diff --git a/LICENSE.md b/LICENSE.md index 19720a9a537e30e2ad804ae85602fcc458f330e5..73ad3714d3139f6edadcd0cf5b3f9c0c3add0cec 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -13,3 +13,9 @@ FastBrain-Instruct is a collection of upstream material and adaptations. Each co Changes in v2 include filtering, grouping and split isolation; assistant-reviewed intent descriptions and alias mapping; candidate sampling and paired target removal; restored available policy/context; and schema/metadata reformatting. Source utterances are retained. The MTOP/TOPv2 derived semantic data, including the adapted catalog, is distributed under CC BY-SA 4.0. Attribution does not imply upstream endorsement of this adaptation. The frozen `default` and retained legacy replay include Google Research SGD (CC BY-SA 4.0), Amazon MASSIVE (CC BY 4.0), Glaive v2 and NousResearch Hermes (Apache 2.0 as recorded in the original card), attributed Nemotron records, NL2Bash (consult its source terms), Git natural-language commands (consult its source terms), and maintainer DevOps templates (Apache 2.0). Their original links and notices remain in the [archived legacy data card](docs/legacy-data-card.md). The v2 release does not claim to resolve unspecified legacy-source terms. The four original data files are unchanged from `a84ea0337a424e287dc6f36f6b9fa320fb920fb0`. + +## v3 additions and modifications + +The repaired semantic descriptions, conflict catalog and MTOP/TOPv2 examples retain CC BY-SA 4.0. Recovered Hermes requests, call sets and schemas retain the original NousResearch source terms; native Nemotron tool schemas retain the NVIDIA component terms listed above. Repairs include filtering, descriptor clarification, candidate regeneration, source recovery, additional group reservations and explicit supervision masks. These changes do not certify upstream outputs or imply author endorsement. + +HINT3 v2 (Curekart and SOFMattress), by Haptik and Gaurav Arora, Chirag Jain, Manas Chaturvedi and Krupal Modi, is provided as a separate native auxiliary database. The database is under ODbL 1.0 and individual contents under DbCL 1.0. The adapted HINT3 view retains those terms. See the [verbatim original notice](licenses/HINT3-ODbL-DbCL.txt), [attribution and source links](licenses/HINT3-attribution.md), and [pinned file hashes](curation/v3/hint3_source_manifest.json). HINT3 labels are not silently relicensed as MIT or merged into the choice catalog. diff --git a/README.md b/README.md index f1479deb2edc3e35936278965d81af1a351be9b0..a7af9b6fb42927fd28aa4e083f2a9330a7d7b59e 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,8 @@ tags: - multi-label - synthetic - aggregated +- dataset-repair +- fastbrain-v3 source_datasets: - glaiveai/glaive-function-calling-v2 - NousResearch/hermes-function-calling-v1 @@ -80,66 +82,130 @@ configs: path: data/semantic_original_labels/validation.parquet - split: test path: data/semantic_original_labels/test.parquet +- config_name: choice_v3 + data_files: + - split: train + path: data/choice_v3/train.parquet + - split: validation + path: data/choice_v3/validation.parquet + - split: test + path: data/choice_v3/test.parquet +- config_name: legacy_replay_v3 + data_files: + - split: train + path: data/legacy_replay_v3/train.parquet + - split: validation + path: data/legacy_replay_v3/validation.parquet + - split: test + path: data/legacy_replay_v3/test.parquet +- config_name: semantic_v3 + data_files: + - split: train + path: data/semantic_v3/train.parquet + - split: validation + path: data/semantic_v3/validation.parquet + - split: test + path: data/semantic_v3/test.parquet +- config_name: tool_context_v3 + data_files: + - split: train + path: data/tool_context_v3/train.parquet + - split: validation + path: data/tool_context_v3/validation.parquet + - split: test + path: data/tool_context_v3/test.parquet +- config_name: tool_environment_holdout_v3 + data_files: + - split: validation + path: data/tool_environment_holdout_v3/validation.parquet + - split: test + path: data/tool_environment_holdout_v3/test.parquet +- config_name: hermes_actions_v3 + data_files: + - split: train + path: data/hermes_actions_v3/train.parquet + - split: validation + path: data/hermes_actions_v3/validation.parquet + - split: test + path: data/hermes_actions_v3/test.parquet +- config_name: nlupp_hotels_v3 + data_files: + - split: train + path: data/nlupp_hotels_v3/train.parquet + - split: validation + path: data/nlupp_hotels_v3/validation.parquet + - split: test + path: data/nlupp_hotels_v3/test.parquet +- config_name: hint3_native_v3 + data_files: + - split: train + path: data/hint3_native_v3/train.parquet + - split: validation + path: data/hint3_native_v3/validation.parquet + - split: test + path: data/hint3_native_v3/test.parquet --- # FastBrain-Instruct -FastBrain-Instruct aggregates public intent and tool-selection data for decision heads. The **2026-10-14 v2** release adds semantic boundary pairs, richer tool context and a separate multi-label task while preserving the original files. - -Load the integrated release explicitly: +The **2026-10-14 v3** release repairs documented supervision and evaluation defects in v2. It regenerates semantic candidate pairs, restores full Hermes requests and native call sets, recovers Nemotron schemas, restores legacy validation, and disables unsupported NLU++ Boolean labels. Model training and capability-retention experiments have not been run for this release. ```python from datasets import load_dataset -ds = load_dataset("TypeScript/FastBrain-Instruct", "choice_v2") -print(ds["train"][0]) +ds = load_dataset("TypeScript/FastBrain-Instruct", "choice_v3") ``` ## Configurations -| Config | Train | Validation | Test | Use | +| Config | Train | Validation | Test | Purpose | |---|---:|---:|---:|---| +| `choice_v3` | 113,163 | 9,505 | 21,502 | Integrated repaired choice corpus; recommended for the choice head | +| `legacy_replay_v3` | 61,131 | 825 | 7,181 | Legacy coarse routing and restored Hermes single-call choices | +| `semantic_v3` | 26,922 | 7,230 | 12,220 | Regenerated MTOP/TOPv2 candidate-relative match/no-match pairs | +| `tool_context_v3` | 25,110 | 1,450 | 2,101 | Observed Funcdex/Nemotron tool choices with available full schemas | +| `tool_environment_holdout_v3` | — | 534 | 561 | Additional held-out Nemotron policy/catalog groups; evaluation only | +| `hermes_actions_v3` | 868 | 24 | 123 | Native observed call sets; separate auxiliary task | +| `nlupp_hotels_v3` | 765 | 97 | 98 | Native multi-label intents and masked slot values; separate auxiliary task | +| `hint3_native_v3` | 801 | 68 | 1,348 | Native brand-relative intent/OOS labels; separate auxiliary task | | `default` | 97,450 | — | 10,828 | Frozen original release | -| `choice_v2` | 116,440 | 8,206 | 24,639 | Integrated choice corpus: legacy replay + semantic pairs + tool context | -| `semantic_v2` | 26,976 | 7,230 | 12,228 | MTOP/TOPv2 intent boundaries and paired candidate-relative no-match | -| `tool_context_v2` | 26,241 | 976 | 1,583 | Funcdex and repaired Nemotron observed next-tool steps | -| `nlupp_hotels_v2` | 765 | 97 | 98 | Original multi-label intent modules and slots; separate task format | -| `semantic_original_labels` | 21,966 | 3,151 | 9,131 | Original labels for complex/ambiguous/unsupported/low-support cases; auxiliary only | +| `choice_v2` | 116,440 | 8,206 | 24,639 | Frozen previous integrated release | + +`legacy_replay_v3`, `semantic_v3`, and `tool_context_v3` are exact subsets of `choice_v3`. **Do not concatenate these subsets with the integrated config.** `tool_environment_holdout_v3` overlaps the tool validation/test splits. All three native auxiliary configs have their own schema and are not automatic choice-head training data. Some native Hermes single-call observations also appear in the choice view; `included_in_choice_v3` records this overlap. + +Calling `load_dataset("TypeScript/FastBrain-Instruct")` still loads `default`. All original and v2 data files remain unchanged from [the pre-repair revision](https://huggingface.co/datasets/TypeScript/FastBrain-Instruct/tree/11e6f657f6d8830da230d4e49c65d0f67f106585). The [v2 data card](docs/v2-data-card.md) is archived; its known defects are addressed by the explicit v3 configs, not by silently replacing old splits. -`semantic_v2` and `tool_context_v2` are **overlapping views of `choice_v2`**, not additional examples to concatenate with it. `nlupp_hotels_v2` and `semantic_original_labels` use their own schemas. Calling `load_dataset("TypeScript/FastBrain-Instruct")` still loads `default`. +## Repairs and evidence -The original `train.parquet`, `test.parquet`, `train.jsonl` and `test.jsonl` remain unchanged from [the frozen baseline](https://huggingface.co/datasets/TypeScript/FastBrain-Instruct/tree/a84ea0337a424e287dc6f36f6b9fa320fb920fb0). All 10,828 original test rows also appear in `choice_v2/test` with their original seven core fields intact. The original data card is [archived here](docs/legacy-data-card.md); its historical claims should be read together with the new audit below. +- **Semantic supervision:** all retained pairs are regenerated with three repaired operation descriptions and a conservative conflict graph. All 44 previously confirmed false-no-match cases are repaired or explicitly quarantined. Known context-dependent/ambiguous requests and unsupported persistent call rules are excluded from the choice view. See [case-level outcomes](audit/v3/known_issue_resolution.json) and [the versioned catalog](catalogs/semantic_v3.json). The catalog still contains 68 operations; this is not a 151-operation natural intent dataset. +- **Legacy replay:** restore 825 protected validation rows across all seven legacy sources. Repair documented Git/shell/MASSIVE descriptions, replace a known equivalent pager distractor, and reserve lexical groups against existing holdouts. MASSIVE cooking-label boundaries and other unreviewed labels remain limitations. Missing context is not guessed. +- **Hermes:** all 1,147 original rows are uniquely mapped to the pinned native file, with full requests, system messages, tool schemas and original call sets recovered. Contract failures, documented semantic contradictions and specifically reviewed financial operations are quarantined. Multi-call responses are retained as observed sets in `hermes_actions_v3`; their first call is not made unique gold. Only eligible single-call records with at least two native tools enter the choice view. Source order does not certify parallel independence or a unique execution order. +- **Tool context:** quarantine 31 reviewed inconsistent/ambiguous parents, including contaminated preceding histories. Recover the primary catalog for every published Nemotron source step before filtering: 10,810 native conversations / 23,275 targets. In the final tool view, 23,259 Nemotron rows have full native schemas. Only 10,792 pass both the strict source-prefix check and native argument contract checks needed to expose argument supervision. Other recovered arguments remain explicitly unverified. No execution-correctness or unique-valid-action claim is made. +- **NLU++:** preserve all 960 native texts, intent lists and original slot dictionaries. Disable the generic Boolean export (`head_questions_json={}`, `head_expected_json={}`, `use_for_head_training=false`), because an absent native annotation does not certify a broadly worded predicate false. Mask the 22 reversed stay-date pairs from `slots_for_value_training_json`; retain the original values in `slots_json`. +- **HINT3:** add a separate native auxiliary view of Curekart/SOFMattress with pinned source files and ODbL/DbCL attribution. Source OOS refers to its original brand catalog, not FastBrain's global no-match option. Native test/OOS is never promoted into training. Financing, lexical overlap, empty text and duplicates are handled explicitly; this filtered view is not the canonical HINT3 benchmark. -## What changed +## Choice adapter and task contract -| Main training component | Rows | -|---|---:| -| Retained legacy replay | 63,223 | -| MTOP/TOPv2 semantic pairs | 26,976 | -| Funcdex | 4,010 | -| Repaired Nemotron | 22,231 | +Use [head_adapter_v3.py](scripts/v3/head_adapter_v3.py) for the current Clef joint-schema format. It assigns deterministic random option IDs per record, preserves the ID-to-name answer mapping, and accounts for the encoder sorting IDs. Merely shuffling the `choices` list does not randomize the encoder's final order. `encode_without_truncation` raises on oversized input instead of letting the upstream encoder crop state. -- **Semantic coverage:** 68 retained intent classes after alias consolidation, split/template isolation, label screening and a minimum of 20 independent training requests per class. Each selected request has one positive and one answer-removed no-match example. Candidate budgets target 16/32/64 ordinary operations and shrink when conflict exclusions require it; `__no_match__` is counted separately. This release does not provide a 151- or 256-intent natural-language catalog. -- **Tool context:** preserve the full available pre-answer prefix for Funcdex; restore Nemotron system policy and exclude steps with known missing earlier history or non-system truncation. Tools remain **observed expert-action labels**; schema validity and context recovery do not establish that every other tool is invalid. No text-only/no-call record is relabeled as no-match. -- **Holdout isolation:** the audit identified 2,687 raw Nemotron conversations shared by the original train and test sets. The v2 rebuild blocks 3,286 historical held-out parents before extracting training steps. The frozen `default` retains its historical split limitations. -- **Multi-label preservation:** NLU++ hotels keeps all annotated intent modules and slots. Only 576 of its 765 train rows are eligible for intent training; empty-intent context fragments have `use_for_intent_training=false` and no fabricated negative labels. +The documented adapter includes the complete published `query` plus candidate names/descriptions; it does **not** append `tools_json` to state. All 144,170 choice records were encoded with the actual Clef tokenizer/encoder: maximum 11,482 tokens, below 16,384. See [the measured token budget](audit/v3/token_budget.json). Adding full schemas, more head questions or media requires a new budget check. -## Record format +For observed tool records, `target` is the source-observed action and `acceptable_targets` is a declared positive label list, **not an exhaustive set of valid actions**. Do not turn unobserved alternatives into independently certified `noul=false` or score-zero labels. The supplied adapter exports only the observed choice task. Auxiliary call-set and multi-label records are rejected by that adapter. -Choice configs retain `id`, `domain`, `category`, `query`, `target`, `choices` (a list of name/description objects), and `source`. Added fields record `parent_id`, `source_family`, `source_repo`, `source_revision`, `source_split`, `source_row_id`, `task_type`, `label_status`, `is_rejection`, `candidate_count`, `catalog_id`, `provenance_json`, `context_json`, and `tools_json`. +## Evaluation and retention -`provenance_json` contains the source file and row, construction method, group, and applicable license for new rows. For tool records, `query` renders the structured `context_json`. Funcdex retains full original schemas in `tools_json`; Nemotron retains only names, descriptions and required-parameter names because the intermediate source discarded the rest. +The v3 test has changed requests, descriptions, candidates and filtering; its accuracy is not directly comparable to v2 accuracy. Preserve old regression runs on frozen files, and re-evaluate both the baseline head and candidate head on the same v3 protocol. -NLU++ uses `text`, `intents`, `slots_json`, source/fold identifiers, `use_for_intent_training`, `head_questions_json` and `head_expected_json`. The optional head fields contain 40 independent boolean module questions. Filter on `use_for_intent_training` before intent training. Auxiliary semantic rows retain source labels and parse trees and explicitly set `use_for_single_choice=false`. +The additional Nemotron environment holdout reserves 23 validation and 23 test policy/catalog groups absent from v3 tool training. Existing in-environment validation/test remains in `tool_context_v3` and is identified by provenance. This is exact policy/catalog grouping within this dataset, not proof that a previous model has never seen the public source or semantically similar workflows. -## Curation and use +The historical legacy validation is a restored reserved set, not newly unseen data. Historical MASSIVE native-split mixing and unavailable original conversation identities in several legacy sources remain disclosed. Conservative lexical grouping does not establish original dialogue or template ancestry. -[Curation recipe and split policy](docs/curation-v2.md) · [Source revisions and file hashes](audit/source_manifest.json) · [Dataset counts and hashes](audit/release_summary.json) · [Validation report](audit/validation.json) · [Semantic catalog](catalogs/semantic_v2.json) · [Source exclusions](curation/excluded_source_families.json) +Use [fixed evaluation membership](curation/v3/evaluation_membership.jsonl.gz), [evaluation contract](curation/v3/evaluation_protocol.json), and [ablation definitions](curation/v3/ablation_configs.json). Report source/operation-family slices, candidate-count buckets, present/absent results and in-/out-of-environment tool results separately. Paired semantic rows share a parent; uncertainty estimates should group by parent/template/environment as applicable. Predeclare retention tolerances before training; a lower aggregate loss is not a retention gate. -Use `source`, `source_family`, `task_type` and `provenance_json.group` to make explicit ablations. The paired semantic data has an artificial 1:1 present/absent ratio within that view; this is not a deployment class prior. The release does not establish model improvement or preservation of an existing head's capabilities; those require training and evaluation. +## Provenance, exclusions and limitations -The source-family exclusions from the original corpus are retained. Additional lexical checks use the exact cached files recorded in the exclusion manifest. They do not certify absence of paraphrases or unobserved source ancestry. Curation uses assistant ontology review, deterministic rules and targeted training-example inspection, **not exhaustive human annotation**. Ambiguous single-choice cases are retained only in the explicitly auxiliary view when possible. +[Release counts and hashes](audit/v3/release_summary.json) · [Validation](audit/v3/validation.json) · [Repair recipe](docs/curation-v3.md) · [Input evidence](audit/v3/input_manifest.json) · [Quarantine ledger](curation/v3/quarantine.jsonl.gz) · [Historical membership](curation/v3/historical_holdout_membership.json) · [Licenses](LICENSE.md) -## Attribution and licenses +The quarantine ledger distinguishes quarantined rows, split reservations, deduplication and records retained in a separate task. It is audit data, not a training config; its row count is not a label-error rate. Native source text and original labels remain available for traceability. -This is an aggregation and adaptation of upstream data. MTOP and TOPv2 are **CC BY-SA 4.0**; NLU++ and the attributed Nemotron component are **CC BY 4.0**; Funcdex declares **MIT**. Legacy records retain their original source terms, including SGD's CC BY-SA 4.0. The collection is not licensed uniformly as MIT. See [license and attribution notices](LICENSE.md), the pinned source manifest and the archived legacy card. +Existing excluded source families/benchmarks remain excluded. Finance filtering is scoped and recorded: it does not establish that every legacy row is finance-free. Ordinary retail price, refund and cash-on-delivery questions are not automatically equated with banking. The semantic 1:1 present/absent construction is an artificial sampling ratio, not a deployment prior. This release does not establish real no-call/clarification policy competence, exhaustive semantic correctness, human gold annotation, or a model-quality gain. diff --git a/audit/v3/hint3_repair.json b/audit/v3/hint3_repair.json new file mode 100644 index 0000000000000000000000000000000000000000..03c300a733b7a8cad93139dff961098f48cc49bf --- /dev/null +++ b/audit/v3/hint3_repair.json @@ -0,0 +1,105 @@ +{ + "counters": { + "excluded_train": 50, + "excluded_test": 40, + "train_duplicates": 8 + }, + "counts": { + "train": { + "rows": 801, + "bytes": 40869, + "sha256": "d789a0db0c2a5e70c3275569a05fa5993f60f4f1bf72dddb134ad219c0932291", + "native_oos": 0, + "by_brand": { + "curekart": 535, + "sofmattress": 266 + } + }, + "validation": { + "rows": 68, + "bytes": 12648, + "sha256": "2da5a239515f0a8ac85454c19607a7184cb0e805476113d713ee06a2071b27c6", + "native_oos": 0, + "by_brand": { + "curekart": 49, + "sofmattress": 19 + } + }, + "test": { + "rows": 1348, + "bytes": 73960, + "sha256": 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"DISTRIBUTORS", + "EMI", + "ERGO_FEATURES", + "LEAD_GEN", + "MATTRESS_COST", + "OFFERS", + "ORDER_STATUS", + "ORTHO_FEATURES", + "PILLOWS", + "PRODUCT_VARIANTS", + "RETURN_EXCHANGE", + "SIZE_CUSTOMIZATION", + "WARRANTY", + "WHAT_SIZE_TO_ORDER" + ] + }, + "seed": 2026101403, + "no_native_oos_training_data": true, + "global_head_no_match_conversion": false, + "main_choice_normalized_queries_screened": 120975, + "test_note": "Filtered native test is auxiliary evaluation, not the canonical HINT3 paper benchmark.", + "true_151_candidate_semantic_coverage_established": false +} diff --git a/audit/v3/input_manifest.json b/audit/v3/input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f5d4867581db2b4c10fcfcc093610da18e1a455e --- /dev/null +++ b/audit/v3/input_manifest.json @@ -0,0 +1,83 @@ +{ + "previous_dataset": { + "repo": "TypeScript/FastBrain-Instruct", + "revision": "11e6f657f6d8830da230d4e49c65d0f67f106585" + }, + "original_dataset_revision": 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Per-row published provenance carries native locators; original source files remain at the linked pinned revisions." +} diff --git a/audit/v3/known_issue_resolution.json b/audit/v3/known_issue_resolution.json new file mode 100644 index 0000000000000000000000000000000000000000..41284159fbe22f51f6ae1a52a4c73af618149631 --- /dev/null +++ b/audit/v3/known_issue_resolution.json @@ -0,0 +1,20 @@ +{ + "S01_route_retrieval_alternative_remains": { + "regenerated_with_repair": 35 + }, + "S02_online_contact_filter_alternative_remains": { + "regenerated_with_repair": 9 + }, + "S03_nutritional_query_description_gap": { + "regenerated_with_repair": 97 + }, + "S04_alarm_rename_description_gap": { + "regenerated_with_repair": 12 + }, + "S05_timer_history_description_gap": { + "regenerated_with_repair": 2 + }, + "S06_persistent_call_policy_description_gap": { + "explicitly_quarantined": 7 + } +} diff --git a/audit/v3/legacy_repair.json b/audit/v3/legacy_repair.json new file mode 100644 index 0000000000000000000000000000000000000000..68b5dc54f50799798b02f6c17d9033179699833d --- /dev/null +++ b/audit/v3/legacy_repair.json @@ -0,0 +1,162 @@ +{ + "counters": { + "records_with_description_repairs": 17314, + "records_with_candidate_repairs": 1, + "The request explicitly names mypy and flake8 but the target is an undefined pylint_typecheck wrapper. 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Unannotated modules are not certified false natural-language predicates. Head adapter refuses these rows until an independently verified boolean task exists.", + "original_slots_kept_for_provenance": true, + "value_training_field": "slots_for_value_training_json", + "value_mask_field": "slot_value_mask_json", + "automatic_date_corrections_guessed": 0 +} diff --git a/audit/v3/quarantine_summary.json b/audit/v3/quarantine_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e546504e496068b5e0b8ac38cda16a3d6a5b21db --- /dev/null +++ b/audit/v3/quarantine_summary.json @@ -0,0 +1,95 @@ +{ + "records": 2524, + "by_reason": { + "The request explicitly names mypy and flake8 but the target is an undefined pylint_typecheck wrapper. No implementation/native schema establishes that it invokes the requested tools.": 1, + "The request combines filesystem search and FTP upload; matching native commands invoke curl inside find -exec. The exposed find capability alone does not certify the complete operation, and multiple native rows remain matched.": 1, + "The native command is git rm -r --cached build/ followed by appending build/ to .gitignore. git_rm alone cannot perform the requested ignore-file edit.": 1, + "The entire request is a seat reservation confirmation; both bus-ticket purchase and one-way-flight reservation are offered. The service/dialogue context that distinguishes them was not recovered.": 1, + "Let us call it Trip to Hawaii identifies a name but not the object or action; the earlier conversation needed to justify create_todo_list is absent.": 1, + "The request covers all pods in a namespace, while k8s_rollout_restart describes a single deployment. The missing cluster/controller state does not establish equivalence.": 1, + "The same native query maps both to a full comm/sort/grep process-substitution command and to corrupt comm -23 < <. Preserve both matches; neither first-match selection nor description rewriting recovers a unique executable source command.": 1, + "Weird things people want to know is labeled cooking_recipe without any visible cooking request. Do not expand a recipe description to cover it.": 1, + "Get me a one-way flight does not uniquely distinguish searching from reserving in the exposed SearchOnewayFlight/ReserveOnewayFlight choice set.": 1, + "The only input is olly, labeled recommendation_events; no event-discovery request is visible.": 1, + "The request asks for things to do and dates of events. FindEvents and GetEventDates are both candidates; the missing service state does not establish a unique action.": 1, + "The only input is fast, labeled audio_volume_up; no volume-increase intent is established.": 1, + "scope_finance: Calculate the payment on a loan from principal, interest rate, and loan term.": 1, + "Go back is labeled play_podcasts but no podcast/media context is present.": 1, + "The user requests a reminder tomorrow, while create_todo only promises creating a todo item. No recovered native schema establishes timed-reminder support.": 1, + "How many hours ahead is NYC is natively datetime_query; retained training examples also place offset questions in datetime_convert. Do not invent an exclusive boundary or silently relabel.": 1, + "What are jalapenos is natively labeled cooking_recipe, but it asks for an ingredient definition. No automatic relabeling or expansion of recipe to all food questions is justified.": 1, + "Change my alarm is labeled alarm_remove, but the request does not distinguish deletion, snoozing, or editing. Do not redefine remove as all alarm changes.": 1, + "Send a text to Alice is labeled email_querycontact; contact lookup may be a prerequisite, but no native dialogue/tool state establishes it as the unique next action.": 1, + "The nature of artificial intelligence is labeled general_quirky. Its boundary with factual/definition questions is not established by an authoritative ontology contract; retain the native label as evidence, not a repaired unique action.": 1, + "Lights out is labeled iot_hue_lighton. Its ordinary reading does not justify turning lights on; isolate without silently flipping the label.": 1, + "indistinguishable_target_candidate_descriptions": 1, + "native_call_set_kept_outside_single_choice": 752, + "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.": 49, + "scope_finance: Initiate a USD transfer to a PayPal recipient.": 1, + "scope_finance: Create an actual Venmo peer-to-peer payment.": 1, + "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.": 60, + "scope_finance: Approve a consumer loan and compute its payments and amortization schedule.": 1, + "scope_finance: Transfer Bitcoin between blockchain wallets.": 1, + "scope_finance: Initiate a credit-card payment through a payment gateway; this is an explicit payment API operation, not merely a shopping-price mention.": 1, + "scope_finance: Retrieve cryptocurrency-wallet financial transaction history.": 1, + "scope_finance: Retrieve PayPal financial transaction history.": 1, + "scope_finance: Transfer USD funds between e-wallet users.": 1, + "scope_finance: Send a Venmo peer-to-peer payment.": 1, + "scope_finance: Link a credit card to Coinbase as the first action of a cryptocurrency-purchase workflow.": 1, + "extract_files_by_extension creates and populates Compliance_Audit_2023, then generate_file_report reads that folder. The user also says parallel, but the visible dependency and contradictory timing prevent certifying the co-emitted set as ready to run in parallel.": 1, + "scope_finance: Initiate a Revolut international money transfer.": 1, + "scope_finance: Purchase Bitcoin through Coinbase.": 1, + "scope_finance: Create a cryptocurrency wallet for asset management and payments.": 1, + "scope_finance: Create a Klarna buy-now-pay-later installment-payment session; the requested operation is credit/payment processing.": 1, + "scope_finance: Transfer funds between checking and savings bank accounts.": 1, + "scope_finance: Create a PayPal payment request; distinguish this explicit payment-network action from ordinary invoice document generation.": 1, + "list_container_services receives cluster=\"web-app-service\", although the user provided a service name and task definition, not its cluster. Passing the native schema does not ground this argument.": 1, + "scope_finance: Create a brokerage account and update an investment profile for securities trading.": 1, + "scope_finance: Link a checking bank account to PayPal as a funding source.": 1, + "scope_finance: Retrieve PayPal transaction history and account balance.": 1, + "scope_finance: Send a USD peer-to-peer payment through Cash App.": 1, + "scope_finance: Purchase Bitcoin through Cash App and query the USD/BTC exchange rate.": 1, + "lexical_group_reserved_for_existing_holdout": 1319, + "reviewed_semantic_ambiguous_multiple_valid": 16, + "reviewed_semantic_needs_context": 24, + "persistent_call_rule_requires_distinct_operation": 14, + "persistent_call_rule_screen": 8, + "reviewed_tool_parent_inconsistent_or_ambiguous": 139, + "normalized_overlap_with_selected_native_test": 15, + "benchmark_exact|normalized_overlap_with_selected_native_test": 1, + "benchmark_exact": 14, + "query_already_in_main_choice_config": 4, + "financial_scope_requires_review": 1, + "explicit_financing_lexical_match": 7, + "empty_or_nonlexical_query_requires_review": 10, + "explicit_financing_lexical_match|explicit_financing_quarantine|native_label_installment_financing": 22, + "explicit_financing_lexical_match|explicit_financing_quarantine|native_label_installment_financing|normalized_overlap_with_selected_native_test": 1, + "explicit_financing_quarantine|financial_scope_requires_review|native_label_installment_financing": 1, + "explicit_financing_quarantine|native_label_installment_financing": 1, + "native_brand_query_duplicate": 8, + "explicit_financing_lexical_match|native_label_installment_financing": 10, + "explicit_financing_lexical_match|financial_scope_requires_review|native_label_installment_financing": 1, + "native_label_installment_financing": 2 + }, + "by_disposition": { + "quarantine": 426, + "separate_task": 752, + "split_reserved": 1338, + "deduplicated": 8 + }, + "by_group": { + "legacy": 22, + "hermes": 884, + "legacy_split": 1319, + "semantic": 62, + "tools": 139, + "hint3": 98 + }, + "by_original_split": { + "train": 2240, + "test": 190, + "validation": 94 + }, + "note": "Quarantined, split-reserved and separate-task rows are distinguished. These counts are not an error-rate estimate.", + "sha256": "73214987fbaec508b01d478dac26e863fabc7daa6c24bcd98a5bef7c0ce42b22" +} diff --git a/audit/v3/release_summary.json b/audit/v3/release_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..a271c5fee0a3f03aecdd8579ae10367501fcf501 --- /dev/null +++ b/audit/v3/release_summary.json @@ -0,0 +1,544 @@ +{ + "version": "2026-10-14-v3", + "base_revision": "11e6f657f6d8830da230d4e49c65d0f67f106585", + "configurations": { + "choice_v3": { + "train": { + "rows": 113163, + "bytes": 165993665, + "sha256": "1a6f5d3a1dead75be71ac23ce61f7812eecc5fa65289770adfbf38b470e2fb9c", + "parents": 84904, + "sources": { + "topv2": 20040, + "sgd": 33724, + "nemotron_agentic": 21131, + "glaive_v2": 9992, + "nl2bash": 2634, + "hermes_v1": 223, + "amazon_massive": 13763, + "git_nl_commands": 777, + "mtop": 6882, + "funcdex": 3979, + "curated_devops": 18 + }, + "task_types": { + "intent_match": 13461, + "single_choice": 60908, + 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legacy_replay_v3, semantic_v3 and tool_context_v3 views; environment holdouts overlap the tools view. Auxiliary configs are not concatenated automatically.", + "old_data_files_modified": [], + "no_training_experiments_run": true, + "no_human_gold_annotation_claim": true, + "fixed_evaluation_membership_sha256": "b2ee4187546f5326bfb20de788290f445459f903e14098001405e1b7b5c6a0ff", + "legacy_native_split_isolation": false, + "new_source_test_promoted_to_train": false +} diff --git a/audit/v3/semantic_repair.json b/audit/v3/semantic_repair.json new file mode 100644 index 0000000000000000000000000000000000000000..739f9b6202d5375e79aad027a920ddeb6a521f49 --- /dev/null +++ b/audit/v3/semantic_repair.json @@ -0,0 +1,54 @@ +{ + "counters": { + "retained_train_parents": 13461, + "reviewed_semantic_ambiguous_multiple_valid_parents": 8, + "reviewed_semantic_needs_context_parents": 12, + "persistent_call_rule_requires_distinct_operation_parents": 7, + "persistent_call_rule_screen_parents": 4, + "retained_validation_parents": 3615, + "retained_test_parents": 6110 + }, + "inspected_ambiguous_parents": 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+ "native_test_rows": 13, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "FRANCHISE", + "description_zh": "加盟、卖家或零售合作咨询", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 12, + "native_test_rows": 12, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "IMMUNITY", + "description_zh": "免疫力相关健康信息", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 6, + "native_test_rows": 0, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "INTERNATIONAL_SHIPPING", + "description_zh": "国际配送可用性", + "finance_category": "nonfinancial_retail_health_or_service_label", + 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"相对于该品牌完整原生目录的 OOS；不是全局无匹配", + "finance_category": "source_relative_oos", + "recommendation": "native_test_only_and_text_finance_screen", + "native_train_rows": 0, + "native_test_rows": 532, + "explicit_financing_quarantine_rows": 4, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "ORDER_QUERY", + "description_zh": "一般订单支持请求", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 7, + "native_test_rows": 0, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "ORDER_STATUS", + "description_zh": "零售订单与物流状态", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 47, + "native_test_rows": 12, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "ORDER_TAKING", + "description_zh": "是否接受订单，含疫情期间运营", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 39, + "native_test_rows": 19, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "ORIGINAL_PRODUCT", + "description_zh": "商品真伪或正品保证", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 23, + "native_test_rows": 43, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "PAYMENT_AND_BILL", + "description_zh": "零售支付、扣款、账单或订单未生成问题", + "finance_category": "retail_transaction_or_monetary_offer", + "recommendation": "apply_shared_retail_policy_and_text_financing_screen", + "native_train_rows": 26, + "native_test_rows": 3, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 1 + }, + { + "brand": "curekart", + "native_label": "PORTAL_ISSUE", + "description_zh": "购物网站或界面故障", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 4, + "native_test_rows": 0, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "RECOMMEND_PRODUCT", + "description_zh": "营养或健康商品推荐与商品咨询", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 95, + "native_test_rows": 205, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "REFER_EARN", + "description_zh": "零售推荐奖励及兑换", + "finance_category": "retail_transaction_or_monetary_offer", + "recommendation": "apply_shared_retail_policy_and_text_financing_screen", + "native_train_rows": 13, + "native_test_rows": 0, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "REFUNDS_RETURNS_REPLACEMENTS", + "description_zh": "零售退款、退货或更换", + "finance_category": "retail_transaction_or_monetary_offer", + "recommendation": "apply_shared_retail_policy_and_text_financing_screen", + "native_train_rows": 54, + "native_test_rows": 20, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "RESUME_DELIVERY", + "description_zh": "何时恢复配送，含疫情影响", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 51, + "native_test_rows": 9, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "SIDE_EFFECT", + "description_zh": "商品副作用咨询", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 4, + "native_test_rows": 5, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "SIGN_UP", + "description_zh": "注册购物服务账号", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 7, + "native_test_rows": 3, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "START_OVER", + "description_zh": "重新开始或回到主菜单的对话控制", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 5, + "native_test_rows": 0, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "STORE_INFORMATION", + "description_zh": "线下店铺信息与营业状态", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 14, + "native_test_rows": 11, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "USER_GOAL_FORM", + "description_zh": "用户健康目标或资料重新评估", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 12, + "native_test_rows": 2, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "curekart", + "native_label": "WORK_FROM_HOME", + "description_zh": "公司办公室疫情期间运营状态", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 10, + "native_test_rows": 0, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "100_NIGHT_TRIAL_OFFER", + "description_zh": "床垫 100 晚试睡政策", + "finance_category": "retail_transaction_or_monetary_offer", + "recommendation": "apply_shared_retail_policy_and_text_financing_screen", + "native_train_rows": 18, + "native_test_rows": 5, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "ABOUT_SOF_MATTRESS", + "description_zh": "品牌介绍及区别", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 11, + "native_test_rows": 3, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "CANCEL_ORDER", + "description_zh": "取消零售订单", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 10, + "native_test_rows": 6, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "CHECK_PINCODE", + "description_zh": "邮编区域是否配送；不是金融 PIN", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 10, + "native_test_rows": 25, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "COD", + "description_zh": "零售货到付款可用性", + "finance_category": "retail_transaction_or_monetary_offer", + "recommendation": "apply_shared_retail_policy_and_text_financing_screen", + "native_train_rows": 12, + "native_test_rows": 12, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "COMPARISON", + "description_zh": "Ergo 与 Ortho 产品比较", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 11, + "native_test_rows": 18, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "DELAY_IN_DELIVERY", + "description_zh": "配送延迟；一些原生表达依赖对话上下文", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 11, + "native_test_rows": 15, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "DISTRIBUTORS", + "description_zh": "线下展厅、经销商或体验地点", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 34, + "native_test_rows": 13, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "EMI", + "description_zh": "分期付款、零息融资及首付", + "finance_category": "explicit_financing", + "recommendation": "quarantine_all_rows_preserve_native_label", + "native_train_rows": 25, + "native_test_rows": 13, + "explicit_financing_quarantine_rows": 38, + "other_finance_lexical_review_rows": 2 + }, + { + "brand": "sofmattress", + "native_label": "ERGO_FEATURES", + "description_zh": "Ergo 床垫功能；产品变体不代表新通用意图", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 11, + "native_test_rows": 3, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "LEAD_GEN", + "description_zh": "联系销售或人工服务", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 21, + "native_test_rows": 17, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "MATTRESS_COST", + "description_zh": "床垫零售价格", + "finance_category": "retail_transaction_or_monetary_offer", + "recommendation": "apply_shared_retail_policy_and_text_financing_screen", + "native_train_rows": 22, + "native_test_rows": 23, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "NO_NODES_DETECTED", + "description_zh": "相对于该品牌完整原生目录的 OOS；不是全局无匹配", + "finance_category": "source_relative_oos", + "recommendation": "native_test_only_and_text_finance_screen", + "native_train_rows": 0, + "native_test_rows": 144, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "OFFERS", + "description_zh": "零售优惠；个别测试查询同时涉及 EMI", + "finance_category": "retail_transaction_or_monetary_offer", + "recommendation": "apply_shared_retail_policy_and_text_financing_screen", + "native_train_rows": 10, + "native_test_rows": 11, + "explicit_financing_quarantine_rows": 1, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "ORDER_STATUS", + "description_zh": "零售订单或配送状态", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 21, + "native_test_rows": 8, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "ORTHO_FEATURES", + "description_zh": "Ortho 床垫功能；产品变体不代表新通用意图", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 17, + "native_test_rows": 11, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "PILLOWS", + "description_zh": "枕头商品是否销售或相关咨询", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 10, + "native_test_rows": 13, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "PRODUCT_VARIANTS", + "description_zh": "床垫产品变体信息", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 21, + "native_test_rows": 8, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "RETURN_EXCHANGE", + "description_zh": "零售退货、换货或退款；个别测试查询同时涉及 EMI", + "finance_category": "retail_transaction_or_monetary_offer", + "recommendation": "apply_shared_retail_policy_and_text_financing_screen", + "native_train_rows": 14, + "native_test_rows": 12, + "explicit_financing_quarantine_rows": 2, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "SIZE_CUSTOMIZATION", + "description_zh": "床垫尺寸修改或定制", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 9, + "native_test_rows": 15, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "WARRANTY", + "description_zh": "商品保修政策", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 10, + "native_test_rows": 0, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + }, + { + "brand": "sofmattress", + "native_label": "WHAT_SIZE_TO_ORDER", + "description_zh": "选择购买尺寸", + "finance_category": "nonfinancial_retail_health_or_service_label", + "recommendation": "apply_row_level_filters", + "native_train_rows": 20, + "native_test_rows": 22, + "explicit_financing_quarantine_rows": 0, + "other_finance_lexical_review_rows": 0 + } +] diff --git a/catalogs/nlupp_hotels_modules_v3.json b/catalogs/nlupp_hotels_modules_v3.json new file mode 100644 index 0000000000000000000000000000000000000000..f2d388757a76e32fb5a93ecad01f64086e01dafd --- /dev/null +++ b/catalogs/nlupp_hotels_modules_v3.json @@ -0,0 +1,243 @@ +{ + "accesibility": { + "description": "is the intent to ask something related to accessibility?", + "domain": [ + "hotels" + ] + }, + "acknowledge": { + "description": "is the intent to acknowledge what was said?", + "domain": [ + "general" + ] + }, + "affirm": { + "description": "is the intent to affirm something?", + "domain": [ + "general" + ] + }, + "booking": { + "description": "is the intent to talk about some booking?", + "domain": [ + "hotels" + ] + }, + "cancel_close_leave_freeze": { + "description": "is the intent asking about canceling or closing something?", + "domain": [ + "general" + ] + }, + "change": { + "description": "is the intent to change or modify something?", + "domain": [ + "general" + ] + }, + "check_in": { + "description": "is the intent to ask about check in?", + "domain": [ + "hotels" + ] + }, + "check_out": { + "description": "is the intent to ask about check out?", + "domain": [ + "hotels" + ] + }, + "deny": { + "description": "is the intent to deny something?", + "domain": [ + "general" + ] + }, + "dont_know": { + "description": "is the intent to say I don’t know?", + "domain": [ + "general" + ] + }, + "end_call": { + "description": "is the intent to end call or say goodbye?", + "domain": [ + "general" + ] + }, + "existing": { + "description": "is the intent asking about something that already exists?", + "domain": [ + "general" + ] + }, + "greet": { + "description": "is the intent to greet someone?", + "domain": [ + "general" + ] + }, + "gym": { + "description": "is the intent to ask something related to gym?", + "domain": [ + "hotels" + ] + }, + "handoff": { + "description": "is the intent to speak to a human or hand off?", + "domain": [ + "general" + ] + }, + "housekeeping": { + "description": "is the intent to talk about housekeeping issues?", + "domain": [ + "hotels" + ] + }, + "how": { + "description": "is the intent asking how to do something?", + "domain": [ + "general" + ] + }, + "how_long": { + "description": "is the intent asking about how long something takes?", + "domain": [ + "general" + ] + }, + "how_much": { + "description": "is the intent asking about some quantity or how much?", + "domain": [ + "general" + ] + }, + "less_lower_before": { + "description": "is the intent to indicate something less, lower, before or decreasing?", + "domain": [ + "general" + ] + }, + "limits": { + "description": "is the intent asking about some sort of limit?", + "domain": [ + "general" + ] + }, + "lost_stolen": { + "description": "is the intent asking about something being lost or stolen?", + "domain": [ + "general" + ] + }, + "make_open_apply_setup_get_activate": { + "description": "is the intent to make, open, apply, set up or activate something?", + "domain": [ + "general" + ] + }, + "more_higher_after": { + "description": "is the intent to indicate something more, higher, after or increasing?", + "domain": [ + "general" + ] + }, + "new": { + "description": "is the intent asking about something new?", + "domain": [ + "general" + ] + }, + "parking": { + "description": "is the intent to ask something related to parking?", + "domain": [ + "hotels" + ] + }, + "pets": { + "description": "is the intent to ask something related to pets?", + "domain": [ + "hotels" + ] + }, + "refund": { + "description": "is the intent to ask about some refund?", + "domain": [ + "banking", + "hotels" + ] + }, + "repeat": { + "description": "is the intent asking to repeat the previous sentence?", + "domain": [ + "general" + ] + }, + "request_info": { + "description": "is the intent to ask or request some information?", + "domain": [ + "general" + ] + }, + "restaurant": { + "description": "is the intent to ask something related to restaurant?", + "domain": [ + "hotels" + ] + }, + "room_ammenities": { + "description": "is the intent to ask something related to some room amenities?", + "domain": [ + "hotels" + ] + }, + "room_service": { + "description": "is the intent to talk about room service?", + "domain": [ + "hotels" + ] + }, + "spa": { + "description": "is the intent to ask something related to spa or beauty services?", + "domain": [ + "hotels" + ] + }, + "swimming_pool": { + "description": "is the intent to ask something related to the swimming pool?", + "domain": [ + "hotels" + ] + }, + "thank": { + "description": "is the intent to thank someone?", + "domain": [ + "general" + ] + }, + "when": { + "description": "is the intent to ask about when or what time something happens?", + "domain": [ + "general" + ] + }, + "why": { + "description": "is the intent to ask why something happened or needs to be done?", + "domain": [ + "general" + ] + }, + "wifi": { + "description": "is the intent to ask something related to wifi or wireless?", + "domain": [ + "hotels" + ] + }, + "wrong_notworking_notshowing": { + "description": "is the intent asking about something wrong, missing or not working?", + "domain": [ + "general" + ] + } +} diff --git a/catalogs/semantic_v3.json b/catalogs/semantic_v3.json new file mode 100644 index 0000000000000000000000000000000000000000..87da02c2535d08282d8f5bb56d2bf5190dab7df2 --- /dev/null +++ b/catalogs/semantic_v3.json @@ -0,0 +1,3097 @@ +{ + "version": "d8855f35640ddfb75d58fd06f604725c", + "candidate_count": 68, + "catalog": [ + { + "canonical_id": "ADD_TIME_TIMER", + "description": "Add a stated amount of time to an existing timer.", + "description_zh": "给已有计时器增加相对时长。", + "boundary_group": "timer_modify", + "raw_labels": [ + "ADD_TIME_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1098, + "unique_normalized_queries": 664, + "single_intent_marker_rows": 1098, + "single_intent_marker_unique_nonconflicting_queries": 663, + "ambiguous_normalized_queries": 1 + }, + "definition_boundary": "ADD/SUBTRACT 是相对增减，UPDATE_TIMER 仅绝对新时长；fix my timer 等缺变化方向与新值的句子隔离。", + "equivalent_raw_labels": [ + "ADD_TIME_TIMER" + ], + "equivalent_source_labels": [ + "mtop:ADD_TIME_TIMER", + "topv2:ADD_TIME_TIMER" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "SUBTRACT_TIME_TIMER", + "UPDATE_TIMER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "ADD_TO_PLAYLIST_MUSIC", + "description": "Add a track or album to a named or current playlist.", + "description_zh": "把曲目或专辑加入指定播放列表。", + "boundary_group": "music_library_feedback", + "raw_labels": [ + "ADD_TO_PLAYLIST_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 159, + "unique_normalized_queries": 119, + "single_intent_marker_rows": 159, + "single_intent_marker_unique_nonconflicting_queries": 117, + "ambiguous_normalized_queries": 2 + }, + "definition_boundary": "点赞/负反馈、增删播放列表成员、创建删除列表有不同状态变化。favorite/add to favorites 或 don't play again 可能属于多个操作，需上下文或明确 playlist 动作才可唯一标注。", + "equivalent_raw_labels": [ + "ADD_TO_PLAYLIST_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:ADD_TO_PLAYLIST_MUSIC", + "topv2:ADD_TO_PLAYLIST_MUSIC" + ], + "potential_semantic_conflicts": [ + "CREATE_PLAYLIST_MUSIC", + "LIKE_MUSIC" + ], + "same_boundary_group_alternatives": [ + "CREATE_PLAYLIST_MUSIC", + "DELETE_PLAYLIST_MUSIC", + "DISLIKE_MUSIC", + "FOLLOW_MUSIC", + "LIKE_MUSIC", + "REMOVE_FROM_PLAYLIST_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "CREATE_ALARM", + "description": "Create a new alarm for a specified time or schedule.", + "description_zh": "新建按时间点触发的闹钟。", + "boundary_group": "alarm_operation", + "raw_labels": [ + "CREATE_ALARM" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 12080, + "unique_normalized_queries": 10444, + "single_intent_marker_rows": 12078, + "single_intent_marker_unique_nonconflicting_queries": 10433, + "ambiguous_normalized_queries": 9 + }, + "definition_boundary": "新建/修改/查询/删除计划与停止响铃/稍后再响分开。turn off/stop alarm 缺乏正在响铃或删除计划证据时隔离，不能只按 stop 猜标签。", + "equivalent_raw_labels": [ + "CREATE_ALARM" + ], + "equivalent_source_labels": [ + "mtop:CREATE_ALARM", + "topv2:CREATE_ALARM" + ], + "potential_semantic_conflicts": [ + "CREATE_REMINDER", + "CREATE_TIMER", + "UPDATE_ALARM" + ], + "same_boundary_group_alternatives": [ + "DELETE_ALARM", + "GET_ALARM", + "SILENCE_ALARM", + "SNOOZE_ALARM", + "UPDATE_ALARM" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "CREATE_CALL", + "description": "Initiate a voice or video call to one or more contacts.", + "description_zh": "向指定联系人发起语音或视频通话。", + "boundary_group": "call_lifecycle", + "raw_labels": [ + "CREATE_CALL" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1177, + "unique_normalized_queries": 1175, + "single_intent_marker_rows": 791, + "single_intent_marker_unique_nonconflicting_queries": 790, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "接听/忽略的对象是呼入通话；结束是已接通；hold/resume/switch 需要对应通话状态。无状态的 cancel 不自动映射。", + "equivalent_raw_labels": [ + "CREATE_CALL" + ], + "equivalent_source_labels": [ + "mtop:CREATE_CALL" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "ANSWER_CALL", + "CANCEL_CALL", + "END_CALL", + "HOLD_CALL", + "IGNORE_CALL", + "RESUME_CALL", + "SWITCH_CALL" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "CREATE_PLAYLIST_MUSIC", + "description": "Create a new music playlist or station.", + "description_zh": "创建新的音乐播放列表或电台。", + "boundary_group": "music_library_feedback", + "raw_labels": [ + "CREATE_PLAYLIST_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 197, + "unique_normalized_queries": 173, + "single_intent_marker_rows": 197, + "single_intent_marker_unique_nonconflicting_queries": 173, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "点赞/负反馈、增删播放列表成员、创建删除列表有不同状态变化。favorite/add to favorites 或 don't play again 可能属于多个操作，需上下文或明确 playlist 动作才可唯一标注。", + "equivalent_raw_labels": [ + "CREATE_PLAYLIST_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:CREATE_PLAYLIST_MUSIC", + "topv2:CREATE_PLAYLIST_MUSIC" + ], + "potential_semantic_conflicts": [ + "ADD_TO_PLAYLIST_MUSIC" + ], + "same_boundary_group_alternatives": [ + "ADD_TO_PLAYLIST_MUSIC", + "DELETE_PLAYLIST_MUSIC", + "DISLIKE_MUSIC", + "FOLLOW_MUSIC", + "LIKE_MUSIC", + "REMOVE_FROM_PLAYLIST_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "CREATE_REMINDER", + "description": "Create a reminder for a task at a later time or location.", + "description_zh": "创建提醒，把待办事项安排在以后提醒。", + "boundary_group": "reminder_operation", + "raw_labels": [ + "CREATE_REMINDER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 11561, + "unique_normalized_queries": 10375, + "single_intent_marker_rows": 8275, + "single_intent_marker_unique_nonconflicting_queries": 7340, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "合并提醒修改的日期/地点/待办子字段；HELP 只接操作说明请求。remind me to call X 的最外层是提醒，不能把内嵌 call 当即时通话。", + "equivalent_raw_labels": [ + "CREATE_REMINDER" + ], + "equivalent_source_labels": [ + "mtop:CREATE_REMINDER", + "topv2:CREATE_REMINDER" + ], + "potential_semantic_conflicts": [ + "CREATE_ALARM", + "HELP_REMINDER" + ], + "same_boundary_group_alternatives": [ + "DELETE_REMINDER", + "HELP_REMINDER", + "UPDATE_REMINDER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "CREATE_TIMER", + "description": "Create and start a countdown timer for a duration.", + "description_zh": "创建并启动一个按时长倒数的计时器。", + "boundary_group": "timer_operation", + "raw_labels": [ + "CREATE_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 4014, + "unique_normalized_queries": 2686, + "single_intent_marker_rows": 4014, + "single_intent_marker_unique_nonconflicting_queries": 2683, + "ambiguous_normalized_queries": 3 + }, + "definition_boundary": "删除、暂停、恢复和从头重启必须有可区分的动作语义。stop/reset/refresh 等短句可能多解，保留原标注但不自动进入单选主集。", + "equivalent_raw_labels": [ + "CREATE_TIMER" + ], + "equivalent_source_labels": [ + "mtop:CREATE_TIMER", + "topv2:CREATE_TIMER" + ], + "potential_semantic_conflicts": [ + "CREATE_ALARM" + ], + "same_boundary_group_alternatives": [ + "DELETE_TIMER", + "PAUSE_TIMER", + "RESTART_TIMER", + "RESUME_TIMER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "DELETE_ALARM", + "description": "Delete a scheduled alarm or alarm schedule.", + "description_zh": "删除或取消已安排的闹钟计划。", + "boundary_group": "alarm_operation", + "raw_labels": [ + "DELETE_ALARM" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 2709, + "unique_normalized_queries": 1869, + "single_intent_marker_rows": 1364, + "single_intent_marker_unique_nonconflicting_queries": 745, + "ambiguous_normalized_queries": 11 + }, + "definition_boundary": "新建/修改/查询/删除计划与停止响铃/稍后再响分开。turn off/stop alarm 缺乏正在响铃或删除计划证据时隔离，不能只按 stop 猜标签。", + "equivalent_raw_labels": [ + "DELETE_ALARM" + ], + "equivalent_source_labels": [ + "mtop:DELETE_ALARM", + "topv2:DELETE_ALARM" + ], + "potential_semantic_conflicts": [ + "SILENCE_ALARM" + ], + "same_boundary_group_alternatives": [ + "CREATE_ALARM", + "GET_ALARM", + "SILENCE_ALARM", + "SNOOZE_ALARM", + "UPDATE_ALARM" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "DELETE_REMINDER", + "description": "Delete an existing reminder.", + "description_zh": "删除已创建的提醒。", + "boundary_group": "reminder_operation", + "raw_labels": [ + "DELETE_REMINDER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 3331, + "unique_normalized_queries": 3004, + "single_intent_marker_rows": 2812, + "single_intent_marker_unique_nonconflicting_queries": 2517, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "合并提醒修改的日期/地点/待办子字段；HELP 只接操作说明请求。remind me to call X 的最外层是提醒，不能把内嵌 call 当即时通话。", + "equivalent_raw_labels": [ + "DELETE_REMINDER" + ], + "equivalent_source_labels": [ + "mtop:DELETE_REMINDER", + "topv2:DELETE_REMINDER" + ], + "potential_semantic_conflicts": [ + "HELP_REMINDER" + ], + "same_boundary_group_alternatives": [ + "CREATE_REMINDER", + "HELP_REMINDER", + "UPDATE_REMINDER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "DELETE_TIMER", + "description": "Delete or cancel a timer instance, or clear saved timer history.", + "description_zh": "删除或取消一个计时器实例。", + "boundary_group": "timer_operation", + "raw_labels": [ + "DELETE_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 852, + "unique_normalized_queries": 423, + "single_intent_marker_rows": 798, + "single_intent_marker_unique_nonconflicting_queries": 371, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "删除、暂停、恢复和从头重启必须有可区分的动作语义。stop/reset/refresh 等短句可能多解，保留原标注但不自动进入单选主集。", + "equivalent_raw_labels": [ + "DELETE_TIMER" + ], + "equivalent_source_labels": [ + "mtop:DELETE_TIMER", + "topv2:DELETE_TIMER" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "CREATE_TIMER", + "PAUSE_TIMER", + "RESTART_TIMER", + "RESUME_TIMER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "DISLIKE_MUSIC", + "description": "Give negative preference feedback on a song, artist, or music item.", + "description_zh": "对音乐表达不喜欢或负反馈。", + "boundary_group": "music_library_feedback", + "raw_labels": [ + "DISLIKE_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 126, + "unique_normalized_queries": 96, + "single_intent_marker_rows": 126, + "single_intent_marker_unique_nonconflicting_queries": 96, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "点赞/负反馈、增删播放列表成员、创建删除列表有不同状态变化。favorite/add to favorites 或 don't play again 可能属于多个操作，需上下文或明确 playlist 动作才可唯一标注。", + "equivalent_raw_labels": [ + "DISLIKE_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:DISLIKE_MUSIC", + "topv2:DISLIKE_MUSIC" + ], + "potential_semantic_conflicts": [ + "REMOVE_FROM_PLAYLIST_MUSIC", + "SKIP_TRACK_MUSIC", + "STOP_MUSIC" + ], + "same_boundary_group_alternatives": [ + "ADD_TO_PLAYLIST_MUSIC", + "CREATE_PLAYLIST_MUSIC", + "DELETE_PLAYLIST_MUSIC", + "FOLLOW_MUSIC", + "LIKE_MUSIC", + "REMOVE_FROM_PLAYLIST_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "END_CALL", + "description": "End an active call.", + "description_zh": "挂断正在进行的通话。", + "boundary_group": "call_lifecycle", + "raw_labels": [ + "END_CALL" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 121, + "unique_normalized_queries": 118, + "single_intent_marker_rows": 113, + "single_intent_marker_unique_nonconflicting_queries": 110, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "接听/忽略的对象是呼入通话；结束是已接通；hold/resume/switch 需要对应通话状态。无状态的 cancel 不自动映射。", + "equivalent_raw_labels": [ + "END_CALL" + ], + "equivalent_source_labels": [ + "mtop:END_CALL" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "ANSWER_CALL", + "CANCEL_CALL", + "CREATE_CALL", + "HOLD_CALL", + "IGNORE_CALL", + "RESUME_CALL", + "SWITCH_CALL" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_AGE", + "description": "Query a specified person's age.", + "description_zh": "查询指定人物的年龄。", + "boundary_group": "person_attribute", + "raw_labels": [ + "GET_AGE" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 51, + "unique_normalized_queries": 51, + "single_intent_marker_rows": 51, + "single_intent_marker_unique_nonconflicting_queries": 51, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "年龄、联系方式、性别、语言、所属小组是不同明确属性；GET_CONTACT 的泛描述不得宣称覆盖所有属性。", + "equivalent_raw_labels": [ + "GET_AGE" + ], + "equivalent_source_labels": [ + "mtop:GET_AGE" + ], + "potential_semantic_conflicts": [ + "GET_CONTACT", + "GET_EDUCATION_TIME", + "GET_EMPLOYER", + "GET_EMPLOYMENT_TIME" + ], + "same_boundary_group_alternatives": [ + "GET_CONTACT_METHOD", + "GET_GENDER", + "GET_GROUP", + "GET_LANGUAGE" + ], + "parent_child_conflicts": [ + { + "parent": "GET_CONTACT", + "children": [ + "GET_AGE", + "GET_EMPLOYER", + "GET_JOB", + "GET_CONTACT_METHOD", + "GET_EDUCATION_TIME", + "GET_EMPLOYMENT_TIME" + ], + "resolution": "restrict_parent_to_identity_search_or_general_profile; isolate_ambiguous_attribute_queries" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_ALARM", + "description": "List existing alarms or inspect their scheduled times.", + "description_zh": "查询已设闹钟或其触发时间。", + "boundary_group": "alarm_operation", + "raw_labels": [ + "GET_ALARM" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 2901, + "unique_normalized_queries": 2085, + "single_intent_marker_rows": 1904, + "single_intent_marker_unique_nonconflicting_queries": 1246, + "ambiguous_normalized_queries": 1 + }, + "definition_boundary": "新建/修改/查询/删除计划与停止响铃/稍后再响分开。turn off/stop alarm 缺乏正在响铃或删除计划证据时隔离，不能只按 stop 猜标签。", + "equivalent_raw_labels": [ + "GET_ALARM" + ], + "equivalent_source_labels": [ + "mtop:GET_ALARM", + "topv2:GET_ALARM" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "CREATE_ALARM", + "DELETE_ALARM", + "SILENCE_ALARM", + "SNOOZE_ALARM", + "UPDATE_ALARM" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_AVAILABILITY", + "description": "Query whether contacts are online or available.", + "description_zh": "查询联系人是否在线或可用。", + "boundary_group": "availability", + "raw_labels": [ + "GET_AVAILABILITY" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 214, + "unique_normalized_queries": 208, + "single_intent_marker_rows": 191, + "single_intent_marker_unique_nonconflicting_queries": 185, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "查询别人是否可用与设置自己的 presence 分开；display availability 等语句缺动作极性时隔离。", + "equivalent_raw_labels": [ + "GET_AVAILABILITY" + ], + "equivalent_source_labels": [ + "mtop:GET_AVAILABILITY" + ], + "potential_semantic_conflicts": [ + "GET_CONTACT" + ], + "same_boundary_group_alternatives": [ + "SET_AVAILABLE", + "SET_UNAVAILABLE" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_CALL", + "description": "Retrieve call history or call details, including participants and times.", + "description_zh": "查询通话记录、来电信息、参与者或通话时间。", + "boundary_group": "call_information", + "raw_labels": [ + "GET_CALL", + "GET_CALL_CONTACT", + "GET_CALL_TIME" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 107, + "unique_normalized_queries": 107, + "single_intent_marker_rows": 87, + "single_intent_marker_unique_nonconflicting_queries": 87, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "合并通话记录与联系人/时间子字段，避免泛查询和细字段同时成为可接受答案。", + "equivalent_raw_labels": [ + "GET_CALL", + "GET_CALL_CONTACT", + "GET_CALL_TIME" + ], + "equivalent_source_labels": [ + "mtop:GET_CALL", + "mtop:GET_CALL_CONTACT", + "mtop:GET_CALL_TIME" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [], + "parent_child_conflicts": [ + { + "parent": "GET_CALL", + "children": [ + "GET_CALL_CONTACT", + "GET_CALL_TIME" + ], + "resolution": "merge_children_into_parent" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_CONTACT", + "description": "Find or identify people using names, relationships, or filters, or retrieve a general person profile.", + "description_zh": "查找、识别人物或读取一般人物资料。", + "boundary_group": "person_identity", + "raw_labels": [ + "GET_CONTACT", + "GET_INFO_CONTACT" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 727, + "unique_normalized_queries": 726, + "single_intent_marker_rows": 727, + "single_intent_marker_unique_nonconflicting_queries": 726, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "人物搜索/身份/一般资料与请求单个具体属性分开；姓名或身份匹配而不是回答真实私人属性。可能同时属于具体属性类的源句需隔离。", + "equivalent_raw_labels": [ + "GET_CONTACT", + "GET_INFO_CONTACT" + ], + "equivalent_source_labels": [ + "mtop:GET_CONTACT", + "mtop:GET_INFO_CONTACT" + ], + "potential_semantic_conflicts": [ + "GET_AGE", + "GET_AVAILABILITY", + "GET_EDUCATION_TIME", + "GET_EMPLOYER", + "GET_EMPLOYMENT_TIME", + "GET_TRACK_INFO_MUSIC" + ], + "same_boundary_group_alternatives": [ + "GET_MUTUAL_FRIENDS" + ], + "parent_child_conflicts": [ + { + "parent": "GET_CONTACT", + "children": [ + "GET_AGE", + "GET_EMPLOYER", + "GET_JOB", + "GET_CONTACT_METHOD", + "GET_EDUCATION_TIME", + "GET_EMPLOYMENT_TIME" + ], + "resolution": "restrict_parent_to_identity_search_or_general_profile; isolate_ambiguous_attribute_queries" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_DIRECTIONS", + "description": "Obtain directions or a route to a destination.", + "description_zh": "首次获取前往目的地的路线或导航指引。", + "boundary_group": "navigation_route", + "raw_labels": [ + "GET_DIRECTIONS" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1859, + "unique_normalized_queries": 1852, + "single_intent_marker_rows": 488, + "single_intent_marker_unique_nonconflicting_queries": 484, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "首次获取路线与修改已有路线分开；GET_INFO_ROUTE 只读路线属性。next/alternative/current 等上下文不足的句子不硬判。", + "equivalent_raw_labels": [ + "GET_DIRECTIONS" + ], + "equivalent_source_labels": [ + "topv2:GET_DIRECTIONS" + ], + "potential_semantic_conflicts": [ + "UPDATE_DIRECTIONS" + ], + "same_boundary_group_alternatives": [ + "GET_INFO_ROUTE", + "UPDATE_DIRECTIONS" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_DISTANCE", + "description": "Query the distance between locations.", + "description_zh": "查询地点之间的距离。", + "boundary_group": "navigation_estimate", + "raw_labels": [ + "GET_DISTANCE" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1742, + "unique_normalized_queries": 1732, + "single_intent_marker_rows": 1384, + "single_intent_marker_unique_nonconflicting_queries": 1374, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "距离、行程时长、到达时刻、出发时刻分别为不同量，不能把多个量同时请求的 query 强制改单选。", + "equivalent_raw_labels": [ + "GET_DISTANCE" + ], + "equivalent_source_labels": [ + "topv2:GET_DISTANCE" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "GET_ESTIMATED_ARRIVAL", + "GET_ESTIMATED_DEPARTURE", + "GET_ESTIMATED_DURATION" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_EDUCATION_TIME", + "description": "Query when, or for how long, a person studied or attended school, including enrollment and graduation times.", + "description_zh": "查询人物入学、就读或毕业的时间。", + "boundary_group": "person_education", + "raw_labels": [ + "GET_EDUCATION_TIME" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 46, + "unique_normalized_queries": 46, + "single_intent_marker_rows": 46, + "single_intent_marker_unique_nonconflicting_queries": 46, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "学校、专业、学位和时间需要明确字段；笼统 educational background 不可凭原类别强制分派。", + "equivalent_raw_labels": [ + "GET_EDUCATION_TIME" + ], + "equivalent_source_labels": [ + "mtop:GET_EDUCATION_TIME" + ], + "potential_semantic_conflicts": [ + "GET_AGE", + "GET_CONTACT", + "GET_EMPLOYER", + "GET_EMPLOYMENT_TIME" + ], + "same_boundary_group_alternatives": [ + "GET_EDUCATION_DEGREE", + "GET_MAJOR", + "GET_UNDERGRAD" + ], + "parent_child_conflicts": [ + { + "parent": "GET_CONTACT", + "children": [ + "GET_AGE", + "GET_EMPLOYER", + "GET_JOB", + "GET_CONTACT_METHOD", + "GET_EDUCATION_TIME", + "GET_EMPLOYMENT_TIME" + ], + "resolution": "restrict_parent_to_identity_search_or_general_profile; isolate_ambiguous_attribute_queries" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_EMPLOYER", + "description": "Query the employer or organization a person works for.", + "description_zh": "查询人物的雇主或任职机构。", + "boundary_group": "person_employment", + "raw_labels": [ + "GET_EMPLOYER" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 99, + "unique_normalized_queries": 99, + "single_intent_marker_rows": 99, + "single_intent_marker_unique_nonconflicting_queries": 99, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "雇主机构、职业岗位、任职时间分开；where does X work 若无法区分雇主与工作地点则隔离。", + "equivalent_raw_labels": [ + "GET_EMPLOYER" + ], + "equivalent_source_labels": [ + "mtop:GET_EMPLOYER" + ], + "potential_semantic_conflicts": [ + "GET_AGE", + "GET_CONTACT", + "GET_EDUCATION_TIME", + "GET_EMPLOYMENT_TIME" + ], + "same_boundary_group_alternatives": [ + "GET_EMPLOYMENT_TIME", + "GET_JOB" + ], + "parent_child_conflicts": [ + { + "parent": "GET_CONTACT", + "children": [ + "GET_AGE", + "GET_EMPLOYER", + "GET_JOB", + "GET_CONTACT_METHOD", + "GET_EDUCATION_TIME", + "GET_EMPLOYMENT_TIME" + ], + "resolution": "restrict_parent_to_identity_search_or_general_profile; isolate_ambiguous_attribute_queries" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_EMPLOYMENT_TIME", + "description": "Query employment dates, duration, or employment chronology.", + "description_zh": "查询人物就职、离职或任职时长。", + "boundary_group": "person_employment", + "raw_labels": [ + "GET_EMPLOYMENT_TIME" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 56, + "unique_normalized_queries": 56, + "single_intent_marker_rows": 56, + "single_intent_marker_unique_nonconflicting_queries": 56, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "雇主机构、职业岗位、任职时间分开；where does X work 若无法区分雇主与工作地点则隔离。", + "equivalent_raw_labels": [ + "GET_EMPLOYMENT_TIME" + ], + "equivalent_source_labels": [ + "mtop:GET_EMPLOYMENT_TIME" + ], + "potential_semantic_conflicts": [ + "GET_AGE", + "GET_CONTACT", + "GET_EDUCATION_TIME", + "GET_EMPLOYER" + ], + "same_boundary_group_alternatives": [ + "GET_EMPLOYER", + "GET_JOB" + ], + "parent_child_conflicts": [ + { + "parent": "GET_CONTACT", + "children": [ + "GET_AGE", + "GET_EMPLOYER", + "GET_JOB", + "GET_CONTACT_METHOD", + "GET_EDUCATION_TIME", + "GET_EMPLOYMENT_TIME" + ], + "resolution": "restrict_parent_to_identity_search_or_general_profile; isolate_ambiguous_attribute_queries" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_ESTIMATED_ARRIVAL", + "description": "Estimate the arrival time for a trip.", + "description_zh": "估计按给定出发条件何时到达目的地。", + "boundary_group": "navigation_estimate", + "raw_labels": [ + "GET_ESTIMATED_ARRIVAL" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1495, + "unique_normalized_queries": 1493, + "single_intent_marker_rows": 382, + "single_intent_marker_unique_nonconflicting_queries": 382, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "距离、行程时长、到达时刻、出发时刻分别为不同量，不能把多个量同时请求的 query 强制改单选。", + "equivalent_raw_labels": [ + "GET_ESTIMATED_ARRIVAL" + ], + "equivalent_source_labels": [ + "topv2:GET_ESTIMATED_ARRIVAL" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "GET_DISTANCE", + "GET_ESTIMATED_DEPARTURE", + "GET_ESTIMATED_DURATION" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_ESTIMATED_DEPARTURE", + "description": "Determine when to depart to arrive by a target time.", + "description_zh": "计算为按时到达而应在何时出发。", + "boundary_group": "navigation_estimate", + "raw_labels": [ + "GET_ESTIMATED_DEPARTURE" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 738, + "unique_normalized_queries": 738, + "single_intent_marker_rows": 108, + "single_intent_marker_unique_nonconflicting_queries": 108, + "ambiguous_normalized_queries": 1 + }, + "definition_boundary": "距离、行程时长、到达时刻、出发时刻分别为不同量，不能把多个量同时请求的 query 强制改单选。", + "equivalent_raw_labels": [ + "GET_ESTIMATED_DEPARTURE" + ], + "equivalent_source_labels": [ + "topv2:GET_ESTIMATED_DEPARTURE" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "GET_DISTANCE", + "GET_ESTIMATED_ARRIVAL", + "GET_ESTIMATED_DURATION" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_ESTIMATED_DURATION", + "description": "Estimate a trip's travel duration.", + "description_zh": "估计一段行程需要的时间。", + "boundary_group": "navigation_estimate", + "raw_labels": [ + "GET_ESTIMATED_DURATION" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 4158, + "unique_normalized_queries": 4095, + "single_intent_marker_rows": 1588, + "single_intent_marker_unique_nonconflicting_queries": 1575, + "ambiguous_normalized_queries": 2 + }, + "definition_boundary": "距离、行程时长、到达时刻、出发时刻分别为不同量，不能把多个量同时请求的 query 强制改单选。", + "equivalent_raw_labels": [ + "GET_ESTIMATED_DURATION" + ], + "equivalent_source_labels": [ + "topv2:GET_ESTIMATED_DURATION" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "GET_DISTANCE", + "GET_ESTIMATED_ARRIVAL", + "GET_ESTIMATED_DEPARTURE" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_EVENT", + "description": "Search for or list events matching stated criteria.", + "description_zh": "搜索、列出或查找符合条件的活动。", + "boundary_group": "event_information", + "raw_labels": [ + "GET_EVENT" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 9580, + "unique_normalized_queries": 8819, + "single_intent_marker_rows": 7725, + "single_intent_marker_unique_nonconflicting_queries": 6991, + "ambiguous_normalized_queries": 4 + }, + "definition_boundary": "GET_EVENT 仅活动搜索/列表；参加者信息合并两个词序标签及人数子字段。特定活动时间/类型/组织者另行核实，不能与笼统‘全部活动详情’泛能力并列。", + "equivalent_raw_labels": [ + "GET_EVENT" + ], + "equivalent_source_labels": [ + "mtop:GET_EVENT", + "topv2:GET_EVENT" + ], + "potential_semantic_conflicts": [ + "GET_TRACK_INFO_MUSIC" + ], + "same_boundary_group_alternatives": [ + "GET_ATTENDEE_EVENT", + "GET_CATEGORY_EVENT", + "GET_DATE_TIME_EVENT", + "GET_EVENT_ORGANIZER" + ], + "parent_child_conflicts": [ + { + "parent": "GET_EVENT", + "children": [ + "GET_ATTENDEE_EVENT", + "GET_CATEGORY_EVENT", + "GET_DATE_TIME_EVENT", + "GET_EVENT_ORGANIZER" + ], + "resolution": "restrict_parent_to_event_search; do_not_claim_generic_full_event_details" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_INFO_RECIPES", + "description": "Answer a specific question about food or cooking, including nutritional content, calories, ingredients, cooking methods, temperature, duration, or conditions; this is not a request for a complete recipe.", + "description_zh": "回答有关食材、做法条件、温度或时长的具体烹饪问题。", + "boundary_group": "recipe_information", + "raw_labels": [ + "GET_INFO_RECIPES", + "IS_TRUE_RECIPES" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 861, + "unique_normalized_queries": 854, + "single_intent_marker_rows": 861, + "single_intent_marker_unique_nonconflicting_queries": 854, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "GET_RECIPES 获取完整食谱，GET_INFO_RECIPES 回答具体烹饪问题（含是非式）；how to cook X 不能同时向完整食谱和泛烹饪问答标唯一答案。", + "equivalent_raw_labels": [ + "GET_INFO_RECIPES", + "IS_TRUE_RECIPES" + ], + "equivalent_source_labels": [ + "mtop:GET_INFO_RECIPES", + "mtop:IS_TRUE_RECIPES" + ], + "potential_semantic_conflicts": [ + "GET_RECIPES" + ], + "same_boundary_group_alternatives": [ + "GET_RECIPES" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_INFO_ROAD_CONDITION", + "description": "Query physical road conditions such as ice, flooding, or weather damage.", + "description_zh": "查询道路是否结冰、积水或受天气影响。", + "boundary_group": "navigation_condition", + "raw_labels": [ + "GET_INFO_ROAD_CONDITION" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 376, + "unique_normalized_queries": 375, + "single_intent_marker_rows": 229, + "single_intent_marker_unique_nonconflicting_queries": 224, + "ambiguous_normalized_queries": 4 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"single_intent_marker_unique_nonconflicting_queries": 5842, + "ambiguous_normalized_queries": 6 + }, + "definition_boundary": "道路物理状态与车流拥堵分开；天气导致拥堵且同时询问两者的语句需多标签或隔离。", + "equivalent_raw_labels": [ + "GET_INFO_TRAFFIC" + ], + "equivalent_source_labels": [ + "topv2:GET_INFO_TRAFFIC" + ], + "potential_semantic_conflicts": [ + "GET_INFO_ROAD_CONDITION" + ], + "same_boundary_group_alternatives": [ + "GET_INFO_ROAD_CONDITION" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_MESSAGE", + "description": "Retrieve or read messages and inspect their sender or other details.", + "description_zh": "查询或读取消息及其发送者等信息。", + "boundary_group": "messaging_information", + "raw_labels": [ + "GET_MESSAGE", + "GET_MESSAGE_CONTACT" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1558, + "unique_normalized_queries": 1256, + "single_intent_marker_rows": 1380, + "single_intent_marker_unique_nonconflicting_queries": 1085, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "消息读取与发送者查询合并；不会因为发送者字段再建一个同等有效的泛查询竞争项。", + "equivalent_raw_labels": [ + "GET_MESSAGE", + "GET_MESSAGE_CONTACT" + ], + "equivalent_source_labels": [ + "mtop:GET_MESSAGE", + "mtop:GET_MESSAGE_CONTACT", + "topv2:GET_MESSAGE" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [], + "parent_child_conflicts": [ + { + "parent": "GET_MESSAGE", + "children": [ + "GET_MESSAGE_CONTACT" + ], + "resolution": "merge_children_into_parent" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_RECIPES", + "description": "Find a recipe or complete cooking instructions for a dish.", + "description_zh": "寻找某道菜的食谱或完整制作步骤。", + "boundary_group": "recipe_information", + "raw_labels": [ + "GET_RECIPES" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 442, + "unique_normalized_queries": 435, + "single_intent_marker_rows": 442, + "single_intent_marker_unique_nonconflicting_queries": 435, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "GET_RECIPES 获取完整食谱，GET_INFO_RECIPES 回答具体烹饪问题（含是非式）；how to cook X 不能同时向完整食谱和泛烹饪问答标唯一答案。", + "equivalent_raw_labels": [ + "GET_RECIPES" + ], + "equivalent_source_labels": [ + "mtop:GET_RECIPES" + ], + "potential_semantic_conflicts": [ + "GET_INFO_RECIPES" + ], + "same_boundary_group_alternatives": [ + "GET_INFO_RECIPES" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_REMINDER", + "description": "Retrieve existing reminders and their content, times, locations, or counts.", + "description_zh": "查询现有提醒及其内容、时间、地点或数量。", + "boundary_group": "reminder_information", + "raw_labels": [ + "GET_REMINDER", + "GET_REMINDER_AMOUNT", + "GET_REMINDER_DATE_TIME", + "GET_REMINDER_LOCATION" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 3423, + "unique_normalized_queries": 2963, + "single_intent_marker_rows": 3157, + "single_intent_marker_unique_nonconflicting_queries": 2716, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "合并提醒查询的数量/时间/地点子字段，避免泛查询与细字段相互冲突。", + "equivalent_raw_labels": [ + "GET_REMINDER", + "GET_REMINDER_AMOUNT", + "GET_REMINDER_DATE_TIME", + "GET_REMINDER_LOCATION" + ], + "equivalent_source_labels": [ + "mtop:GET_REMINDER", + "mtop:GET_REMINDER_AMOUNT", + "mtop:GET_REMINDER_DATE_TIME", + "mtop:GET_REMINDER_LOCATION", + "topv2:GET_REMINDER", + "topv2:GET_REMINDER_AMOUNT", + "topv2:GET_REMINDER_DATE_TIME", + "topv2:GET_REMINDER_LOCATION" + ], + "potential_semantic_conflicts": [ + "HELP_REMINDER" + ], + "same_boundary_group_alternatives": [], + "parent_child_conflicts": [ + { + "parent": "GET_REMINDER", + "children": [ + "GET_REMINDER_AMOUNT", + "GET_REMINDER_DATE_TIME", + "GET_REMINDER_LOCATION" + ], + "resolution": "merge_children_into_parent" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_STORIES_NEWS", + "description": "Retrieve news updates, read news stories, or answer questions about current events.", + "description_zh": "检索新闻、阅读报道或查询时事信息。", + "boundary_group": "news_information", + "raw_labels": [ + "GET_DETAILS_NEWS", + "GET_STORIES_NEWS", + "QUESTION_NEWS" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1219, + "unique_normalized_queries": 1207, + "single_intent_marker_rows": 1219, + "single_intent_marker_unique_nonconflicting_queries": 1207, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "合并新闻/详情/时事问答；普通人物资料、常识与音乐信息不因带名人名字而自动进入新闻能力。", + "equivalent_raw_labels": [ + "GET_DETAILS_NEWS", + "GET_STORIES_NEWS", + "QUESTION_NEWS" + ], + "equivalent_source_labels": [ + "mtop:GET_DETAILS_NEWS", + "mtop:GET_STORIES_NEWS", + "mtop:QUESTION_NEWS" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_SUNRISE", + "description": "Query sunrise time for a date and location.", + "description_zh": "查询日出时间。", + "boundary_group": "weather_attribute", + "raw_labels": [ + "GET_SUNRISE" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 179, + "unique_normalized_queries": 119, + "single_intent_marker_rows": 179, + "single_intent_marker_unique_nonconflicting_queries": 119, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "GET_WEATHER 限天气条件与预报；日出/日落时刻各独立。查询日出时的温度是天气，不是仅询问日出时刻。", + "equivalent_raw_labels": [ + "GET_SUNRISE" + ], + "equivalent_source_labels": [ + "mtop:GET_SUNRISE", + "topv2:GET_SUNRISE" + ], + "potential_semantic_conflicts": [ + "GET_SUNSET", + "GET_WEATHER" + ], + "same_boundary_group_alternatives": [ + "GET_AIRQUALITY", + "GET_SUNSET", + "GET_WEATHER" + ], + "parent_child_conflicts": [ + { + "parent": "GET_WEATHER", + "children": [ + "GET_SUNRISE", + "GET_SUNSET" + ], + "resolution": "restrict_parent_to_weather_conditions; sunrise_sunset_classes_only_for_times" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_SUNSET", + "description": "Query sunset time for a date and location.", + "description_zh": "查询日落时间。", + "boundary_group": "weather_attribute", + "raw_labels": [ + "GET_SUNSET" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 259, + "unique_normalized_queries": 172, + "single_intent_marker_rows": 257, + "single_intent_marker_unique_nonconflicting_queries": 169, + "ambiguous_normalized_queries": 1 + }, + "definition_boundary": "GET_WEATHER 限天气条件与预报；日出/日落时刻各独立。查询日出时的温度是天气，不是仅询问日出时刻。", + "equivalent_raw_labels": [ + "GET_SUNSET" + ], + "equivalent_source_labels": [ + "mtop:GET_SUNSET", + "topv2:GET_SUNSET" + ], + "potential_semantic_conflicts": [ + "GET_SUNRISE", + "GET_WEATHER" + ], + "same_boundary_group_alternatives": [ + "GET_AIRQUALITY", + "GET_SUNRISE", + "GET_WEATHER" + ], + "parent_child_conflicts": [ + { + "parent": "GET_WEATHER", + "children": [ + "GET_SUNRISE", + "GET_SUNSET" + ], + "resolution": "restrict_parent_to_weather_conditions; sunrise_sunset_classes_only_for_times" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_TIMER", + "description": "Inspect a timer's state, elapsed time, or remaining time.", + "description_zh": "查询计时器状态、剩余时长或已过时长。", + "boundary_group": "timer_information", + "raw_labels": [ + "GET_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 2214, + "unique_normalized_queries": 1393, + "single_intent_marker_rows": 2040, + "single_intent_marker_unique_nonconflicting_queries": 1219, + "ambiguous_normalized_queries": 2 + }, + "definition_boundary": "计时器查询只读状态或时长；不能与改变其计时进度混标。", + "equivalent_raw_labels": [ + "GET_TIMER" + ], + "equivalent_source_labels": [ + "mtop:GET_TIMER", + "topv2:GET_TIMER" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_TRACK_INFO_MUSIC", + "description": "Retrieve information about songs, albums, musicians, or the currently playing music.", + "description_zh": "查询曲目、专辑、音乐人或当前播放音乐的信息。", + "boundary_group": "music_information", + "raw_labels": [ + "GET_TRACK_INFO_MUSIC", + "QUESTION_MUSIC" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 106, + "unique_normalized_queries": 104, + "single_intent_marker_rows": 106, + "single_intent_marker_unique_nonconflicting_queries": 104, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "查询曲目元数据、音乐知识与歌词需要具体请求；QUESTION_MUSIC 中演唱会搜索可与 GET_EVENT 重叠，需移除或另审。", + "equivalent_raw_labels": [ + "GET_TRACK_INFO_MUSIC", + "QUESTION_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:GET_TRACK_INFO_MUSIC", + "mtop:QUESTION_MUSIC" + ], + "potential_semantic_conflicts": [ + "GET_CONTACT", + "GET_EVENT" + ], + "same_boundary_group_alternatives": [ + "GET_LYRICS_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "GET_WEATHER", + "description": "Query weather conditions or forecasts, including temperature, precipitation, and wind.", + "description_zh": "查询天气、温度、降水、风况或预报。", + "boundary_group": "weather_attribute", + "raw_labels": [ + "GET_WEATHER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 23392, + "unique_normalized_queries": 17584, + "single_intent_marker_rows": 23372, + "single_intent_marker_unique_nonconflicting_queries": 17544, + "ambiguous_normalized_queries": 20 + }, + "definition_boundary": "GET_WEATHER 限天气条件与预报；日出/日落时刻各独立。查询日出时的温度是天气，不是仅询问日出时刻。", + "equivalent_raw_labels": [ + "GET_WEATHER" + ], + "equivalent_source_labels": [ + "mtop:GET_WEATHER", + "topv2:GET_WEATHER" + ], + "potential_semantic_conflicts": [ + "GET_SUNRISE", + "GET_SUNSET" + ], + "same_boundary_group_alternatives": [ + "GET_AIRQUALITY", + "GET_SUNRISE", + "GET_SUNSET" + ], + "parent_child_conflicts": [ + { + "parent": "GET_WEATHER", + "children": [ + "GET_SUNRISE", + "GET_SUNSET" + ], + "resolution": "restrict_parent_to_weather_conditions; sunrise_sunset_classes_only_for_times" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "HELP_REMINDER", + "description": "Explain how to use or manage reminders.", + "description_zh": "解释提醒功能如何使用或如何进行某项提醒操作。", + "boundary_group": "reminder_operation", + "raw_labels": [ + "HELP_REMINDER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 43, + "unique_normalized_queries": 41, + "single_intent_marker_rows": 43, + "single_intent_marker_unique_nonconflicting_queries": 41, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "合并提醒修改的日期/地点/待办子字段；HELP 只接操作说明请求。remind me to call X 的最外层是提醒，不能把内嵌 call 当即时通话。", + "equivalent_raw_labels": [ + "HELP_REMINDER" + ], + "equivalent_source_labels": [ + "mtop:HELP_REMINDER", + "topv2:HELP_REMINDER" + ], + "potential_semantic_conflicts": [ + "CREATE_REMINDER", + "DELETE_REMINDER", + "GET_REMINDER", + "UPDATE_REMINDER" + ], + "same_boundary_group_alternatives": [ + "CREATE_REMINDER", + "DELETE_REMINDER", + "UPDATE_REMINDER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "IGNORE_CALL", + "description": "Ignore or decline an incoming call.", + "description_zh": "拒接或忽略呼入的通话。", + "boundary_group": "call_lifecycle", + "raw_labels": [ + "IGNORE_CALL" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 53, + "unique_normalized_queries": 53, + "single_intent_marker_rows": 40, + "single_intent_marker_unique_nonconflicting_queries": 40, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "接听/忽略的对象是呼入通话；结束是已接通；hold/resume/switch 需要对应通话状态。无状态的 cancel 不自动映射。", + "equivalent_raw_labels": [ + "IGNORE_CALL" + ], + "equivalent_source_labels": [ + "mtop:IGNORE_CALL" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "ANSWER_CALL", + "CANCEL_CALL", + "CREATE_CALL", + "END_CALL", + "HOLD_CALL", + "RESUME_CALL", + "SWITCH_CALL" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "IGNORE_MESSAGE", + "description": "Ignore an incoming message.", + "description_zh": "忽略一条收到的消息。", + "boundary_group": "messaging_action", + "raw_labels": [ + "IGNORE_MESSAGE" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 67, + "unique_normalized_queries": 61, + "single_intent_marker_rows": 67, + "single_intent_marker_unique_nonconflicting_queries": 61, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "创建发送、取消未发消息、忽略已收消息、对已收消息作反应分开。待发/已收状态不清的操作需要隔离。", + "equivalent_raw_labels": [ + "IGNORE_MESSAGE" + ], + "equivalent_source_labels": [ + "topv2:IGNORE_MESSAGE" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "CANCEL_MESSAGE", + "REACT_MESSAGE", + "SEND_MESSAGE" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "LIKE_MUSIC", + "description": "Give positive preference feedback on music, such as a like or thumbs-up.", + "description_zh": "对曲目或音乐表达喜欢、点赞等正反馈。", + "boundary_group": "music_library_feedback", + "raw_labels": [ + "LIKE_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 136, + "unique_normalized_queries": 106, + "single_intent_marker_rows": 136, + "single_intent_marker_unique_nonconflicting_queries": 104, + "ambiguous_normalized_queries": 2 + }, + "definition_boundary": "点赞/负反馈、增删播放列表成员、创建删除列表有不同状态变化。favorite/add to favorites 或 don't play again 可能属于多个操作，需上下文或明确 playlist 动作才可唯一标注。", + "equivalent_raw_labels": [ + "LIKE_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:LIKE_MUSIC", + "topv2:LIKE_MUSIC" + ], + "potential_semantic_conflicts": [ + "ADD_TO_PLAYLIST_MUSIC" + ], + "same_boundary_group_alternatives": [ + "ADD_TO_PLAYLIST_MUSIC", + "CREATE_PLAYLIST_MUSIC", + "DELETE_PLAYLIST_MUSIC", + "DISLIKE_MUSIC", + "FOLLOW_MUSIC", + "REMOVE_FROM_PLAYLIST_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "PAUSE_MUSIC", + "description": "Pause music playback while preserving the current position.", + "description_zh": "暂时暂停音乐并保留稍后继续的位置。", + "boundary_group": "music_playback", + "raw_labels": [ + "PAUSE_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 435, + "unique_normalized_queries": 116, + "single_intent_marker_rows": 435, + "single_intent_marker_unique_nonconflicting_queries": 116, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "当前曲目内快进/后退、跳转上一首/下一首、暂停/继续/停止保持区别。previous/last 与 replay、stop 与 pause 语义重叠的源句隔离。", + "equivalent_raw_labels": [ + "PAUSE_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:PAUSE_MUSIC", + "topv2:PAUSE_MUSIC" + ], + "potential_semantic_conflicts": [ + "STOP_MUSIC" + ], + "same_boundary_group_alternatives": [ + "FAST_FORWARD_MUSIC", + "PREVIOUS_TRACK_MUSIC", + "RESUME_MUSIC", + "REWIND_MUSIC", + "SKIP_TRACK_MUSIC", + "STOP_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "PAUSE_TIMER", + "description": "Pause a timer while preserving its current progress.", + "description_zh": "暂时暂停计时器并保留其进度。", + "boundary_group": "timer_operation", + "raw_labels": [ + "PAUSE_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1424, + "unique_normalized_queries": 523, + "single_intent_marker_rows": 1401, + "single_intent_marker_unique_nonconflicting_queries": 498, + "ambiguous_normalized_queries": 2 + }, + "definition_boundary": "删除、暂停、恢复和从头重启必须有可区分的动作语义。stop/reset/refresh 等短句可能多解，保留原标注但不自动进入单选主集。", + "equivalent_raw_labels": [ + "PAUSE_TIMER" + ], + "equivalent_source_labels": [ + "mtop:PAUSE_TIMER", + "topv2:PAUSE_TIMER" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "CREATE_TIMER", + "DELETE_TIMER", + "RESTART_TIMER", + "RESUME_TIMER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "PLAY_MUSIC", + "description": "Find and play songs, albums, playlists, or music stations.", + "description_zh": "查找并播放曲目、专辑、播放列表或音乐电台。", + "boundary_group": "music_start", + "raw_labels": [ + "PLAY_MEDIA", + "PLAY_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 7745, + "unique_normalized_queries": 6474, + "single_intent_marker_rows": 7745, + "single_intent_marker_unique_nonconflicting_queries": 6473, + "ambiguous_normalized_queries": 1 + }, + "definition_boundary": "PLAY_MUSIC/PLAY_MEDIA 合并为音乐检索播放。单纯询问作者/年代是音乐信息查询。", + "equivalent_raw_labels": [ + "PLAY_MEDIA", + "PLAY_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:PLAY_MEDIA", + "mtop:PLAY_MUSIC", + "topv2:PLAY_MUSIC" + ], + "potential_semantic_conflicts": [ + "REPLAY_MUSIC" + ], + "same_boundary_group_alternatives": [], + "parent_child_conflicts": [ + { + "parent": "PLAY_MUSIC", + "children": [ + "RESUME_MUSIC", + "REPLAY_MUSIC", + "PREVIOUS_TRACK_MUSIC" + ], + "resolution": "restrict_parent_to_new_music_selection; require_explicit_playback_state_for_children" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "REACT_MESSAGE", + "description": "Add a like or other reaction to an existing message.", + "description_zh": "对已有消息添加点赞或其他表情反应。", + "boundary_group": "messaging_action", + "raw_labels": [ + "REACT_MESSAGE" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 626, + "unique_normalized_queries": 622, + "single_intent_marker_rows": 550, + "single_intent_marker_unique_nonconflicting_queries": 546, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "创建发送、取消未发消息、忽略已收消息、对已收消息作反应分开。待发/已收状态不清的操作需要隔离。", + "equivalent_raw_labels": [ + "REACT_MESSAGE" + ], + "equivalent_source_labels": [ + "topv2:REACT_MESSAGE" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "CANCEL_MESSAGE", + "IGNORE_MESSAGE", + "SEND_MESSAGE" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "REMOVE_FROM_PLAYLIST_MUSIC", + "description": "Remove a track from a playlist; source annotations also include playback bans.", + "description_zh": "从播放列表移除曲目；源标注也混有禁止再播的偏好。", + "boundary_group": "music_library_feedback", + "raw_labels": [ + "REMOVE_FROM_PLAYLIST_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 249, + "unique_normalized_queries": 201, + "single_intent_marker_rows": 249, + "single_intent_marker_unique_nonconflicting_queries": 201, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "点赞/负反馈、增删播放列表成员、创建删除列表有不同状态变化。favorite/add to favorites 或 don't play again 可能属于多个操作，需上下文或明确 playlist 动作才可唯一标注。", + "equivalent_raw_labels": [ + "REMOVE_FROM_PLAYLIST_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:REMOVE_FROM_PLAYLIST_MUSIC", + "topv2:REMOVE_FROM_PLAYLIST_MUSIC" + ], + "potential_semantic_conflicts": [ + "DISLIKE_MUSIC", + "STOP_MUSIC" + ], + "same_boundary_group_alternatives": [ + "ADD_TO_PLAYLIST_MUSIC", + "CREATE_PLAYLIST_MUSIC", + "DELETE_PLAYLIST_MUSIC", + "DISLIKE_MUSIC", + "FOLLOW_MUSIC", + "LIKE_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "REPLAY_MUSIC", + "description": "Repeat a current or explicitly specified song, album, or playlist, once or continuously.", + "description_zh": "再次播放或循环当前、指定的曲目、专辑或列表。", + "boundary_group": "music_repeat", + "raw_labels": [ + "LOOP_MUSIC", + "REPEAT_ALL_MUSIC", + "REPLAY_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 994, + "unique_normalized_queries": 567, + "single_intent_marker_rows": 994, + "single_intent_marker_unique_nonconflicting_queries": 567, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "合并重复一次/循环/重复全队列为 REPLAY_MUSIC；关闭重复为 UNLOOP_MUSIC。明确上一首跳转仍是 PREVIOUS_TRACK_MUSIC，模糊 last song 句子不靠源类硬判。", + "equivalent_raw_labels": [ + "LOOP_MUSIC", + "REPEAT_ALL_MUSIC", + "REPLAY_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:LOOP_MUSIC", + "mtop:REPEAT_ALL_MUSIC", + "mtop:REPLAY_MUSIC", + "topv2:LOOP_MUSIC", + "topv2:REPLAY_MUSIC" + ], + "potential_semantic_conflicts": [ + "PLAY_MUSIC" + ], + "same_boundary_group_alternatives": [ + "UNLOOP_MUSIC" + ], + "parent_child_conflicts": [ + { + "parent": "PLAY_MUSIC", + "children": [ + "RESUME_MUSIC", + "REPLAY_MUSIC", + "PREVIOUS_TRACK_MUSIC" + ], + "resolution": "restrict_parent_to_new_music_selection; require_explicit_playback_state_for_children" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "RESTART_TIMER", + "description": "Restart an existing timer from its initial duration.", + "description_zh": "让已有计时器从头重新开始。", + "boundary_group": "timer_operation", + "raw_labels": [ + "RESTART_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 576, + "unique_normalized_queries": 252, + "single_intent_marker_rows": 532, + "single_intent_marker_unique_nonconflicting_queries": 208, + "ambiguous_normalized_queries": 2 + }, + "definition_boundary": "删除、暂停、恢复和从头重启必须有可区分的动作语义。stop/reset/refresh 等短句可能多解，保留原标注但不自动进入单选主集。", + "equivalent_raw_labels": [ + "RESTART_TIMER" + ], + "equivalent_source_labels": [ + "mtop:RESTART_TIMER", + "topv2:RESTART_TIMER" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "CREATE_TIMER", + "DELETE_TIMER", + "PAUSE_TIMER", + "RESUME_TIMER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "RESUME_TIMER", + "description": "Resume a paused timer from its saved progress.", + "description_zh": "让暂停的计时器继续计时。", + "boundary_group": "timer_operation", + "raw_labels": [ + "RESUME_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 622, + "unique_normalized_queries": 322, + "single_intent_marker_rows": 589, + "single_intent_marker_unique_nonconflicting_queries": 288, + "ambiguous_normalized_queries": 3 + }, + "definition_boundary": "删除、暂停、恢复和从头重启必须有可区分的动作语义。stop/reset/refresh 等短句可能多解，保留原标注但不自动进入单选主集。", + "equivalent_raw_labels": [ + "RESUME_TIMER" + ], + "equivalent_source_labels": [ + "mtop:RESUME_TIMER", + "topv2:RESUME_TIMER" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "CREATE_TIMER", + "DELETE_TIMER", + "PAUSE_TIMER", + "RESTART_TIMER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SEND_MESSAGE", + "description": "Compose, record, or send a text, audio, or video message.", + "description_zh": "编写、录制或发送文本、语音、视频消息。", + "boundary_group": "messaging_action", + "raw_labels": [ + "SEND_MESSAGE", + "SEND_TEXT_MESSAGE" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 8888, + "unique_normalized_queries": 6610, + "single_intent_marker_rows": 7256, + "single_intent_marker_unique_nonconflicting_queries": 5410, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "创建发送、取消未发消息、忽略已收消息、对已收消息作反应分开。待发/已收状态不清的操作需要隔离。", + "equivalent_raw_labels": [ + "SEND_MESSAGE", + "SEND_TEXT_MESSAGE" + ], + "equivalent_source_labels": [ + "mtop:SEND_MESSAGE", + "topv2:SEND_MESSAGE", + "topv2:SEND_TEXT_MESSAGE" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "CANCEL_MESSAGE", + "IGNORE_MESSAGE", + "REACT_MESSAGE" + ], + "parent_child_conflicts": [ + { + "parent": "SEND_MESSAGE", + "children": [ + "SEND_TEXT_MESSAGE" + ], + "resolution": "merge_children_into_parent" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SET_AVAILABLE", + "description": "Set the user's presence status to available.", + "description_zh": "把自己的在线状态设为可用。", + "boundary_group": "availability", + "raw_labels": [ + "SET_AVAILABLE" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 52, + "unique_normalized_queries": 51, + "single_intent_marker_rows": 52, + "single_intent_marker_unique_nonconflicting_queries": 51, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "查询别人是否可用与设置自己的 presence 分开；display availability 等语句缺动作极性时隔离。", + "equivalent_raw_labels": [ + "SET_AVAILABLE" + ], + "equivalent_source_labels": [ + "mtop:SET_AVAILABLE" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "GET_AVAILABILITY", + "SET_UNAVAILABLE" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SET_RSVP_INTERESTED", + "description": "RSVP to an event as interested or tentative.", + "description_zh": "将活动回复状态设为感兴趣或待定。", + "boundary_group": "event_rsvp", + "raw_labels": [ + "SET_RSVP_INTERESTED" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 44, + "unique_normalized_queries": 42, + "single_intent_marker_rows": 44, + "single_intent_marker_unique_nonconflicting_queries": 42, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "参加/不参加/感兴趣三种 RSVP 状态明确区分；用户仅评价活动而非要求设置状态时隔离。", + "equivalent_raw_labels": [ + "SET_RSVP_INTERESTED" + ], + "equivalent_source_labels": [ + "mtop:SET_RSVP_INTERESTED" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "SET_RSVP_NO", + "SET_RSVP_YES" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SET_RSVP_NO", + "description": "RSVP to an event as not attending.", + "description_zh": "将活动回复状态设为不参加。", + "boundary_group": "event_rsvp", + "raw_labels": [ + "SET_RSVP_NO" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 41, + "unique_normalized_queries": 41, + "single_intent_marker_rows": 41, + "single_intent_marker_unique_nonconflicting_queries": 41, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "参加/不参加/感兴趣三种 RSVP 状态明确区分；用户仅评价活动而非要求设置状态时隔离。", + "equivalent_raw_labels": [ + "SET_RSVP_NO" + ], + "equivalent_source_labels": [ + "mtop:SET_RSVP_NO" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "SET_RSVP_INTERESTED", + "SET_RSVP_YES" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SET_UNAVAILABLE", + "description": "Set the user's presence status to busy or unavailable.", + "description_zh": "把自己的在线状态设为忙碌或不可用。", + "boundary_group": "availability", + "raw_labels": [ + "SET_UNAVAILABLE" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 83, + "unique_normalized_queries": 80, + "single_intent_marker_rows": 83, + "single_intent_marker_unique_nonconflicting_queries": 80, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "查询别人是否可用与设置自己的 presence 分开；display availability 等语句缺动作极性时隔离。", + "equivalent_raw_labels": [ + "SET_UNAVAILABLE" + ], + "equivalent_source_labels": [ + "mtop:SET_UNAVAILABLE" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "GET_AVAILABILITY", + "SET_AVAILABLE" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SILENCE_ALARM", + "description": "Dismiss a currently ringing alarm without deleting its schedule.", + "description_zh": "停止正在响铃的闹钟，保留既有计划。", + "boundary_group": "alarm_operation", + "raw_labels": [ + "SILENCE_ALARM" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1108, + "unique_normalized_queries": 702, + "single_intent_marker_rows": 885, + "single_intent_marker_unique_nonconflicting_queries": 476, + "ambiguous_normalized_queries": 20 + }, + "definition_boundary": "新建/修改/查询/删除计划与停止响铃/稍后再响分开。turn off/stop alarm 缺乏正在响铃或删除计划证据时隔离，不能只按 stop 猜标签。", + "equivalent_raw_labels": [ + "SILENCE_ALARM" + ], + "equivalent_source_labels": [ + "mtop:SILENCE_ALARM", + "topv2:SILENCE_ALARM" + ], + "potential_semantic_conflicts": [ + "DELETE_ALARM" + ], + "same_boundary_group_alternatives": [ + "CREATE_ALARM", + "DELETE_ALARM", + "GET_ALARM", + "SNOOZE_ALARM", + "UPDATE_ALARM" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SKIP_TRACK_MUSIC", + "description": "Skip the current track and advance to the next one.", + "description_zh": "跳过当前曲目并转到下一首。", + "boundary_group": "music_playback", + "raw_labels": [ + "SKIP_TRACK_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1302, + "unique_normalized_queries": 450, + "single_intent_marker_rows": 1302, + "single_intent_marker_unique_nonconflicting_queries": 450, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "当前曲目内快进/后退、跳转上一首/下一首、暂停/继续/停止保持区别。previous/last 与 replay、stop 与 pause 语义重叠的源句隔离。", + "equivalent_raw_labels": [ + "SKIP_TRACK_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:SKIP_TRACK_MUSIC", + "topv2:SKIP_TRACK_MUSIC" + ], + "potential_semantic_conflicts": [ + "DISLIKE_MUSIC" + ], + "same_boundary_group_alternatives": [ + "FAST_FORWARD_MUSIC", + "PAUSE_MUSIC", + "PREVIOUS_TRACK_MUSIC", + "RESUME_MUSIC", + "REWIND_MUSIC", + "STOP_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SNOOZE_ALARM", + "description": "Snooze a ringing alarm so it rings again after a delay.", + "description_zh": "把当前闹钟推迟片刻再响。", + "boundary_group": "alarm_operation", + "raw_labels": [ + "SNOOZE_ALARM" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 933, + "unique_normalized_queries": 613, + "single_intent_marker_rows": 922, + "single_intent_marker_unique_nonconflicting_queries": 601, + "ambiguous_normalized_queries": 1 + }, + "definition_boundary": "新建/修改/查询/删除计划与停止响铃/稍后再响分开。turn off/stop alarm 缺乏正在响铃或删除计划证据时隔离，不能只按 stop 猜标签。", + "equivalent_raw_labels": [ + "SNOOZE_ALARM" + ], + "equivalent_source_labels": [ + "mtop:SNOOZE_ALARM", + "topv2:SNOOZE_ALARM" + ], + "potential_semantic_conflicts": [ + "UPDATE_ALARM" + ], + "same_boundary_group_alternatives": [ + "CREATE_ALARM", + "DELETE_ALARM", + "GET_ALARM", + "SILENCE_ALARM", + "UPDATE_ALARM" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "START_SHUFFLE_MUSIC", + "description": "Enable shuffled music playback.", + "description_zh": "开启随机播放。", + "boundary_group": "music_shuffle", + "raw_labels": [ + "START_SHUFFLE_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 246, + "unique_normalized_queries": 178, + "single_intent_marker_rows": 246, + "single_intent_marker_unique_nonconflicting_queries": 177, + "ambiguous_normalized_queries": 1 + }, + "definition_boundary": "开启和关闭随机播放的极性必须清楚；数量不足时不新增主目录类。", + "equivalent_raw_labels": [ + "START_SHUFFLE_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:START_SHUFFLE_MUSIC", + "topv2:START_SHUFFLE_MUSIC" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "STOP_SHUFFLE_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "STOP_MUSIC", + "description": "Stop music playback.", + "description_zh": "停止音乐播放。", + "boundary_group": "music_playback", + "raw_labels": [ + "STOP_MUSIC" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 274, + "unique_normalized_queries": 123, + "single_intent_marker_rows": 274, + "single_intent_marker_unique_nonconflicting_queries": 123, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "当前曲目内快进/后退、跳转上一首/下一首、暂停/继续/停止保持区别。previous/last 与 replay、stop 与 pause 语义重叠的源句隔离。", + "equivalent_raw_labels": [ + "STOP_MUSIC" + ], + "equivalent_source_labels": [ + "mtop:STOP_MUSIC", + "topv2:STOP_MUSIC" + ], + "potential_semantic_conflicts": [ + "DISLIKE_MUSIC", + "PAUSE_MUSIC", + "REMOVE_FROM_PLAYLIST_MUSIC" + ], + "same_boundary_group_alternatives": [ + "FAST_FORWARD_MUSIC", + "PAUSE_MUSIC", + "PREVIOUS_TRACK_MUSIC", + "RESUME_MUSIC", + "REWIND_MUSIC", + "SKIP_TRACK_MUSIC" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SUBTRACT_TIME_TIMER", + "description": "Subtract a stated amount of time from an existing timer.", + "description_zh": "从已有计时器减去相对时长。", + "boundary_group": "timer_modify", + "raw_labels": [ + "SUBTRACT_TIME_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 253, + "unique_normalized_queries": 209, + "single_intent_marker_rows": 252, + "single_intent_marker_unique_nonconflicting_queries": 208, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "ADD/SUBTRACT 是相对增减，UPDATE_TIMER 仅绝对新时长；fix my timer 等缺变化方向与新值的句子隔离。", + "equivalent_raw_labels": [ + "SUBTRACT_TIME_TIMER" + ], + "equivalent_source_labels": [ + "mtop:SUBTRACT_TIME_TIMER", + "topv2:SUBTRACT_TIME_TIMER" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "ADD_TIME_TIMER", + "UPDATE_TIMER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "SWITCH_CALL", + "description": "Switch from the current call to another existing or incoming call.", + "description_zh": "切换到另一条已存在或正在呼入的通话。", + "boundary_group": "call_lifecycle", + "raw_labels": [ + "SWITCH_CALL" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 49, + "unique_normalized_queries": 48, + "single_intent_marker_rows": 45, + "single_intent_marker_unique_nonconflicting_queries": 44, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "接听/忽略的对象是呼入通话；结束是已接通；hold/resume/switch 需要对应通话状态。无状态的 cancel 不自动映射。", + "equivalent_raw_labels": [ + "SWITCH_CALL" + ], + "equivalent_source_labels": [ + "mtop:SWITCH_CALL" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "ANSWER_CALL", + "CANCEL_CALL", + "CREATE_CALL", + "END_CALL", + "HOLD_CALL", + "IGNORE_CALL", + "RESUME_CALL" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "UPDATE_ALARM", + "description": "Modify an existing alarm, including its time, schedule, name, or label.", + "description_zh": "修改已有闹钟的时间或重复安排。", + "boundary_group": "alarm_operation", + "raw_labels": [ + "UPDATE_ALARM" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1678, + "unique_normalized_queries": 1597, + "single_intent_marker_rows": 747, + "single_intent_marker_unique_nonconflicting_queries": 670, + "ambiguous_normalized_queries": 3 + }, + "definition_boundary": "新建/修改/查询/删除计划与停止响铃/稍后再响分开。turn off/stop alarm 缺乏正在响铃或删除计划证据时隔离，不能只按 stop 猜标签。", + "equivalent_raw_labels": [ + "UPDATE_ALARM" + ], + "equivalent_source_labels": [ + "mtop:UPDATE_ALARM", + "topv2:UPDATE_ALARM" + ], + "potential_semantic_conflicts": [ + "CREATE_ALARM", + "SNOOZE_ALARM" + ], + "same_boundary_group_alternatives": [ + "CREATE_ALARM", + "DELETE_ALARM", + "GET_ALARM", + "SILENCE_ALARM", + "SNOOZE_ALARM" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "UPDATE_CALL", + "description": "Add, remove, or change participants in an existing call.", + "description_zh": "增加、移除或更改当前通话的参与者。", + "boundary_group": "call_participants_method", + "raw_labels": [ + "UPDATE_CALL" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 218, + "unique_normalized_queries": 218, + "single_intent_marker_rows": 141, + "single_intent_marker_unique_nonconflicting_queries": 141, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "参与者变化与音视频/显示模式变化分开；同时请求两种变化的 query 不进入单一答案任务。", + "equivalent_raw_labels": [ + "UPDATE_CALL" + ], + "equivalent_source_labels": [ + "mtop:UPDATE_CALL" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "MERGE_CALL", + "UPDATE_METHOD_CALL" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "UPDATE_DIRECTIONS", + "description": "Modify an existing route or choose an alternative to the current route.", + "description_zh": "调整正在使用或已选定的导航路线。", + "boundary_group": "navigation_route", + "raw_labels": [ + "UPDATE_DIRECTIONS" + ], + "sources": [ + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 245, + "unique_normalized_queries": 239, + "single_intent_marker_rows": 157, + "single_intent_marker_unique_nonconflicting_queries": 152, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "首次获取路线与修改已有路线分开；GET_INFO_ROUTE 只读路线属性。next/alternative/current 等上下文不足的句子不硬判。", + "equivalent_raw_labels": [ + "UPDATE_DIRECTIONS" + ], + "equivalent_source_labels": [ + "topv2:UPDATE_DIRECTIONS" + ], + "potential_semantic_conflicts": [ + "GET_DIRECTIONS" + ], + "same_boundary_group_alternatives": [ + "GET_DIRECTIONS", + "GET_INFO_ROUTE" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "UPDATE_METHOD_CALL", + "description": "Change an active call's audio, video, or display mode.", + "description_zh": "改变当前通话的音视频方式或显示模式。", + "boundary_group": "call_participants_method", + "raw_labels": [ + "UPDATE_METHOD_CALL" + ], + "sources": [ + "mtop" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 44, + "unique_normalized_queries": 44, + "single_intent_marker_rows": 42, + "single_intent_marker_unique_nonconflicting_queries": 42, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "参与者变化与音视频/显示模式变化分开；同时请求两种变化的 query 不进入单一答案任务。", + "equivalent_raw_labels": [ + "UPDATE_METHOD_CALL" + ], + "equivalent_source_labels": [ + "mtop:UPDATE_METHOD_CALL" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "MERGE_CALL", + "UPDATE_CALL" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "UPDATE_REMINDER", + "description": "Modify an existing reminder's content, time, location, or recurrence.", + "description_zh": "修改已有提醒的内容、时间、地点或重复安排。", + "boundary_group": "reminder_operation", + "raw_labels": [ + "UPDATE_REMINDER", + "UPDATE_REMINDER_DATE_TIME", + "UPDATE_REMINDER_LOCATION", + "UPDATE_REMINDER_TODO" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 1639, + "unique_normalized_queries": 1509, + "single_intent_marker_rows": 1414, + "single_intent_marker_unique_nonconflicting_queries": 1290, + "ambiguous_normalized_queries": 0 + }, + "definition_boundary": "合并提醒修改的日期/地点/待办子字段；HELP 只接操作说明请求。remind me to call X 的最外层是提醒，不能把内嵌 call 当即时通话。", + "equivalent_raw_labels": [ + "UPDATE_REMINDER", + "UPDATE_REMINDER_DATE_TIME", + "UPDATE_REMINDER_LOCATION", + "UPDATE_REMINDER_TODO" + ], + "equivalent_source_labels": [ + "mtop:UPDATE_REMINDER", + "mtop:UPDATE_REMINDER_DATE_TIME", + "mtop:UPDATE_REMINDER_LOCATION", + "mtop:UPDATE_REMINDER_TODO", + "topv2:UPDATE_REMINDER", + "topv2:UPDATE_REMINDER_DATE_TIME", + "topv2:UPDATE_REMINDER_TODO" + ], + "potential_semantic_conflicts": [ + "HELP_REMINDER" + ], + "same_boundary_group_alternatives": [ + "CREATE_REMINDER", + "DELETE_REMINDER", + "HELP_REMINDER" + ], + "parent_child_conflicts": [ + { + "parent": "UPDATE_REMINDER", + "children": [ + "UPDATE_REMINDER_DATE_TIME", + "UPDATE_REMINDER_LOCATION", + "UPDATE_REMINDER_TODO" + ], + "resolution": "merge_children_into_parent" + } + ], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + }, + { + "canonical_id": "UPDATE_TIMER", + "description": "Set an existing timer to a new absolute duration.", + "description_zh": "把已有计时器的时长改成指定的新值。", + "boundary_group": "timer_modify", + "raw_labels": [ + "UPDATE_TIMER" + ], + "sources": [ + "mtop", + "topv2" + ], + "eligible_for_main_choice": true, + "exclusion_reason": null, + "counts": { + "source_rows": 347, + "unique_normalized_queries": 301, + "single_intent_marker_rows": 317, + "single_intent_marker_unique_nonconflicting_queries": 270, + "ambiguous_normalized_queries": 1 + }, + "definition_boundary": "ADD/SUBTRACT 是相对增减，UPDATE_TIMER 仅绝对新时长；fix my timer 等缺变化方向与新值的句子隔离。", + "equivalent_raw_labels": [ + "UPDATE_TIMER" + ], + "equivalent_source_labels": [ + "mtop:UPDATE_TIMER", + "topv2:UPDATE_TIMER" + ], + "potential_semantic_conflicts": [], + "same_boundary_group_alternatives": [ + "ADD_TIME_TIMER", + "SUBTRACT_TIME_TIMER" + ], + "parent_child_conflicts": [], + "note": "same_boundary_group_alternatives are potential hard negatives, not equivalence assertions or an automatic exclusion list" + } + ], + "excluded_neighbors": { + "ADD_TIME_TIMER": [], + "ADD_TO_PLAYLIST_MUSIC": [ + "CREATE_PLAYLIST_MUSIC", + "LIKE_MUSIC" + ], + "CREATE_ALARM": [ + "CREATE_REMINDER", + "CREATE_TIMER", + "UPDATE_ALARM" + ], + "CREATE_CALL": [], + "CREATE_PLAYLIST_MUSIC": [ + "ADD_TO_PLAYLIST_MUSIC" + ], + "CREATE_REMINDER": [ + "CREATE_ALARM", + "HELP_REMINDER" + ], + "CREATE_TIMER": [ + "CREATE_ALARM" + ], + "DELETE_ALARM": [ + "SILENCE_ALARM" + ], + "DELETE_REMINDER": [ + "HELP_REMINDER" + ], + "DELETE_TIMER": [], + "DISLIKE_MUSIC": [ + "REMOVE_FROM_PLAYLIST_MUSIC", + "SKIP_TRACK_MUSIC", + "STOP_MUSIC" + ], + "END_CALL": [], + "GET_AGE": [ + "GET_CONTACT", + "GET_EDUCATION_TIME", + "GET_EMPLOYER", + "GET_EMPLOYMENT_TIME" + ], + "GET_ALARM": [], + "GET_AVAILABILITY": [ + "GET_CONTACT" + ], + "GET_CALL": [], + "GET_CONTACT": [ + "GET_AGE", + "GET_AVAILABILITY", + "GET_EDUCATION_TIME", + "GET_EMPLOYER", + "GET_EMPLOYMENT_TIME", + "GET_TRACK_INFO_MUSIC" + ], + "GET_DIRECTIONS": [ + "UPDATE_DIRECTIONS" + ], + "GET_DISTANCE": [], + "GET_EDUCATION_TIME": [ + "GET_AGE", + "GET_CONTACT", + "GET_EMPLOYER", + "GET_EMPLOYMENT_TIME" + ], + "GET_EMPLOYER": [ + "GET_AGE", + "GET_CONTACT", + "GET_EDUCATION_TIME", + "GET_EMPLOYMENT_TIME" + ], + "GET_EMPLOYMENT_TIME": [ + "GET_AGE", + "GET_CONTACT", + "GET_EDUCATION_TIME", + "GET_EMPLOYER" + ], + "GET_ESTIMATED_ARRIVAL": [], + "GET_ESTIMATED_DEPARTURE": [], + "GET_ESTIMATED_DURATION": [], + "GET_EVENT": [ + "GET_TRACK_INFO_MUSIC" + ], + "GET_INFO_RECIPES": [ + "GET_RECIPES" + ], + "GET_INFO_ROAD_CONDITION": [ + "GET_INFO_TRAFFIC" + ], + "GET_INFO_TRAFFIC": [ + "GET_INFO_ROAD_CONDITION" + ], + "GET_MESSAGE": [], + "GET_RECIPES": [ + "GET_INFO_RECIPES" + ], + "GET_REMINDER": [ + "HELP_REMINDER" + ], + "GET_STORIES_NEWS": [], + "GET_SUNRISE": [ + "GET_SUNSET", + "GET_WEATHER" + ], + "GET_SUNSET": [ + "GET_SUNRISE", + "GET_WEATHER" + ], + "GET_TIMER": [], + "GET_TRACK_INFO_MUSIC": [ + "GET_CONTACT", + "GET_EVENT" + ], + "GET_WEATHER": [ + "GET_SUNRISE", + "GET_SUNSET" + ], + "HELP_REMINDER": [ + "CREATE_REMINDER", + "DELETE_REMINDER", + "GET_REMINDER", + "UPDATE_REMINDER" + ], + "IGNORE_CALL": [], + "IGNORE_MESSAGE": [], + "LIKE_MUSIC": [ + "ADD_TO_PLAYLIST_MUSIC" + ], + "PAUSE_MUSIC": [ + "STOP_MUSIC" + ], + "PAUSE_TIMER": [], + "PLAY_MUSIC": [ + "REPLAY_MUSIC" + ], + "REACT_MESSAGE": [], + "REMOVE_FROM_PLAYLIST_MUSIC": [ + "DISLIKE_MUSIC", + "STOP_MUSIC" + ], + "REPLAY_MUSIC": [ + "PLAY_MUSIC" + ], + "RESTART_TIMER": [], + "RESUME_TIMER": [], + "SEND_MESSAGE": [], + "SET_AVAILABLE": [], + "SET_RSVP_INTERESTED": [], + "SET_RSVP_NO": [], + "SET_UNAVAILABLE": [], + "SILENCE_ALARM": [ + "DELETE_ALARM" + ], + "SKIP_TRACK_MUSIC": [ + "DISLIKE_MUSIC" + ], + "SNOOZE_ALARM": [ + "UPDATE_ALARM" + ], + "START_SHUFFLE_MUSIC": [], + "STOP_MUSIC": [ + "DISLIKE_MUSIC", + "PAUSE_MUSIC", + "REMOVE_FROM_PLAYLIST_MUSIC" + ], + "SUBTRACT_TIME_TIMER": [], + "SWITCH_CALL": [], + "UPDATE_ALARM": [ + "CREATE_ALARM", + "SNOOZE_ALARM" + ], + "UPDATE_CALL": [], + "UPDATE_DIRECTIONS": [ + "GET_DIRECTIONS" + ], + "UPDATE_METHOD_CALL": [], + "UPDATE_REMINDER": [ + "HELP_REMINDER" + ], + "UPDATE_TIMER": [] + }, + "description_updates": { + "GET_INFO_RECIPES": "Answer a specific question about food or cooking, including nutritional content, calories, ingredients, cooking methods, temperature, duration, or conditions; this is not a request for a complete recipe.", + "UPDATE_ALARM": "Modify an existing alarm, including its time, schedule, name, or label.", + "DELETE_TIMER": "Delete or cancel a timer instance, or clear saved timer history." + }, + "added_conflict_pairs": [ + [ + "GET_DIRECTIONS", + "UPDATE_DIRECTIONS" + ], + [ + "GET_CONTACT", + "GET_AVAILABILITY" + ] + ], + "additional_review_supported_conflicts": [ + [ + "CREATE_ALARM", + "CREATE_REMINDER" + ], + [ + "CREATE_ALARM", + "CREATE_TIMER" + ], + [ + "CREATE_ALARM", + "UPDATE_ALARM" + ], + [ + "DELETE_ALARM", + "SILENCE_ALARM" + ], + [ + "GET_DIRECTIONS", + "UPDATE_DIRECTIONS" + ], + [ + "GET_INFO_ROAD_CONDITION", + "GET_INFO_TRAFFIC" + ], + [ + "SNOOZE_ALARM", + "UPDATE_ALARM" + ] + ], + "descriptions_origin": "Source ontology plus documented assistant repair; no exhaustive human annotation claim" +} diff --git a/curation/v3/ablation_configs.json b/curation/v3/ablation_configs.json new file mode 100644 index 0000000000000000000000000000000000000000..169af6f81869968fb599db6068a39b57f5161235 --- /dev/null +++ b/curation/v3/ablation_configs.json @@ -0,0 +1,47 @@ +{ + "version": "2026-10-14-v3", + "execution_status": "data_definitions_only_no_training_run", + "experiments": [ + { + "name": "legacy_baseline", + "choice_configs": [ + "legacy_replay_v3" + ] + }, + { + "name": "legacy_plus_semantic", + "choice_configs": [ + "legacy_replay_v3", + "semantic_v3" + ] + }, + { + "name": "legacy_plus_tools", + "choice_configs": [ + "legacy_replay_v3", + "tool_context_v3" + ] + }, + { + "name": "legacy_plus_semantic_plus_tools", + "choice_configs": [ + "choice_v3" + ] + } + ], + "separate_objective_experiments": [ + "hermes_actions_v3", + "nlupp_hotels_v3", + "hint3_native_v3" + ], + "required_controls": [ + "same initial frozen backbone/head checkpoint revision", + "same optimizer and scheduler", + "matched training token/update budget", + "predeclared source and parent sampling weights", + "same fixed validation and test membership", + "same retention/distillation configuration", + "checkpoint selection on validation only; test evaluated after selection" + ], + "disallowed_concatenation": "Do not combine choice_v3 with any of its subsets, or auto-convert native auxiliary tasks to choice." +} diff --git a/curation/v3/evaluation_membership.jsonl.gz b/curation/v3/evaluation_membership.jsonl.gz new file mode 100644 index 0000000000000000000000000000000000000000..93cdefb5b17c1f48e328a7c47ddd33932f80b6de --- /dev/null +++ b/curation/v3/evaluation_membership.jsonl.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2ee4187546f5326bfb20de788290f445459f903e14098001405e1b7b5c6a0ff +size 936365 diff --git a/curation/v3/evaluation_protocol.json b/curation/v3/evaluation_protocol.json new file mode 100644 index 0000000000000000000000000000000000000000..b067bd30f7da452c56fc2b0a411de0916ef670af --- /dev/null +++ b/curation/v3/evaluation_protocol.json @@ -0,0 +1,21 @@ +{ + "version": "2026-10-14-v3", + "membership_sha256": "b2ee4187546f5326bfb20de788290f445459f903e14098001405e1b7b5c6a0ff", + "selection_metrics": [ + "legacy accuracy by source and operation family", + "semantic present and absent accuracy separately", + "tool observed-action accuracy by source and environment exposure", + "candidate-count buckets" + ], + "do_not_select_on_loss_alone": true, + "no_test_driven_checkpoint_selection": true, + "baseline_must_be_recomputed_on_same_v3_inputs": true, + "retention_tolerances": "Predeclare per-slice noninferiority tolerances before training; no fabricated universal tolerance is implied by these data.", + "uncertainty_units": { + "semantic": "source query parent", + "legacy": "available query/template group; native dialogues not all recovered", + "tools": "trajectory parent, with environment-cluster sensitivity analysis" + }, + "small_sample_policy": "Report counts and uncertainty. Tiny DevOps/per-tool slices do not prove broad capability retention.", + "score_comparability": "Frozen default/v2 regression and cleaned v3 evaluation are different protocols." +} diff --git a/curation/v3/hermes_parent_groups.json b/curation/v3/hermes_parent_groups.json new file mode 100644 index 0000000000000000000000000000000000000000..7f535f2349470d971924c7ab5056ed4447298b43 --- /dev/null +++ b/curation/v3/hermes_parent_groups.json @@ -0,0 +1,30644 @@ +{ + "groups": [ + { + "request_group_id": "hermes_request:001179ce3c39db686ba03cf44f45b1126518cc24ea8010319e7e8a9d12b1c988", + "source_parent_ids": [ + "hermes_record:a64c4bf4-0ad1-4323-9330-8f1c71671321" + ], + "source_record_ids": [ + "a64c4bf4-0ad1-4323-9330-8f1c71671321" + ], + "all_upstream_records_with_same_full_request": [ + { + "source_file_row": 66, + "source_record_id": "a64c4bf4-0ad1-4323-9330-8f1c71671321" + } + ], + "canonical_ids": [ + "fastbrain_069354" + ], + "original_splits": [ + "train" + ], + 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"https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1/blob/dae3e1d28cfbcf4b915c04ea1e072030529b4bda/func-calling-singleturn.json", + "local_file": "scratch/fastbrain_dataset_reaudit_20261014/legacy/primary/hermes_func_calling_singleturn.json", + "sha256": "fb2df9fe3f295dfd65b223c2dbf04d6b90006357d737f8f8927749f0bea09527" + }, + "policy": { + "query_crop": 400, + "description_crop": 160, + "matching": "Exact only; unresolved/ambiguous matches must remain quarantined.", + "correction_from_previous_audit": "Previous audit required full-description equality, leaving truncated-description rows unverified. Current exact [:160] description reconstruction resolves them without fuzzy matching.", + "parent_key": "Native source-record UUID identifies the original multi-call request record; an additional normalized full-user request group prevents duplicate bodies crossing splits.", + "split_precedence": [ + "test", + "validation", + "train" + ] + }, + "summary": { + "train": { + "canonical_rows": 1015, + "unique_source_recovered": 1015, + "cropped_query": 1001, + "cropped_target_description": 23, + "multi_call_source_rows": 539, + "first_round_schema_valid": 914, + "first_round_schema_invalid": 101, + "schema_valid_multi_call": 513, + "schema_valid_single_call": 401 + }, + "test": { + "canonical_rows": 132, + "unique_source_recovered": 132, + "cropped_query": 130, + "cropped_target_description": 5, + "multi_call_source_rows": 71, + "first_round_schema_valid": 124, + "first_round_schema_invalid": 8, + "schema_valid_multi_call": 69, + "schema_valid_single_call": 55 + } + }, + "records": [ + { + "canonical_id": "fastbrain_000024", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1046 + ], + "match_source_rows": [ + 1046 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm about to have an important FaceTime call with my family who are overseas, and I want to make sure that the call quality is excellent without any hiccups. I am currently connected to a WiFi network and would like to verify if the network quality is optimal for FaceTime. Also, I prefer a high-definition video call, so I would like to enable HD video. For audio clarity, especially since I'll be i", + "legacy_target": "check_network_quality", + "legacy_target_description": "Check the quality of the network connection for FaceTime calls.", + "legacy_target_description_chars": 63, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1046, + "source_record_id": "0f3116c3-63d0-4b0e-84ad-a50c89247921", + "source_parent_id": "hermes_record:0f3116c3-63d0-4b0e-84ad-a50c89247921", + "request_group_id": "hermes_request:c9a86d1a3de6f38cfb26bc4b41db8e2bd87aa5c8166d6b61fda1e2507fcb8254", + "full_human_chars": 854, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "check_network_quality", + "configure_facetime_settings" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_000099", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 753 + ], + "match_source_rows": [ + 753 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I've recorded a podcast episode focused on the latest advancements in renewable energy, which I'd like to transcribe accurately into text. The episode features two speakers discussing the impact of solar power innovations, and it contains key technical terms specific to the renewable energy sector. 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Specifically, I require the matrix to include the top 1000 terms, and the feature extra", + "legacy_target": "count_vectorize_text", + "legacy_target_description": "Convert a collection of text documents to a matrix of token counts.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 146, + "source_record_id": "81e0f317-975b-4486-86d7-2e7cfacfe36b", + "source_parent_id": "hermes_record:81e0f317-975b-4486-86d7-2e7cfacfe36b", + "request_group_id": "hermes_request:fd6abc7501cb589ac8e9d39f8db9b544f356e83d0e0a22e3cfab241157b77d8c", + "full_human_chars": 1033, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "count_vectorize_text" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_001166", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 178 + ], + "match_source_rows": [ + 178 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to transfer $500 to my friend's PayPal account for an urgent expense that has come up. His email that is linked to his PayPal account is friend@example.com. Please initiate this money transfer and include a note that says \"Urgent expense cover.\" Can you provide me with the transaction ID once the transfer is initiated?", + "legacy_target": "initiate_money_transfer", + "legacy_target_description": "Initiates a money transfer to a recipient's PayPal account.", + "legacy_target_description_chars": 59, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 178, + "source_record_id": "636262b7-9d59-44bc-8fec-aa6171913ca6", + "source_parent_id": "hermes_record:636262b7-9d59-44bc-8fec-aa6171913ca6", + "request_group_id": "hermes_request:6447d83e3ee5ebaa3e967fdf3d9e3e1a2ebbe0f016d4f549dcf72a75bee2a511", + "full_human_chars": 333, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "initiate_money_transfer" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_001227", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1089 + ], + "match_source_rows": [ + 1089 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to establish a new SQL warehouse within our Databricks environment to support our growing data science team. The warehouse should be set up with the default configurations suitable for our initial requirements. The name of this new warehouse will be 'new_sql_warehouse'. Following the creation of the SQL warehouse, I need to ensure that the 'data-scientists' group in our workspace has ", + "legacy_target": "create_sql_warehouse", + "legacy_target_description": "Create a new SQL warehouse with specified settings.", + "legacy_target_description_chars": 51, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1089, + "source_record_id": "70fe8bf0-baa6-41cb-823b-06e5abba1114", + "source_parent_id": "hermes_record:70fe8bf0-baa6-41cb-823b-06e5abba1114", + "request_group_id": "hermes_request:0e0198d8ec3043ed3fe498b23c56eefbbd64fdda65797ebef0de3164b009fe74", + "full_human_chars": 622, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_sql_warehouse", + "update_group_entitlements" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_001714", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 752 + ], + "match_source_rows": [ + 752 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a set of images from a recent photoshoot at an arboretum, featuring an array of beautiful plants, intricate landscapes, and interactions between humans and nature. These images are destined for an online gallery, and I wish to accompany each with a creative caption that captures the essence and story of the visuals. I seek to utilize the OpenAI API's vision capabilities to generate compelli", + "legacy_target": "generate_image_captions", + "legacy_target_description": "Generates captions for a list of images using the OpenAI API with vision capabilities.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 752, + "source_record_id": "f8caf977-8639-4d6f-a075-e00d9ed2b1c4", + "source_parent_id": "hermes_record:f8caf977-8639-4d6f-a075-e00d9ed2b1c4", + "request_group_id": "hermes_request:608ff29b4902a82007dc602d356a1bc182f7045b3ae228f93b6d4e2c4875b455", + "full_human_chars": 953, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "generate_image_captions", + "generate_image_captions", + "generate_image_captions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_001741", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 565 + ], + "match_source_rows": [ + 565 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I oversee operations at TechGadgets Inc., and we are aiming to enhance our order processing system's efficiency and accuracy by implementing RPA automation. Our system needs to handle customer orders received in email format as PDF attachments, extract the relevant order information from these PDFs, validate the extracted data, update our ERP system, and perform inventory updates. Once orders are ", + "legacy_target": "extract_order_data", + "legacy_target_description": "Extracts order data from various sources such as emails or online forms.", + "legacy_target_description_chars": 72, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 565, + "source_record_id": "bc24683e-8092-4e3f-9a12-17a55439d9c7", + "source_parent_id": "hermes_record:bc24683e-8092-4e3f-9a12-17a55439d9c7", + "request_group_id": "hermes_request:63653ad5a8e4246cfb9869dc92b8f2f8d5e723ce47f3a456b9665d58cc228690", + "full_human_chars": 1983, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_order_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_001748", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 354 + ], + "match_source_rows": [ + 354 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am an energy trader managing a portfolio with a variety of assets. I've recently acquired several new energy assets that need to be added to my trading system. For instance, I purchased 500 MWh of electricity futures at a price of $50 per MWh with a maturity date of October 30, 2023. Additionally, I have 200 Brent Crude oil futures at $65 per barrel expiring on September 15, 2023, and 1000 allow", + "legacy_target": "add_asset_to_portfolio", + "legacy_target_description": "Adds a new asset to the specified energy trading portfolio.", + "legacy_target_description_chars": 59, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 354, + "source_record_id": "54393f7a-d5cb-4a23-a5aa-a0d979ffc14d", + "source_parent_id": "hermes_record:54393f7a-d5cb-4a23-a5aa-a0d979ffc14d", + "request_group_id": "hermes_request:719abb34747357c2d969dfa042bfd57215780814fdb2d7105f0f7625ed8fd815", + "full_human_chars": 1653, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 5, + "valid_top_level_call_names": [ + "add_asset_to_portfolio", + "add_asset_to_portfolio", + "add_asset_to_portfolio", + "calculate_portfolio_value", + "forecast_portfolio_performance" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_001960", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 283 + ], + "match_source_rows": [ + 283 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the head of risk assessment for our algorithmic trading division, it's imperative to manage the risk factors that could significantly affect our algorithmic trading activities. Specifically, we're focusing on the potential impacts of sudden market downturns, regulatory changes, and trading volume fluctuations. \n\nTo execute an effective risk assessment, we will analyze market analysis reports an", + "legacy_target": "assess_algorithmic_risks", + "legacy_target_description": "Assesses the potential risks in algorithmic trading using specified metrics and considering market analysis reports and historical trading data.", + "legacy_target_description_chars": 144, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 283, + "source_record_id": "e035f654-dc19-4199-b52d-afc3d393e55d", + "source_parent_id": "hermes_record:e035f654-dc19-4199-b52d-afc3d393e55d", + "request_group_id": "hermes_request:aac1aa8577c6d4181eacc59a73836d06a86faa100650796f8b2ff337a7474622", + "full_human_chars": 2463, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "assess_algorithmic_risks", + "simulate_risk_impact" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002065", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 724 + ], + "match_source_rows": [ + 724 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am creating a desktop graphics editor application designed to support graphic designers with vector graphic creation. My current focus is integrating a feature that enables the precise drawing of bezier curves. For this, I require the function 'drawBezierCurve' to be called with specific parameters. To demonstrate, I need a bezier curve beginning at the origin point (0,0), with the first control", + "legacy_target": "drawBezierCurve", + "legacy_target_description": "Draws a bezier curve on the canvas with given control points.", + "legacy_target_description_chars": 61, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 724, + "source_record_id": "9df18625-aa6a-4bb0-9734-9a73619678fe", + "source_parent_id": "hermes_record:9df18625-aa6a-4bb0-9734-9a73619678fe", + "request_group_id": "hermes_request:5aefa706e5bd6eed386041755aa07861ed21d2d7f6dc21d04154068a5798b867", + "full_human_chars": 805, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "drawBezierCurve" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002078", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 192 + ], + "match_source_rows": [ + 192 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I've been managing my finances through my Revolut account and for the last month, I have been meticulously recording all of my transactions. I'd like to get a comprehensive analysis of my spending for the month of March this year. Could you generate a detailed monthly spending report for the period from March 1st to March 31st, 2023? This report should categorize my expenses to help in budgeting, ", + "legacy_target": "generate_monthly_spending_report", + "legacy_target_description": "Generates a detailed monthly spending report for the user, categorizing expenses and providing insights on spending habits and potential savings.", + "legacy_target_description_chars": 145, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 192, + "source_record_id": "e2bd2207-1636-42e2-b75a-5a15a0f79e0f", + "source_parent_id": "hermes_record:e2bd2207-1636-42e2-b75a-5a15a0f79e0f", + "request_group_id": "hermes_request:541bb9d36c750bf28f7301bea9d2ce969f2b10b44aaebd7b87ba4688286b6ce6", + "full_human_chars": 700, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_monthly_spending_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002205", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 449 + ], + "match_source_rows": [ + 449 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for managing the firewall for our corporate network and need to update the rules to enhance our security posture. We've identified a set of IP addresses that have been flagged as sources of malicious activity, and we need to ensure that our firewall blocks any incoming traffic from these particular addresses to protect our network. Additionally, our remote workforce has been alloc", + "legacy_target": "block_incoming_traffic", + "legacy_target_description": "Blocks incoming traffic from a list of specified IP addresses.", + "legacy_target_description_chars": 62, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 449, + "source_record_id": "3356f2ac-3860-485b-bf5f-8a7125212e96", + "source_parent_id": "hermes_record:3356f2ac-3860-485b-bf5f-8a7125212e96", + "request_group_id": "hermes_request:1345222a53618bea5107233950292856b2372fa70acffa855532d53249d328e3", + "full_human_chars": 1157, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "block_incoming_traffic", + "allow_vpn_traffic" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002220", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 317 + ], + "match_source_rows": [ + 317 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the manager of the municipal water utility, I need to schedule regular inspections for our key assets. 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The pump's ", + "legacy_target": "schedule_asset_inspection", + "legacy_target_description": "Schedules an inspection for a specific asset type on a given date and frequency.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 317, + "source_record_id": "f4c6391d-9cf2-460d-8b9a-65435884a19d", + "source_parent_id": "hermes_record:f4c6391d-9cf2-460d-8b9a-65435884a19d", + "request_group_id": "hermes_request:5bc23463d53eceeb2863ad1f49c3c66efa536a86561404ac599b16846ccbb573", + "full_human_chars": 1013, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "schedule_asset_inspection", + "record_inspection_outcome", + "analyze_maintenance_requirements" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002241", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 513 + ], + "match_source_rows": [ + 513 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a series of recorded customer service calls in English that I need to convert into written transcripts for a qualitative analysis project. The recordings are approximately 30 minutes in duration each. I require transcription with detailed accuracy, including punctuation, to maintain the integrity of the conversations. It's also important to identify when different customer service represent", + "legacy_target": "transcribe_audio", + "legacy_target_description": "Transcribes the given audio file into text with options for punctuation, speaker diarization, and word timestamps.", + "legacy_target_description_chars": 114, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 513, + "source_record_id": "bcbfd34a-7989-4201-85bb-6627509c73b3", + "source_parent_id": "hermes_record:bcbfd34a-7989-4201-85bb-6627509c73b3", + "request_group_id": "hermes_request:52009aeee45df0b3ba02c6116858a2c346eeaeff2cca6d789371b85868691a13", + "full_human_chars": 1153, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "transcribe_audio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002492", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 190 + ], + "match_source_rows": [ + 190 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to reimburse my friend, Alex_Jones_99, for the pizza we shared during our movie night. The amount owed is $45.00. The note for the payment should read \"🍕 Pizza night - Thanks for treating us!\" and I prefer the transaction to be private. Please create this payment on Venmo with these specifics.\n\nFunction call: \n```json\n{\n \"recipient_username\": \"Alex_Jones_99\",\n \"amount\": 45.00,\n \"no", + "legacy_target": "create_venmo_payment", + "legacy_target_description": "Create a payment request or send money to a Venmo user with an optional note and emoji.", + "legacy_target_description_chars": 87, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 190, + "source_record_id": "64c881e6-0bf2-48f6-a06b-da4571ae8731", + "source_parent_id": "hermes_record:64c881e6-0bf2-48f6-a06b-da4571ae8731", + "request_group_id": "hermes_request:f950515b9852b2903c3ff0745826f1a0c82993f48933f135fd9478dc4d7a84d9", + "full_human_chars": 585, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_venmo_payment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002635", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 343 + ], + "match_source_rows": [ + 343 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Based on the objective to assist with forecasting energy demand for a utility company's power production optimization, I'd like to request the execution of several functions to analyze data and provide accurate forecasts. Below are the details and real-world data required for the function calls:\n\nWe need to forecast the energy demand for the upcoming quarter, from July 1, 2023, to September 30, 20", + "legacy_target": "predict_energy_demand", + "legacy_target_description": "Predicts energy demand for a specified period using historical consumption data, weather predictions, and planned outages information.", + "legacy_target_description_chars": 134, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 343, + "source_record_id": "8a239dbf-de0e-47f3-a487-18286ea67dbb", + "source_parent_id": "hermes_record:8a239dbf-de0e-47f3-a487-18286ea67dbb", + "request_group_id": "hermes_request:b8ac69102a42714d065390dd3ac953db582631de746fe18d2fe5a017ebc58c33", + "full_human_chars": 2036, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "predict_energy_demand", + "identify_peak_load_times" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002682", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 854 + ], + "match_source_rows": [ + 854 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our project collaboration, we are currently editing a significant document titled '2023 Project Overview' using the collaborative features of Confluence. Our team needs to make crucial updates to this shared draft, specifically by including detailed sections about 'Market Analysis' and 'Competitor Insights.' \n\nFor 'Market Analysis,' we have compiled extensive research on market trends, ", + "legacy_target": "update_shared_draft", + "legacy_target_description": "Update the content of a shared draft in a collaborative editing session.", + "legacy_target_description_chars": 72, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 854, + "source_record_id": "ccf4587c-d545-412e-8437-d1982bce4a12", + "source_parent_id": "hermes_record:ccf4587c-d545-412e-8437-d1982bce4a12", + "request_group_id": "hermes_request:a11c4ee0e2f6d04fdeb79e1c53f2887cceb363a1a3011aa3ed3a49129876580c", + "full_human_chars": 1251, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "update_shared_draft" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_002823", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 648 + ], + "match_source_rows": [ + 648 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing the development of a software product with ID 12345 and require assistance in several key areas. I need to create a detailed product roadmap that includes milestones such as the completion of the prototype by June 30, 2023, and a beta release by September 15, 2023. Currently, the product is in the Design phase, and we aim to maintain progress aligned with these milestones. In additio", + "legacy_target": "create_product_roadmap", + "legacy_target_description": "Creates a product roadmap with key milestones for the product lifecycle.", + "legacy_target_description_chars": 72, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 648, + "source_record_id": "14a8eb93-20e0-421a-9aeb-f365adc5e18c", + "source_parent_id": "hermes_record:14a8eb93-20e0-421a-9aeb-f365adc5e18c", + "request_group_id": "hermes_request:542fe90a15c3460dd07d1fba479ebafd3bba331ee657a550be9e1d8a4c638a04", + "full_human_chars": 1275, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "create_product_roadmap", + "track_development_progress", + "manage_product_requirements", + "handle_release_planning" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_003038", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 221 + ], + "match_source_rows": [ + 221 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am interested in finding mobile applications that can help me stay up-to-date with the latest fashion trends and also allow me to shop for new outfits right from my phone. Specifically, I am looking for apps that provide AI-based style curation and have features for secure in-app purchases. Additionally, I'd like the app to have a personalized feed feature with direct purchase options. Could you", + "legacy_target": "find_fashion_apps", + "legacy_target_description": "Find fashion and shopping apps with specified features for a given platform.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 221, + "source_record_id": "280d2f40-a29c-4068-9f10-5ebb8744beb8", + "source_parent_id": "hermes_record:280d2f40-a29c-4068-9f10-5ebb8744beb8", + "request_group_id": "hermes_request:23ae0d96974661dd7c14ca48e5eff48a43604915840a09a967417920c5a35e2f", + "full_human_chars": 498, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "find_fashion_apps", + "find_fashion_apps" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_003105", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 234 + ], + "match_source_rows": [ + 234 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently installed some music apps on my Android smartphone, and I've accumulated a large collection of music files. I want to streamline my listening experience by creating mood-based and genre-based playlists. Can you organize my music files into playlists for me? I'm particularly interested in having separate playlists that cater to different moods like \"Chill Vibes\" with soothing tracks, \"Wo", + "legacy_target": "organize_music_files", + "legacy_target_description": "Organize music files into playlists based on specified criteria such as genre and mood.", + "legacy_target_description_chars": 87, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 234, + "source_record_id": "e5acb4eb-b8df-4b72-aa7c-de4e99f00ee0", + "source_parent_id": "hermes_record:e5acb4eb-b8df-4b72-aa7c-de4e99f00ee0", + "request_group_id": "hermes_request:0305a3431adfd990c862ff7ed8f0af977cd40c12f993bca26a7aa72f13babc56", + "full_human_chars": 954, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "organize_music_files" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_003236", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 725 + ], + "match_source_rows": [ + 725 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm organizing a video conference for my team to discuss the integration of new features into our desktop communication app. We require a tool that offers video calling capabilities, enables screen sharing for effective presentation of design mockups, and ensures secure, encrypted discussions to protect our proprietary information. Ideally, the platform should accommodate at least 10 participants.", + "legacy_target": "find_communication_tool", + "legacy_target_description": "Find a communication tool that matches the specified features and participant limit.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 725, + "source_record_id": "d18e51fa-fd54-4c1a-b985-7a554a3e394c", + "source_parent_id": "hermes_record:d18e51fa-fd54-4c1a-b985-7a554a3e394c", + "request_group_id": "hermes_request:5ba54a293dfef2e5e5219b91173a53c16fce659cb73b57c1fea2b299c4942b04", + "full_human_chars": 756, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "find_communication_tool" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_003314", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 342 + ], + "match_source_rows": [ + 342 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As an energy trader focusing on the North American market, I'm interested in the current NYMEX prices for natural gas. I'd like to place an order for 50 natural gas futures as soon as the price falls below $3.00 per MMBtu. Additionally, to manage my trading strategy dynamically, I need to set a price alert for when the natural gas price goes above $3.50 per MMBtu.\n\nPlease execute the following fun", + "legacy_target": "get_real_time_prices", + "legacy_target_description": "Retrieves the latest real-time prices for a specified energy type from a given market.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 342, + "source_record_id": "8cc53a0f-3d80-4ae2-bb7a-df1813e2eb34", + "source_parent_id": "hermes_record:8cc53a0f-3d80-4ae2-bb7a-df1813e2eb34", + "request_group_id": "hermes_request:042adca2054bca13dfd856ffa1444224da8837e142783246d68461e832d5c36e", + "full_human_chars": 853, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_real_time_prices", + "set_price_alert" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_003379", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 389 + ], + "match_source_rows": [ + 389 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently came across an issue within our organization that requires immediate attention: a user has reported being unable to access their email account and has requested IT support. I need to log this specific problem in our helpdesk and ticketing system. The issue should be categorized under 'Access Issues' and it's important to handle it with a medium priority due to its potential impact on da", + "legacy_target": "create_issue_ticket", + "legacy_target_description": "Create a new issue ticket with details about the problem reported.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 389, + "source_record_id": "e6887770-d855-419e-8b8f-0d4df202fe7b", + "source_parent_id": "hermes_record:e6887770-d855-419e-8b8f-0d4df202fe7b", + "request_group_id": "hermes_request:cefdec9b5145c2e4424f1fb96716eb5a9c9b6b66e8d873f41b65adc8c1bedd47", + "full_human_chars": 1583, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_issue_ticket", + "generate_ticket_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_003445", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 848 + ], + "match_source_rows": [ + 848 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently working on securing API access for our system and require authorization tokens that adhere to the OAuth 2.0 specifications. Using the client credentials grant type, we need to generate these tokens through a POST request. Our client identifier during the registration process is `123456` and the corresponding client secret issued to us is `abcdef`. We're aiming to secure both read and", + "legacy_target": "generate_authentication_token", + "legacy_target_description": "Generates an authentication token based on the provided client credentials.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 848, + "source_record_id": "24327e3a-00b2-45a6-b131-e6ae4675d4a3", + "source_parent_id": "hermes_record:24327e3a-00b2-45a6-b131-e6ae4675d4a3", + "request_group_id": "hermes_request:a074c07d6554b9a9e187c789032726b60f13daa03ee08ec99d2dc1b6de3c3e36", + "full_human_chars": 524, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_authentication_token" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_003466", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 242 + ], + "match_source_rows": [ + 242 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Office Administrator managing our company's Dropbox file organization, I need to append the suffix \"_2023\" to every file name within our 'Invoices' folder to reflect the current year. Subsequently, these renamed files should be moved to a newly created folder titled 'Invoices_2023'. 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Here's the data for each category:\n\n**Loan History:**\n- $250,000 home loan acquired in January 2020, currently $150,000 remaining.\n- $20,000 car loan taken ", + "legacy_target": "calculate_credit_score", + "legacy_target_description": "Calculates the credit score of an individual based on their loan history, credit card usage, and payment timeliness.", + "legacy_target_description_chars": 116, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 787, + "source_record_id": "431357ed-b832-4b2a-940f-1dcf8eb1723c", + "source_parent_id": "hermes_record:431357ed-b832-4b2a-940f-1dcf8eb1723c", + "request_group_id": "hermes_request:6b7bcb50271a940c20cf4e4567481256f035f9e6be2b73912fac6f3ec8b09a29", + "full_human_chars": 1332, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "calculate_credit_score", + "analyze_credit_utilization", + "evaluate_payment_history", + "assess_loan_to_income_ratio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_004265", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 664 + ], + "match_source_rows": [ + 664 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the product manager for a voice-enabled application, I want to analyze user engagement with our grocery and pharmacy services offered through Amazon Alexa and Google Assistant. 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We have sales data showing trends among our various channels; for instance, online sales have been growing with a revenue of $500,000 and a growth rate of 10%, retail has been in decline with a revenue of $300,000 and a decline rate of 5%, and our whol", + "legacy_target": "assess_distribution_performance", + "legacy_target_description": "Assess the performance of current distribution channels using sales and channel data.", + "legacy_target_description_chars": 85, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 467, + "source_record_id": "1c46c5a6-18f4-4594-9781-4a96c2d78a75", + "source_parent_id": "hermes_record:1c46c5a6-18f4-4594-9781-4a96c2d78a75", + "request_group_id": "hermes_request:bf3910e06ba2e86b821a086067829c4672d2414b785380f0f14854fb0ddb0bd6", + "full_human_chars": 2919, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "assess_distribution_performance", + "identify_potential_channels", + "forecast_distribution_impact" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_004588", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1141, + 1180, + 1189, + 1211, + 1419, + 1421, + 1423, + 1452, + 1483, + 1533, + 1543, + 1621, + 1646 + ], + "match_source_rows": [ + 1141 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage through it. Do you worry excessively about writing assignments? Do they make you feel uneasy or agitated? Do you have negative feelings about certain types of writing? If you answered yes to any of these questions, you might be dealing with writing anxiety. Writing anxiety simply means that a writer is experiencing negative feeli", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for an assessment or critique of an idea, argument, or proposal in the document. 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My goal is to set up and maintain a system that leverages real-time market data to identify momentum-based trading opportunities and execute trades accordingly, while dynamically adjusting for risk tolerance based on market conditions. In addition, the system will perform regular portfolio rebalancin", + "legacy_target": "connect_market_data_stream", + "legacy_target_description": "Connects to the specified market data sources and begins monitoring for momentum-based trade opportunities.", + "legacy_target_description_chars": 107, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 266, + "source_record_id": "2ab8d296-bc68-4efd-816d-240a2b7823c4", + "source_parent_id": "hermes_record:2ab8d296-bc68-4efd-816d-240a2b7823c4", + "request_group_id": "hermes_request:4b60599280aae50953751b903b303b649bd692b362c78a96b1c5e4146dffb711", + "full_human_chars": 1158, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "connect_market_data_stream", + "execute_automatic_trades", + "adjust_risk_tolerance", + "rebalance_portfolio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_004705", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 950 + ], + "match_source_rows": [ + 950 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To effectively manage our sales cycle, I need to fetch all the open sales opportunities in our Salesforce CRM that have had interaction since the beginning of last month. 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Once the financial data is e", + "legacy_target": "extract_financial_statements", + "legacy_target_description": "Extracts financial statements such as profit and loss, balance sheets, and transaction details for a specified period from QuickBooks.", + "legacy_target_description_chars": 134, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1099, + "source_record_id": "35575fcf-06dd-4e92-ab27-f18147d8002d", + "source_parent_id": "hermes_record:35575fcf-06dd-4e92-ab27-f18147d8002d", + "request_group_id": "hermes_request:62ebdf0f82ed667272bfe4600157472bba34f88619192eb9a7204b2d99289c54", + "full_human_chars": 1075, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_financial_statements" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_004776", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 239 + ], + "match_source_rows": [ + 239 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am updating our CRM records and need to modify the details for one of our contacts, Jane Smith. We have recently received her updated contact information. Her new phone number is 555-1234, and her new email address is jane.smith@example.com. Jane's unique identifier in our CRM system is C123456. Please call the appropriate function to update Jane Smith's CRM record with her new contact details.", + "legacy_target": "update_contact_info", + "legacy_target_description": "Update the phone number and email address for a contact in the CRM system.", + "legacy_target_description_chars": 74, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 239, + "source_record_id": "f076122f-ff48-4685-b9df-a244256dbb03", + "source_parent_id": "hermes_record:f076122f-ff48-4685-b9df-a244256dbb03", + "request_group_id": "hermes_request:6cdd23ff25b74eb58a0bcb28927692b0cef13a7abe40de98bbcc1c9b2b39652e", + "full_human_chars": 399, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "update_contact_info" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_005111", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 277 + ], + "match_source_rows": [ + 277 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the operations manager for an investment firm, I am spearheading an initiative to enhance our algorithmic trading capabilities to keep up with expected growth. We have historically processed an average of 500 trades per day. Looking ahead, we aim to quadruple our capacity to 2000 trades per day within a six-month window. Our trading infrastructure primarily facilitates high-frequency trading an", + "legacy_target": "analyze_infrastructure_scalability", + "legacy_target_description": "Analyzes the current trading infrastructure to determine its ability to scale to a higher trade volume.", + "legacy_target_description_chars": 103, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 277, + "source_record_id": "328f0ea2-b1ba-42c9-84a9-efe6c34a9144", + "source_parent_id": "hermes_record:328f0ea2-b1ba-42c9-84a9-efe6c34a9144", + "request_group_id": "hermes_request:eca3a566347a02bacd8edba47df935f1545318eb1e92a71f342557717f1bc5a8", + "full_human_chars": 1570, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_infrastructure_scalability", + "upgrade_trading_infrastructure" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_005317", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 28 + ], + "match_source_rows": [ + 28 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the administrator of a healthcare facility, I am currently digitalizing and optimizing our EHR system. 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We expect up to 150 industry professionals to attend this event. I require assistance with setting up the event in Salesforce, registering participants, and dispatching confirmations. Here are the specifics:\n\nEvent Name:", + "legacy_target": "create_event", + "legacy_target_description": "Creates a new event record in Salesforce with the specified details.", + "legacy_target_description_chars": 68, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 524, + "source_record_id": "591522ce-e1b2-4b68-b66a-4f2616489cba", + "source_parent_id": "hermes_record:591522ce-e1b2-4b68-b66a-4f2616489cba", + "request_group_id": "hermes_request:0d872130dab60caf1bfc4ef004757eb2c29aadb623289af659e19f48aaee6def", + "full_human_chars": 838, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_event" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_005715", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 755 + ], + "match_source_rows": [ + 755 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I run an e-commerce platform specializing in fashion, and I'm working on adding a feature to help customers find products similar to what they're looking for. 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I have the raw EHR data in a JSON file located at `path/to/ehr_records.json`. Also, I've been provided with a JSON schema template that defines the structure for patient demographics, which is available at `", + "legacy_target": "annotate_ehr_with_demographics", + "legacy_target_description": "Annotates electronic health records with patient demographics and medical history details according to a given JSON schema.", + "legacy_target_description_chars": 123, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 142, + "source_record_id": "630d8f71-91e8-4249-ab77-01cb3f0cda7d", + "source_parent_id": "hermes_record:630d8f71-91e8-4249-ab77-01cb3f0cda7d", + "request_group_id": "hermes_request:c71ec7dc44f0d5b56ad51bbe5dd15b49013984a6738061d9df05c6b4ea460911", + "full_human_chars": 1708, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "annotate_ehr_with_demographics", + "validate_ehr_schema" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_006314", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1128, + 1234, + 1244, + 1345, + 1435, + 1443, + 1493, + 1514, + 1607, + 1619, + 1665, + 1785, + 1877 + ], + "match_source_rows": [ + 1128 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage : A directed graph. \n weighted, by the way. 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We have an extensive dataset that includes specific data types such as contact details, interaction history, and sales records. These datasets are vital to our day-to-day operations and must be securely transferred to the new system with absolute accuracy. To ensure minim", + "legacy_target": "automate_data_migration", + "legacy_target_description": "Automates the data migration process from a specified source system to a target system, including scheduling and data validation.", + "legacy_target_description_chars": 129, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 559, + "source_record_id": "94cd2ec6-9c4b-460b-8402-754144eafebb", + "source_parent_id": "hermes_record:94cd2ec6-9c4b-460b-8402-754144eafebb", + "request_group_id": "hermes_request:f464e53a3a81128ca8ae5c77dfa9a37a75dfb18e76e11191b70b7d0ec44ad98e", + "full_human_chars": 1566, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "automate_data_migration", + "validate_data_integrity" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_006536", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 124 + ], + "match_source_rows": [ + 124 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing a high-performance computing cluster designated as 'HPC-Cluster-01', which comprises multiple nodes that require constant monitoring to assess and optimize their computational efficiency. To accomplish this, I need to track and analyze key performance metrics over the most recent hour. These metrics include CPU load, memory usage, network throughput, and disk I/O operations", + "legacy_target": "get_cluster_cpu_load", + "legacy_target_description": "Retrieve the CPU load metrics for a specific cluster over a defined time period.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 124, + "source_record_id": "9f5aed4c-35ed-4f73-92be-d8ac48acb758", + "source_parent_id": "hermes_record:9f5aed4c-35ed-4f73-92be-d8ac48acb758", + "request_group_id": "hermes_request:2c3e4fc9eb86b4fda03d16bfa0e92f10354f0d908afa01d4023a0ca488ba2586", + "full_human_chars": 1300, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "get_cluster_cpu_load", + "get_cluster_memory_usage", + "get_cluster_network_throughput", + "get_cluster_disk_io" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_006650", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 264 + ], + "match_source_rows": [ + 264 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a trading strategy that I've developed which is tailored to momentum-based trading and I would like to implement it using the Alpaca trading platform. 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In addition to the reminders, I also want to track my medication adherence for the same duration to ensure that I am fo", + "legacy_target": "create_medication_reminder", + "legacy_target_description": "Create a reminder for a specific medication, dosage, and frequency.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1030, + "source_record_id": "d442b35d-ee67-444b-91ec-f19dc5b308c5", + "source_parent_id": "hermes_record:d442b35d-ee67-444b-91ec-f19dc5b308c5", + "request_group_id": "hermes_request:1eb8b79bfccb85a3870ce157f295847518359ed877a80bd654696691658d7c23", + "full_human_chars": 558, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_medication_reminder", + "track_medication_adherence" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_006713", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 692 + ], + "match_source_rows": [ + 692 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to establish and track a 'Basic Life Support' (BLS) certification training program for our healthcare facility staff. We have identified the following staff roles that require this certification: Registered Nurses, Certified Nursing Assistants, and Medical Technicians. 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We recognize the need to implement accessibility features to accommodate users with visual and motor disabilities. We require an analysis of the current state of accessibility features on our media content, specifi", + "legacy_target": "analyze_media_for_accessibility", + "legacy_target_description": "Analyzes the media content for accessibility features and generates a report.", + "legacy_target_description_chars": 77, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 84, + "source_record_id": "c1ab9a16-6911-420f-96b2-11a07c184141", + "source_parent_id": "hermes_record:c1ab9a16-6911-420f-96b2-11a07c184141", + "request_group_id": "hermes_request:e0755b0dccef38be5af5c464da1d869b150357fa513ba6b00d2718b89c512045", + "full_human_chars": 1630, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_media_for_accessibility", + "generate_audio_descriptions", + "generate_keyboard_navigation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_008355", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 529 + ], + "match_source_rows": [ + 529 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As a physician at our clinic, I need to review the comprehensive health record of a patient who recently visited. 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Please re", + "legacy_target": "get_patient_health_record", + "legacy_target_description": "Retrieves the full health record of a patient from the Mendix Healthcare Record App.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 529, + "source_record_id": "27317c70-1049-4055-956d-059cef17760a", + "source_parent_id": "hermes_record:27317c70-1049-4055-956d-059cef17760a", + "request_group_id": "hermes_request:267295d1b2faed03df673f5bef82f6e5587af5f3910e9977a4d03082733c4471", + "full_human_chars": 633, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_patient_health_record" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_008499", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1058 + ], + "match_source_rows": [ + 1058 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently monitoring multiple stock market websites and need to view them simultaneously for real-time updates. 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The data is stored in a CSV file named \"AAPL_GOOGLE_MSFT_St", + "legacy_target": "calculate_moving_averages", + "legacy_target_description": "Calculates moving averages for given stock data over specified time windows.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 276, + "source_record_id": "b8743006-332d-4a3e-bece-5085e0847be0", + "source_parent_id": "hermes_record:b8743006-332d-4a3e-bece-5085e0847be0", + "request_group_id": "hermes_request:6cbb31a915ae0b7c5292398946018e93524592e9046a56469906905f0c9511f2", + "full_human_chars": 1622, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_moving_averages", + "calculate_volatility", + "calculate_momentum_indicators" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_008615", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 265 + ], + "match_source_rows": [ + 265 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm working on enhancing the network performance for our algorithmic trading system. Our analysis shows significant latency issues in specific network segments, especially those connected to the New York and London trading servers. We've diligently collected and compiled detailed network performance metrics, including precise timestamps, server locations, round-trip times (RTT), and packet sizes o", + "legacy_target": "analyze_network_latency", + "legacy_target_description": "Analyzes network performance data to identify latency hotspots and recommend optimizations.", + "legacy_target_description_chars": 91, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 265, + "source_record_id": "5f06a8cb-6390-4385-a9ce-d62927c2a749", + "source_parent_id": "hermes_record:5f06a8cb-6390-4385-a9ce-d62927c2a749", + "request_group_id": "hermes_request:8a50f5aa19872db8b676a94d3f1b73e1a704218d9153bcf6165a83387e99a04a", + "full_human_chars": 1477, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "analyze_network_latency" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_008621", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 415 + ], + "match_source_rows": [ + 415 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the registrar of a university, I am working with our student database and need to make an update to a student's academic major. The student, with an ID of 'S1001', has decided to switch their major to 'Computer Science'. I need to update their record in the 'Students' table to reflect this change. Additionally, I'm preparing a report on the recent cohort of students and require a list of studen", + "legacy_target": "update_student_major", + "legacy_target_description": "Updates the major of a student in the 'Students' table.", + "legacy_target_description_chars": 55, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 415, + "source_record_id": "8a0fb58e-da18-4bf5-a069-f4e3ead03e34", + "source_parent_id": "hermes_record:8a0fb58e-da18-4bf5-a069-f4e3ead03e34", + "request_group_id": "hermes_request:aed706d031117eba672bd7209195da56ac434556f09ef691aefbe255a08dbfbb", + "full_human_chars": 1301, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "update_student_major", + "retrieve_students_by_enrollment_year" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_008630", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 132 + ], + "match_source_rows": [ + 132 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "In light of the recent surge in sophisticated phishing attacks against our employees, as the head of cybersecurity operations, I am proposing a strategic initiative to bolster our cybersecurity defenses through a multi-faceted approach that encompasses the analysis of phishing threats, comprehensive employee training, and well-defined organizational security policy updates.\n\n1. **Phishing Threat A", + "legacy_target": "analyze_phishing_threats", + "legacy_target_description": "Analyzes recent phishing attempts within a specified time range to identify common characteristics and tactics used by attackers.", + "legacy_target_description_chars": 129, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 132, + "source_record_id": "60c29931-0b62-4f26-b65d-1371f78f7027", + "source_parent_id": "hermes_record:60c29931-0b62-4f26-b65d-1371f78f7027", + "request_group_id": "hermes_request:b8824119bf3c45397f562c3fa1ad366078d4542ce08da466f528296c32a1cb17", + "full_human_chars": 2113, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_phishing_threats", + "create_cybersecurity_training", + "update_security_policy" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_009034", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 695 + ], + "match_source_rows": [ + 695 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for managing the inventory of a pharmacy and need to ensure that we maintain adequate levels of each medication. Our pharmacy uses a digital inventory system that stores information like medication IDs, names, current stock, pricing, and expiration dates in a JSON database. I'd like to call several functions to automate different aspects of the inventory management: monitoring sto", + "legacy_target": "monitor_stock_levels", + "legacy_target_description": "Monitors the stock levels of medications in the inventory and identifies items that need restocking.", + "legacy_target_description_chars": 100, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 695, + "source_record_id": "4b2e7c0a-a2d1-41a5-a098-d545a9fb3ef4", + "source_parent_id": "hermes_record:4b2e7c0a-a2d1-41a5-a098-d545a9fb3ef4", + "request_group_id": "hermes_request:a5f776ab27c0bc70b969d294425f9ecd02db74cf701d9fa55e3f547bc4404495", + "full_human_chars": 2226, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 5, + "valid_top_level_call_names": [ + "monitor_stock_levels", + "alert_low_or_expiring_stock", + "generate_inventory_report", + "generate_inventory_report", + "generate_inventory_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_009282", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 883 + ], + "match_source_rows": [ + 883 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm analyzing the social impact of law enforcement policies and would like to examine the New Orleans Police Department in light of the discussions following the death of George Floyd. Specifically, I want to look at the latest metrics related to police funding, incidents of force, and racial bias in enforcement actions. Could you use the `fetchPoliceDepartmentMetrics` function to obtain the most ", + "legacy_target": "fetchPoliceDepartmentMetrics", + "legacy_target_description": "Fetches a set of metrics for a specified police department over a given time period.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 883, + "source_record_id": "6a9eacab-7eef-4fc2-acd7-56f828730485", + "source_parent_id": "hermes_record:6a9eacab-7eef-4fc2-acd7-56f828730485", + "request_group_id": "hermes_request:f30669158d80e7feb871c8fe793bac50cb6a7fe8037b54ddcc8b8f7995d0fb15", + "full_human_chars": 1070, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetchPoliceDepartmentMetrics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_009396", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1102, + 1113, + 1140, + 1178, + 1183, + 1260, + 1273, + 1350, + 1398, + 1701, + 1722, + 1755, + 1763 + ], + "match_source_rows": [ + 1102 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage of them occurs. You might first think that we'd add the two probabilities for the two events and be done with it, but a glance at the diagram tells you this means trouble. Pr(U ) is .4, and Pr(R) is .7. 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Addi", + "legacy_target": "find_gamification_apps", + "legacy_target_description": "Find gamification apps suitable for team building and educational purposes.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 202, + "source_record_id": "3da05b9c-2695-4e9e-848e-8652c6a3820b", + "source_parent_id": "hermes_record:3da05b9c-2695-4e9e-848e-8652c6a3820b", + "request_group_id": "hermes_request:29754747a5f4b76b217e92d658c4be19b2f71b6abba3e4ba127df50d871e0696", + "full_human_chars": 669, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "find_gamification_apps", + "find_gamification_apps" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_010736", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 749 + ], + "match_source_rows": [ + 749 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm designing a visual series for an environmental initiative titled \"Green Energy for a Sustainable Future.\" To vividly convey our campaign's concept, I'd like to have a set of high-quality images created. The image ideas are as follows: \n\n1. A city of the future with sustainable energy practices, featuring a skyline dotted with wind turbines and solar panels.\n2. A visualization of eco-friendly f", + "legacy_target": "generate_dalle_images", + "legacy_target_description": "Generates a set of images based on the provided prompts using the DALL-E Image Generation feature of the OpenAI API.", + "legacy_target_description_chars": 116, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 749, + "source_record_id": "9e3cfe8a-5b52-4fc2-aa47-65b1c5d0cd59", + "source_parent_id": "hermes_record:9e3cfe8a-5b52-4fc2-aa47-65b1c5d0cd59", + "request_group_id": "hermes_request:60a41b5edcf68a9460e021dec5d9182f5c8144b31d5e03e9bf83001a4225e854", + "full_human_chars": 1029, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "generate_dalle_images", + "generate_dalle_images", + "generate_dalle_images" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_010821", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 9 + ], + "match_source_rows": [ + 9 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to adjust the smart window blinds in my living room to precisely 75% openness in order to balance natural light and minimize screen glare while I'm working from home today. 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Thank you for assisting with this task to maintain a comfortable work environment.", + "legacy_target": "set_blind_openness", + "legacy_target_description": "Sets the openness of the smart window blinds to a specified percentage.", + "legacy_target_description_chars": 71, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 9, + "source_record_id": "8e0d5c89-d6d2-4724-9e5c-249860fc3cfc", + "source_parent_id": "hermes_record:8e0d5c89-d6d2-4724-9e5c-249860fc3cfc", + "request_group_id": "hermes_request:308af9adf3e7a67298afed4ef39d54177c60906be194d07d23d52a48280061ae", + "full_human_chars": 371, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "set_blind_openness" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_011206", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 680 + ], + "match_source_rows": [ + 680 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm coordinating with a team of healthcare providers and we have a patient, John Doe, with the patient ID 'P12345'. We need to update our partners on his health status by sending his detailed medical history via the HIE platform. The medical history to be sent should include John's known allergies to Penicillin and Aspirin, his past medical condition of Asthma, his current prescription of Albutero", + "legacy_target": "send_patient_medical_history", + "legacy_target_description": "Sends the medical history of a patient to other healthcare providers via the HIE platform.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 680, + "source_record_id": "f84ebc4f-4171-4622-ac59-986d36269dfb", + "source_parent_id": "hermes_record:f84ebc4f-4171-4622-ac59-986d36269dfb", + "request_group_id": "hermes_request:82da6f4299b59dac29469877a2ae6772982419cd6a418660ae8a7f2456c5a154", + "full_human_chars": 794, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "send_patient_medical_history", + "request_patient_lab_results" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_011831", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 637 + ], + "match_source_rows": [ + 637 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the inventory manager at Seaport Logistics, I'm overseeing inventory for a diverse range of products including anchor chains, navigational equipment, container latches, and fuel supplies. Our inventory database, `seaport_inventory.json`, tracks item IDs, current stock levels, restock thresholds, and storage locations. Today, I'd like to perform three tasks: first, monitor current stock levels f", + "legacy_target": "monitor_port_inventory", + "legacy_target_description": "Monitors the inventory levels for items at the marine port and provides a summary of current stock.", + "legacy_target_description_chars": 99, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 637, + "source_record_id": "28498356-ac78-4170-8fb8-7d1b08a70a06", + "source_parent_id": "hermes_record:28498356-ac78-4170-8fb8-7d1b08a70a06", + "request_group_id": "hermes_request:2929e0a8b4c41bdcd415b7559b0a9c6bcf8bd3de88fea3d7dace41ebebcb17cd", + "full_human_chars": 1409, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "monitor_port_inventory", + "update_inventory_on_shipment", + "update_inventory_on_shipment", + "generate_restock_alerts" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_011858", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 534 + ], + "match_source_rows": [ + 534 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing the logistics for our company's Order Processing App developed on a low-code platform similar to Google App Maker. I need to monitor the progress of specific orders. For instance, I'd like to check the current status of an order with ID 'ORD1023'. 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We prioritize secure cloud-based quantum solutions and require detailed comparisons of QCaaS providers that specialize in the cybersecurity field. It's essential for us to evaluate hardware specifications such as the qubit count and connectivity opt", + "legacy_target": "list_qcaas_providers", + "legacy_target_description": "Retrieve a list of QCaaS providers with details on their service offerings and hardware specifications.", + "legacy_target_description_chars": 103, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 21, + "source_record_id": "1e9a5790-61b9-485f-bc6e-6d9d7335bb0d", + "source_parent_id": "hermes_record:1e9a5790-61b9-485f-bc6e-6d9d7335bb0d", + "request_group_id": "hermes_request:bf1a5d05d818e0cbf8b5c6d3a365676e39e4da9de2f927d8890b43b3cf83e3c4", + "full_human_chars": 800, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "list_qcaas_providers" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_011987", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 961 + ], + "match_source_rows": [ + 961 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have an order with the unique ID 1001 on my Shopify store that needs processing. I need assistance with the following tasks for this specific order: Firstly, I need to retrieve the complete details of the order including item descriptions, customer information, and transaction details. Once I have reviewed the details, the second task will be to update the order status to 'shipped', ensuring tha", + "legacy_target": "get_order_details", + "legacy_target_description": "Retrieve the details of an order using its unique ID.", + "legacy_target_description_chars": 53, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 961, + "source_record_id": "ecba4aa1-58c4-45b2-be79-375154297f4b", + "source_parent_id": "hermes_record:ecba4aa1-58c4-45b2-be79-375154297f4b", + "request_group_id": "hermes_request:0a44c381f0c11f58599357258094189cffbfe60d25ba5026b3fb062f8bb7ac61", + "full_human_chars": 696, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_order_details" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_012076", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 259 + ], + "match_source_rows": [ + 259 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing an algorithmic trading model targeting tech stocks and require assistance in setting up an end-to-end machine learning pipeline. My dataset contains historical stock prices, trading volumes, and a variety of technical metrics over the past two years. 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Our database `inventory.json` consists of current fuel stock for different fuel types such as diesel, gasoline, and kerosene, with their respective quantities. We have historical data `usage_logs.json` reflecting past fuel consumption. We need to:\n\n- Track the current fuel levels in real-time.", + "legacy_target": "track_fuel_levels", + "legacy_target_description": "Tracks the current fuel levels for each type of fuel in the inventory.", + "legacy_target_description_chars": 70, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 302, + "source_record_id": "bb3e6261-fd39-42bf-ac6d-ecd35727287c", + "source_parent_id": "hermes_record:bb3e6261-fd39-42bf-ac6d-ecd35727287c", + "request_group_id": "hermes_request:127c74049b331c4a98aba0a474f1050c33cfecb8cd210b1a103304a0b9f85b15", + "full_human_chars": 1556, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "track_fuel_levels", + "forecast_fuel_demand", + "alert_low_fuel_levels", + "generate_fuel_usage_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_012216", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 709 + ], + "match_source_rows": [ + 709 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of an ongoing study in our pharmaceuticals research department, we have accumulated extensive data on a new class of compounds being evaluated for treatment of hypertension. We have already performed clinical trials, which have been documented thoroughly. Our dataset includes information about compound efficacy, quantified as a percentage decrease in systolic blood pressure, and side effec", + "legacy_target": "categorize_compounds", + "legacy_target_description": "Categorizes compounds based on efficacy and side effects thresholds.", + "legacy_target_description_chars": 68, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 709, + "source_record_id": "e449d01a-763e-4c28-a785-f0804105bd4a", + "source_parent_id": "hermes_record:e449d01a-763e-4c28-a785-f0804105bd4a", + "request_group_id": "hermes_request:2d0f8a3fdf41e0ca20db8461ed98df3ff5890f46df5be087348d52e7c40da831", + "full_human_chars": 2076, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "categorize_compounds", + "correlate_dosage_with_outcomes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_012242", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 605 + ], + "match_source_rows": [ + 605 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am tasked with managing the inventory of our transportation service company. Our inventory includes essential vehicle parts like filters, brakes, and lubricants, as well as maintenance supplies. Here is the current inventory list in JSON format that includes part numbers, descriptions, current stock levels, and the minimum required quantities:\n\n```json\n[\n {\n \"part_number\": \"PN123\",\n \"desc", + "legacy_target": "monitor_stock_levels", + "legacy_target_description": "Monitors the current stock levels against minimum required quantities and provides a report.", + "legacy_target_description_chars": 92, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 605, + "source_record_id": "ee0cf83b-6572-4ac7-8bff-06bff66630f8", + "source_parent_id": "hermes_record:ee0cf83b-6572-4ac7-8bff-06bff66630f8", + "request_group_id": "hermes_request:25a810c172ac3d45ca0972ce44d902d984eff3b6dba414a73019daef2114cdea", + "full_human_chars": 1246, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "monitor_stock_levels", + "update_inventory_stock", + "notify_purchasing_department" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_012277", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 563 + ], + "match_source_rows": [ + 563 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for the marketing sector at our company, TrendyTech. 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We need to routinely gather and assess our customer interaction benchmarks from our social media platforms, specifically Facebook,", + "legacy_target": "gather_social_media_data", + "legacy_target_description": "Automates the collection and analysis of social media engagement data from specified platforms.", + "legacy_target_description_chars": 95, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 563, + "source_record_id": "54978baa-5ec1-4559-8168-2d74a63aebac", + "source_parent_id": "hermes_record:54978baa-5ec1-4559-8168-2d74a63aebac", + "request_group_id": "hermes_request:90badb4feb1c5b72191600fa3b8df773f3b97b7f32a33a5244facf64d64b32fc", + "full_human_chars": 1232, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "gather_social_media_data", + "optimize_google_ads_bids", + "generate_seo_reports" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_012278", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 308 + ], + "match_source_rows": [ + 308 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing an electric utility network and we've just encountered a significant power outage that needs immediate attention. The outage occurred in Sector 4B, and it's a high severity incident that took place on April 12th, 2023, at 2:30 PM UTC. 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Additionally, using an interest rate of 3.99%, I need to calculate the monthl", + "legacy_target": "approve_loan_application", + "legacy_target_description": "Approves or rejects a loan application based on the applicant's credit score, annual income, loan amount, and loan purpose.", + "legacy_target_description_chars": 123, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 801, + "source_record_id": "3892dde2-f530-4b91-b05c-240c94e61fe8", + "source_parent_id": "hermes_record:3892dde2-f530-4b91-b05c-240c94e61fe8", + "request_group_id": "hermes_request:9fdf821645198ef04edb452bdcdabc7e321bbd1fd755d489e9190221797b77e4", + "full_human_chars": 1300, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "approve_loan_application", + "calculate_monthly_payments", + "generate_loan_amortization_schedule" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_012885", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 341 + ], + "match_source_rows": [ + 341 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "We have a network of sensors installed throughout our gas utility infrastructure to monitor for potential leaks. 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We need to update their account information with their new service plans and send them a confirmati", + "legacy_target": "update_customer_service_plan", + "legacy_target_description": "Updates the service plan details for a list of customers and sends a confirmation message to their registered contact.", + "legacy_target_description_chars": 118, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 550, + "source_record_id": "53443e28-1804-47df-b4f3-5c3b1cdf8360", + "source_parent_id": "hermes_record:53443e28-1804-47df-b4f3-5c3b1cdf8360", + "request_group_id": "hermes_request:d0b9862117f33d83efb49325e45b05ab25b53bd1d5dcb4a1769c4512a7d5528f", + "full_human_chars": 1100, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "update_customer_service_plan" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_012935", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1028 + ], + "match_source_rows": [ + 1028 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently launched a new loyalty program at my cafe called \"Java Junction Rewards\" and I'm looking to provide digital loyalty cards to my customers. 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Our objective is to reduce waiting times, optimize vessel scheduling, and enhance cargo throughput. Currently, we are facing scheduling inefficiencies due to varying cargo loads and limited berth availability.\n\nTo address this challenge, I am looking to call ", + "legacy_target": "optimize_port_scheduling", + "legacy_target_description": "Analyzes vessel arrival times, cargo loads, and berth availability to create an optimized scheduling plan for port operations.", + "legacy_target_description_chars": 126, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 615, + "source_record_id": "2c385fbe-da0c-4467-9904-d6cd5864a3a7", + "source_parent_id": "hermes_record:2c385fbe-da0c-4467-9904-d6cd5864a3a7", + "request_group_id": "hermes_request:deb4ef07d2be7b77f2fb22164b66cd6ca7b98468ba8ba4e727afcbeeb9f58e72", + "full_human_chars": 2467, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "optimize_port_scheduling", + "calculate_berth_utilization", + "generate_cargo_handling_plan" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_013285", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 263 + ], + "match_source_rows": [ + 263 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am implementing a pairs trading strategy and have identified two potential stock pairs based on their historical price correlation. 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Here are the specifics to assist with function calls:\n\n- For **Sales Analysis**:\n - Time Period: \"Q1 2023\"\n - Metrics: [\"total sales\", \"average transaction value\", \"sell-t", + "legacy_target": "analyze_sales_data", + "legacy_target_description": "Analyze sales data for a given time period and return key metrics.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 466, + "source_record_id": "582e71a3-0593-44e6-9747-4c744b26c803", + "source_parent_id": "hermes_record:582e71a3-0593-44e6-9747-4c744b26c803", + "request_group_id": "hermes_request:31ca7a10726ca11074f33e69a48cb49663829fbba812ccb24d2a354f9acc4c14", + "full_human_chars": 1271, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "analyze_sales_data", + "predict_inventory_needs", + "process_transactions", + "manage_customer_interactions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_013476", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 95 + ], + "match_source_rows": [ + 95 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm interested in purchasing some electronic items and would like to explore the best deals available through Klarna's platform. 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Once I've identified an item I wan", + "legacy_target": "search_deals", + "legacy_target_description": "Search for deals within a specific category and sort them by a given criterion.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 95, + "source_record_id": "a9aa46d2-c924-4a36-99f3-52384c314a6a", + "source_parent_id": "hermes_record:a9aa46d2-c924-4a36-99f3-52384c314a6a", + "request_group_id": "hermes_request:b2a0345bb1c13bacfc6dd955924c311014bbd40f95ecfa1a71acdcda4719c9c8", + "full_human_chars": 972, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "search_deals", + "add_to_cart", + "initiate_klarna_payment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_013509", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 428 + ], + "match_source_rows": [ + 428 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently using a Mac and I need to perform a couple of operations in the terminal that I used to do on Ubuntu Linux. Firstly, I need to list all the contents of my current directory, which include the following files: '.bash_profile', '.gitignore', 'report.txt', 'project_data.csv', and 'script.sh'. It's important to include hidden files in the listing. Secondly, I have a file named 'report.tx", + "legacy_target": "list_directory_contents", + "legacy_target_description": "List all files and directories in the specified path, with an option to include hidden files.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 428, + "source_record_id": "e50da6d3-921f-470c-a98c-0f717000d293", + "source_parent_id": "hermes_record:e50da6d3-921f-470c-a98c-0f717000d293", + "request_group_id": "hermes_request:11c37e419cd5e87ee8b75a1e74b95fe33cd6764ea3987f7220b6db62c3a51e5c", + "full_human_chars": 672, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "list_directory_contents", + "count_file_lines" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_013518", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 941 + ], + "match_source_rows": [ + 941 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our team is finalizing the presentation 'Annual Tech Innovations' for the upcoming conference, and we aim to foster a collaborative environment for its completion. To facilitate this process, I would like to share the Google Slides presentation, which has the unique identifier '1a2b3c4d5e6f', with my team members whose email addresses are user@example.com and collaborator@example.com. I want to en", + "legacy_target": "share_presentation", + "legacy_target_description": "Share a Google Slides presentation with specified team members and set their permission levels.", + "legacy_target_description_chars": 95, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 941, + "source_record_id": "3c6db2a0-2fa1-43ea-b368-cd3ce78909ef", + "source_parent_id": "hermes_record:3c6db2a0-2fa1-43ea-b368-cd3ce78909ef", + "request_group_id": "hermes_request:d42d4ffc462c595b9ccf3f20acf846eee1bed096123160dcfd541beeb93fd1eb", + "full_human_chars": 914, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "share_presentation", + "track_revision_history" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_013653", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 837 + ], + "match_source_rows": [ + 837 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am analyzing the impact of technological advancements on the market, and for this purpose, I need the latest news headlines related to technology. To compile my report, I require information from trusted sources within the last 24 hours. The details should include the name of the news source, the headline title, and the exact time when the news was published. Given the high volume of news, I wou", + "legacy_target": "get_top_tech_headlines", + "legacy_target_description": "Retrieve the top news headlines in the technology category from various sources within the last 24 hours.", + "legacy_target_description_chars": 105, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 837, + "source_record_id": "b4fe0fa6-561e-4178-8271-90d175fd9e5a", + "source_parent_id": "hermes_record:b4fe0fa6-561e-4178-8271-90d175fd9e5a", + "request_group_id": "hermes_request:3fd06e9f044634d6c0cc0c75de4cf75620e0809b1858a609362e8cffa653d8ec", + "full_human_chars": 517, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_top_tech_headlines" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_013930", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 747 + ], + "match_source_rows": [ + 747 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am setting up a conversational AI named \"BookBot\" for my bookstore. This agent should be capable of discussing various book genres, recommending titles based on individual customer preferences, and providing details for books in our inventory. For initializing the agent, the name \"BookBot\" will be used, and it will have the capabilities of genre discussion, book recommendation, and inventory que", + "legacy_target": "initialize_conversational_agent", + "legacy_target_description": "Initializes a conversational agent with specified capabilities.", + "legacy_target_description_chars": 63, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 747, + "source_record_id": "47a47543-6f9a-4a52-8e8a-4b11a256c5a3", + "source_parent_id": "hermes_record:47a47543-6f9a-4a52-8e8a-4b11a256c5a3", + "request_group_id": "hermes_request:f4ec26ae0e828678d71ea5f124a45983c554299a5d0fde327c332cb41e132e2c", + "full_human_chars": 1370, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "initialize_conversational_agent", + "process_user_preferences", + "generate_book_recommendations", + "query_book_inventory" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_013994", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 127 + ], + "match_source_rows": [ + 127 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a specific video file named \"family_beach_day.mov\" that I recorded on a recent vacation. 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The file is currently stored on my computer in the fol", + "legacy_target": "extract_video_metadata", + "legacy_target_description": "Extract metadata from a video file, including capture date and time, file size, and format.", + "legacy_target_description_chars": 91, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 127, + "source_record_id": "72f126ad-e203-48e6-840e-e3047e9f8dfa", + "source_parent_id": "hermes_record:72f126ad-e203-48e6-840e-e3047e9f8dfa", + "request_group_id": "hermes_request:e838b2120214aa4b92a6a41c33b8a30954dc2f88fb0876a729336d290d3aa2e1", + "full_human_chars": 698, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_video_metadata" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_014007", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 481 + ], + "match_source_rows": [ + 481 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "In my possession are numerous healthcare documents filled with medical codes and related entities that encompass symptoms, diagnoses, and treatments. 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I have a budget of $100 to $1000 and I'm open to different brands. I'd like to see a list of available smartphones within this price range. Once I have the list, I would appreciate help in comparing the features of two specific models that catch my interest. 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Could you please automate the pr", + "legacy_target": "retrieve_customer_interactions", + "legacy_target_description": "Retrieves a list of customers from the CRM system who have had interactions of a specified type within a given timeframe.", + "legacy_target_description_chars": 121, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 567, + "source_record_id": "ad4e483f-1388-4a3d-acd6-6f740ca0c89a", + "source_parent_id": "hermes_record:ad4e483f-1388-4a3d-acd6-6f740ca0c89a", + "request_group_id": "hermes_request:de71d4eb28c8f63a327325645d47d33d5e0b60067f9675642d9ff364f2235cf5", + "full_human_chars": 1490, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "retrieve_customer_interactions", + "update_contact_list" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_014768", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 429 + ], + "match_source_rows": [ + 429 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like assistance with organizing '.log' files on my Linux server by automatically performing the following file manipulation tasks: First, I need to create a new directory specifically for storing backup '.log' files. 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The analysis ", + "legacy_target": "calculate_investment_performance_metrics", + "legacy_target_description": "Calculates various investment performance metrics for a given portfolio over a specified time period.", + "legacy_target_description_chars": 101, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 794, + "source_record_id": "f0ad356b-dedb-404b-9c06-1b07eabb97cf", + "source_parent_id": "hermes_record:f0ad356b-dedb-404b-9c06-1b07eabb97cf", + "request_group_id": "hermes_request:efcaaecfee34e1230d30d4c0144e32ab967f2326e8bca65b66efd92a0f3a3a19", + "full_human_chars": 937, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "calculate_investment_performance_metrics", + "compare_performance_to_benchmark" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015051", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 294 + ], + "match_source_rows": [ + 294 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating the enhancement of our financial institution's algorithmic trading infrastructure to facilitate high-frequency trading (HFT). 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It must also be capable of handling high-volume data processing, enabling us to process vast quantities of market data from sources s", + "legacy_target": "initializeHFTInfrastructure", + "legacy_target_description": "Initializes the high-frequency trading infrastructure with specified execution speed and data processing capabilities.", + "legacy_target_description_chars": 118, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 294, + "source_record_id": "136bbb66-5d80-424b-856f-5894aee9be99", + "source_parent_id": "hermes_record:136bbb66-5d80-424b-856f-5894aee9be99", + "request_group_id": "hermes_request:e9da58b835256862044f18a323c9433343ad92acdbb43ae2111feadc9883659a", + "full_human_chars": 1708, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "initializeHFTInfrastructure", + "configureMarketDataProcessing", + "setupDataStorageSolution" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015057", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 200 + ], + "match_source_rows": [ + 200 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently developing a DApp focused on trading digital collectibles, specifically NFTs, on the Ethereum blockchain. I require real-time market data for prominent NFT collections like CryptoKitties, AxieInfinity, and ArtBlocks. Key data I need includes trading volume, average prices, and details of recent sales for each collection. Moreover, for an in-depth understanding of individual NFTs, I n", + "legacy_target": "retrieve_NFT_market_data", + "legacy_target_description": "Retrieves market data for specified NFT collections on the Ethereum blockchain.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 200, + "source_record_id": "077dbfa2-0c73-4d8a-b0d7-ce0483f027a4", + "source_parent_id": "hermes_record:077dbfa2-0c73-4d8a-b0d7-ce0483f027a4", + "request_group_id": "hermes_request:492e6039c8e1c4ac30581ab05ead550ed213af6a158aa818155aeb0e6ac42908", + "full_human_chars": 1155, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "retrieve_NFT_market_data", + "get_NFT_details" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015075", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 199 + ], + "match_source_rows": [ + 199 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I've developed a new digital artwork titled \"Celestial Dreams\" and I intend to create a unique non-fungible token (NFT) for it. I'd like to mint this NFT on the Ethereum blockchain and list it for sale on a marketplace. My Ethereum address is `0x1A2b3C4d5E6f7G8H9I0J1K2L3M4N5O6P7Q8R9S0T`. The digital asset's metadata, including the image and description, is hosted at `https://ipfs.io/ipfs/QmT123XYZ", + "legacy_target": "mintNFT", + "legacy_target_description": "Mints a new NFT with the specified metadata and assigns it to the creator's address.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 199, + "source_record_id": "554cb54c-a1ab-4713-bbed-c009624992a9", + "source_parent_id": "hermes_record:554cb54c-a1ab-4713-bbed-c009624992a9", + "request_group_id": "hermes_request:767570664f0dd97c6f4750291ee24502d49530bdb4bf3154f9098213b5b3158c", + "full_human_chars": 979, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "mintNFT" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015105", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 894 + ], + "match_source_rows": [ + 894 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the IT manager at HealthFirst Clinics, and our goal is to enhance operational efficiency by automating our patient appointment scheduling workflow currently processed through phone, email, and our website. Here are the specific task details structured for RPA functions:\n\n1. **Capture Appointment Requests**: Automatically capture appointment requests submitted via the mentioned contact methods", + "legacy_target": "capture_appointment_requests", + "legacy_target_description": "Captures appointment requests from specified contact methods.", + "legacy_target_description_chars": 61, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 894, + "source_record_id": "fabb2ab5-18c3-4aee-a325-ded6f07e424e", + "source_parent_id": "hermes_record:fabb2ab5-18c3-4aee-a325-ded6f07e424e", + "request_group_id": "hermes_request:972b62cb5f70d70cdff18030438ae2a15b74a7f9106e5074fc1670f0f87b8e6d", + "full_human_chars": 1498, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "capture_appointment_requests", + "check_staff_availability" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015166", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 344 + ], + "match_source_rows": [ + 344 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing a smart grid network that integrates solar arrays, wind farms, and battery storage facilities. Our objective is to enhance the efficiency of renewable energy utilization and maintain grid stability amidst fluctuating supply and demand. I have collected detailed datasets on our current operations. For energy production, we have a JSON file named `solar_wind_production.json` ", + "legacy_target": "analyze_distributed_energy_resources", + "legacy_target_description": "Analyzes data on energy production, storage levels, and local demand to provide insights for resource management.", + "legacy_target_description_chars": 113, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 344, + "source_record_id": "2e0352ec-40a4-4121-8555-b815690ae200", + "source_parent_id": "hermes_record:2e0352ec-40a4-4121-8555-b815690ae200", + "request_group_id": "hermes_request:124256460accb1a02d924dc0959940328667f5e6f5265a7a9e9dbd69f9e47ad3", + "full_human_chars": 1293, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_distributed_energy_resources", + "predict_energy_needs" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015234", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 297 + ], + "match_source_rows": [ + 297 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm overseeing the exploration and production activities for our oil & gas ventures in the Permian Basin. We've conducted comprehensive geological surveys and accumulated detailed historical production records from our existing wells. Additionally, we have access to up-to-date market trends reports that provide insights into current commodity pricing, supply-demand dynamics, and technological adva", + "legacy_target": "optimize_drilling_operations", + "legacy_target_description": "Processes geological survey data and historical production records to optimize drilling operations.", + "legacy_target_description_chars": 99, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 297, + "source_record_id": "ad10a93b-ea97-458d-921e-c3e475de4a5e", + "source_parent_id": "hermes_record:ad10a93b-ea97-458d-921e-c3e475de4a5e", + "request_group_id": "hermes_request:837f0b08b87fd16692819bcbaf8ca27b0333f366f77c65c803e17981a390d106", + "full_human_chars": 3280, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "optimize_drilling_operations", + "forecast_production_rates", + "manage_resource_portfolio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015286", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 369 + ], + "match_source_rows": [ + 369 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to implement an update to our company's firewall rules across three specific network locations: our main data center, a branch office, and a remote site. The task includes adding three new IP addresses to the whitelist for enhanced security and collaboration. These IPs are 192.168.1.100, 192.168.1.101, and 192.168.1.102. Additionally, we need to remove two IP addresses that are no lon", + "legacy_target": "update_firewall_rules", + "legacy_target_description": "Updates firewall rules by adding or removing IP addresses from the whitelist across multiple network locations.", + "legacy_target_description_chars": 111, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 369, + "source_record_id": "8ceb674d-a617-48dc-b8c8-15320b96ad99", + "source_parent_id": "hermes_record:8ceb674d-a617-48dc-b8c8-15320b96ad99", + "request_group_id": "hermes_request:41c62fccb8bf853537e51114fdc34e957275c2f44e497088454458647b73e646", + "full_human_chars": 1086, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "update_firewall_rules", + "update_firewall_rules", + "update_firewall_rules" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015313", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 822 + ], + "match_source_rows": [ + 822 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning to immerse myself in a gripping TV series during the weekend, and I prefer genres that involve mystery and thrill. I've been captivated by series such as 'Breaking Bad' and 'Mindhunter', and I'm in search of similar shows that will keep me enthralled. Please could you fire up the recommendation engine and provide me with a list of TV series that align with my taste in the mystery and ", + "legacy_target": "getSeriesRecommendations", + "legacy_target_description": "Retrieves a list of TV series recommendations based on user's preferred genres and previously liked series.", + "legacy_target_description_chars": 107, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 822, + "source_record_id": "40ad9eaf-b7a0-4415-8133-554eb1198c86", + "source_parent_id": "hermes_record:40ad9eaf-b7a0-4415-8133-554eb1198c86", + "request_group_id": "hermes_request:427b31f174969a20ca20ea4cb56ae653b079c9fa204f445c9a3a471eca9b3fb1", + "full_human_chars": 1004, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "getSeriesRecommendations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015316", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 786 + ], + "match_source_rows": [ + 786 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently using the Savings account with the unique identifier \"123456789\" for my mobile banking operations. As a part of my banking app functionality check, I need to access the latest account balance and the most recent five transactions. I would like to request the retrieval of the current balance available in this account, as well as a list of the last 5 transactions made. \nCould you plea", + "legacy_target": "get_account_balance", + "legacy_target_description": "Retrieves the current balance of a specified bank account.", + "legacy_target_description_chars": 58, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 786, + "source_record_id": "fb4a683d-f035-4f45-8e1b-95d7de3bd7ef", + "source_parent_id": "hermes_record:fb4a683d-f035-4f45-8e1b-95d7de3bd7ef", + "request_group_id": "hermes_request:3f2f595f4e8654db9b5c6eb686d9662fefa3275a715d9a5424a1a4f0562162f4", + "full_human_chars": 760, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_account_balance", + "get_recent_transactions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015346", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 408 + ], + "match_source_rows": [ + 408 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently experienced an unexpected loss of a critical PowerPoint presentation from my USB flash drive. The file is named 'Q1_Sales_Report.pptx' and it is essential for an upcoming meeting. Unfortunately, I do not have a backup of this file. I urgently require assistance in retrieving this deleted presentation file. Based on my understanding, there are data recovery methods that can restore such ", + "legacy_target": "recover_deleted_file", + "legacy_target_description": "Recover a deleted file from a specified storage device.", + "legacy_target_description_chars": 55, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 408, + "source_record_id": "d43f93b3-343f-4db1-9aad-8a76bda24d56", + "source_parent_id": "hermes_record:d43f93b3-343f-4db1-9aad-8a76bda24d56", + "request_group_id": "hermes_request:5b47f98391f350f4389b69202e7212fcd5d271e68a035062d7ea0f6f18d4dcbb", + "full_human_chars": 589, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "recover_deleted_file" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015561", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 291 + ], + "match_source_rows": [ + 291 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently evaluating the stock performance of several prominent tech companies and aim to understand their latest price trends. Specifically, I need a detailed analysis of the stocks of Apple Inc. (AAPL), Alphabet Inc. (GOOGL), and Microsoft Corporation (MSFT) over the past hour. The analysis should focus on the most recent price changes, trading volumes, and any relevant news headlines that ", + "legacy_target": "analyze_price_trends", + "legacy_target_description": "Analyzes the price trends of specified stocks over a given time period, including price changes, trading volumes, and relevant news.", + "legacy_target_description_chars": 132, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 291, + "source_record_id": "b67baa99-e7a5-41b9-a338-534f715a0751", + "source_parent_id": "hermes_record:b67baa99-e7a5-41b9-a338-534f715a0751", + "request_group_id": "hermes_request:6f5f3b5601998e5298eb96efe39dd50b8147d50b7085d1181438a8a8aefae894", + "full_human_chars": 1407, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "analyze_price_trends" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015576", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 863 + ], + "match_source_rows": [ + 863 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm a radiologist reviewing a patient's case with the ID P123456. The patient has a history of lung nodules, and I need to evaluate any changes in these nodules since their last study on December 1, 2022. Please retrieve the most recent DICOM images from the PACS system using the function `retrieve_dicom_images` for the study date of April 1, 2023. Then, analyze these images for nodule changes by ", + "legacy_target": "retrieve_dicom_images", + "legacy_target_description": "Retrieves DICOM images for a patient from the PACS system based on patient ID and study date.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 863, + "source_record_id": "12588a66-00c1-4db8-bd5f-a11933c870c4", + "source_parent_id": "hermes_record:12588a66-00c1-4db8-bd5f-a11933c870c4", + "request_group_id": "hermes_request:b51e740b20beb79a079d9a0ea33b8187fd66aa6a582109bef65ddfb079f9aa8b", + "full_human_chars": 711, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "retrieve_dicom_images" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_015688", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 986 + ], + "match_source_rows": [ + 986 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the manager of a food retail chain seeking to enhance the efficiency of our supply chain. Our objective is to analyze historical sales trends, optimize current inventory levels, and synchronize supplier delivery schedules with our forecasted demand. 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Finally, to proactively handle potential", + "legacy_target": "monitor_warehouse_load", + "legacy_target_description": "Monitor the current load on a specified Snowflake warehouse.", + "legacy_target_description_chars": 60, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1097, + "source_record_id": "cfd07961-7037-4aa2-94ac-e5a09662e655", + "source_parent_id": "hermes_record:cfd07961-7037-4aa2-94ac-e5a09662e655", + "request_group_id": "hermes_request:2472ee85444a32e6a96a3570e9611b47a6bd33c007c9dff9024f1377c5fa134a", + "full_human_chars": 1429, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "monitor_warehouse_load", + "analyze_usage_history", + "setup_usage_alerts" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_017160", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 818 + ], + "match_source_rows": [ + 818 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to obtain the most recent market data for a set of specific stocks, as well as assess their historical performance over the last year. Could you please fetch the latest stock data, including prices and trading volumes, for Apple Inc. (AAPL), Microsoft Corporation (MSFT), and Tesla, Inc. (TSLA) from the New York Stock Exchange (NYSE) and NASDAQ? Additionally, I need to review the histo", + "legacy_target": "fetch_latest_stock_data", + "legacy_target_description": "Fetch the latest stock data such as price and volume for selected symbols from specified exchanges.", + "legacy_target_description_chars": 99, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 818, + "source_record_id": "d6c45b10-60ca-4e97-a8ac-8d0176d0910b", + "source_parent_id": "hermes_record:d6c45b10-60ca-4e97-a8ac-8d0176d0910b", + "request_group_id": "hermes_request:3b0ea1d4e85b30df8275966bdcc23bd9ca841535ad1163fd845af5b07383e5f0", + "full_human_chars": 1082, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "fetch_latest_stock_data", + "get_historical_stock_performance" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_017211", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 816 + ], + "match_source_rows": [ + 816 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to develop a feature for our financial application that allows our clients to manage cryptocurrencies. Specifically, I want to create a cryptocurrency wallet called 'UserCryptoWallet' for a user with an ID 'U1001', which should support transactions in Bitcoin (BTC) and Ethereum (ETH). Furthermore, I need to initiate a transaction to send 0.5 BTC to an external address '1A8JiWcwvpY7tAo", + "legacy_target": "create_wallet", + "legacy_target_description": "Creates a new cryptocurrency wallet for the user with support for multiple cryptocurrencies.", + "legacy_target_description_chars": 92, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 816, + "source_record_id": "8bd339ac-8589-4c36-bc14-670bb55865fc", + "source_parent_id": "hermes_record:8bd339ac-8589-4c36-bc14-670bb55865fc", + "request_group_id": "hermes_request:f36dbe6cf6ad9c0e31169b9e06f6deb182411bd00f072a8265400ff00598bd08", + "full_human_chars": 1328, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "create_wallet", + "initiate_crypto_payment", + "receive_crypto_payment", + "get_wallet_balance" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_017316", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 485 + ], + "match_source_rows": [ + 485 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting research on hotels in New York City that adhere to eco-friendly practices and sustainability. Specifically, I require reviews for hotels that have achieved a minimum of 4 stars, with the reviews being gathered from the time span of the past year. These reviews must highlight environmental consciousness, such as waste management, energy efficiency, or any other sustainability measur", + "legacy_target": "fetchEcoFriendlyHotelReviews", + "legacy_target_description": "Retrieves hotel reviews based on eco-friendly practices and sustainability, filtered by city, minimum rating, and time period.", + "legacy_target_description_chars": 126, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 485, + "source_record_id": "06b6e360-544e-48bc-8a14-1ff3de663bc5", + "source_parent_id": "hermes_record:06b6e360-544e-48bc-8a14-1ff3de663bc5", + "request_group_id": "hermes_request:7d7aa87922f38599365e583b9a1edfaf39ca308acc7aab5c4b418ef3137cf9d0", + "full_human_chars": 1006, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetchEcoFriendlyHotelReviews" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_017547", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 642 + ], + "match_source_rows": [ + 642 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to enhance the security measures at JFK International Airport by incorporating sophisticated security features. Specifically, my request involves three areas:\n\n1. 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We want to depart on May 15th, 2023, and return on May 30th, 2023. Our preference is for non-stop flights and we're looking to travel in economy class. Could you assist me by searching for available options that meet these criteria?", + "legacy_target": "search_flights", + "legacy_target_description": "Search for flights based on origin, destination, travel dates, number of passengers, and preferences such as non-stop flights and travel class.", + "legacy_target_description_chars": 143, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 114, + "source_record_id": "06e2d751-59d7-4525-88c9-0f5caf835557", + "source_parent_id": "hermes_record:06e2d751-59d7-4525-88c9-0f5caf835557", + "request_group_id": "hermes_request:f27d616cc734ea7f36f098c99898628efdc8a94a341ae4eac2a858da32d01882", + "full_human_chars": 333, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "search_flights" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_017656", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 531 + ], + "match_source_rows": [ + 531 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently overseeing the financial aspect of a project team within our organization and we frequently engage in business travel resulting in miscellaneous expenses. As part of streamlining our operations using AgilePoint, I want to call the relevant functions to handle our expense approval process. Here's what I need:\n\n1. 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We've issued a letter of credit for $100,000 with an annual interest rate of 5% that has been active for 90 days, and I need to calculate the accrued interest. Additionally, we're evaluating a trading partner with ID TP12345, whose transaction history indicates frequent late payments, and they have a credit sco", + "legacy_target": "calculate_letter_of_credit_interest", + "legacy_target_description": "Calculates the interest on a letter of credit given the principal amount, interest rate, and number of days active.", + "legacy_target_description_chars": 115, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 622, + "source_record_id": "c6bdff8f-0ca3-4315-886c-2616c4f499ca", + "source_parent_id": "hermes_record:c6bdff8f-0ca3-4315-886c-2616c4f499ca", + "request_group_id": "hermes_request:56c437e37ac2c0c28a57dabeef12b3cb92b63e8a8aebef73fba26afaf2b0cbb7", + "full_human_chars": 1509, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_letter_of_credit_interest", + "assess_trade_partner_risk", + "generate_trade_finance_payment_schedule" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018192", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 15 + ], + "match_source_rows": [ + 15 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm conducting a research project where I aim to simulate one-dimensional periodic functions within a quantum system through Fourier expansion techniques. For this purpose, I need to design quantum circuits that are capable of estimating the output of these functions. The circuits must have the capacity to store information on various Fourier components, therefore requiring a certain number of qub", + "legacy_target": "design_quantum_circuit", + "legacy_target_description": "Generates a quantum circuit to estimate arbitrary finite continuous periodic 1D functions based on Fourier expansion.", + "legacy_target_description_chars": 117, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 15, + "source_record_id": "4195cbf2-2257-4963-9fa0-1a5e55ea4a35", + "source_parent_id": "hermes_record:4195cbf2-2257-4963-9fa0-1a5e55ea4a35", + "request_group_id": "hermes_request:7bb2b9e5d3aef60c3afc70b0c90118d89295abbae78a7d0bd8d2d8e16160369c", + "full_human_chars": 1301, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "design_quantum_circuit" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018215", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 723 + ], + "match_source_rows": [ + 723 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "After recently relocating to a new home, I need to update my personal contact information in our company's employee directory to ensure all communications and records are current. 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The ne", + "legacy_target": "update_employee_contact_info", + "legacy_target_description": "Update the contact information of an employee in the organization's directory.", + "legacy_target_description_chars": 78, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 723, + "source_record_id": "ee1ffeb7-7b35-4e69-b2ab-20c27da5de96", + "source_parent_id": "hermes_record:ee1ffeb7-7b35-4e69-b2ab-20c27da5de96", + "request_group_id": "hermes_request:431e1f9414e321c68ab2c361b4e892c7a656d6e69c7546d83b72eba002f31477", + "full_human_chars": 1066, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "update_employee_contact_info" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018388", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 204 + ], + "match_source_rows": [ + 204 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As an independent contractor with a wide range of business-related expenses, I'm seeking a more integrated approach to managing my financial obligations. Specifically, I need assistance with the following tasks for my account: firstly, integrating my bank transaction data from my main business account, which includes an array of transactions such as payments received from clients and various busin", + "legacy_target": "integrate_bank_transactions", + "legacy_target_description": "Integrates the user's bank transactions into the expense tracking system.", + "legacy_target_description_chars": 73, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 204, + "source_record_id": "8e4bc2e8-2ed8-482c-a967-dfcfd24989d7", + "source_parent_id": "hermes_record:8e4bc2e8-2ed8-482c-a967-dfcfd24989d7", + "request_group_id": "hermes_request:1d18894a318c17c17419ef217679e30f8349302d862634371ac5b32f26a46fbd", + "full_human_chars": 1199, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "integrate_bank_transactions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018409", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1109, + 1122, + 1151, + 1173, + 1338, + 1569, + 1752, + 1782, + 1825, + 1834, + 1841, + 1848, + 1888 + ], + "match_source_rows": [ + 1122 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage have encountered. This will be especially important when you are asked to construct proofs whose conclusion states that two objects are equal. If you have an electronic copy of the book, such as the PDF version, searching on \"Definition CVE\" can be an instructive exercise. See how often, and where, the definition is employed. OK,", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for reasoning behind a design decision, product specification, or method described in the document. These reveals thought ", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1122, + "source_record_id": "8c250196-c23d-4c91-88be-b578d7afd181", + "source_parent_id": "hermes_record:8c250196-c23d-4c91-88be-b578d7afd181", + "request_group_id": "hermes_request:75bfc52208e94b7debd567d06c690c28f3e6ce19efb82592ebfc0a8a3c033b95", + "full_human_chars": 9349, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018613", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1062 + ], + "match_source_rows": [ + 1062 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing a content management system through a web interface, and I need to perform clean-up operations by deleting multiple outdated entries from the system. 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We need to examine critical metrics such as click-through rate (CTR), conversion rate, and return on ad spend (ROAS) over the last quarter. The date range for this analysis is from 2023-01-01 to 2023-03-31. We require the retrieval of these specific m", + "legacy_target": "get_ad_performance_metrics", + "legacy_target_description": "Retrieve advertising performance metrics for specified campaigns within a date range.", + "legacy_target_description_chars": 85, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 76, + "source_record_id": "0997b574-370a-4871-897e-5260843b1bc3", + "source_parent_id": "hermes_record:0997b574-370a-4871-897e-5260843b1bc3", + "request_group_id": "hermes_request:165860320451e79e98061288e7b1d74db7e35046b3e349edbd9d004837252411", + "full_human_chars": 843, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_ad_performance_metrics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018814", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 72 + ], + "match_source_rows": [ + 72 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to explore the latest movies in the action genre that have been released this month for our entertainment streaming application. Can you fetch a list of new release action movies sorted by their ratings, limiting the results to five? Once we have the list, please begin streaming the highest-rated new action movie available. The streaming should start with the movie titled 'Explosive S", + "legacy_target": "fetch_new_releases", + "legacy_target_description": "Fetches a list of new release movies in a specified genre, sorted by a specified criterion, and limited to a specified number of results.", + "legacy_target_description_chars": 137, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 72, + "source_record_id": "e0d9d502-4ee3-4ca8-9008-617cd607baf3", + "source_parent_id": "hermes_record:e0d9d502-4ee3-4ca8-9008-617cd607baf3", + "request_group_id": "hermes_request:a45157b62afd5e11bb67481d7ba3791aca1ba5886a73f65359829415c13ad1e3", + "full_human_chars": 898, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "fetch_new_releases", + "stream_movie" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018848", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1018 + ], + "match_source_rows": [ + 1018 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing a comprehensive health and fitness program called \"Wellness Journey\" and require an AI conversational agent named \"WellnessGuide\" with capabilities to assist users. The agent should provide personalized dietary guidance including vegetarian options, exercise recommendations, and track health metrics for our users. Our user base has varying needs; for instance, a female in her 30s w", + "legacy_target": "initialize_conversational_agent", + "legacy_target_description": "Initializes the conversational agent with specified features.", + "legacy_target_description_chars": 61, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1018, + "source_record_id": "15aee723-6519-42f1-9d8c-3e4bdab49a96", + "source_parent_id": "hermes_record:15aee723-6519-42f1-9d8c-3e4bdab49a96", + "request_group_id": "hermes_request:e7752ae7a8e85bc5c8b29f09740229e60685f375c878240ec602dc74b5c7795f", + "full_human_chars": 1341, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "initialize_conversational_agent", + "interpret_user_input", + "fetch_health_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018866", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 701 + ], + "match_source_rows": [ + 701 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To efficiently manage our lab's workflow, I would like to incorporate a system to track and process our patient samples. For a specific upcoming batch, we have collected blood samples from multiple patients on April 20, 2023. For patient with ID PAT001, a sample labeled SAMP001 was taken, and for patient ID PAT002, sample SAMP002 was collected. I need to register these samples in our LIMS.\n\nSubseq", + "legacy_target": "register_new_sample", + "legacy_target_description": "Registers a new sample in the LIMS with patient and sample details.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 701, + "source_record_id": "6394230f-fdd8-4f8e-886c-b9fc412717ba", + "source_parent_id": "hermes_record:6394230f-fdd8-4f8e-886c-b9fc412717ba", + "request_group_id": "hermes_request:071f9f81050224af85829ed597390932eaada719e9bf863625acbf77d7b82258", + "full_human_chars": 1028, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "register_new_sample", + "register_new_sample" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_018951", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 246 + ], + "match_source_rows": [ + 246 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to automate the assignment of new leads to our sales representatives in Salesforce. The automation should be based on the geographic location of each lead, ensuring that leads are matched with reps who manage the corresponding regions. Please use the 'LeadAddress' field from the lead records to determine the location. The assignment rules should be as follows: leads from North America", + "legacy_target": "assign_leads_to_sales_reps", + "legacy_target_description": "Automatically assigns new leads to sales representatives based on predefined location criteria.", + "legacy_target_description_chars": 95, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 246, + "source_record_id": "e9f08e76-2079-47d9-b337-d943421f32fe", + "source_parent_id": "hermes_record:e9f08e76-2079-47d9-b337-d943421f32fe", + "request_group_id": "hermes_request:adb5e212f09121bf49a05a50e6b0650c9f5b2512b306bb18c7188c1fc3103204", + "full_human_chars": 621, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "assign_leads_to_sales_reps" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_019204", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 671 + ], + "match_source_rows": [ + 671 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "At the Jackson Memorial Hospital, we need to enhance our healthcare facility's inventory management for items such as medications, medical supplies, and equipment. As the hospital's inventory manager, I have specific requirements for which I'll need the following actions to be executed:\n\n1. A regular examination of our stock levels to identify items that need restocking, utilizing our current inve", + "legacy_target": "monitor_stock_levels", + "legacy_target_description": "Monitors the stock levels of items in the healthcare facility's inventory and identifies items that need restocking.", + "legacy_target_description_chars": 116, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 671, + "source_record_id": "566f866b-d13a-423b-be54-d3ad3400ba75", + "source_parent_id": "hermes_record:566f866b-d13a-423b-be54-d3ad3400ba75", + "request_group_id": "hermes_request:147c58c0fb4bac5f6d3bc0c5db214404c9af1aa2dc118d3e4429e33b345ed988", + "full_human_chars": 1857, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 6, + "valid_top_level_call_names": [ + "monitor_stock_levels", + "update_inventory_transaction", + "notify_low_stock", + "generate_inventory_report", + "generate_inventory_report", + "generate_inventory_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_019265", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1006 + ], + "match_source_rows": [ + 1006 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "We are undertaking a security overhaul at our office complex (Facility ID: FAC123) and require a comprehensive security management system. The system must integrate an access control setup that employs a multi-factor authentication approach, including the use of access cards, biometric scanners, and PIN codes to ensure secure and restricted entry. Additionally, we need to install a surveillance sy", + "legacy_target": "setupAccessControl", + "legacy_target_description": "Configures the access control system with specified authentication methods for a facility.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1006, + "source_record_id": "62ea8528-a24e-4bd5-bf9d-d47152c9d77f", + "source_parent_id": "hermes_record:62ea8528-a24e-4bd5-bf9d-d47152c9d77f", + "request_group_id": "hermes_request:9fde109b7bb65fb385edda7a7b19cb71dbd649013461d756819ccdd8ffa5541d", + "full_human_chars": 1430, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "setupAccessControl", + "installSurveillanceSystem", + "configureSecurityAlerts" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_019292", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 33 + ], + "match_source_rows": [ + 33 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our healthcare facility is in the process of optimizing our EMR system and needs assistance with organizing our patient medical records. We have a directory at `/path/to/emr/data` that contains all patient records. The records need to be sorted into three specific categories: diagnosis, treatment, and medication history. We also have identified issues with duplicate patient profiles within the sam", + "legacy_target": "sort_records_by_category", + "legacy_target_description": "Sorts patient records into specified categories such as diagnosis, treatment, and medication history.", + "legacy_target_description_chars": 101, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 33, + "source_record_id": "366b2e42-9c0b-4bc5-aa29-f611b7dcddfc", + "source_parent_id": "hermes_record:366b2e42-9c0b-4bc5-aa29-f611b7dcddfc", + "request_group_id": "hermes_request:bb0fb3c9a30e0753dabdc6d00d2ac5a93b1a5c8d9035dae4051d17269ebbfc1a", + "full_human_chars": 1354, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "sort_records_by_category", + "merge_duplicate_profiles", + "update_contact_information" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_019421", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 111 + ], + "match_source_rows": [ + 111 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm setting up a cafe and need to purchase a high-quality coffee maker available on Amazon. I want to ensure the coffee maker has received positive reviews, so it should have a rating of at least 4 stars. Specifically, I'd like to see the top-rated options sorted by customer ratings in descending order to help me make a decision. From the available options, I only need the first 5 results to consi", + "legacy_target": "search_amazon_products", + "legacy_target_description": "Search for products on Amazon with various filters such as category, minimum rating, and sorting options.", + "legacy_target_description_chars": 105, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 111, + "source_record_id": "a10919e0-2278-4b65-865f-b252806ebff1", + "source_parent_id": "hermes_record:a10919e0-2278-4b65-865f-b252806ebff1", + "request_group_id": "hermes_request:aebec9d99eee1d26af177228fc9ef312505a71654b41594c220a1b73936e0672", + "full_human_chars": 735, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "search_amazon_products" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_019581", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 795 + ], + "match_source_rows": [ + 795 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our investment bank's M&A division, we are currently managing a deal with transaction ID T456. Our goal is to conduct a comprehensive assessment of the target company's financials, update our internal records with the latest deal status, and generate a transaction report for senior management and stakeholders. I have the following information ready for processing:\n\n1. For the financial ", + "legacy_target": "analyze_financial_health", + "legacy_target_description": "Analyzes the financial health of a target company by reviewing specified financial statements.", + "legacy_target_description_chars": 94, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 795, + "source_record_id": "69ebc8fa-502c-470e-88f2-5d71654f91e9", + "source_parent_id": "hermes_record:69ebc8fa-502c-470e-88f2-5d71654f91e9", + "request_group_id": "hermes_request:9d72beefe83f93fa74b1cc3c9a569ec948305de8cd722a71ef6aa394e64d3250", + "full_human_chars": 992, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_financial_health", + "update_deal_status", + "generate_transaction_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_019650", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1188, + 1194, + 1200, + 1207, + 1262, + 1386, + 1461, + 1498, + 1529, + 1632, + 1686, + 1774, + 1791 + ], + "match_source_rows": [ + 1188 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage t C o u r s e i n L i n e a r A l g e b r a 40 \nsentence is pretty vague, so let us be more precise. Using our notation for the sets D and F from the reduced row-echelon form (Definition RREF): xdi = [B]i,n+1 , 1 <= i <= r xfi = 0, 1 <= i <= n - r These values for the variables make the equations represented by the first r rows o", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1188, + "source_record_id": "116b0a9c-d7a2-4efb-bfd9-10470728798b", + "source_parent_id": "hermes_record:116b0a9c-d7a2-4efb-bfd9-10470728798b", + "request_group_id": "hermes_request:913550eef32a9c0b614b342263547e283d1240cbb3727080ad88f7c7ec9d34ed", + "full_human_chars": 11544, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_019701", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 1196, + 1219, + 1299, + 1313, + 1318, + 1359, + 1361, + 1446, + 1531, + 1704, + 1800, + 1855, + 1862 + ], + "match_source_rows": [ + 1196 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage or insane, so we won't discuss it more here. For now, just realize that every set is either finite or infinite. \nYou might think, by the way, that there's no way to define an infinite set extensionally, since that would require infinite paper. This isn't true, though, if we creatively use an ellipsis: \n I = { 3 , 6 , 9 , 12 , 15 ", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that request for an opinion on a topic found in the document.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1196, + "source_record_id": "92c2216d-471b-4b98-aa93-f2d9c19799d7", + "source_parent_id": "hermes_record:92c2216d-471b-4b98-aa93-f2d9c19799d7", + "request_group_id": "hermes_request:c46f1d6a3cef851dfb2f6f4e8332f598d765fbedc170d3ce4d978d9ab2a881c2", + "full_human_chars": 11981, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_019803", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 118 + ], + "match_source_rows": [ + 118 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am interested in transferring Bitcoin using blockchain technology and require assistance with completing a transaction. I need to send 0.75 BTC from my wallet to a recipient's wallet. For secure and transparent processing, I would like this transfer to be recorded on the blockchain with all relevant details such as the transaction ID, timestamp, and the wallet addresses of both the sender and th", + "legacy_target": "transfer_funds", + "legacy_target_description": "Executes a secure transfer of Bitcoin from one wallet to another and records the transaction on the blockchain.", + "legacy_target_description_chars": 111, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 118, + "source_record_id": "38a25478-7e0f-4186-b472-e3a07fa58606", + "source_parent_id": "hermes_record:38a25478-7e0f-4186-b472-e3a07fa58606", + "request_group_id": "hermes_request:3bab8cb02c08fae6778857ce1a7e4a82117f8b5eac42b014ac85d2cd9d260b85", + "full_human_chars": 931, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "transfer_funds" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_019814", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 505 + ], + "match_source_rows": [ + 505 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing a project through Microsoft Planner, and I have a particular task, identified by the task ID \"task456\", for which I need to set up a notification system. I want to create a webhook that triggers when this task is marked as complete. Could you please establish this webhook for my Planner with the planner ID \"abc123\"? Additionally, once the task is completed, I need an automa", + "legacy_target": "create_task_completed_webhook", + "legacy_target_description": "Create a webhook that triggers when a task is marked as complete in Microsoft Planner.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 505, + "source_record_id": "1e40ffdc-cb01-4d93-b8a5-28fe2f9a04da", + "source_parent_id": "hermes_record:1e40ffdc-cb01-4d93-b8a5-28fe2f9a04da", + "request_group_id": "hermes_request:8408a3d7240c0d0e85a33d60a821c97ed1384b08b059ca7234a65a9c6b67d378", + "full_human_chars": 662, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_task_completed_webhook", + "post_message_to_slack" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020078", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 316 + ], + "match_source_rows": [ + 316 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to request assistance in forecasting renewable electricity generation, specifically by analyzing the impact of weather conditions on the production of solar and wind energy and estimating how well the forecasted generation will meet the expected demand. We have historical production data, weather forecast data, and demand forecast data available for this purpose.\n\n- **Historical Produ", + "legacy_target": "forecast_generation", + "legacy_target_description": "Forecasts the amount of electricity generation from renewable sources using historical production data and weather forecasts.", + "legacy_target_description_chars": 125, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 316, + "source_record_id": "f43c5b28-8589-4c5b-a7fe-bb0ccc84b800", + "source_parent_id": "hermes_record:f43c5b28-8589-4c5b-a7fe-bb0ccc84b800", + "request_group_id": "hermes_request:dd001a87db2e162f551d7a972872763daaaa62b0af06dec98eb5e37bc7ef16c5", + "full_human_chars": 1732, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "forecast_generation", + "analyze_weather_impact", + "estimate_generation_demand_match" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020193", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 448 + ], + "match_source_rows": [ + 448 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently assumed the role of Systems Administrator at our company, and I am currently focused on enhancing our access control systems. Our tasks include updating server permissions for the IT department, setting access policies for a new repository, and revoking user access permissions for a former employee.\n\nOur main server 'srv-main-01' should only be accessible by our IT department 'grp-it-de", + "legacy_target": "update_access_control_list", + "legacy_target_description": "Updates the access control list for a specified server to grant or restrict access to a specified group.", + "legacy_target_description_chars": 104, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 448, + "source_record_id": "8a45d79c-6a51-42ce-8fae-6747e7c7a337", + "source_parent_id": "hermes_record:8a45d79c-6a51-42ce-8fae-6747e7c7a337", + "request_group_id": "hermes_request:594470ef5eca439c7b277b5b2bef9a05da50a4651e3b420736644bfcc7e73f93", + "full_human_chars": 1396, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "update_access_control_list", + "create_access_control_policy", + "revoke_user_access" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020229", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 489 + ], + "match_source_rows": [ + 489 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm conducting a detailed analysis of our production line performance and need to access comprehensive manufacturing data for our main product models. Specifically, I require data on the output numbers, defect rates, and machine utilization for the following product models: X100, Y200, and Z300. Additionally, it is critical to obtain up-to-date inventory levels for these models to ensure our stock", + "legacy_target": "fetch_production_data", + "legacy_target_description": "Fetches production data for specified product models, including output, defect rates, machine utilization, and inventory levels.", + "legacy_target_description_chars": 128, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 489, + "source_record_id": "f1e3dd27-0f8b-4e69-9c7d-74759cc980c5", + "source_parent_id": "hermes_record:f1e3dd27-0f8b-4e69-9c7d-74759cc980c5", + "request_group_id": "hermes_request:cfa73c01306f3ae641b91893b1fa9aa6f5394a4f34da9b462e94d40bb51dbc89", + "full_human_chars": 1589, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetch_production_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020433", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1021 + ], + "match_source_rows": [ + 1021 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Head of Compliance at FutureTech Innovations, we are mindful of the ethical footprint of the AI models we deploy. To this end, we need to scrutinize our new AI model, designed to enhance user experience in our product recommendation engine. The model in question is referenced by its identifier \"product_recommendation_ai_1\". We seek to conduct an audit to identify any potential biases regard", + "legacy_target": "audit_model_for_bias", + "legacy_target_description": "Audits an AI model for potential biases based on specified types.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1021, + "source_record_id": "fad62e60-4fd6-4f21-83fd-7ff864bdeca6", + "source_parent_id": "hermes_record:fad62e60-4fd6-4f21-83fd-7ff864bdeca6", + "request_group_id": "hermes_request:29daba4f64228e9aeae71d4588ba2e47a3f47f3a752e3f20875f953253665896", + "full_human_chars": 1672, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "audit_model_for_bias", + "ensure_gdpr_compliance", + "verify_ai_content_copyright" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020641", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 891 + ], + "match_source_rows": [ + 891 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am in the process of transitioning my career and seeking guidance on suitable career paths that match my professional background and aspirations. My educational and professional background is in software engineering, and my expertise lies in full-stack development. I am particularly drawn to positions where I can exercise team leadership and oversee project management. Furthermore, it is importa", + "legacy_target": "analyzeCareerProfile", + "legacy_target_description": "Analyzes a user's professional background, skills, and interests to suggest suitable career options.", + "legacy_target_description_chars": 100, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 891, + "source_record_id": "71ae6a2e-700a-4a53-9c3c-f20ea56ca1ab", + "source_parent_id": "hermes_record:71ae6a2e-700a-4a53-9c3c-f20ea56ca1ab", + "request_group_id": "hermes_request:627b4dd9e6bb5c279fb609b7e5d0f0cd3623ba63d22b904dc1a8b3c1dc67bfb3", + "full_human_chars": 1067, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "analyzeCareerProfile" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020661", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 889 + ], + "match_source_rows": [ + 889 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am preparing an article focused on the significant role APIs play in the realm of modern software development, emphasizing aspects such as integration, security enhancements, and contributions to user experience. I intend to publish this insightful piece on my personal blog platform. To facilitate this, I require assistance in executing a POST request to share the content effectively. \n\nHere are", + "legacy_target": "share_content", + "legacy_target_description": "Share content on a specified platform using a POST request.", + "legacy_target_description_chars": 59, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 889, + "source_record_id": "2aeb4583-f336-43eb-aa8c-9a20c0216644", + "source_parent_id": "hermes_record:2aeb4583-f336-43eb-aa8c-9a20c0216644", + "request_group_id": "hermes_request:c4fc229904c1b71349cbfa9aa131d4dca7997609618102bb8d1361c32901a35b", + "full_human_chars": 1027, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "share_content" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020682", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 591 + ], + "match_source_rows": [ + 591 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage the financial analysis team for a mid-sized corporation where we consolidate extensive financial data to produce monthly reports. These reports critically influence strategic business decisions but have been plagued by the inefficiencies and inaccuracies of manual compilation from multiple data sources. To rectify this, we're implementing RPA to automate our data analysis and reporting pr", + "legacy_target": "setupDataAnalysisAutomation", + "legacy_target_description": "Configures RPA to extract data from various sources, analyze financial metrics, and generate a monthly financial report.", + "legacy_target_description_chars": 120, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 591, + "source_record_id": "73feb24b-4920-47c2-aa3c-b3cb06c991f6", + "source_parent_id": "hermes_record:73feb24b-4920-47c2-aa3c-b3cb06c991f6", + "request_group_id": "hermes_request:50070263463e85fbe34aa6152bd3d7e19e2d7e7e2980207660911c80fb6a76b1", + "full_human_chars": 1331, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "setupDataAnalysisAutomation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020858", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 673 + ], + "match_source_rows": [ + 673 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing patient billing at our healthcare facility and need to generate an itemized bill for a particular patient. The patient's ID is P123456, and they have received a few services from us. On April 15, 2023, the patient had a consultation, for which we need to bill them $150.00, and also underwent two X-Ray services, each costing $75.00. To efficiently process this billing, I wou", + "legacy_target": "generate_itemized_bill", + "legacy_target_description": "Generates an itemized bill for a patient with details of services rendered.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 673, + "source_record_id": "1b37a8c0-f314-4735-865b-bdf813098fe5", + "source_parent_id": "hermes_record:1b37a8c0-f314-4735-865b-bdf813098fe5", + "request_group_id": "hermes_request:0aa8ac6dd51925b65568277f9d51ea746b9e9ae1831efdd7973d72fbeb742a9d", + "full_human_chars": 861, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_itemized_bill" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_020955", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 870 + ], + "match_source_rows": [ + 870 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating robot deployment and diagnostics at our logistics center today and require assistance with the following tasks:\n\n1. Robot Unit RX-900 needs to be deployed for the transportation of Shipment SHP-1122. Please ensure it is moved from the loading dock to storage area SA-3 without delay, as this shipment contains sensitive items that must be handled with care.\n\n2. We have observed pot", + "legacy_target": "deploy_robot_for_transport", + "legacy_target_description": "Deploys a robot to transport a shipment to a specified destination within the facility.", + "legacy_target_description_chars": 87, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 870, + "source_record_id": "9bc54ece-1b9d-46fe-a577-57cd4c0c674c", + "source_parent_id": "hermes_record:9bc54ece-1b9d-46fe-a577-57cd4c0c674c", + "request_group_id": "hermes_request:2535efa302cad5aedaf884f6f5c0bd266076d98c6d55de3efe8de429df27266c", + "full_human_chars": 1146, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "deploy_robot_for_transport", + "initiate_robot_diagnostic", + "generate_robot_activity_log" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_021009", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1150, + 1191, + 1198, + 1336, + 1367, + 1393, + 1399, + 1470, + 1540, + 1600, + 1729, + 1882, + 1884 + ], + "match_source_rows": [ + 1150 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 'r'^ } (Definition RREF). There are four steps in the proof, and the first three are about showing that B and C have the same number of pivot columns, in the same places. In other words, the \"primed\" symbols are a necessary fiction. First Step. Suppose that d 1 < d' 1. Then 1 = [B] 1 d 1 Definition RREF \n = \n ^ m \n k=1 \n d 1 k [C", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that request for an opinion on a topic found in the document.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1150, + "source_record_id": "77095778-9e4e-46ae-b97c-1e84c726b4a9", + "source_parent_id": "hermes_record:77095778-9e4e-46ae-b97c-1e84c726b4a9", + "request_group_id": "hermes_request:410e87568a80bd8d362dd1fd7c9a7fdbe9e9796a9b30d7786358c87b4c186ff5", + "full_human_chars": 6443, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_021079", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 826 + ], + "match_source_rows": [ + 826 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need to ensure my grandmother, Mrs. Emma Thompson, receives proper care at her home located at 742 Evergreen Terrace. Firstly, could you schedule a nurse visit for a routine check-up on April 18, 2023? Additionally, she requires a week of meal deliveries starting from April 14, 2023, to April 21, 2023. Lastly, it is essential to arrange transportation for her to get to Springfield Clinic for a p", + "legacy_target": "schedule_nurse_visit", + "legacy_target_description": "Schedules a home visit by a nurse for a routine check-up.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 826, + "source_record_id": "bec2f5c4-dd5b-4f54-af6c-0c06c5ec6b48", + "source_parent_id": "hermes_record:bec2f5c4-dd5b-4f54-af6c-0c06c5ec6b48", + "request_group_id": "hermes_request:16dc803638b7098b92c3f1c2c1ec6c703caa6446224c5b5be4587532afc72622", + "full_human_chars": 936, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "schedule_nurse_visit", + "order_meal_delivery", + "book_transportation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_021163", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 633 + ], + "match_source_rows": [ + 633 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the manager of logistics for our shipping company, we're currently working to improve our marine transportation logistics. We have an updated set of vessel itineraries and port schedules that detail the planned movements and operations of our maritime fleet and port docking times. Our goal is to enhance the efficiency of vessel scheduling to reduce delays, maintain precision in cargo tracking t", + "legacy_target": "optimize_vessel_scheduling", + "legacy_target_description": "Optimizes the scheduling of the vessel fleet to maximize efficiency and minimize delays.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 633, + "source_record_id": "b21aefc6-e89e-417d-bbd9-f3b34a3d3529", + "source_parent_id": "hermes_record:b21aefc6-e89e-417d-bbd9-f3b34a3d3529", + "request_group_id": "hermes_request:4a92b5b70be6f07dd214f65a29ea1b8e47b14ec7a16a6e7f5a2c3493728765ab", + "full_human_chars": 1913, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "optimize_vessel_scheduling", + "track_cargo", + "manage_port_operations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_021307", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 163 + ], + "match_source_rows": [ + 163 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to perform Named Entity Recognition on a collection of text to identify key entities such as people, organizations, and locations. Specifically, I have extracted paragraphs from multiple open-source Large Language Models (LLM) related web pages, and I need to process them to find entities. For this task, can you please load a Named Entity Recognition model from TensorFlow Hub using th", + "legacy_target": "load_ner_model", + "legacy_target_description": "Load a Named Entity Recognition model from TensorFlow Hub.", + "legacy_target_description_chars": 58, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 163, + "source_record_id": "63b5dd55-ec11-45b2-af27-aa1d6cffe759", + "source_parent_id": "hermes_record:63b5dd55-ec11-45b2-af27-aa1d6cffe759", + "request_group_id": "hermes_request:6535dc8ed2441cc9dce31fe8154a075a8f901b269bb750d3e165f4c444d37707", + "full_human_chars": 1494, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "load_ner_model" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_021559", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 788 + ], + "match_source_rows": [ + 788 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working on designing an online banking portal and need to implement several key functionalities. To test the system, I would like to perform these operations using actual data:\n\n1. Authenticate a customer with the username \"johndoe123\" and the corresponding password \"JDpass!2023\" to access their online banking.\n2. Check the account balance for customer ID \"JD929292\" who has a checki", + "legacy_target": "authenticate_user", + "legacy_target_description": "Authenticates a user's credentials to provide access to the online banking portal.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 788, + "source_record_id": "bd02c626-b376-4873-80bb-a7e9ab4fb13d", + "source_parent_id": "hermes_record:bd02c626-b376-4873-80bb-a7e9ab4fb13d", + "request_group_id": "hermes_request:a0d677a47e3c44d10f780ba8b3950ab19457c39d73243806f897cdb67d98ca89", + "full_human_chars": 921, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "authenticate_user", + "check_account_balance", + "transfer_funds", + "retrieve_transaction_history" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_021560", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 540 + ], + "match_source_rows": [ + 540 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the project manager for an enterprise using Vinyl’s low-code platform to enhance our internal operations through a Change Management Application. We're rolling out a new feature that needs to facilitate employee submissions of change requests related to our ERP system updates. It must include a facility to capture the request title, a detailed description including impact analysis, associated", + "legacy_target": "submitChangeRequest", + "legacy_target_description": "Submits a new change request to the system.", + "legacy_target_description_chars": 43, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 540, + "source_record_id": "f4da5839-b354-4b93-b5c0-cb76390fd08e", + "source_parent_id": "hermes_record:f4da5839-b354-4b93-b5c0-cb76390fd08e", + "request_group_id": "hermes_request:15d5f661979afec4e46c96d59fac26b72c803f1f26dd04625c364da7a8a916bf", + "full_human_chars": 1857, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "submitChangeRequest" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_021743", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 704 + ], + "match_source_rows": [ + 704 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing the deliveries for a healthcare distributor, and we have a consignment of critical medical supplies that needs to be tracked and managed meticulously. We have a package with delivery ID \"DL12345XYZ\" that was expected to arrive at Mercy Hospital by April 15, 2023, at 14:00 UTC. However, it's currently April 16, 2023, 16:00 UTC, and we have no confirmation of its delivery yet. I need", + "legacy_target": "track_delivery_progress", + "legacy_target_description": "Tracks the progress of a specific delivery using its unique delivery ID.", + "legacy_target_description_chars": 72, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 704, + "source_record_id": "b2a5ad15-8a7b-40cd-8af0-93805324ce42", + "source_parent_id": "hermes_record:b2a5ad15-8a7b-40cd-8af0-93805324ce42", + "request_group_id": "hermes_request:94a93666e48bea31b1bd05f6c3793c35702a186d522e6ef63183140b52fa8126", + "full_human_chars": 1275, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "track_delivery_progress", + "alert_delivery_delay" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_021937", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 154 + ], + "match_source_rows": [ + 154 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have an ONNX model for classifying real-time sensor readings to monitor the condition of heavy machinery. The model, saved at `./models/machinery_condition_model.onnx`, needs to process input data stored in `./data/sensor_readings.json`. 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Specifically, I'm looking for entertainment apps that have high popularity and user engagement. I would appreciate a curated list of the top 5 entertainment VR apps that are suitable for my Oculus Rift. 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Additionally, I have set up a callback URL on ", + "legacy_target": "initiate_payment", + "legacy_target_description": "Initiates a payment request to the third-party payment gateway.", + "legacy_target_description_chars": 63, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 830, + "source_record_id": "4f7718f6-e3e7-44fb-8893-154a0859d590", + "source_parent_id": "hermes_record:4f7718f6-e3e7-44fb-8893-154a0859d590", + "request_group_id": "hermes_request:c20dbe434a0e7a5ca12de31e868c7ea705381d3d152128354e48a101eb0a455a", + "full_human_chars": 1008, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "initiate_payment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_022526", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 438 + ], + "match_source_rows": [ + 438 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To ensure the smooth operation of our automated warehouse facility, I would like to request assistance with orchestrating the activities of our robot, model WRX-2023. 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My user ID is 'user123', and I really enjoyed the genre 'action' lately. I'd like to add it to my list of favorite genres. Also, I recently watched 'The Matrix', and I'd want to make sure this is included in my recently watched list to better tailor future recommendations. Could you please update my user prof", + "legacy_target": "updateUserPreferences", + "legacy_target_description": "Updates the user's preferences for genres and recently watched movies.", + "legacy_target_description_chars": 70, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 922, + "source_record_id": "f773ccd6-a684-4ad1-ae87-61e07098ee73", + "source_parent_id": "hermes_record:f773ccd6-a684-4ad1-ae87-61e07098ee73", + "request_group_id": "hermes_request:c7016afc26041f4196519b28073c9106f38777e0c7edd1e71737b50fe97f8dac", + "full_human_chars": 437, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "updateUserPreferences" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_022663", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 640 + ], + "match_source_rows": [ + 640 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the production manager at ABC Manufacturing, I am tasked with optimizing our MRP II system to handle our increased workload effectively. We have specific datasets detailing our current supply chain, machine availability, and labor forces which need to be integrated for smoother operation. Here are the precise details:\n\nOur supply chain data includes vendor information such as inventory levels a", + "legacy_target": "synchronize_supply_chain_data", + "legacy_target_description": "Synchronizes supply chain data to match inventory requirements with vendor capabilities.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 640, + "source_record_id": "2816a4b3-0f37-4cf4-90de-e0bf71757fb2", + "source_parent_id": "hermes_record:2816a4b3-0f37-4cf4-90de-e0bf71757fb2", + "request_group_id": "hermes_request:fa62aa5410371fbea95e11fccd0639d9cdc029a832ca1e00c43d89b6da6f9173", + "full_human_chars": 2092, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "synchronize_supply_chain_data", + "align_production_schedules", + "allocate_labor_resources" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_022780", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 521 + ], + "match_source_rows": [ + 521 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently setting up an online store section on my website dedicated to electronics, and I'm particularly interested in offering a range of laptops to my customers. I need to implement functionality that allows customers to easily search for laptops within the electronics category, sorted by relevance. The search result should list no more than 10 products to maintain clarity on the page. Onc", + "legacy_target": "search_products", + "legacy_target_description": "Search for products based on a query and other optional filters.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 521, + "source_record_id": "8e678c66-e149-4ecb-8b3a-9a958bec2c7d", + "source_parent_id": "hermes_record:8e678c66-e149-4ecb-8b3a-9a958bec2c7d", + "request_group_id": "hermes_request:f683552486840f424524f02406fb6ce5871bbb3e958080a9b4c2ba9917dba5fe", + "full_human_chars": 1398, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "search_products", + "get_product_details" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_022795", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 770 + ], + "match_source_rows": [ + 770 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently crafting a fantasy novel and wish to enrich the narrative with unexpected plot twists and further develop my protagonist's character arc. The novel unfolds in a medieval realm inhabited by mystical dragons and widespread sorcery. The central character is Elara, a youthful mage on an epic expedition to retrieve a storied relic. To overcome my creative hurdles, I seek assistance in ge", + "legacy_target": "generate_plot_twists", + "legacy_target_description": "Generates plot twists for a given story context and genre.", + "legacy_target_description_chars": 58, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 770, + "source_record_id": "71d33c77-92dc-4bd8-8ec6-7a7012588206", + "source_parent_id": "hermes_record:71d33c77-92dc-4bd8-8ec6-7a7012588206", + "request_group_id": "hermes_request:c86c4e42b25100f579ddadc3d4d2b1d6d871421b58b368e9a736796b4124aa1c", + "full_human_chars": 1563, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "generate_plot_twists", + "develop_character" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_022977", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 90 + ], + "match_source_rows": [ + 90 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our company is currently producing a new batch of polyethylene terephthalate (PET) for packaging materials. To maintain the highest quality standards, we need to perform quality control checks on the polymer samples. Specifically, we require an analysis to identify and classify any impurities present above a threshold of 0.02% concentration. The polymer samples have been prepped, and we've already", + "legacy_target": "analyze_material_samples", + "legacy_target_description": "Analyzes material samples to detect impurities using spectral analysis.", + "legacy_target_description_chars": 71, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 90, + "source_record_id": "eb999173-72ab-4bef-9010-bf222fcee93d", + "source_parent_id": "hermes_record:eb999173-72ab-4bef-9010-bf222fcee93d", + "request_group_id": "hermes_request:611dca026f300207e89d4ff2afe0a1a33aad7ca8e6eb386354d043466fe08869", + "full_human_chars": 1656, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "analyze_material_samples" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023118", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1000 + ], + "match_source_rows": [ + 1000 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I oversee a food distribution network, and we are in the process of enhancing our supply chain operations. Our current challenges include high inventory levels with low turnover, unnecessary wastage due to poor shelf-life management, and inconsistencies in our supply chain that affect timely delivery. Below I've provided details regarding our inventory, supplier network, and logistics system in JS", + "legacy_target": "optimize_inventory_turnover", + "legacy_target_description": "Analyzes inventory data to optimize stock levels, reduce waste, and improve turnover rates.", + "legacy_target_description_chars": 91, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1000, + "source_record_id": "3b5e8dc5-026d-4042-aabf-912f9a411251", + "source_parent_id": "hermes_record:3b5e8dc5-026d-4042-aabf-912f9a411251", + "request_group_id": "hermes_request:3488875255f12161b40b206a756948e47ce64d35449dc0769fc6f341b43e5602", + "full_human_chars": 2257, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "optimize_inventory_turnover", + "coordinate_suppliers", + "improve_logistics_tracking" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023231", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 55 + ], + "match_source_rows": [ + 55 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am organizing a music festival called \"Sunset Melodies\" that requires a comprehensive ERP system to manage various aspects of the event. Specifically, I need to book a jazz artist, Nina Simone, for a performance on July 15, 2023, from 8:00 PM to 10:00 PM, with the special requirement of a grand piano on stage. The event will be held at the \"Harborfront Center,\" which must be set up by 2:00 PM on", + "legacy_target": "book_artist", + "legacy_target_description": "Books an artist for a specific event, including date, duration, and special requirements.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 55, + "source_record_id": "c92649be-0256-4b85-a0ef-77fcf0f65af8", + "source_parent_id": "hermes_record:c92649be-0256-4b85-a0ef-77fcf0f65af8", + "request_group_id": "hermes_request:111ba00f1665089affe981b2edf3e438d9c6c54fdd7263bf1914142541db6329", + "full_human_chars": 1157, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "book_artist", + "manage_venue", + "schedule_event" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023298", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 339 + ], + "match_source_rows": [ + 339 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our city's water utility department aims to initiate water conservation programs and to do so, we need to analyze our recent water usage patterns and demographic characteristics to craft effective conservation strategies. We have collected detailed water usage records for the past year, which outline the consumption by neighborhood, property type (residential, commercial, industrial), and season. ", + "legacy_target": "analyze_water_usage", + "legacy_target_description": "Analyzes current water usage patterns in conjunction with demographic data to identify key areas for conservation efforts.", + "legacy_target_description_chars": 122, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 339, + "source_record_id": "72c30e68-850f-4a72-9043-ec503915cc46", + "source_parent_id": "hermes_record:72c30e68-850f-4a72-9043-ec503915cc46", + "request_group_id": "hermes_request:f6debad662534c3835757a50261b1cd0a8a7754b436bcae6360e907955b6afea", + "full_human_chars": 1865, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_water_usage", + "segment_population" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023338", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 121 + ], + "match_source_rows": [ + 121 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I require assistance in compiling a comprehensive transaction history for my cryptocurrency wallet to ensure accurate record-keeping for tax reporting purposes. Specifically, my wallet address is '0x123abc', and I need to extract all transactions that occurred during the last fiscal year, beginning on April 1, 2022, and ending on March 31, 2023. The transaction details should include the unique tr", + "legacy_target": "retrieve_wallet_transaction_history", + "legacy_target_description": "Retrieves the transaction history for a specified wallet address from the blockchain within a given date range, including details such as transaction IDs, times", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 121, + "source_record_id": "13f93ea4-19df-41a9-9072-1baca0202863", + "source_parent_id": "hermes_record:13f93ea4-19df-41a9-9072-1baca0202863", + "request_group_id": "hermes_request:db08876df836ccd49b12004ae5f4de92f8eb3570db93633f2fe8f97960d4db79", + "full_human_chars": 636, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "retrieve_wallet_transaction_history" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_023506", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 496 + ], + "match_source_rows": [ + 496 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating travel arrangements for a team of 6 from New York City to Chicago. I need to arrange flights departing on June 12, 2023, and returning on June 16, 2023. Additionally, I'm looking for accommodation options for 6 guests near the Willis Tower, checking in on June 12 and checking out on June 16. To enhance the trip, I want to discover some good dining venues and places for relaxation", + "legacy_target": "get_flight_options", + "legacy_target_description": "Retrieve available flight options for a group of passengers between specified origin and destination cities on given travel dates.", + "legacy_target_description_chars": 130, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 496, + "source_record_id": "d3cce6b0-569e-419f-b87c-320ea4d8f807", + "source_parent_id": "hermes_record:d3cce6b0-569e-419f-b87c-320ea4d8f807", + "request_group_id": "hermes_request:721e6f9336d8c4e66810cfb9906ec94e6ff9670f7f6dd6de9298d2de39459949", + "full_human_chars": 1509, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_flight_options", + "book_hotel_nearby", + "get_local_recommendations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023582", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 116 + ], + "match_source_rows": [ + 116 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am planning a trip to Tokyo and would like to ensure I have comprehensive travel insurance coverage. Specifically, I'm interested in plans that cover trip cancellations, medical emergencies, and lost or stolen luggage. I prefer to explore the options available through Expedia for these specific coverage areas. Could you retrieve a list of travel insurance plans from Expedia that include these th", + "legacy_target": "get_travel_insurance_plans", + "legacy_target_description": "Retrieve available travel insurance plans from a provider with specified coverage options for a given destination.", + "legacy_target_description_chars": 114, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 116, + "source_record_id": "964ecb04-6d6d-4735-a4be-1cfdcdf1e9b8", + "source_parent_id": "hermes_record:964ecb04-6d6d-4735-a4be-1cfdcdf1e9b8", + "request_group_id": "hermes_request:5a0ba0f04c8f66b5870db299b18db3a03a8f1dfe0ed22ea26a0d0622d18b852c", + "full_human_chars": 844, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_travel_insurance_plans" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023841", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 714 + ], + "match_source_rows": [ + 714 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently exploring various technology stacks used in desktop applications, particularly focusing on functionalities within social media apps that allow users to engage with posts. I would like to perform four specific actions using the respective functions: \n\n1. Retrieve the latest two posts from Twitter.\n2. Post a new update on Twitter with the content: \"Excited to learn about tech stacks f", + "legacy_target": "retrieve_latest_posts", + "legacy_target_description": "Retrieve the latest posts from a specified social media platform.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 714, + "source_record_id": "adb40093-0217-47d6-b790-2af48b093613", + "source_parent_id": "hermes_record:adb40093-0217-47d6-b790-2af48b093613", + "request_group_id": "hermes_request:72d8ead88535053fd5b7b8d21c03060801113f496858f95700cc305d3ddc2bbd", + "full_human_chars": 903, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "retrieve_latest_posts", + "post_new_update", + "like_post", + "comment_on_post" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023893", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 180 + ], + "match_source_rows": [ + 180 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to retrieve my PayPal transaction history from last month to assist with reconciling my accounting records. Specifically, I need details such as transaction IDs, dates, transaction amounts, and the status of each transaction. Please call the appropriate function, providing the start and end dates for the past month, and include fields for transaction ID, date, amount, and status in th", + "legacy_target": "retrieve_paypal_transaction_history", + "legacy_target_description": "Retrieves the user's PayPal transaction history for a specified date range and fields.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 180, + "source_record_id": "8169f166-03c4-4e80-b3f5-11bc17c12460", + "source_parent_id": "hermes_record:8169f166-03c4-4e80-b3f5-11bc17c12460", + "request_group_id": "hermes_request:5cfbcf259b58fa09f6d9842d3dc2901b9dba744815e24ba9ac4f109800dba6fd", + "full_human_chars": 485, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "retrieve_paypal_transaction_history" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023951", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 363 + ], + "match_source_rows": [ + 363 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for maintaining the security of our company's cloud resources on AWS. We have two S3 buckets named \"app-data-bucket\" and \"logs-bucket\", and I need to confirm that they are configured according to industry security standards. Moreover, we manage two IAM roles, \"AdminRole\" and \"DeveloperRole\", for which I require an audit to identify any potential misconfigurations or compliance iss", + "legacy_target": "assess_s3_bucket_security", + "legacy_target_description": "Assess the security compliance of specified S3 buckets against security standards.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 363, + "source_record_id": "0c7d6a20-98e5-4339-9a0b-d4c0d8a431fe", + "source_parent_id": "hermes_record:0c7d6a20-98e5-4339-9a0b-d4c0d8a431fe", + "request_group_id": "hermes_request:e051791744bda3bd309fe93d3d6f9c665d56494ead77784cf89115733e062266", + "full_human_chars": 1227, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "assess_s3_bucket_security", + "audit_iam_roles", + "verify_ec2_instance_amis" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_023969", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 793 + ], + "match_source_rows": [ + 793 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing three investment portfolios with identifiers ABC123, XYZ456, and LMN789. My aim is to track their recent performance, so I require the latest valuation as of April 5, 2023, for each portfolio. Also, I need to compare their performance against the S&P 500 and NASDAQ benchmarks over the last year. Besides, I'm interested in the current asset allocation for the portfolio with ID XYZ45", + "legacy_target": "retrieve_portfolio_valuation", + "legacy_target_description": "Retrieves the latest valuation of specified investment portfolios on a given date.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 793, + "source_record_id": "d12c445b-36a0-4643-8767-3184b43eb7ba", + "source_parent_id": "hermes_record:d12c445b-36a0-4643-8767-3184b43eb7ba", + "request_group_id": "hermes_request:cc5f687d5d3f9b3b1f786de6ca72d72b158cbb28bd6833ee7207b6ac76259334", + "full_human_chars": 964, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "retrieve_portfolio_valuation", + "assess_portfolio_performance", + "get_portfolio_asset_allocation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_024164", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 847 + ], + "match_source_rows": [ + 847 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on enhancing the security of our application's interactions with our backend API. To accomplish this, I require the generation of OAuth 2.0 compliant API tokens utilizing the client credentials grant type. Our application has been registered with the necessary OAuth client credentials, specifically the client ID 'app-client-321' and the client secret 's3cr3t-key'. These credentials mu", + "legacy_target": "generateOAuthToken", + "legacy_target_description": "Generates an OAuth 2.0 token for API access using client credentials flow.", + "legacy_target_description_chars": 74, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 847, + "source_record_id": "65b14703-ff63-4a6e-8b62-cfbd6bf43211", + "source_parent_id": "hermes_record:65b14703-ff63-4a6e-8b62-cfbd6bf43211", + "request_group_id": "hermes_request:825082f844b968e819871d5ca5b2e59ea66ff554bf67d88b832db263e3c7539b", + "full_human_chars": 951, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generateOAuthToken" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_024279", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 686 + ], + "match_source_rows": [ + 686 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To assist with streamlining pharmaceutical R&D processes, I require a detailed analysis of clinical trial data for a new medication intended to treat chronic inflammation. The specific clinical trial has generated a substantial dataset detailing patient responses, biomarker levels, and safety outcomes. I have the dataset accessible at `https://clinicaltrials.gov/data/trial_data_inflammation_study.", + "legacy_target": "analyze_clinical_trial_data", + "legacy_target_description": "Analyze clinical trial data using various statistical or machine learning methods.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 686, + "source_record_id": "999a397b-360d-4189-8f27-d17d87398403", + "source_parent_id": "hermes_record:999a397b-360d-4189-8f27-d17d87398403", + "request_group_id": "hermes_request:e62357a061cf350b474e22da7b195546e04c1c52b9678450bc2162ac24a1f677", + "full_human_chars": 1418, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_clinical_trial_data", + "predict_side_effects", + "identify_optimal_dosing" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_024301", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1023 + ], + "match_source_rows": [ + 1023 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning a trip to Miami for the next week and would like to be prepared for the weather conditions. I need to know what kind of clothes to pack, so I require up-to-date weather information. Please provide me with the current weather conditions in Miami, including temperature, general weather conditions, and humidity level. I also need a detailed 7-day weather forecast for Miami. The temperatu", + "legacy_target": "getCurrentWeather", + "legacy_target_description": "Get the current weather for a specified city.", + "legacy_target_description_chars": 45, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1023, + "source_record_id": "41bbab31-34a9-47a9-b899-6cc6ace1f012", + "source_parent_id": "hermes_record:41bbab31-34a9-47a9-b899-6cc6ace1f012", + "request_group_id": "hermes_request:98a9dfb2863830cf56594ea862eeccd6b8ee6dbc2b7bed126b0feb4204d0b365", + "full_human_chars": 515, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "getCurrentWeather", + "getWeeklyForecast" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_024365", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 593 + ], + "match_source_rows": [ + 593 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for setting up container orchestration for our organization to efficiently manage and scale our cloud-native microservices. We need to establish a Kubernetes cluster specifically designed for our production environment and deploy our microservices with precise networking and accessibility configurations. Here are the detailed requirements for the function call arguments:\n\n- For th", + "legacy_target": "create_kubernetes_cluster", + "legacy_target_description": "Creates a new Kubernetes cluster in the specified region with the desired number of nodes.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 593, + "source_record_id": "a3d9b81c-7a80-46f1-88f4-6a1478971f85", + "source_parent_id": "hermes_record:a3d9b81c-7a80-46f1-88f4-6a1478971f85", + "request_group_id": "hermes_request:f3b453dad7860e931143332ef4ad1e993b454728c40e2ecc51267bf10c6060c2", + "full_human_chars": 1421, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_kubernetes_cluster", + "deploy_microservices" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_024543", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 327 + ], + "match_source_rows": [ + 327 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As an energy trader focused on electricity and natural gas markets, I am looking to create an algorithmic trading solution. The system should be tailored to execute trades for EU Allowance (EUA) and Henry Hub Natural Gas futures contracts. I need it to utilize a momentum-based trading strategy, with a moderate risk tolerance level to balance potential returns and risks. The solution must be able t", + "legacy_target": "initialize_trading_algorithm", + "legacy_target_description": "Initializes the trading algorithm with the specified parameters.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 327, + "source_record_id": "4f30eb1b-b0c1-4b78-8a7e-1c31a4761447", + "source_parent_id": "hermes_record:4f30eb1b-b0c1-4b78-8a7e-1c31a4761447", + "request_group_id": "hermes_request:972605e7fa1170d8251d183d4f3101f25d14aced69e87047e11a5d72acc1be50", + "full_human_chars": 1400, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "initialize_trading_algorithm", + "analyze_market_trends", + "predict_energy_prices", + "manage_portfolio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_024669", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 454 + ], + "match_source_rows": [ + 454 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage the inventory for LuxeDecor Home Store, and I'm looking to optimize our stock tracking process for our array of home furnishing products, including furniture, decor items, and home accessories. To achieve this, I have an inventory database in JSON format that contains each item's product ID, current stock level, and minimum stock requirement. I would like to execute functions that can ana", + "legacy_target": "track_inventory_levels", + "legacy_target_description": "Tracks the current inventory levels against minimum stock requirements and provides a report.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 454, + "source_record_id": "bf9bfa5e-a74a-4bab-8c35-8b17bed6abad", + "source_parent_id": "hermes_record:bf9bfa5e-a74a-4bab-8c35-8b17bed6abad", + "request_group_id": "hermes_request:796791395f10e4ed88785a10961df132c7a33a5ff044d48ad0128e39f993862a", + "full_human_chars": 2109, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "track_inventory_levels", + "update_inventory_stock", + "update_inventory_stock", + "generate_reorder_alerts" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_024967", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 145 + ], + "match_source_rows": [ + 145 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm conducting a study on a dataset containing information such as geographic locations, customer demographics, and purchase history. My objective is to uncover natural groupings within this data to better understand customer segments. For this purpose, I want to utilize various clustering techniques such as K-Means, DBSCAN, and Agglomerative Clustering available in Scikit-learn.\n\nThe dataset has ", + "legacy_target": "k_means_clustering", + "legacy_target_description": "Perform K-Means clustering on the dataset.", + "legacy_target_description_chars": 42, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 145, + "source_record_id": "a71ae604-10bf-48de-8dee-610af9eeb142", + "source_parent_id": "hermes_record:a71ae604-10bf-48de-8dee-610af9eeb142", + "request_group_id": "hermes_request:945a505fdf4ccb00345ba9f7999c0b5aef75a44a21c29262cc4f36c0a976ba3e", + "full_human_chars": 1635, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "k_means_clustering", + "dbscan_clustering", + "agglomerative_clustering" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025221", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1286, + 1293, + 1294, + 1319, + 1340, + 1390, + 1489, + 1650, + 1698, + 1700, + 1803, + 1871, + 1874 + ], + "match_source_rows": [ + 1286 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 3. (Step 1) Now compute 1838 mod 16. This gives us the value 14, which is of course a base 10 number. The equivalent hex digit is E. So we now write down E to the left of the 3: \nE3 \n4. (Step 2) Dividing 1838 by 16 and taking the floor gives us \n114. Since this is again not zero, we perform step 2b: make 114 our new value", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that request for an opinion on a topic found in the document.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1286, + "source_record_id": "f8acf133-90f4-47cf-b2c0-ef69480f8893", + "source_parent_id": "hermes_record:f8acf133-90f4-47cf-b2c0-ef69480f8893", + "request_group_id": "hermes_request:f1695b89fcaa37e4859cef4fbc389f19e3d699c792b2c8ddef99c18c67bf8333", + "full_human_chars": 9775, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025257", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 390 + ], + "match_source_rows": [ + 390 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am in the process of configuring a wireless communication system for IoT devices in a residential smart home. The aim is to ensure the IoT devices can communicate with our central hub securely and effectively. For this, I need to: \n\n1. Initialize a WiFi wireless module to operate on the 2.4GHz frequency band.\n2. Scan for all wireless networks within range to identify available options.\n3. Connec", + "legacy_target": "initialize_wireless_module", + "legacy_target_description": "Initializes the wireless module with the specified type and frequency band.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 390, + "source_record_id": "1b158748-9d5a-41d1-9581-b8276da668e7", + "source_parent_id": "hermes_record:1b158748-9d5a-41d1-9581-b8276da668e7", + "request_group_id": "hermes_request:b24a27180408dc6d41b5afd4a474cc4e4a5b04bb04f0ed48dc90b42b2558edb4", + "full_human_chars": 1169, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "initialize_wireless_module", + "scan_for_networks" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025285", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 240 + ], + "match_source_rows": [ + 240 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the office administrator for XYZ Corp, I need to implement a comprehensive solution for managing our routine weekly staff meetings to enhance efficiency and ensure consistent attendance tracking. Specifically, I need to set up a recurring staff meeting every Monday at 10 AM. To facilitate timely attendance, I want to automate reminders that will be sent to all staff members 24 hours before the ", + "legacy_target": "schedule_meeting", + "legacy_target_description": "Schedules a recurring meeting on a specified day and time.", + "legacy_target_description_chars": 58, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 240, + "source_record_id": "86c8f17a-f149-4506-8c5a-472709263c55", + "source_parent_id": "hermes_record:86c8f17a-f149-4506-8c5a-472709263c55", + "request_group_id": "hermes_request:793e92f917a0eb8c0b3adddcf278deb0c5e7af762ccfcdd47736082fb81f6ffa", + "full_human_chars": 1464, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "schedule_meeting", + "send_reminder" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025397", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 419 + ], + "match_source_rows": [ + 419 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am in the process of migrating a database from a PostgreSQL server to a MySQL server. For this purpose, I need to establish connections to both databases. Firstly, the source PostgreSQL server is located at `source.example.com` on port 5432. The username for accessing the PostgreSQL server is `source_user` with the password `source_password`, and the database name is `source_db`. Secondly, the d", + "legacy_target": "connect_to_postgresql", + "legacy_target_description": "Establishes a connection to a PostgreSQL database server.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 419, + "source_record_id": "df9fe9b8-ff7b-4193-ac8a-22f77d2e725a", + "source_parent_id": "hermes_record:df9fe9b8-ff7b-4193-ac8a-22f77d2e725a", + "request_group_id": "hermes_request:7610593246925dc4911ffb9030d62db37885b3836282680d4dbb0b3d2d4cdd69", + "full_human_chars": 764, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "connect_to_postgresql", + "connect_to_mysql" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025446", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 688 + ], + "match_source_rows": [ + 688 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing the operations of a healthcare clinic and need to perform some updates on our electronic health records system. Firstly, I must add a new patient record for Jane Rogers, born on June 10, 1982, living at 456 Elm Street, Lakeside, LN. She has had past treatments for asthma and is currently on Ventolin. She's allergic to penicillin. Additionally, we need to update the record for patie", + "legacy_target": "add_patient_record", + "legacy_target_description": "Adds a new patient record to the EHR system.", + "legacy_target_description_chars": 44, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 688, + "source_record_id": "46e8e543-b119-4daf-84d6-d8b1a84091a7", + "source_parent_id": "hermes_record:46e8e543-b119-4daf-84d6-d8b1a84091a7", + "request_group_id": "hermes_request:b06e42df0d84d14d8f33d0fb83214e7f09349ef174d4bb5aa7c2145f7519aa73", + "full_human_chars": 1167, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "add_patient_record", + "update_patient_record", + "remove_duplicate_records" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025527", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1142, + 1208, + 1240, + 1409, + 1451, + 1532, + 1637, + 1706, + 1762, + 1786, + 1796, + 1839, + 1844 + ], + "match_source_rows": [ + 1142 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage ounced \"oh-of-n\"), it means that the time it takes to execute the algorithm is _proportional to the number of nodes._ This doesn't imply any specific number of milliseconds or anything -- that is highly dependent on the type of computer hardware, you have, the programming language, and a myriad of other things. But what we _can_ ", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of open-ended questions related to the document, that are potentially ambiguous.", + "legacy_target_description_chars": 96, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1142, + "source_record_id": "18556c45-f8ec-4d01-b43e-eafda339660b", + "source_parent_id": "hermes_record:18556c45-f8ec-4d01-b43e-eafda339660b", + "request_group_id": "hermes_request:c03130267f8200b64c070e589ceacbe5deb2349b3b2f1276a4a143edeccd9bf2", + "full_human_chars": 12028, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025657", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1056 + ], + "match_source_rows": [ + 1056 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning a team meeting via a conference call on my iPhone and I'd like to start with five team members. It's important that I have the option to add additional participants during the call as we might need to bring in some specialists to the discussion. I also want to ensure that the conference call doesn't drop if I need to use another app on my phone, as I'll likely have to multitask during", + "legacy_target": "start_conference_call", + "legacy_target_description": "Initiates a conference call with the specified number of participants and options for adding participants and multitasking support.", + "legacy_target_description_chars": 131, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1056, + "source_record_id": "2d3e9433-5bb2-4bf1-b122-9ebabaa0c67e", + "source_parent_id": "hermes_record:2d3e9433-5bb2-4bf1-b122-9ebabaa0c67e", + "request_group_id": "hermes_request:2d508d361c35f0536c6bd89b0c98cb2a3e6afdd57cf429e8f203b40b26bf79cb", + "full_human_chars": 739, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "start_conference_call" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025867", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 198 + ], + "match_source_rows": [ + 198 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to establish a specific savings goal in my Chime account for a significant purchase I'm planning. The item I wish to save for is a new laptop, and I aim to collect a total of $1500 by the end date of December 31, 2023. To facilitate tracking my progress towards this objective, could you please create this savings goal in my Chime account, properly setting the target amount and the spe", + "legacy_target": "create_savings_goal", + "legacy_target_description": "Creates a savings goal in the user's Chime account with a specified name, target amount, and target date.", + "legacy_target_description_chars": 105, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 198, + "source_record_id": "cfce1eae-5b6a-4c83-b6bc-de0923a63dc6", + "source_parent_id": "hermes_record:cfce1eae-5b6a-4c83-b6bc-de0923a63dc6", + "request_group_id": "hermes_request:28327ca5e0cda9136c1cdcb9460254d4c77dae5082869c2a025543ed4f7757e6", + "full_human_chars": 938, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_savings_goal" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025929", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 450 + ], + "match_source_rows": [ + 450 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the head of cybersecurity at our organization, I've analyzed the trends over the past week and identified a troubling increase in cyber threats that could potentially impact our network and data security. To take proactive measures, I would like to request the following:\n\n1. Generate a comprehensive report on recent cyber threats detected from March 25, 2023, to April 1, 2023. I need details in", + "legacy_target": "get_recent_cyber_threats", + "legacy_target_description": "Retrieves a list of recent cyber threats identified within a specified time range.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 450, + "source_record_id": "67e28a15-99ae-4e8f-a708-3cfa28ade0c7", + "source_parent_id": "hermes_record:67e28a15-99ae-4e8f-a708-3cfa28ade0c7", + "request_group_id": "hermes_request:5956aa538b35d80c5427df791a1ade1258a1586491052458bea6591b296478f4", + "full_human_chars": 1293, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_recent_cyber_threats", + "get_apts_by_sector", + "get_latest_malware_signatures" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025945", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 196 + ], + "match_source_rows": [ + 196 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently at the intersection of West 3rd Street and South Grand Avenue in downtown Los Angeles, and I am looking to withdraw cash without paying any ATM fees. As a Chime user, I would like to find the closest ATM that allows me to do so without any charges. Can you help me locate nearby fee-free ATMs within a 5-kilometer radius of my current position? My current location coordinates are 34.0", + "legacy_target": "find_fee_free_atms", + "legacy_target_description": "Locate nearby ATMs that do not charge fees for cash withdrawals for Chime users.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 196, + "source_record_id": "0a939886-651f-44d9-8011-6e8a75c48fc1", + "source_parent_id": "hermes_record:0a939886-651f-44d9-8011-6e8a75c48fc1", + "request_group_id": "hermes_request:36ee595b0e76fc6c2c8adf64c82d564cc87d8d4d0fa78e1603cf3a3fbf6a3abf", + "full_human_chars": 528, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "find_fee_free_atms" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_025947", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 876 + ], + "match_source_rows": [ + 876 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently analyzing the betting market for the next English Premier League soccer match between Manchester United and Chelsea. To make informed betting decisions, I need the latest head-to-head moneyline odds. Please retrieve the real-time betting odds from Unibet and Betfair for this specific match. The data should reflect the odds for each team to win, as well as the odds for a draw. Can yo", + "legacy_target": "getSportsBettingOdds", + "legacy_target_description": "Retrieves real-time betting odds for a specified sports match from various bookmakers.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 876, + "source_record_id": "876b9f61-977a-4c90-93a2-8467b64d32d3", + "source_parent_id": "hermes_record:876b9f61-977a-4c90-93a2-8467b64d32d3", + "request_group_id": "hermes_request:3f3fb540c13dd3fdbb2db2f322e75bfa42234f37de6e22424bd2560caca6dc9f", + "full_human_chars": 684, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "getSportsBettingOdds", + "getSportsBettingOdds" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026007", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1094 + ], + "match_source_rows": [ + 1094 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our ongoing security initiatives, I have identified two critical actions to improve our security posture within our Google Cloud Platform project named \"my-gcp-project\". Firstly, I need to conduct a thorough audit of our current IAM policies to identify any potential misconfigurations that could lead to unauthorized access or data breaches. This audit must include an examination of IAM ", + "legacy_target": "audit_iam_policies", + "legacy_target_description": "Audit IAM policies for a GCP project to detect misconfigurations.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1094, + "source_record_id": "f239e6f5-92bd-45be-887c-d50aad78e079", + "source_parent_id": "hermes_record:f239e6f5-92bd-45be-887c-d50aad78e079", + "request_group_id": "hermes_request:7a892e9b7f9ea6aa0f5fa5ce81d5d49735c57b53bac16ae42acd7733a1da82fa", + "full_human_chars": 1441, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "audit_iam_policies", + "check_network_security" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026201", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 838 + ], + "match_source_rows": [ + 838 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing a frontend interface for our client's online store and I need to display detailed information about specific products. The items in question are identified by their unique product IDs: 101 which corresponds to a Wireless Mouse, 102 for a Bluetooth Keyboard, and 103 for a USB-C Charging Cable. I need to fetch the name, price, and current availability status for each of these product", + "legacy_target": "fetch_product_details", + "legacy_target_description": "Fetches the details for a list of product IDs including name, price, and availability status.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 838, + "source_record_id": "f66983e6-00ae-403e-869d-cf596007b93e", + "source_parent_id": "hermes_record:f66983e6-00ae-403e-869d-cf596007b93e", + "request_group_id": "hermes_request:8b9ca840e4007e90d32739a71b5142d626aa9b9243d7c0798fd3397d28dce572", + "full_human_chars": 510, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetch_product_details" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026309", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 98 + ], + "match_source_rows": [ + 98 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to view customer reviews for a specific product listed on our e-commerce platform to enhance our product page with real user experiences. The product in question has the unique identifier 'W12345'. In addition to displaying existing reviews, I want to contribute a new customer review and detailed feedback for this product to share my positive experience with future customers. The new ", + "legacy_target": "get_product_reviews", + "legacy_target_description": "Retrieve customer reviews for a specific product.", + "legacy_target_description_chars": 49, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 98, + "source_record_id": "1763bd56-7418-450f-88a9-642a50203700", + "source_parent_id": "hermes_record:1763bd56-7418-450f-88a9-642a50203700", + "request_group_id": "hermes_request:da030e8b221ef81f610265dcb5c21a96e3e174130e9beac9a8dcacfd332f779a", + "full_human_chars": 783, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_product_reviews", + "submit_product_review" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026363", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 463 + ], + "match_source_rows": [ + 463 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage a home furnishings retail store and had an interaction today with one of our customers, identified as CU789. The customer visited our store on April 12, 2023, at 3:30 PM and inquired about our new sofa collections. Additionally, the same customer has a pending service request due to a damaged furniture delivery that was logged today. I need to update our CRM system with this interaction, ", + "legacy_target": "record_customer_interaction", + "legacy_target_description": "Records a customer interaction in the CRM system, including details such as date, type, and notes.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 463, + "source_record_id": "224b9202-e0c0-4113-84f3-1f8198086b69", + "source_parent_id": "hermes_record:224b9202-e0c0-4113-84f3-1f8198086b69", + "request_group_id": "hermes_request:4faeb1536c5b96d969b9917b729d92f43441a3f728c8e92b61e44bf2948f1666", + "full_human_chars": 1174, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "record_customer_interaction", + "retrieve_purchase_history", + "log_service_request" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026529", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 554 + ], + "match_source_rows": [ + 554 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the project manager overseeing customer onboarding for our company, I require an efficient and error-free process to transfer new customer data from our web portal directly into our customer relationship management system. We receive customer information via our online portal, where individuals provide their full name, residential address, contact phone number, and specify which of our services", + "legacy_target": "automate_customer_onboarding", + "legacy_target_description": "Automates the data entry process for new customer onboarding from a web portal to a CRM system, with an option for data validation.", + "legacy_target_description_chars": 131, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 554, + "source_record_id": "4ce9b6f1-2868-4772-8e0d-2c94222646c9", + "source_parent_id": "hermes_record:4ce9b6f1-2868-4772-8e0d-2c94222646c9", + "request_group_id": "hermes_request:a543093732c16f5928bd7694a1e70d98e7f4df1239918a9bc8c10bdf2beded5b", + "full_human_chars": 1312, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "automate_customer_onboarding" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026547", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 820 + ], + "match_source_rows": [ + 820 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on a project that involves monitoring and analyzing the development of artificial intelligence, machine learning, and robotics sectors. For my research, I need to collect the latest news articles that are related to these fields from various authoritative news sources, but only within the last 24 hours to ensure the information is current. I want to keep the results concise, so I woul", + "legacy_target": "fetch_personalized_news", + "legacy_target_description": "Fetches a list of news articles based on user's interests, within a specified time range, and limits the number of articles returned.", + "legacy_target_description_chars": 133, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 820, + "source_record_id": "d80daadd-a605-44fd-987b-68bdea710d25", + "source_parent_id": "hermes_record:d80daadd-a605-44fd-987b-68bdea710d25", + "request_group_id": "hermes_request:e56a1a37dc0b5e95cb46dba9c43599e633e384320ea15c889f9068f9d8da94ca", + "full_human_chars": 934, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetch_personalized_news" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026563", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 177 + ], + "match_source_rows": [ + 177 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to execute a sale of my cryptocurrency holdings on the Coinbase platform. Specifically, I'm looking to sell 2 Bitcoins (BTC). Please retrieve the current market rate for BTC on Coinbase and calculate the transaction fees associated with this sale. Once I have the market rate and the calculated fees, I'd like to proceed with the sale. Can you confirm the transaction with details such a", + "legacy_target": "get_current_market_rate", + "legacy_target_description": "Retrieves the current market rate for the specified cryptocurrency on the given platform.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 177, + "source_record_id": "38b0d5b8-595e-43fd-8e1d-3a2304e5de73", + "source_parent_id": "hermes_record:38b0d5b8-595e-43fd-8e1d-3a2304e5de73", + "request_group_id": "hermes_request:f540cbb7bfa6566f8dde845f009f043b8a793a99473d3e378c9a59e1e8ee2b43", + "full_human_chars": 503, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_current_market_rate", + "calculate_transaction_fees" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026748", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 309 + ], + "match_source_rows": [ + 309 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As a power production company, we are looking to enhance our grid with renewable energy sources. We have collected specific data on the potential of solar energy, such as average sunlight hours, solar panel efficiency, and conversion rates, in a JSON format named \"solar_potential_data.json.\" Similarly, data on wind energy is compiled detailing average wind speeds, turbine efficiency, and possible ", + "legacy_target": "analyze_renewable_potential", + "legacy_target_description": "Analyzes data on solar and wind energy to determine the potential for renewable energy generation.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 309, + "source_record_id": "ac5e8305-dc33-40f8-8950-9b9a7515f969", + "source_parent_id": "hermes_record:ac5e8305-dc33-40f8-8950-9b9a7515f969", + "request_group_id": "hermes_request:a17251adcf6bd2efcce09e3975dff943ec48dc5aed36b0f55496e4a7c68a6e48", + "full_human_chars": 1717, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_renewable_potential", + "forecast_grid_demand" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_026912", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1100, + 1147, + 1184, + 1238, + 1296, + 1300, + 1315, + 1327, + 1334, + 1405, + 1631, + 1684, + 1797 + ], + "match_source_rows": [ + 1100 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage y Kelly is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted. \n#### WORKS CITED \n241 COLLECTED WORKS \n###### CHAPTER 30. \n## GIVING CREDIT WHERE IT'S DUE \n Why and How to Cite Your Sources LIZ DELF Using outside sources in your paper is a great move. 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Specifically, I require the latest exchange rate from USD to EUR, and then I need to show how much 1000 USD would be in EUR using that rate. Please execute the necessary functions to retrieve the latest USD to EUR exchange rate and also convert an amount of 10", + "legacy_target": "get_latest_exchange_rate", + "legacy_target_description": "Retrieve the latest exchange rate between two specified currencies.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 780, + "source_record_id": "9d0e5ae0-ea63-413c-9e26-117e5ef45140", + "source_parent_id": "hermes_record:9d0e5ae0-ea63-413c-9e26-117e5ef45140", + "request_group_id": "hermes_request:e851d68ea85481d1e4b80c3588ca9c928c427b4e2a3e13cb1e9cb09fb78a7a80", + "full_human_chars": 683, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_latest_exchange_rate", + "convert_currency_amount" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_027320", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1020 + ], + "match_source_rows": [ + 1020 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing an augmented reality app for a retail environment that can recognize and translate product labels for a more enhanced customer experience. Specifically, the app should identify products on store shelves and provide detailed information including price, reviews, and similar product recommendations. 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Here's the code snippet that needs analysis and formatting:\n\n```python\ndef example_function(param1, param2):\n return param1 + param2\n", + "legacy_target": "run_linter", + "legacy_target_description": "Analyzes the provided code snippet for style issues based on specified linter rules.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 253, + "source_record_id": "3a376f27-0042-45bd-9f02-3626bfd11ed9", + "source_parent_id": "hermes_record:3a376f27-0042-45bd-9f02-3626bfd11ed9", + "request_group_id": "hermes_request:483ea009631e0a80e02d0963348fdbfe49e9076d0aa9c89f9dab327d8c23c84d", + "full_human_chars": 568, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "run_linter", + "format_code" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_030953", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 721 + ], + "match_source_rows": [ + 721 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm working on a C# project in Visual Studio Code and have run into a problem where the debugger won't trigger any breakpoints. I suspect there's an issue with my `launch.json` configuration file. The configuration looks like this:\n\n```json\n{\n \"version\": \"0.2.0\",\n \"configurations\": [\n {\n \"name\": \"C#\",\n \"type\": \"coreclr\",\n \"request\": \"launch\",\n \"program\": \"${workspaceFolder", + "legacy_target": "analyze_debug_configuration", + "legacy_target_description": "Analyzes the launch.json configuration for common issues that prevent the debugger from hitting breakpoints and provides suggestions for corrections.", + "legacy_target_description_chars": 149, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 721, + "source_record_id": "0951fd9c-22ee-49a9-a611-7991704284cd", + "source_parent_id": "hermes_record:0951fd9c-22ee-49a9-a611-7991704284cd", + "request_group_id": "hermes_request:f909bed7783115b2d2272580be0f2fe8d3a7c86ce8a71d8fd88f0e6241303c77", + "full_human_chars": 810, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "analyze_debug_configuration" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_031217", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 924 + ], + "match_source_rows": [ + 924 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need assistance with updating our customer records. Specifically, for our customer Samantha Carter, who has the customer ID 'SC_307', I require her new address to be updated in our system. Her current address needs to be changed to '456 Stargate Avenue, SG1 7XZ'. Could you please initiate a POST request using the provided customer ID and the new address to ensure our records are current and accu", + "legacy_target": "update_customer_address", + "legacy_target_description": "Updates the address for a specific customer in the system using a POST request.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 924, + "source_record_id": "817491b5-4ada-4c59-865d-9ed72e70c308", + "source_parent_id": "hermes_record:817491b5-4ada-4c59-865d-9ed72e70c308", + "request_group_id": "hermes_request:71d0f97d3c4239c8f9d3b5af2371923e5d5e0c24c73836df9c2a808a18f2e5c3", + "full_human_chars": 405, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "update_customer_address" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_031324", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 71 + ], + "match_source_rows": [ + 71 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to enhance our Interactive Home Entertainment User Feedback System by implementing a feature that allows us to collect detailed feedback from users about the new interface. Specifically, we have Feature ID #12345 that several users have experienced and we want to capture their insights. For instance, user John Smith with User ID #67890 rated the feature a 4 out of 5 and commented, \"Th", + "legacy_target": "collectUserFeedback", + "legacy_target_description": "Collects user feedback for a specific feature of the home entertainment system.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 71, + "source_record_id": "68ff2eea-a598-4fab-8907-90c6d867d778", + "source_parent_id": "hermes_record:68ff2eea-a598-4fab-8907-90c6d867d778", + "request_group_id": "hermes_request:5d58b87a696c567a9cd75ee07ef629a10e7b108bdd32bb47a963bdad242e938c", + "full_human_chars": 1041, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "collectUserFeedback", + "analyzeFeedbackSentiment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_031415", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1230, + 1292, + 1297, + 1302, + 1314, + 1538, + 1557, + 1560, + 1652, + 1655, + 1660, + 1753, + 1881 + ], + "match_source_rows": [ + 1230 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage The table below summarizes the types of secondary sources in four tiers. All sources have their legitimate uses, but the top-tier ones are preferable for citation in an academic context. \n#### AH, THE RESEARCH PAPER \n#### SOME SOURCES ARE BETTER THAN OTHERS \n A DAM GOOD ARGUMENT 154 \n Table 20.1 Analyzing the quality of secondary", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for reasoning behind a design decision, product specification, or method described in the document. These reveals thought ", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1230, + "source_record_id": "5044d7b8-3c83-43ba-9df2-361cd550b89b", + "source_parent_id": "hermes_record:5044d7b8-3c83-43ba-9df2-361cd550b89b", + "request_group_id": "hermes_request:4d52a00d7171ae2e5a9e1640d147442caabf78aef86bc617a8170832429417b5", + "full_human_chars": 15112, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_031658", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 147 + ], + "match_source_rows": [ + 147 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm analyzing a healthcare dataset containing various patient records to predict the likelihood of developing heart disease. The features include age, sex, blood pressure, cholesterol levels, and other medical indicators, while the target labels indicate the presence or absence of heart disease. I've prepared the dataset with the following structure:\n\n```markdown\n| Age | Sex | Blood Pressure | Cho", + "legacy_target": "initialize_classifier", + "legacy_target_description": "Initializes a classification algorithm with specified parameters.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 147, + "source_record_id": "4e1e97d4-8e15-41b9-bb7a-cb7741be1877", + "source_parent_id": "hermes_record:4e1e97d4-8e15-41b9-bb7a-cb7741be1877", + "request_group_id": "hermes_request:d9be8535c51f5043d065b65cecf30a0924732ffb771896f282beb41abc2991dd", + "full_human_chars": 1530, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "initialize_classifier" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_031752", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 967 + ], + "match_source_rows": [ + 967 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a collection of digitized historical texts that detail various relationships among historical figures and significant events. These texts are available in JSON format, and my aim is to generate a structured JSON schema that accurately represents the complex web of interactions and associations outlined in these documents. Specifically, the schema should facilitate the visualization of these", + "legacy_target": "extract_relations_from_historical_texts", + "legacy_target_description": "Processes digitized historical texts in JSON format to extract relationships between historical figures and events, and structures the output as a JSON schema.", + "legacy_target_description_chars": 159, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 967, + "source_record_id": "99e3be1d-5290-448c-b55e-9d784574a8b6", + "source_parent_id": "hermes_record:99e3be1d-5290-448c-b55e-9d784574a8b6", + "request_group_id": "hermes_request:d3756370d8103add8edfc9c24adad36bf0d7e5bc946be0e679524598789172af", + "full_human_chars": 2285, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_relations_from_historical_texts" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_031949", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1029 + ], + "match_source_rows": [ + 1029 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am seeking a health tracking app for my Android smartphone that includes comprehensive features for monitoring my heart rate and activity levels. The app should be able to provide real-time heart rate monitoring during different exercises and physical activities. I also require the app to compile daily summaries and reports of my heart rate and activity data, allowing me to keep track of my fitn", + "legacy_target": "find_health_tracking_app", + "legacy_target_description": "Find a health tracking app on Android that includes specific features.", + "legacy_target_description_chars": 70, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1029, + "source_record_id": "f800a3cb-06c7-4bd9-a099-7fe758c241ee", + "source_parent_id": "hermes_record:f800a3cb-06c7-4bd9-a099-7fe758c241ee", + "request_group_id": "hermes_request:c1c48ed3279a93bd73dc09c4b9a73898fc8c11780657e81d0656d70e081ee09d", + "full_human_chars": 810, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "find_health_tracking_app" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_031967", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 926 + ], + "match_source_rows": [ + 926 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently shopping on your website and have come across an excellent pair of headphones that I would like to purchase. The product ID for the headphones is 'HP1234', and I would like to buy two units of this product. My user ID on your platform is 'user5678'. To proceed with my order, I need these headphones added to my shopping cart. Could you assist by executing the necessary function to add", + "legacy_target": "addProductToCart", + "legacy_target_description": "Adds a specified quantity of a product to the user's shopping cart.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 926, + "source_record_id": "45b88639-985b-49b7-a424-9e48c5766a35", + "source_parent_id": "hermes_record:45b88639-985b-49b7-a424-9e48c5766a35", + "request_group_id": "hermes_request:7137b1bd852ba6c8a0b9bb0d8a07953a2ee034b245e4396cceb5ef3d02808293", + "full_human_chars": 688, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "addProductToCart" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_032090", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 290 + ], + "match_source_rows": [ + 290 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Risk Manager for our algorithmic trading operations, my task is to implement protocols to mitigate risk associated with our diversified portfolio of equities and derivatives. I need to determine our maximum potential loss in case of adverse market movements, monitor our trading algorithms' performance for significant drawdowns, and adjust our trading strategies in response to market volatil", + "legacy_target": "calculate_portfolio_var", + "legacy_target_description": "Calculates the Value at Risk (VaR) for a portfolio using a specified method and confidence level.", + "legacy_target_description_chars": 97, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 290, + "source_record_id": "65e6ecac-2595-4c7a-8400-f7bea1d765ce", + "source_parent_id": "hermes_record:65e6ecac-2595-4c7a-8400-f7bea1d765ce", + "request_group_id": "hermes_request:5550fc556cae15d1b1b6cbc5f812ee1aa90c37e10d239b9f62f6dc0fa4cbb1d6", + "full_human_chars": 2016, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_portfolio_var", + "monitor_algorithm_performance", + "adjust_trading_limits" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_032107", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 395 + ], + "match_source_rows": [ + 395 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the IT manager for our company, I have noticed that our cloud-based image dataset, stored at \"/company/dataset/images\", contains a significant number of duplicate files. Accumulated from recurrent backups and user uploads, these duplicates are consuming valuable storage space. To address this, I am looking for a robust data deduplication solution that can scan the entire dataset and identify an", + "legacy_target": "image_data_deduplication", + "legacy_target_description": "Performs data deduplication on an image dataset, ensuring no loss of unique data.", + "legacy_target_description_chars": 81, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 395, + "source_record_id": "fe6f1d30-9fb8-48c4-8654-fa4b2b8227cf", + "source_parent_id": "hermes_record:fe6f1d30-9fb8-48c4-8654-fa4b2b8227cf", + "request_group_id": "hermes_request:c75f63b54128bf542a6968368a457932f370af7c5bd28a82a242e74fda0af45c", + "full_human_chars": 1310, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "image_data_deduplication" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_032121", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 380 + ], + "match_source_rows": [ + 380 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our organization operates a variety of IT infrastructure including physical servers, virtual machines, and cloud-based services. We intend to set up an automated backup system to secure our critical business data such as employee files, customer records, contracts, and more. We require daily scheduled backups with robust data integrity checks. Additionally, we need a ready-to-execute recovery proc", + "legacy_target": "configure_backup_automation", + "legacy_target_description": "Configures automated backups across various server types with specified frequency and data integrity checks.", + "legacy_target_description_chars": 108, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 380, + "source_record_id": "d40e12e7-7c74-4727-8f06-64b66cf184e3", + "source_parent_id": "hermes_record:d40e12e7-7c74-4727-8f06-64b66cf184e3", + "request_group_id": "hermes_request:57e73a4278bd70cf99289537833bdd1a178559e538c0d39608276a8ee1612024", + "full_human_chars": 1363, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "configure_backup_automation", + "initiate_recovery_process" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_032159", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 89 + ], + "match_source_rows": [ + 89 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing a mining exploration project and require specialized software analysis for data-driven decision-making. We have conducted comprehensive geological surveys of the mining area, which have produced extensive datasets, and we have also completed drilling in various locations, resulting in detailed reports. 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Specifically, we need to establish a reliable and efficient data replication process between our primary data center, identified as \"MainDataCenter,\" and our designat", + "legacy_target": "synchronous_data_replication", + "legacy_target_description": "Perform synchronous data replication between two storage locations to ensure high availability and data consistency.", + "legacy_target_description_chars": 116, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 409, + "source_record_id": "a90a87bd-f6e2-4a5b-987c-39edd31945d4", + "source_parent_id": "hermes_record:a90a87bd-f6e2-4a5b-987c-39edd31945d4", + "request_group_id": "hermes_request:fea46b2ca72f087eebc797501773dab4bfb46d90feabc632aa157a1acf3497a8", + "full_human_chars": 1006, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "synchronous_data_replication" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_034013", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 185 + ], + "match_source_rows": [ + 185 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to invest in Bitcoin through my Robinhood account with the unique identifier RH12345678. 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Additionally, I'd like to place chairs that are each 0.5 meters long, 0.5", + "legacy_target": "create_kitchen_model", + "legacy_target_description": "Create a digital 3D model of a kitchen with customizable wall heights and furniture placement.", + "legacy_target_description_chars": 94, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 219, + "source_record_id": "3f7b47f0-b966-42d2-b4f4-d5e66c73548c", + "source_parent_id": "hermes_record:3f7b47f0-b966-42d2-b4f4-d5e66c73548c", + "request_group_id": "hermes_request:0457128ad599c5742adbbc8d47fbbcae5f755ac2acf0cc3d8ccd02468370f3cc", + "full_human_chars": 712, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_kitchen_model" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_034638", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1081 + ], + "match_source_rows": [ + 1081 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting an employee review for an individual currently in our system and I need to collect a comprehensive set of data from Workday for employee ID WD12345. 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Could ", + "legacy_target": "getCustomerInfo", + "legacy_target_description": "Retrieves detailed information for a specific customer using their unique identifier.", + "legacy_target_description_chars": 85, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 834, + "source_record_id": "da964ad4-ef13-49ee-afa3-59ca3c90d2d1", + "source_parent_id": "hermes_record:da964ad4-ef13-49ee-afa3-59ca3c90d2d1", + "request_group_id": "hermes_request:8da018b56a47503564338ae4fa5c8d52dbad1dc120c58aa49ab297e44125d433", + "full_human_chars": 876, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "getCustomerInfo" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_034856", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 472 + ], + "match_source_rows": [ + 472 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working with a retail chain that specializes in general merchandise, and we've just compiled our sales data for the last quarter. To enhance our decision-making, we need to identify our best-selling products, anticipate sales for the next quarter, and segment our customers based on their recent purchases. Here are the details for the function calls:\n\n1. To identify top-performing products wit", + "legacy_target": "identify_top_products", + "legacy_target_description": "Identify top-performing products within a specified category and time period.", + "legacy_target_description_chars": 77, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 472, + "source_record_id": "35904813-205d-43a0-a9cb-0e17eb82d76d", + "source_parent_id": "hermes_record:35904813-205d-43a0-a9cb-0e17eb82d76d", + "request_group_id": "hermes_request:91421be58d027333bd0d42b97094df1661e1bf14adf8b5e6a06eaf06d6781a10", + "full_human_chars": 1014, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "identify_top_products", + "forecast_sales", + "segment_customers" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_034935", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1129, + 1179, + 1185, + 1228, + 1267, + 1268, + 1503, + 1577, + 1585, + 1662, + 1737, + 1784, + 1790 + ], + "match_source_rows": [ + 1129 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage for five years, which they call Midwestern University, do not help facilitate social mobility. Frequently, the students who entered college best prepared were those who were already middle or upper class, meaning the opportunities the workingand lower-class students received were more limited (Armstrong and Hamilton 1-26). When y", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for advice or suggestions on how to approach a specific scenario found in the document.", + "legacy_target_description_chars": 125, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1129, + "source_record_id": "a630342f-0940-4225-b281-d92387000ae9", + "source_parent_id": "hermes_record:a630342f-0940-4225-b281-d92387000ae9", + "request_group_id": "hermes_request:78d84ed5a5244a0e4fbb4c544a4a741cd3abea39ec65c17c1e3025c92ccb8f29", + "full_human_chars": 14765, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035025", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1190, + 1351, + 1373, + 1392, + 1517, + 1539, + 1641, + 1670, + 1672, + 1681, + 1702, + 1727, + 1781 + ], + "match_source_rows": [ + 1190 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage answers, higher stakes, very specific \n##### Health \n Are people in the United States more obese than they used to be? Too straightforward, not specific enough Have obesity rates increased in the United States over the last one hundred years? More specific Is there a correlation between obesity rates and economic instability in t", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for advice or suggestions on how to approach a specific scenario found in the document.", + "legacy_target_description_chars": 125, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1190, + "source_record_id": "5f906392-bad2-4313-a5dc-355f0856330c", + "source_parent_id": "hermes_record:5f906392-bad2-4313-a5dc-355f0856330c", + "request_group_id": "hermes_request:e8f5a9b8c3974f8ef13e73f788ea7db269ec1c4a9e5030f9492d8a7f509e17c1", + "full_human_chars": 15224, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035043", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 388 + ], + "match_source_rows": [ + 388 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Please assist by executing the following specific tasks for our IT service catalog management system:\n\n1. Add a new IT service named \"Cloud Storage Service\" to the catalog with the following details: \n - Service Name: Cloud Storage Service\n - Description: A cloud storage service offering secure and scalable storage options for enterprise data.\n - Category: Cloud Services\n - Lifecycle Statu", + "legacy_target": "add_service_to_catalog", + "legacy_target_description": "Adds a new service to the IT service catalog with the provided details.", + "legacy_target_description_chars": 71, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 388, + "source_record_id": "ce86341e-d040-49bb-a7d4-103b352b03e9", + "source_parent_id": "hermes_record:ce86341e-d040-49bb-a7d4-103b352b03e9", + "request_group_id": "hermes_request:3033aa26f5f7fd123ee6337f2a40d0abc00a9c61ca239e2b7ab7953727a18fa8", + "full_human_chars": 1049, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "add_service_to_catalog", + "update_service_in_catalog", + "remove_service_from_catalog" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035060", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 56 + ], + "match_source_rows": [ + 56 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning to host a virtual game night called \"Saturday Night Trivia\" for my friends. The game should include categories such as General Knowledge, Movies, Science, and History. I'm aiming for a medium difficulty level and would like to have 20 questions in total. For this interactive home entertainment experience, I'd need to create a trivia game session and send out invitations to my friends ", + "legacy_target": "create_trivia_game_session", + "legacy_target_description": "Creates a new trivia game session with specified parameters.", + "legacy_target_description_chars": 60, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 56, + "source_record_id": "464878f8-a902-40e1-a80d-f34d5aa57d14", + "source_parent_id": "hermes_record:464878f8-a902-40e1-a80d-f34d5aa57d14", + "request_group_id": "hermes_request:eeb8369b13fe5093287bf59992a10ba704346ddc1e566c9232934cb84e6fbe1d", + "full_human_chars": 918, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_trivia_game_session", + "invite_players_to_game" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035119", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 797 + ], + "match_source_rows": [ + 797 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm implementing a POS system for our boutique store, and I need to execute specific transactions. Firstly, I would like to process a credit card payment for a customer who has purchased items totaling $250.35. The credit card details are as follows: the card number is 4532 8770 5567 8899, expiration date 07/25, CVV code 321, and the transaction ID is TXN4567.\n\nSecondly, another customer paid with", + "legacy_target": "process_credit_card_payment", + "legacy_target_description": "Processes a payment transaction using a credit card.", + "legacy_target_description_chars": 52, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 797, + "source_record_id": "a5c9def7-3d35-48e4-82f9-b85511c71e12", + "source_parent_id": "hermes_record:a5c9def7-3d35-48e4-82f9-b85511c71e12", + "request_group_id": "hermes_request:37d2b164643dd35358387c12ab822f80fdb667b0587f855b85d9910f51786883", + "full_human_chars": 1052, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "process_credit_card_payment", + "handle_cash_transaction", + "apply_member_discount", + "update_inventory" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035163", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 711 + ], + "match_source_rows": [ + 711 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working on securing sensitive files on my Mac, and I have a particular document that I need to restrict access to. The document is called 'FinancialReport.xlsx', and it's located in the '/Users/johndoe/Documents' directory. I want to ensure that other users on the machine can't modify this file, but they can still read it. Can you set the permissions of this file to be read-only for", + "legacy_target": "change_file_permissions", + "legacy_target_description": "Change the permissions of a file for a specific user or group.", + "legacy_target_description_chars": 62, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 711, + "source_record_id": "f56872b5-ee8f-4e21-8024-bfd35caa5da8", + "source_parent_id": "hermes_record:f56872b5-ee8f-4e21-8024-bfd35caa5da8", + "request_group_id": "hermes_request:7a0f56e8b71f023f781d523d5e4d2137cd152b232cb4b3cec7835e6ab1ff6e76", + "full_human_chars": 575, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "change_file_permissions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035181", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 440 + ], + "match_source_rows": [ + 440 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "In our robotics lab, we are currently upgrading the precision of our RM-2045 robotic manipulator. We have equipped it with an Inertial Measurement Unit (IMU) sensor and plan to employ the Factored Quaternion Algorithm combined with a Kalman Filter for enhanced orientation estimates. The objective is to ensure the orientation accuracy of this manipulator is within stringent tolerances for our upcom", + "legacy_target": "estimate_orientation", + "legacy_target_description": "Estimates the orientation of a robotic manipulator using specified algorithms and sensor data.", + "legacy_target_description_chars": 94, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 440, + "source_record_id": "12f3489b-ea8d-465b-9319-b18040089030", + "source_parent_id": "hermes_record:12f3489b-ea8d-465b-9319-b18040089030", + "request_group_id": "hermes_request:2a5722cb7c2e6e5335f07e2e5565eda7847379ddeea83a0b9a0077036bbf58fa", + "full_human_chars": 1644, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "estimate_orientation", + "estimate_kinematic_errors" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035255", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 860 + ], + "match_source_rows": [ + 860 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have composed an article titled \"The Social Shift: Communication in the Digital Age,\" which explores the transformative effect of social media on the way we communicate. I'd like to share an excerpt of this article on Twitter to engage with a wider audience. The excerpt is as follows: \"Social media has revolutionized the way we connect, creating new opportunities for engagement and dialogue. Dis", + "legacy_target": "create_twitter_post", + "legacy_target_description": "Create a new Twitter post with the given title, excerpt, and optional hashtags.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 860, + "source_record_id": "529f25eb-b31c-4e0e-9222-ee57825acdd7", + "source_parent_id": "hermes_record:529f25eb-b31c-4e0e-9222-ee57825acdd7", + "request_group_id": "hermes_request:8ca13a826c8cf3e1b9f30b3cd427810fae7479a3c68806acbc40d9cb63b2ceac", + "full_human_chars": 1273, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_twitter_post" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035332", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 912 + ], + "match_source_rows": [ + 912 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing the management of a precision farming operation and need to closely monitor the environmental conditions in specific agricultural fields. We have a network of IoT sensors in place that continuously measure key variables affecting crop health, including soil moisture content, ambient temperature, and nutrient levels. In order to make data-driven decisions and adjust our farming stra", + "legacy_target": "get_field_sensor_data", + "legacy_target_description": "Retrieves the latest sensor data for specified fields from the agricultural data API.", + "legacy_target_description_chars": 85, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 912, + "source_record_id": "bebe2c7c-ebe5-441b-9274-8ae4998dc323", + "source_parent_id": "hermes_record:bebe2c7c-ebe5-441b-9274-8ae4998dc323", + "request_group_id": "hermes_request:d52e5d73b8bcbbaa55bfcbdf7c00642897da972db77379d180d22e5cb8c7095a", + "full_human_chars": 1830, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_field_sensor_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035334", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 900 + ], + "match_source_rows": [ + 900 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working on a project that involves simulating a quantum Fourier transform using a cloud-based quantum processor. The objective is to apply this transformation to a 5-qubit system, a fundamental construct within quantum computing that is particularly useful for applications in quantum algorithms and quantum computing research. Upon initiating this process, I intend to analyze the res", + "legacy_target": "create_quantum_circuit", + "legacy_target_description": "Creates a new quantum circuit with the specified number of qubits and applies the given operation.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 900, + "source_record_id": "5f182b19-4271-41ab-92c8-ea264678e435", + "source_parent_id": "hermes_record:5f182b19-4271-41ab-92c8-ea264678e435", + "request_group_id": "hermes_request:46c51c7c072c0702d2ac3b2d26d193558f9e8c7affed86ae7e2d088bd0394a4e", + "full_human_chars": 1556, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_quantum_circuit" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035400", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 324 + ], + "match_source_rows": [ + 324 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the water conservation manager at City Utilities, I'm tasked with implementing a comprehensive water conservation program. We've gathered extensive demographic data on our city's population, including age distribution, average household size, income levels, and education, which will be used to tailor our educational outreach. We have also aggregated our residential water usage data, providing i", + "legacy_target": "create_educational_materials", + "legacy_target_description": "Generates targeted educational materials for water conservation based on demographic data and selected topics.", + "legacy_target_description_chars": 110, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 324, + "source_record_id": "fdbb5c8c-1b1e-4aac-ba0c-4cdb7cd6f940", + "source_parent_id": "hermes_record:fdbb5c8c-1b1e-4aac-ba0c-4cdb7cd6f940", + "request_group_id": "hermes_request:e735cb8d097646033106decd2b231e9ba29d86992f058c5ba01553c9bd941d4d", + "full_human_chars": 1999, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "create_educational_materials", + "analyze_residential_water_usage", + "develop_incentive_schemes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035431", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 944 + ], + "match_source_rows": [ + 944 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am leading a project coordinated through a Microsoft 365 spreadsheet and require assistance with several tasks to enhance our team collaboration. Firstly, I would like to add a comment \"Please review the budget allocation here.\" to cell B2 for specific feedback. Additionally, I need to track any modifications made by our team member with the email address team_member@example.com to ensure we're ", + "legacy_target": "add_comment_to_cell", + "legacy_target_description": "Add a comment to a specific cell in a Microsoft 365 spreadsheet.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 944, + "source_record_id": "3cd71c79-ec6d-4113-ac77-f8a5f2d12d02", + "source_parent_id": "hermes_record:3cd71c79-ec6d-4113-ac77-f8a5f2d12d02", + "request_group_id": "hermes_request:00fc781ca6588f31912533a5486a9c9cf2b0666f4c954441da3b3187aafc44e1", + "full_human_chars": 712, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "add_comment_to_cell", + "track_changes", + "setup_notifications" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035451", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 669 + ], + "match_source_rows": [ + 669 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning a business trip to San Francisco and need to book accommodations. Specifically, I'm looking for hotels that can host 2 guests from May 15th to May 20th. I've heard about Hotel California and The Grand Bixby, both with high ratings. Could you assist me by calling the appropriate functions to fetch a list of available hotels, including these two if possible, ensuring they have a high cu", + "legacy_target": "get_available_hotels", + "legacy_target_description": "Retrieves a list of available hotels based on the specified city, check-in and check-out dates, and number of guests.", + "legacy_target_description_chars": 117, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 669, + "source_record_id": "9b498cd1-9fa5-4335-a541-c4254e1d3fc6", + "source_parent_id": "hermes_record:9b498cd1-9fa5-4335-a541-c4254e1d3fc6", + "request_group_id": "hermes_request:699a5372cf04f845f63205e577ef636501782a558b1982727002a952c128f35e", + "full_human_chars": 557, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_available_hotels" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035551", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 641 + ], + "match_source_rows": [ + 641 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like assistance in optimizing the rail routes for our transportation network. Our fleet consists of three trains: Train A capable of 70 mph with a cargo capacity of 120 tons, Train B capable of 60 mph with a cargo capacity of 140 tons, and Train C which can reach 65 mph and hold 100 tons. Below is the rail network map along with the cargo details we need to transport and the maintenance sc", + "legacy_target": "optimize_rail_routes", + "legacy_target_description": "Computes the most efficient rail routes for trains based on train specifications, rail network maps, cargo details, and scheduled maintenance times.", + "legacy_target_description_chars": 148, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 641, + "source_record_id": "e2179bbd-eb9e-4ac2-9105-17b3b5db6393", + "source_parent_id": "hermes_record:e2179bbd-eb9e-4ac2-9105-17b3b5db6393", + "request_group_id": "hermes_request:9e0f0809edcfc6feb0cd4c8b4238617b13886ae3c01952656c353d61477be4bf", + "full_human_chars": 1508, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "optimize_rail_routes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_035668", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 396 + ], + "match_source_rows": [ + 396 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "The IT department of our enterprise has initiated a data migration project to transition our substantial IT infrastructure, consisting of multiple petabytes of data, from its current on-premises setup to a cloud computing environment. Our objective is to minimize downtime, specifically limiting it to a window of 2 hours, while also ensuring the integrity of data during the migration process. To fa", + "legacy_target": "generate_migration_plan", + "legacy_target_description": "Generates a data migration plan based on the provided parameters.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 396, + "source_record_id": "34ea0eea-4c02-4253-b30a-31a2237255df", + "source_parent_id": "hermes_record:34ea0eea-4c02-4253-b30a-31a2237255df", + "request_group_id": "hermes_request:9529cdb08aac6a43fe6037eedbfb2ddee061458f333b39079f6061d699c1d825", + "full_human_chars": 1607, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_migration_plan" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036027", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 410 + ], + "match_source_rows": [ + 410 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a dataset from our recent market research focused on analyzing customer demographics and their buying behavior. The dataset includes variables such as Age, Income, and Spending Score. I need to conduct an initial data exploration to understand the basic distribution and relationships within this data.\n\nFirstly, I aim to summarize the main characteristics of the dataset, including the mean, ", + "legacy_target": "summarize_dataset", + "legacy_target_description": "Summarize the dataset with descriptive statistics such as mean, median, and mode.", + "legacy_target_description_chars": 81, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 410, + "source_record_id": "9fba5fbe-35f7-41f8-9caa-f70cfb5f88ee", + "source_parent_id": "hermes_record:9fba5fbe-35f7-41f8-9caa-f70cfb5f88ee", + "request_group_id": "hermes_request:db551a2cd3ecc59c1d99986fa88f0d19e6c71dbc7fd016a506da30bcb29d5d63", + "full_human_chars": 2210, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "summarize_dataset", + "detect_outliers", + "calculate_correlations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036203", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 272 + ], + "match_source_rows": [ + 272 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently leading a training session for my team on algorithmic trading strategies with a focus on trend following, mean reversion, and arbitrage techniques. To provide a hands-on learning experience, we intend to simulate these strategies using actual historical stock data from the S&P 500 index that we've collected over the past year. This dataset, named \"SP500_historical_stock_data.csv\", co", + "legacy_target": "simulate_trend_following", + "legacy_target_description": "Simulates a trend following strategy using historical stock data and real-time market data feeds.", + "legacy_target_description_chars": 97, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 272, + "source_record_id": "6c444eaa-999c-4f0e-90c7-cde62ab6bfaa", + "source_parent_id": "hermes_record:6c444eaa-999c-4f0e-90c7-cde62ab6bfaa", + "request_group_id": "hermes_request:a4cb791582d2e65a730d4ee837fe88ff44b52e0bc9903340f6b5d265a684ece5", + "full_human_chars": 1341, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "simulate_trend_following", + "simulate_mean_reversion", + "simulate_arbitrage" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036258", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 855 + ], + "match_source_rows": [ + 855 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our project management process, our team utilizes a document titled 'Research_Paper.docx' for collaborative editing. We store this document in a specific shared workspace, specifically located at the path '/projects/collab_edit/'. I need to retrieve the latest updates and contributions made by team members to this document. Can you perform a GET request to fetch the most recent collabor", + "legacy_target": "fetch_collaborative_data", + "legacy_target_description": "Retrieve the latest collaborative data for a specified document within a shared workspace.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 855, + "source_record_id": "a801f6ab-47ed-41c1-a8e2-bbb2787e2462", + "source_parent_id": "hermes_record:a801f6ab-47ed-41c1-a8e2-bbb2787e2462", + "request_group_id": "hermes_request:2b562769e8a5f1ff9a54d5434e097ddb596d53fc315a01dff79f036d79a21d19", + "full_human_chars": 531, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetch_collaborative_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036351", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 474 + ], + "match_source_rows": [ + 474 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As a Service Manager at an automotive dealership, I need to arrange a maintenance service appointment for one of our loyal customers, John Smith, who owns a 2017 Honda Accord with the VIN number HNDA2017XYZ123456. I would like to schedule his vehicle for service on May 20th, 2023, at 9:00 AM. Additionally, I want to review any past services that have been completed on his vehicle to ensure we're u", + "legacy_target": "schedule_service_appointment", + "legacy_target_description": "Schedules a service appointment for a customer's vehicle.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 474, + "source_record_id": "8dc6a51e-180a-4ff9-a532-312ca98a9923", + "source_parent_id": "hermes_record:8dc6a51e-180a-4ff9-a532-312ca98a9923", + "request_group_id": "hermes_request:ec25620cd8d6fa17fa7a24855f06bfb5bbda532c0688d48191e18dc903d6d9b8", + "full_human_chars": 914, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "schedule_service_appointment", + "track_vehicle_service_history", + "send_service_reminder" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036359", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 515 + ], + "match_source_rows": [ + 515 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently captured a series of images at our office party and I'd like to digitally catalog the items and attendees in each. There's one image, in particular, featuring office furniture and a colleague that I want to start with. The URL for the image is `http://example.com/office-party.jpg`. Could you analyze this image to detect and recognize any objects present, such as furniture, as well as id", + "legacy_target": "recognize_objects_in_image", + "legacy_target_description": "Identify and classify objects within an image.", + "legacy_target_description_chars": 46, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 515, + "source_record_id": "450a743c-d523-4c0f-9987-81e18a37923b", + "source_parent_id": "hermes_record:450a743c-d523-4c0f-9987-81e18a37923b", + "request_group_id": "hermes_request:ae51ab3b00ffcef64e540da067f80f98657c39d8cc5c33583ad0b55194e49d8e", + "full_human_chars": 871, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "recognize_objects_in_image", + "detect_faces" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036631", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 468 + ], + "match_source_rows": [ + 468 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing an automotive retail store and today we completed a sale, processed a return, and provided vehicle servicing. I require your help to call the respective functions to record these transactions with precision.\n\nFor the sale transaction, here are the details:\n- Transaction ID: TXN-4501\n- Customer ID: CUST-9801\n- Vehicle ID: VHCL-3021\n- Sale Amount: $23,500\n- Payment Method: credit_car", + "legacy_target": "process_sale_transaction", + "legacy_target_description": "Processes a sale transaction for a vehicle in the automotive retail store.", + "legacy_target_description_chars": 74, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 468, + "source_record_id": "09a41044-b7b4-4080-b832-e84554c3b725", + "source_parent_id": "hermes_record:09a41044-b7b4-4080-b832-e84554c3b725", + "request_group_id": "hermes_request:2381f440622370225d4655889f1d41f43f07482e7eadc5b74318f69cca0197c7", + "full_human_chars": 905, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "process_sale_transaction", + "process_return_transaction", + "process_service_order" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036760", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 40 + ], + "match_source_rows": [ + 40 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently purchased a range of smart home devices that I would like to integrate with my OpenHAB system for centralized control. Specifically, I have acquired a 'SecureLock SLT2' smart lock that I want to add to my front door for enhanced security. Additionally, I'm aiming to create a more comfortable and energy-efficient environment by installing 'BrightBulb BBL22' smart lights in my living room", + "legacy_target": "add_device_to_openhab", + "legacy_target_description": "Adds a new smart device to the OpenHAB system and configures it for control.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 40, + "source_record_id": "5c1edf8e-9732-4346-80fb-66c7307b70da", + "source_parent_id": "hermes_record:5c1edf8e-9732-4346-80fb-66c7307b70da", + "request_group_id": "hermes_request:5c95fcefd37ba62ecc2555a2636417c865fbe0b7cd0882a5996107eb4bb561b6", + "full_human_chars": 1134, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "add_device_to_openhab", + "add_device_to_openhab", + "add_device_to_openhab" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036781", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 250 + ], + "match_source_rows": [ + 250 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working on a Python project where I need to create a specific virtual environment to ensure compatibility and manage dependencies. For this project, I would like to use Python version 3.8. I also need to install certain Python packages that are essential for my project's functionality. The packages I need to install are numpy for numerical computing, pandas for data manipulation, an", + "legacy_target": "create_virtual_environment", + "legacy_target_description": "Creates a new virtual environment for a Python project at the specified path with the given Python version.", + "legacy_target_description_chars": 107, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 250, + "source_record_id": "e6eb4aa3-4db2-4f01-84d1-e804867956fa", + "source_parent_id": "hermes_record:e6eb4aa3-4db2-4f01-84d1-e804867956fa", + "request_group_id": "hermes_request:951802284844684a0c17f4bcf3d9b8a7946b391158a391ddbef4330ef86d8d83", + "full_human_chars": 791, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_virtual_environment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_036850", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 189 + ], + "match_source_rows": [ + 189 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to send a peer-to-peer payment for $45.00 to my friend @AlexDinner45 on Venmo. This amount is for our shared dinner expenses. Please include the note 'Dinner last night' with the payment to provide context for the transaction.", + "legacy_target": "send_venmo_payment", + "legacy_target_description": "Send a peer-to-peer payment to a user on Venmo.", + "legacy_target_description_chars": 47, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 189, + "source_record_id": "9c46f3db-7e4e-4873-a9bc-a8e0251c45fe", + "source_parent_id": "hermes_record:9c46f3db-7e4e-4873-a9bc-a8e0251c45fe", + "request_group_id": "hermes_request:3758422dfe6846daacea267cbd839deafa9c7a5dc6a0f0799bd5a116bfbf3a8f", + "full_human_chars": 239, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "send_venmo_payment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_037028", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 979 + ], + "match_source_rows": [ + 979 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm operating a food retail store and today we've had several customers checking out with various items. I need to process these sales and would like to call the relevant functions to do so. Here are the details for a specific transaction:\n\nCustomer ID: 783920D\n\nItems:\n- Product ID: A1B2C3, Quantity: 2, Unit Price: $3.99\n- Product ID: D4E5F6, Quantity: 1, Unit Price: $5.49\n- Product ID: G7H8I9, Qu", + "legacy_target": "process_sale_transaction", + "legacy_target_description": "Processes a sales transaction with items and customer information.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 979, + "source_record_id": "1f42637c-1d8b-42c1-bbf4-60c58ff8e24c", + "source_parent_id": "hermes_record:1f42637c-1d8b-42c1-bbf4-60c58ff8e24c", + "request_group_id": "hermes_request:ee519bc8ee99302d5c50c9cc152d8136296531883ec34266221afaf4d976a40f", + "full_human_chars": 1045, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "process_sale_transaction" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_037222", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 17 + ], + "match_source_rows": [ + 17 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently researching the simulation of a two-level quantum system and its energy eigenstates. In order to achieve this, I need to generate a quantum circuit based on a specific Hamiltonian over a fixed evolution time. The Hamiltonian I intend to use consists of terms with a coefficient of 1 for the Pauli X operator, and a 0.5 coefficient for the Pauli Z operator. The system will be evolved f", + "legacy_target": "generate_quantum_circuit", + "legacy_target_description": "Generate a quantum circuit based on the provided Hamiltonian and evolution time.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 17, + "source_record_id": "64f1174b-dca3-4863-9202-5888503701e1", + "source_parent_id": "hermes_record:64f1174b-dca3-4863-9202-5888503701e1", + "request_group_id": "hermes_request:cab71f2703e90bc18d264b61b247fc4a1b28dac758f37d61ce862625cd54abf7", + "full_human_chars": 1625, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_quantum_circuit" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_037269", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1032 + ], + "match_source_rows": [ + 1032 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing a travel guide section within my Android application focused on Hungarian destinations. As part of the user experience, I want to provide a feature that allows users to translate common English phrases to Hungarian directly within the app. The phrases to translate include greetings and essential questions a traveler might need, such as \"Hello\", \"Thank you\", \"How much does it cost?\"", + "legacy_target": "translate_phrases", + "legacy_target_description": "Translates a list of phrases from a source language to a target language.", + "legacy_target_description_chars": 73, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1032, + "source_record_id": "1343117d-7519-4c36-8aa0-c053eb490b47", + "source_parent_id": "hermes_record:1343117d-7519-4c36-8aa0-c053eb490b47", + "request_group_id": "hermes_request:f81e7dbb152303d9f74697fdc821ab53208a47b5cf5cb4523f9e4694f1af886a", + "full_human_chars": 795, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "translate_phrases" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_037448", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 275 + ], + "match_source_rows": [ + 275 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently researching the impacts of algorithmic trading within the Indian financial markets, focusing on the National Stock Exchange of India's Nifty 50 stocks. For this purpose, I have compiled a dataset named \"Nifty_50_Stocks_Data.csv\" containing detailed trade information over a period from September 2012 to August 2013. I am particularly interested in examining how algorithmic trading ha", + "legacy_target": "analyze_algorithmic_impact", + "legacy_target_description": "Analyzes the impact of algorithmic trading on trade sizes, order-to-trade ratios, and market liquidity using historical stock data.", + "legacy_target_description_chars": 131, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 275, + "source_record_id": "3e150b90-9dcd-4256-9d7f-fe002e5a3bb6", + "source_parent_id": "hermes_record:3e150b90-9dcd-4256-9d7f-fe002e5a3bb6", + "request_group_id": "hermes_request:f9b188fcf17e31011a066b6e259b9fb9efad9164464174db997c2460473f6d47", + "full_human_chars": 1364, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "analyze_algorithmic_impact" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_037573", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 948 + ], + "match_source_rows": [ + 948 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently finalizing a PowerPoint presentation for our upcoming joint project and need to collaborate with external partners from different organizations. The presentation is stored at \"path/to/presentation.pptx\". I want to share this presentation with our partners at 'partner1@example.com' and 'partner2@example.com' granting them edit permissions. Additionally, I need to collect detailed fee", + "legacy_target": "share_presentation", + "legacy_target_description": "Share a PowerPoint presentation with specified recipients and set permissions.", + "legacy_target_description_chars": 78, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 948, + "source_record_id": "8d72ac0b-2a7b-44d7-b49b-957f23dd5a00", + "source_parent_id": "hermes_record:8d72ac0b-2a7b-44d7-b49b-957f23dd5a00", + "request_group_id": "hermes_request:040d1fb5285b4524b824ef9b90871918b25a62a3756a508418f711fb2a4b4631", + "full_human_chars": 787, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "share_presentation", + "collect_presentation_feedback", + "track_presentation_changes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_037630", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 575 + ], + "match_source_rows": [ + 575 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the head of our customer support department, I am spearheading the initiative to refine our knowledge base by leveraging RPA technology. We've accumulated extensive customer interaction records, compiled in a CSV file named `customer_interactions.csv`, over various communication channels—these need insightful analysis for identifying prevalent queries and concerns. Based on this analysis, we pl", + "legacy_target": "analyze_customer_interactions", + "legacy_target_description": "Analyzes customer interaction data to identify common issues and suggest new help topics for the knowledge base.", + "legacy_target_description_chars": 112, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 575, + "source_record_id": "0eddae73-1bf3-4113-8bc9-fe27348e18d9", + "source_parent_id": "hermes_record:0eddae73-1bf3-4113-8bc9-fe27348e18d9", + "request_group_id": "hermes_request:49a6315400acc1f0ad4cbefa94a976f5d50b1768b2da661a665a115a3a5494fa", + "full_human_chars": 1531, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_customer_interactions", + "update_knowledge_base", + "format_knowledge_base" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038109", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 445 + ], + "match_source_rows": [ + 445 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm overseeing the monitoring of a robotic system operating within a high-precision manufacturing facility, and it's vital to keep track of the environmental conditions for the robots. We have a set of sensors installed on our robot which include temperature, humidity, and proximity detectors. It is imperative that we gather the most recent sensor data to ensure the robot functions within safe env", + "legacy_target": "get_sensor_data", + "legacy_target_description": "Retrieves real-time data from specified sensors through a GET request to the robot's API endpoint.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 445, + "source_record_id": "5a0db2ad-1cb7-4dfe-ae50-d12d309b7b96", + "source_parent_id": "hermes_record:5a0db2ad-1cb7-4dfe-ae50-d12d309b7b96", + "request_group_id": "hermes_request:4b839ba6f23251e0c7b563ca52bca9419b0fabf5b228f90f71adcfb869ce669b", + "full_human_chars": 1262, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_sensor_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038141", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1013 + ], + "match_source_rows": [ + 1013 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm working on optimizing our retail chain's performance and require a sales forecast for the upcoming quarter considering various influencing factors. The objective is to integrate historical sales figures, promotional strategies, and external elements like weather patterns and holiday seasons into this forecast. We have compiled comprehensive data sets, including daily sales figures over the pas", + "legacy_target": "forecast_sales", + "legacy_target_description": "Generates a sales forecast for a specified period based on historical sales data, promotional schedules, and external factors.", + "legacy_target_description_chars": 126, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1013, + "source_record_id": "9d957d9d-e506-4aa7-b5d1-6e328458eb68", + "source_parent_id": "hermes_record:9d957d9d-e506-4aa7-b5d1-6e328458eb68", + "request_group_id": "hermes_request:dd1afe43ffa3cb81b9fe7d865014c290ed294b3f437a50b46e6074261d55fe0b", + "full_human_chars": 1100, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "forecast_sales" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038168", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 702 + ], + "match_source_rows": [ + 702 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing the EMR for our healthcare facility and need to perform several updates and retrievals. Firstly, I need to add a new patient record for Jane Smith. Her personal information is as follows: \n\n- Name: Jane Smith\n- Date of Birth: 1985-08-25\n- Address: 742 Evergreen Terrace\n- Contact Number: 555-0123\n\nHer medical information includes:\n\n- Known Allergies: [Penicillin]\n- Current Medications", + "legacy_target": "add_patient_record", + "legacy_target_description": "Adds a new patient record to the EMR system with personal and medical information.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 702, + "source_record_id": "5389fb44-a6b7-4a41-b2be-b85a5a6c84c1", + "source_parent_id": "hermes_record:5389fb44-a6b7-4a41-b2be-b85a5a6c84c1", + "request_group_id": "hermes_request:1a0ca1e3874065107097c62df7ba551941638e7182a8d8fe8510371dedaf25c6", + "full_human_chars": 1155, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "add_patient_record", + "update_patient_record", + "retrieve_patient_history", + "generate_patient_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038171", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 718 + ], + "match_source_rows": [ + 718 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently developing a desktop application using MonoDevelop, and I'm looking to integrate Git for version control in my Visual Studio Code environment to better manage the project. The project is located at the following path on my system: `/Users/myusername/projects/my-application`. I need to initialize a Git repository in this project directory. \n\nOnce the repository is set up, I would lik", + "legacy_target": "initialize_git_repository", + "legacy_target_description": "Initialize a new Git repository in the specified project path.", + "legacy_target_description_chars": 62, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 718, + "source_record_id": "43069657-c694-4e1e-bdd2-8253b57afb21", + "source_parent_id": "hermes_record:43069657-c694-4e1e-bdd2-8253b57afb21", + "request_group_id": "hermes_request:aed83475c712877c761a9cd5415ab687d093c77d7582bb5c79debac10e44fd69", + "full_human_chars": 1038, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "initialize_git_repository", + "create_branch" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038321", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 973 + ], + "match_source_rows": [ + 973 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To effectively manage our brewery's customer relationships, I need to perform three key tasks. First, I'd like to track all recent interactions with our VIP customer, John Doe, whose unique customer identifier is \"C12345.\" This should include purchases made in the past 3 months, recorded support tickets, and communication logs.\n\nSecondly, there is a promotion campaign where customers can earn doub", + "legacy_target": "track_customer_interactions", + "legacy_target_description": "Tracks all interactions with a specific customer, including purchases, support tickets, and communication logs.", + "legacy_target_description_chars": 111, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 973, + "source_record_id": "fd628360-935d-4d40-b35f-110c046ba8a4", + "source_parent_id": "hermes_record:fd628360-935d-4d40-b35f-110c046ba8a4", + "request_group_id": "hermes_request:6b9a2e5aaf5528a21f4277e84efd4fa483c8dc52556ba011260a31f617e7875f", + "full_human_chars": 1377, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "track_customer_interactions", + "manage_loyalty_program", + "analyze_customer_feedback" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038364", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 729 + ], + "match_source_rows": [ + 729 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a directory on my Windows machine located at 'C:\\Users\\ExampleUser\\Documents' which contains several files including 'file1.txt', 'file2.txt', and 'report.docx'. I need to organize this directory by listing all the contents within it to keep track of the files present. Additionally, I want to rename each file in the directory by appending the current date in the 'yyyyMMdd' format to ensure ", + "legacy_target": "list_directory_contents", + "legacy_target_description": "List the contents of the specified directory path.", + "legacy_target_description_chars": 50, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 729, + "source_record_id": "c5df1c00-05ac-497b-9a6e-a14ae299c301", + "source_parent_id": "hermes_record:c5df1c00-05ac-497b-9a6e-a14ae299c301", + "request_group_id": "hermes_request:6b042e6d7e5b48f29fc6422e49894c175c3ecee1ada3becad5fc2239730f315a", + "full_human_chars": 577, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "list_directory_contents", + "rename_files_add_date" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038452", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 654 + ], + "match_source_rows": [ + 654 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To enhance our company's efficiency in supply chain management, we have identified four critical areas that require optimization: inventory analysis, demand forecasting, transportation route optimization, and delivery scheduling. As part of our efforts to streamline our supply chain logistics, we have collected extensive data that we wish to analyze and utilize for improvement.\n\nFor inventory anal", + "legacy_target": "analyze_current_inventory", + "legacy_target_description": "Analyzes the current inventory levels and identifies potential overstock or understock situations.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 654, + "source_record_id": "cac519de-7fe8-4c23-af9c-40d37760390f", + "source_parent_id": "hermes_record:cac519de-7fe8-4c23-af9c-40d37760390f", + "request_group_id": "hermes_request:8428358f0483bf52f89cd007f40caf06cb6b7ad79dea01d094c689c17e0b6ccc", + "full_human_chars": 2100, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_current_inventory", + "forecast_product_demand", + "calculate_optimal_routes" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038478", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1145, + 1214, + 1349, + 1366, + 1382, + 1418, + 1420, + 1474, + 1520, + 1571, + 1575, + 1735, + 1777 + ], + "match_source_rows": [ + 1145 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 6. Is this graph weighted? \n No. (No numbers annotating the edges.) \n7. Is it a tree? \n No. (A tree must be connected, and must also have no cycles, which this graph clearly does: e.g. , B-to-A-to-E-to-B.) \n8. Is it a DAG? \n Not remotely: it is neither directed nor acyclic. \n9. If this graph represented an en- dorelation, how", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that require making logical inferences based on the information in the document. These test understanding.", + "legacy_target_description_chars": 135, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1145, + "source_record_id": "1737bbd7-9e80-4133-bb34-1095efc8d1a1", + "source_parent_id": "hermes_record:1737bbd7-9e80-4133-bb34-1095efc8d1a1", + "request_group_id": "hermes_request:14f643478a9e90a66c9ec6daee50d3bac26843aad5321b7b55ae0c170d3a3343", + "full_human_chars": 11568, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038565", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 849 + ], + "match_source_rows": [ + 849 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have an existing account with Google's API service, specifically for managing and accessing calendar data. To authenticate and retrieve my calendar information, I am providing my unique client ID, '12345.apps.googleusercontent.com,' and I have securely prepared my client secret for this process. My intention is to utilize the OAuth 2.0 protocol to authenticate with Google's authorization servers", + "legacy_target": "authenticateOAuth2", + "legacy_target_description": "Authenticates a user using OAuth 2.0 protocol and returns an access token.", + "legacy_target_description_chars": 74, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 849, + "source_record_id": "dc129a31-63a2-4ca7-a956-bd92af2edad3", + "source_parent_id": "hermes_record:dc129a31-63a2-4ca7-a956-bd92af2edad3", + "request_group_id": "hermes_request:cccb7416e55ff46c64ecfa663caea65245c030f6d46947a1d84a61bf5e0900be", + "full_human_chars": 948, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "authenticateOAuth2" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038671", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1075 + ], + "match_source_rows": [ + 1075 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing the implementation of Single Sign-On (SSO) for our company's suite of online services. We need to enable SSO for our three core internal applications: 'HR Portal', 'Project Management', and 'Customer Support Dashboard'. The authentication protocol required is 'SAML 2.0', and we need to align the setup with security standards including 'OAuth 2.0' and 'OpenID Connect'. \n\nWe are targ", + "legacy_target": "configureSSO", + "legacy_target_description": "Configures Single Sign-On for specified applications using the chosen authentication protocol and ensures compliance with security standards.", + "legacy_target_description_chars": 141, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1075, + "source_record_id": "14f0ebb5-95be-479b-9e2f-c23892ff1ac7", + "source_parent_id": "hermes_record:14f0ebb5-95be-479b-9e2f-c23892ff1ac7", + "request_group_id": "hermes_request:37ecec7b0a87b32fa4aa21921ae6ab5aac376d1026527cdc51e0516ad4fba787", + "full_human_chars": 1121, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "configureSSO", + "enableSSOForEmployees", + "testSSOIntegration" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038681", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 548 + ], + "match_source_rows": [ + 548 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm overseeing the customer support at AiseraGPT, which offers AI-powered services. We're keen on furthering our automation by integrating RPA for an efficient support system. To achieve this, I would like to set up our platform to automatically handle specific customer queries using AI chatbots. The types of queries include billing, technical support, and account management queries. Additionally,", + "legacy_target": "automate_customer_queries", + "legacy_target_description": "Automates the handling of common customer queries using AI chatbots on the specified platform.", + "legacy_target_description_chars": 94, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 548, + "source_record_id": "8ca67d04-43ab-4bc7-a171-85d873a37fe7", + "source_parent_id": "hermes_record:8ca67d04-43ab-4bc7-a171-85d873a37fe7", + "request_group_id": "hermes_request:8edb78b7991247f66ba455102de1584e653ea081517d821050bad71680653679", + "full_human_chars": 828, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "automate_customer_queries", + "escalate_to_human_agent", + "compile_interaction_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038718", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 767 + ], + "match_source_rows": [ + 767 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm developing an augmented reality application for interior designers, enabling them to offer clients a way to envision customized furniture pieces in their own spaces. One feature we're excited about is allowing the user to describe their ideal piece of furniture using natural language, and our app will generate a 3D model that matches their description for AR viewing. For instance, a client mig", + "legacy_target": "generate_3d_model_from_description", + "legacy_target_description": "Generates a 3D model from a textual description using AI.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 767, + "source_record_id": "7bc7702e-7915-45fa-9640-4a698a3c8649", + "source_parent_id": "hermes_record:7bc7702e-7915-45fa-9640-4a698a3c8649", + "request_group_id": "hermes_request:37492c1a84872f9239d75eb9acfdaeb2d807ef9e6b9fd685ff61e8cdf83cf129", + "full_human_chars": 964, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_3d_model_from_description" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038731", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1164, + 1254, + 1265, + 1374, + 1463, + 1524, + 1556, + 1625, + 1635, + 1657, + 1685, + 1716, + 1758 + ], + "match_source_rows": [ + 1164 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage students not simply think about the \"so what\" of their writing but also consider and analyze the prompt more carefully. I've found that students go through a layered understanding of a prompt, at first understanding the basic concept and then looking more carefully at the prompt's specific requirements. But what makes their paper", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for clarification on an ambiguous or unclear statement in the document. These help resolve confusion.", + "legacy_target_description_chars": 139, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1164, + "source_record_id": "0a9ed99a-4cda-45ad-9bfe-74652befffd7", + "source_parent_id": "hermes_record:0a9ed99a-4cda-45ad-9bfe-74652befffd7", + "request_group_id": "hermes_request:bc0e31a82e4f501e3d33919b99f7aa82e25ff69960a2fb05c324d238c13f054c", + "full_human_chars": 14374, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038782", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 844 + ], + "match_source_rows": [ + 844 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to gather comprehensive information on the film titled 'Inception', which was released in the year 2010. Specifically, I am interested in obtaining the full list of cast members, the name of the director, the genre classification of the film, and its overall viewer rating. Please use the getMovieDetails function with the following parameters to retrieve this data:\n\n- **Movie Title:** ", + "legacy_target": "getMovieDetails", + "legacy_target_description": "Retrieves detailed information about a specific movie by title and release year.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 844, + "source_record_id": "34cd1e4c-0d1d-43b4-a342-83d7729e10aa", + "source_parent_id": "hermes_record:34cd1e4c-0d1d-43b4-a342-83d7729e10aa", + "request_group_id": "hermes_request:6f5516bd8f6eef814a5f6f6cdfda4f651514f669c54128340e5db54d1d171408", + "full_human_chars": 532, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "getMovieDetails" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_038817", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 20 + ], + "match_source_rows": [ + 20 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am seeking assistance in learning about quantum programming languages such as Qiskit, Cirq, and Q#, which are pivotal in the realm of quantum computing. 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Specifically, I would like to evaluate their performance over the last quarter by reviewing both price movements and trading volumes. 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I have identified the following exhibits to be featur", + "legacy_target": "findVRContentService", + "legacy_target_description": "Finds VR content creation services compatible with a specific headset model and required content features.", + "legacy_target_description_chars": 106, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 882, + "source_record_id": "a63189c5-495f-4e10-ba02-a0a349f61bc7", + "source_parent_id": "hermes_record:a63189c5-495f-4e10-ba02-a0a349f61bc7", + "request_group_id": "hermes_request:de673e2455985064e1f90d8cd1937af6aa64cbbe07d5304fb4c98793ad06c915", + "full_human_chars": 925, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "findVRContentService" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_039089", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 482 + ], + "match_source_rows": [ + 482 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on analyzing a set of manufacturing documents that detail various production units and machinery within our facility. The objective is to extract specific information such as types of machinery, their models, and associated production units from these texts. This data is curated in a structured JSON file named \"manufacturing_details.json\", which captures descriptions and specification", + "legacy_target": "identify_production_entities", + "legacy_target_description": "Identifies and categorizes production units and machinery details in manufacturing texts and outputs the data in JSON Schema format.", + "legacy_target_description_chars": 132, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 482, + "source_record_id": "9dab6fe7-00e3-4b1a-b107-91c8893e18e2", + "source_parent_id": "hermes_record:9dab6fe7-00e3-4b1a-b107-91c8893e18e2", + "request_group_id": "hermes_request:f6e27fec0405c1ef432beae071779bd7a56f25d02e99c2070df552848d3a79cf", + "full_human_chars": 1776, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "identify_production_entities" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_039140", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 812 + ], + "match_source_rows": [ + 812 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing a batch of recent insurance claims that arose from severe weather events. The claims need to be meticulously verified against their respective policy coverages to check eligibility. Following verification, a detailed assessment of the damage linked to each claim is required. Consequently, accurate settlement amounts must be calculated for those assessments. Finally, the settlements m", + "legacy_target": "verify_claims_coverage", + "legacy_target_description": "Verifies each insurance claim against the policy coverage information.", + "legacy_target_description_chars": 70, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 812, + "source_record_id": "739caa28-bb48-4cc0-b879-bbc9463af369", + "source_parent_id": "hermes_record:739caa28-bb48-4cc0-b879-bbc9463af369", + "request_group_id": "hermes_request:6e8c7c6733dfa2c527e769683ce42fb61a3948326f2a8a51ca973726bbb8d90f", + "full_human_chars": 1550, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "verify_claims_coverage" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_039197", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 377 + ], + "match_source_rows": [ + 377 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am in the process of enhancing our IT infrastructure with virtualization capabilities. For this purpose, I would like to create a new virtual machine named 'WebServerVM' configured with a Linux operating system. The VM should have resources allocated to it as follows: 4 CPU cores to handle multiple processes smoothly, 16GB of memory for efficient operation, and 100GB of storage for our applicati", + "legacy_target": "create_virtual_machine", + "legacy_target_description": "Create a new virtual machine with specified resources and network settings.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 377, + "source_record_id": "5a1605b0-31f9-41b7-a2f2-878610e10831", + "source_parent_id": "hermes_record:5a1605b0-31f9-41b7-a2f2-878610e10831", + "request_group_id": "hermes_request:d997c9809a1de2d208da2045d9d59c6730df5f0be687ce25f887e57e11dc1f1a", + "full_human_chars": 1604, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_virtual_machine" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_039226", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 981 + ], + "match_source_rows": [ + 981 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the supply chain manager for our personal care products company, I'm seeking assistance to improve our operations. 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Our supplier selection process also needs refinement to ensure consistent quality and timely deliv", + "legacy_target": "optimize_inventory", + "legacy_target_description": "Analyzes current inventory levels and uses predictive modeling to suggest optimal stock levels, reducing overstock and preventing stockouts.", + "legacy_target_description_chars": 140, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 981, + "source_record_id": "e1e23d6b-7c98-4503-bb42-717af6435acf", + "source_parent_id": "hermes_record:e1e23d6b-7c98-4503-bb42-717af6435acf", + "request_group_id": "hermes_request:899c449c772ab77a2cbae5031dd6d5a680a5203cf0b0df32f98da71c89bc9361", + "full_human_chars": 3386, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "optimize_inventory", + "evaluate_suppliers", + "track_shipments" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_039340", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 59 + ], + "match_source_rows": [ + 59 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to enhance our telecom CRM system with several updates. Firstly, I need to update the profile of a customer, John Doe, with ID \"JD_8098\". His new address is \"1234 Telecom Lane, Datacity, DC 90909\", his email has changed to \"john.doe@email.com\", and his phone plan should now be set to \"Unlimited Data Plan\". Secondly, I want to track service usage patterns for customer Jane Smith, ID \"J", + "legacy_target": "update_customer_profile", + "legacy_target_description": "Updates the customer profile with new information such as address, email, and phone plan.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 59, + "source_record_id": "860c38a2-ef33-4002-bcf3-b94044795ceb", + "source_parent_id": "hermes_record:860c38a2-ef33-4002-bcf3-b94044795ceb", + "request_group_id": "hermes_request:57b6132e5ee8c2436e074705511694a35a271e38c9a49ecbad5d4f0a56cc2560", + "full_human_chars": 1888, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "update_customer_profile", + "track_service_usage", + "manage_support_tickets", + "analyze_customer_feedback" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_039428", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 176 + ], + "match_source_rows": [ + 176 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to add a new credit card to my Coinbase account to facilitate cryptocurrency transactions. The card details for the addition are as follows: card number 1234567890123456, expiration date 09/25, and CVV 123. The billing address associated with the card is John Doe, 123 Main St, Metropolis, New York, 10001, USA. Please proceed to add this credit card and then carry out its verification.", + "legacy_target": "add_credit_card", + "legacy_target_description": "Adds a new credit card to the user's Coinbase account.", + "legacy_target_description_chars": 54, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 176, + "source_record_id": "a0b55b75-0b32-4e82-9c04-79963e4225b1", + "source_parent_id": "hermes_record:a0b55b75-0b32-4e82-9c04-79963e4225b1", + "request_group_id": "hermes_request:291b94e10cff5413497f3ea4dea61b9e93a006e78b138ac57c633c97eecb94b8", + "full_human_chars": 607, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "add_credit_card" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_039682", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1001 + ], + "match_source_rows": [ + 1001 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage a chain of food retail stores and have recently compiled a comprehensive report on our point-of-sale (POS) transactions. I've got a large dataset in a CSV format ('food_retail_sales_data.csv') that details every product sold, including quantities, prices, dates, and associated customer information. Our goal is to:\n\n1. Understand monthly sales trends for the past year.\n2. 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For convenience and security, I'm looking to automate the opening and closing process using my smartphone. I have received my unique device identifier as \"SGDO12345678\" and a secure authorization token \"a1b2c3d4e5f6g7h8\". \n\nThe task is to integrate these credentials into my home automation system to enable remote control ", + "legacy_target": "open_garage_door", + "legacy_target_description": "Opens the smart garage door given the device ID and authorization token.", + "legacy_target_description_chars": 72, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 5, + "source_record_id": "7d99abac-f27f-4ed2-a9ea-31faab5ad5e8", + "source_parent_id": "hermes_record:7d99abac-f27f-4ed2-a9ea-31faab5ad5e8", + "request_group_id": "hermes_request:dc863be0ce1e4cde9f32051f48e85b588e93c9229b4ea93e6e425001fcada4d8", + "full_human_chars": 689, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "open_garage_door" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_040854", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1043 + ], + "match_source_rows": [ + 1043 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing an Android application that should allow users to contact our customer service team with ease. I want to incorporate a feature that checks if the device supports phone calling, automatically fills in our customer service number, and then initiates the call after the user confirms. The customer service number is +18005550199. Could you execute the functions needed to determine if ca", + "legacy_target": "check_phone_call_availability", + "legacy_target_description": "Check if the phone call feature is available on the Android device.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1043, + "source_record_id": "bb2d9bee-ad7c-4275-a689-64c35f88b860", + "source_parent_id": "hermes_record:bb2d9bee-ad7c-4275-a689-64c35f88b860", + "request_group_id": "hermes_request:031801272eff88b8c0ac2328ccc29ccf5bf9ebd760d6b16177c05764f26361b9", + "full_human_chars": 827, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "check_phone_call_availability" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_040959", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1012 + ], + "match_source_rows": [ + 1012 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our data analytics enhancement initiative, my team aims to investigate the efficacy of integrating quantum computing with AI models, particularly focusing on tasks related to pattern recognition and optimization. We are intrigued by the potential benefits quantum algorithms may offer in these domains. To forward our exploration, we seek assistance in obtaining comprehensive information ", + "legacy_target": "get_quantum_ai_integration_info", + "legacy_target_description": "Retrieves information on how quantum computing can be integrated with AI models for specific tasks such as pattern recognition and optimization.", + "legacy_target_description_chars": 144, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1012, + "source_record_id": "656738b2-e3d0-4df0-a5da-93cb6a1af169", + "source_parent_id": "hermes_record:656738b2-e3d0-4df0-a5da-93cb6a1af169", + "request_group_id": "hermes_request:ffd391ba50006d68efb5d954f9d5aa7daac0e952986167169be0ececf0764736", + "full_human_chars": 1297, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_quantum_ai_integration_info", + "simulate_quantum_optimization" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041143", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 268 + ], + "match_source_rows": [ + 268 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing a portfolio with a focus on technology stocks such as Apple (AAPL), Microsoft (MSFT), Alphabet (GOOGL), Amazon (AMZN), and Facebook (FB). I want to initiate an event-driven trading strategy that hinges on real-time technology market news, specifically targeting events like earnings reports, product launches, or regulatory changes. The strategy should incorporate sentiment analysis of", + "legacy_target": "initialize_event_driven_strategy", + "legacy_target_description": "Initializes an event-driven trading strategy for a given list of stocks and event types on a specified trading platform.", + "legacy_target_description_chars": 120, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 268, + "source_record_id": "1e16f372-a861-461a-82e4-27ce4df0caa0", + "source_parent_id": "hermes_record:1e16f372-a861-461a-82e4-27ce4df0caa0", + "request_group_id": "hermes_request:558538d90140e35cf301707a27ecdcecf52bfe7b07c7ca9dc1b816433496bb0f", + "full_human_chars": 752, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "initialize_event_driven_strategy" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041338", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1121, + 1149, + 1161, + 1239, + 1394, + 1434, + 1636, + 1679, + 1703, + 1712, + 1721, + 1734, + 1794 + ], + "match_source_rows": [ + 1121 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage .2. PERMUTATIONS 151 \n R S K I R K I S R K S I I R S K I R K S I S R K I S K R I K R S I K S R S R I K * * * \nThen, for the 5-letter word: \n B R I S K B R I K S B R S I K B R S K I B R K I S B R K S I B I R S K B I R K S * * * \n**Partial permutations** \nSometimes we want to count the permutations of a set, but only want to choose", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of open-ended questions related to the document, that are potentially ambiguous.", + "legacy_target_description_chars": 96, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1121, + "source_record_id": "4a2e1536-a29a-48fd-9204-e33f17e412ea", + "source_parent_id": "hermes_record:4a2e1536-a29a-48fd-9204-e33f17e412ea", + "request_group_id": "hermes_request:201771607b08eb452fc4bd59fa77b7a1f878b3d889f1d14c5d5b0c24ade72ab6", + "full_human_chars": 10751, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041408", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 318 + ], + "match_source_rows": [ + 318 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing a renewable energy project aimed at optimizing the energy storage system of a grid that is transitioning to incorporate more solar and wind power. We need to accurately forecast our upcoming energy storage needs, effectively manage the charging and discharging cycles of our batteries to meet these needs, and ensure the longevity and health of our battery systems.\n\nFor this task, I ", + "legacy_target": "forecast_storage_needs", + "legacy_target_description": "Forecasts future energy storage needs based on historical consumption data, energy generation forecasts, and current battery storage metrics.", + "legacy_target_description_chars": 141, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 318, + "source_record_id": "9f251a1e-f899-48ab-bd8a-8e04d41275fe", + "source_parent_id": "hermes_record:9f251a1e-f899-48ab-bd8a-8e04d41275fe", + "request_group_id": "hermes_request:5b557300553baa41c89c1e4758e2bfbd178dbfc73bcdf2b1a4937f84e8e3db41", + "full_human_chars": 1514, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "forecast_storage_needs" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041472", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 129 + ], + "match_source_rows": [ + 129 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on a podcast project where I need to extract the audio tracks from several videos that feature interesting interviews and dialogues. The videos are hosted on the MVP Sports Bar & Grille website for their past events, and I want to capture the audio in high-quality MP3 format to use as content for my podcast episodes.\n\nThe videos I am specifically interested in are from different event", + "legacy_target": "batch_extract_audio", + "legacy_target_description": "Extracts audio tracks from multiple video files in a batch process.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 129, + "source_record_id": "b84c29d9-6a3f-4e62-8955-372e043df4f4", + "source_parent_id": "hermes_record:b84c29d9-6a3f-4e62-8955-372e043df4f4", + "request_group_id": "hermes_request:f4101d1ef352a7ed4c3c644c50b2d557d7e171bc111c84bfa0dc23aaf9bdd9bd", + "full_human_chars": 1014, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "batch_extract_audio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041537", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 443 + ], + "match_source_rows": [ + 443 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing the setup of our new XR-2000 robotic model at our manufacturing plant and need to configure it to carry out a series of precise tasks. The sequence includes having the robot pick components from storage bin B1, place them on conveyor belt C1, and perform a welding operation at station S1. Each component used has a specific part number, PN-4520, which must be tracked and deducted from", + "legacy_target": "configure_robotic_sequence", + "legacy_target_description": "Configures the robotic system to perform a specified sequence of tasks.", + "legacy_target_description_chars": 71, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 443, + "source_record_id": "369c896e-0cb8-406b-b8af-8759851d587b", + "source_parent_id": "hermes_record:369c896e-0cb8-406b-b8af-8759851d587b", + "request_group_id": "hermes_request:f43ef5ab2cb1c2b5034d41d21a67593f27d6cb81b0411be7effc6d924cb16121", + "full_human_chars": 1230, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "configure_robotic_sequence", + "update_inventory_count", + "log_robot_actions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041603", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 585 + ], + "match_source_rows": [ + 585 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the IT manager at Acme Corp, looking to streamline our monthly sales report generation and distribution process for April 2023. We have a comprehensive sales database identified as \"sales_db_01,\" which records crucial data including transaction dates, product IDs, quantities sold, and sales figures. \n\nOur goal is to produce a detailed report in PDF format that captures total sales, top-perfor", + "legacy_target": "generate_sales_report", + "legacy_target_description": "Generates a sales report from the specified database for the given month and formats it as requested.", + "legacy_target_description_chars": 101, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 585, + "source_record_id": "1fd944c2-7a2f-4522-a27c-747361e57d21", + "source_parent_id": "hermes_record:1fd944c2-7a2f-4522-a27c-747361e57d21", + "request_group_id": "hermes_request:382c8b0b36fcaee7beed654124b6bf7570a2fb18ab757ed2bd65153042e27bad", + "full_human_chars": 1228, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "generate_sales_report", + "retrieve_contact_list" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041636", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 560 + ], + "match_source_rows": [ + 560 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the HR coordinator at Innovative Tech Solutions, and we're currently digitizing the onboarding process for our new employees. For each employee, we need to complete an onboarding form that includes their personal details, a record of their previous employment, and their current contact information. For instance, one of our new team members is Sarah Miller, born on 1990-05-05. Sarah has notabl", + "legacy_target": "fill_form", + "legacy_target_description": "Fills out a web form with the provided personal details, employment history, and contact information, then submits the form.", + "legacy_target_description_chars": 124, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 560, + "source_record_id": "63db9252-853c-4a82-a840-ed17a6a3c394", + "source_parent_id": "hermes_record:63db9252-853c-4a82-a840-ed17a6a3c394", + "request_group_id": "hermes_request:0756cd8f8e3d56e5a5ba5c62cd3c60eb2d5827ecebdc6004c5c0efac9a4f778b", + "full_human_chars": 705, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fill_form" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041747", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 236 + ], + "match_source_rows": [ + 236 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am interested in experiencing the capabilities of 1Password on my iPhone, particularly its integration with iOS features. For this purpose, I would like to set up a trial account that includes specific functionalities such as biometric login for secure access, secure password sharing options for collaboration, and dark web monitoring for enhanced security surveillance. As I plan to extensively t", + "legacy_target": "create_trial_account", + "legacy_target_description": "Create a trial account for a specified password manager app with requested features.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 236, + "source_record_id": "41d37a42-2aa2-455a-a364-29c437c7bfc5", + "source_parent_id": "hermes_record:41d37a42-2aa2-455a-a364-29c437c7bfc5", + "request_group_id": "hermes_request:9ab01d61fbb6ad859ae86562a50f6fd8c4fb8b761f020b3bd9b5679ef667fb5b", + "full_human_chars": 1241, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_trial_account", + "verify_integration" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041838", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 319 + ], + "match_source_rows": [ + 319 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to enhance our microgrid's efficiency by accurately predicting renewable energy output, managing our battery storage optimally, and distributing energy according to usage needs. For this, I need to call specific functions with real data.\n\n1. Predict the solar and wind energy production for the next 24 hours using historical data. The files `solar_production.json` and `wind_production.", + "legacy_target": "predict_renewable_energy_output", + "legacy_target_description": "Predicts the energy output from renewable sources such as solar and wind for a given time horizon.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 319, + "source_record_id": "af2767a1-e477-4635-adb1-ae2409404df9", + "source_parent_id": "hermes_record:af2767a1-e477-4635-adb1-ae2409404df9", + "request_group_id": "hermes_request:6ea91d40ff69332b724f384ab5bb272e0179d6c6e59baabcdf720805cce2f6ad", + "full_human_chars": 1713, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "predict_renewable_energy_output", + "manage_battery_storage", + "optimize_energy_distribution" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041930", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1 + ], + "match_source_rows": [ + 1 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I've recently equipped my home with various smart devices to enhance my living experience. The products I've chosen include Philips Hue smart bulbs, a Nest Thermostat, and an Arlo Pro 3 security camera. My goal is to create a cohesive smart home environment where I can control all these devices through a unified interface. To achieve this, I would like to request the initialization of a smart home", + "legacy_target": "initialize_smart_home_system", + "legacy_target_description": "Initializes the smart home system to connect various smart devices.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1, + "source_record_id": "89ef3c87-66bd-46ee-9297-15398fd9a235", + "source_parent_id": "hermes_record:89ef3c87-66bd-46ee-9297-15398fd9a235", + "request_group_id": "hermes_request:14059bb2682338d68b6b7dc76e9eec9f3a29c536dce29e66198eced34c19bfe4", + "full_human_chars": 916, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "initialize_smart_home_system", + "create_device_group" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_041953", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1115, + 1330, + 1401, + 1410, + 1468, + 1547, + 1579, + 1610, + 1651, + 1767, + 1821, + 1829, + 1892 + ], + "match_source_rows": [ + 1115 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage = - 3 x + y - z = 0 \nC50+^ A three-digit number has two properties. 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Additionally, I would like to withdraw $50 from it. I am currently near the Downtown Branch ATM, which should accept my Cash Card. Could you please call the necessary functions to first retrieve the current balance of my Cash Card and then proceed to withdraw the spe", + "legacy_target": "get_cash_card_balance", + "legacy_target_description": "Retrieve the current balance of a specified Cash Card.", + "legacy_target_description_chars": 54, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 172, + "source_record_id": "d951c2e6-6cb4-42b6-a6a5-b82642fada5d", + "source_parent_id": "hermes_record:d951c2e6-6cb4-42b6-a6a5-b82642fada5d", + "request_group_id": "hermes_request:339b050d4344bf888d3643e7b6673b81982084e594984788efa6925ad44fbad9", + "full_human_chars": 444, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_cash_card_balance" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_042111", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 861 + ], + "match_source_rows": [ + 861 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have an article on our CMS website that requires an important update in the 'Server Security' section. The title of the article is 'Security Best Practices,' which is hosted at https://www.dotcms.com/docs/latest/security-best-practices. In light of recent cyber threats, I would like to emphasize the critical role of firewalls in combating zero-day vulnerabilities. Please add the following new pa", + "legacy_target": "updateArticleContent", + "legacy_target_description": "Updates the content of an article at a specified URL by adding new content under a specific section.", + "legacy_target_description_chars": 100, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 861, + "source_record_id": "f9221027-2206-48f8-8109-1c2d00a97b02", + "source_parent_id": "hermes_record:f9221027-2206-48f8-8109-1c2d00a97b02", + "request_group_id": "hermes_request:2d7dac4373e6a3f2350d51c2114687237361bf8c7bc186a21d4daabda8dfd414", + "full_human_chars": 1142, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "updateArticleContent" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_042248", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 378 + ], + "match_source_rows": [ + 378 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing a software development project for an IT company and need to streamline task creation, team member assignments, monitoring, and resource optimization. I require specific functions to enhance our project management efficiency. Here are the details:\n\n- For task creation: Develop a user authentication module with high priority. The task is critical for the security of our application, a", + "legacy_target": "create_task", + "legacy_target_description": "Creates a new task within a project with specified details.", + "legacy_target_description_chars": 59, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 378, + "source_record_id": "14d7f746-4c32-44d2-9a1b-5509c6c7fe5a", + "source_parent_id": "hermes_record:14d7f746-4c32-44d2-9a1b-5509c6c7fe5a", + "request_group_id": "hermes_request:ec7686290a9d97a855a6452f8432839bfdbe6351aaf748dad31a2764dd4c6dae", + "full_human_chars": 1491, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "create_task", + "monitor_progress", + "optimize_resources" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_042268", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 307 + ], + "match_source_rows": [ + 307 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our power production company is focused on optimizing assets across our operations. 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Additionally, our detailed maintenance recor", + "legacy_target": "optimize_power_plant_efficiency", + "legacy_target_description": "Analyzes power generation data, weather patterns, and maintenance records to suggest improvements in power plant efficiency.", + "legacy_target_description_chars": 124, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 307, + "source_record_id": "cf7d3a30-6c2a-464c-8388-a8c2799631f9", + "source_parent_id": "hermes_record:cf7d3a30-6c2a-464c-8388-a8c2799631f9", + "request_group_id": "hermes_request:f4728f25f82d96e736ba100a8e7198025f7fbab7db098dcc89449b5b27f38685", + "full_human_chars": 1816, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "optimize_power_plant_efficiency", + "improve_load_forecasting", + "manage_energy_portfolio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_042312", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 522 + ], + "match_source_rows": [ + 522 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our IT Service Desk is currently dealing with an email service outage impacting a significant number of users across multiple departments, resulting in considerable disruption. To address this efficiently, I need to create an incident ticket in our ServiceNow system with the following specifications: a description detailing the \"Email service outage,\" an urgency level of \"high\" due to its immediat", + "legacy_target": "create_incident", + "legacy_target_description": "Creates a new incident ticket with the specified description, urgency, and impact level.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 522, + "source_record_id": "a1f1cac7-8813-476e-b28d-1e80885ae2bf", + "source_parent_id": "hermes_record:a1f1cac7-8813-476e-b28d-1e80885ae2bf", + "request_group_id": "hermes_request:91a0217a0b1fb110d46efe65eb6e35a22e8b35a9360bcc6fd716661077bfe074", + "full_human_chars": 926, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_incident", + "update_incident_status" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_042460", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1061 + ], + "match_source_rows": [ + 1061 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working on a project where I need to download a large set of podcast transcripts for analysis. These transcripts are available on the website located at `http://example.com/podcasts/transcripts/` and are named sequentially, starting from 'episode1.txt' all the way up to 'episode600.txt'. To ensure that I comply with the website's terms of service and do not overload their server, I ", + "legacy_target": "check_robots_txt", + "legacy_target_description": "Checks the robots.txt file of a website to determine the crawl-delay and allowed paths for web scraping.", + "legacy_target_description_chars": 104, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1061, + "source_record_id": "8c00a0e5-d3c9-4498-a724-3030e0d3ab12", + "source_parent_id": "hermes_record:8c00a0e5-d3c9-4498-a724-3030e0d3ab12", + "request_group_id": "hermes_request:68d3d9a7c0237047bdfec656caa5064806ade5fe14c4c9fa3478dbabf929e027", + "full_human_chars": 1127, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "check_robots_txt" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_042463", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 710 + ], + "match_source_rows": [ + 710 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I mistakenly deleted an important file named 'Q1_Report.pdf' to the Trash on my Mac and urgently need it restored to my Desktop, which is located at '/Users/username/Desktop'. Also, I have a folder named 'Old_Backups' on my external drive 'BackupDrive', where there are many temporary files that I no longer need. These files have an extension of '.tmp' and I wish to permanently delete them to free ", + "legacy_target": "restore_file_from_trash", + "legacy_target_description": "Restore a specific file from the trash to a specified location on the Mac.", + "legacy_target_description_chars": 74, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 710, + "source_record_id": "c906c7c3-eef7-4133-a37c-8f8b81c427aa", + "source_parent_id": "hermes_record:c906c7c3-eef7-4133-a37c-8f8b81c427aa", + "request_group_id": "hermes_request:743eb13054ae00f7494519512fa8dfefba4e2c5e1bad8801d8469d3d29459268", + "full_human_chars": 1040, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "restore_file_from_trash", + "delete_files_permanently" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_042482", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 592 + ], + "match_source_rows": [ + 592 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To streamline our web application deployment process, I require assistance with a sequence of functions to automate the transition from development to production. 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I've compiled a list of English descriptions that need to be translated while keeping their original tone and context for cultural relevance. 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Here's a brief exc", + "legacy_target": "manage_supplier_relationships", + "legacy_target_description": "Manages and analyzes supplier relationships to ensure a reliable and efficient supply chain.", + "legacy_target_description_chars": 92, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 460, + "source_record_id": "67987b94-f71f-4c99-a254-12a1be13d3fb", + "source_parent_id": "hermes_record:67987b94-f71f-4c99-a254-12a1be13d3fb", + "request_group_id": "hermes_request:9b65c954dda873cbe1d2355c486f10de8987c0d74a7e09c4127e81ea1dff72ec", + "full_human_chars": 1580, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "manage_supplier_relationships", + "optimize_inventory_levels", + "track_parts_in_supply_chain" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_044994", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 525 + ], + "match_source_rows": [ + 525 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm tasked with managing our electronics store's inventory system built on Appian, and I've noticed a discrepancy in the stock levels for a few key products. 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Once I have the current numbers, I need to amend the stock quantity for product ID 1002 specifically—I've confirmed we ", + "legacy_target": "retrieve_inventory_details", + "legacy_target_description": "Retrieves the inventory details for a list of product IDs.", + "legacy_target_description_chars": 58, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 525, + "source_record_id": "91c4cd77-280a-44da-97f2-677bb6737bf2", + "source_parent_id": "hermes_record:91c4cd77-280a-44da-97f2-677bb6737bf2", + "request_group_id": "hermes_request:5e40d86cf92248f2ed38b2ab0c480a4b7001839b29c75cd220f0435f7634e701", + "full_human_chars": 716, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "retrieve_inventory_details" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_044998", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 287 + ], + "match_source_rows": [ + 287 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Over the past six months, our trading team has been collecting extensive historical trade data for our algorithmic trading strategies. We have noticed areas for improvement and have decided to optimize our trading models' parameters. The historical trade data includes detailed daily records of stock prices, trade volumes, and our executed trade signals. Our strategies primarily rely on technical i", + "legacy_target": "optimize_moving_averages", + "legacy_target_description": "Optimizes the moving average windows based on historical trade data and a specified risk-reward profile.", + "legacy_target_description_chars": 104, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 287, + "source_record_id": "45b4d592-89cc-437a-8361-fd08b258481d", + "source_parent_id": "hermes_record:45b4d592-89cc-437a-8361-fd08b258481d", + "request_group_id": "hermes_request:c09f5a8dd76d234ab78fb9609690f19f57fcba5af21cb670c4bb34cb221fa7e7", + "full_human_chars": 2188, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "optimize_moving_averages", + "optimize_RSI_thresholds" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_044999", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 620 + ], + "match_source_rows": [ + 620 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating the scheduling for our rail transportation network and I need to optimize the schedule for efficiency. The objective is to reduce travel times, decrease delays, and schedule maintenance without interrupting service. Here are our datasets that need to be considered for the optimization:\n\nTrain Routes (train_routes.csv):\n```\nTrain_ID,Route_Name,Departure_Station,Arrival_Station\nT10", + "legacy_target": "optimize_train_schedule", + "legacy_target_description": "Optimizes the train schedule to reduce transit times, minimize delays, and respect maintenance windows, considering station capacities and passenger demand fore", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 620, + "source_record_id": "48074b33-cc5c-43fa-ac3c-19821f1a5cb2", + "source_parent_id": "hermes_record:48074b33-cc5c-43fa-ac3c-19821f1a5cb2", + "request_group_id": "hermes_request:cc0e2ce964956137157c13c805d7cdd1607c88ac79423dcc889020c35378c1b9", + "full_human_chars": 1234, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "optimize_train_schedule" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_045100", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 672 + ], + "match_source_rows": [ + 672 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to call certain functions to manage and address infection control in our hospital. 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Furthermore, we need a comprehensive report on infection trends between M", + "legacy_target": "record_infection_incident", + "legacy_target_description": "Records an incident of infection for a patient within the healthcare facility.", + "legacy_target_description_chars": 78, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 672, + "source_record_id": "6dcdb88f-4de8-4397-9749-ebdac4dea3ca", + "source_parent_id": "hermes_record:6dcdb88f-4de8-4397-9749-ebdac4dea3ca", + "request_group_id": "hermes_request:970cd728f8b8673b1a3c918709eb35c44d03b0590c380204c46f5422fb63f81d", + "full_human_chars": 1045, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "record_infection_incident", + "monitor_infection_control", + "generate_infection_trend_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_045106", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 619 + ], + "match_source_rows": [ + 619 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to utilize our CRM system to perform specific tasks aimed at enhancing customer service for our transportation services. These tasks include logging a phone call interaction with a customer, addressing a service change request made by another customer, and analyzing received feedback scores for insight into our service quality.\n\nHere are the details for logging the interaction:\n- Cust", + "legacy_target": "log_customer_interaction", + "legacy_target_description": "Logs a customer interaction in the CRM system, including type and details of the interaction.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 619, + "source_record_id": "09f03d49-e532-449c-8b3b-87dc8bc4e2a6", + "source_parent_id": "hermes_record:09f03d49-e532-449c-8b3b-87dc8bc4e2a6", + "request_group_id": "hermes_request:93182daa332e6af3c4a72457a21416c690b11fc5ac7c95c2f82480fe0cdeb663", + "full_human_chars": 1491, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "log_customer_interaction", + "handle_service_request", + "analyze_customer_feedback" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_045187", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 898 + ], + "match_source_rows": [ + 898 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently visited Springfield General Hospital and had some tests and consultations. I would like to fetch my health records using my patient ID SGH-45127. Specifically, I need to access my latest lab results, the current list of my medications, and the notes from my doctor's visit on March 25th, 2023. 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Specifically, I'd like to start by checking the live feed from the camera located at the front door to monitor any activity. The camera has a unique identifier, which I've already configured to be \"front_door.\" I'd prefer to view the live stream in high definition, so a 1080p qual", + "legacy_target": "get_camera_live_feed", + "legacy_target_description": "Retrieves the live feed from a specified security camera.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 0, + "source_record_id": "85f6c398-69c7-4df2-aed1-29d614a93a26", + "source_parent_id": "hermes_record:85f6c398-69c7-4df2-aed1-29d614a93a26", + "request_group_id": "hermes_request:0ff1b28130c53d024541ddf6777f80a30f00fdb22b13801bd0503c22a29e7a18", + "full_human_chars": 1670, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_camera_live_feed", + "record_camera_feed", + "get_recorded_feed" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_045477", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 683 + ], + "match_source_rows": [ + 683 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm responsible for managing the inventory of the 'NextGen Biosciences' research facility. We need to track the stock of essential items like PCR machines, reagents, and consumables to ensure smooth operation of our ongoing experiments. We have a JSON file that lists our current inventory status. The actions we want to perform include: updating the system when an item is used or restocked, receivi", + "legacy_target": "update_inventory", + "legacy_target_description": "Updates the inventory based on consumption or purchase of items.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 683, + "source_record_id": "c023c43f-d7cc-405d-a27a-492e2947b366", + "source_parent_id": "hermes_record:c023c43f-d7cc-405d-a27a-492e2947b366", + "request_group_id": "hermes_request:58bd0c455a1101d4b68a84920fd4cb480b757bf0ce0f30a5afff622f80d59fb4", + "full_human_chars": 1696, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "update_inventory", + "update_inventory", + "alert_low_stock", + "generate_inventory_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_045689", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 12 + ], + "match_source_rows": [ + 12 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to upgrade the security features of my smart home and ensure everything is properly set up. Specifically, I need to install a YRD256 Yale Assure Lock SL smart lock on my front door. The installation code for the smart lock is \"73829SL\". In addition, I want to configure Honeywell SiXPIR motion detectors in my living room to have better motion sensing accuracy for my home security; the ", + "legacy_target": "install_smart_lock", + "legacy_target_description": "Installs a new smart lock on a specified door using the provided model details and installation code.", + "legacy_target_description_chars": 101, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 12, + "source_record_id": "ec0e73f1-1f85-4963-b840-4a7e76b1c5b3", + "source_parent_id": "hermes_record:ec0e73f1-1f85-4963-b840-4a7e76b1c5b3", + "request_group_id": "hermes_request:dc85be521efca5356d8d04cb0988582834bd659192982911c0e9b45c56be5bbf", + "full_human_chars": 1055, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "install_smart_lock", + "configure_motion_detectors", + "activate_security_alarm" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_045812", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 49 + ], + "match_source_rows": [ + 49 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our marketing team has conducted three digital advertising campaigns identified as 'camp_001', 'camp_002', and 'camp_003'. 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Initialize a new Git ", + "legacy_target": "initialize_git_repository", + "legacy_target_description": "Initialize a new Git repository in the specified project path.", + "legacy_target_description_chars": 62, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 594, + "source_record_id": "a2bc2db1-128f-4b6c-9ae5-8f57751e9047", + "source_parent_id": "hermes_record:a2bc2db1-128f-4b6c-9ae5-8f57751e9047", + "request_group_id": "hermes_request:b7a8a411d59c9724eb4fe03d08c9f7ae9536c9bf529acd472f62a3c72b99e190", + "full_human_chars": 1313, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "initialize_git_repository", + "commit_all_files", + "push_to_remote" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048021", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 413 + ], + "match_source_rows": [ + 413 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage the database for our company's sales department, and I need to obtain the most recent sales figures for analysis. Specifically, I am looking for the sales data from the 'QuarterlySales' table for our current fiscal quarter. Here is the precise SQL query I need to execute on our database named 'SalesDB':\n\n```sql\nSELECT * FROM QuarterlySales WHERE Quarter = (SELECT QUARTER(CURRENT_DATE()) F", + "legacy_target": "get_current_quarter", + "legacy_target_description": "Retrieves the current quarter based on the system date.", + "legacy_target_description_chars": 55, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 413, + "source_record_id": "4833674a-8706-4acf-909e-398490182af0", + "source_parent_id": "hermes_record:4833674a-8706-4acf-909e-398490182af0", + "request_group_id": "hermes_request:0c4913255d847cf70003dfdf0d78a63d4c309247c67012a3d3d18d79014b73dd", + "full_human_chars": 625, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_current_quarter", + "execute_sql_query" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048123", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 23 + ], + "match_source_rows": [ + 23 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to secure a hotel room at the Grand Oak Hotel for an upcoming business stay. I plan to check in on April 25th and check out on April 30th. The reservation will be for one adult. My preference is a room that offers a king-size bed and boasts a view of the city skyline. I also would like to ensure my loyalty rewards are applied, so please use my loyalty account number, which is 94837261", + "legacy_target": "createReservation", + "legacy_target_description": "Creates a hotel room reservation with specified preferences and loyalty account information.", + "legacy_target_description_chars": 92, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 23, + "source_record_id": "b2165b46-1320-48de-a1f1-3202f99168d6", + "source_parent_id": "hermes_record:b2165b46-1320-48de-a1f1-3202f99168d6", + "request_group_id": "hermes_request:4f39eb171aece0f2567a194b4d204615c4fc12e554dcc4069abd19b7a57a099c", + "full_human_chars": 741, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "createReservation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048151", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 690 + ], + "match_source_rows": [ + 690 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Healthcare Services Revenue Cycle Manager at our clinic, I am tasked with optimizing several crucial aspects of our billing and claims processes. For instance, I need to automate the billing for patient John Doe, with patient ID JD789321, who had services on April 15, 2023. He received a general consultation (service code SVC1001) and a follow-up X-ray (service code SVC1002). I also need to", + "legacy_target": "automate_patient_billing", + "legacy_target_description": "Automates the billing process for a patient by generating invoices based on service codes and dates.", + "legacy_target_description_chars": 100, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 690, + "source_record_id": "ce28568a-26dc-4bcf-b9c8-8a1142f70cad", + "source_parent_id": "hermes_record:ce28568a-26dc-4bcf-b9c8-8a1142f70cad", + "request_group_id": "hermes_request:b4fa3dfcb71819a08dfc9649e0ef13884ef1e68e54247f489bbf6ed2897c8f1f", + "full_human_chars": 1499, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "automate_patient_billing", + "verify_coding_accuracy", + "track_claim_submissions", + "follow_up_unpaid_claims" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048194", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 886 + ], + "match_source_rows": [ + 886 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently enhancing our online retail platform to include product personalization features. We have a customizable smartphone case with a unique product ID 'P12345', which we're ready to offer with various customization options. I would like to request the API to be called to enable the following customization settings for this product:\n\n- Color choices: Black, White, and Blue\n- Engraving opt", + "legacy_target": "enable_product_customization", + "legacy_target_description": "Enables customization options for a specific product.", + "legacy_target_description_chars": 53, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 886, + "source_record_id": "aa4d9b09-b5d4-4596-8489-6fddc38ef990", + "source_parent_id": "hermes_record:aa4d9b09-b5d4-4596-8489-6fddc38ef990", + "request_group_id": "hermes_request:3c810a34d14a7a78d8e72d8100fa0ea1904c707dc40f6a69e9accfc71bb06dcb", + "full_human_chars": 806, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "enable_product_customization" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048283", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 235 + ], + "match_source_rows": [ + 235 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning a special weekend getaway in New York City and I'm on the lookout for the best local deals to enhance my experience. Specifically, I would like to dine at some fabulous Italian restaurants that offer great deals and coupons. Additionally, I'm interested in exploring the city's vibrant culture through live music events, and I would appreciate any deals related to them as well. My exact", + "legacy_target": "search_local_deals", + "legacy_target_description": "Search for local deals and coupons based on user's location and preferences.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 235, + "source_record_id": "d8d7e5a8-4e69-4f9b-ae6a-b611f18e9904", + "source_parent_id": "hermes_record:d8d7e5a8-4e69-4f9b-ae6a-b611f18e9904", + "request_group_id": "hermes_request:8db991aa9b1ebba77b368ef7ee0d7aba530fd1c71531a92e44ba4e051dc2705a", + "full_human_chars": 656, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "search_local_deals", + "search_local_deals" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048302", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 909 + ], + "match_source_rows": [ + 909 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am finalizing a promotional video for a real estate company and need to polish some drone footage we've captured. The footage is a bit shaky, so the first step is to stabilize it to ensure a professional look. Afterwards, I need to extract frames at certain moments to highlight property features. The specific timestamps for extraction are 2 minutes, 5 minutes, and 7 minutes and 30 seconds into t", + "legacy_target": "stabilizeVideo", + "legacy_target_description": "Stabilizes the input video to remove shakiness and outputs the stabilized video to the specified path.", + "legacy_target_description_chars": 102, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 909, + "source_record_id": "3d620774-cdd8-43dc-91f5-62703ba6a00c", + "source_parent_id": "hermes_record:3d620774-cdd8-43dc-91f5-62703ba6a00c", + "request_group_id": "hermes_request:cd4e422f9e8474f0eea755f9815e008c4173eda5937fc8a0ee725a3f331d9284", + "full_human_chars": 770, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "stabilizeVideo", + "extractFrames" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048341", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1285, + 1306, + 1310, + 1343, + 1357, + 1424, + 1583, + 1614, + 1683, + 1745, + 1765, + 1766, + 1837 + ], + "match_source_rows": [ + 1285 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage it again? \n#### ACTIVITIES \n119 COLLECTED WORKS \n**1.** For more about the relationships between revision, writing experience, and writing processes, see Alice Horning and Anne Becker's _Revision: History,_ _Theory, and Practice_ (Parlor Press) and Linda Adler-Kassner and Elizabeth Wardle's _Naming What We Know: Thres", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for advice or suggestions on how to approach a specific scenario found in the document.", + "legacy_target_description_chars": 125, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1285, + "source_record_id": "67c3c4f6-02a4-4d6c-a65f-fb3eb49ece80", + "source_parent_id": "hermes_record:67c3c4f6-02a4-4d6c-a65f-fb3eb49ece80", + "request_group_id": "hermes_request:ffd1c7f5f9e3fdbfbfe287d157f953f32a85707334a5fa9c36a424158c5cafc1", + "full_human_chars": 14278, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048382", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 959 + ], + "match_source_rows": [ + 959 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am operating a Shopify store specializing in vintage clothing and require assistance with product management and sales tracking. I would like to add a new item to our collection: a Vintage Leather Jacket. The product details are as follows: the title is \"Vintage Leather Jacket\", it has an HTML description highlighting \"Great quality leather jacket\", the vendor name is \"Vintage Boutique\", and the", + "legacy_target": "add_product_to_shopify", + "legacy_target_description": "Add a new product to the Shopify store with detailed information.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 959, + "source_record_id": "f52d76bc-b14d-4105-ab67-2ea1eb033503", + "source_parent_id": "hermes_record:f52d76bc-b14d-4105-ab67-2ea1eb033503", + "request_group_id": "hermes_request:77e86398a9a694e358c307a03c80c95957d18ea82d04474e1eaf67df52c7f7c9", + "full_human_chars": 1198, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "add_product_to_shopify", + "update_inventory_level", + "generate_sales_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048398", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 974 + ], + "match_source_rows": [ + 974 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the manager of Meadowlark Pharmacy, I require assistance in managing our medication inventory effectively. Our inventory system is digitized, and I have a JSON formatted database file named \"medication_inventory.json\" containing medication IDs, names, current stock levels, and minimum stock thresholds. I'd like to call three functions with specific tasks:\n\n1. Monitor the current stock levels ag", + "legacy_target": "monitor_medication_stock_levels", + "legacy_target_description": "Monitors the current stock levels of medications against minimum required quantities and provides a report.", + "legacy_target_description_chars": 107, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 974, + "source_record_id": "8d7466fa-f83d-4721-a05e-d8275ffb1679", + "source_parent_id": "hermes_record:8d7466fa-f83d-4721-a05e-d8275ffb1679", + "request_group_id": "hermes_request:74d78e0b3a71c64e655a07ee7fc58c15218b9a25f7430e283d7fd1c043729b62", + "full_human_chars": 1089, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "monitor_medication_stock_levels", + "generate_medication_reorder_alerts", + "update_medication_inventory", + "update_medication_inventory" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048468", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 975 + ], + "match_source_rows": [ + 975 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently experiencing seasonal allergies and I'm looking to purchase an antihistamine that is effective for sneezing and runny noses. Additionally, I need a pain reliever for my occasional headaches, preferably acetaminophen. I would appreciate assistance in searching for these products by name and symptom, viewing their details, adding them to a cart, and proceeding to checkout. For the anti", + "legacy_target": "search_products_by_name_or_symptoms", + "legacy_target_description": "Search for products based on their names or symptoms they address.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 975, + "source_record_id": "00695312-0d0b-4f2b-9976-892c92618e49", + "source_parent_id": "hermes_record:00695312-0d0b-4f2b-9976-892c92618e49", + "request_group_id": "hermes_request:b9fed719152d27bd4fd8ff800dd8f2feb622263d5a758ffe4aca8d2d3a5e716f", + "full_human_chars": 1898, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "search_products_by_name_or_symptoms" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048703", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1101, + 1118, + 1136, + 1137, + 1170, + 1269, + 1406, + 1440, + 1455, + 1472, + 1618, + 1761, + 1799 + ], + "match_source_rows": [ + 1101 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage audience to see how their _other_ latent beliefs (in this case, tacit acceptance of inhumane treatment of their country's less well-off) are not only wrong but immoral. That's a rather extreme example, but we can see how it applies to the roommate example as well. The roommates share a respect for the history that shapes the visi", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for clarification on an ambiguous or unclear statement in the document. These help resolve confusion.", + "legacy_target_description_chars": 139, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1101, + "source_record_id": "0a84b63a-e32d-4583-9fe7-3c45f516cc56", + "source_parent_id": "hermes_record:0a84b63a-e32d-4583-9fe7-3c45f516cc56", + "request_group_id": "hermes_request:9b8a1569666c421ea540a367c4943c298bee57141a484ec991708d5f902fe799", + "full_human_chars": 14829, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048721", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 768 + ], + "match_source_rows": [ + 768 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting an urban development study that requires a detailed analysis of several street scenes. The analysis should focus on traffic patterns, including vehicle spacing and flow, pedestrian behavior such as adherence to crosswalks, and specific details about the storefronts present in the images. 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Secondly, I require instant notifications to be sent out whenever there'", + "legacy_target": "configure_task_deadline_alerts", + "legacy_target_description": "Sets up automated notifications for tasks nearing their deadlines.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 169, + "source_record_id": "aace50b3-1f4a-4e22-9dce-1de0d0ea863f", + "source_parent_id": "hermes_record:aace50b3-1f4a-4e22-9dce-1de0d0ea863f", + "request_group_id": "hermes_request:3fa4f83e561b7c08a602964e0024a419089b3db4b9df2e421346bd142c4342e9", + "full_human_chars": 956, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "configure_task_deadline_alerts", + "configure_status_change_alerts" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_048926", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 368 + ], + "match_source_rows": [ + 368 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To ensure our network's security, I'd like to establish a robust defense system against potential cyber threats. Firstly, I require a firewall configuration that strictly denies all incoming traffic by default, specifically blocking any IP address attempting to access the subnet 192.168.1.0/24. Additionally, I need to deploy an intrusion detection system with high-level monitoring for network traf", + "legacy_target": "configure_firewall", + "legacy_target_description": "Configure firewall rules to control network traffic.", + "legacy_target_description_chars": 52, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 368, + "source_record_id": "6317b007-d61b-45ff-a9cd-cc3ddf289b6b", + "source_parent_id": "hermes_record:6317b007-d61b-45ff-a9cd-cc3ddf289b6b", + "request_group_id": "hermes_request:cfd9fe253f79e888b0c28b8f82769dfec90753bfbced8a7023314fe7fa62f7a1", + "full_human_chars": 1260, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "configure_firewall", + "setup_intrusion_detection", + "manage_data_encryption" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_048947", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 453 + ], + "match_source_rows": [ + 453 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Customer Relationship Manager at our automobile manufacturing company, I'm looking to perform a few critical updates in our CRM system for one of our valued customers, identified as CUST001. 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Ideally, the a", + "legacy_target": "search_music_organizer_apps", + "legacy_target_description": "Search for music organizer apps that allow categorization by genre, artist, album, and custom playlist creation.", + "legacy_target_description_chars": 112, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1048, + "source_record_id": "c6093d66-768d-49dd-99c4-29c8ff6653c9", + "source_parent_id": "hermes_record:c6093d66-768d-49dd-99c4-29c8ff6653c9", + "request_group_id": "hermes_request:38b3064717e1ee0b6fc5caf4d9d63c6ca0bf116fe81e73d17f5ebd47096787c3", + "full_human_chars": 722, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "search_music_organizer_apps" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_049153", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 989 + ], + "match_source_rows": [ + 989 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently sold several cases of our signature Pale Ale to a customer at our brewery store. 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Specifically, I need to conduct a thorough UI examination, identify any regressions, and validate site responsiveness on an iPhone 12 Pro Max using the Safari browser. The website in question is `https://www.companywebsite.com`. \n\nCould you initiate a remote testing session on an iPhon", + "legacy_target": "initiate_remote_testing_session", + "legacy_target_description": "Initiate a remote testing session for a specified device and browser.", + "legacy_target_description_chars": 69, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1047, + "source_record_id": "2e293a91-67f1-4cc3-aad5-fab08058c5ff", + "source_parent_id": "hermes_record:2e293a91-67f1-4cc3-aad5-fab08058c5ff", + "request_group_id": "hermes_request:31940b3df042897f40949bc130f51024e7d6509f03fb5ffe30cd2c837fdc01ad", + "full_human_chars": 689, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "initiate_remote_testing_session" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_049231", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 393 + ], + "match_source_rows": [ + 393 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently analyzing a dataset of GNSS displacement measurements to assess the risk of landslides and need to apply different filtering methods to reduce data scatter and enhance the detection of trends. 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Addition", + "legacy_target": "simple_moving_average", + "legacy_target_description": "Apply a simple moving average filter to reduce scatter in displacement measurements.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 393, + "source_record_id": "3cb03d10-1f9a-4ce5-86f8-5c146c80d51f", + "source_parent_id": "hermes_record:3cb03d10-1f9a-4ce5-86f8-5c146c80d51f", + "request_group_id": "hermes_request:fca2cefc9ce81680bc068e485c27572ad0e71a0ff4afef0ea663be0889407f19", + "full_human_chars": 1521, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "simple_moving_average", + "gaussian_weighted_moving_average", + "savitzky_golay_filter" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_049292", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 411 + ], + "match_source_rows": [ + 411 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our e-commerce platform's database has been experiencing performance lags, particularly when processing complex SQL queries with multiple joins and subqueries. These queries are crucial for generating customer order reports by region and managing our inventory system. We have identified specific queries and tables that need optimization:\n\nFor the `customers` table, we are running the following que", + "legacy_target": "analyze_sql_queries", + "legacy_target_description": "Analyzes the provided SQL queries and gives recommendations for performance improvement.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 411, + "source_record_id": "bf3beac3-3cb8-4e66-9aab-ee9b18f781e9", + "source_parent_id": "hermes_record:bf3beac3-3cb8-4e66-9aab-ee9b18f781e9", + "request_group_id": "hermes_request:1d6d1e1dfa16f41f41f043c09551a19250b4128b466012e674f1a36735cdd2c1", + "full_human_chars": 1336, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "analyze_sql_queries", + "recommend_index_optimization", + "recommend_index_optimization", + "recommend_index_optimization" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_049850", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 254 + ], + "match_source_rows": [ + 254 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting a performance analysis on a specific Python script named `data_processor.py`, which is part of our data analysis toolset. 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We have observed some latency issues when dealing with very large datasets, and we suspect there are inefficien", + "legacy_target": "profileScriptExecution", + "legacy_target_description": "Profiles the execution of a Python script, reporting the execution time and memory usage.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 254, + "source_record_id": "e08ebce5-df7a-4ad8-a571-9d8b21f450ad", + "source_parent_id": "hermes_record:e08ebce5-df7a-4ad8-a571-9d8b21f450ad", + "request_group_id": "hermes_request:16c34af8aa03530dc8b7a524b5053f92c3ac402f581c28a6b807131649200240", + "full_human_chars": 1297, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "profileScriptExecution" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_049910", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 220 + ], + "match_source_rows": [ + 220 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am interested in obtaining a comprehensive analysis of social media usage trends for the year 2023. Specifically, I'd like to know the total number of users, daily active users, and a breakdown of demographics including age, gender, and location for popular platforms like Facebook, Instagram, Twitter, TikTok, Snapchat, LinkedIn, Pinterest, Reddit, and YouTube.\n\nPlease provide the following infor", + "legacy_target": "get_social_media_statistics", + "legacy_target_description": "Retrieve social media usage statistics for specified year, demographics, and platforms.", + "legacy_target_description_chars": 87, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 220, + "source_record_id": "7d0c27a3-2973-4b4d-a4fd-b47b82fda1a5", + "source_parent_id": "hermes_record:7d0c27a3-2973-4b4d-a4fd-b47b82fda1a5", + "request_group_id": "hermes_request:1824294b826070fb377fd27ebe6581d84c71340cd8e4e0a1b2bc9737cef30f45", + "full_human_chars": 1312, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 9, + "valid_top_level_call_names": [ + "get_social_media_statistics", + "get_social_media_statistics", + "get_social_media_statistics", + "get_social_media_statistics", + "get_social_media_statistics", + "get_social_media_statistics", + "get_social_media_statistics", + "get_social_media_statistics", + "get_social_media_statistics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_049931", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 402 + ], + "match_source_rows": [ + 402 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a dataset containing our company's historical sales records from January 2005 to December 2022. These records are pivotal for financial analysis and regulatory compliance owing to their sensitive nature. The data is meticulously organized in a CSV file named 'sales_records.csv' with detailed columns for 'Date', 'Product_ID', 'Quantity_Sold', 'Unit_Price', and 'Total_Revenue'. To ensure secu", + "legacy_target": "archive_data_to_cloud", + "legacy_target_description": "Archives a specified file to cloud-based storage with options for storage tier, encryption, and indexing.", + "legacy_target_description_chars": 105, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 402, + "source_record_id": "17f722b8-16ef-41aa-bf7f-2a35875bea05", + "source_parent_id": "hermes_record:17f722b8-16ef-41aa-bf7f-2a35875bea05", + "request_group_id": "hermes_request:a0f9150c1f9e28a1e96cd6285b877c6b8903f341aae70e0681962091b2f5ebe5", + "full_human_chars": 1038, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "archive_data_to_cloud" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_049971", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 608 + ], + "match_source_rows": [ + 608 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing the inventory for our manufacturing plant and need to carry out a couple of important tasks. Firstly, I need to verify the existing stock levels for a few specific items. The item IDs for these products are 5001, 5002, and 5005. I would also like to update the inventory for one of our products. For item ID 5002, we've received a new shipment and need to increase the stock l", + "legacy_target": "get_inventory_levels", + "legacy_target_description": "Retrieves the current stock levels for specified item IDs.", + "legacy_target_description_chars": 58, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 608, + "source_record_id": "4bcbcb42-16f0-4185-83b8-62ca1b5266da", + "source_parent_id": "hermes_record:4bcbcb42-16f0-4185-83b8-62ca1b5266da", + "request_group_id": "hermes_request:af7035ec2dc88224a4826633c3458a8cbff40427182fe4c5c2b949d1c44b9814", + "full_human_chars": 864, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_inventory_levels", + "update_inventory_level" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050093", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 217 + ], + "match_source_rows": [ + 217 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm interested in finding a nutrition tracker app that can assist me with my daily dietary planning. Specifically, I need an app that offers features such as detailed calorie tracking, macronutrient breakdown including proteins, carbohydrates, and fats, as well as the ability to analyze my meals to provide a comprehensive nutritional profile with vitamins and minerals. Additionally, I'd appreciate", + "legacy_target": "find_nutrition_tracker_app", + "legacy_target_description": "Finds a nutrition tracker app with specified features.", + "legacy_target_description_chars": 54, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 217, + "source_record_id": "f40ee003-1584-4acc-8b70-b740ef92de46", + "source_parent_id": "hermes_record:f40ee003-1584-4acc-8b70-b740ef92de46", + "request_group_id": "hermes_request:c13fc9a4942427ac257b87305ff2295cf8985e0517b574b88489bb83d8bccfde", + "full_human_chars": 525, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "find_nutrition_tracker_app" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050120", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 532 + ], + "match_source_rows": [ + 532 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing an Employee Onboarding App with Microsoft Power Apps for Acme Corp. We've recently hired John Smith, who will join the Legal Department as a Paralegal. His email is john.smith@acmecorp.com and he will start on May 15th, 2023. We need to register John in our system, assign him to the correct department, schedule an orientation session for him at our headquarters in Conference Room B", + "legacy_target": "register_new_employee", + "legacy_target_description": "Registers a new employee in the system with their personal and professional details.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 532, + "source_record_id": "77380e9a-53f9-4a0c-b48f-2a0ebf658d1e", + "source_parent_id": "hermes_record:77380e9a-53f9-4a0c-b48f-2a0ebf658d1e", + "request_group_id": "hermes_request:cff0515523ed69e68a8d8dec51fd1d4b02adbab72dcf9be6aff632eae040430b", + "full_human_chars": 1072, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "register_new_employee" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050271", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 998 + ], + "match_source_rows": [ + 998 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As a manager of a chain of pharmacies, I need to analyze our point-of-sale (POS) data to inform our restocking and marketing strategies. We have a comprehensive dataset of POS transactions from the last quarter (January 1 to March 31, 2023), with detailed records, including timestamps, medication names, quantities sold, and total sales. The dataset is stored in \"Q1_2023_Pharmacy_Sales.csv\". We als", + "legacy_target": "identify_frequently_purchased_medications", + "legacy_target_description": "Identifies the most frequently purchased medications from POS sales data over a specified time period.", + "legacy_target_description_chars": 102, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 998, + "source_record_id": "67eb9fe8-1d1b-4f05-9702-29701b422ca3", + "source_parent_id": "hermes_record:67eb9fe8-1d1b-4f05-9702-29701b422ca3", + "request_group_id": "hermes_request:39f5e34f7a6b17d0c50755898fd728140682a7b6ecedb97057c6230e29b1ad95", + "full_human_chars": 1581, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "identify_frequently_purchased_medications", + "track_sales_trends_by_category", + "assess_campaign_impact_on_sales" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050436", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 908 + ], + "match_source_rows": [ + 908 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning an astronomy event in New York City and need detailed information on space weather conditions to anticipate any potential disruptions to the night sky visibility. Specifically, I require up-to-date data on current geomagnetic storm conditions and solar wind parameters at the coordinates 40.7128° N, 74.0060° W. In addition, it is crucial to be informed about any solar activity, such as", + "legacy_target": "getCurrentGeomagneticStormConditions", + "legacy_target_description": "Retrieve the current geomagnetic storm conditions for a specified location.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 908, + "source_record_id": "419d62ec-b6e9-4dcc-8052-77f21fb39c6d", + "source_parent_id": "hermes_record:419d62ec-b6e9-4dcc-8052-77f21fb39c6d", + "request_group_id": "hermes_request:40f3c22361c8237792dbbd8220e19bd33ed251c3935c14fe534607f92448e8dd", + "full_human_chars": 978, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "getCurrentGeomagneticStormConditions", + "getSolarWindParameters", + "getSolarActivityForecast" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050486", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 400 + ], + "match_source_rows": [ + 400 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a dataset, `dataset_normalized.csv`, which contains several features for a machine learning project. However, this data encompasses features with heterogeneous scales and units, which may hinder the predictive model's performance. To ensure that each feature has an equal impact on the model, the features need to be normalized to the same scale. I would like to perform Min-Max normalization ", + "legacy_target": "min_max_normalize", + "legacy_target_description": "Apply Min-Max normalization to scale features in the dataset to a specified range.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 400, + "source_record_id": "f9b15d86-fb69-4f05-a65e-18f2f7b5369d", + "source_parent_id": "hermes_record:f9b15d86-fb69-4f05-a65e-18f2f7b5369d", + "request_group_id": "hermes_request:a63c15dcfe69b8fc61192f053ba4c0c96e6833eabf42390bafc560b0275d9e24", + "full_human_chars": 1150, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "min_max_normalize" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050548", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 479 + ], + "match_source_rows": [ + 479 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working with a dataset of consulting reports that detail various discussions around market strategies, mergers, acquisitions, and financial operations of corporate entities. The dataset is in JSON format and consists of intricate business terminologies and references to specific corporate entities that are crucial for my analysis. I require assistance in extracting these terms and e", + "legacy_target": "extract_entities_from_consulting_reports", + "legacy_target_description": "Extracts and categorizes business terms and corporate entities from consulting reports presented in a JSON file and structures the output according to a JSON Sc", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 479, + "source_record_id": "fe0d6c1e-511d-4c0b-a8d8-6fcb65a01eb7", + "source_parent_id": "hermes_record:fe0d6c1e-511d-4c0b-a8d8-6fcb65a01eb7", + "request_group_id": "hermes_request:4226f7801fdb0bcc9636242039a966e16c437aeff46de79e073c4e503dcf1a79", + "full_human_chars": 2080, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_entities_from_consulting_reports" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050558", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1010 + ], + "match_source_rows": [ + 1010 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to conduct a detailed analysis of a real estate investment property I'm interested in. Specifically, could you calculate the cash flow projections for the next 10 years, determine the cap rate, and assess the annual ROI over this 10-year period?\n\nHere are the details for the property and financing:\n- Purchase price: $1,200,000\n- Expected monthly rental income: $15,000\n- Monthly operat", + "legacy_target": "calculate_cash_flow_projections", + "legacy_target_description": "Calculates the projected cash flow for a real estate investment property over a specified period.", + "legacy_target_description_chars": 97, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1010, + "source_record_id": "5e13e49d-e179-4c2f-88b9-d3baa477d9d2", + "source_parent_id": "hermes_record:5e13e49d-e179-4c2f-88b9-d3baa477d9d2", + "request_group_id": "hermes_request:b085db1e9e04bd002fabe492f968b6156c494e5b27bb7efc93190666b317422a", + "full_human_chars": 1007, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_cash_flow_projections", + "calculate_cap_rate", + "assess_return_on_investment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050647", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 161 + ], + "match_source_rows": [ + 161 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have collected feedback in English from a mobile app that I need to translate into French, Spanish, and Japanese for international partners. The text includes specific user comments such as \"The new update has significantly improved user experience with its intuitive interface design and faster performance on various devices.\" I seek assistance in translating this text to maintain accuracy and p", + "legacy_target": "translate_text", + "legacy_target_description": "Translate text from a source language to a target language using Hugging Face's translation models.", + "legacy_target_description_chars": 99, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 161, + "source_record_id": "86b75e1f-30e1-4225-8a0a-89f2957e30eb", + "source_parent_id": "hermes_record:86b75e1f-30e1-4225-8a0a-89f2957e30eb", + "request_group_id": "hermes_request:ea2f60864dcc4ab7fd2cc33d882e6f269ac56564b18362ffa709a8f139894fcc", + "full_human_chars": 1162, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "translate_text", + "translate_text", + "translate_text" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_050691", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 91 + ], + "match_source_rows": [ + 91 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our recycling plant requires an overhaul of our metals recycling management system to optimize efficiency. We have accrued comprehensive data, including the types of metals collected, the volume of each, and timestamps of collections. For the sorting process, we possess detailed records of sorting durations per metal type and accuracy rates. 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Moreover, our client with the identifier \"CLIENT67890Y\" should be updated about the progress of their ", + "legacy_target": "retrieve_order_status", + "legacy_target_description": "Retrieves the current status of a specific order using its unique identifier.", + "legacy_target_description_chars": 77, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 681, + "source_record_id": "e4534622-0a63-4601-979d-7dfe96444498", + "source_parent_id": "hermes_record:e4534622-0a63-4601-979d-7dfe96444498", + "request_group_id": "hermes_request:89ef1ead67a9af64cb47aaa9047d58b8d45e6af6bf5ff70f670b14e54581d62d", + "full_human_chars": 1152, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "retrieve_order_status", + "update_order_details", + "notify_client_order_progress", + "generate_tracking_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052191", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 442 + ], + "match_source_rows": [ + 442 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As Operations Manager for our automated warehouse facility, today's activity involves a sequence of critical tasks to optimize inventory management and maintain our robotics system's efficiency. \n\nFirstly, I request dispatching Robot Unit RBT-340 for an item retrieval operation. 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This item is pivotal for our u", + "legacy_target": "dispatch_robot", + "legacy_target_description": "Dispatches a robot to a specified location to perform a task such as retrieving or placing items.", + "legacy_target_description_chars": 97, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 442, + "source_record_id": "900c1d30-44bc-43b7-979e-2a05fd141f50", + "source_parent_id": "hermes_record:900c1d30-44bc-43b7-979e-2a05fd141f50", + "request_group_id": "hermes_request:78169ad07bdc6b74be2ec48647d35592749b2d87857bea4c6576bff741115e7f", + "full_human_chars": 1376, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "dispatch_robot", + "schedule_maintenance", + "generate_inventory_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052252", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 223 + ], + "match_source_rows": [ + 223 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm interested in keeping up with the latest advancements and trends in technology gadgets. I’m particularly looking for mobile applications that can curate news articles related to tech gadgets from reputable sources and also allow me to compare prices for specific gadgets across various online retailers. Specifically, I want the application to gather news from sources like The Verge, Engadget, T", + "legacy_target": "find_tech_gadget_apps", + "legacy_target_description": "Find apps that provide news curation and price comparison features for tech gadgets on a mobile platform.", + "legacy_target_description_chars": 105, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 223, + "source_record_id": "86f631de-4cc5-4618-9b33-34426a422868", + "source_parent_id": "hermes_record:86f631de-4cc5-4618-9b33-34426a422868", + "request_group_id": "hermes_request:e59c9d1b6c89518b18bbfe83bf028f227bc1c5d515edad5504856304e4066d89", + "full_human_chars": 1135, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "find_tech_gadget_apps", + "get_gadget_news", + "compare_gadget_prices" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052279", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1112, + 1156, + 1376, + 1417, + 1486, + 1545, + 1578, + 1582, + 1591, + 1697, + 1726, + 1804, + 1805 + ], + "match_source_rows": [ + 1112 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 4 2 4 - 1 - 7 4 \n \nC31+^ Find the null space of the matrix B, N (B). \n B = \n \n - 6 4 - 36 6 2 - 1 10 - 1 - 3 2 - 18 3 \n \nM45 Without doing any computations, and without examining any solutions, say as much as possible about the form of the solution set for corresponding homogeneous system of equations of each archetype tha", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that require making logical inferences based on the information in the document. These test understanding.", + "legacy_target_description_chars": 135, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1112, + "source_record_id": "181a4420-1d5a-4304-bbc5-a5c297766807", + "source_parent_id": "hermes_record:181a4420-1d5a-4304-bbc5-a5c297766807", + "request_group_id": "hermes_request:4909fe5d033714238ed3af307e7fd5886c93bf3a21ec7e5b9f4a6c3cd31ea3c7", + "full_human_chars": 10906, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052287", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 212 + ], + "match_source_rows": [ + 212 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm looking to streamline my financial management and need a budgeting app that can offer comprehensive assistance. The app should be capable of syncing with my bank and credit card accounts for real-time tracking of my financial transactions. I need it to assist me in tracking my spending across various categories and effectively manage my bills. It's also essential that the app supports the sett", + "legacy_target": "find_budgeting_app", + "legacy_target_description": "Find a budgeting app based on user preferences such as account syncing, spending tracking, bill management, financial goal setting, and alert notifications.", + "legacy_target_description_chars": 156, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 212, + "source_record_id": "b81cbe2b-5cf3-4a6f-903c-44e0218d2673", + "source_parent_id": "hermes_record:b81cbe2b-5cf3-4a6f-903c-44e0218d2673", + "request_group_id": "hermes_request:daf73be24a2619195fadb0c8a144886e03c86f72c3e91eafd0f7e6978b1d7991", + "full_human_chars": 865, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "find_budgeting_app" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052347", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 570 + ], + "match_source_rows": [ + 570 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working with our company's sales data from the first quarter, which is stored in a CSV file located at `https://company-datasets.com/q1-sales.csv`. The CSV file includes essential information such as the date of sale, product ID, product name, quantity sold, and the sales amount in USD. For integration with our legacy system, I need to convert this data into XML format, with \"SalesR", + "legacy_target": "convertCsvToXml", + "legacy_target_description": "Converts a CSV file into an XML file with specified root and record elements.", + "legacy_target_description_chars": 77, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 570, + "source_record_id": "8ca0ef0d-dd57-40bb-a20d-46046b10e687", + "source_parent_id": "hermes_record:8ca0ef0d-dd57-40bb-a20d-46046b10e687", + "request_group_id": "hermes_request:80907638d92b07ac91e1cc6bcd6ec87dde11370d37c0777b6c428927c837bd60", + "full_human_chars": 1438, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "convertCsvToXml" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052430", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 693 + ], + "match_source_rows": [ + 693 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the lab manager for Omega Biotech, I need to maintain our inventory for lab equipment meticulously to ensure uninterrupted research activities. Our lab uses an inventory database \"lab_equipment_inventory.json\" for tracking. It lists equipment IDs, names, current stock, and minimum required stock levels. I have three specific tasks to manage the inventory:\n\n1. Monitor the current stock levels ag", + "legacy_target": "track_equipment_stock_levels", + "legacy_target_description": "Monitors the current stock levels of lab equipment against minimum required quantities and provides a report.", + "legacy_target_description_chars": 109, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 693, + "source_record_id": "bdbb5f86-542b-4ac0-947d-61a90673e53e", + "source_parent_id": "hermes_record:bdbb5f86-542b-4ac0-947d-61a90673e53e", + "request_group_id": "hermes_request:84365fd4bced8c29a26e15e00a8467e6eed417910340c973d5f6893524aca219", + "full_human_chars": 1184, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "track_equipment_stock_levels", + "update_equipment_inventory", + "update_equipment_inventory", + "generate_equipment_reorder_alerts" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052441", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 2 + ], + "match_source_rows": [ + 2 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently installed a smart thermostat model with ID thermo123, and I plan to optimize energy usage in my home according to a specific weekly schedule. Starting from tomorrow, on weekdays I want the temperature to be set at 68 degrees Fahrenheit at 7:00 in the morning to wake up to a warm house, and then automatically drop to 62 degrees at 23:00 for an energy-efficient and cooler environment duri", + "legacy_target": "set_thermostat_schedule", + "legacy_target_description": "Sets a temperature schedule for a smart thermostat.", + "legacy_target_description_chars": 51, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 2, + "source_record_id": "14657d01-d6d1-46df-8eb1-7267ba820683", + "source_parent_id": "hermes_record:14657d01-d6d1-46df-8eb1-7267ba820683", + "request_group_id": "hermes_request:eae371c24f07ca597965dd7355e79775be13155752ffac4daa33ce607823dd7c", + "full_human_chars": 757, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "set_thermostat_schedule" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052588", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 119 + ], + "match_source_rows": [ + 119 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Chief Technology Officer for our finance company, I'm interested in taking our operations to the next level by automating the execution of smart contracts on the Ethereum blockchain. We're particularly focused on a corporate bond that we offer. For this financial instrument, the smart contract has to autonomously execute actions at predefined intervals and conditions—without any human overs", + "legacy_target": "deploy_smart_contract", + "legacy_target_description": "Deploys a smart contract to the blockchain with the specified financial instrument details.", + "legacy_target_description_chars": 91, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 119, + "source_record_id": "645222ca-a20d-4d7f-8fb8-f04341fe7c4a", + "source_parent_id": "hermes_record:645222ca-a20d-4d7f-8fb8-f04341fe7c4a", + "request_group_id": "hermes_request:74a35963b33021723b47fb6eaf9234b104792af148087bfe1eed9d4056ef023f", + "full_human_chars": 823, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "deploy_smart_contract", + "execute_contract_automation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052623", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1215, + 1325, + 1354, + 1490, + 1595, + 1613, + 1756, + 1788, + 1820, + 1851, + 1853, + 1859, + 1873 + ], + "match_source_rows": [ + 1215 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage # A First Course in Linear Algebra \n## Robert A. Beezer \n## University of Puget Sound \n## Version 3.40 \n## Congruent Press \nRobert A. Beezer is a Professor of Mathematics at the University of Puget Sound, where he has been on the faculty since 1984. He received a B.S. in Mathematics (with an Emphasis in Computer Science) from the", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask how to diagnose or solve a problem outlined in the document. These assesses ability to troubleshoot.", + "legacy_target_description_chars": 138, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1215, + "source_record_id": "e68a4eb3-9e56-4cab-897e-80be2d5c63a8", + "source_parent_id": "hermes_record:e68a4eb3-9e56-4cab-897e-80be2d5c63a8", + "request_group_id": "hermes_request:2aa21c76867ee8c00ebe952d265ca1312b4a92ba53029c62222af27fe4925f0d", + "full_human_chars": 15710, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052626", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 737 + ], + "match_source_rows": [ + 737 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm working on a project and need to organize my text documents on my Windows computer. Specifically, I need to find all .txt files within the \"Documents\" directory, including any subdirectories. These text files, such as 'ProjectProposal.txt' and 'MeetingNotes.txt,' should be located and I would also like to obtain their file sizes and the dates they were created. The 'ProjectProposal.txt' file w", + "legacy_target": "search_files_by_extension", + "legacy_target_description": "Search for files with a specific extension within a directory and optionally in subdirectories.", + "legacy_target_description_chars": 95, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 737, + "source_record_id": "bec97596-b004-467a-bcf4-1b8b7efff589", + "source_parent_id": "hermes_record:bec97596-b004-467a-bcf4-1b8b7efff589", + "request_group_id": "hermes_request:c1521dd84496173694b141e320dc6bd3ecdec3ecdb57d2d1067a01d2312e013e", + "full_human_chars": 876, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "search_files_by_extension" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052734", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 345 + ], + "match_source_rows": [ + 345 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our electric utility company is currently updating our grid automation system and we require specific functionalities to ensure the stability and efficiency of our grid. We have accumulated real-time demand data and grid topology that need to be processed. To accomplish this, we first need to manage load balancing using the following JSON file: `/path/to/load_data.json` which contains historical l", + "legacy_target": "manage_load_balancing", + "legacy_target_description": "Balances the load across the electric grid to prevent overloading and ensure stable supply.", + "legacy_target_description_chars": 91, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 345, + "source_record_id": "b5328f4e-6a03-451d-98e8-53f9bc1fa136", + "source_parent_id": "hermes_record:b5328f4e-6a03-451d-98e8-53f9bc1fa136", + "request_group_id": "hermes_request:d60332811321d758ac3584c05eb968baf0f36a3cb13461fe11b7e4d9dae970ff", + "full_human_chars": 1088, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "manage_load_balancing", + "detect_and_respond_to_outages", + "optimize_energy_distribution" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052785", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 874 + ], + "match_source_rows": [ + 874 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am analyzing team performances in the NBA for the 2022-2023 season for an upcoming sports analytics report. Specifically, I'm looking at the Los Angeles Lakers and Miami Heat. I need to collect detailed team statistics for both teams, including each team's overall win-loss record, average points scored per game, and information on their top five scorers, which entails the players' names and thei", + "legacy_target": "get_team_statistics", + "legacy_target_description": "Retrieve statistics for a specified basketball team and season.", + "legacy_target_description_chars": 63, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 874, + "source_record_id": "a5d7d3dd-71e8-4cf4-ac92-22818def799e", + "source_parent_id": "hermes_record:a5d7d3dd-71e8-4cf4-ac92-22818def799e", + "request_group_id": "hermes_request:5b631d9dac85a43ccbefc6a4206f6f0b6aaa37db16331de927469da1a75c9df1", + "full_human_chars": 1041, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_team_statistics", + "get_team_statistics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052847", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1195, + 1199, + 1246, + 1308, + 1317, + 1431, + 1506, + 1678, + 1687, + 1772, + 1798, + 1864, + 1889 + ], + "match_source_rows": [ + 1195 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage is an argument about the effects of global warming rhetoric, not global warming itself. It would break the standpoint rule to argue that the writer/ speaker does not believe in global warming. This is not the issue at hand. Similar to #3, this rule assures that the evidence you use must actually relate to your standpoint. Let's s", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for clarification on an ambiguous or unclear statement in the document. These help resolve confusion.", + "legacy_target_description_chars": 139, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1195, + "source_record_id": "50613121-ffa0-4bde-97e7-be5391e11077", + "source_parent_id": "hermes_record:50613121-ffa0-4bde-97e7-be5391e11077", + "request_group_id": "hermes_request:09c7e89c7cd74b8b1510dbc7f1240735572a8a7d0153092c585b2332e32c649c", + "full_human_chars": 13462, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_052927", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 63 + ], + "match_source_rows": [ + 63 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have recently purchased a smart speaker, and I would like to integrate it into my home entertainment system. The speaker's device ID is \"SPK12345X\", and my home entertainment system has an ID of \"SYS67890H\". I prefer using a Wi-Fi connection for this setup. Additionally, I have personal preferences that I'd like to configure: my default volume is 65, I enjoy watching Science Fiction and Comedy g", + "legacy_target": "connect_smart_device", + "legacy_target_description": "Connects a smart device to the home entertainment system using a specified protocol.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 63, + "source_record_id": "722af2c1-7445-4ca4-b5fc-9266bccc3098", + "source_parent_id": "hermes_record:722af2c1-7445-4ca4-b5fc-9266bccc3098", + "request_group_id": "hermes_request:62f38fc13c21b093e6eaae05cd504ae9807de4d4c7596d763e2d87ec56e571dd", + "full_human_chars": 1432, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "connect_smart_device", + "manage_user_preferences", + "control_streaming_content" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053010", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 923 + ], + "match_source_rows": [ + 923 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I require assistance with managing our inventory system. We have an inventory table named 'Inventory' that includes records of items with their respective expiry dates. To maintain the accuracy of our inventory data and adhere to our data retention policy, I need to delete all records in the 'Inventory' table where the 'expiry_date' has already passed as of '2022-01-15'. Based on our records, for ", + "legacy_target": "deleteExpiredRecords", + "legacy_target_description": "Deletes records from the specified table where the 'expiry_date' is in the past.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 923, + "source_record_id": "fb998249-86d4-465b-a1d7-b9a34a39bce3", + "source_parent_id": "hermes_record:fb998249-86d4-465b-a1d7-b9a34a39bce3", + "request_group_id": "hermes_request:949543e0bd644488e458751ec45d1732b2c3e97e8b4da53fc54a2b23ae982119", + "full_human_chars": 842, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "deleteExpiredRecords" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053065", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 972 + ], + "match_source_rows": [ + 972 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As we prepare for the upcoming audit, I require assistance with gathering and organizing all compliance-related documents stored on our company's Dropbox. Specifically, please extract all files with the .pdf extension located within the '/Compliance' folder. These files are essential for adhering to our compliance standards and will include various policy documents, compliance reports, and records", + "legacy_target": "extract_files_by_extension", + "legacy_target_description": "Extracts all files with a specified extension from a given Dropbox folder and organizes them into a new folder.", + "legacy_target_description_chars": 111, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 972, + "source_record_id": "c1714874-1e2a-45b5-bf5e-e55306046abc", + "source_parent_id": "hermes_record:c1714874-1e2a-45b5-bf5e-e55306046abc", + "request_group_id": "hermes_request:6f544a67601704560f3e370d47d6833ee1afd2ffbc5f21679d9d85e47a01411d", + "full_human_chars": 1391, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "extract_files_by_extension", + "generate_file_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053170", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 157 + ], + "match_source_rows": [ + 157 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm conducting a sentiment analysis of customer reviews from our latest product release to gauge public opinion. I have collated a list of individual reviews from various sources, including our website, emails, and social media platforms. To ensure accurate sentiment classification, I would like to use a pre-trained Hugging Face model. Specifically, I'm interested in classifying each review as pos", + "legacy_target": "batch_classify_sentiments", + "legacy_target_description": "Perform sentiment analysis on a batch of comments using a specified Hugging Face model.", + "legacy_target_description_chars": 87, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 157, + "source_record_id": "6ea64aa3-6aff-47f5-92d6-2e5a25d28191", + "source_parent_id": "hermes_record:6ea64aa3-6aff-47f5-92d6-2e5a25d28191", + "request_group_id": "hermes_request:56069ce4517d9b0a6d2f392116fd780b94a3108baa73cf7f213d1a2194e30deb", + "full_human_chars": 801, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "batch_classify_sentiments" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053176", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 70 + ], + "match_source_rows": [ + 70 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Recently, we've observed a decrease in the quality of our telecommunications service, with customers reporting dropped calls and fluctuating data speeds in sector 123, particularly over the last 24 hours. This sector includes a densely populated downtown area and a stadium, which could be contributing to network congestion during peak times. To address these issues, I would like to request two spe", + "legacy_target": "analyze_signal_strength", + "legacy_target_description": "Analyzes the signal strength of a specific sector over a given time frame and provides recommendations for improvement.", + "legacy_target_description_chars": 119, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 70, + "source_record_id": "3e153b9d-05dd-4186-9047-2803b610aeaa", + "source_parent_id": "hermes_record:3e153b9d-05dd-4186-9047-2803b610aeaa", + "request_group_id": "hermes_request:f1b286493cf5e525033ac86a4aa60bd056be19d8440f748f81cf9b96272ec365", + "full_human_chars": 1218, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_signal_strength", + "assess_network_traffic" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053210", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 93 + ], + "match_source_rows": [ + 93 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "We are implementing a comprehensive sustainable materials management strategy at our production facility. To achieve this, we require a thorough analysis of our materials usage, including a detailed assessment of potential waste reduction. We have acquired accurate data on our material inputs and waste outputs, categorized by type and quantity, which is documented in our 'materials_data.json' and ", + "legacy_target": "analyze_materials_usage", + "legacy_target_description": "Analyzes the current materials usage to identify patterns and potential areas for reducing waste.", + "legacy_target_description_chars": 97, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 93, + "source_record_id": "fbf00f8e-f06b-413e-a844-3304bf5856b6", + "source_parent_id": "hermes_record:fbf00f8e-f06b-413e-a844-3304bf5856b6", + "request_group_id": "hermes_request:92ba803b44720daa544ab42c16a5a685f5e0cfeb458a1cd797d010827e2aa6e6", + "full_human_chars": 1955, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_materials_usage", + "identify_improvement_areas" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053273", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 478 + ], + "match_source_rows": [ + 478 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting a thorough analysis of our transportation documents to better understand our logistics operations. The documents are freight invoices, cargo manifests, and shipping routes. I need to precisely extract entities such as carriers, cargo types, shipment sizes, and route details from these texts. The extracted data should be structured according to our 'supply_chain_entity_schema' JSON ", + "legacy_target": "extract_supply_chain_entities", + "legacy_target_description": "Extracts and categorizes supply chain entities and logistics terms from transportation texts and structures the output according to a given JSON Schema.", + "legacy_target_description_chars": 152, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 478, + "source_record_id": "5148f611-767d-40cd-9db0-ad6cd23bbb04", + "source_parent_id": "hermes_record:5148f611-767d-40cd-9db0-ad6cd23bbb04", + "request_group_id": "hermes_request:4bbfd00e42e1454a0442d8c587ccef27d241a4db4ec64d0344897b38eb568a06", + "full_human_chars": 1197, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_supply_chain_entities" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053321", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 315 + ], + "match_source_rows": [ + 315 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am an energy trader looking to gain insights into the electricity market for strategic decision-making. I require a detailed analysis of historical and real-time energy market data, focusing on electricity price fluctuations and demand patterns over the past three months. Additionally, I need to understand the impact of recent weather conditions on energy trading to better predict future market ", + "legacy_target": "fetch_energy_market_data", + "legacy_target_description": "Fetches historical and real-time energy market data such as price and demand for a specified time range and frequency.", + "legacy_target_description_chars": 118, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 315, + "source_record_id": "ad45366d-1f67-4b28-84ce-70566974fea5", + "source_parent_id": "hermes_record:ad45366d-1f67-4b28-84ce-70566974fea5", + "request_group_id": "hermes_request:15bfaeb4ba5d5ab7bd0e089c16c0a4312b6b1480106dc40f75a76d286c383445", + "full_human_chars": 2053, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetch_energy_market_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053547", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 987 + ], + "match_source_rows": [ + 987 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing a food retail store and aiming to upgrade our CRM capabilities. Specifically, we need to onboard a new customer, Alison O'Brien, whose details are as follows: full name 'Alison O'Brien', email 'alison.obrien@email.com', and phone number '555-342-2178'. Additionally, we want to track the purchase history of an existing customer, with the unique identifier 'CST123456', to und", + "legacy_target": "register_new_customer", + "legacy_target_description": "Registers a new customer in the CRM system with their personal details and initializes loyalty points.", + "legacy_target_description_chars": 102, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 987, + "source_record_id": "90d184f8-966b-42a0-82b6-b969935737e4", + "source_parent_id": "hermes_record:90d184f8-966b-42a0-82b6-b969935737e4", + "request_group_id": "hermes_request:c05415cf63b28dd209150ec90bf6146ca13beeef5c5afe92497f67e593ea7994", + "full_human_chars": 1652, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "register_new_customer", + "track_purchase_history", + "offer_personalized_promotions", + "gather_customer_feedback" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053657", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 44 + ], + "match_source_rows": [ + 44 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our smart city initiative, we have deployed environmental sensors labeled from 'sensor-001' to 'sensor-005' throughout the city. These sensors are equipped to measure key environmental indicators including temperature, humidity, and air quality. I need to register each sensor as a separate device with Google Cloud IoT Core within our project 'smart-city-123', specifically in the 'us-cen", + "legacy_target": "registerDeviceWithIoTCore", + "legacy_target_description": "Register a new device with Google Cloud IoT Core.", + "legacy_target_description_chars": 49, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 44, + "source_record_id": "ebc5a118-1748-43b2-86bf-95c0a47e036d", + "source_parent_id": "hermes_record:ebc5a118-1748-43b2-86bf-95c0a47e036d", + "request_group_id": "hermes_request:7d7dccb0928e7ab8056a4599440e9cfe553f663bd2742abbe3ba40ee63db3f16", + "full_human_chars": 1216, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 10, + "valid_top_level_call_names": [ + "registerDeviceWithIoTCore", + "registerDeviceWithIoTCore", + "registerDeviceWithIoTCore", + "registerDeviceWithIoTCore", + "registerDeviceWithIoTCore", + "configureDeviceMQTT", + "configureDeviceMQTT", + "configureDeviceMQTT", + "configureDeviceMQTT", + "configureDeviceMQTT" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053825", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 769 + ], + "match_source_rows": [ + 769 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm a primary care physician reviewing cases for three of my patients and require assistance in determining their potential medical conditions using the OpenAI API. I will provide their symptoms, medical history, and lab results for analysis. \n\nPatient A's symptoms include persistent cough, fever, and shortness of breath. Their medical background comprises a smoking habit and a recent trip to a re", + "legacy_target": "diagnose_medical_conditions", + "legacy_target_description": "Processes patient descriptions and suggests possible medical conditions using the OpenAI API.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 769, + "source_record_id": "ad343ff6-f32c-4780-b897-7befd139b899", + "source_parent_id": "hermes_record:ad343ff6-f32c-4780-b897-7befd139b899", + "request_group_id": "hermes_request:b2515c846fd7fc5b162d7adfb9b20ac13f9b740e31f40da2e87d61145ee6f98b", + "full_human_chars": 1080, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "diagnose_medical_conditions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_053844", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 366 + ], + "match_source_rows": [ + 366 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As a distributor for technology products, we are looking to upgrade our distribution systems to better manage our extensive inventory and ensure efficient delivery to our retail partners. We have gathered a comprehensive sales history in JSON format detailing our product movement and a list of products with their specifications to assist with demand forecasting. Our warehouse data includes product", + "legacy_target": "predict_product_demand", + "legacy_target_description": "Predicts future product demand based on historical sales data and current market trends.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 366, + "source_record_id": "41b45dc8-6ad6-4d48-a4c5-49025dc13c46", + "source_parent_id": "hermes_record:41b45dc8-6ad6-4d48-a4c5-49025dc13c46", + "request_group_id": "hermes_request:4a1d418f8bd49c78746206da6ea3ddd6ef2ecc77853a55fb85b9e76e27f39c7c", + "full_human_chars": 1622, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "predict_product_demand", + "optimize_warehouse_storage", + "select_best_logistic_routes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_054312", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 726 + ], + "match_source_rows": [ + 726 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need to update my colleague on the progress of our project and share the latest report with them. I want to send a text message directly to their desktop application to keep them in the loop. The message should include a summary of the recent milestones we've achieved and any upcoming deadlines. Along with the text update, I need to attach the latest project report in PDF format, which outlines ", + "legacy_target": "sendMessageWithAttachment", + "legacy_target_description": "Send a message with an attachment to a specified recipient through a desktop application.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 726, + "source_record_id": "cfdee00a-6fc9-4326-8552-bf87a37ed04b", + "source_parent_id": "hermes_record:cfdee00a-6fc9-4326-8552-bf87a37ed04b", + "request_group_id": "hermes_request:d0355d13d498f21d2776fc98eb20c5cca4c62814fe8148bb3f45b5c116a465d2", + "full_human_chars": 958, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "sendMessageWithAttachment" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_054331", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1025 + ], + "match_source_rows": [ + 1025 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I've organized a team meeting for our project and I need to notify everyone about it. The meeting is scheduled for 5 PM today. I have a contact group on my Android device specifically for our team members, but I'm unsure of the exact list ID. Could you help me retrieve the group ID for my team contact list and then send out a group message informing them about the meeting details? The content of t", + "legacy_target": "retrieve_contact_list", + "legacy_target_description": "Retrieve a list of contacts from an Android device.", + "legacy_target_description_chars": 51, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1025, + "source_record_id": "4f095e20-6dbe-4fa5-99f5-4d7073ec8eb7", + "source_parent_id": "hermes_record:4f095e20-6dbe-4fa5-99f5-4d7073ec8eb7", + "request_group_id": "hermes_request:d8d7d2845cedee017125d2b4a5ee1776950ed8486274e274aeaab601297df47d", + "full_human_chars": 579, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "retrieve_contact_list" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_054518", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 815 + ], + "match_source_rows": [ + 815 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently analyzing a portfolio of properties to manage reinsurance risks effectively. We have identified several properties that require assessment for potential losses due to catastrophic events such as earthquakes and hurricanes. Our list includes properties with the identifiers P123, P456, and P789. We aim to calculate the probable maximum loss for these properties at a 95% confidence lev", + "legacy_target": "calculate_probable_maximum_loss", + "legacy_target_description": "Calculates the probable maximum loss for a set of properties given a specific catastrophic event and confidence level.", + "legacy_target_description_chars": 118, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 815, + "source_record_id": "52b36a3f-c797-4e28-bf47-1476d5f0ce95", + "source_parent_id": "hermes_record:52b36a3f-c797-4e28-bf47-1476d5f0ce95", + "request_group_id": "hermes_request:34a65f2cc0bded9a8f0cbb7734f5e36ab63e275ebbc3ea59465551bd7f0a03ae", + "full_human_chars": 1340, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_probable_maximum_loss", + "assess_risk_distribution", + "suggest_optimal_reinsurance_strategy" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_054681", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 82 + ], + "match_source_rows": [ + 82 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am tasked with managing the expansion of our telecommunication network. To accurately plan for the necessary increase in capacity, I request assistance with forecasting network demand over the next 24 months. We have historical traffic data from the past three years that shows monthly active users, data usage, and peak traffic times. The expected growth rate is estimated at 25% per annum. Additi", + "legacy_target": "forecast_network_demand", + "legacy_target_description": "Forecasts future network demand based on historical traffic data and expected growth rate.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 82, + "source_record_id": "15ce3784-0902-4ccb-b0fb-30a329a10193", + "source_parent_id": "hermes_record:15ce3784-0902-4ccb-b0fb-30a329a10193", + "request_group_id": "hermes_request:0533e4149ffccbf830d9cd2599401c5f1f4b186b4cc9558da1d427cb3c19e6d1", + "full_human_chars": 1591, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "forecast_network_demand", + "simulate_network_load", + "optimize_resource_allocation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_054701", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 398 + ], + "match_source_rows": [ + 398 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a dataset of hyperspectral images used for agricultural land classification. This dataset suffers from a class imbalance problem that I need to address in order to improve the performance of my classification model. Specifically, there are certain classes of crops that are underrepresented. To tackle this, I plan to use three different resampling techniques: random undersampling to reduce t", + "legacy_target": "random_undersampling", + "legacy_target_description": "Perform random undersampling on a dataset to balance the classes.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 398, + "source_record_id": "e99e3aa8-c795-4a53-abee-a505ba6f204d", + "source_parent_id": "hermes_record:e99e3aa8-c795-4a53-abee-a505ba6f204d", + "request_group_id": "hermes_request:6601d05b3192eafc34715743711f3945dc318a51aa35fb50dbfe4803e80c72d2", + "full_human_chars": 1988, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "random_undersampling", + "random_oversampling", + "synthetic_data_generation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_054892", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1051 + ], + "match_source_rows": [ + 1051 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a credit card that I'd like to add to my iPhone Wallet for easy and secure payments at contactless pay terminals. I need to set up my iPhone to use my credit card for both in-store purchases and sending money to friends. The cardholder name is Jane Doe, the card number is 1234 5678 9101 1121, the expiry is 09/25, and the CVV is 321. Once the card is added, I'd like to make an immediate paym", + "legacy_target": "add_credit_card_to_wallet", + "legacy_target_description": "Add a credit card to the iPhone Wallet app for contactless payments.", + "legacy_target_description_chars": 68, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1051, + "source_record_id": "cad212a1-4360-44c5-ad6c-4ddd3072ba3e", + "source_parent_id": "hermes_record:cad212a1-4360-44c5-ad6c-4ddd3072ba3e", + "request_group_id": "hermes_request:f21e48a83f2eae624d9f2de95d98550db3fef9ad005e794699a5d72c4f516707", + "full_human_chars": 666, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "add_credit_card_to_wallet", + "initiate_contactless_payment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_054919", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1132, + 1139, + 1186, + 1360, + 1442, + 1482, + 1546, + 1654, + 1742, + 1773, + 1811, + 1867, + 1878 + ], + "match_source_rows": [ + 1132 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage a great variety of emotions to support their argument, from fear, passion, and joy to pity, kinship, and rage. By playing on the audience's feelings, writers can increase the impact of their arguments. There are two especially effective techniques for cultivating pathos: \n**1.** **_Make the audience aware of the issue's relevance", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of open-ended questions related to the document, that are potentially ambiguous.", + "legacy_target_description_chars": 96, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1132, + "source_record_id": "d579b07a-bc0a-4786-9206-aac8bdb3d7d2", + "source_parent_id": "hermes_record:d579b07a-bc0a-4786-9206-aac8bdb3d7d2", + "request_group_id": "hermes_request:b22bd46971f8c604e5c2b6b56e8be4353ed7ce153ac3f91d5d181ac2c4f1e4d6", + "full_human_chars": 14561, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_055047", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 668 + ], + "match_source_rows": [ + 668 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to enhance the user experience of my application by integrating a voice search feature. The functionality should be as follows: when a user says the phrase \"Start search,\" the app should activate the voice search mode. Subsequently, the app will listen for the user's spoken search query and convert the speech into text. 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Additionally, I want to evaluate potential future returns under three specific scenarios: an optimistic scena", + "legacy_target": "fetch_historical_stock_prices", + "legacy_target_description": "Fetches historical stock prices for a list of tickers between specified start and end dates.", + "legacy_target_description_chars": 92, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 37, + "source_record_id": "558711c2-6939-4e4b-8135-47daec637d13", + "source_parent_id": "hermes_record:558711c2-6939-4e4b-8135-47daec637d13", + "request_group_id": "hermes_request:bc1f9c6c4875001b521625c6d4fc63679795e5d58438f8202576cd33e51dc161", + "full_human_chars": 1205, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "fetch_historical_stock_prices", + "calculate_future_returns", + "assess_portfolio_risk" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_055997", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1079 + ], + "match_source_rows": [ + 1079 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently processing our company's monthly payroll and need to obtain specific payroll-related data for each of our employees from our HR system. 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This file includes details such as timestamps, transaction amounts, and customer IDs. I need to aggregate the total spending per customer to track spending patterns. The transactions are timestamped, but I require them in a 'YYYY-MM-DD' date format for ease of analysis. Could you ple", + "legacy_target": "aggregate_customer_spending", + "legacy_target_description": "Aggregate the total spending per customer from transaction data.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 401, + "source_record_id": "e8758d80-2382-4139-b96d-eacbb103f6ba", + "source_parent_id": "hermes_record:e8758d80-2382-4139-b96d-eacbb103f6ba", + "request_group_id": "hermes_request:8483e1d061f8ead7fb1ef75b29d5c59661180a02883ce641538c5c87faed6535", + "full_human_chars": 1380, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "aggregate_customer_spending", + "convert_timestamps" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_056315", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1068 + ], + "match_source_rows": [ + 1068 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have been working on improving our AI model for the project 'AI-Model', and as part of the update, I have made several key modifications. Specifically, the 'model.py' file now has an enhanced architecture, and 'train.py' has been updated to refine the training process. Additionally, the file 'data_old.csv', which contained outdated information, has been deleted as it's no longer needed. These ch", + "legacy_target": "stage_files", + "legacy_target_description": "Stage files to be included in the next commit.", + "legacy_target_description_chars": 46, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1068, + "source_record_id": "7f592a06-c623-4917-af94-676e9c23734d", + "source_parent_id": "hermes_record:7f592a06-c623-4917-af94-676e9c23734d", + "request_group_id": "hermes_request:dd9945d470a1cc7a9cb8e29ed4029a097d2f6acf968c97130f0936f84228b9cd", + "full_human_chars": 923, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "stage_files", + "commit_changes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_056657", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1082 + ], + "match_source_rows": [ + 1082 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for the Azure Functions deployed under the subscription ID sub-12345, within the resource group myResourceGroup. These functions are crucial to our operations, and I need to keep a close eye on their health and performance metrics. Specifically, I would like to monitor their current CPU and memory usage, which should ideally be at 60% and 70% respectively. Additionally, I want to ", + "legacy_target": "monitor_azure_services_health", + "legacy_target_description": "Monitor the health and performance metrics of specified Azure services.", + "legacy_target_description_chars": 71, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1082, + "source_record_id": "3ad84ed4-9556-45f2-978f-0cba0c6d6263", + "source_parent_id": "hermes_record:3ad84ed4-9556-45f2-978f-0cba0c6d6263", + "request_group_id": "hermes_request:10db667005d1496dc6ab17a76996918745a196b4e7448d8923a38668bf8906fa", + "full_human_chars": 870, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "monitor_azure_services_health", + "set_alert_rules", + "retrieve_logs" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_056725", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 24 + ], + "match_source_rows": [ + 24 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the manager of a dental practice, I'm looking to streamline our booking process. I need to schedule an appointment for our patient, John Doe with ID 'p123', with Dr. Sarah Smith, whose dentist ID is 'd456'. Please book this appointment for May 15, 2023, at 2:00 PM. Additionally, I would like to set up an automated reminder for John Doe to ensure he remembers his appointment. Can you book this a", + "legacy_target": "book_appointment", + "legacy_target_description": "Books an appointment for a patient with a specific dentist at a given date and time.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 24, + "source_record_id": "cb260921-b346-4076-9232-a175cf82d32e", + "source_parent_id": "hermes_record:cb260921-b346-4076-9232-a175cf82d32e", + "request_group_id": "hermes_request:a72779a50e64692af1ac08504391c616b9dea65e42b2a8f8d0d52ec7d05c8b2e", + "full_human_chars": 466, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "book_appointment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_056747", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1039 + ], + "match_source_rows": [ + 1039 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm developing an Android app for gamers where they need to save their progress and load it when they return to the game. I have structured the game progress data in JSON format, which includes the level, score, and inventory items. Here is the data I want to save: `{\"level\": 5, \"score\": 4500, \"items\": [\"sword\", \"shield\"]}`. I need to ensure that this data is stored securely on the device and can ", + "legacy_target": "save_to_file", + "legacy_target_description": "Saves content to a specified file path on the Android device.", + "legacy_target_description_chars": 61, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1039, + "source_record_id": "77172ed8-85a7-4d65-aa52-75d99b85755c", + "source_parent_id": "hermes_record:77172ed8-85a7-4d65-aa52-75d99b85755c", + "request_group_id": "hermes_request:f302d7b1223d0fe92ee7c1cec2d8f074d2de25bafa337b0c101790bb6b27cf9b", + "full_human_chars": 985, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "save_to_file", + "read_from_file" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_056780", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 193 + ], + "match_source_rows": [ + 193 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to execute a global money transfer through Revolut to my friend residing in Spain. The transfer amount is 1500 EUR and it's important that the transaction is processed urgently, as I need it to be completed before the end of this week. To facilitate this, I would like to initiate an express money transfer. Moreover, to keep track of the transfer's progress, I would appreciate if you c", + "legacy_target": "initiate_money_transfer", + "legacy_target_description": "Initiate a global money transfer to a recipient using Revolut.", + "legacy_target_description_chars": 62, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 193, + "source_record_id": "7d6dd5c8-9e1e-443d-92af-9cfdb910fbb1", + "source_parent_id": "hermes_record:7d6dd5c8-9e1e-443d-92af-9cfdb910fbb1", + "request_group_id": "hermes_request:d44090ccc43b36c6f5cab491e321fe5c821bb5ef057359f2cc32097b4e67bca6", + "full_human_chars": 717, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "initiate_money_transfer" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_056806", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 636 + ], + "match_source_rows": [ + 636 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing our company's railcar fleet to enhance our rail transportation logistics. Currently, we possess a detailed database of our railcars, including identifiers, types, current locations, and load capacities. To maintain optimal operations, I request the following functions be executed:\n\n1. Monitor the real-time locations of our railcars using the provided comprehensive railcar database:", + "legacy_target": "monitor_railcar_locations", + "legacy_target_description": "Monitors the current locations of railcars and provides a real-time tracking report.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 636, + "source_record_id": "e01ae42f-d06e-43fc-9310-1dc4413b409a", + "source_parent_id": "hermes_record:e01ae42f-d06e-43fc-9310-1dc4413b409a", + "request_group_id": "hermes_request:3b45e2786ef64f042fdc9c053707517a9137810d99b5dd78fd58f456e9820379", + "full_human_chars": 1736, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "monitor_railcar_locations", + "schedule_railcar_maintenance", + "update_railcar_load_capacity", + "generate_railcar_utilization_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_056956", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 731 + ], + "match_source_rows": [ + 731 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I've recently had a productive meeting with my project team where we discussed the important timeline and deliverables for our upcoming project. I would like to create a new note in my desktop application titled 'Meeting Summary' and populate it with the details from this discussion so that I can reference them later. Additionally, I have an existing note with the ID '12345', which needs to be upd", + "legacy_target": "create_note", + "legacy_target_description": "Create a new note with a specified title and content.", + "legacy_target_description_chars": 53, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 731, + "source_record_id": "ebd8b80e-1673-4393-9ee1-25310990dd6a", + "source_parent_id": "hermes_record:ebd8b80e-1673-4393-9ee1-25310990dd6a", + "request_group_id": "hermes_request:bd1c7ea29adc9bb356f55b36b5dd8e1a985aaa38ae0ad7a71973986e94180413", + "full_human_chars": 726, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_note" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057080", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 685 + ], + "match_source_rows": [ + 685 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm managing a logistics operation for distributing life sciences tools and services to multiple healthcare facilities across the region. We require a route optimization solution to streamline our deliveries, catering to varying priorities and deadlines associated with each location, and taking into account the specific capacities and schedules of our delivery fleet. Below are the delivery locatio", + "legacy_target": "calculate_efficient_routes", + "legacy_target_description": "Calculates the most efficient delivery routes for life sciences tools and services considering delivery locations, vehicle capacities, and constraints.", + "legacy_target_description_chars": 151, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 685, + "source_record_id": "12d4d5ac-7a38-4d50-afb6-d18f763fe123", + "source_parent_id": "hermes_record:12d4d5ac-7a38-4d50-afb6-d18f763fe123", + "request_group_id": "hermes_request:43ca80bf073406e466fed4c67dc3348254314e3a99473f7dde88692094560f88", + "full_human_chars": 1538, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "calculate_efficient_routes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057109", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 431 + ], + "match_source_rows": [ + 431 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am monitoring a server that currently has 32GB of total memory, and I would like to perform a detailed analysis of its memory usage to identify potential optimizations. I've noted that 24GB of memory is in use, leaving 8GB free. Additionally, there are 3GB allocated for buffers and 10GB for caching. The swap space totals 1GB, with 0.5GB currently used. Could you execute the 'get_memory_statistic", + "legacy_target": "get_memory_statistics", + "legacy_target_description": "Retrieve detailed statistics of the system's memory usage.", + "legacy_target_description_chars": 58, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 431, + "source_record_id": "c0d083d7-4353-4f82-8a1a-48f655f8037c", + "source_parent_id": "hermes_record:c0d083d7-4353-4f82-8a1a-48f655f8037c", + "request_group_id": "hermes_request:ec72aaf4758b0c1f0fda382ecb31db5650636c407c970b366b37c78eea917bcf", + "full_human_chars": 608, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_memory_statistics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057143", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1155, + 1218, + 1220, + 1227, + 1232, + 1291, + 1438, + 1441, + 1551, + 1589, + 1639, + 1847, + 1891 + ], + "match_source_rows": [ + 1155 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage Let's look at the **weak form** first. It says: \n1. _If_ a predicate is true for a certain number, \n2. _and_ its being true for some number would reliably mean that it's also true for the next number ( _i.e._ , one number greater), \n3. _then_ it's true for all numbers. \nAll you have to do is prove those two things, and you've", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that request for an opinion on a topic found in the document.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1155, + "source_record_id": "c3348b81-bedd-4f20-a873-8cc099b979d6", + "source_parent_id": "hermes_record:c3348b81-bedd-4f20-a873-8cc099b979d6", + "request_group_id": "hermes_request:f1a425e29309abffada2b77a52c2f1e05a68e477e7498079ad1e32b7cd007acb", + "full_human_chars": 10049, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057171", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 888 + ], + "match_source_rows": [ + 888 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to submit a rating for a product that I recently purchased from our online store. The specific product has an identification of 'P123', and as a verified buyer with a user ID 'U456', I'm eligible to provide feedback. I have decided to give this product a 4-star rating due to its excellent quality and fast shipping. Along with the star rating, I'd like to include a comment stating \"Exc", + "legacy_target": "submit_product_rating", + "legacy_target_description": "Submit a user's rating and review for a specific product.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 888, + "source_record_id": "c58a21dd-1024-463a-a494-9541a3aa2286", + "source_parent_id": "hermes_record:c58a21dd-1024-463a-a494-9541a3aa2286", + "request_group_id": "hermes_request:6a5ac76b26aa41dcbb28acbe8d2da9f117722ac9fd3f39d63cd6dd6d11dc82a2", + "full_human_chars": 641, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "submit_product_rating" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057174", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 846 + ], + "match_source_rows": [ + 846 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have been actively monitoring my fitness regime using my SmartFit wristband, and I'm looking to aggregate the collected data from the past week onto my health dashboard. Specifically, I need to extract daily step counts, along with daily heart rate summaries that include average, peak, and resting heart rates, as well as daily calorie intake. The date range for this data retrieval is from March ", + "legacy_target": "getWearableData", + "legacy_target_description": "Retrieves specified health and fitness data from a user's wearable device over a given time range.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 846, + "source_record_id": "a9f46367-14a0-4082-8417-0cf13fd6504d", + "source_parent_id": "hermes_record:a9f46367-14a0-4082-8417-0cf13fd6504d", + "request_group_id": "hermes_request:04e7f14fa0e611f11d9bd5f96d7379c05d56a9084a86b881847a217af1874711", + "full_human_chars": 665, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "getWearableData" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057223", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 943 + ], + "match_source_rows": [ + 943 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating a project named \"Project Alpha\" and would like assistance with establishing a collaborative environment within Microsoft 365. Specifically, I need to create a dedicated team on Microsoft Teams, ensuring it's a central hub for teamwork. The team should be named \"Project Alpha\" and described as a \"Workspace for Project Alpha team collaboration.\" Additionally, I require the team own", + "legacy_target": "create_team", + "legacy_target_description": "Create a new team in Microsoft Teams with a specified name, description, and owner.", + "legacy_target_description_chars": 83, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 943, + "source_record_id": "d76c4db8-8954-48eb-afec-7eecdaef411d", + "source_parent_id": "hermes_record:d76c4db8-8954-48eb-afec-7eecdaef411d", + "request_group_id": "hermes_request:98ba651a64dca131854ec60abae79f2c09110c4fc2af6e26e35876813533963c", + "full_human_chars": 1248, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_team" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057239", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 350 + ], + "match_source_rows": [ + 350 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working with an electric utilities company to improve the resilience of our power grid. We need to analyze our current grid's structure and past disruptions to pinpoint vulnerabilities. Here is the detailed infrastructure data and historical disruption record:\n\n**Grid Infrastructure Data (grid_infrastructure.json):**\n```json\n{\n \"substations\": [\n { \"id\": \"SUB001\", \"capacity\": \"500MW\", \"coo", + "legacy_target": "assess_grid_vulnerabilities", + "legacy_target_description": "Assesses the vulnerabilities of the electric grid using infrastructure data and historical disruption records.", + "legacy_target_description_chars": 110, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 350, + "source_record_id": "cbbbbb70-bedf-40bd-a6f9-ab87e6a6001d", + "source_parent_id": "hermes_record:cbbbbb70-bedf-40bd-a6f9-ab87e6a6001d", + "request_group_id": "hermes_request:26dedc51afdb1f3e3681ab2354b4d005ea52496d2bc99cf289afc40003b88f26", + "full_human_chars": 1988, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "assess_grid_vulnerabilities" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057350", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 911 + ], + "match_source_rows": [ + 911 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our company has recently concluded an employee satisfaction survey, collecting feedback from various departments. I have collated the results, including identifiable data, department allocations, specific ratings for the work environment and management, as well as any remarks provided by the staff. I wish to delve into this data to extract insights on the overall employee sentiment towards their w", + "legacy_target": "analyzeEmployeeSurvey", + "legacy_target_description": "Analyzes the employee satisfaction survey data to calculate average ratings and perform sentiment analysis on comments.", + "legacy_target_description_chars": 119, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 911, + "source_record_id": "282d3700-8f16-44b1-bfa0-46d4bbc5817a", + "source_parent_id": "hermes_record:282d3700-8f16-44b1-bfa0-46d4bbc5817a", + "request_group_id": "hermes_request:8c5e22b098eda19570ff2f4dd049380f05dbd005fe3ca4e56cbab889e493a051", + "full_human_chars": 1424, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyzeEmployeeSurvey", + "identifyImprovementAreas" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057507", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 255 + ], + "match_source_rows": [ + 255 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to conduct a unit test to verify the parsing accuracy of a Python 'for' loop code snippet within the code interpreter I am working with. The specific code snippet to analyze is `for i in range(5): print(i)`, and I expect the parsing to yield the following sequence of tokens: `['for', 'i', 'in', 'range', '(', '5', ')', ':', 'print', '(', 'i', ')']`. Please execute the function that tes", + "legacy_target": "test_parse_for_loop", + "legacy_target_description": "Tests the parsing of a Python 'for' loop code snippet against an expected result.", + "legacy_target_description_chars": 81, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 255, + "source_record_id": "fb1dc9c5-3643-4646-8ef9-5220b4f44305", + "source_parent_id": "hermes_record:fb1dc9c5-3643-4646-8ef9-5220b4f44305", + "request_group_id": "hermes_request:6cae75cf261fcfd347d45d6638f25ec4b0fa8185e754d4e4b5f4f42659815fa4", + "full_human_chars": 646, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "test_parse_for_loop" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057755", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 156 + ], + "match_source_rows": [ + 156 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on an analysis project focusing on social media impact and require assistance in processing a dataset of tweets retrieved from a marketing campaign. The goal is to identify any mentions of specific public figures, companies, and organizations within these tweets. The tweets have been collected and stored in a JSON file named `campaign_tweets.json`. I need to utilize a Hugging Face Nam", + "legacy_target": "load_ner_model", + "legacy_target_description": "Load a Named Entity Recognition model from Hugging Face.", + "legacy_target_description_chars": 56, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 156, + "source_record_id": "4990a806-9794-4ed4-9a58-617072bfb43f", + "source_parent_id": "hermes_record:4990a806-9794-4ed4-9a58-617072bfb43f", + "request_group_id": "hermes_request:e5affc08903f9687f3ee5c9f5dba1aa165b716de7d2b28648711cd00392f1bd3", + "full_human_chars": 934, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "load_ner_model", + "extract_entities" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_057828", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 271 + ], + "match_source_rows": [ + 271 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am looking to enhance my knowledge of algorithmic trading strategies, focusing on specific methods including momentum trading, mean reversion, and arbitrage. I have a dataset containing five years of historical trading data for the S&P 500 index, titled \"SP500_historical_data_5_years.csv\". My objective is to use this data to simulate the mentioned strategies and analyze their effectiveness. Furt", + "legacy_target": "get_algorithmic_trading_education", + "legacy_target_description": "Provides educational information on various algorithmic trading strategies, their risks, potential returns, and the integration of machine learning.", + "legacy_target_description_chars": 148, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 271, + "source_record_id": "c40a49ee-2d57-4e37-8a1e-ae68b5626811", + "source_parent_id": "hermes_record:c40a49ee-2d57-4e37-8a1e-ae68b5626811", + "request_group_id": "hermes_request:a18f505eb4f351f24d4c01e45ac7a4ba6ec65d1d3b868b52698301b39caa9470", + "full_human_chars": 827, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_algorithmic_trading_education" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_058009", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 934 + ], + "match_source_rows": [ + 934 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing a project and need to implement an automated notification system using our Jira issue tracking. The requirement is for a webhook to send POST requests to our designated notification service endpoint at 'https://api.notify.example.com' whenever issues are either created or updated in Jira. These notifications must carry essential details such as the issue ID, its summary, and status", + "legacy_target": "configureJiraNotificationWorkflow", + "legacy_target_description": "Sets up an automated workflow to send notifications via POST request when specified events occur in Jira.", + "legacy_target_description_chars": 105, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 934, + "source_record_id": "19004c3d-4502-4a58-aced-385d69c325e9", + "source_parent_id": "hermes_record:19004c3d-4502-4a58-aced-385d69c325e9", + "request_group_id": "hermes_request:1f06291ce32004e08793c7eb3ecbf91127c64e385a2d123bca68e7793e8b13ff", + "full_human_chars": 712, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "configureJiraNotificationWorkflow" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_058387", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 139 + ], + "match_source_rows": [ + 139 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Chief Information Security Officer at InnovaTech Inc., I am implementing enhanced security measures to safeguard our company's critical data, particularly our customer database and financial records. To this end, we wish to establish a comprehensive security workflow automation system with the following objectives:\n\n1. To set up an automatic detection system for unauthorized access attempts", + "legacy_target": "setup_unauthorized_access_detection", + "legacy_target_description": "Sets up a system to detect unauthorized access attempts to specified data sources and alerts designated recipients.", + "legacy_target_description_chars": 115, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 139, + "source_record_id": "af430a61-6635-4ea7-8cd7-7dc862b347d1", + "source_parent_id": "hermes_record:af430a61-6635-4ea7-8cd7-7dc862b347d1", + "request_group_id": "hermes_request:8b01279226c45ce3a49acbff73a3e1b3faf7b0693f333fba5f127475ace8885a", + "full_human_chars": 1949, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "setup_unauthorized_access_detection", + "automate_access_revocation", + "generate_incident_report", + "initiate_data_recovery_plan" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_058529", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1134, + 1168, + 1203, + 1210, + 1245, + 1278, + 1427, + 1491, + 1572, + 1640, + 1661, + 1696, + 1822 + ], + "match_source_rows": [ + 1134 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage . It's like saying, \"I've noticed you are very concerned about X; I have a thought about that subject too.\" If she only included those somewhat vague references to other writers, then the introduction would be weak, but the quotation from Downie and Schudson introduces a specific pressing question that others feel must be answere", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1134, + "source_record_id": "27235d1b-226b-4cd3-80ee-a15543b9b30b", + "source_parent_id": "hermes_record:27235d1b-226b-4cd3-80ee-a15543b9b30b", + "request_group_id": "hermes_request:f962272ba6111345e96f0ced009303b35ed928900073b5170932e64625528532", + "full_human_chars": 14084, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_058775", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 392 + ], + "match_source_rows": [ + 392 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm analyzing our company's sales performance over the past year and need to visualize the data to identify trends and patterns effectively. I have collected the monthly sales figures, categorized our product sales figures, and recorded sales across different regions. I'd like to request the creation of visualizations that will assist us in understanding our sales dynamics better.\n\nHere's the data", + "legacy_target": "generate_line_chart", + "legacy_target_description": "Generates a line chart from the provided data.", + "legacy_target_description_chars": 46, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 392, + "source_record_id": "17f23e81-1620-439c-9996-96f02af76607", + "source_parent_id": "hermes_record:17f23e81-1620-439c-9996-96f02af76607", + "request_group_id": "hermes_request:d09e23e9385c59120c93392b73d160387b6a2babc6c203218dee4f2e46532081", + "full_human_chars": 1511, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "generate_line_chart", + "generate_bar_chart", + "generate_pie_chart" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_058897", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 497 + ], + "match_source_rows": [ + 497 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am undertaking a comprehensive analysis of our educational institute's data, which includes extracting information from various sources to create structured JSON files. I have the performance records of our students in a text file, detailed information on our courses in another document, and teacher evaluation reports saved on our server.\n\nFor the student performance, here's a snippet of the kin", + "legacy_target": "extractStudentPerformanceData", + "legacy_target_description": "Extracts student performance data from the provided records and structures it into a JSON format.", + "legacy_target_description_chars": 97, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 497, + "source_record_id": "e001d382-0097-40a8-ae61-aee2345df94c", + "source_parent_id": "hermes_record:e001d382-0097-40a8-ae61-aee2345df94c", + "request_group_id": "hermes_request:cfde192e7d5f17468a854c80ab8a2e3db9bda18555691e26b7599e338c5208a7", + "full_human_chars": 1918, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "extractStudentPerformanceData", + "extractCourseInformation", + "extractTeacherEvaluations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059127", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 957 + ], + "match_source_rows": [ + 957 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our team is currently utilizing Zendesk as our customer service platform and Salesforce as our CRM system. We're aiming to enhance our team’s efficiency by integrating these two systems with our Slack workspace. Specifically, we’re looking at setting up an automated notification system that alerts our '#support-notifications' Slack channel instantly when a new support ticket is filed in Zendesk. A", + "legacy_target": "create_ticket_notification_integration", + "legacy_target_description": "Create an integration to receive notifications in Slack when a new support ticket is created in a customer service platform.", + "legacy_target_description_chars": 124, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 957, + "source_record_id": "e5c558d0-82e0-489a-8d82-08df8be45517", + "source_parent_id": "hermes_record:e5c558d0-82e0-489a-8d82-08df8be45517", + "request_group_id": "hermes_request:598ed4125df6a2a7e23a172ff2d164413441795f91c792517ae63c687f3fdbde", + "full_human_chars": 726, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_ticket_notification_integration", + "create_crm_contact_integration" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059200", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 436 + ], + "match_source_rows": [ + 436 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I possess a collection of legal opinions with varying content and require them to be accurately categorized by legal domain and case type for a research project. The domains of interest include \"Intellectual Property,\" \"Family Law,\" \"Employment Law,\" and \"Criminal Law.\" Similarly, the case types pertinent to this task are \"Infringement,\" \"Divorce,\" \"Wrongful Termination,\" and \"Fraud.\" The classifi", + "legacy_target": "categorize_legal_documents", + "legacy_target_description": "Categorizes legal documents into specified legal domains and case types, and outputs the results in a JSON Schema format.", + "legacy_target_description_chars": 121, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 436, + "source_record_id": "c9c50cdd-3937-42a3-86bd-30e4bd15f4d1", + "source_parent_id": "hermes_record:c9c50cdd-3937-42a3-86bd-30e4bd15f4d1", + "request_group_id": "hermes_request:e2b06d667732dd17e51f45bbd6bd1b8f90bd917f993ca570d28b1035b5aa332d", + "full_human_chars": 1268, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "categorize_legal_documents" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059241", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 716 + ], + "match_source_rows": [ + 716 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to establish a recurring reminder to ensure that our team is prepared for our weekly meetings. The reminder should be titled \"Team Meeting\" and needs to start on April 10th, 2023, taking place every Monday at 10:00 AM. I want the system to alert me 15 minutes prior to each meeting. Additionally, it should include a note emphasizing the need to \"Discuss project updates\" as part of the ", + "legacy_target": "create_recurring_reminder", + "legacy_target_description": "Create a recurring reminder with specified details.", + "legacy_target_description_chars": 51, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 716, + "source_record_id": "36a604f4-dabe-42a5-92a3-069b0d75f0d2", + "source_parent_id": "hermes_record:36a604f4-dabe-42a5-92a3-069b0d75f0d2", + "request_group_id": "hermes_request:296fb78f7ad9fd2c7787798a12ec6be201e8320c8a487ed300d475a5766df881", + "full_human_chars": 494, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_recurring_reminder" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059264", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1124, + 1346, + 1448, + 1570, + 1596, + 1659, + 1705, + 1709, + 1713, + 1723, + 1783, + 1808, + 1865 + ], + "match_source_rows": [ + 1124 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 1 2 1 2 1 1 8 1 1 0 5 ] and work to reduced row-echelon form, first with j = 1, - 2 R 1 +R 2 ------- [ 1 - 1 2 1 0 3 - 3 6 1 1 0 5 ] - 1 R 1 +R 3 ------- 1 - 1 2 1 0 3 - 3 6 0 2 - 2 4 Now, with j = 2, (^13) R 2 ---- 1 - 1 2 1 0 1 - 1 2 0 2 - 2 4 (^) -^1 -R-^2 +--R-^1 1 0 1 3 0 1 - 1 2 0 2 - 2 4 - 2 R 2 +R 3 ------- ", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1124, + "source_record_id": "14cd0793-09b4-4b9b-b3aa-e7b4c19fcf37", + "source_parent_id": "hermes_record:14cd0793-09b4-4b9b-b3aa-e7b4c19fcf37", + "request_group_id": "hermes_request:2f44c7fa5d7bc201365bba710a63b0fa7109413f937f7c8cdb1dc8b79811bbe3", + "full_human_chars": 7058, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059394", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 336 + ], + "match_source_rows": [ + 336 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "We are upgrading our utility company's operations with smart grid solutions to enhance efficiency. Our specific goals are to forecast upcoming energy demand accurately, manage the deployment and control of distributed energy resources effectively, and ensure optimal energy flow across our grid network. To achieve these, we have collected historical energy usage data and the latest weather forecast", + "legacy_target": "forecast_energy_demand", + "legacy_target_description": "Forecasts energy demand based on historical usage data and weather forecasts for a specified date range.", + "legacy_target_description_chars": 104, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 336, + "source_record_id": "e3701c30-4226-451f-ba32-9a87c41ca0b6", + "source_parent_id": "hermes_record:e3701c30-4226-451f-ba32-9a87c41ca0b6", + "request_group_id": "hermes_request:c887818f3e51e207923130984edac2f19bdeb973043b28b8d025b3a2a8c969c0", + "full_human_chars": 1560, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "forecast_energy_demand", + "manage_distributed_energy_resources", + "optimize_energy_flow" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059426", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 406 + ], + "match_source_rows": [ + 406 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To assist with the analysis of sales data across multiple regions and stores, I have gathered detailed sales data from our company's database. Our database \"SalesDB\" contains comprehensive figures of transactions from various stores categorized by region - North, South, East, and West. I would like to call the `functions.aggregate_sales_data` function to aggregate this data by region. Here's the b", + "legacy_target": "aggregate_sales_data", + "legacy_target_description": "Aggregate sales data from a data source at a specified aggregation level.", + "legacy_target_description_chars": 73, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 406, + "source_record_id": "a5fd3325-fbc1-4f93-b363-22f10d35afdb", + "source_parent_id": "hermes_record:a5fd3325-fbc1-4f93-b363-22f10d35afdb", + "request_group_id": "hermes_request:463ddb096cca825ec7bff1f5d2097e45b90e5417676548db2be2aa28031518bd", + "full_human_chars": 1484, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "aggregate_sales_data", + "drilldown_sales_data", + "drilldown_sales_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059583", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 939 + ], + "match_source_rows": [ + 939 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am organizing an upcoming conference and would like to schedule a keynote event on my primary Google Calendar. The event is titled \"Conference Keynote\" and will take place in the Main Hall of the Convention Center. The keynote speech will be delivered by a prominent industry leader. I would like the event to be scheduled on May 10, 2023, from 9:00 AM to 10:00 AM Pacific Time (America/Los_Angeles", + "legacy_target": "create_calendar_event", + "legacy_target_description": "Creates a new event in the specified Google Calendar.", + "legacy_target_description_chars": 53, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 939, + "source_record_id": "654df200-5357-4e6d-8ed9-0e57bf8577d9", + "source_parent_id": "hermes_record:654df200-5357-4e6d-8ed9-0e57bf8577d9", + "request_group_id": "hermes_request:e92a520d94be2028d1873643776a39a389b3d5b261229ff730986a2532676083", + "full_human_chars": 1362, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_calendar_event", + "check_event_conflict" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059597", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 660 + ], + "match_source_rows": [ + 660 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to create a new skill for Microsoft Cortana to help users effortlessly book taxis from their Windows devices. The skill should allow users to specify their location for pickup, such as \"500 5th Avenue, New York, NY 10110,\" along with their desired destination, like \"John F. Kennedy International Airport.\" It should enable users to set their preferred pickup time, for example, \"2023-06", + "legacy_target": "createTaxiBookingSkill", + "legacy_target_description": "Creates a new Cortana skill for booking a taxi with user-specified details.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 660, + "source_record_id": "f595b8d5-531a-4699-85bc-02620d4e1de8", + "source_parent_id": "hermes_record:f595b8d5-531a-4699-85bc-02620d4e1de8", + "request_group_id": "hermes_request:6a146bb9c8c302262708a6acf52d441fdd51597a737ea40b5c2ad3c67c55f3c8", + "full_human_chars": 675, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "createTaxiBookingSkill" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059631", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 375 + ], + "match_source_rows": [ + 375 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As we are deploying a comprehensive data storage management system in our IT department, we require a solid-state drive (SSD) storage volume with a capacity of 1 terabyte (TB) to accommodate our high-speed data access needs. We further need to attach this storage volume to our main server, identified by the ID 'srv-789123', to ensure seamless data flow for our critical applications. Additionally, ", + "legacy_target": "create_storage_volume", + "legacy_target_description": "Creates a new storage volume with the specified size and storage type.", + "legacy_target_description_chars": 70, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 375, + "source_record_id": "dfdeabe9-ea5c-4e73-ae06-174bd53a71af", + "source_parent_id": "hermes_record:dfdeabe9-ea5c-4e73-ae06-174bd53a71af", + "request_group_id": "hermes_request:ed93437e43c49f0c2419c2a31229a3f2c854344719f9b2b5165ea92d4221fcdb", + "full_human_chars": 1143, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_storage_volume" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059669", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 610 + ], + "match_source_rows": [ + 610 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the CRM manager for Oceanic Terminal Services, I need to onboard a new shipping company as our client, SeaFast Logistics, and track their service request for cargo handling. The primary contact at SeaFast is Jessica Tiller, reachable at [email protected] or by phone at +1-555-0199. Their office is located at 789 Coastal Avenue, Dockside City, DC 10109. Additionally, I must generate a performanc", + "legacy_target": "register_new_client", + "legacy_target_description": "Registers a new client in the CRM system with their company and contact information.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 610, + "source_record_id": "e1648658-c6f2-40db-bb37-a4762cbfbc11", + "source_parent_id": "hermes_record:e1648658-c6f2-40db-bb37-a4762cbfbc11", + "request_group_id": "hermes_request:8381cda3d4f53ceabcdc6ab8d4533a823b7b1bffd919b5bdc95d12b633a9614e", + "full_human_chars": 1450, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "register_new_client" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059692", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 352 + ], + "match_source_rows": [ + 352 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As a facility manager at a power production plant, our objective is to employ predictive maintenance for our turbines to prevent unplanned outages and improve maintenance efficiency. We have collected extensive historical data which includes sensor readings from the turbines, detailed maintenance logs, and operational records over the past year. \n\nSensor data includes temperature, pressure, and vi", + "legacy_target": "predict_equipment_failures", + "legacy_target_description": "Analyzes historical sensor data, maintenance records, and operational logs to predict potential equipment failures.", + "legacy_target_description_chars": 115, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 352, + "source_record_id": "5b697336-0f74-4623-817c-ac4e5117b6a5", + "source_parent_id": "hermes_record:5b697336-0f74-4623-817c-ac4e5117b6a5", + "request_group_id": "hermes_request:b4b65c3ad8b7908c6ad151e838a5f44be919ca86f2e74ff2dc0d44e0e87ef22b", + "full_human_chars": 1567, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "predict_equipment_failures" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059772", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1085 + ], + "match_source_rows": [ + 1085 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our company is planning to transition our on-premises SQL Server 2019 databases to Azure SQL to take advantage of cloud scalability and cost savings. To ensure a smooth migration, we need to assess our current database compatibility with Azure SQL, particularly considering we are utilizing SQL Server 2019's enterprise-level features. Additionally, we require an estimate of the effort involved in m", + "legacy_target": "assess_sql_server_compatibility", + "legacy_target_description": "Assess the compatibility of on-premises SQL Server with Azure SQL.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1085, + "source_record_id": "1859908b-a554-45c8-97b0-6d8fd2405436", + "source_parent_id": "hermes_record:1859908b-a554-45c8-97b0-6d8fd2405436", + "request_group_id": "hermes_request:a81ac6d2618c4c147765709e7f334f2f12af051aa20f06a8448e662e3e69d91e", + "full_human_chars": 1599, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "assess_sql_server_compatibility", + "estimate_migration_effort", + "calculate_cost_savings" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059838", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 564 + ], + "match_source_rows": [ + 564 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Facilities Manager at GreenTech Innovations, I need assistance with the automation of management processes for our campus buildings identified as B1, B2, and B3. We aim for enhanced efficiency and precision in managing the building systems, so I am requesting the following:\n\n1. Implement a monitoring system for building B1 focusing on HVAC, lighting, and security. This should run comprehens", + "legacy_target": "monitor_system_status", + "legacy_target_description": "Monitors the status of various systems within a building and generates reports.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 564, + "source_record_id": "d0b331dc-923d-4ad9-b0da-5f79082238a4", + "source_parent_id": "hermes_record:d0b331dc-923d-4ad9-b0da-5f79082238a4", + "request_group_id": "hermes_request:9007f9d07b9099c8f80c7e8479295286ba69aa126d6956f88e97f92aa5bd6957", + "full_human_chars": 1301, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "monitor_system_status", + "schedule_maintenance", + "manage_room_bookings", + "compile_energy_usage_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059906", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 403 + ], + "match_source_rows": [ + 403 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working with a healthcare dataset that includes patient records. This dataset contains, among various features, 'age' and 'annual_income' for each patient. The 'age' feature has values that range from 0 to 100 years, and the 'annual_income' feature contains values that vary significantly, some incomes being extremely high. In preparation for using a machine learning model that is sensitive to", + "legacy_target": "min_max_normalize", + "legacy_target_description": "Normalize a specific feature in the dataset to a specified range using Min-Max normalization.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 403, + "source_record_id": "8e956fa3-4ce4-49a8-b0f9-ad1d2eff789f", + "source_parent_id": "hermes_record:8e956fa3-4ce4-49a8-b0f9-ad1d2eff789f", + "request_group_id": "hermes_request:62c50529527507e286266d9c1f41461838e97feef425b76c6d94ccc3a3df638b", + "full_human_chars": 1435, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "min_max_normalize", + "z_score_standardize" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_059939", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 477 + ], + "match_source_rows": [ + 477 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a collection of economic reports saved in a JSON file, and I need to extract specific financial terms for analysis. The terms I'm interested in are \"market cap,\" \"dividend yield,\" \"P/E ratio,\" and \"beta.\" These terms need to be accurately identified within the reports and categorized accordingly. I require the output to be structured in a manner that aligns with a predefined JSON Schema. Th", + "legacy_target": "generate_financial_terms_schema", + "legacy_target_description": "Generates a JSON Schema to structure the output data of identified financial terms.", + "legacy_target_description_chars": 83, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 477, + "source_record_id": "fca31ad8-2b30-4f2b-84a7-d3472117201b", + "source_parent_id": "hermes_record:fca31ad8-2b30-4f2b-84a7-d3472117201b", + "request_group_id": "hermes_request:53ef25b8c2d1bfc5fd40de75ecd405edd9f002ec062816f63a68625dafb73ce5", + "full_human_chars": 1648, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_financial_terms_schema" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_060050", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1065 + ], + "match_source_rows": [ + 1065 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on a project that requires up-to-date user agent strings for various web browsers and devices to enhance web scraping capabilities and ensure compatibility. Specifically, I need to collect and analyze the latest user agent strings from the web. I would like to extract these strings from several online resources where they are publicly available. I have identified the following URLs th", + "legacy_target": "scrape_user_agents", + "legacy_target_description": "Scrapes user agent strings from the given URLs using BeautifulSoup to parse the HTML content.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1065, + "source_record_id": "b131c7bf-a25d-4097-b79f-84932be30a35", + "source_parent_id": "hermes_record:b131c7bf-a25d-4097-b79f-84932be30a35", + "request_group_id": "hermes_request:30a330432cd7827111d225464a16229ef90222caccf560428f0e786670c52b61", + "full_human_chars": 1762, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "scrape_user_agents" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_060158", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 978 + ], + "match_source_rows": [ + 978 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the operations manager of a food distribution company, we need to boost efficiency by forecasting product demand, optimizing stock levels, and improving delivery routes for the upcoming quarter. Attached is our historical sales data from the past year (`historical_sales_data.json`), current inventory status (`current_inventory.json`), and our existing delivery routes coupled with delivery desti", + "legacy_target": "forecast_demand", + "legacy_target_description": "Forecasts demand for food products based on historical sales data for a specified future period.", + "legacy_target_description_chars": 96, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 978, + "source_record_id": "b021ce90-53fa-4fc9-97b7-bce1d2e030cf", + "source_parent_id": "hermes_record:b021ce90-53fa-4fc9-97b7-bce1d2e030cf", + "request_group_id": "hermes_request:0fab47d8569313d661b9552ee9cf15aae7c2e9ddf4361a96eaf85a7ed2a71957", + "full_human_chars": 1610, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "forecast_demand", + "improve_delivery_routing" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_060192", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1117, + 1163, + 1181, + 1192, + 1290, + 1436, + 1439, + 1495, + 1594, + 1714, + 1770, + 1824, + 1835 + ], + "match_source_rows": [ + 1117 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage Lille^ Strasbourg \n 250 200 250 150 \n 600 150 300 \n 300 \n 450 \n 150 \n 300 \n 350 \n 500 \n 400 \n 200 \n 350 400 650 \n 2 \n Vichy^450 Lyon Briancon^ Toulouse^200 Marseille^ Nice^ \n Bordeaux^0 \n Nantes^150 \n Paris Lille^ Strasbourg \n 250 200 250 150 \n 600 150 300 \n 300 \n 450 \n 150 \n 300 \n 350 \n 500 \n 400 \n 200 \n 350 400 650 \n 3", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1117, + "source_record_id": "4af09c05-15be-4bd8-8b6b-69af51e0f27e", + "source_parent_id": "hermes_record:4af09c05-15be-4bd8-8b6b-69af51e0f27e", + "request_group_id": "hermes_request:061596c766c24d19db1be22760cea3a7bf5d913502fb67e4991b884305b1992d", + "full_human_chars": 10441, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_060254", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1078 + ], + "match_source_rows": [ + 1078 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the HR manager at our company, I am planning an engaging all-day team-building event to foster better collaboration and morale among our team members. The event is scheduled for May 20th, 2023, and will be assembled as an outdoor activity. I require assistance to create this event in our company calendar, send out the invitations to all our team members, and set reminders to ensure everyone is ", + "legacy_target": "createTeamEvent", + "legacy_target_description": "Create a new team event in the company calendar and send out invites.", + "legacy_target_description_chars": 69, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1078, + "source_record_id": "42f63d56-d419-41ad-b6ea-d744cf5b54c5", + "source_parent_id": "hermes_record:42f63d56-d419-41ad-b6ea-d744cf5b54c5", + "request_group_id": "hermes_request:30269385a53989e2f3569ec4597bb71f699a378538516408fbfceeb4d472f9c4", + "full_human_chars": 1460, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "createTeamEvent" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_060530", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 744 + ], + "match_source_rows": [ + 744 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm working on an article that features visual representations of extraordinary futuristic concepts, and I would like to incorporate digitally generated artwork. 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Observe how sales trends fluctuate wee", + "legacy_target": "identify_top_selling_products", + "legacy_target_description": "Identifies the top-selling personal care products from POS sales data over a specified time period.", + "legacy_target_description_chars": 99, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 984, + "source_record_id": "3bfcdbdf-0e61-4bdc-b6e7-323f634bf7d6", + "source_parent_id": "hermes_record:3bfcdbdf-0e61-4bdc-b6e7-323f634bf7d6", + "request_group_id": "hermes_request:49768e7a2d59a729544cfbb0f6f0819cb334c270c24005d9a06d6d5a8d3a6193", + "full_human_chars": 1046, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "identify_top_selling_products", + "monitor_sales_trends", + "evaluate_marketing_campaign_impact" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_062270", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 572 + ], + "match_source_rows": [ + 572 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm tasked with optimizing IT operations for our company and need assistance in implementing task scheduling automation. 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Additionally, we p", + "legacy_target": "analyze_historical_data", + "legacy_target_description": "Analyzes historical energy usage and peak load data to identify patterns and trends.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 325, + "source_record_id": "9a4d5b45-b8a0-46bf-b09f-3ab0d018e086", + "source_parent_id": "hermes_record:9a4d5b45-b8a0-46bf-b09f-3ab0d018e086", + "request_group_id": "hermes_request:2a88c8c4b97c0ecdff051a5411bbd34bf43111cc2a8dce68c66c501a4bf2724d", + "full_human_chars": 1470, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_historical_data", + "forecast_peak_load" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_062685", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 29 + ], + "match_source_rows": [ + 29 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage billing for multiple consultancy clients, and I am tasked with preparing and dispatching this month's invoices. 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Please create an Azure Quantum workspace named 'ChemSimulatio", + "legacy_target": "create_quantum_workspace", + "legacy_target_description": "Creates a new Azure Quantum workspace with the specified name and location.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 18, + "source_record_id": "5790a757-bbe7-49d7-9fbd-d98cf3e0fd45", + "source_parent_id": "hermes_record:5790a757-bbe7-49d7-9fbd-d98cf3e0fd45", + "request_group_id": "hermes_request:93613dd5705f6b31b53c1dbd9b1b9294c643e3490a407758c0c908a4bbae8125", + "full_human_chars": 1132, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "create_quantum_workspace", + "estimate_quantum_resources", + "submit_quantum_program" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064217", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 433 + ], + "match_source_rows": [ + 433 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently organizing my digital workspace and need to compress a folder containing important project files for archiving and easy distribution to my colleagues. The folder is located on my desktop and is named \"ProjectData.\" Inside, it contains various CSV files related to financial analysis reports and client data. I would like to compress this entire folder into a single ZIP file named \"Data", + "legacy_target": "compress_folder_to_zip", + "legacy_target_description": "Compresses a specified folder into a ZIP file at the given destination path.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 433, + "source_record_id": "3d5d918b-c63c-422f-901f-11eaf2f08d06", + "source_parent_id": "hermes_record:3d5d918b-c63c-422f-901f-11eaf2f08d06", + "request_group_id": "hermes_request:f2f33d6c70446a9769110ef11ca2eed96e4f8ce90829a9551cf3f3a8dd091450", + "full_human_chars": 750, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "compress_folder_to_zip" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064252", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 634 + ], + "match_source_rows": [ + 634 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting a detailed analysis of our manufacturing operations to improve our production efficiency, machine uptime, and maintenance schedules. For this purpose, I have gathered datasets from the past 30 days which include the following:\n\n1. Production data containing timestamps of production cycles indicating the start and end of each manufacturing cycle, reflecting a total output of 15,000 ", + "legacy_target": "calculate_production_efficiency", + "legacy_target_description": "Calculates the production efficiency based on production data over a specified time frame.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 634, + "source_record_id": "11414e77-a064-46b9-8eb5-3da9c04f4b24", + "source_parent_id": "hermes_record:11414e77-a064-46b9-8eb5-3da9c04f4b24", + "request_group_id": "hermes_request:e509329c021b102dbfacb32e30970ba19850b4929bc4373e6e150ca68641683e", + "full_human_chars": 1548, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_production_efficiency", + "analyze_machine_downtime", + "optimize_maintenance_schedule" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064277", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 174 + ], + "match_source_rows": [ + 174 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently trading cryptocurrencies on Coinbase and would like to establish a more efficient asset management system. To enhance my trading capabilities, I request the creation of a cryptocurrency wallet named 'MyCryptoWallet' tailored for my use. Additionally, I need to check the balance of this new wallet to stay informed about my assets. To facilitate seamless cryptocurrency payments, pleas", + "legacy_target": "create_wallet", + "legacy_target_description": "Creates a new cryptocurrency wallet for the user.", + "legacy_target_description_chars": 49, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 174, + "source_record_id": "71f294e6-c40d-44bd-8f1d-661a5c02e4bd", + "source_parent_id": "hermes_record:71f294e6-c40d-44bd-8f1d-661a5c02e4bd", + "request_group_id": "hermes_request:bbbf6a277034826d134ecc85aeffec810a2d6806b8d704718360baf4d5420a4a", + "full_human_chars": 901, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_wallet" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064281", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 81 + ], + "match_source_rows": [ + 81 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to establish an interactive media platform designed to engage users with various types of content, including streaming videos, live chat sessions, and immersive virtual reality experiences. For secure access, I want to implement an OAuth2 authentication method. Please initialize the user authentication with the following specifics: \n\n- Required registration fields: email and password.", + "legacy_target": "initialize_user_authentication", + "legacy_target_description": "Set up the user authentication system with customizable requirements.", + "legacy_target_description_chars": 69, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 81, + "source_record_id": "9314f38e-aab8-4e9e-8cc8-fa6fdcfafdbe", + "source_parent_id": "hermes_record:9314f38e-aab8-4e9e-8cc8-fa6fdcfafdbe", + "request_group_id": "hermes_request:25e3acf459be20eea04a432c6533ce89ab83533adee4a96d2f5ab285781c826a", + "full_human_chars": 967, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 7, + "valid_top_level_call_names": [ + "initialize_user_authentication", + "manage_content", + "manage_content", + "manage_content", + "track_analytics", + "track_analytics", + "track_analytics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064337", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1332, + 1478, + 1484, + 1499, + 1511, + 1552, + 1566, + 1611, + 1667, + 1680, + 1707, + 1757, + 1842 + ], + "match_source_rows": [ + 1332 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage or papers. Was this thesis-first or inquiry-based research? What were the benefits or drawbacks of this approach? What led you to that method? \n**2.** Imagine you had all the time in the world to research a topic that you were personally passionate about. How might your approach to research be different than in a ", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1332, + "source_record_id": "329425b8-b141-495f-8a49-cbaed415bb96", + "source_parent_id": "hermes_record:329425b8-b141-495f-8a49-cbaed415bb96", + "request_group_id": "hermes_request:21ff603c2bfb0791f7149162ae65d2150e815f002275d42ebbddbcaac1acde43", + "full_human_chars": 14802, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064347", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 444 + ], + "match_source_rows": [ + 444 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "During a routine inspection of our robotics system, the control panel flagged an error code 'E-101', indicating a failure in one of the sensors. We need to swiftly address this issue to minimize downtime. Could you please carry out the error diagnostic procedure for the specific malfunction by using the error code 'E-101'? After obtaining the diagnostic result, proceed to attempt an automatic reco", + "legacy_target": "diagnose_sensor_error", + "legacy_target_description": "Diagnoses the error based on the provided error code and identifies the faulty sensor.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 444, + "source_record_id": "833a6ad8-3d45-4f8f-a6b1-499c2c284940", + "source_parent_id": "hermes_record:833a6ad8-3d45-4f8f-a6b1-499c2c284940", + "request_group_id": "hermes_request:416c202649dc9c1e18ce2b0087e341ddc11dd19dff20532be545fc57f312c56c", + "full_human_chars": 1247, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "diagnose_sensor_error", + "attempt_automatic_recovery" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064360", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 754 + ], + "match_source_rows": [ + 754 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on an art project that visualizes different interpretations of futuristic cities during sunset. I'd like to create a set of artistic images that capture this concept using the DALL-E model and the OpenAI API. Specifically, I want images that depict \"A cityscape in the future, silhouetted against a glowing sunset sky, showcasing advanced technology and architecture.\" I need these image", + "legacy_target": "generate_artistic_images", + "legacy_target_description": "Generates artistic images based on a text prompt using DALL-E and the OpenAI API.", + "legacy_target_description_chars": 81, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 754, + "source_record_id": "f560a67b-2cf4-412f-a374-9226b4461ece", + "source_parent_id": "hermes_record:f560a67b-2cf4-412f-a374-9226b4461ece", + "request_group_id": "hermes_request:26832f4ca19d526335b1062a2e947f1ee931eeef565e4c7653c0666dd4e902c2", + "full_human_chars": 907, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_artistic_images" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064680", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 348 + ], + "match_source_rows": [ + 348 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the manager of a multi-utility company overseeing electricity, water, and gas services, I face the challenge of optimizing resource allocation to accommodate varying demand. Over the last 12 months, our data shows fluctuating consumption patterns that need thorough analysis. To better manage resources and prepare for the upcoming quarter, I'd like to request the following actions:\n\n1. Analyze t", + "legacy_target": "analyze_consumption_patterns", + "legacy_target_description": "Analyzes consumption patterns for specified utility types over a given time period.", + "legacy_target_description_chars": 83, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 348, + "source_record_id": "61674673-0824-40e6-bbfe-3b37c2078e02", + "source_parent_id": "hermes_record:61674673-0824-40e6-bbfe-3b37c2078e02", + "request_group_id": "hermes_request:11d4f6d179f736b064e49ce8b9491ae2e1f1a9e7ae2e5974c441fd1ce629cf95", + "full_human_chars": 2040, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_consumption_patterns", + "predict_future_demand" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064932", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 97 + ], + "match_source_rows": [ + 97 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am processing an order on my e-commerce website where the customer has selected to use Klarna's \"Pay in 4 interest-free installments\" option for their purchase. The total cost of the items in their cart is $150.00. I need to create a Klarna payment session with the following details: an order amount of 15000 cents since we use the smallest currency unit, the currency set to USD, the locale set t", + "legacy_target": "create_klarna_payment_session", + "legacy_target_description": "Create a new payment session with Klarna for processing an order.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 97, + "source_record_id": "9ea5bebb-56e0-4245-80f0-af00c09fec09", + "source_parent_id": "hermes_record:9ea5bebb-56e0-4245-80f0-af00c09fec09", + "request_group_id": "hermes_request:dde96a9c66cf710c2dc714828eb055deb0d564b567df0800cc0f75e9251a5be0", + "full_human_chars": 989, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_klarna_payment_session" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_064961", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 1090 + ], + "match_source_rows": [ + 1090 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on a Databricks project titled \"Data Aggregation and ML Model Execution\" which requires processing voluminous datasets stored within an Azure Data Lake Storage Gen2 container located at `https://mydatalake.dfs.core.windows.net/lakehouse/data/`. The job entails two primary tasks: the first is to aggregate data, potentially using Apache Spark SQL queries for performance, and the second ", + "legacy_target": "create_job", + "legacy_target_description": "Create a new job for data processing and machine learning tasks.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1090, + "source_record_id": "fc59a739-2325-48fd-b6f1-c9ce044c999c", + "source_parent_id": "hermes_record:fc59a739-2325-48fd-b6f1-c9ce044c999c", + "request_group_id": "hermes_request:1611ae928398971a33542119e1549f1af71bad06282ebbe3d3a7f29d0e2d0275", + "full_human_chars": 1512, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_job" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_065032", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 314 + ], + "match_source_rows": [ + 314 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As an energy trader, my aim is to gain a strategic edge by comprehensively analyzing energy market dynamics. I have gathered a JSON file containing real-time market data, `real_time_market_data_feed.json`, and a CSV file of historical energy prices, `historical_energy_prices.csv`. I need to forecast energy prices for the next week, identify periods of high market volatility from the past 30 days, ", + "legacy_target": "forecast_energy_prices", + "legacy_target_description": "Forecasts future energy prices based on real-time market data and historical prices.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 314, + "source_record_id": "44233de8-78dc-46da-9b5b-875710700390", + "source_parent_id": "hermes_record:44233de8-78dc-46da-9b5b-875710700390", + "request_group_id": "hermes_request:406dfe0325cfbfa6e45ed65325e4d2f41c55fe6673e180dbb778ba17b73cde22", + "full_human_chars": 1280, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "forecast_energy_prices", + "identify_market_volatility", + "detect_trading_opportunities" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_065059", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 134 + ], + "match_source_rows": [ + 134 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Chief Security Officer at our financial institution, I'm addressing an increase in cyber threats targeting our network infrastructure, potentially endangering customer data and financial operations. To tackle this, I've collected an extensive dataset comprising our network traffic, server activity, and security system events spanning from March 1, 2023, to March 31, 2023. The datasets inclu", + "legacy_target": "analyze_threat_intelligence", + "legacy_target_description": "Analyzes logs from various sources to extract patterns of unusual activity that could indicate potential cyber threats.", + "legacy_target_description_chars": 119, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 134, + "source_record_id": "a50c8800-a249-4856-840d-85f3c7a220b8", + "source_parent_id": "hermes_record:a50c8800-a249-4856-840d-85f3c7a220b8", + "request_group_id": "hermes_request:7cb1fc0375edbfcf281c7ba7ecb92ba7ae9673478d4e5af08bc895c8b5d183e2", + "full_human_chars": 1639, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "analyze_threat_intelligence" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_065190", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 651 + ], + "match_source_rows": [ + 651 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to call a set of functions to manage maintenance activities for our network of highways and railtracks. For monitoring the asset conditions, we have gathered sensor data and inspection reports. The sensor data file, \"sensor_data_highways_railtracks.csv\", contains timestamped measurements from various embedded sensors along the infrastructure. Our inspection reports file, \"inspection_r", + "legacy_target": "monitor_asset_condition", + "legacy_target_description": "Monitors the condition of highways and railtracks to identify areas that require maintenance.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 651, + "source_record_id": "4e1ce78e-eb79-4ed3-8a5a-fb33b034d916", + "source_parent_id": "hermes_record:4e1ce78e-eb79-4ed3-8a5a-fb33b034d916", + "request_group_id": "hermes_request:77ea75e4e512563384e608a7e8aa9187e54dd58930108c17f44776de5e5380dd", + "full_human_chars": 1885, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "monitor_asset_condition", + "schedule_maintenance", + "manage_maintenance_teams" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_065332", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 365 + ], + "match_source_rows": [ + 365 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the IT manager at XYZ Corporation, I am tasked with enhancing our IT vendor management by implementing a comprehensive system. 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Below is a comprehensive list of these shipments, each with its corresponding details such as weight, dimensions, destination airport, and required de", + "legacy_target": "calculate_efficient_loading", + "legacy_target_description": "Calculates the most efficient loading plan for air freight shipments based on their weights, dimensions, and destination airports.", + "legacy_target_description_chars": 130, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 597, + "source_record_id": "1eec9a71-3b82-4497-b4d9-a10e47e203a6", + "source_parent_id": "hermes_record:1eec9a71-3b82-4497-b4d9-a10e47e203a6", + "request_group_id": "hermes_request:6c2b2894b36ed235492c9777896e1499c053d9a9c36fca244774470fd7673857", + "full_human_chars": 2144, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "calculate_efficient_loading", + "select_optimal_flight_routes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_065611", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 78 + ], + "match_source_rows": [ + 78 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to initiate an advertising campaign for our annual Summer Sale event. 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The camp", + "legacy_target": "create_ad_campaign", + "legacy_target_description": "Create a new advertising campaign with specified parameters on multiple platforms.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 78, + "source_record_id": "e3a5533a-fd54-45a9-bfee-6270402004e2", + "source_parent_id": "hermes_record:e3a5533a-fd54-45a9-bfee-6270402004e2", + "request_group_id": "hermes_request:914f04ad12947001b5534bece7bfe260d36e34e73d047d8c3c3ac3576031ac44", + "full_human_chars": 1299, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_ad_campaign" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_065709", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 607 + ], + "match_source_rows": [ + 607 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I require a booking for a shuttle service that will accommodate a group of five individuals, all of whom will be staying at the downtown hotel. We need to arrange transportation to the city's main train station on the morning of April 15th, 2023. Please ensure that the shuttle is ready for pickup at precisely 10:00 AM, and that there's adequate storage space to hold our luggage as we all will have", + "legacy_target": "createGroundTransportReservation", + "legacy_target_description": "Creates a reservation for ground transportation services such as shuttles or taxis.", + "legacy_target_description_chars": 83, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 607, + "source_record_id": "e4fe9589-da95-42d7-8b59-5ce83ce3785d", + "source_parent_id": "hermes_record:e4fe9589-da95-42d7-8b59-5ce83ce3785d", + "request_group_id": "hermes_request:e00960ae770fe725f5e63c0e04f8ad32471cf486d8f6ff1428e83712f4d3fe98", + "full_human_chars": 576, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "createGroundTransportReservation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_065718", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 45 + ], + "match_source_rows": [ + 45 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently deploying a temperature monitoring system using Particle IoT-enabled temperature sensors throughout our warehouse facility. Each sensor has a unique identifier, and I need to ensure they are properly registered with Particle IoT Cloud for management and secure data transmission. For example, one of the sensors is identified as `tempSensor_001` and should be named `Temperature Sensor", + "legacy_target": "registerDevice", + "legacy_target_description": "Register a new device with the Particle IoT Cloud.", + "legacy_target_description_chars": 50, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 45, + "source_record_id": "63eeb057-885b-4884-8c2d-2e22f2ba540c", + "source_parent_id": "hermes_record:63eeb057-885b-4884-8c2d-2e22f2ba540c", + "request_group_id": "hermes_request:e13d08d7c333f49c5bf4a01def2bc28182e7f922b4eccb801d74da78ea884406", + "full_human_chars": 1183, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "registerDevice", + "establishSecureConnection" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_065858", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 542 + ], + "match_source_rows": [ + 542 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the data manager for our company, we have a comprehensive dataset named \"customer_records\" that includes various information about our customers. I need assistance with automating the data cleansing and quality checks to streamline our operations. Here are the specific tasks I need to be performed on the dataset:\n\n1. Identify and remove duplicate records based on the unique \"customer_id\" field ", + "legacy_target": "remove_duplicate_records", + "legacy_target_description": "Removes duplicate records from a dataset based on a specified unique field.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 542, + "source_record_id": "75053add-246a-493a-b6c2-c4c500990fb0", + "source_parent_id": "hermes_record:75053add-246a-493a-b6c2-c4c500990fb0", + "request_group_id": "hermes_request:b988fb95b40a4fbf5a46d314db7568741e36e0dcb71511cfccba477566a893ac", + "full_human_chars": 1575, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "remove_duplicate_records", + "correct_email_format", + "verify_addresses", + "validate_currency_format" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_065901", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1126, + 1209, + 1231, + 1277, + 1287, + 1341, + 1414, + 1480, + 1502, + 1587, + 1676, + 1801, + 1814 + ], + "match_source_rows": [ + 1126 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage two values (0 or 1), so by the Fundamental Theorem of Counting, there are 28 different combinations. 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I will then", + "legacy_target": "createCalendarEvent", + "legacy_target_description": "Create a new calendar event with the specified details.", + "legacy_target_description_chars": 55, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1036, + "source_record_id": "ac8e0b55-bca1-4452-a906-41a5ecd0104e", + "source_parent_id": "hermes_record:ac8e0b55-bca1-4452-a906-41a5ecd0104e", + "request_group_id": "hermes_request:819079520cd966bb5ed18f9fa14780e1a34cfe3a6c1aca98c96ff0fafced0d07", + "full_human_chars": 835, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "createCalendarEvent", + "retrieveEventsForDay" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066171", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 441 + ], + "match_source_rows": [ + 441 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working with a robotic arm, specifically the RA-7X model, in our laboratory for a task that requires precise joint movements to place a sensor correctly on our testing equipment. The task sequence is as follows: initially, the robotic arm's base joint needs to rotate to an angle of 45 degrees, followed by the extension of the elbow joint to reach a 90-degree angle, and concluding wi", + "legacy_target": "rotate_base_joint", + "legacy_target_description": "Rotates the base joint of the specified robotic arm to a given angle.", + "legacy_target_description_chars": 69, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 441, + "source_record_id": "9b498610-4607-44d2-b3be-088747613298", + "source_parent_id": "hermes_record:9b498610-4607-44d2-b3be-088747613298", + "request_group_id": "hermes_request:1e83b60a0e25efc67e9893e4f242124035110b510715713e71d4c6bbbda53da8", + "full_human_chars": 699, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "rotate_base_joint", + "extend_elbow_joint", + "rotate_wrist_joint" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066254", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 203 + ], + "match_source_rows": [ + 203 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm looking for a comprehensive photography app for my smartphone that offers a wide array of features for an upcoming wedding I will be attending. The app should include capabilities such as applying real-time filters, creating photo collages from the event, and editing videos with music overlay. I would like the app to be versatile and user-friendly, with options for both novice and experienced ", + "legacy_target": "find_photo_video_app", + "legacy_target_description": "Find a photo and video app that includes specific features for capturing and editing.", + "legacy_target_description_chars": 85, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 203, + "source_record_id": "9b5abd71-5b84-463d-8868-29515eb6b2ee", + "source_parent_id": "hermes_record:9b5abd71-5b84-463d-8868-29515eb6b2ee", + "request_group_id": "hermes_request:1b62cb9d6f8c172dcc46172785cf4b736f07e04ac150bf930ba904c538e8e4bc", + "full_human_chars": 798, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "find_photo_video_app" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066260", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1111, + 1174, + 1250, + 1298, + 1323, + 1371, + 1407, + 1488, + 1527, + 1528, + 1718, + 1733, + 1751 + ], + "match_source_rows": [ + 1111 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage Stephen Davies, Ph.D. \n Version 2.2.1 \n##### Through Discrete Mathematics \n## A Cool Brisk Walk \nCopyright (c) 2023 Stephen Davies. \nUniversity of Mary Washington Department of Computer Science James Farmer Hall 1301 College Avenue Fredericksburg, VA 22401 \nPermission is granted to copy, distribute, transmit and adapt this work u", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1111, + "source_record_id": "a161982c-0f80-47cc-838c-40461d2e5ca4", + "source_parent_id": "hermes_record:a161982c-0f80-47cc-838c-40461d2e5ca4", + "request_group_id": "hermes_request:c59907df836f76ab3b03894daf8a9a70ac39fc4b26319ca6a8b2edd1bc52b60f", + "full_human_chars": 13401, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066261", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 107 + ], + "match_source_rows": [ + 107 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating a kayaking expedition titled 'Kayaking Adventure,' which includes a group of 10 individuals. We are planning to explore various picturesque kayaking spots, starting with Crystal River, located at latitude 28.902479 and longitude -82.592601. I would like to schedule our first kayaking outing at Crystal River for the 15th of July, 2023 at 09:00 AM. Additionally, our group has a pre", + "legacy_target": "create_itinerary", + "legacy_target_description": "Create a new itinerary with a title, description, and group size.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 107, + "source_record_id": "74a08af3-845d-46d9-93d4-c4993b9f824a", + "source_parent_id": "hermes_record:74a08af3-845d-46d9-93d4-c4993b9f824a", + "request_group_id": "hermes_request:f7bac4deae30894730177134b8aae8259a3a3c5efd683ac0119ca7ff0891ab6a", + "full_human_chars": 810, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_itinerary" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066365", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 871 + ], + "match_source_rows": [ + 871 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm performing research on pharmacogenomics, focusing on how individuals' genetic makeup affects their response to medications. For this purpose, I require detailed genetic data for a specific individual in my study. I have the participant ID 'PGD123', and for this ID, I need the complete genetic profile, including the full genomic sequence and unique profile identifier. Additionally, I require th", + "legacy_target": "get_genetic_profile", + "legacy_target_description": "Retrieves the full genetic profile for a given participant ID.", + "legacy_target_description_chars": 62, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 871, + "source_record_id": "02463934-de5d-4d38-ab5b-dcaa59fb8100", + "source_parent_id": "hermes_record:02463934-de5d-4d38-ab5b-dcaa59fb8100", + "request_group_id": "hermes_request:5e3bd9c2f08375b1f3d5ff4c19a9863d42abde29e7e8c5ca8816e055a40730e8", + "full_human_chars": 1608, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_genetic_profile", + "get_genetic_markers", + "get_marker_annotations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066409", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 760 + ], + "match_source_rows": [ + 760 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I've meticulously cataloged an extensive visual archive of wildlife from the Madagascar rainforest. Each photograph has been analyzed, and an embedding vector representation has been created. I wish to organize these images further by their similarity to a particular image in the dataset: a rare capture of the critically endangered Indri lemur. This reference image has an embedding vector availabl", + "legacy_target": "retrieve_similar_images_by_embedding", + "legacy_target_description": "Retrieves a list of images similar to a reference image based on their embeddings.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 760, + "source_record_id": "25ab1ec2-1800-4d30-a83a-53c7a2c9469d", + "source_parent_id": "hermes_record:25ab1ec2-1800-4d30-a83a-53c7a2c9469d", + "request_group_id": "hermes_request:5d431deeecc06b6ae833d8c68216301e9e89393b973ec734fb2c17845cf24f44", + "full_human_chars": 1419, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "retrieve_similar_images_by_embedding" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066519", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 890 + ], + "match_source_rows": [ + 890 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm enhancing our retail store experience by incorporating an augmented reality feature that allows customers to view products in 3D before purchasing. We have a specific 'Modern Leather Sofa' that we would like customers to be able to visualize through their smartphones by scanning its associated QR code. I need a textured and accurately scaled 3D model of this sofa to be retrieved and displayed ", + "legacy_target": "get_3d_model", + "legacy_target_description": "Retrieves the 3D model for a product based on its name and associated QR code.", + "legacy_target_description_chars": 78, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 890, + "source_record_id": "ae90ebb8-0711-4b2a-b9ef-44cec55a4a86", + "source_parent_id": "hermes_record:ae90ebb8-0711-4b2a-b9ef-44cec55a4a86", + "request_group_id": "hermes_request:49d8c001009bf34d4845b20cf79f8beb4096da4bf8a241128036a1e13c884724", + "full_human_chars": 928, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_3d_model" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066669", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 269 + ], + "match_source_rows": [ + 269 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to establish a comprehensive view of our algo-trading performance by creating a dashboard titled \"AlgoTrade Performance Dashboard.\" This dashboard should prominently feature real-time key performance indicators, including total return, volatility, Sharpe ratio, and drawdown. To achieve this, we'll need to integrate with our trade data source using the API key \"abcd1234\" and access tra", + "legacy_target": "create_algorithmic_trading_dashboard", + "legacy_target_description": "Creates a new dashboard for monitoring algorithmic trading performance with specified KPI metrics and real-time updates.", + "legacy_target_description_chars": 120, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 269, + "source_record_id": "6e3aabe5-7d1f-4725-9404-2ffba6b46cbe", + "source_parent_id": "hermes_record:6e3aabe5-7d1f-4725-9404-2ffba6b46cbe", + "request_group_id": "hermes_request:474f3dd7fd59bd1cf93d2dc24f74e8ed46053dd97f859a34d2a337f8d8ed5279", + "full_human_chars": 1330, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_algorithmic_trading_dashboard" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066770", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 50 + ], + "match_source_rows": [ + 50 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As an avid movie buff, I have curated a digital collection on my home entertainment system, and I want to enhance my viewing experience with tailored content and settings. I've watched 'Riveting Saga' (movie_201) last Friday night and 'Laugh Out Loud' (comedy_789) yesterday evening. My preferred genres are action and drama. I'd like content recommendations based on my history. Also, I like my audi", + "legacy_target": "recommend_content_based_on_viewing_habits", + "legacy_target_description": "Recommends content based on the user's viewing history and genre preferences.", + "legacy_target_description_chars": 77, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 50, + "source_record_id": "79873db7-e520-485a-8526-7ff38442c09d", + "source_parent_id": "hermes_record:79873db7-e520-485a-8526-7ff38442c09d", + "request_group_id": "hermes_request:6a236a2bc0f9407cd9816fd881763962ffe1408e7eeef3ae0b35edee7aca6f89", + "full_human_chars": 1536, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "recommend_content_based_on_viewing_habits", + "adjust_settings_to_user_preferences", + "set_parental_control_options" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_066817", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1106, + 1166, + 1182, + 1204, + 1344, + 1437, + 1500, + 1615, + 1730, + 1817, + 1818, + 1860, + 1887 + ], + "match_source_rows": [ + 1106 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 1 0 3 - 2 0 0 1 1 - 3 0 0 0 0 0 0 \n \nBy Theorem HSC, the system is consistent, and so the computation n - r = 4 - 2 = 2 means the solution set contains two free variables by Theorem FVCS, and hence has infinitely many solutions. We can describe this solution set using the free variables x 3 and x 4 , \n S = \n \n \n \n x 1 x 2", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask how to diagnose or solve a problem outlined in the document. These assesses ability to troubleshoot.", + "legacy_target_description_chars": 138, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1106, + "source_record_id": "56f98bf3-940b-465d-bcfc-294a5f38563b", + "source_parent_id": "hermes_record:56f98bf3-940b-465d-bcfc-294a5f38563b", + "request_group_id": "hermes_request:ce659af872b3eb4549ee26b993fcbc0a9dfa8f3778bfb040be22d684866e6f12", + "full_human_chars": 8034, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_067140", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 274 + ], + "match_source_rows": [ + 274 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for the risk management of our equity-focused algorithmic trading portfolio, which includes a variety of sectors such as technology, healthcare, and consumer goods. Our team requires a comprehensive risk assessment that includes calculating the Value at Risk (VaR), performing stress testing, and evaluating liquidity risk to make informed decisions and mitigate potential losses. We", + "legacy_target": "calculate_portfolio_var", + "legacy_target_description": "Calculates the Value at Risk (VaR) for a portfolio based on historical trade data.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 274, + "source_record_id": "d525c2d0-ff82-407a-83f2-0264c50a8984", + "source_parent_id": "hermes_record:d525c2d0-ff82-407a-83f2-0264c50a8984", + "request_group_id": "hermes_request:fc418e7ecb5d44568f0c910dc38172528eac9bd22900fb236f20f517d16486e2", + "full_human_chars": 1820, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_portfolio_var", + "perform_stress_testing", + "assess_liquidity_risk" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_067177", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1223, + 1249, + 1329, + 1391, + 1453, + 1464, + 1507, + 1509, + 1647, + 1719, + 1725, + 1802, + 1866 + ], + "match_source_rows": [ + 1223 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage Let's be crystal clear where we're going with this. 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I need to ensure that everything complies with trade regulations and that I am aware of all applicable tariffs. Can you calculate the tariffs for these goods categories, considering there might be trade agreements between China and Germany that could affect tariff rates? 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Here's an e", + "legacy_target": "generate_chat_response", + "legacy_target_description": "Generates a chat response using OpenAI's GPT model based on the provided prompt and parameters.", + "legacy_target_description_chars": 95, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 766, + "source_record_id": "df5e1bec-b671-4618-813f-6982b305d325", + "source_parent_id": "hermes_record:df5e1bec-b671-4618-813f-6982b305d325", + "request_group_id": "hermes_request:456719accdbe297683e27a5ae897adba6dfb6dc76ae1352d9a2277a2925c75e1", + "full_human_chars": 969, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_chat_response" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_068097", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 376 + ], + "match_source_rows": [ + 376 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To facilitate the automation on our production line for circuit boards, I would like to initiate the following actions. 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Could you retr", + "legacy_target": "get_mars_rover_data", + "legacy_target_description": "Retrieve data on Mars Rover missions from a space exploration API.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 910, + "source_record_id": "4478245d-5415-4b6c-a47f-3058181e83ac", + "source_parent_id": "hermes_record:4478245d-5415-4b6c-a47f-3058181e83ac", + "request_group_id": "hermes_request:bcc32db5ff6eff6f341e99bfc2c3a2cbac39f6374e153ef52e83d0c3540888e9", + "full_human_chars": 598, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_mars_rover_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068352", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 52 + ], + "match_source_rows": [ + 52 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing a telecommunication network and require detailed monitoring of key network devices to ensure they are fully operational, alongside a comprehensive analysis of the network traffic patterns. Specifically, I need to check the operational status of our router (device ID: router_01), switch (device ID: switch_02), and modem (device ID: modem_03). Additionally, I want to analyze the traf", + "legacy_target": "get_network_device_status", + "legacy_target_description": "Retrieves the current operational status of specified network devices.", + "legacy_target_description_chars": 70, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 52, + "source_record_id": "260c475f-0116-42be-9529-7392a78ad1cf", + "source_parent_id": "hermes_record:260c475f-0116-42be-9529-7392a78ad1cf", + "request_group_id": "hermes_request:129d30be21803996eba6c71ec5776018887264118aaba331f4a3ff9e26a78c05", + "full_human_chars": 1005, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_network_device_status", + "analyze_network_traffic" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068361", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 517 + ], + "match_source_rows": [ + 517 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am planning an outdoor event in San Francisco for the upcoming week and would like to stay updated on the weather conditions. 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Please make sure the games are suitable for iPhone devices and provide me the titles and brief details a", + "legacy_target": "get_trending_games", + "legacy_target_description": "Retrieve the top trending games from the App Store.", + "legacy_target_description_chars": 51, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1052, + "source_record_id": "c2249a4a-9598-4025-851f-6d2684efcf02", + "source_parent_id": "hermes_record:c2249a4a-9598-4025-851f-6d2684efcf02", + "request_group_id": "hermes_request:2e4242655b7ae3531f0609725e9dd8cae4bdbc919f757749ced62c83d298c822", + "full_human_chars": 629, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_trending_games" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068583", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 502 + ], + "match_source_rows": [ + 502 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to analyze the real estate market in Los Angeles for the first quarter of 2023. 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Additionally, I need market trends for the same time period and location, focusing on average sale p", + "legacy_target": "extract_property_data", + "legacy_target_description": "Extracts property listings from a real estate database API and structures them into a JSON schema.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 502, + "source_record_id": "4c4e2648-6717-477e-8008-6592abd7786f", + "source_parent_id": "hermes_record:4c4e2648-6717-477e-8008-6592abd7786f", + "request_group_id": "hermes_request:7cbad8a9890ea0ccd091d011e8bef80fae89309fdf55ec52d3895d04d488fdc8", + "full_human_chars": 1250, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "extract_property_data", + "extract_market_trends", + "extract_historical_prices" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068601", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 988 + ], + "match_source_rows": [ + 988 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm operating a personal care products store and today I had a customer purchase three bottles of Clarifying Shampoo with the product ID \"CPS12345\" using their credit card. They also provided a discount code \"15OFF\" that needs to be applied to their transaction. Additionally, yesterday a customer returned two units of Moisturizing Conditioner with the product ID \"CPS67890\" from the transaction ID ", + "legacy_target": "process_sale_transaction", + "legacy_target_description": "Processes a sale transaction for a given product and quantity using the specified payment method.", + "legacy_target_description_chars": 97, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 988, + "source_record_id": "8ef68b4f-992e-479c-9aa8-1395cbbed149", + "source_parent_id": "hermes_record:8ef68b4f-992e-479c-9aa8-1395cbbed149", + "request_group_id": "hermes_request:7196a967647cb07fca33075fe7284abb5d15e4a3861e12333a5f17c363fb1b15", + "full_human_chars": 1118, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "process_sale_transaction" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068616", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1053 + ], + "match_source_rows": [ + 1053 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I've recently added some new applications to my iPhone and I'm looking to tidy up my app icons. Can you provide me with a list of all productivity-related apps currently installed on my device? Additionally, I've been managing quite a few documents lately and I suspect there might be some duplicates cluttering my system. Could you search through my iPhone's 'Downloads' directory and identify any d", + "legacy_target": "list_category_apps", + "legacy_target_description": "List all apps within a specified category on the iPhone.", + "legacy_target_description_chars": 56, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1053, + "source_record_id": "697351e0-3274-4aa7-b6a4-7329bb871324", + "source_parent_id": "hermes_record:697351e0-3274-4aa7-b6a4-7329bb871324", + "request_group_id": "hermes_request:2d199ea1783369af2ff71bbfadabc07d07d12b138079a18276cc98bf6913d4d4", + "full_human_chars": 999, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "list_category_apps", + "find_duplicate_files" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068665", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 825 + ], + "match_source_rows": [ + 825 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage an online retail store and am currently preparing a comprehensive report on our sales performance for the previous month. I need to analyze the following data to form effective strategies moving forward:\n\n1. Sales Data\n - Total revenue generated\n - Average order value\n - Number of unique customers\n\n2. Customer Behavior\n - Top 5 best-selling products by sales volume\n - Number of ", + "legacy_target": "get_sales_data", + "legacy_target_description": "Retrieve sales data for a specified time period, including total revenue, average order value, and number of unique customers.", + "legacy_target_description_chars": 126, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 825, + "source_record_id": "1949f5c6-e085-44f0-85c4-7d6bc7b770f0", + "source_parent_id": "hermes_record:1949f5c6-e085-44f0-85c4-7d6bc7b770f0", + "request_group_id": "hermes_request:b5b70a58de7d5ab8ec5c827261259b47f92b735abb350902c33a77d47186449b", + "full_human_chars": 1287, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_sales_data", + "get_top_products", + "get_customer_behavior" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068767", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 364 + ], + "match_source_rows": [ + 364 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing cloud resources for a multi-energy cloud platform that incorporates distributed generation, energy storage, microgrids, flexible load, and electric vehicle piles. To ensure high-efficiency application and management, I would like to execute a set of tasks. First, I need to list all resources currently under our Azure cloud provider. Second, a new AWS resource group called \"Production", + "legacy_target": "listResources", + "legacy_target_description": "List resources from a specified cloud provider and resource type.", + "legacy_target_description_chars": 65, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 364, + "source_record_id": "ec104798-602d-43e6-ae22-613b488c6cf4", + "source_parent_id": "hermes_record:ec104798-602d-43e6-ae22-613b488c6cf4", + "request_group_id": "hermes_request:3a351027ac1a0f9e7d79d72b8e05d882ab34c896fd4294e32c39c317b1f6848b", + "full_human_chars": 1067, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "listResources", + "createResourceGroup" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068769", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 420 + ], + "match_source_rows": [ + 420 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to create a new table for organizing our bookstore's inventory. The table should be called `BookInventory`, and it needs to include several specific fields to record details about each book we have in stock. The fields should be: `book_id` as a VARCHAR with a maximum length of 255 characters and set as the PRIMARY KEY, `title` as a VARCHAR with a maximum length of 255 characters, `aut", + "legacy_target": "create_table", + "legacy_target_description": "Creates a new table in the database with the specified name and columns.", + "legacy_target_description_chars": 72, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 420, + "source_record_id": "a9902a01-2851-4072-84a6-f856e0b17c1b", + "source_parent_id": "hermes_record:a9902a01-2851-4072-84a6-f856e0b17c1b", + "request_group_id": "hermes_request:d3050a47de8d66e07341a950f60b81d77da2ec97724400f3c52a17443e0c3c6c", + "full_human_chars": 1263, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_table" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068782", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 159 + ], + "match_source_rows": [ + 159 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm developing an educational platform to assist French learners in improving their pronunciation skills. As part of the interactive lessons, I would like to provide them with high-quality audio examples. For this purpose, I require the conversion of the provided French phrase 'Bonjour, comment allez-vous?' into speech. Please use the Hugging Face speech synthesis model specifically designed for F", + "legacy_target": "synthesize_speech", + "legacy_target_description": "Converts text into speech using a specified Hugging Face model for speech synthesis.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 159, + "source_record_id": "e2419baa-742a-4905-a593-1fcd0bce534d", + "source_parent_id": "hermes_record:e2419baa-742a-4905-a593-1fcd0bce534d", + "request_group_id": "hermes_request:ff751e940952b8dab19b99ef948a1ec79edaaa4a90a7be12582d85e37b68d3d8", + "full_human_chars": 934, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "synthesize_speech" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068847", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 284 + ], + "match_source_rows": [ + 284 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm managing a diverse portfolio and wish to integrate advanced algorithmic trading signals to inform my buy and sell decisions. I have compiled 'Equity_ETF_Historical_Prices.csv' which contains detailed historical price data for the equities and ETFs within my portfolio. Additionally, I have assembled 'Financial_News_Summary.txt' summarizing recent impactful financial news articles that I want to", + "legacy_target": "generate_trading_signals", + "legacy_target_description": "Generates trading signals based on machine learning analysis of historical price data and market sentiment.", + "legacy_target_description_chars": 107, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 284, + "source_record_id": "dad77684-a8d5-4f0f-bbf3-73ed161f79af", + "source_parent_id": "hermes_record:dad77684-a8d5-4f0f-bbf3-73ed161f79af", + "request_group_id": "hermes_request:824a1ba47f924beb683a5b143242277fb353aae20ae31928180eaab6332812cd", + "full_human_chars": 995, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_trading_signals" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068903", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 573 + ], + "match_source_rows": [ + 573 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting a market analysis and require current pricing information for a set of consumer electronics on an e-commerce platform. I have identified 10 specific products I'm interested in, and I need to extract their pricing details and URLs from the website. The product names are:\n\n- Samsung Galaxy S21\n- Apple iPhone 12\n- Sony WH-1000XM4 Headphones\n- Fitbit Charge 4\n- Dell XPS 13 Laptop\n- Nik", + "legacy_target": "search_and_scrape_data", + "legacy_target_description": "Searches for a list of products on an e-commerce website and scrapes specified data fields for each product.", + "legacy_target_description_chars": 108, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 573, + "source_record_id": "79ea2e25-10dd-430d-8961-a73119f70d14", + "source_parent_id": "hermes_record:79ea2e25-10dd-430d-8961-a73119f70d14", + "request_group_id": "hermes_request:0f3b544cde41453b1b79759ff9ffb51679f393e604fa07bce63530c3d436c54b", + "full_human_chars": 1067, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "search_and_scrape_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_068932", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 475 + ], + "match_source_rows": [ + 475 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our consumer electronics company is expanding its product line and experiencing increased sales volumes. To manage this growth effectively, we need specialized functions to optimize our supply chain operations. Specifically, we require assistance with three critical areas:\n\n1. **Inventory Management**: We need to analyze current stock levels of items such as smartphones, laptops, headphones, and a", + "legacy_target": "optimize_inventory_management", + "legacy_target_description": "Optimizes inventory levels by analyzing current stock and predicting future demand to prevent overstocking or stockouts.", + "legacy_target_description_chars": 120, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 475, + "source_record_id": "d35ca281-f082-49bd-82c7-8dbd08079376", + "source_parent_id": "hermes_record:d35ca281-f082-49bd-82c7-8dbd08079376", + "request_group_id": "hermes_request:807a51ac8f5f48fa38c42ed44fb962dd42e4cd2bbc3cf6d23af55735216a27dd", + "full_human_chars": 2653, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "optimize_inventory_management", + "select_optimal_suppliers", + "enhance_product_tracking" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069052", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1103, + 1154, + 1221, + 1242, + 1303, + 1328, + 1494, + 1584, + 1653, + 1711, + 1749, + 1780, + 1868 + ], + "match_source_rows": [ + 1103 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage know it makes all the other equations of the original system true. We then find aj 1 b 1 + * * * + ajnbn = aj 1 b 1 + * * * + ajnbn + abi - abi = aj 1 b 1 + * * * + ajnbn + (aai 1 b 1 + * * * + aainbn) - abi = aj 1 b 1 + aai 1 b 1 + * * * + ajnbn + aainbn - abi = (aai 1 + aj 1 )b 1 + * * * + (aain + ajn)bn - abi = abi + bj - abi ", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1103, + "source_record_id": "ad105e11-f880-42f8-858d-26ffee84b4f1", + "source_parent_id": "hermes_record:ad105e11-f880-42f8-858d-26ffee84b4f1", + "request_group_id": "hermes_request:5612c7c320446ef7ccfea9f7b53758b14dbc69d8c7c40a7a03025943b246272a", + "full_human_chars": 8629, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069075", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 186 + ], + "match_source_rows": [ + 186 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to conduct a thorough analysis of my Robinhood investment account. Please retrieve the current value of my portfolio as of today's date, which is April 15, 2023. Additionally, I am interested in understanding the historical performance of my portfolio; particularly I want to see the performance data from one year ago, on April 12, 2022, to the present date. Lastly, I require a detaile", + "legacy_target": "get_portfolio_value", + "legacy_target_description": "Retrieves the current value of the user's investment portfolio on a specified date.", + "legacy_target_description_chars": 83, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 186, + "source_record_id": "60e990e9-d82a-4e71-9c0a-dcfb42948b2b", + "source_parent_id": "hermes_record:60e990e9-d82a-4e71-9c0a-dcfb42948b2b", + "request_group_id": "hermes_request:1a79c82cdefa68ed488952682e6e57ee6680a46f808b54c2ee9f2675090ac152", + "full_human_chars": 641, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_portfolio_value", + "get_historical_performance", + "get_asset_allocation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069188", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 965 + ], + "match_source_rows": [ + 965 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently overseeing a project that requires efficient coordination between our Customer Relationship Management (CRM) system and our project management tool. We utilize Salesforce as our CRM platform, where we register new sales leads. The requirement is to automate the creation of tasks in our project management tool, Trello, corresponding to each new lead registered.\n\nFor every new lead ca", + "legacy_target": "create_crm_to_project_management_integration", + "legacy_target_description": "Sets up an integration between a CRM system and a project management tool using Zapier to automate task creation based on new CRM entries.", + "legacy_target_description_chars": 138, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 965, + "source_record_id": "5d962e6b-d7f6-4bab-9b67-d89dd1908409", + "source_parent_id": "hermes_record:5d962e6b-d7f6-4bab-9b67-d89dd1908409", + "request_group_id": "hermes_request:cc20f6111280521fe0d06c8d51ffec8564c9f9a06e90137fa1bd1424f2665e19", + "full_human_chars": 1210, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_crm_to_project_management_integration" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069324", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1092 + ], + "match_source_rows": [ + 1092 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently engaged in a machine learning initiative and require a secure environment for storing substantial datasets. To facilitate this, I've identified a need to create a new GCP storage bucket with specific configurations. The bucket, which will be named 'ml-datasets-bucket', must be set up in the 'US' location and should employ the 'STANDARD' storage class. Post-creation, I plan to upload", + "legacy_target": "create_storage_bucket", + "legacy_target_description": "Create a new storage bucket in the specified project with the given parameters.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1092, + "source_record_id": "5656ad1a-5c5d-4811-bb59-1d7b6557ac74", + "source_parent_id": "hermes_record:5656ad1a-5c5d-4811-bb59-1d7b6557ac74", + "request_group_id": "hermes_request:77f2a6565965716972c86aa868d39005ddf3d838b63214bd1feace22460729c7", + "full_human_chars": 892, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "create_storage_bucket", + "upload_files_to_bucket", + "encrypt_bucket_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069354", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 66 + ], + "match_source_rows": [ + 66 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "For the \"Soundwave\" music festival organized by \"Harmony Events,\" taking place on July 15, 2023, I would like to manage the artist lineup on two stages: \"Main Stage\" and \"Acoustic Stage.\" We have the following schedule:\n\n- Artist ID \"A123,\" DJ Alex, is set to perform at the Main Stage at 4 PM (2023-07-15T16:00:00).\n- Artist ID \"B456,\" The Strings Band, is scheduled for the Acoustic Stage at 6 PM (", + "legacy_target": "manage_artist_lineup", + "legacy_target_description": "Manages the lineup of artists for an event, including stage assignments and performance times.", + "legacy_target_description_chars": 94, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 66, + "source_record_id": "a64c4bf4-0ad1-4323-9330-8f1c71671321", + "source_parent_id": "hermes_record:a64c4bf4-0ad1-4323-9330-8f1c71671321", + "request_group_id": "hermes_request:001179ce3c39db686ba03cf44f45b1126518cc24ea8010319e7e8a9d12b1c988", + "full_human_chars": 1237, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "manage_artist_lineup", + "track_ticket_sales", + "coordinate_event_logistics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069410", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 32 + ], + "match_source_rows": [ + 32 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing the student database for a college and I require assistance with two distinct tasks. Firstly, I would like to add a new student into our Education Management System. Here are the details for the new student:\n\nName: John Doe\nAge: 20\nMajor: Computer Science\nEnrollment Year: 2023\n\nSecondly, I need to access the academic records of an existing student. The specific student’s ID is S1023.", + "legacy_target": "addStudent", + "legacy_target_description": "Adds a new student to the Education Management System with the provided details.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 32, + "source_record_id": "3a06afa8-2222-42e3-a707-8e533ade54de", + "source_parent_id": "hermes_record:3a06afa8-2222-42e3-a707-8e533ade54de", + "request_group_id": "hermes_request:7e4d9edf31ab677bb6ddf1a3975887e14fdbf447c9195317b2f440f5668388e5", + "full_human_chars": 637, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "addStudent", + "getAcademicRecords" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069458", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 629 + ], + "match_source_rows": [ + 629 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the inventory manager for our marine transportation company, I am handling a fleet of vessels that require constant monitoring to maintain optimal inventory levels. We have a comprehensive vessel inventory database that lists all our vessels along with their current stock of supplies, equipment, and consumables. I would like to utilize three functions to streamline our inventory management proc", + "legacy_target": "monitor_inventory_levels", + "legacy_target_description": "Monitors the inventory levels for each vessel and provides a summary of current stock.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 629, + "source_record_id": "4fdf6525-0485-49d3-a43d-ebe6514de476", + "source_parent_id": "hermes_record:4fdf6525-0485-49d3-a43d-ebe6514de476", + "request_group_id": "hermes_request:2da73ce837cee90ac017e00bbe9e20c487e6cd31a93aa366ee306f8b051a7d9a", + "full_human_chars": 1996, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "monitor_inventory_levels", + "predict_restocking_needs", + "generate_inventory_reports" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069493", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 27 + ], + "match_source_rows": [ + 27 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the HR manager at TechFusion Inc. and we're implementing new procedures to improve our employee onboarding workflow. For our most recent hire, James Ford, we'll need to create his employee profile with the following specifics: full name \"James Ford\", email address \"james.ford@techfusion.com\", department \"Development\", and position \"Software Engineer\". His start date will be \"2023-05-15\".\n\nAdd", + "legacy_target": "create_employee_profile", + "legacy_target_description": "Creates a new employee profile in the HRMS with personal and professional details.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 27, + "source_record_id": "24b4de96-700f-42e1-b42f-d7f3fb7c563b", + "source_parent_id": "hermes_record:24b4de96-700f-42e1-b42f-d7f3fb7c563b", + "request_group_id": "hermes_request:d56aff936728baffe143c5d5c6f4b4e9619b88d3fc453aa20eac54f109a64e84", + "full_human_chars": 1978, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_employee_profile" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069545", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 983 + ], + "match_source_rows": [ + 983 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage a brewery and we need to meticulously track and manage our inventory, which includes raw materials like hops, malt, and yeast, as well as other brewing supplies. The goal is to maintain an optimal stock level that aligns with our production schedule, avoiding both shortages and excess inventory. We record our stock levels in a digital inventory database and maintain detailed logs of our h", + "legacy_target": "monitor_inventory", + "legacy_target_description": "Monitors current inventory levels and provides a report on stock status.", + "legacy_target_description_chars": 72, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 983, + "source_record_id": "081f5f08-0208-4aa7-9118-5583e7d2c166", + "source_parent_id": "hermes_record:081f5f08-0208-4aa7-9118-5583e7d2c166", + "request_group_id": "hermes_request:696d81f03415d76376b7e98125310683d0c9e159ec2a3e48d86d079082ca1d2a", + "full_human_chars": 1936, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "monitor_inventory", + "predict_inventory_needs", + "automate_reordering" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069725", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 658 + ], + "match_source_rows": [ + 658 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the manager of check-in operations for Flight AA123, I need to facilitate an efficient process for our passengers due at 3:00pm today. There are specific tasks we must address:\n\n1. Assigning seats to passengers based on their preferences, where some require additional legroom or are traveling with infants and need bassinet-accessible seats.\n2. Managing special assistance for those with disabili", + "legacy_target": "assignSeats", + "legacy_target_description": "Assigns seats to passengers based on their preferences and any additional requirements they may have.", + "legacy_target_description_chars": 101, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 658, + "source_record_id": "145d47a9-7301-43f7-9487-12549be522c4", + "source_parent_id": "hermes_record:145d47a9-7301-43f7-9487-12549be522c4", + "request_group_id": "hermes_request:9ffb29df67ad4671f8dda519cb61e5ea425b7eacdda14fab260b4a4d168757d7", + "full_human_chars": 1655, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "assignSeats", + "manageSpecialAssistance", + "optimizeBaggageHandling" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069738", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1067 + ], + "match_source_rows": [ + 1067 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need assistance with managing Chrome browser extensions across our company’s network. The task involves installing the Microsoft Defender Browser Protection extension on all Chrome browsers within the organization. The web store URL for the Microsoft Defender Browser Protection extension is `https://chrome.google.com/webstore/detail/microsoft-defender-browse/bkbeeeffjjeopflfhgeknacdieedcoml`. Af", + "legacy_target": "install_browser_extension", + "legacy_target_description": "Install a browser extension using the extension's web store URL.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1067, + "source_record_id": "6f1d2e3d-b3e3-4bf6-bfcd-133b4565f6cf", + "source_parent_id": "hermes_record:6f1d2e3d-b3e3-4bf6-bfcd-133b4565f6cf", + "request_group_id": "hermes_request:2a24b3a3b0aa093b4bfe1442a7fd984b485b6f0849f54df9fc73dcc8b5f9dcdd", + "full_human_chars": 1261, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "install_browser_extension", + "enable_browser_extension", + "list_installed_extensions" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069842", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1157, + 1175, + 1213, + 1321, + 1477, + 1519, + 1563, + 1588, + 1622, + 1806, + 1870, + 1875, + 1885 + ], + "match_source_rows": [ + 1157 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage do, and they found the analysis to be well constructed. Thus, you can accept the findings as legitimate and just focus on the passages that explain the findings and their significance in plainer language. If you have a really good article that's a few years old, you can use article databases to find newer articles that cited it i", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for advice or suggestions on how to approach a specific scenario found in the document.", + "legacy_target_description_chars": 125, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1157, + "source_record_id": "5177fe0e-a3d4-4621-a77b-9aa5b4086a56", + "source_parent_id": "hermes_record:5177fe0e-a3d4-4621-a77b-9aa5b4086a56", + "request_group_id": "hermes_request:1effd7aeba84ab07a9f969dba4fa62ff6ac0fdf63dfe038fd3268110b1aa2c6a", + "full_human_chars": 14392, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069917", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1193, + 1235, + 1281, + 1309, + 1333, + 1337, + 1449, + 1576, + 1629, + 1663, + 1732, + 1744, + 1792 + ], + "match_source_rows": [ + 1193 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage is mapped to by _at most_ one x; with a surjective function, every y is mapped to by _at least_ one x; so with a bijective function, every y is mapped to by _exactly_ one x. Needless to say, the domain and the codomain must have the same cardinality for this to be possible. \n50 CHAPTER 3. RELATIONS \n The function ", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for advice or suggestions on how to approach a specific scenario found in the document.", + "legacy_target_description_chars": 125, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1193, + "source_record_id": "3697c0c4-a2d4-4522-8dc6-24ae30d6e8dd", + "source_parent_id": "hermes_record:3697c0c4-a2d4-4522-8dc6-24ae30d6e8dd", + "request_group_id": "hermes_request:d75e93e3e3be109e49596ed9f7596192edfd5e8b74e9f75c00f4ed985cd342d2", + "full_human_chars": 10797, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_069953", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 414 + ], + "match_source_rows": [ + 414 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am administering a SQL Server database named 'SQLServerDB' which is quite extensive in size. In order to secure our data, I need to perform a full backup of this database. However, it is crucial to maintain system performance during the backup operation; therefore, I request that the impact on the system be kept minimal. Additionally, for security reasons, the backup must be encrypted. Could you", + "legacy_target": "backup_database", + "legacy_target_description": "Perform a full or differential backup of a specified database with optional encryption and performance impact level.", + "legacy_target_description_chars": 116, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 414, + "source_record_id": "0866536f-055a-4838-ac61-026628e3b106", + "source_parent_id": "hermes_record:0866536f-055a-4838-ac61-026628e3b106", + "request_group_id": "hermes_request:f02ffbf4495006e1520f796874540a23d7af6a6d7abe508b8307310f6099211f", + "full_human_chars": 587, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "backup_database" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_070008", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 970 + ], + "match_source_rows": [ + 970 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our efforts to comply with the Sarbanes-Oxley Act (SOX), we need to prepare several detailed compliance reports for our upcoming audit covering the period from January 1, 2022, to December 31, 2022. 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My car's unique identifier is 'Car123'. I have a secure authorization token 'authTokenSecure' for accessing the vehicle's remote control functions. For my departure, could you please start the engine of my car to warm it up? Additionally, I want the cabin temperature preset to a comfortable 72 d", + "legacy_target": "startEngine", + "legacy_target_description": "Starts the engine of the specified car using the car identifier and authorization token.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 896, + "source_record_id": "1641d663-7172-4ff7-a5af-3c54555a2904", + "source_parent_id": "hermes_record:1641d663-7172-4ff7-a5af-3c54555a2904", + "request_group_id": "hermes_request:2a2e9e8aded743e93e2b9463f92995fb7858251a08c7b666596ad9826092c0f1", + "full_human_chars": 1117, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "startEngine", + "setCabinTemperature", + "tuneRadio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_070160", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 85 + ], + "match_source_rows": [ + 85 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To streamline our metals and mining operations, we require an advanced resource planning system. For mineral reserves, we have compiled extensive data including copper and iron estimated quantities, their grades, and specific mine locations. Alongside, we have production schedules for copper wire and iron rods with their quantities and timelines, as well as complete maintenance logs for our excava", + "legacy_target": "manage_mineral_reserves", + "legacy_target_description": "Manages the mineral reserves data, including types, quantities, grades, and locations.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 85, + "source_record_id": "4a77cd94-904a-4ac1-8b40-707512d73f6b", + "source_parent_id": "hermes_record:4a77cd94-904a-4ac1-8b40-707512d73f6b", + "request_group_id": "hermes_request:b73b15d1561f3b383e05220a1177d465935ebfbe992c559c93c311ad4a03167c", + "full_human_chars": 1438, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "manage_mineral_reserves", + "schedule_production", + "maintain_equipment", + "allocate_labor" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_070237", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 110 + ], + "match_source_rows": [ + 110 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently purchased a Canon EOS 5D Mark IV Full Frame Digital SLR Camera on Amazon. 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I would like to initiate a product return and request a refund to the original payment method I ", + "legacy_target": "check_return_eligibility", + "legacy_target_description": "Check if a product is eligible for return on Amazon.", + "legacy_target_description_chars": 52, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 110, + "source_record_id": "413f098e-d2a6-494c-b988-bc4a2bbf3310", + "source_parent_id": "hermes_record:413f098e-d2a6-494c-b988-bc4a2bbf3310", + "request_group_id": "hermes_request:0bd8cc591b88a4685374f9b8acc6a13234b90bef92093d04b421794634eb5011", + "full_human_chars": 508, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "check_return_eligibility" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_070334", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 361 + ], + "match_source_rows": [ + 361 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the IT manager of Acme Corp, I'm tasked with planning for our IT infrastructure's capacity over the next 5 years. Our data from the last 12 months indicates that our server utilization is at 75%, and our network utilization is at 60%. Given the company's growth projection, we expect a significant increase in demand for our services, which could increase our server needs by 50% and network capac", + "legacy_target": "analyze_current_usage", + "legacy_target_description": "Analyzes the current usage of IT resources over a specified time period.", + "legacy_target_description_chars": 72, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 361, + "source_record_id": "4f0c410c-63cb-4c00-96ea-89d87f58f239", + "source_parent_id": "hermes_record:4f0c410c-63cb-4c00-96ea-89d87f58f239", + "request_group_id": "hermes_request:db446b1bef38d6389742d57a9e026ef19688a4094d01453eb92c4771ebc67c25", + "full_human_chars": 1792, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "analyze_current_usage" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_070360", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 434 + ], + "match_source_rows": [ + 434 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing IT operations for our company and need to monitor the disk space on all computer systems in the network to prevent any storage issues. I require detailed disk space usage information for all types of drives, including local, removable, and network disks. For effective management, it's important to know the current free space, total size for each drive, the drive letters, as well as", + "legacy_target": "get_disk_space_usage", + "legacy_target_description": "Retrieves the disk space usage information for specified drive types on a given computer.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 434, + "source_record_id": "30229340-429c-499b-9753-94f271a5df25", + "source_parent_id": "hermes_record:30229340-429c-499b-9753-94f271a5df25", + "request_group_id": "hermes_request:fba9712e20b8dc81771d532540cd08226384e35a4735598ec6b39d7597be638a", + "full_human_chars": 881, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_disk_space_usage", + "get_drive_file_system", + "get_drive_letter_and_size" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_070427", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 764 + ], + "match_source_rows": [ + 764 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to create a captivating interactive story on my website, enabling users to guide the narrative based on their decisions. The story revolves around a courageous explorer on a thrilling expedition to uncover the Atlantis remains. I need to initiate this interactive journey with an engaging introduction, following which I want to utilize user choices to forge the story's path. Could you ", + "legacy_target": "initialize_interactive_story", + "legacy_target_description": "Initializes the interactive story with a genre, character role, quest objective, and a brief introduction.", + "legacy_target_description_chars": 106, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 764, + "source_record_id": "0dca0e03-c3ae-4363-a5c1-d296c0459a82", + "source_parent_id": "hermes_record:0dca0e03-c3ae-4363-a5c1-d296c0459a82", + "request_group_id": "hermes_request:cec31c6e79c933d2e77d2db75be1619f8b81d3d6723334c673da1a724ca6ce95", + "full_human_chars": 1684, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "initialize_interactive_story", + "generate_story_continuation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_070682", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1093 + ], + "match_source_rows": [ + 1093 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to update the networking configuration for a specific Cloud Function within my Google Cloud project to enhance security and traffic management. The function, named `my-secure-function`, is part of a sensitive system, and I need to ensure that it can only be accessed by other resources within the same GCP network. In addition, I want to route all of its outbound network traffic exclusi", + "legacy_target": "set_function_ingress_settings", + "legacy_target_description": "Configure ingress settings for a specific Cloud Function.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1093, + "source_record_id": "3725d66e-4187-42e6-8142-5766e205747f", + "source_parent_id": "hermes_record:3725d66e-4187-42e6-8142-5766e205747f", + "request_group_id": "hermes_request:773284bdf8481e7c516d9d2e7e04c6b574dfbb2276296cd0df5c71b5e1fa5979", + "full_human_chars": 1110, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "set_function_ingress_settings", + "set_function_egress_settings" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_070928", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 985 + ], + "match_source_rows": [ + 985 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the CRM manager for a community pharmacy, I need to enhance our database with vital customer prescription details and interact more efficiently with our clientele. I have the following specific tasks:\n\n1. Track customer prescriptions including medication details and refill due dates. For example, customer John Doe (ID: JD2398) has a prescription (ID: RX83743) for Metformin, dosage 500mg, prescr", + "legacy_target": "track_prescriptions", + "legacy_target_description": "Tracks customer prescriptions including medication details and refill due dates.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 985, + "source_record_id": "284a72fd-f2b5-42a7-85dc-d90942b0530b", + "source_parent_id": "hermes_record:284a72fd-f2b5-42a7-85dc-d90942b0530b", + "request_group_id": "hermes_request:47083540aef6a7db099fa8662c3161ffa461474328e9d4df55517ac0c20769e7", + "full_human_chars": 1176, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "track_prescriptions", + "set_medication_reminder", + "provide_personalized_offers", + "gather_customer_feedback" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_071194", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 875 + ], + "match_source_rows": [ + 875 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating an environmental initiative where we are hosting a river cleanup on June 20, 2023, at Greenbank Park. For this event, we specifically need volunteers who are skilled in aquatic biology and environmental education. The cleanup is scheduled from 9 AM to 3 PM. Could you locate volunteers who are available during these hours and match these skills? Afterwards, I'd like to delegate sp", + "legacy_target": "findAvailableVolunteers", + "legacy_target_description": "Finds volunteers with the specified skills and availability for an event.", + "legacy_target_description_chars": 73, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 875, + "source_record_id": "2ae7e1c4-28c8-4c5e-aca4-7d64116e09a7", + "source_parent_id": "hermes_record:2ae7e1c4-28c8-4c5e-aca4-7d64116e09a7", + "request_group_id": "hermes_request:08033f48794f6879d22f3ce44eec87f045b15cb0107f5d47ed0fc95713639ab8", + "full_human_chars": 1418, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "findAvailableVolunteers" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_071369", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 552 + ], + "match_source_rows": [ + 552 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I manage IT operations at our company, and we're implementing event-driven automation to streamline our processes. We've identified three key areas where RPA could significantly improve our workflow:\n\n1. For new sales orders received through our online sales platform, I need an RPA function that will automatically initiate an inventory check for the ordered items. This check should update the orde", + "legacy_target": "initiateInventoryCheck", + "legacy_target_description": "Initiates an inventory check when a new sales order is received.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 552, + "source_record_id": "4af1e134-5013-413b-b3a3-3984ea3f992c", + "source_parent_id": "hermes_record:4af1e134-5013-413b-b3a3-3984ea3f992c", + "request_group_id": "hermes_request:d37f2c6f4d51b4a962a7f4fc6c42bbc24be3b7e57fb7c55aff43e95a2a3a1afe", + "full_human_chars": 1470, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "initiateInventoryCheck", + "triggerPurchaseOrder", + "generateAndSendInvoice" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_071436", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 785 + ], + "match_source_rows": [ + 785 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing an investment banking deal with the deal ID \"IBD12345X\". To assist in this management process, I need to calculate key financial metrics such as \"net present value\", \"internal rate of return\", and \"payback period\". Additionally, I want to assess the deal's risk factors, including \"credit risk\", \"market risk\", and \"operational risk\". Finally, for our stakeholders, I require ", + "legacy_target": "calculate_financial_metrics", + "legacy_target_description": "Calculates key financial metrics for a given investment banking deal.", + "legacy_target_description_chars": 69, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 785, + "source_record_id": "673bb323-01f6-4b84-bff2-5e87b2687fd9", + "source_parent_id": "hermes_record:673bb323-01f6-4b84-bff2-5e87b2687fd9", + "request_group_id": "hermes_request:6e802f417bd60e6b7c04a72aa11d13f1badd28fa54abadaa28b5b9d3afeba434", + "full_human_chars": 1003, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_financial_metrics", + "assess_deal_risk_profile", + "prepare_deal_summary" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_071601", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1024 + ], + "match_source_rows": [ + 1024 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing an Android application that provides a feature for instant video calling. I'd like to incorporate a functionality where before initiating a video call, the app checks if the specific user is available. For instance, user A (with user ID 'userA123') wants to video call user B (with user ID 'userB456'). The application should first verify that user B is ready to receive a video call.", + "legacy_target": "check_user_availability", + "legacy_target_description": "Check if a user is available for a video call.", + "legacy_target_description_chars": 46, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1024, + "source_record_id": "b030c9ac-7f96-4232-81fb-7427c20382c3", + "source_parent_id": "hermes_record:b030c9ac-7f96-4232-81fb-7427c20382c3", + "request_group_id": "hermes_request:aa4745998cd963cc778db33bcdfd4b6f957cc0a400da56739d33e25e04822983", + "full_human_chars": 1040, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "check_user_availability" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_071871", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1044 + ], + "match_source_rows": [ + 1044 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning a hiking trip to the Rocky Mountains this weekend, and I need detailed weather information to ensure I'm adequately prepared. I'm specifically looking for the current weather conditions, including temperature in Fahrenheit and sky conditions. Additionally, I require a 3-day weather forecast starting from April 14, 2023, with daily temperatures and weather descriptions, such as chances", + "legacy_target": "get_current_weather", + "legacy_target_description": "Get the current weather in a given location", + "legacy_target_description_chars": 43, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1044, + "source_record_id": "f6734c2b-5d60-4d50-bd0a-3494259bde36", + "source_parent_id": "hermes_record:f6734c2b-5d60-4d50-bd0a-3494259bde36", + "request_group_id": "hermes_request:f90190f222bbb83ee5011af05b8ba45e8eec5954fa0f564a4b44f32bda7d1034", + "full_human_chars": 1345, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_current_weather", + "get_n_day_weather_forecast", + "get_weather_alerts" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_071898", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 841 + ], + "match_source_rows": [ + 841 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing a dashboard that displays user profiles and requires the integration of user data from an external source. To populate this dashboard, I need to fetch profiles for a specific number of users, including detailed information such as their names, phone numbers, physical addresses, and ages. This information needs to be retrieved from a well-documented API source that generates random ", + "legacy_target": "fetchUserProfiles", + "legacy_target_description": "Fetches a list of user profiles from an external API.", + "legacy_target_description_chars": 53, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 841, + "source_record_id": "0e4167f3-46d0-49fa-ae91-63e10c37ae5b", + "source_parent_id": "hermes_record:0e4167f3-46d0-49fa-ae91-63e10c37ae5b", + "request_group_id": "hermes_request:1126b6884561713f51c1062857094cb4064a879a922a78cdadc2824abd364d6a", + "full_human_chars": 956, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetchUserProfiles" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_072026", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 999 + ], + "match_source_rows": [ + 999 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm working at a mid-size brewery and we're currently facing challenges with production inefficiencies. To address these issues, we've compiled comprehensive datasets that we would like analyzed. \n\nFirstly, we'd like to evaluate our brewing schedule to pinpoint any bottlenecks and gain insights into potential improvements. We have a detailed JSON file (`brewing_schedule_data.json`) that includes i", + "legacy_target": "analyze_brewing_schedule", + "legacy_target_description": "Analyzes the current brewing schedules to identify bottlenecks and suggest improvements.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 999, + "source_record_id": "6f965d9a-2747-46ad-85cc-e414864d693b", + "source_parent_id": "hermes_record:6f965d9a-2747-46ad-85cc-e414864d693b", + "request_group_id": "hermes_request:cfc92bee4bf1d50468fe025157cf33850990e32e04e72614dea25637612e0279", + "full_human_chars": 1630, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_brewing_schedule", + "optimize_ingredient_inventory", + "evaluate_equipment_efficiency" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_072145", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 286 + ], + "match_source_rows": [ + 286 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am planning to enhance our asset management firm's trading capabilities by building a sophisticated algorithmic trading platform tailored for equities and ETFs. This platform will be designed to process high volumes of trades with unparalleled precision. I require the system to integrate seamlessly with our current data infrastructure, including real-time market data and extensive historical pri", + "legacy_target": "integrate_data_sources", + "legacy_target_description": "Integrates various data sources into the trading platform for market analysis and strategy execution.", + "legacy_target_description_chars": 101, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 286, + "source_record_id": "5abe92ab-85c4-463b-b3e8-e5afdbb27a28", + "source_parent_id": "hermes_record:5abe92ab-85c4-463b-b3e8-e5afdbb27a28", + "request_group_id": "hermes_request:c6690dbad65e35d3a76c222231c62020816aab688d47d2534c5373c4ee419ae9", + "full_human_chars": 1944, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "integrate_data_sources", + "implement_trading_strategies", + "setup_risk_management" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_072216", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1251, + 1253, + 1266, + 1326, + 1412, + 1476, + 1497, + 1516, + 1522, + 1559, + 1580, + 1677, + 1779 + ], + "match_source_rows": [ + 1251 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage RREFU), \n 1 4 0 0 2 1 - 3 4 0 0 1 0 1 - 3 5 2 0 0 0 1 2 - 6 6 1 0 0 0 0 0 0 0 0 \n \nSo we find that r = 3 and D = {d 1 , d 2 , d 3 } = { 1 , 3 , 4 } F = {f 1 , f 2 , f 3 , f 4 , f 5 } = { 2 , 5 , 6 , 7 , 8 } Let i denote any one of the r = 3 nonzero rows. Then the index di is a pivot column. It will be easy in this case to", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask how to diagnose or solve a problem outlined in the document. These assesses ability to troubleshoot.", + "legacy_target_description_chars": 138, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1251, + "source_record_id": "f2641cef-1cd6-418e-940a-0f2b31956b9c", + "source_parent_id": "hermes_record:f2641cef-1cd6-418e-940a-0f2b31956b9c", + "request_group_id": "hermes_request:0022726af5e0816972bfed58603bc3918e85582c03b5fe7e7c02d8f011489e9e", + "full_human_chars": 9132, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_072479", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 152 + ], + "match_source_rows": [ + 152 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm finalizing the setup for a building's security system that employs facial recognition technology to grant access only to authorized personnel. I have uploaded an image to our secure server for testing purposes, which is available at 'http://example.com/face.jpg'. This image needs to be evaluated to verify the identity of the individual captured in the photo.\n\nTo accomplish this, I require the ", + "legacy_target": "load_facial_recognition_model", + "legacy_target_description": "Load a pre-trained facial recognition model from Torch Hub.", + "legacy_target_description_chars": 59, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 152, + "source_record_id": "e075faf3-0c36-4d84-94ab-a5900de6a740", + "source_parent_id": "hermes_record:e075faf3-0c36-4d84-94ab-a5900de6a740", + "request_group_id": "hermes_request:560bd6ecea362ccd08a40c6471195d7b7ab587f037b74289b8c33a875d87a99d", + "full_human_chars": 812, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "load_facial_recognition_model", + "recognize_face" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_072521", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 381 + ], + "match_source_rows": [ + 381 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our IT operations team has been monitoring system performance and would like to proactively address any potential issues before they impact our services. We have collected system logs over the past 24 hours and would like to analyze them for any anomalies using the IsolationForest machine learning model. Here is the encoded data of the system logs for analysis:\n\n```\n< a CC BY-NC-SA (Attribution NonCommercial ShareAlike) license \n#### WORKS CITED \n 79 COLLECTED WORKS \nFind the Good Argument by Rebecca Jones; Liz Delf; Rob Drummond; and Kristy Kelly is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. A DAM GOOD ARGUME", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for reasoning behind a design decision, product specification, or method described in the document. These reveals thought ", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1133, + "source_record_id": "81a8b207-9772-49c7-a51b-b98113d2b63b", + "source_parent_id": "hermes_record:81a8b207-9772-49c7-a51b-b98113d2b63b", + "request_group_id": "hermes_request:11674e7263d2598ccd713f51c5e2210d292733946abc25098290c0cc4fed6582", + "full_human_chars": 14122, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_072676", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 676 + ], + "match_source_rows": [ + 676 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the CRM manager of a healthcare distribution company, I'm looking to implement several functions into our system to optimize our customer relationship management. I'd like to track the recent order of a long-standing customer with an ID of \"CUST12345\". The order number is \"ORD67890\" and it was placed on April 4th, 2023. The order included 50 units of Product A (ID: PROD001) and 20 units of Prod", + "legacy_target": "track_customer_orders", + "legacy_target_description": "Tracks orders placed by a specific customer, including order details and product quantities.", + "legacy_target_description_chars": 92, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 676, + "source_record_id": "b15e754c-660e-4758-9c63-6cdf02995056", + "source_parent_id": "hermes_record:b15e754c-660e-4758-9c63-6cdf02995056", + "request_group_id": "hermes_request:a52c79780375f8380e811da96f218a07669b34a9571a9414e6447f44e41159af", + "full_human_chars": 1718, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "track_customer_orders", + "manage_communication_preferences", + "segment_customers", + "provide_customer_insights" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_072710", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 712 + ], + "match_source_rows": [ + 712 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need to create a backup of my project work by duplicating an entire folder from my desktop. The folder is specifically located at `/Users/username/Desktop/ProjectX`. I want this folder to be copied to my external backup drive, which is mounted at `/Volumes/BackupDrive/`. Within the backup drive, there's a directory named `Archives` where I need the duplicated folder to be placed.\n\nCould you plea", + "legacy_target": "copy_folder", + "legacy_target_description": "Copy a folder from a source path to a destination path on the Mac filesystem.", + "legacy_target_description_chars": 77, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 712, + "source_record_id": "31ea09a6-cd2b-4e70-b852-058e0e5a7d12", + "source_parent_id": "hermes_record:31ea09a6-cd2b-4e70-b852-058e0e5a7d12", + "request_group_id": "hermes_request:d9a2073f4823cc3abd1d38e925324c9deb7165c132084a315abfcba6a8247edf", + "full_human_chars": 650, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "copy_folder" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_072727", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 181 + ], + "match_source_rows": [ + 181 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have recently completed a web design project for my client and I would like to utilize PayPal to send them an invoice for the agreed amount of $500.00. Since my client prefers PayPal for transactions, could you assist me by generating a PayPal payment request addressed to my client's email client@example.com? I need to include a short description stating that it's for web design services provide", + "legacy_target": "create_paypal_payment_request", + "legacy_target_description": "Create a PayPal payment request to a specified recipient.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 181, + "source_record_id": "25bc6ea8-0661-4506-a38a-ffadf15029ba", + "source_parent_id": "hermes_record:25bc6ea8-0661-4506-a38a-ffadf15029ba", + "request_group_id": "hermes_request:5bdacfa5e8aaff82421b0e6451a5300f0fdb97f5d5a10c124ca0b463218e72b4", + "full_human_chars": 402, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_paypal_payment_request" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_072885", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 583 + ], + "match_source_rows": [ + 583 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm coordinating travel arrangements for a group of colleagues attending a business conference in Paris during the first week of May. I need to book flights and hotels that meet their individual preferences and requirements. Could you assist with the following bookings?\n\n1. Alice Johnson requires a flight from New York to Paris on May 1st, 2023. She prefers a window seat and a vegetarian meal. For", + "legacy_target": "bookFlights", + "legacy_target_description": "Automates the process of booking flights for travelers with specific preferences and requirements.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 583, + "source_record_id": "d121e97c-da80-4642-bb57-00fe4570fb1f", + "source_parent_id": "hermes_record:d121e97c-da80-4642-bb57-00fe4570fb1f", + "request_group_id": "hermes_request:de6bb998276e711a8b14428649bcd62bfc40c39faf68c9edc3d526f049e365f8", + "full_human_chars": 2368, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "bookFlights", + "bookHotels" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073053", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 6 + ], + "match_source_rows": [ + 6 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently automating the irrigation system of my home garden, and I need to ensure that zone 01 receives adequate water without over-irrigation. The recent soil moisture measurements indicate that the soil moisture level in zone 01 is at 25%, which is below the optimal threshold. The local weather forecast reports clear skies and sunny conditions for the next few days, therefore there's no ex", + "legacy_target": "schedule_watering", + "legacy_target_description": "Schedules watering for a specific zone based on soil moisture and weather conditions.", + "legacy_target_description_chars": 85, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 6, + "source_record_id": "d1bff923-dbd1-45da-b0a0-7bd33c9cbc46", + "source_parent_id": "hermes_record:d1bff923-dbd1-45da-b0a0-7bd33c9cbc46", + "request_group_id": "hermes_request:037929766eaefa24adfd90d699b659d3f22f7a949441f1e754c56160d887cbaf", + "full_human_chars": 1275, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "schedule_watering", + "activate_irrigation" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073062", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 530 + ], + "match_source_rows": [ + 530 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently overseeing a project that requires us to systematically transition tasks from an 'in_progress' state to a 'Completed' state as they are finalized. Three specific tasks have reached their completion, and I need to ensure our Quickbase project management system reflects this update. The task IDs are as follows: 1001, which dealt with finalizing our vendor contracts; 1003 involving the", + "legacy_target": "batch_update_task_status", + "legacy_target_description": "Updates the status of multiple tasks in a Quickbase project management app.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 530, + "source_record_id": "401343c9-07c0-4092-a2b1-4c3a2d1313fa", + "source_parent_id": "hermes_record:401343c9-07c0-4092-a2b1-4c3a2d1313fa", + "request_group_id": "hermes_request:91bab39ddf063190e80d1b2a57259b9158181156ffc06d385d7eec3823e82bb4", + "full_human_chars": 772, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "batch_update_task_status" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073101", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 494 + ], + "match_source_rows": [ + 494 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "In order to conduct comprehensive research on government and public sector initiatives, I am compiling a dataset that includes the following elements: recent governmental policy updates for the past quarter, detailed demographic information from specific regions, and extensive details on public services across healthcare, education, and transportation sectors. The data should be curated and struct", + "legacy_target": "fetch_policy_updates", + "legacy_target_description": "Fetches recent policy updates from a specified government policy portal within a given date range.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 494, + "source_record_id": "2b095d7e-40e1-4cfd-a31e-2c2b74a04bed", + "source_parent_id": "hermes_record:2b095d7e-40e1-4cfd-a31e-2c2b74a04bed", + "request_group_id": "hermes_request:84af46f784aa33accf23eef103fdd038c90d4eada56b9f3f4c0d989398e1b89f", + "full_human_chars": 1394, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "fetch_policy_updates", + "fetch_demographic_data", + "fetch_public_service_details" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073155", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 675 + ], + "match_source_rows": [ + 675 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As Head of Customer Engagement at a life sciences company, I need assistance managing our customer relationships. Today, I would like to log a customer inquiry we received; a service request needs updating, and we need to analyze our latest customer interaction logs.\n\nHere are the specifics for the customer inquiry logging:\n- Customer ID: 58293\n- Date and Time of Inquiry: 2023-04-10T15:20:00Z\n- Su", + "legacy_target": "track_customer_inquiries", + "legacy_target_description": "Tracks customer inquiries and logs them into the CRM system.", + "legacy_target_description_chars": 60, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 675, + "source_record_id": "e0173917-2775-4998-a5ba-6340e405a0ef", + "source_parent_id": "hermes_record:e0173917-2775-4998-a5ba-6340e405a0ef", + "request_group_id": "hermes_request:764404c3e076ad0d92645d89068eeaaafc7f4cd38d529e6754ce2bd5cdf74c2d", + "full_human_chars": 1329, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "track_customer_inquiries", + "manage_service_requests", + "analyze_customer_interactions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073228", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 26 + ], + "match_source_rows": [ + 26 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As a case manager for the firm, I'm currently handling a complex lawsuit identified as \"CASE-12345\". We have four parties involved: two plaintiffs named \"Emma Johnson\" and \"Liam Smith\", and two defendants named \"Sophia Williams\" and \"Oliver Brown\". I require assistance with organizing their roles accurately. Furthermore, we have accumulated a vast array of case documents that need to be catalogued", + "legacy_target": "categorize_parties", + "legacy_target_description": "Categorizes the involved parties in a case by their roles.", + "legacy_target_description_chars": 58, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 26, + "source_record_id": "de713f49-102c-4136-80ab-a7fca54ccbb1", + "source_parent_id": "hermes_record:de713f49-102c-4136-80ab-a7fca54ccbb1", + "request_group_id": "hermes_request:6dac32183797eebe202b74fdd9c552d03c36d96daf10b827966a53aa3798d462", + "full_human_chars": 2071, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "categorize_parties", + "index_documents", + "setup_case_timeline" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073343", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 391 + ], + "match_source_rows": [ + 391 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the Chief Information Security Officer (CISO) for our company, I've identified a need to enhance our cybersecurity posture due to a recent upsurge in cyber threats. The following measures are essential to solidify our defenses:\n\n1. **Network Traffic Analysis**: \n - **Network ID**: net-12345\n - **Time Range**:\n - **Start Time**: 2023-04-01T00:00:00Z\n - **End Time**: 2023-04-05T23:59:", + "legacy_target": "analyze_network_traffic", + "legacy_target_description": "Analyzes network traffic within a specified time range to identify suspicious activities.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 391, + "source_record_id": "c39ff7d2-2597-430a-ab81-1dbe083017e2", + "source_parent_id": "hermes_record:c39ff7d2-2597-430a-ab81-1dbe083017e2", + "request_group_id": "hermes_request:bd820e0019b96866a040105cb220c9b380af30bd1335b8bf94403e3bf2ec1a84", + "full_human_chars": 1548, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_network_traffic", + "scan_system_vulnerabilities", + "ensure_data_encryption" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073433", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 73 + ], + "match_source_rows": [ + 73 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'd like to get personalized content suggestions tailored to my taste. I have a strong preference for sci-fi and comedy genres, and I've recently enjoyed watching movies like \"The Martian\" and TV shows such as \"Brooklyn Nine-Nine.\" Could you analyze my preferences and past viewing history to recommend new movies and series that I'm likely to enjoy?\n\nTo assist with the task, I'd like you to call re", + "legacy_target": "generate_personalized_recommendations", + "legacy_target_description": "Generates personalized content recommendations based on user's preferred genres and viewing history.", + "legacy_target_description_chars": 100, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 73, + "source_record_id": "fcc3b730-0876-446a-ab70-7b1f1c3313b7", + "source_parent_id": "hermes_record:fcc3b730-0876-446a-ab70-7b1f1c3313b7", + "request_group_id": "hermes_request:f43530b5c35237eb5c08da87e2478d5b5f15a2eb46cf50f1d9e5b0b841e94b6d", + "full_human_chars": 1167, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "generate_personalized_recommendations", + "update_user_preferences" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073541", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 807 + ], + "match_source_rows": [ + 807 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing the financial reporting for our asset management fund, ID AMF12345, and require an up-to-date financial report. The report needs to include the latest net asset value (NAV) as of April 28, 2023, along with both realized and unrealized gains, and income distributions for the first quarter of the year (January 1, 2023, to March 31, 2023). Could you please call the appropriate functio", + "legacy_target": "calculate_net_asset_value", + "legacy_target_description": "Calculates the net asset value for an asset management fund as of a specific date.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 807, + "source_record_id": "2ca54d32-0070-4cc1-aa65-5e533234c10d", + "source_parent_id": "hermes_record:2ca54d32-0070-4cc1-aa65-5e533234c10d", + "request_group_id": "hermes_request:50a4f37dffd10bb25e1874ab0c4ef482e639e3bf082aea8fb6ef9b80cd29b229", + "full_human_chars": 1129, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "calculate_net_asset_value", + "retrieve_gains_and_distributions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073565", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1229, + 1255, + 1261, + 1304, + 1487, + 1565, + 1638, + 1669, + 1673, + 1731, + 1759, + 1789, + 1809 + ], + "match_source_rows": [ + 1229 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 1 + x^32 /x 4 - x 3 x 4 x^25 = 0 tan(ab) + log(c - d) = - 7 \n SSW I L A B e e z e r : A F i r s t C o u r s e i n L i n e a r A l g e b r a 2 \nThe exception will be that we will on occasion need to take a square root. You have probably heard the word \"algebra\" frequently in your mathematical preparation for this course. Most like", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that require making logical inferences based on the information in the document. These test understanding.", + "legacy_target_description_chars": 135, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1229, + "source_record_id": "c1519e05-f59b-49ad-b245-26728361150a", + "source_parent_id": "hermes_record:c1519e05-f59b-49ad-b245-26728361150a", + "request_group_id": "hermes_request:003cc68efd04a4f8f753ee2ea32c0ece48414cf64426d95f56e91f52e20ab689", + "full_human_chars": 11776, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073627", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 581 + ], + "match_source_rows": [ + 581 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am part of the operations team at Anderson & Murdock Law Firm. We're enhancing our document management system to automatically handle our growing collection of legal documents such as client agreements, NDAs, and case files. Our goals are to automate the generation of these documents, organize them into specific categories, and ensure they comply with current laws such as GDPR and HIPAA.\n\nWe req", + "legacy_target": "create_legal_document", + "legacy_target_description": "Automates the creation of legal documents such as NDAs, agreements, and case files with client-specific details.", + "legacy_target_description_chars": 112, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 581, + "source_record_id": "f304065d-6722-4f3e-b18f-69955891b54b", + "source_parent_id": "hermes_record:f304065d-6722-4f3e-b18f-69955891b54b", + "request_group_id": "hermes_request:c9daba503a8d1d876f320a0ffd0a8ae9a46a5f4090c33811e77fc9dc931a4dbd", + "full_human_chars": 1132, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "create_legal_document", + "categorize_document", + "check_compliance" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073802", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 58 + ], + "match_source_rows": [ + 58 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to watch the latest episode of 'Planet Earth II' on my streaming device. My username for the streaming service is 'NatureFan88', and my password is 'EcoLover2023!'. The show I'm interested in has the unique identifier 'pe2_show_001'. The episode I want to stream is number 6. I'm planning to watch it on my tablet, which has the unique device identifier 'device_987654321'. Could you ple", + "legacy_target": "authenticate_user", + "legacy_target_description": "Authenticates a user with the streaming service and returns a token for subsequent requests.", + "legacy_target_description_chars": 92, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 58, + "source_record_id": "c82abc25-206c-4776-a1b1-d6fbc5769bce", + "source_parent_id": "hermes_record:c82abc25-206c-4776-a1b1-d6fbc5769bce", + "request_group_id": "hermes_request:a07a89e1f97c5afa8b9711606b343184b28cbc710e8c87213865787601789e36", + "full_human_chars": 859, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "authenticate_user" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073868", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 927 + ], + "match_source_rows": [ + 927 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need to update the profile information for a specific user in our system. The user's unique identifier is 'user-321'. The updated information should include the new email address 'new.email@example.com' and the new phone number '987-654-3210'. Could you perform the function call to modify the user profile using these details with a POST request? Thank you.", + "legacy_target": "modifyUserProfile", + "legacy_target_description": "Modifies the user profile information such as email and phone number using a POST request.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 927, + "source_record_id": "387c087f-05e5-4b14-80ad-18a93f17a7b2", + "source_parent_id": "hermes_record:387c087f-05e5-4b14-80ad-18a93f17a7b2", + "request_group_id": "hermes_request:d66163f950d83a647ece0f81658b7bda2d109f8c81b9b262dfeba7f6b7d0547d", + "full_human_chars": 360, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "modifyUserProfile" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073901", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 850 + ], + "match_source_rows": [ + 850 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need to access a secure service for which I have my login details ready. I would like to request assistance to authenticate my login through an API POST request. My username for the login is 'user123' and the password is 'securePass!'. Would you please authenticate my credentials and provide me with an access token, assuming the login attempt is successful?", + "legacy_target": "authenticateUser", + "legacy_target_description": "Authenticates a user with the provided username and password, returning an access token upon successful authentication.", + "legacy_target_description_chars": 119, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 850, + "source_record_id": "ee60a112-d8e0-4440-8230-58deb1fb32bd", + "source_parent_id": "hermes_record:ee60a112-d8e0-4440-8230-58deb1fb32bd", + "request_group_id": "hermes_request:757b63c8f5e28e82fab6ad2fd8457fcfa7fe8424d9d948399002d9290776f888", + "full_human_chars": 361, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "authenticateUser" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073907", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 353 + ], + "match_source_rows": [ + 353 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "We are operating a fleet of turbines at our power generation facility and require real-time monitoring to ensure optimal performance. Specifically, we need to monitor the power output, which should not exceed our safety threshold of 5000 kW. We also have critical temperature limits on our equipment set at 150 degrees Celsius, and vibration level thresholds set at 5.0 m/s². Our systems are currentl", + "legacy_target": "monitor_power_output", + "legacy_target_description": "Continuously monitors the power output data stream and triggers an alert if the value exceeds the specified threshold.", + "legacy_target_description_chars": 118, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 353, + "source_record_id": "3e2bf1ff-fca4-4d4c-8da5-05ad1ff67625", + "source_parent_id": "hermes_record:3e2bf1ff-fca4-4d4c-8da5-05ad1ff67625", + "request_group_id": "hermes_request:3f85af9bff60b2012722dda462d51a637a3e2ce1d5effaae2b7eb622025d6560", + "full_human_chars": 1349, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "monitor_power_output", + "monitor_equipment_temperature", + "monitor_vibration_levels" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_073977", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 598 + ], + "match_source_rows": [ + 598 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing a large fleet of industrial equipment that requires precise monitoring of their locations across multiple facilities. In order to ensure the efficient utilization and security of these assets, I need to establish a real-time tracking system. Specifically, I'd like to monitor the asset labeled 'asset_12345'. The last known coordinates for this asset were latitude 37.7749 and", + "legacy_target": "getCurrentAssetLocation", + "legacy_target_description": "Retrieves the current location of a specified asset.", + "legacy_target_description_chars": 52, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 598, + "source_record_id": "0fbf090d-4fc8-4b69-acb1-4cc762d1a530", + "source_parent_id": "hermes_record:0fbf090d-4fc8-4b69-acb1-4cc762d1a530", + "request_group_id": "hermes_request:04687e5e17157ab5ef29464a97519d8d2e998d52b6b0190291213566d4796b51", + "full_human_chars": 1025, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "getCurrentAssetLocation", + "subscribeToAssetLocationUpdates" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_074038", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1130, + 1165, + 1288, + 1411, + 1541, + 1553, + 1574, + 1603, + 1627, + 1628, + 1643, + 1715, + 1738 + ], + "match_source_rows": [ + 1130 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage Fig 16.3 How will your group develop feedback? \n##### Global Revision Activity For A Narrative Essay \n This assignment challenges you to try new approaches to a draft you've already written. Although you will be \"rewriting\" in this exercise, you are not abandoning your earlier draft: this exercise is generative, meaning it is des", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1130, + "source_record_id": "89b5bcf5-98d9-4bdb-927f-429a64bcc798", + "source_parent_id": "hermes_record:89b5bcf5-98d9-4bdb-927f-429a64bcc798", + "request_group_id": "hermes_request:19f2ffe7f7259420fae363ce99ef45128744723901d3fada8fde7c2bbd29f18a", + "full_human_chars": 13740, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_074161", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 773 + ], + "match_source_rows": [ + 773 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to manage the underwriting processes for a new reinsurance application we've received from a client. GlobalRisk Inc., a company operating within the property insurance industry, is seeking reinsurance coverage. They have requested a coverage amount of $100,000,000 for Property type coverage. Included with their application is their historical claims data, which I've compiled into a JS", + "legacy_target": "evaluate_reinsurance_risk", + "legacy_target_description": "Evaluates the risk associated with a new reinsurance application and suggests a premium.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 773, + "source_record_id": "b0daedc1-28ec-48d5-9178-e370d9337dc1", + "source_parent_id": "hermes_record:b0daedc1-28ec-48d5-9178-e370d9337dc1", + "request_group_id": "hermes_request:289e56cee20ceaefe4901233262dcda98fa621536edd1abebd839ddf432d9af6", + "full_human_chars": 1673, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "evaluate_reinsurance_risk" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_074284", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 951 + ], + "match_source_rows": [ + 951 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing the sales operations at our company and require direct assistance with automating Salesforce tasks. First, I have a lead generated from a recent webinar attendee - John Doe, who works at Acme Inc. He provided his email as johndoe@acmeinc.com during registration. I need to create a new lead entry for him in Salesforce with the source marked as 'Webinar'. Secondly, I need to update t", + "legacy_target": "create_lead", + "legacy_target_description": "Creates a new lead in Salesforce from provided lead information.", + "legacy_target_description_chars": 64, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 951, + "source_record_id": "3e13a6b8-9719-4d0a-8adb-d8c66fed1ac8", + "source_parent_id": "hermes_record:3e13a6b8-9719-4d0a-8adb-d8c66fed1ac8", + "request_group_id": "hermes_request:df1dfee7b3b6cde7a9e666316d185c2a130afe3e9079a12eb6bbdf629f0063b3", + "full_human_chars": 889, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "create_lead", + "update_lead_status", + "generate_opportunities_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_074360", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 280 + ], + "match_source_rows": [ + 280 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our compliance with the updated FX Global Code, which emphasizes the importance of transaction cost analysis in foreign exchange markets, we need to analyze the transaction costs involved in our algorithmic trading operations from the last quarter. We have compiled data comprising timestamps, currency pairs, traded volumes, and execution prices in the file 'FX_Trade_Data_Q4.csv'. Please", + "legacy_target": "calculate_explicit_costs", + "legacy_target_description": "Calculates the explicit transaction costs such as commissions and fees from trade data.", + "legacy_target_description_chars": 87, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 280, + "source_record_id": "f02d5e87-57ed-4572-b31c-1ee49454041b", + "source_parent_id": "hermes_record:f02d5e87-57ed-4572-b31c-1ee49454041b", + "request_group_id": "hermes_request:c3c685516bc11bb3b7bc9e2da2e1d68e25390fab9cbc316bff01add791fb103e", + "full_human_chars": 1544, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "calculate_explicit_costs", + "calculate_implicit_costs" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_074388", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 805 + ], + "match_source_rows": [ + 805 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As Head of Risk Management at our insurance company, I've identified several critical risk factors that could affect our policyholders and overall business sustainability. Given recent trends and unforeseen global events, I'd like to conduct an in-depth analysis of how natural disasters, economic market shifts, and unexpected changes in policyholder behavior might impact our insurance policies. 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We're preparing for a large-scale global shipment and require an optimized packing strategy to maximize container space utilization. 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Additionally, I’d like to create a customer token for one of my ", + "legacy_target": "initiate_payment_session", + "legacy_target_description": "Initiates a payment session with Klarna for an order.", + "legacy_target_description_chars": 53, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 96, + "source_record_id": "22a2717c-9f27-4725-85f3-0fa921e6246f", + "source_parent_id": "hermes_record:22a2717c-9f27-4725-85f3-0fa921e6246f", + "request_group_id": "hermes_request:19852fcf901c2c975d10542fc1cb6171bef66f3b141b58dd0ce90d20bf924235", + "full_human_chars": 1622, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "initiate_payment_session", + "create_customer_token", + "process_refund" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_076762", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 126 + ], + "match_source_rows": [ + 126 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our company's IT department needs to enhance its data protection with a secure backup and recovery plan for our mix of on-premises servers and cloud services. To comply with our stringent data security protocols, we require nightly backups of all systems, with these backups being encrypted to safeguard sensitive information. The recovery strategy must permit selective recovery of data, enabling us", + "legacy_target": "initiate_backup", + "legacy_target_description": "Initiates the backup process for specified server types with encryption and frequency options.", + "legacy_target_description_chars": 94, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 126, + "source_record_id": "14c5f909-cc29-4f54-87cb-6535a171771e", + "source_parent_id": "hermes_record:14c5f909-cc29-4f54-87cb-6535a171771e", + "request_group_id": "hermes_request:46c1a2a4935eeb25bc9b8cb1528629a5283ac4b481301c7d2166872afb016cd8", + "full_human_chars": 1096, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "initiate_backup", + "configure_recovery" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_076860", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 811 + ], + "match_source_rows": [ + 811 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "We have recently received a new batch of reinsurance claims related to natural disasters such as hurricanes, floods, and wildfires. 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Our objective is to bolster data privacy and streamline real-time incident responses. 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Our current inventory database reflects up-to-date quantities and product details for our entire range of he", + "legacy_target": "generate_demand_forecast", + "legacy_target_description": "Generates a demand forecast for healthcare products by analyzing historical sales data, current inventory levels, and promotional schedules over a specified for", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 707, + "source_record_id": "76a35c36-cbfb-4291-b919-6795a31af1a6", + "source_parent_id": "hermes_record:76a35c36-cbfb-4291-b919-6795a31af1a6", + "request_group_id": "hermes_request:b76fe5f8732e61473331059316cc654aa08f688ec22f70926c7a62860a48324b", + "full_human_chars": 2012, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_demand_forecast" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_077412", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 595 + ], + "match_source_rows": [ + 595 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating logistics for our trucking company and need to optimize our delivery routes to save time and fuel. 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The deliveries must be completed with", + "legacy_target": "generate_optimized_trucking_routes", + "legacy_target_description": "Generates the most efficient trucking routes based on fleet information and delivery locations with time windows.", + "legacy_target_description_chars": 113, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 595, + "source_record_id": "08d2c01f-d72b-40f7-a16f-ad6d73834275", + "source_parent_id": "hermes_record:08d2c01f-d72b-40f7-a16f-ad6d73834275", + "request_group_id": "hermes_request:8e8b51cdafda83109de17441d618a013306af6720f9f5a788adf4a73c84153ec", + "full_human_chars": 1281, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_optimized_trucking_routes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_077483", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1035 + ], + "match_source_rows": [ + 1035 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently developing an Android mobile application for our company's monthly reporting, and I need to integrate a feature that allows the app to send an email with a PDF attachment of the report. The email should be dispatched to our financial analyst at finance@company.com. 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Additionally, I want a list of container s", + "legacy_target": "describe_ec2_instances", + "legacy_target_description": "Retrieve information about EC2 instances based on specified filters.", + "legacy_target_description_chars": 68, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1087, + "source_record_id": "6ee8e53b-8934-47e7-8c86-ab301eb52995", + "source_parent_id": "hermes_record:6ee8e53b-8934-47e7-8c86-ab301eb52995", + "request_group_id": "hermes_request:9f9abdacdf2911ef52650cd2e5933c1eeb48abee27a058577dde5c688b8c7467", + "full_human_chars": 1450, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "describe_ec2_instances", + "list_container_services", + "get_serverless_options" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_083049", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 617 + ], + "match_source_rows": [ + 617 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm in charge of optimizing the logistics for our air freight company to ensure timely deliveries while minimizing operational costs. We currently have multiple shipments that need to be allocated to our fleet of aircraft. Each shipment has specific details such as weight, dimensions, and the destination airport, along with the deadline for delivery. I'd like to know the optimal distribution of th", + "legacy_target": "allocate_shipments_to_flights", + "legacy_target_description": "Allocates shipments to available flights based on aircraft payload capacities, shipment weights, dimensions, and destination airports.", + "legacy_target_description_chars": 134, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 617, + "source_record_id": "282c1d4d-08ae-45e2-b1f0-7f61a5d70d2e", + "source_parent_id": "hermes_record:282c1d4d-08ae-45e2-b1f0-7f61a5d70d2e", + "request_group_id": "hermes_request:54e421a4965ad496adb52ef2092a169573f8ce2db88d17812d9f28fac026d5d8", + "full_human_chars": 2123, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "allocate_shipments_to_flights" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_083328", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1158, + 1237, + 1276, + 1307, + 1454, + 1521, + 1564, + 1567, + 1658, + 1724, + 1746, + 1833, + 1852 + ], + "match_source_rows": [ + 1158 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage . For instance, if I had drawn node I further to the right, in order to make the lines between D-O-I less steep, that I node might have been pushed physically to the right of K. But that wouldn't change the order and have K visited earlier. \nFinally, it's worth mentioning that all of these traversal methods make elegant use of **", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask how to diagnose or solve a problem outlined in the document. These assesses ability to troubleshoot.", + "legacy_target_description_chars": 138, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1158, + "source_record_id": "71996882-77f1-4a7c-be42-e59d551145d4", + "source_parent_id": "hermes_record:71996882-77f1-4a7c-be42-e59d551145d4", + "request_group_id": "hermes_request:462084f59da2aae05dac5becbf53fbc5dded6ed5be63c80c054c91628cc92690", + "full_human_chars": 12309, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_083423", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 464 + ], + "match_source_rows": [ + 464 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the manager of a distribution network specializing in consumer electronic goods, I require specific functions to be executed in order to maintain and enhance our customer relationship management. I need to add a new customer profile for \"TechGadgets Ltd.,\" with contact details: phone number \"123-456-7890,\" email \"contact@techgadgets.com,\" and physical address \"123 Tech Avenue, Silicon Valley, C", + "legacy_target": "add_customer_profile", + "legacy_target_description": "Adds a new customer profile to the CRM system with their contact details and preferences.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 464, + "source_record_id": "9b93de24-50b0-49b1-958b-fe2bf4969104", + "source_parent_id": "hermes_record:9b93de24-50b0-49b1-958b-fe2bf4969104", + "request_group_id": "hermes_request:17d026f984af16a6d7ec70a3c9d2ce2e2f10915071673591b8709322a4a06d4e", + "full_human_chars": 1558, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "add_customer_profile", + "update_customer_information", + "track_order_status", + "analyze_customer_feedback" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_083505", + "original_split": "train", + "v2_split": null, + "historical_validation": true, + "historical_test": false, + "prefix_match_source_rows": [ + 541 + ], + "match_source_rows": [ + 541 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for the data accuracy in our company's marketing database. We have a CSV file named \"customer_information.csv\" stored on our secure server, located at \"path/to/customer_dataset.csv\". The important fields in this dataset that need validation are email addresses, postal codes, and opt-in statuses. I need to ensure that all email addresses adhere to a standard format, postal codes ar", + "legacy_target": "validate_customer_data", + "legacy_target_description": "Validates customer data against specified fields and rules.", + "legacy_target_description_chars": 59, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 541, + "source_record_id": "bff2d531-e357-4665-a4c2-d1415af0ceaa", + "source_parent_id": "hermes_record:bff2d531-e357-4665-a4c2-d1415af0ceaa", + "request_group_id": "hermes_request:4186cbcc995f8e4069e09de472ff82f1c51116db867e4c7bcb1f368574480d04", + "full_human_chars": 1312, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "validate_customer_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_083535", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 917 + ], + "match_source_rows": [ + 917 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working with a customer dataset stored in JSON format at '/data/customer_profiles.json'. The dataset contains detailed customer profiles which include their demographic information, account details, and a comprehensive summary of their transaction history. I need to leverage a machine learning model to predict the likelihood of each customer churning. Can you call the relevant funct", + "legacy_target": "predict_customer_churn", + "legacy_target_description": "Predicts the likelihood of customers churning by analyzing their profile and transaction history.", + "legacy_target_description_chars": 97, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 917, + "source_record_id": "804d1b3d-10ea-4373-8eb5-8edfc8cd29c0", + "source_parent_id": "hermes_record:804d1b3d-10ea-4373-8eb5-8edfc8cd29c0", + "request_group_id": "hermes_request:b705b3bccd85de7ff20d03f8e77ec42a37ff09e39320c8e9e7b55fd3e1d6d246", + "full_human_chars": 490, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "predict_customer_churn" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_083659", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 814 + ], + "match_source_rows": [ + 814 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our reinsurance company has received a batch of claims following a recent spate of natural disasters. We need to ensure these claims are processed accurately and efficiently. The first step is to assess the validity of these claims. Here's a detailed list of the current claims batch that includes dates, amounts requested, policy numbers, claimant details, and incident descriptions:\n\n```\nClaim ID,D", + "legacy_target": "assess_claim_validity", + "legacy_target_description": "Assesses the validity of reinsurance claims based on predefined criteria.", + "legacy_target_description_chars": 73, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 814, + "source_record_id": "458b31d0-9dd2-43c7-9d0c-5833ee35fa29", + "source_parent_id": "hermes_record:458b31d0-9dd2-43c7-9d0c-5833ee35fa29", + "request_group_id": "hermes_request:cd7c29fa01d6ca4bd6a2190139ffff77ac9893f98c20a3b246e7fbb57b592e78", + "full_human_chars": 1662, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "assess_claim_validity" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_083819", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 483 + ], + "match_source_rows": [ + 483 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently working with a dataset of law texts and require assistance in extracting and categorizing specific legal terms and entities. The data comprises various legal documents and contracts, and I need to identify categories such as 'Contract Clauses', 'Legal Obligations', 'Rights', and 'Parties'. The dataset is conveniently formatted in JSON, and my goal is to systematically analyze these ", + "legacy_target": "extract_legal_entities", + "legacy_target_description": "Extracts and categorizes legal terms and entities from law texts provided in JSON format and outputs the results in JSON Schema format.", + "legacy_target_description_chars": 135, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 483, + "source_record_id": "3ad21de2-a8d5-4056-98b1-e6ca06002ca8", + "source_parent_id": "hermes_record:3ad21de2-a8d5-4056-98b1-e6ca06002ca8", + "request_group_id": "hermes_request:e305d385dd85f41e75aba50f64f323156497fe1e7c98df258a07fb6f8d1b807a", + "full_human_chars": 1733, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_legal_entities" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_084164", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 233 + ], + "match_source_rows": [ + 233 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am interested in attending technology events focusing on artificial intelligence, virtual reality, and blockchain technologies in San Francisco. I need to find events that align with these interests and are scheduled to take place next month. Please provide me with a list of such events, including their details like date, location, and description, within the specified date range. Here are the d", + "legacy_target": "searchEventsByCategory", + "legacy_target_description": "Search for events in a specific location and categories within a date range.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 233, + "source_record_id": "9efc99ec-fe40-4aa1-8ccd-b05da8aa19a0", + "source_parent_id": "hermes_record:9efc99ec-fe40-4aa1-8ccd-b05da8aa19a0", + "request_group_id": "hermes_request:767bd14c99de6879345bea1123e6869feed2aafb26b2fed702e66d7515cbb2a9", + "full_human_chars": 724, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "searchEventsByCategory" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_084187", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 993 + ], + "match_source_rows": [ + 993 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to analyze online sales data for our drug retail e-commerce platform, focusing specifically on pharmaceutical and healthcare categories. For this analysis, I require a comprehensive data extraction covering the sales performance of these categories over the last quarter, from January 1, 2023, to March 31, 2023. Additionally, I am interested in understanding the customer demographics t", + "legacy_target": "extract_sales_performance", + "legacy_target_description": "Extract sales performance data for specified product categories within a date range from an online sales database.", + "legacy_target_description_chars": 114, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 993, + "source_record_id": "79773ebe-01fb-4710-8a45-26cc30e78661", + "source_parent_id": "hermes_record:79773ebe-01fb-4710-8a45-26cc30e78661", + "request_group_id": "hermes_request:8e395aee747a793ba4b13d466ac5a3253c7a18fd9cdab446ae6f8aa92f1340a5", + "full_human_chars": 1305, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "extract_sales_performance", + "analyze_customer_demographics", + "identify_seasonal_trends" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_084352", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1222, + 1225, + 1226, + 1272, + 1389, + 1415, + 1515, + 1534, + 1535, + 1674, + 1760, + 1771, + 1828 + ], + "match_source_rows": [ + 1222 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage bets are off. The conclusion was conditional on the premise. \n The reason this operator is so important is that in artificial intelligence, the name of the game is concluding new facts from known existing facts, so that knowledge is increased. Every time a 'bot learns that X=Y is true, and then also learns that the premise (X) is", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for an assessment or critique of an idea, argument, or proposal in the document. These test critical thinking.", + "legacy_target_description_chars": 148, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1222, + "source_record_id": "4de3e63b-ce13-4780-815d-1bdf5a13ff1d", + "source_parent_id": "hermes_record:4de3e63b-ce13-4780-815d-1bdf5a13ff1d", + "request_group_id": "hermes_request:c553bfec901015cb55d66174183d1718181f6a94abba0cc281be882993ce72f0", + "full_human_chars": 8533, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_084354", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 809 + ], + "match_source_rows": [ + 809 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting a thorough performance analysis of our trading strategies for the first quarter of 2023 and require assistance in evaluating our recent trades, followed by optimizing our trading algorithms. We need to calculate various financial metrics, including ROI, alpha, beta, and the Sharpe ratio, for which I have prepared a comprehensive trade data file named \"Q1_2023_Trade_Data.csv\". 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We have collected a comprehensive dataset that includes the latest quarterly financial reports, historical stock prices, and market sentiment analysis for a range of companies we're considering for investment.\n\nOur goal is to accurately predict sto", + "legacy_target": "predictStockTrends", + "legacy_target_description": "Processes financial datasets to identify key indicators and predict stock performance trends.", + "legacy_target_description_chars": 93, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 761, + "source_record_id": "51885460-d2e4-4484-bbff-ace0c7516c34", + "source_parent_id": "hermes_record:51885460-d2e4-4484-bbff-ace0c7516c34", + "request_group_id": "hermes_request:d0e91e9c52c44d07d70908d630c747403ba89470ba03fe4fbe77474b0147ce01", + "full_human_chars": 1727, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "predictStockTrends", + "generateInvestmentRecommendations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_085541", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 471 + ], + "match_source_rows": [ + 471 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I just assisted a customer who purchased a Samsung Galaxy S21 smartphone, along with a pair of JBL Tune 220TWS earbuds and an Otterbox Defender Series case. The transaction total came to $950, and the customer chose to pay using their Visa credit card. I need to ensure the payment is processed and the sale is correctly recorded in our POS system. The product ID for the Samsung Galaxy S21 is \"SGS21", + "legacy_target": "process_payment", + "legacy_target_description": "Processes a payment with the given amount and payment method.", + "legacy_target_description_chars": 61, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 471, + "source_record_id": "52381037-0343-46ab-a45f-84a68bde89e8", + "source_parent_id": "hermes_record:52381037-0343-46ab-a45f-84a68bde89e8", + "request_group_id": "hermes_request:f93f6bc329c403851610448687825e66ac60e02e1cd2dec1819909c698734b05", + "full_human_chars": 762, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "process_payment", + "update_sales_record" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_085722", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 120 + ], + "match_source_rows": [ + 120 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently trading cryptocurrencies and need an integration solution for my application to interact with Coinbase, Binance, and Kraken exchanges. Specifically, I require functions to fetch the latest market prices for Bitcoin (BTC) and Ethereum (ETH), as well as functions to execute both buy and sell orders based on real-time pricing information. Additionally, I need to access my account balanc", + "legacy_target": "get_market_prices", + "legacy_target_description": "Retrieves the current market prices for specified cryptocurrencies from a given exchange.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 120, + "source_record_id": "c7432f8a-4417-4132-b3ff-6ff9dc857449", + "source_parent_id": "hermes_record:c7432f8a-4417-4132-b3ff-6ff9dc857449", + "request_group_id": "hermes_request:a8a08bd23b64d67242f6c20d4078bff790bc6b976c7235c6041e0d2af27c6e97", + "full_human_chars": 1225, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 9, + "valid_top_level_call_names": [ + "get_market_prices", + "get_market_prices", + "get_market_prices", + "get_account_balance", + "get_account_balance", + "get_account_balance", + "get_account_balance", + "get_account_balance", + "get_account_balance" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_085743", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 995 + ], + "match_source_rows": [ + 995 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To maintain an effective inventory management system for our food retail operation, we need to track the freshness of our perishable items, adjust stock levels post-sale, and get alerts for products that are running low. Our current database includes various details such as product IDs, names, current stock, and expiration dates. We would like to request the use of functions to monitor expiration ", + "legacy_target": "track_perishable_goods", + "legacy_target_description": "Tracks perishable goods in the inventory to monitor expiration dates and reduce waste.", + "legacy_target_description_chars": 86, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 995, + "source_record_id": "8d933fd0-0629-4759-a0de-903aac95a137", + "source_parent_id": "hermes_record:8d933fd0-0629-4759-a0de-903aac95a137", + "request_group_id": "hermes_request:c3934920bbe4f755e31835fd334d8f9c00437a5877355e35990ab6eb73920513", + "full_human_chars": 1388, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "track_perishable_goods", + "update_inventory_levels", + "notify_low_stock" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_085756", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 574 + ], + "match_source_rows": [ + 574 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the manager of a residential property portfolio, I'm seeking to implement Robotic Process Automation to enhance our management processes effectively. I would like to establish a quarterly property inspection schedule across all properties. Each inspection should cover a comprehensive checklist including smoke detectors, plumbing, electrical systems, HVAC, and structural integrity.\n\nAdditionally", + "legacy_target": "schedule_property_inspections", + "legacy_target_description": "Schedules property inspections at a specified frequency and provides inspectors with a checklist of items to review.", + "legacy_target_description_chars": 116, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 574, + "source_record_id": "30c9f17b-f6fc-48c5-a40a-7590e0f15ad3", + "source_parent_id": "hermes_record:30c9f17b-f6fc-48c5-a40a-7590e0f15ad3", + "request_group_id": "hermes_request:8faa75b9b49ff3870d0ae92919552260818acc6784bc0b1b72bd9da42fe9fc05", + "full_human_chars": 1405, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "schedule_property_inspections", + "automate_rent_collection", + "manage_maintenance_requests" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_085871", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 432 + ], + "match_source_rows": [ + 432 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a specific file, 'report.txt', located in the directory '/home/user/documents'. I need to modify the file permissions to restrict it to read-only for the owner, and ensure that there are no permissions given to either the group or others. To accomplish this, the file permissions should be set to '400', corresponding to read-only for the owner (4), and no permissions (0) for both the group a", + "legacy_target": "change_file_permissions", + "legacy_target_description": "Change the permissions of a file or directory.", + "legacy_target_description_chars": 46, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 432, + "source_record_id": "f5bdb572-8bc0-4fca-b54f-65d4ec50455b", + "source_parent_id": "hermes_record:f5bdb572-8bc0-4fca-b54f-65d4ec50455b", + "request_group_id": "hermes_request:91e935daa7acd991db6e73551f6a0636b709e25b126ba99ed06a910c6fcc6b93", + "full_human_chars": 812, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "change_file_permissions" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_085974", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 713 + ], + "match_source_rows": [ + 713 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm organizing my project files on my Mac, and I need to find all documents with the '.docx' extension stored in my 'Documents' folder. Specifically, I am looking for files such as 'ProjectProposal.docx', 'MeetingMinutes.docx', and 'ReportDraft.docx'. Could you search within the directory path '~/Documents' to identify these files? Additionally, I need to locate a particular file named 'budget.xls", + "legacy_target": "find_files_by_extension", + "legacy_target_description": "Finds all files with a specified extension within a given directory.", + "legacy_target_description_chars": 68, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 713, + "source_record_id": "c396fdcc-93eb-407e-84d5-950bfa8bda6b", + "source_parent_id": "hermes_record:c396fdcc-93eb-407e-84d5-950bfa8bda6b", + "request_group_id": "hermes_request:076a9d79db35e3d79698e147a8af031ace4408ae2f11bcefad755afb2c670ac7", + "full_human_chars": 703, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "find_files_by_extension", + "search_file_by_name" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_086247", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 982 + ], + "match_source_rows": [ + 982 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to execute a series of tasks for our drug retail POS system to efficiently manage sales and transactions for the day. Firstly, we have a prescription that needs to be processed for a customer. The prescription ID is 'RX987654321', for a customer with the ID 'CUST123456', paying with a credit card. \n\nSecondly, we have an over-the-counter sale with the following details: two units of pa", + "legacy_target": "process_prescription_sale", + "legacy_target_description": "Processes a prescription sale transaction with prescription details, customer information, and payment method.", + "legacy_target_description_chars": 110, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 982, + "source_record_id": "b7d73e14-a0e5-4c2d-86d7-3a2bc21b6cba", + "source_parent_id": "hermes_record:b7d73e14-a0e5-4c2d-86d7-3a2bc21b6cba", + "request_group_id": "hermes_request:c3035f1db4b1be8aa560dbfaeeb602922c1d1049a49dd47422f127baf71042d0", + "full_human_chars": 1015, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 5, + "valid_top_level_call_names": [ + "process_prescription_sale", + "handle_otc_sale", + "manage_inventory", + "manage_inventory", + "generate_transaction_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_086360", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 526 + ], + "match_source_rows": [ + 526 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently overseeing a sales team utilizing the Zoho Creator platform for our business operations. We're entering Q2 of 2023 and need to evaluate our performance from the previous quarter. I would like to request the following data analyses to enhance our Sales Dashboard App and make strategic decisions for the upcoming quarter:\n\n1. Retrieve the sales figures for Q1 of 2023. This data is crit", + "legacy_target": "retrieve_quarterly_sales", + "legacy_target_description": "Retrieves sales figures for a specified year and quarter.", + "legacy_target_description_chars": 57, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 526, + "source_record_id": "34f4bcf0-aaf5-4f09-9b8a-cab361584468", + "source_parent_id": "hermes_record:34f4bcf0-aaf5-4f09-9b8a-cab361584468", + "request_group_id": "hermes_request:393dd1946d5c42446b81981123d6fc0db9525d57c1918499f4656cf616a504ba", + "full_human_chars": 1249, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "retrieve_quarterly_sales", + "display_top_performing_products", + "update_sales_forecast" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_086383", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 843 + ], + "match_source_rows": [ + 843 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the lead data analyst for our company's user engagement reporting system, I must compile comprehensive user activity logs for an upcoming presentation. The data should include timestamps, activity types, user IDs, and detailed descriptions of their interactions on our platform over the previous week. To obtain this, could you please execute a GET request through the `fetchUserActivityLogs` func", + "legacy_target": "fetchUserActivityLogs", + "legacy_target_description": "Fetches user activity logs from a specified API endpoint within a given date range for selected user IDs.", + "legacy_target_description_chars": 105, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 843, + "source_record_id": "f98a4f69-cae3-477e-af49-40f6225f6822", + "source_parent_id": "hermes_record:f98a4f69-cae3-477e-af49-40f6225f6822", + "request_group_id": "hermes_request:747a231a7297fb1231a2da03a15f10b10367412f1e84f9f33405cacf8bd3337a", + "full_human_chars": 684, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetchUserActivityLogs" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_086394", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 151 + ], + "match_source_rows": [ + 151 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have captured a variety of wildlife images and would like to identify the species present in one particular image. The image is hosted online at 'https://mywebsite.com/uploads/eagle-in-the-wild.jpg'. I have selected the MobileNetV2 model from Torch Hub for its balance between accuracy and computational efficiency, and I want to utilize this pre-trained model to classify the species in the image.", + "legacy_target": "load_model_from_torch_hub", + "legacy_target_description": "Load a pre-trained model from Torch Hub.", + "legacy_target_description_chars": 40, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 151, + "source_record_id": "43228856-d6c0-47bd-9f1a-cd03c7698cd5", + "source_parent_id": "hermes_record:43228856-d6c0-47bd-9f1a-cd03c7698cd5", + "request_group_id": "hermes_request:7fd741e9b0af4de8458aef1127c99196d0bff68abdb7919b2b544c6030a53ab0", + "full_human_chars": 838, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "load_model_from_torch_hub", + "classify_image" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_086404", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 885 + ], + "match_source_rows": [ + 885 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm preparing to unveil an exclusive fashion collection and my vision includes developing five distinct dress designs that are inspired by particular aesthetic themes. I want these designs to resonate with the following themes: 'Vintage Elegance', 'Modern Chic', 'Bohemian Rhapsody', 'Avant-Garde', and 'Classic Minimalism'. The designs should all be catered to evening wear, capturing the essence of", + "legacy_target": "generate_fashion_designs", + "legacy_target_description": "Generates a series of fashion designs based on specified themes and categories using AI.", + "legacy_target_description_chars": 88, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 885, + "source_record_id": "03c52af9-3040-4760-b52a-d33b6c33cbc5", + "source_parent_id": "hermes_record:03c52af9-3040-4760-b52a-d33b6c33cbc5", + "request_group_id": "hermes_request:42760043af8150ec99e4c0a9b010d2ef11ef8c47f3fe8e837158cde33b1aa412", + "full_human_chars": 660, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 5, + "valid_top_level_call_names": [ + "generate_fashion_designs", + "generate_fashion_designs", + "generate_fashion_designs", + "generate_fashion_designs", + "generate_fashion_designs" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_086466", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 618 + ], + "match_source_rows": [ + 618 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "To enhance efficiency in our passenger ground transportation service, we run a shuttle operation assisting travelers to various destinations from the airport. Recently, we've seen an increase in demand, and our current system requires optimization. I would like to utilize your function to plan the most efficient routes for our shuttles. 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For the reaction yield analysis, we have a detailed dataset in a JSON file (reaction_yields.json) containing information about different batches, including reactants, catalysts, reaction conditions", + "legacy_target": "analyze_reaction_yields", + "legacy_target_description": "Analyzes reaction yield data to identify trends and areas for improvement in the chemical processes.", + "legacy_target_description_chars": 100, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 86, + "source_record_id": "6c555dc2-b02b-43c9-90b3-d54f208ab2ff", + "source_parent_id": "hermes_record:6c555dc2-b02b-43c9-90b3-d54f208ab2ff", + "request_group_id": "hermes_request:385536018af3a87352acb75edb29553ef9cbad9c2214aedc5b04de893531754a", + "full_human_chars": 3074, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_reaction_yields", + "calculate_energy_efficiency", + "optimize_raw_material_costs" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_088775", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 557 + ], + "match_source_rows": [ + 557 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our electronics retail company is taking strides to improve our procurement process by implementing automation in reordering, supplier selection, and invoice processing. 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To ensure I arrive on time for the opening session, I'd like to schedule an UberX ride for next Monday. The pickup should be from my hotel, Hilton Garden Inn, located at 815 14th St NW, Washington, DC 20005, and the drop-off should be at the Washingto", + "legacy_target": "request_uber_ride", + "legacy_target_description": "Request an Uber ride to be scheduled for a specific time and location.", + "legacy_target_description_chars": 70, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 227, + "source_record_id": "4929f36f-9133-49c4-97d7-5a881084eb9b", + "source_parent_id": "hermes_record:4929f36f-9133-49c4-97d7-5a881084eb9b", + "request_group_id": "hermes_request:5883043e2f990bc3ce8659f2fb0f98a38b6f1ae349e053e2a981558ce993033c", + "full_human_chars": 651, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "request_uber_ride" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "validation" + }, + { + "canonical_id": "fastbrain_088814", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 853 + ], + "match_source_rows": [ + 853 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As part of our environmental research study, we've been surveying the general public to gather opinions on various ecological issues. We have an ongoing online survey with the identifier \"env_survey_123\", where participants share detailed insights into their perceptions and attitudes. For the next phase of data analysis, we require the most recent batch of responses gathered via this survey platfo", + "legacy_target": "fetchSurveyResponses", + "legacy_target_description": "Fetches a list of survey responses from a crowdsourced data collection platform.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 853, + "source_record_id": "5bd3f9b2-6406-462a-8a74-cf75434de239", + "source_parent_id": "hermes_record:5bd3f9b2-6406-462a-8a74-cf75434de239", + "request_group_id": "hermes_request:9873c9abf4d6bd910adc5064e8b4bb1adbb0f8d7ae28d029e4cbfa0f7079d3be", + "full_human_chars": 1067, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetchSurveyResponses" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_088887", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1017 + ], + "match_source_rows": [ + 1017 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a dataset of fashion product images that I would like to have classified into different categories, such as 'shirts', 'shoes', and 'dresses'. The image dataset is hosted on a cloud storage container, and the URL is 'https://cloudstorage.example.com/fashion-dataset/'. I am looking to train an image classification model using this dataset. I would like to use the ResNet-50 architecture for th", + "legacy_target": "load_image_dataset", + "legacy_target_description": "Load an image dataset from a specified cloud storage URL for image classification.", + "legacy_target_description_chars": 82, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1017, + "source_record_id": "e0a1eb2d-b95f-49fa-a45f-d1f663c1f0ec", + "source_parent_id": "hermes_record:e0a1eb2d-b95f-49fa-a45f-d1f663c1f0ec", + "request_group_id": "hermes_request:ce527e0faded9b5e866bd8d458e3d213086d22cb918b48f555598358aaef566d", + "full_human_chars": 948, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "load_image_dataset" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_088945", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 435 + ], + "match_source_rows": [ + 435 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have gathered a collection of customer reviews specifically related to various financial services offered by my company. 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I need assistance in processing this file to classify each", + "legacy_target": "classify_financial_reviews", + "legacy_target_description": "Processes a JSON file of customer reviews, classifies each review into financial service categories, and outputs the results in a JSON Schema format.", + "legacy_target_description_chars": 149, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 435, + "source_record_id": "1991b039-52d1-45f4-8e80-c4724dcd90a2", + "source_parent_id": "hermes_record:1991b039-52d1-45f4-8e80-c4724dcd90a2", + "request_group_id": "hermes_request:a18917f5f170f9a014d9715f4b8a99d916c8ad0abdeca7d027c0f423e025f6bb", + "full_human_chars": 981, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "classify_financial_reviews", + "generate_financial_review_schema" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_089107", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 260 + ], + "match_source_rows": [ + 260 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing a portfolio with an aim to optimize market making on the NASDAQ exchange. 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My objective is to collect and structure customer transaction data for analysis—this data is presented in the system's display inside a table. The table is identifiable by its identifier `//table[@id='transaction_table']` and includes key columns such as 'Trans", + "legacy_target": "initialize_screen_scraping_session", + "legacy_target_description": "Initializes a screen scraping session for a specified system interface.", + "legacy_target_description_chars": 71, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 586, + "source_record_id": "d47a084d-ee20-4170-87f8-a1544b848375", + "source_parent_id": "hermes_record:d47a084d-ee20-4170-87f8-a1544b848375", + "request_group_id": "hermes_request:6fccacfd612321fc917e03dd1a9f938e9cc44b9303c0b9751455648766e9d979", + "full_human_chars": 1234, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "initialize_screen_scraping_session", + "scrape_table_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_089990", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 578 + ], + "match_source_rows": [ + 578 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the knowledge manager at InnoTech Solutions, and our firm has amassed a significant number of project reports in PDF form, which contain valuable insights that we want to share across the organization. Currently, we're aiming to implement RPA to streamline our knowledge sharing processes.\n\nTo achieve this, I have compiled all the project reports into a compressed file named \"project_reports.z", + "legacy_target": "extract_insights_from_reports", + "legacy_target_description": "Extracts key insights from a collection of project reports in PDF format.", + "legacy_target_description_chars": 73, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 578, + "source_record_id": "5d84265c-dd9b-44c5-995a-a09531b536c6", + "source_parent_id": "hermes_record:5d84265c-dd9b-44c5-995a-a09531b536c6", + "request_group_id": "hermes_request:d9189df256a23280eea8da3b323ba917df1bb67b25ffb92a08ce0c1a51a5f903", + "full_human_chars": 1586, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_insights_from_reports" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_090206", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 727 + ], + "match_source_rows": [ + 727 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a Java Swing application, specifically a file named `HelloWorldSwing.java` located in a subdirectory called 'start'. I need to compile this Java file and execute the compiled program, but I want the execution to happen from the parent directory of 'start'. I assume there is already a compiled version of this Java program in the parent directory. Could you assist me by calling the appropriat", + "legacy_target": "compile_java_file", + "legacy_target_description": "Compiles a Java file from the specified directory and outputs the compiled class to the given directory.", + "legacy_target_description_chars": 104, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 727, + "source_record_id": "3b2ab99b-4050-43c3-9d59-88dbeed46acf", + "source_parent_id": "hermes_record:3b2ab99b-4050-43c3-9d59-88dbeed46acf", + "request_group_id": "hermes_request:8598e16aa0525002af2dd6276835ac898059281db27af88b83b6fa13d18e7e0f", + "full_human_chars": 1049, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "compile_java_file", + "execute_java_program" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_090223", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1275, + 1279, + 1342, + 1365, + 1388, + 1496, + 1598, + 1617, + 1624, + 1671, + 1708, + 1728, + 1807 + ], + "match_source_rows": [ + 1275 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage your \n##### position \n##### (something that you present as a \n##### perspective ) \n Women are unfairly criticized on social media. A peer-reviewed scholarly article: \n##### Sills, Sophie, et al. \"Rape Culture and Social \n##### Media: Young Critics and a Feminist \n##### Counterpublic.\" Feminist Media Studies , vol. \n##### 16, no. ", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for reasoning behind a design decision, product specification, or method described in the document. These reveals thought ", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1275, + "source_record_id": "6e4394cf-f5f1-4311-872d-42305551c9cd", + "source_parent_id": "hermes_record:6e4394cf-f5f1-4311-872d-42305551c9cd", + "request_group_id": "hermes_request:078e19a50482d5d48c612d8adcd44fddb809d13e877b269ec9e507b63159605a", + "full_human_chars": 14188, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_090502", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 149 + ], + "match_source_rows": [ + 149 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am working on a project where I need to analyze an audio lecture for accessibility features. The audio file is available online and contains a spoken presentation on the topic of reinforcement learning with RLlib. The audio is hosted at 'http://example.com/audio.wav' and is approximately 30 minutes long. I would like to utilize the 'wav2vec2' speech recognition model available on Torch Hub, whic", + "legacy_target": "load_speech_recognition_model", + "legacy_target_description": "Load a pre-trained speech recognition model from Torch Hub.", + "legacy_target_description_chars": 59, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 149, + "source_record_id": "48c04b4f-9d74-4b42-b8e0-0dce6d71d85b", + "source_parent_id": "hermes_record:48c04b4f-9d74-4b42-b8e0-0dce6d71d85b", + "request_group_id": "hermes_request:f3c2b442b4c6c31f426eb74e5a2fe4d1dcd28ac2b432cb9a998a309dbe8a6803", + "full_human_chars": 831, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "load_speech_recognition_model", + "transcribe_audio" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_090519", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 215 + ], + "match_source_rows": [ + 215 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'd like to enhance my in-car entertainment experience by watching a movie from Netflix on the car's infotainment system. To accomplish this, I need to connect my smartphone, with the ID \"smartphone123\", to the car's infotainment system, which has the ID \"infotainment456\". The connection should be established using an HDMI cable. After the connection is made, I would like to initiate the Netflix a", + "legacy_target": "connect_smartphone_to_infotainment", + "legacy_target_description": "Connects a smartphone to a car's infotainment system using a specified connection type.", + "legacy_target_description_chars": 87, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 215, + "source_record_id": "fb28a02b-4eaa-41a5-a1b8-b9f4556b6231", + "source_parent_id": "hermes_record:fb28a02b-4eaa-41a5-a1b8-b9f4556b6231", + "request_group_id": "hermes_request:58cb05a8d5b3bbdc05112c08da73a6c39f6bc3ecd84c6b4936ab5374906fda59", + "full_human_chars": 578, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "connect_smartphone_to_infotainment", + "initiate_streaming_service" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_090728", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 69 + ], + "match_source_rows": [ + 69 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our company is launching operations in remote regions of Australia and South America. We require satellite communication services to support a team of 50 personnel, with needs for both voice communication and internet access that includes heavy data transfer for our field operations. We plan to support video conferencing but also need a contingency for unreliable internet connections. We have the ", + "legacy_target": "determine_optimal_satellite_coverage", + "legacy_target_description": "Determines the optimal satellite coverage for a given set of location coordinates and required reliability percentage.", + "legacy_target_description_chars": 118, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 69, + "source_record_id": "f5ac86c3-fe26-41d3-a537-928664d2b43d", + "source_parent_id": "hermes_record:f5ac86c3-fe26-41d3-a537-928664d2b43d", + "request_group_id": "hermes_request:f0bdc05c346a987715510c44ff5fbe6b10b80726a1a0380be381e991eae071e8", + "full_human_chars": 1486, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "determine_optimal_satellite_coverage", + "estimate_required_bandwidth", + "select_satellite_service_provider" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_090932", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 904 + ], + "match_source_rows": [ + 904 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing a multilingual website and we are preparing to introduce a new product to our international audience. The product description in English reads: \"Introducing our latest innovation – the SmartWidget 3000, designed to simplify your life with its advanced technology and user-friendly interface. Experience the future of home automation today!\" I need to provide this description in multipl", + "legacy_target": "translate_text", + "legacy_target_description": "Translates a given text from a source language to a target language using a language translation service API.", + "legacy_target_description_chars": 109, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 904, + "source_record_id": "260772b5-b443-4982-9d45-90bd55407f63", + "source_parent_id": "hermes_record:260772b5-b443-4982-9d45-90bd55407f63", + "request_group_id": "hermes_request:bffd5bea6f92711dc727d05ddf8f7734b6de808f330988d0774bef7232c3e08b", + "full_human_chars": 668, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "translate_text", + "translate_text", + "translate_text" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_090984", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1049 + ], + "match_source_rows": [ + 1049 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently preparing for a trip to Spain and will need to interact with Spanish speakers while I'm there. To facilitate communication, I would like to translate some essential English phrases into Spanish. Specifically, I would like to get translations for the phrases \"Where is the nearest hospital?\" and \"How much does this cost?\" on my iPhone. Could you assist me by using the translation tool", + "legacy_target": "translate_text", + "legacy_target_description": "Translate text from one language to another.", + "legacy_target_description_chars": 44, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1049, + "source_record_id": "1e8d1165-b219-40e4-9663-bd06d06c3420", + "source_parent_id": "hermes_record:1e8d1165-b219-40e4-9663-bd06d06c3420", + "request_group_id": "hermes_request:653fe2a73a27cb0642b6080120152dcf9afe06e7851457af5505b9bbf23f5e5a", + "full_human_chars": 959, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "translate_text", + "translate_text" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_091068", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 347 + ], + "match_source_rows": [ + 347 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As an energy trader at Zenith Energy, I'm tasked with refining our risk management processes. I need to know the potential losses our portfolio might incur due to market volatility over the next 10 days. We have a portfolio with ID \"ZEN-319\", and we want to calculate a Value at Risk (VaR) at a 95% confidence level. Additionally, we're currently dealing with a new counterparty, Flux Capacitor Energ", + "legacy_target": "calculate_portfolio_VaR", + "legacy_target_description": "Calculates the Value at Risk (VaR) for a given energy trading portfolio over a specified time horizon at a certain confidence level.", + "legacy_target_description_chars": 132, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 347, + "source_record_id": "d7927d11-7b17-4bf0-a446-aafa87277a9f", + "source_parent_id": "hermes_record:d7927d11-7b17-4bf0-a446-aafa87277a9f", + "request_group_id": "hermes_request:01b3fa4f60aaacfb27ea6fe8b2782c6e740ff9c9c6eb29129f896faa7f7a24c8", + "full_human_chars": 1757, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_portfolio_VaR", + "assess_counterparty_credit_risk", + "simulate_market_scenarios" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_091186", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 349 + ], + "match_source_rows": [ + 349 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the operations manager of our city's water treatment facility, I am looking to enhance our operational efficiency and ensure compliance with environmental regulations. We have collected comprehensive data over the past two years, which includes the following:\n\n1. Water quality data with parameters recorded monthly, such as pH (6.5-8.5), turbidity (0-5 NTU), and contaminant levels (e.g., Nitrate", + "legacy_target": "analyze_water_quality", + "legacy_target_description": "Analyzes historical water quality data to identify trends and areas for improvement.", + "legacy_target_description_chars": 84, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 349, + "source_record_id": "e90caaf7-d183-4537-9809-a4269fe9d7ca", + "source_parent_id": "hermes_record:e90caaf7-d183-4537-9809-a4269fe9d7ca", + "request_group_id": "hermes_request:271b6f4c2204868ec52c158b6f1982a8cddeb25ab27adb75df08c68419cee52b", + "full_human_chars": 1636, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_water_quality", + "calculate_chemical_usage_efficiency", + "evaluate_plant_performance" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_091368", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 616 + ], + "match_source_rows": [ + 616 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing a portfolio of industrial projects with the portfolio ID \"PORT12345\" and need to evaluate their current performance based on key metrics such as 'cost efficiency', 'timeliness', 'quality of output', and 'stakeholder satisfaction'. Additionally, I require resources to be strategically allocated across the portfolio projects, with an emphasis on priority-based distribution to ensure ", + "legacy_target": "analyze_project_performance", + "legacy_target_description": "Analyzes the performance of projects within a portfolio based on specified metrics.", + "legacy_target_description_chars": 83, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 616, + "source_record_id": "7ac1277c-ec8e-4dba-a017-1dce928f5c98", + "source_parent_id": "hermes_record:7ac1277c-ec8e-4dba-a017-1dce928f5c98", + "request_group_id": "hermes_request:3886160d974f442247e3958a26417d9874ca650ff95c1e4ca44c000a9f3f2da0", + "full_human_chars": 1268, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_project_performance", + "allocate_portfolio_resources", + "generate_portfolio_reports" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_091373", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1257, + 1270, + 1378, + 1395, + 1426, + 1450, + 1504, + 1612, + 1649, + 1668, + 1778, + 1812, + 1890 + ], + "match_source_rows": [ + 1257 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage possible? \n Ignoring the at-least-one-child-andadult constraint for the moment, the total number of groups would seem to be \n ( 17 3 \n ) + \n ( 17 4 \n ) + \n ( 17 5 \n ) = 680+2380+ 6188 = 9, 248 possible groups. But of course this is an overcount, since it includes groups with no children and groups with no adults. We'll use the tr", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for reasoning behind a design decision, product specification, or method described in the document. These reveals thought ", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1257, + "source_record_id": "a885d2c8-ff74-4265-b0f4-2ddc9efc7cc4", + "source_parent_id": "hermes_record:a885d2c8-ff74-4265-b0f4-2ddc9efc7cc4", + "request_group_id": "hermes_request:25c7c2c39fa9e4c56bf2db8d16d555527c66105205be0a13dc29f91ae6fe03b2", + "full_human_chars": 11526, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_091448", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 545 + ], + "match_source_rows": [ + 545 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I oversee inventory management for our retail chain, and to enhance operational efficiency, we are keen to leverage RPA in automating our inventory management system. Specifically, we need to undertake the following actions:\n\n1. Conduct a thorough stock level assessment for our core products, ensuring we understand how much is available and at which warehouse locations. \n2. When product stock dips", + "legacy_target": "check_stock_levels", + "legacy_target_description": "Checks the current stock levels for a list of product IDs and returns their quantities and locations.", + "legacy_target_description_chars": 101, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 545, + "source_record_id": "598fd2ee-c7c9-4176-a116-fc990caa1abd", + "source_parent_id": "hermes_record:598fd2ee-c7c9-4176-a116-fc990caa1abd", + "request_group_id": "hermes_request:5eaa140bd4aea118efcd2ef3a6d47db9925cc5a6ed6bfefa702dafda2c7332cb", + "full_human_chars": 1696, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "check_stock_levels", + "reorder_product", + "update_inventory_record" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_091458", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 41 + ], + "match_source_rows": [ + 41 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing a SmartThings setup and need to assess the overall health and connectivity of the ecosystem. Specifically, I require the status of all devices, including those that might be offline, at a particular location identified by the SmartThings location identifier \"12345\". Could you use the appropriate function to fetch and provide me with the current status of all connected—and p", + "legacy_target": "getConnectedDevicesStatus", + "legacy_target_description": "Fetches the current status of all devices connected to a SmartThings location.", + "legacy_target_description_chars": 78, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 41, + "source_record_id": "d31cc0b1-5a6d-43a5-950b-a4649096103a", + "source_parent_id": "hermes_record:d31cc0b1-5a6d-43a5-950b-a4649096103a", + "request_group_id": "hermes_request:439388140c01dd9cf10cf896c0118fbf0e8ad2d62aa19621717dbd206933da52", + "full_human_chars": 575, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "getConnectedDevicesStatus" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_091827", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 677 + ], + "match_source_rows": [ + 677 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the administrator at a healthcare clinic, and I need to arrange an appointment for one of our patients with their preferred doctor. Specifically, I need to schedule an appointment for patient ID 'pt678' with Doctor ID 'dr123' on July 20, 2023. The appointment should be set for 10:00 AM on that day. Following the scheduling, I want to ensure that the patient receives a confirmation message. Ad", + "legacy_target": "schedule_appointment", + "legacy_target_description": "Schedules a new appointment for a patient with a specific doctor at a given date and time.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 677, + "source_record_id": "5b72641f-bf72-4633-b1d3-c075805f8061", + "source_parent_id": "hermes_record:5b72641f-bf72-4633-b1d3-c075805f8061", + "request_group_id": "hermes_request:4de95de280ec0cfde7ddf6f613b7505f7e17e718948bcf64e9d16e266ee03225", + "full_human_chars": 852, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "schedule_appointment" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_091906", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 54 + ], + "match_source_rows": [ + 54 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am the owner of the interactive media platform \"PlayLearn\" with ID \"media_content_123\", and I would like to implement monetization strategies to enhance revenue streams. My content offers educational games targeting audiences interested in learning through interactive experiences. I want to establish a paywall that divides content accessibility into 'free' and 'premium' levels, with the premium ", + "legacy_target": "setup_paywall", + "legacy_target_description": "Sets up a paywall for interactive media content with different access levels and pricing.", + "legacy_target_description_chars": 89, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 54, + "source_record_id": "5ff5fe50-ef4f-4920-a5c0-7f51663d5649", + "source_parent_id": "hermes_record:5ff5fe50-ef4f-4920-a5c0-7f51663d5649", + "request_group_id": "hermes_request:72413fcb97052e308c04222a6f04adb1ea69399dfa06b969408f15163187286e", + "full_human_chars": 1513, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "setup_paywall", + "manage_subscriptions", + "serve_targeted_ads" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_092413", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1217, + 1247, + 1385, + 1433, + 1466, + 1471, + 1526, + 1544, + 1609, + 1642, + 1819, + 1831, + 1832 + ], + "match_source_rows": [ + 1217 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage . This last point is often not fully appreciated, leading to errors. Be careful not to cavalierly add the cardinalities of nonmutually-exclusive sets! You'll end up double-counting items. \nSo we know that the number of possible license plates is equal to: \n the # of 7-character plates + the # of 6-character plates + the # of 5-ch", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask what is likely to happen next based on the information in the document. These assess ability to extrapolate.", + "legacy_target_description_chars": 146, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1217, + "source_record_id": "f86f70af-5a84-4be1-80b8-221db22068a3", + "source_parent_id": "hermes_record:f86f70af-5a84-4be1-80b8-221db22068a3", + "request_group_id": "hermes_request:3edf52f1ef1189339a7176426b25e5a0f3d0176d7f9b984bdff71f5679dd5cc1", + "full_human_chars": 11656, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_092620", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 511 + ], + "match_source_rows": [ + 511 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As an entrepreneur managing a small business, I have a packed schedule with frequent activities like weekly team meetings every Tuesday at 10 am, client consultations on Thursdays twice a month, and quarterly project deadlines. It's essential for me to synchronize all these commitments in a calendar to oversee my daily commitments, as well as to anticipate weekly tasks and monthly agendas. I prefe", + "legacy_target": "integrate_calendar_app", + "legacy_target_description": "Integrate a calendar app with the user's account to manage their schedule.", + "legacy_target_description_chars": 74, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 511, + "source_record_id": "4860feb5-7370-4633-95d1-f8f1429c0b1a", + "source_parent_id": "hermes_record:4860feb5-7370-4633-95d1-f8f1429c0b1a", + "request_group_id": "hermes_request:15c49fb899e06476587e7b6a03d62b48b5d780dd7ce0ea489c671ef30b748eaa", + "full_human_chars": 1085, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "integrate_calendar_app" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_092661", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1116, + 1120, + 1368, + 1372, + 1397, + 1402, + 1597, + 1689, + 1710, + 1813, + 1845, + 1856, + 1858 + ], + "match_source_rows": [ + 1116 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage instance change Sam from basketball to volleyball. Now both of the codomain elements are \"reachable\" by some domain element, so it's surjective. \n33. Is faveSport now also bijec- tive? \n No, because it's still not injective. \n34. How can we alter things so that it's bijective? \n One way is to add a third sport -- say, kic", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that focus on summarizing a specific topic found in the document.", + "legacy_target_description_chars": 94, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1116, + "source_record_id": "2d96f513-4625-4c28-b295-549bb0b704c9", + "source_parent_id": "hermes_record:2d96f513-4625-4c28-b295-549bb0b704c9", + "request_group_id": "hermes_request:a72c55de27236212e1306db4ed2d865f21aae7e8e88df3713e07ff4817750274", + "full_human_chars": 12211, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_092671", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 387 + ], + "match_source_rows": [ + 387 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Our organization requires a robust and efficient IT service desk system to ensure optimal support for our staff and users. The desired system must include modules for incident management to capture and address IT issues, service requests to process standard user needs, and user communication to interact with users via different channels. These modules are essential for managing the lifecycle of su", + "legacy_target": "initialize_service_desk", + "legacy_target_description": "Initialize the IT service desk system with the required modules and communication channels.", + "legacy_target_description_chars": 91, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 387, + "source_record_id": "0a1e8b32-d6f5-4d9f-b01a-53192f528a5b", + "source_parent_id": "hermes_record:0a1e8b32-d6f5-4d9f-b01a-53192f528a5b", + "request_group_id": "hermes_request:8928789336834708d42355b8e2e10928d5d55dec26d404652c3b6c0191afb290", + "full_human_chars": 1846, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "initialize_service_desk", + "create_knowledge_base" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_092792", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1077 + ], + "match_source_rows": [ + 1077 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am involved in a project for evaluating our company's financial performance over the last fiscal year. We utilize an SAP system identified as SAP_PROD, and we need to review and analyze general ledger balances across all our accounts. The range of accounts we want to look at starts with account number 100000 and ends with account number 199999, all of which pertain to the fiscal year 2022. The l", + "legacy_target": "extract_general_ledger_balances", + "legacy_target_description": "Extracts general ledger balances for a range of accounts for a specified fiscal year from an SAP Finance system.", + "legacy_target_description_chars": 112, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1077, + "source_record_id": "9e0434a3-80ef-4646-aaff-136063685830", + "source_parent_id": "hermes_record:9e0434a3-80ef-4646-aaff-136063685830", + "request_group_id": "hermes_request:c18808d7be6c2eb1729ee73e152a08a4c94bf33851563a91b5b6a712cd1b7fa5", + "full_human_chars": 983, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "extract_general_ledger_balances" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_092794", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 331 + ], + "match_source_rows": [ + 331 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "We are currently managing a power grid that requires integration of renewable energy from solar and wind sources. 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We need to find a time that works for everyone and send out Google Calendar invites accordingly. The participants are located in New York, London, and Tokyo. 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Additionally, for breach detection, we aim to utilize both anomaly detection and signature-based methods to promptly i", + "legacy_target": "encrypt_network_data", + "legacy_target_description": "Encrypts data on the telecommunication network using a specified encryption standard and key management system.", + "legacy_target_description_chars": 111, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 57, + "source_record_id": "83752703-6ade-41b9-8e70-027d3fcca335", + "source_parent_id": "hermes_record:83752703-6ade-41b9-8e70-027d3fcca335", + "request_group_id": "hermes_request:e657f7b3062faeb6c840ba077a08007c77cb46304f5535edbe3e8a023b389218", + "full_human_chars": 1081, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "encrypt_network_data", + "setup_breach_detection" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_095147", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 819 + ], + "match_source_rows": [ + 819 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm preparing for an upcoming visit to New York City, and I'd like a tailored selection of activities and accommodation options. 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It is also imperati", + "legacy_target": "get_river_water_levels", + "legacy_target_description": "Retrieves current water levels of rivers within a specified county and date.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 892, + "source_record_id": "cb24a2b8-69e0-4d6f-8afd-8fe69aaff4f0", + "source_parent_id": "hermes_record:cb24a2b8-69e0-4d6f-8afd-8fe69aaff4f0", + "request_group_id": "hermes_request:5921a1516c77bccd0590a44885bfac84a83513c2775efedd3cfb45d0a267fd13", + "full_human_chars": 1334, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "get_river_water_levels", + "get_flood_barrier_status", + "get_safe_evacuation_routes", + "get_emergency_supplies_status" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_095287", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 160 + ], + "match_source_rows": [ + 160 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am creating educational content about major historical events and need accurate dates for each. 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Additionally, I want an app that provides in-depth details about Ye", + "legacy_target": "find_wildlife_identification_apps", + "legacy_target_description": "Finds apps that assist with identifying and learning about wildlife and plants in a specific location.", + "legacy_target_description_chars": 102, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 232, + "source_record_id": "fe11461e-d2c3-41ac-9118-d5b508a1c287", + "source_parent_id": "hermes_record:fe11461e-d2c3-41ac-9118-d5b508a1c287", + "request_group_id": "hermes_request:4d13766e59c3f8f311425a187b943a0f3da7d0555b72a0bd6fe13112f2713976", + "full_human_chars": 838, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "find_wildlife_identification_apps" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_095443", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1045 + ], + "match_source_rows": [ + 1045 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need to communicate with my friend John Doe using my iPhone's Messages app. Please send an iMessage to him with the content \"Hey John, are we still meeting up this weekend?\" Since John Doe is a contact on my device, the message should go through without a hitch. Additionally, I'd like to review my most recent messages to ensure I haven't missed any important conversations. Can you retrieve the l", + "legacy_target": "send_iMessage", + "legacy_target_description": "Send an iMessage to a specified contact.", + "legacy_target_description_chars": 40, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1045, + "source_record_id": "d7a1a5d1-c745-4284-a846-727aa56a0c99", + "source_parent_id": "hermes_record:d7a1a5d1-c745-4284-a846-727aa56a0c99", + "request_group_id": "hermes_request:9110ea897f42ca9bbcca5a8997e616ab6094ead9b0b4512c44105d550e1a2962", + "full_human_chars": 972, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "send_iMessage", + "get_recent_messages", + "check_unread_messages" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_095540", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 457 + ], + "match_source_rows": [ + 457 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm collaborating with a department store chain to scrutinize our POS sales data for the previous quarter. Our goal is to identify the top-selling products in our 'Home Appliances' department for the last month, assess overall sales trends from the past six months, and evaluate the success of our 'Holiday Season' promotion which ran for two weeks in December compared to sales in November.\n\nFor det", + "legacy_target": "identify_top_selling_products", + "legacy_target_description": "Identify top-selling products within a specified department and time period from POS sales data.", + "legacy_target_description_chars": 96, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 457, + "source_record_id": "85d0ca57-194a-4984-8aae-d4bed688ebe1", + "source_parent_id": "hermes_record:85d0ca57-194a-4984-8aae-d4bed688ebe1", + "request_group_id": "hermes_request:6bf6d3c8e7c617cf8f37dafcdf54d4fd7245503dbddeaf04657516cd8162c854", + "full_human_chars": 1117, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "identify_top_selling_products", + "assess_sales_trends", + "evaluate_promotion_effectiveness" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_095597", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 46 + ], + "match_source_rows": [ + 46 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to integrate my IoT devices, which consists of a smart lock with device ID 'device_001', a smart light with device ID 'device_002', and a thermostat with device ID 'device_003', within my Home Assistant setup. These devices are connected on my home network identified as 'home_network_123'. I need to discover these IoT devices on the network, add them to Home Assistant, and establish a", + "legacy_target": "discover_iot_devices", + "legacy_target_description": "Discovers IoT devices on the specified network.", + "legacy_target_description_chars": 47, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 46, + "source_record_id": "800e361c-9949-4085-9969-6618791ef835", + "source_parent_id": "hermes_record:800e361c-9949-4085-9969-6618791ef835", + "request_group_id": "hermes_request:b14166101ffb627dc5f1c4cace0f3b410c4d03d7dfe4eee743efa091470bb13c", + "full_human_chars": 804, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "discover_iot_devices", + "add_device_to_home_assistant", + "create_automation_rule" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_095614", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 551 + ], + "match_source_rows": [ + 551 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As a data analyst at StreamFlow Solutions, my current project involves analyzing the influx of real-time data captured by our IoT sensors across the city. These sensors keep track of critical environmental conditions, including temperature, humidity, and air quality indices. 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Check for the proper", + "legacy_target": "launch_android_app", + "legacy_target_description": "Launch an Android application on a specified device.", + "legacy_target_description_chars": 52, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1038, + "source_record_id": "6b7ba520-01a7-421e-9097-f47401807e6c", + "source_parent_id": "hermes_record:6b7ba520-01a7-421e-9097-f47401807e6c", + "request_group_id": "hermes_request:a680f0f8ede461eb5c11da57f1be0d221536d00d4ea775d49129a625bc03c9d5", + "full_human_chars": 1139, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "launch_android_app", + "navigate_to_url_in_chrome", + "check_element_rendering" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_095755", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 83 + ], + "match_source_rows": [ + 83 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing several advertising campaigns with IDs camp_001, camp_002, and camp_003. 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To accompli", + "legacy_target": "fetch_campaign_metrics", + "legacy_target_description": "Retrieve real-time performance metrics for specified advertising campaigns.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 83, + "source_record_id": "3996ce16-9477-4aeb-8f18-2cb58ec1cff6", + "source_parent_id": "hermes_record:3996ce16-9477-4aeb-8f18-2cb58ec1cff6", + "request_group_id": "hermes_request:37fb6ce9d3a6ad551d890bce298d7f360af57948fa57c59a812f6db13f8c23cb", + "full_human_chars": 1248, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "fetch_campaign_metrics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_096064", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 22 + ], + "match_source_rows": [ + 22 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am a quantum computing researcher using the Qiskit framework to design an algorithm that involves controlled NOT gates and Hadamard gates. 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The expected distribution was to show equal probabilities amongst the possible quantum states, while the observed outpu", + "legacy_target": "debug_quantum_circuit", + "legacy_target_description": "Analyzes a quantum circuit to identify potential errors and suggests adjustments to achieve the expected behavior.", + "legacy_target_description_chars": 114, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 22, + "source_record_id": "84bff146-4486-42c2-98da-a7b22919ce2d", + "source_parent_id": "hermes_record:84bff146-4486-42c2-98da-a7b22919ce2d", + "request_group_id": "hermes_request:33c1e79e2dc19f776905e5b29204bb1db45088949ceff6665db76cf21414b311", + "full_human_chars": 1685, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "debug_quantum_circuit" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_096219", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 218 + ], + "match_source_rows": [ + 218 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am planning a holiday trip to Paris from December 20, 2023, to January 5, 2024. My total budget for this trip is $1500, and I would like assistance finding the best travel deals within this budget, including flights and accommodations. Additionally, I am interested in local culture, food, and entertainment, so I am looking for local experiences and dining options in Paris that fit these interest", + "legacy_target": "search_travel_deals", + "legacy_target_description": "Search for the best travel deals including flights and accommodations within a specified budget.", + "legacy_target_description_chars": 96, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 218, + "source_record_id": "417a4600-a94d-481c-9b62-1522737b5c3f", + "source_parent_id": "hermes_record:417a4600-a94d-481c-9b62-1522737b5c3f", + "request_group_id": "hermes_request:c52f7c2a1dc62ebdcefdc62fa405a498363b32cdd1cd4ba74f55547d988d2aa5", + "full_human_chars": 716, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "search_travel_deals", + "find_local_experiences", + "track_travel_expenses" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_096619", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 405 + ], + "match_source_rows": [ + 405 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting a market analysis focused on the technology and energy sectors, with the aim of creating a dynamic dashboard that captures market sentiments and stock price movements. To achieve this, I need to gather the latest financial news articles from reputable sources such as Bloomberg, Reuters, and CNBC, and obtain real-time data on specific stocks like AAPL from NYSE and GOOGL from NASDAQ", + "legacy_target": "aggregate_financial_news", + "legacy_target_description": "Aggregate news articles from specified financial news sources based on given keywords and time range.", + "legacy_target_description_chars": 101, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 405, + "source_record_id": "93f03ec0-f6ae-41f3-917a-a32c6e59ba8f", + "source_parent_id": "hermes_record:93f03ec0-f6ae-41f3-917a-a32c6e59ba8f", + "request_group_id": "hermes_request:7011d0a96fc3ff5d68e441899fa78bef6dd8b1554e118a1919da9e4e5865517e", + "full_human_chars": 1612, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "aggregate_financial_news", + "fetch_stock_market_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_096742", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 397 + ], + "match_source_rows": [ + 397 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing a machine learning model to categorize various types of vehicles within a set of images. 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I'd like to randomly rotate the ", + "legacy_target": "rotate_images", + "legacy_target_description": "Rotate images in the dataset by a random angle within the specified range.", + "legacy_target_description_chars": 74, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 397, + "source_record_id": "32372914-c129-4170-a7b1-0bb79e2e1854", + "source_parent_id": "hermes_record:32372914-c129-4170-a7b1-0bb79e2e1854", + "request_group_id": "hermes_request:83ef7aa8404aa3d52a3fc4d357ba250a813a6ecb2bf472602128813bfd0aeb6e", + "full_human_chars": 1069, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "rotate_images", + "adjust_brightness", + "random_crop" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_096899", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 109 + ], + "match_source_rows": [ + 109 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I recently placed an order on Amazon for a book and received an order confirmation with the ID 'AMZ12345'. To stay updated on my shipment, I would like to track the current status and estimated delivery date of this order. Could you please provide me with the most recent information available for my Amazon order using this specific order ID?", + "legacy_target": "get_order_status", + "legacy_target_description": "Retrieve the current status and estimated delivery date of an Amazon order.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 109, + "source_record_id": "8908569d-7723-4c52-9172-cd22b89faef3", + "source_parent_id": "hermes_record:8908569d-7723-4c52-9172-cd22b89faef3", + "request_group_id": "hermes_request:6fbdba37e459fb95d1218afd44369b2c4f9e11dbd234f09f7680159a2d6f5a2e", + "full_human_chars": 343, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "get_order_status" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097041", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 936 + ], + "match_source_rows": [ + 936 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently managing the time tracking for our project 'Website Redesign' using Clockify, and I've encountered an error with the entry for one of our team members. Specifically, on March 15th, 2023, the time entry for our employee, Alice Johnson (User ID: ajohnson), was incorrectly registered as 2 hours. The actual duration worked was 3.5 hours. I need assistance to first retrieve this particula", + "legacy_target": "retrieveTimeEntry", + "legacy_target_description": "Retrieves a specific time entry for a user on a given project and date.", + "legacy_target_description_chars": 71, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 936, + "source_record_id": "475416f1-2c9b-45cd-b242-005fa75c7c83", + "source_parent_id": "hermes_record:475416f1-2c9b-45cd-b242-005fa75c7c83", + "request_group_id": "hermes_request:bde87c187699d3dd217df38e661e74fa0afcbb131c3986e925e1e8fd0653cce2", + "full_human_chars": 653, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "retrieveTimeEntry", + "updateTimeEntry" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097077", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 491 + ], + "match_source_rows": [ + 491 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm working on an automotive analysis project and require detailed vehicle data for integration into my business intelligence tool. Specifically, I need vehicle specifications for a 2020 Tesla Model S, customer reviews for this vehicle, and an analysis of market trends in the electric vehicle segment from January 1, 2020, to December 31, 2020. The information should be structured as a JSON object ", + "legacy_target": "fetch_vehicle_specifications", + "legacy_target_description": "Retrieves detailed specifications for a specific vehicle make, model, and year.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 491, + "source_record_id": "46396b82-7a7d-4c2e-9838-74da7b71880e", + "source_parent_id": "hermes_record:46396b82-7a7d-4c2e-9838-74da7b71880e", + "request_group_id": "hermes_request:7ef95e223d1105022bdc37a3550d010b1eebe4a47644de30e49ae99b9f7d87dd", + "full_human_chars": 1043, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "fetch_vehicle_specifications", + "fetch_vehicle_reviews", + "analyze_ev_market_trends" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097119", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 225 + ], + "match_source_rows": [ + 225 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am interested in beginning a meditation practice to help manage stress. I've heard that 'Mindful Moments' is a good meditation app for beginners, and I would like to set up my first session. I am aiming for a 10-minute guided session focusing on stress relief. Could you please assist me in setting up this meditation session in the app 'Mindful Moments'? I am ready to find a quiet place, wear com", + "legacy_target": "initiate_meditation_session", + "legacy_target_description": "Set up a meditation session in the specified app with the given parameters.", + "legacy_target_description_chars": 75, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 225, + "source_record_id": "1e33295e-74f7-43af-b90c-28207ba64c33", + "source_parent_id": "hermes_record:1e33295e-74f7-43af-b90c-28207ba64c33", + "request_group_id": "hermes_request:7214a0eedfc33b19b85b4ba20e7833d985629c7c73431373505f20ce08d9742a", + "full_human_chars": 522, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "initiate_meditation_session" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097123", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 789 + ], + "match_source_rows": [ + 789 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am implementing a new process for onboarding banking customers and require assistance with automating several steps. I have a JSON file named \"customer_application_form_123.json\" that contains the application form data of a new customer, John Doe, who has applied for a bank account. Additionally, I've scanned his government-issued identity document and saved it as \"identity_document_123.pdf\". I ", + "legacy_target": "create_customer_profile", + "legacy_target_description": "Creates a new customer profile using the application form and identity document.", + "legacy_target_description_chars": 80, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 789, + "source_record_id": "560a820a-4c3c-4514-aff5-c4accfb0ee45", + "source_parent_id": "hermes_record:560a820a-4c3c-4514-aff5-c4accfb0ee45", + "request_group_id": "hermes_request:c7a075553b7c47739443c163c4ab53253f19e496c0a33f728c30e88afdca49ef", + "full_human_chars": 1376, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "create_customer_profile" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097130", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 840 + ], + "match_source_rows": [ + 840 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm planning a road trip from Los Angeles to San Francisco this weekend along the I-5 highway and I need timely and detailed traffic updates to ensure a smooth journey. Could you please use the `get_traffic_updates` function to provide me with the following real-time traffic information:\n\n- Current traffic conditions on the I-5 highway.\n- Reports of any accidents, specifically if there's one near ", + "legacy_target": "get_traffic_updates", + "legacy_target_description": "Retrieves real-time traffic updates, incidents, and alternative routes for a specified highway between two cities.", + "legacy_target_description_chars": 114, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 840, + "source_record_id": "b4de7c2d-5383-4d0d-a507-d2124483ad5a", + "source_parent_id": "hermes_record:b4de7c2d-5383-4d0d-a507-d2124483ad5a", + "request_group_id": "hermes_request:8af46e96057bb59f7e1937bc7bc0c55be1708abf8d62008c7e68d4e0229a4be5", + "full_human_chars": 928, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "get_traffic_updates", + "get_traffic_updates" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097266", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 1305, + 1312, + 1331, + 1444, + 1485, + 1513, + 1561, + 1581, + 1620, + 1675, + 1699, + 1816, + 1854 + ], + "match_source_rows": [ + 1305 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage paragraph theme (figure 12.1), it hardly matters which of the three reasons you explain first or second. In the more organic structure (figure 12.2), each paragraph specifically leads to the next. The last key difference is seen in the conclusion. Because the organic essay is driven by an ambitious, nonobvious argument, the reade", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that request for an opinion on a topic found in the document.", + "legacy_target_description_chars": 90, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1305, + "source_record_id": "62cfb91c-35e2-4b04-ae6e-dca98205e4be", + "source_parent_id": "hermes_record:62cfb91c-35e2-4b04-ae6e-dca98205e4be", + "request_group_id": "hermes_request:a439711033a50ff66142407e1782083afd292dc3ba7b697a9a474a9a5f11e74c", + "full_human_chars": 15008, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097316", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 887 + ], + "match_source_rows": [ + 887 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I discovered an insightful post with ID '987654321' on a social media platform that resonated with me. I intend to show my appreciatioin by liking it. For this purpose, I have my personal authentication token on hand, which is \"my_auth_token\". Could you execute the necessary API call utilizing the 'like_post' function to like this post on my behalf? Please ensure the post with the specified ID is ", + "legacy_target": "like_post", + "legacy_target_description": "This function allows a user to like a post on a social media platform using a POST request.", + "legacy_target_description_chars": 91, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 887, + "source_record_id": "05e37f38-0288-49c8-b7e3-39faf4e875e1", + "source_parent_id": "hermes_record:05e37f38-0288-49c8-b7e3-39faf4e875e1", + "request_group_id": "hermes_request:5deb7e6e8c3e6f88525ace7704180253130b7ddab1c51b27a84a639491d03f16", + "full_human_chars": 419, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "like_post" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097324", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 205 + ], + "match_source_rows": [ + 205 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm in the process of redecorating my living room and I'm searching for an app that offers robust features to assist in this endeavor. Specifically, I need an app that allows me to create a comprehensive virtual layout of the space, complete with the ability to adjust wall heights for a more accurate depiction. Additionally, the app should have a vast library of customizable furniture and accessor", + "legacy_target": "find_home_decor_app", + "legacy_target_description": "Finds a home decor and design app based on specified features.", + "legacy_target_description_chars": 62, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 205, + "source_record_id": "c5b090ae-6826-4b5c-8f2b-f440e85e97a6", + "source_parent_id": "hermes_record:c5b090ae-6826-4b5c-8f2b-f440e85e97a6", + "request_group_id": "hermes_request:e8fd3a69b7c69f0b6d0edb798e7295b38582e2d7946541d90919d64a3be31c47", + "full_human_chars": 868, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "find_home_decor_app" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097423", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 715 + ], + "match_source_rows": [ + 715 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am finalizing the launch of our new product and have created a promotional video that needs to be shared across our company's social media channels. The video is ready for upload and is located at \"path/to/promotional_video.mp4\". I would like the video to be accompanied by the following caption: \"Introducing our latest innovation - the future of technology. #NewProduct #Innovation #TechLaunch\". ", + "legacy_target": "post_to_social_media", + "legacy_target_description": "Post a video with a caption and hashtags to specified social media accounts.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 715, + "source_record_id": "d2a23043-37c9-4bbe-b8bc-a8090567b4e1", + "source_parent_id": "hermes_record:d2a23043-37c9-4bbe-b8bc-a8090567b4e1", + "request_group_id": "hermes_request:b1ce45e42d9290e3d50d2a85774135f77e17043265d770879de2fde952f9dd18", + "full_human_chars": 829, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "post_to_social_media" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097432", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 627 + ], + "match_source_rows": [ + 627 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am coordinating the maintenance scheduling for our transportation infrastructure, which includes both highways and railtracks. We have gathered comprehensive datasets that encompass daily and hourly traffic patterns over the last quarter, detailed logs of previous maintenance works and their outcomes, upcoming weather forecasts for the region, and detailed rosters outlining the availability of o", + "legacy_target": "generate_optimized_maintenance_schedule", + "legacy_target_description": "Generates an optimized maintenance schedule for highways and railtracks considering traffic patterns, maintenance history, weather forecasts, and crew availabil", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 627, + "source_record_id": "6f33c897-6c64-47bc-beca-f76dccad85f7", + "source_parent_id": "hermes_record:6f33c897-6c64-47bc-beca-f76dccad85f7", + "request_group_id": "hermes_request:5146cf5bc1db965223d77ef0180e3a72fe5ab7dbac3ed6f819d3fadd891293ab", + "full_human_chars": 1804, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "generate_optimized_maintenance_schedule" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "train" + }, + { + "canonical_id": "fastbrain_097442", + "original_split": "train", + "v2_split": "train", + "historical_validation": false, + "historical_test": false, + "prefix_match_source_rows": [ + 62 + ], + "match_source_rows": [ + 62 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm leading the analytics for our multi-channel advertising campaign, and I have detailed performance data that I need to analyze for cost-effectiveness and audience engagement. We've recently concluded a campaign spanning Facebook, Google, and Instagram. We invested $1,200 in Facebook with 30,000 impressions leading to 1,500 clicks and 150 conversions. 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To maximize efficiency and accuracy, we're looking to implement RPA in several key areas. Our objectives include:\n\n1. 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We have gathered extensive geographical data including latitude, longitude, altitude, and land use classifications for several potential sites. Alongside this, we've compiled historical weather data such as solar insolation levels, wind speeds, temperature ranges, and precipitati", + "legacy_target": "assess_location_viability", + "legacy_target_description": "Assesses the viability of locations for solar and wind energy production using geographical and historical weather data.", + "legacy_target_description_chars": 120, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 300, + "source_record_id": "71ccc51c-4d3e-40f8-a04f-160ca9135849", + "source_parent_id": "hermes_record:71ccc51c-4d3e-40f8-a04f-160ca9135849", + "request_group_id": "hermes_request:97156b5865bfc0de1d7a432ba01f699943c3c348770fae1d7e4e33e316bb92a7", + "full_human_chars": 2323, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "assess_location_viability" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_102619", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 1110, + 1114, + 1143, + 1148, + 1172, + 1252, + 1295, + 1377, + 1383, + 1387, + 1542, + 1604, + 1694 + ], + "match_source_rows": [ + 1110 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage blinked, she sighed. \n**7.** Rewrite each of the following sentences in three different ways (same content and ideas, just a different order). Which version do you like best? \n#### ACTIVITIES \n A DAM GOOD ARGUMENT 212 \n She walked the dog past the empty post office, and the dog barked twice. The soup may be brothy, but it i", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that ask for reasoning behind a design decision, product specification, or method described in the document. 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We have a comprehensive JSON file containing customer transaction history, which can be utilized to segment customers for targetted services. For one specific customer with ID \"C12345678\", who holds a savings account and has a high volume", + "legacy_target": "segment_customers", + "legacy_target_description": "Segments customers based on their transaction history to provide targeted services.", + "legacy_target_description_chars": 83, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 808, + "source_record_id": "1ecf1c49-fba6-4ab7-abb8-2fedb46ea1fd", + "source_parent_id": "hermes_record:1ecf1c49-fba6-4ab7-abb8-2fedb46ea1fd", + "request_group_id": "hermes_request:1a8994f7eac40c049e205aa5149bbdb580946adab49547cd8596df58c53f3e17", + "full_human_chars": 2805, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "segment_customers", + "provide_personalized_offers", + "track_customer_service_interactions", + "analyze_customer_satisfaction" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_102781", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 1146, + 1259, + 1348, + 1352, + 1400, + 1403, + 1475, + 1479, + 1512, + 1537, + 1695, + 1768, + 1826 + ], + "match_source_rows": [ + 1146 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage to be rhetorically effective in our writing, and our strategies may vary based on the particular rhetorical situation in which we find ourselves. As writers, it is a powerful tool to be able to move back and forth between stylistic modes and communication styles--the better to reach our readers. The following elements of style (i", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of questions that require making logical inferences based on the information in the document. 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To precisely evaluate the credit risk, I wish to calculate the probability of default for each loan, considering historical data. I also need to determine the exposure at default, incorporating the current exposure values and estimate the loss given default, utilizing specif", + "legacy_target": "calculate_probability_of_default", + "legacy_target_description": "Calculates the probability of default for each loan based on historical data.", + "legacy_target_description_chars": 77, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 791, + "source_record_id": "3ed8e87c-0dfe-42fc-9aab-456a7a6e903c", + "source_parent_id": "hermes_record:3ed8e87c-0dfe-42fc-9aab-456a7a6e903c", + "request_group_id": "hermes_request:a051c6d82b4c998f0c9d3d3f605ed57c35170d4234ea4f507b70bb68cd28073f", + "full_human_chars": 1548, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "calculate_probability_of_default", + "determine_exposure_at_default", + "estimate_loss_given_default" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_102865", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 580 + ], + "match_source_rows": [ + 580 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for modernizing our company's invoicing operations. We need to transition from a legacy system that processes a multitude of invoice formats, including PDFs, paper copies, and digital documents, to a more efficient cloud-based invoicing platform. Our objective is to automate the extraction and standardization process for these various invoice formats, ensuring a secure and accurat", + "legacy_target": "automate_invoice_processing", + "legacy_target_description": "Automates the extraction and standardization of invoices from various formats into a specified target format, with options for security checks and data verifica", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 580, + "source_record_id": "c29d4ec6-dc56-4528-a14e-ccbb040d6174", + "source_parent_id": "hermes_record:c29d4ec6-dc56-4528-a14e-ccbb040d6174", + "request_group_id": "hermes_request:ea6f41b63905c3160ea7ca01681eefd968c50dbdc4431f2660348f49e82cdada", + "full_human_chars": 1160, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "automate_invoice_processing", + "verify_data_migration" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_102876", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 214 + ], + "match_source_rows": [ + 214 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As someone who wants to improve my physical and emotional wellness through yoga, I've been researching the Daily Yoga app. I've discovered that the app offers various yoga classes tailored for beginners like myself. Specifically, I'm interested in a beginner-friendly class focused on overall wellness. Additionally, to ensure consistent practice and progress, I'd like to establish a structured week", + "legacy_target": "find_yoga_class", + "legacy_target_description": "Find a yoga class based on the user's level and focus.", + "legacy_target_description_chars": 54, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 214, + "source_record_id": "e558ba4f-3e78-48a9-8174-cacc14b19f4e", + "source_parent_id": "hermes_record:e558ba4f-3e78-48a9-8174-cacc14b19f4e", + "request_group_id": "hermes_request:25b79a89c1a54690aa5ea5efd16d8f2104fb256c4db23625e2d193249f5b11ff", + "full_human_chars": 1065, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "find_yoga_class", + "create_target_plan" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_103054", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 1011 + ], + "match_source_rows": [ + 1011 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have an extensive and informative article from Egnyte's blog on their AI engine, detailing how it's embedded into various content-related workflows for tasks such as file administration, document search, discovery, and data compliance. The article also delves into the components of Egnyte's architecture, the selection and integration of various AI models, and how these enhance customer experienc", + "legacy_target": "summarize_document", + "legacy_target_description": "Generates a summary of the provided document text.", + "legacy_target_description_chars": 50, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1011, + "source_record_id": "b4268291-6c85-4311-9680-8950efaf2e32", + "source_parent_id": "hermes_record:b4268291-6c85-4311-9680-8950efaf2e32", + "request_group_id": "hermes_request:33f5ea9c8159db45ce4cd07cdabcca58405e7d53e3531f0c483b97d73d3074ad", + "full_human_chars": 974, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "summarize_document" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_103111", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 1123, + 1131, + 1138, + 1243, + 1263, + 1339, + 1425, + 1430, + 1623, + 1693, + 1748, + 1815, + 1843 + ], + "match_source_rows": [ + 1123 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 8. LOGIC \nSo these two cases both result in true. But perhaps surprisingly, we also get true for oatmeal: \n Human(oatmeal) = Adult(oatmeal) Child(oatmeal) false = false false false = false true X \nWhoa, how did _true_ pop out of that? Simply because the premise was false, and so all bets were off. 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We frequently interact with customers who speak a variety of languages, and it's crucial to provide real-time assistance in their native tongues. To illustrate, when a customer sends us an inquiry in French, such as \"Pouvez-vous m'aider avec ma commande?\" (Can you help me with my order?), we need to", + "legacy_target": "translate_realtime_message", + "legacy_target_description": "Translates a message from the source language to the target language in real-time using a translation API.", + "legacy_target_description_chars": 106, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 827, + "source_record_id": "b4286425-8dcd-46cd-baf0-15f44b9c7c96", + "source_parent_id": "hermes_record:b4286425-8dcd-46cd-baf0-15f44b9c7c96", + "request_group_id": "hermes_request:e4435588101e49f9b824f2ffffb011966e035e10fe064524a79ff90176d5438a", + "full_human_chars": 716, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "translate_realtime_message" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105133", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 804 + ], + "match_source_rows": [ + 804 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to enhance our insurance CRM system with specific customer data analysis. We have compiled detailed policy information for hundreds of our customers into a JSON file, and we need to segment them by policy type to tailor our services better. Additionally, we require monitoring of the claim history of a certain customer, specifically customer with ID \"1234567890.\" For this particular cu", + "legacy_target": "segment_customers_by_policy", + "legacy_target_description": "Segments customers based on their policy types to provide targeted services.", + "legacy_target_description_chars": 76, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 804, + "source_record_id": "45517319-9bef-4920-8fcc-4e69e82268ec", + "source_parent_id": "hermes_record:45517319-9bef-4920-8fcc-4e69e82268ec", + "request_group_id": "hermes_request:2d250d22a9f926f743f23f846171e741a7ec4bba60274cb37ec2948d0eafdf60", + "full_human_chars": 1992, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "segment_customers_by_policy", + "track_claim_history", + "provide_personalized_recommendations", + "analyze_customer_feedback" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105171", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 1004 + ], + "match_source_rows": [ + 1004 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am overseeing a complex construction project and require assistance in streamlining our operations to enhance efficiency and productivity. We have an extensive project timeline that outlines all the key tasks, milestones, and deadlines. Our resource list includes detailed profiles and schedules of our construction personnel, as well as a complete inventory of our equipment and materials. Current", + "legacy_target": "schedule_tasks", + "legacy_target_description": "Schedules tasks based on the project timeline and available resources.", + "legacy_target_description_chars": 70, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1004, + "source_record_id": "ceccf2d1-0ee7-4fec-a3b6-1be4f23a54cd", + "source_parent_id": "hermes_record:ceccf2d1-0ee7-4fec-a3b6-1be4f23a54cd", + "request_group_id": "hermes_request:c47fca47006d0e6f4d8aac92b7bc804a71fc7493d052bd075852e6f4e449f637", + "full_human_chars": 2158, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "schedule_tasks", + "assign_resources", + "track_project_progress", + "generate_progress_report" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105186", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 30 + ], + "match_source_rows": [ + 30 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As the operations manager of the Grandeur Hotel (ID: H1001), I'm focused on streamlining our services for the month of April. To achieve this, I require assistance with multiple tasks:\n\n1. Synchronize our room inventory across leading online booking platforms—Booking.com, Expedia, and Airbnb—from April 1st to April 30th, 2023.\n2. Implement an automated system for guest check-ins and check-outs to ", + "legacy_target": "synchronizeRoomAvailability", + "legacy_target_description": "Synchronizes room availability across multiple booking platforms for a given hotel and date range.", + "legacy_target_description_chars": 98, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 30, + "source_record_id": "37c43506-c016-4947-9746-b6d28b6bc4ef", + "source_parent_id": "hermes_record:37c43506-c016-4947-9746-b6d28b6bc4ef", + "request_group_id": "hermes_request:ba510677675a0fa8edfad1c2aad2902296dc0dac635b503635f39d87edb616bf", + "full_human_chars": 737, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "synchronizeRoomAvailability", + "automateGuestCheckInOut", + "scheduleHousekeeping" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105201", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 167 + ], + "match_source_rows": [ + 167 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am managing a software development project and require assistance with organizing and overseeing various tasks for my team. I'd like to establish a project timeline for the \"Software Development Project\" initiating on the 1st of April, 2023, and culminating on the 1st of October, 2023. Furthermore, I need to delegate specific tasks to my team members, such as \"Design Database Schema\" to be assig", + "legacy_target": "createProjectTimeline", + "legacy_target_description": "Creates a new project timeline with a specified start and end date.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 167, + "source_record_id": "391b61e4-c0f3-4a58-bd79-4eb8013bcc6e", + "source_parent_id": "hermes_record:391b61e4-c0f3-4a58-bd79-4eb8013bcc6e", + "request_group_id": "hermes_request:6e4e2382fb7bdbbad963b11ab6b5b009766ed345b1db787fffe940b90566cd4e", + "full_human_chars": 1138, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "createProjectTimeline", + "assignTasksToTeam", + "trackTaskProgress" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105217", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 424 + ], + "match_source_rows": [ + 424 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently structuring my project directories and need assistance with creating a new folder specifically for organizing documents related to our client 'A'. This folder should reside directly within the root 'Projects' folder. The name of this new directory should be 'Client_A_Files'. I also need to ensure an 'Archive' folder is available within the 'Projects' directory for storing completed ", + "legacy_target": "create_directory", + "legacy_target_description": "Create a new directory at the specified path.", + "legacy_target_description_chars": 45, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 424, + "source_record_id": "aeb2b370-b2cc-4861-8c5d-e1f75c3b504f", + "source_parent_id": "hermes_record:aeb2b370-b2cc-4861-8c5d-e1f75c3b504f", + "request_group_id": "hermes_request:a1de99932b166303cba74e81f48bf9c2aa7210bc0a3b1715f470f60f24a9752b", + "full_human_chars": 977, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_directory", + "check_directory_exists" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105386", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 647 + ], + "match_source_rows": [ + 647 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need to update the status of three specific orders within our Order Management System. The order with ID 'ORD456' has been dispatched and should be marked as 'Shipped'. Similarly, orders 'ORD457' and 'ORD458' have also left the warehouse and need their statuses updated to 'Shipped' to reflect their current states. Please execute the function calls to update the statuses of these orders.\n\nCan you", + "legacy_target": "update_order_status", + "legacy_target_description": "Updates the status of an existing order in the Order Management System.", + "legacy_target_description_chars": 71, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 647, + "source_record_id": "768bac42-e03d-40c0-aa43-fcab3c571847", + "source_parent_id": "hermes_record:768bac42-e03d-40c0-aa43-fcab3c571847", + "request_group_id": "hermes_request:6a887a28de9525ca25dcc63d2c0d484b4d585518da1b6200d87817f76991b822", + "full_human_chars": 843, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "update_order_status", + "update_order_status", + "update_order_status" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105430", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 1224, + 1284, + 1289, + 1320, + 1364, + 1422, + 1432, + 1458, + 1462, + 1656, + 1720, + 1769, + 1823 + ], + "match_source_rows": [ + 1224 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Can you help me extract queries from the following passage 4 }, is R Q? The answer is yes, since both 2 and 4 are also members of Q. \nNotice that by the definition, every set is a subset of itself. Sometimes, though, it's useful to talk about whether a set is really a _sub_ set of another, and you don't want it to \"count\" if the two sets are actually equal. This is called a **proper sub", + "legacy_target": "ExpertQAExtractor", + "legacy_target_description": "Extracts a list of direct questions related to the document that have a single, unambiguous answer.", + "legacy_target_description_chars": 99, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1224, + "source_record_id": "f3ce3383-8994-458a-ac9c-f75ea893f881", + "source_parent_id": "hermes_record:f3ce3383-8994-458a-ac9c-f75ea893f881", + "request_group_id": "hermes_request:5c07dda0e3dad61c23e61ba7a61c561b65d64412a31e0e726764fa4c18375431", + "full_human_chars": 11119, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "missing_top_level_name" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105495", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 446 + ], + "match_source_rows": [ + 446 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I need to finalize the setup of a new router for our development team's network segment. The router requires enabling SSH access for remote administration, setting up a dedicated VLAN for development activities, and establishing a static route to ensure proper traffic flow to our internal server. Below are the exact configurations required:\n\n**SSH Configuration**:\n- Username: devAdmin\n- Password: ", + "legacy_target": "configureSSH", + "legacy_target_description": "Enables SSH access on the network device with specified credentials.", + "legacy_target_description_chars": 68, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 446, + "source_record_id": "de0edc62-c2bf-414a-990f-43d49caeff29", + "source_parent_id": "hermes_record:de0edc62-c2bf-414a-990f-43d49caeff29", + "request_group_id": "hermes_request:8e01a7c398ddcd18b2af2ec1d664c77b8ce732e714448f7b79159f05537f079f", + "full_human_chars": 939, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "configureSSH", + "setupVLAN", + "configureStaticRoute" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105603", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 487 + ], + "match_source_rows": [ + 487 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am conducting a comprehensive analysis of hypertension treatment outcomes among patients taking the medication Lisinopril. For my research, I need to obtain detailed patient records that include medication dosage, side effects, and blood pressure readings, explicitly for those patients who are on Lisinopril and exhibit a systolic blood pressure reading of 150 mmHg or higher.\n\nCould you please re", + "legacy_target": "retrieve_hypertension_patient_records", + "legacy_target_description": "Retrieves patient records for individuals with hypertension, filtered by a specific medication and a systolic blood pressure threshold, returning the data in a ", + "legacy_target_description_chars": 160, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 487, + "source_record_id": "8ad73826-e3c9-471f-885b-cc5f28e8c179", + "source_parent_id": "hermes_record:8ad73826-e3c9-471f-885b-cc5f28e8c179", + "request_group_id": "hermes_request:43adc4a0d7aa758082a75d8a0fb0cb6a6dd088436605d2c7abc691ebd2890d7a", + "full_human_chars": 953, + "query_prefix_cropped": true, + "description_prefix_cropped": true, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "retrieve_hypertension_patient_records", + "get_patient_medication_details", + "get_patient_blood_pressure_readings", + "get_patient_side_effects" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105668", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 918 + ], + "match_source_rows": [ + 918 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm interested in discovering some new movies to watch that align with my taste in film genres, specifically those in the action and sci-fi categories. Could you utilize a machine learning service to provide me with a tailored list of movies that fall within these two genres? I'm hoping to gain access to this recommendation list via a simple GET request. Based on my preferences, please invoke the ", + "legacy_target": "getMovieRecommendations", + "legacy_target_description": "Retrieves a list of movie recommendations based on specified genres.", + "legacy_target_description_chars": 68, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 918, + "source_record_id": "c362ef4e-81ea-44ec-a33c-50edb2cc38d1", + "source_parent_id": "hermes_record:c362ef4e-81ea-44ec-a33c-50edb2cc38d1", + "request_group_id": "hermes_request:8333855b18822f411af8ce4cf7c77de5abd936343ed250b736b1c0c864ffcf7f", + "full_human_chars": 454, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "getMovieRecommendations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105747", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 858 + ], + "match_source_rows": [ + 858 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "Good morning! I'm preparing for my day and need to have my smart mirror updated with today's essential information. I'm located in San Francisco, CA. Please call the getWeatherInfo function to provide the current weather details for San Francisco. Also, my user ID is user123; use the getPersonalSchedule function to fetch my personal schedule for the day. Lastly, I like to stay informed about world", + "legacy_target": "getWeatherInfo", + "legacy_target_description": "Retrieves the current weather information for a specified location.", + "legacy_target_description_chars": 67, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 858, + "source_record_id": "5d54b4ed-37f5-4ada-84c0-b7920252f9b8", + "source_parent_id": "hermes_record:5d54b4ed-37f5-4ada-84c0-b7920252f9b8", + "request_group_id": "hermes_request:ec7817594ca4aaa5921d61c610017c3523caabc4200b54afd373a1366b8b5813", + "full_human_chars": 1180, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "getWeatherInfo", + "getPersonalSchedule", + "getLatestNewsHeadlines" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_105982", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 267 + ], + "match_source_rows": [ + 267 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am actively trading cryptocurrencies on the Binance exchange and need assistance with implementing algorithmic trading algorithms for my portfolio, which includes Bitcoin (BTC) and Ethereum (ETH). I would like to automate the following tasks:\n \n1. Analyze the market sentiment for BTC and ETH using data from key sources: Twitter, Reddit, and various Crypto News Websites.\n2. Employ an LSTM Neural ", + "legacy_target": "analyze_market_sentiment", + "legacy_target_description": "Analyzes the market sentiment for specified cryptocurrencies on a given exchange using data from social media and news sources.", + "legacy_target_description_chars": 127, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 267, + "source_record_id": "d6915c89-7913-4fb9-bc89-817c6f2504c8", + "source_parent_id": "hermes_record:d6915c89-7913-4fb9-bc89-817c6f2504c8", + "request_group_id": "hermes_request:0f907ce6f777ed415dbd0b009bc5f5d981b4c01f0638e4d2e2cbc6046c159969", + "full_human_chars": 933, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "analyze_market_sentiment", + "predict_price_movements" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106053", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 916 + ], + "match_source_rows": [ + 916 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I oversee our brand's social media strategy and need to assess the performance of our recent 'Summer Blast' campaign, which ran on Facebook, Twitter, and Instagram from June 1 to June 30, 2023. I have detailed campaign IDs for each platform and I'm seeking specific engagement metrics: the number of likes, shares, comments, and the total reach of each campaign. Please use the functions to fetch the", + "legacy_target": "get_social_media_metrics", + "legacy_target_description": "Retrieves engagement metrics for a specific social media campaign identified by its campaign ID.", + "legacy_target_description_chars": 96, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 916, + "source_record_id": "0cbf7089-2bdb-440c-a5bc-e10d3ccdf912", + "source_parent_id": "hermes_record:0cbf7089-2bdb-440c-a5bc-e10d3ccdf912", + "request_group_id": "hermes_request:cb1b8d6ce252abb7e75586df7c9bb26b0449dc96e8a2fb34de9bac1058b918eb", + "full_human_chars": 1202, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_social_media_metrics", + "get_social_media_metrics", + "get_social_media_metrics" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106214", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 258 + ], + "match_source_rows": [ + 258 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I'm currently evaluating the performance of our momentum trading strategy over the past year and require detailed visualizations to better understand the points of action within the strategy's execution. I have compiled CSV files containing trade data for Tesla (TSLA), Amazon (AMZN), and Google (GOOG) over the specified period. The data includes dates, price movements, volumes, and the trading sig", + "legacy_target": "visualize_trading_strategy", + "legacy_target_description": "Generates visualizations for algorithmic trading strategies, including entry and exit points, trade P&L, and cumulative P&L.", + "legacy_target_description_chars": 124, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 258, + "source_record_id": "b1ba89fc-8836-478b-bc8c-e917a81e2295", + "source_parent_id": "hermes_record:b1ba89fc-8836-478b-bc8c-e917a81e2295", + "request_group_id": "hermes_request:210ae90ed99663b0b9b0844f0ca274152ef3ffb9e285eb5fb37f061944010458", + "full_human_chars": 1691, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "visualize_trading_strategy", + "visualize_trading_strategy", + "visualize_trading_strategy" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106315", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 802 + ], + "match_source_rows": [ + 802 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently managing an investment portfolio with an ID of `PF12345678`, and I'd like to assess its asset allocation as of today, `2023-06-01`. Furthermore, I intend to examine how the portfolio would perform in upcoming years considering a bull market, bear market, and stagnant market. For this, I need to project the potential returns over a 5-year period. Additionally, my portfolio requires r", + "legacy_target": "analyze_asset_allocations", + "legacy_target_description": "Analyzes the current asset allocations within a specified portfolio and provides a detailed breakdown.", + "legacy_target_description_chars": 102, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 802, + "source_record_id": "197cf5f6-affc-403a-a1ed-2ad28f9abce9", + "source_parent_id": "hermes_record:197cf5f6-affc-403a-a1ed-2ad28f9abce9", + "request_group_id": "hermes_request:762b4bb04995d0aa4f9dd1e12976053ef5f52fb779952eead2d6b137754f94c9", + "full_human_chars": 1200, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "analyze_asset_allocations", + "forecast_portfolio_returns", + "generate_rebalancing_recommendations" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106322", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 16 + ], + "match_source_rows": [ + 16 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently exploring quantum computing through a simulation exercise and need to create and visualize a Bell state using a quantum simulator that supports Qiskit. The exercise involves initializing a circuit with two qubits in the \\(|00\\rangle\\) state, applying a Hadamard gate to the first qubit, followed by a CNOT gate between the first and second qubits, which will entangle them into a Bell ", + "legacy_target": "initialize_qubits", + "legacy_target_description": "Initialize the qubits in the quantum circuit to a specified state.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 16, + "source_record_id": "c0d622cf-fee6-4b5d-8b05-e58edc313a25", + "source_parent_id": "hermes_record:c0d622cf-fee6-4b5d-8b05-e58edc313a25", + "request_group_id": "hermes_request:d4e653ded9936bdae36c1900d26276caffef408637250be20a873fe627804c7c", + "full_human_chars": 803, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 4, + "valid_top_level_call_names": [ + "initialize_qubits", + "apply_gates", + "run_circuit", + "visualize_quantum_state" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106411", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 79 + ], + "match_source_rows": [ + 79 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am planning an interactive virtual game night named \"FunTriviaNight\" and require assistance in setting up the platform. Specifically, I need a virtual game room created with the capacity to host up to 10 players at a time. The game we'll be playing has the identifier \"trivia_001\". Additionally, I want the players to have a fun experience by choosing their own avatars. There should be a selection", + "legacy_target": "create_virtual_game_room", + "legacy_target_description": "Creates a virtual room for hosting online games with participants.", + "legacy_target_description_chars": 66, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 79, + "source_record_id": "f4515ed2-556d-4b21-bddb-70f1f2c4458f", + "source_parent_id": "hermes_record:f4515ed2-556d-4b21-bddb-70f1f2c4458f", + "request_group_id": "hermes_request:e90e794970e00d6b69fa66de8c903b69fb7c1ac32e83b65fb6842966cd070568", + "full_human_chars": 576, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "create_virtual_game_room", + "select_avatar" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106414", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 133 + ], + "match_source_rows": [ + 133 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am tasked with improving our customer due diligence process in our financial services company by implementing an automated Anti-Money Laundering (AML) risk scoring mechanism. We need to analyze our customers and calculate the AML risk scores based on specific risk attributes such as the customer's country, political exposure, and occupation, which are essential factors in our risk assessment.\n\nW", + "legacy_target": "calculate_aml_risk_scores", + "legacy_target_description": "Calculates the AML risk scores for customers based on specified risk factors.", + "legacy_target_description_chars": 77, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 133, + "source_record_id": "b44dd2fc-08a2-4118-862a-4edb8e3307dd", + "source_parent_id": "hermes_record:b44dd2fc-08a2-4118-862a-4edb8e3307dd", + "request_group_id": "hermes_request:a5bf98a066b3e8662cd6f4b5a4caf1a40513fbe6d6c95dd13aab4e1d39c7e981", + "full_human_chars": 1748, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "calculate_aml_risk_scores" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106576", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 893 + ], + "match_source_rows": [ + 893 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am responsible for overseeing the operational efficiency of our production line at Industrial Facility X. We have recently integrated new IoT sensors into our Azure IoT infrastructure. There is an immediate requirement to collect and analyze real-time sensor data to monitor key performance indicators for our equipment. For this task, I need to access the most recent readings from three specific ", + "legacy_target": "get_iot_sensor_data", + "legacy_target_description": "Retrieves real-time data from specified IoT sensors connected to Azure IoT Hub.", + "legacy_target_description_chars": 79, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 893, + "source_record_id": "4173b1ec-5a01-49a3-ae99-00aaa42f400f", + "source_parent_id": "hermes_record:4173b1ec-5a01-49a3-ae99-00aaa42f400f", + "request_group_id": "hermes_request:5e960b65f1072f50819ad4922262a4bc6740666135e76d0d32a619fb12195d4a", + "full_human_chars": 1072, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 3, + "valid_top_level_call_names": [ + "get_iot_sensor_data", + "get_iot_sensor_data", + "get_iot_sensor_data" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106671", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 665 + ], + "match_source_rows": [ + 665 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am creating educational content for an e-learning platform aimed at visually impaired students. The material includes a chapter on the solar system, and I need to provide an audio version for accessibility. I would like the text to be read aloud in a clear, natural-sounding female voice that can be easily heard over classroom background noise. The text that needs converting is: \n\n\"The solar syst", + "legacy_target": "convertTextToSpeech", + "legacy_target_description": "Converts the given text into audible speech with options for voice type and environmental optimization.", + "legacy_target_description_chars": 103, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 665, + "source_record_id": "779fa945-e380-4e1d-8e59-2c5f7f5465fa", + "source_parent_id": "hermes_record:779fa945-e380-4e1d-8e59-2c5f7f5465fa", + "request_group_id": "hermes_request:8e93602d78e26fb3def3e96535d5e5dc1c7a8a7d347035d817df6569db1f6d63", + "full_human_chars": 719, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "convertTextToSpeech" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106744", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 869 + ], + "match_source_rows": [ + 869 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am currently subscribed to the 'Premium Content' service of 'ContentHub' under the subscription ID 'CH123456789'. I have decided to switch my subscription plan from 'Monthly' to 'Annual' to benefit from the yearly discount. Additionally, I would like to include the 'eBook Access' add-on to enhance my subscription package. Please proceed with updating my subscription preferences accordingly.", + "legacy_target": "updateSubscriptionPreferences", + "legacy_target_description": "Updates the subscription plan and add-ons for a user's subscription.", + "legacy_target_description_chars": 68, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 869, + "source_record_id": "1d9afe30-ac97-4077-8249-f2f1a328a2bf", + "source_parent_id": "hermes_record:1d9afe30-ac97-4077-8249-f2f1a328a2bf", + "request_group_id": "hermes_request:0931042ba4af331ae14ee65c5bfb1c56dee4df86c93f5caf24d6bb377577d1f0", + "full_human_chars": 395, + "query_prefix_cropped": false, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "updateSubscriptionPreferences" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106751", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 781 + ], + "match_source_rows": [ + 781 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I am developing a trading algorithm for financial exchanges that should be able to execute trades autonomously based on market analysis and predefined criteria. Specifically, I need to analyze the latest market data for certain stocks and identify trading opportunities that meet my strategy parameters. For this task, please analyze the real-time market data for the stocks Apple Inc. 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We have a dedicated folder in Google Drive where all project-related files are stored, such as 'ProjectProposal.docx' and 'Budget.xlsx'. Firstly, I need to verify that no new documents have been added recently by listing all files within the folder. 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The recipes should be simple enough to be prepared in under an hour since I won", + "legacy_target": "find_recipes", + "legacy_target_description": "Find recipes based on dietary preferences and preparation time.", + "legacy_target_description_chars": 63, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 228, + "source_record_id": "e3636475-60a5-457b-9f17-aafe5e8792ef", + "source_parent_id": "hermes_record:e3636475-60a5-457b-9f17-aafe5e8792ef", + "request_group_id": "hermes_request:f4c66e0d7363b8f5cca11627225b124ed0b35f342a7523eb53d83fed43f79d94", + "full_human_chars": 765, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 2, + "valid_top_level_call_names": [ + "find_recipes", + "find_recipes" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "hermes_multiaction_v3", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_106931", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 11 + ], + "match_source_rows": [ + 11 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "As I am preparing for my upcoming vacation, I want to make sure my home stays secure in my absence. I'd like to request the locking of all our smart doors, which include the front door, the back door, and the garage door. I need confirmation that each of these doors is indeed locked. To ensure this, could you please lock all the smart doors and send me a confirmation message to verify that they ha", + "legacy_target": "lock_all_smart_doors", + "legacy_target_description": "Locks all smart doors in the home and optionally sends a confirmation message.", + "legacy_target_description_chars": 78, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 11, + "source_record_id": "edfc63f9-9534-4205-87ca-e37f3630eabe", + "source_parent_id": "hermes_record:edfc63f9-9534-4205-87ca-e37f3630eabe", + "request_group_id": "hermes_request:a0d8160d94b5684dd80a2534eec1fff30c2b582046d1021dedc5e5f5b6cf2413", + "full_human_chars": 708, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "lock_all_smart_doors" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_107008", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 1040 + ], + "match_source_rows": [ + 1040 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have recently taken a memorable trip to Paris and meticulously captured numerous photographs on my Android smartphone. I would now like to consolidate these images into a well-organized photo album for effortless sharing and reminiscence. I request your assistance in initiating a new photo album with the title 'Paris Trip' to encompass the essence of this excursion. The description to accompany ", + "legacy_target": "createPhotoAlbum", + "legacy_target_description": "Creates a new photo album with the given title and description.", + "legacy_target_description_chars": 63, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 1040, + "source_record_id": "079d2cbd-07c5-4666-a65b-78a9f1e357fc", + "source_parent_id": "hermes_record:079d2cbd-07c5-4666-a65b-78a9f1e357fc", + "request_group_id": "hermes_request:06fac29ba16c7006ce9f967e33c0186bb15a1e674fa25b1018f7ff7c0968fd49", + "full_human_chars": 1039, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "createPhotoAlbum" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_107019", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 738 + ], + "match_source_rows": [ + 738 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I would like to download the latest version of Firefox for my Windows desktop. 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Also, I need to ensure that the downloaded file is genuine and has not ", + "legacy_target": "download_file", + "legacy_target_description": "Downloads a file from a specified URL to a destination path on the local machine and optionally checks the file integrity using a checksum.", + "legacy_target_description_chars": 139, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 738, + "source_record_id": "1aaea158-8b7f-4e51-b24d-085e5692b067", + "source_parent_id": "hermes_record:1aaea158-8b7f-4e51-b24d-085e5692b067", + "request_group_id": "hermes_request:7807cc10a3a8c6de5f87567c018a39f28b578900cceab8e047c5c55670bf2406", + "full_human_chars": 611, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "download_file" + ], + "first_round_schema_valid": false, + "first_round_schema_issue_types": [ + "argument_schema" + ], + "default_proposed_view": "quarantine_call_contract", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_107056", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 771 + ], + "match_source_rows": [ + 771 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I have a collection of three articles focused on the impact and advancements of AI in the healthcare sector. 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Vehicle ID: `VH003`, Capacity: 12 passengers, Operational Cost per ", + "legacy_target": "optimize_vehicle_scheduling", + "legacy_target_description": "Generates optimized schedules for a fleet of vehicles to minimize operational costs while adhering to service booking time constraints.", + "legacy_target_description_chars": 135, + "status": "unique_source_recovered", + "source_repo": "NousResearch/hermes-function-calling-v1", + "source_revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "source_file": "func-calling-singleturn.json", + "source_file_row": 631, + "source_record_id": "6c053139-6441-46a5-82d0-00ff0b756741", + "source_parent_id": "hermes_record:6c053139-6441-46a5-82d0-00ff0b756741", + "request_group_id": "hermes_request:5423c991885f5d1a72a8d777dbcc667d997e61703ef545a3a2406fd4fe041097", + "full_human_chars": 1513, + "query_prefix_cropped": true, + "description_prefix_cropped": false, + "first_round_call_count": 1, + "valid_top_level_call_names": [ + "optimize_vehicle_scheduling" + ], + "first_round_schema_valid": true, + "first_round_schema_issue_types": [], + "default_proposed_view": "restored_single_call_review_pool", + "effective_split_by_protected_request_group": "test" + }, + { + "canonical_id": "fastbrain_107499", + "original_split": "test", + "v2_split": "test", + "historical_validation": false, + "historical_test": true, + "prefix_match_source_rows": [ + 971 + ], + "match_source_rows": [ + 971 + ], + "match_count": 1, + "matching_policy": "Exact first-human stripped prefix [:400], exact target name, exact target description [:160]; no fuzzy matching, no first-match tie breaking.", + "legacy_query": "I oversee GDPR compliance within our organization and require real-time monitoring and analysis of key compliance metrics for the past 30 days. 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_000480", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6c114ca5b8df2c0b2fe8ed38e479af9b", + "normalized_query_sha256": "7ed42a2835d21b346fe628882ff4429fd14cbaf01a84588d704ec27986467a38", + "query": "please delete my next calendar event", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 206, + "historical_id": "semantic:validation:fastbrain_000480:present", + "source": "amazon_massive", + "query_hash": "7ed42a2835d21b346fe628882ff4429fd14cbaf01a84588d704ec27986467a38" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 860, + "historical_id": "semantic:validation:fastbrain_000480:absent", + "source": "amazon_massive", + "query_hash": "7ed42a2835d21b346fe628882ff4429fd14cbaf01a84588d704ec27986467a38" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_000496", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:91897556640daf44008bd617aeabce94", + "normalized_query_sha256": "8f850dd8c05346536ceda3f05910ef135e48679d05cef24be0beee5a3b4a4b73", + "query": "Good, also a movie.", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 827, + "historical_id": "fastbrain_000496", + "source": "sgd", + "query_hash": "8f850dd8c05346536ceda3f05910ef135e48679d05cef24be0beee5a3b4a4b73" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 827, + "historical_id": "fastbrain_000496", + "source": "sgd", + "query_hash": "8f850dd8c05346536ceda3f05910ef135e48679d05cef24be0beee5a3b4a4b73" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_000718", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7d32727e1518bf5ed6f15bca18902e0e", + "normalized_query_sha256": "d4c8bb9cbe0118e1f1884f5a9ab0bb1bb2e71b4397bff4d6995c2ba19212f507", + "query": "I'm searching for a salon.", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 302, + "historical_id": "fastbrain_000718", + "source": "sgd", + "query_hash": "d4c8bb9cbe0118e1f1884f5a9ab0bb1bb2e71b4397bff4d6995c2ba19212f507" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 302, + "historical_id": "fastbrain_000718", + "source": "sgd", + "query_hash": "d4c8bb9cbe0118e1f1884f5a9ab0bb1bb2e71b4397bff4d6995c2ba19212f507" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_000937", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4cd1abcd8d672f9f2314c6148897c614", + "normalized_query_sha256": "a7323ba47f53b5f50e15295c23a9cfbd779ef902148b4dc7652421c0e8317841", + "query": "restore app.py from two commits ago", + "target": "git_restore", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 342, + "historical_id": "fastbrain_000937", + "source": "git_nl_commands", + "query_hash": "a7323ba47f53b5f50e15295c23a9cfbd779ef902148b4dc7652421c0e8317841" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 342, + "historical_id": "fastbrain_000937", + "source": "git_nl_commands", + "query_hash": "a7323ba47f53b5f50e15295c23a9cfbd779ef902148b4dc7652421c0e8317841" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_001075", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c566c7978fe853a4caf70f5e5fe5c22a", + "normalized_query_sha256": "1aa10ecc9ed7daded180dafd8b5da24f7ca1315806006381c5167745ffa8e06a", + "query": "i need to add baseball practice on saturday at two p. m.", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 729, + "historical_id": "semantic:validation:fastbrain_001075:present", + "source": "amazon_massive", + "query_hash": "1aa10ecc9ed7daded180dafd8b5da24f7ca1315806006381c5167745ffa8e06a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 889, + "historical_id": "semantic:validation:fastbrain_001075:absent", + "source": "amazon_massive", + "query_hash": "1aa10ecc9ed7daded180dafd8b5da24f7ca1315806006381c5167745ffa8e06a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_001079", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4ac5e64a3fad88aa13cfce166661e102", + "normalized_query_sha256": "1bfa35dc0af08a543457cb290aa2415a2722fe5a24d7ce2c13c04c522cd7429b", + "query": "what are the upcoming reminders", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 851, + "historical_id": "fastbrain_001079", + "source": "amazon_massive", + "query_hash": "1bfa35dc0af08a543457cb290aa2415a2722fe5a24d7ce2c13c04c522cd7429b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 851, + "historical_id": "fastbrain_001079", + "source": "amazon_massive", + "query_hash": "1bfa35dc0af08a543457cb290aa2415a2722fe5a24d7ce2c13c04c522cd7429b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_001419", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dffd12d632b043afb878fce65809a1e0", + "normalized_query_sha256": "d60dae547d2582db43d1a23e42b8f2188c2cd1d7002f875f82c7b57d81455905", + "query": "I need to have 2 rooms at a hotel there.", + "target": "SearchHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 801, + "historical_id": "fastbrain_001419", + "source": "sgd", + "query_hash": "d60dae547d2582db43d1a23e42b8f2188c2cd1d7002f875f82c7b57d81455905" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 801, + "historical_id": "fastbrain_001419", + "source": "sgd", + "query_hash": "d60dae547d2582db43d1a23e42b8f2188c2cd1d7002f875f82c7b57d81455905" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_001496", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fd685de5fb7039c7d9286a1702f4c47b", + "normalized_query_sha256": "fe383d5437478bfd83d72dab5d73dfafdd0c5a2df45424e7881089967bfa5489", + "query": "Play on my bedroom speaker.", + "target": "PlayMedia", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 841, + "historical_id": "fastbrain_001496", + "source": "sgd", + "query_hash": "fe383d5437478bfd83d72dab5d73dfafdd0c5a2df45424e7881089967bfa5489" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 841, + "historical_id": "fastbrain_001496", + "source": "sgd", + "query_hash": "fe383d5437478bfd83d72dab5d73dfafdd0c5a2df45424e7881089967bfa5489" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_001573", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:228a45c295c59efd60121179524551cf", + "normalized_query_sha256": "14044b10b5b283a79edb7d0207a141dc4dc53de7306f6b884881f92230293d49", + "query": " Financial disputes exceeding $500 \n4. Suspected minor on platform \n\nTransfer message: \n\"YOU ARE BEING TRANSFERRED TO A HUMAN SAFETY AGENT. PLEASE WAIT MOMENTARILY.\" \n\n## Tool Execution Rules \n- Require explicit user confirmation for: \n - Subscription changes \n - Permanent account deactivation \n - Location radius expansion \n- One tool per action cycle (e.g., retrieve match → verify status → send message) \n- Validate active subscription before premium feature access\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi there, I'm having an issue with my profile visibility. I set my profile to \"Hidden\" during my vacation, and now I've turned it back to \"Active,\" but my profile isn't showing up in matches even after I refreshed my photos. I thought the monthly photo refresh would count toward maintaining my Active status. Can you confirm if there's a specific requirement for the monthly photo refresh to keep my profile visible?\nassistant: Hello! I'd be happy to help you with your profile visibility issue. First, I need to check your current profile status to understand what might be happening.\n\nCould you please provide your User ID so I can look up your profile details?\nuser: Sure, my User ID is 123456. Let me know what you find!\nassistant: \ntool: \n\n{\"account_status\": \"active\", \"verification_progress\": \"Email and phone verified. Profile photo last refreshed 2 days ago.\", \"location_settings\": {\"radius_km\": 50, \"last_modified\": \"2024-03-15T08:30:00Z\"}}", + "target": "get_subscription_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 28, + "historical_id": "fastbrain_001573", + "source": "nemotron_agentic", + "query_hash": "14044b10b5b283a79edb7d0207a141dc4dc53de7306f6b884881f92230293d49" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 28, + "historical_id": "fastbrain_001573", + "source": "nemotron_agentic", + "query_hash": "14044b10b5b283a79edb7d0207a141dc4dc53de7306f6b884881f92230293d49" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_001606", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4f6a6f6cee8791c031a31c2ab8465f4b", + "normalized_query_sha256": "66569760c459b164c5ac845c87055eeab0587c01e4e78a8e895cf927ebc6f18d", + "query": "Yes, that will work.", + "target": "BuyBusTicket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 780, + "historical_id": "fastbrain_001606", + "source": "sgd", + "query_hash": "66569760c459b164c5ac845c87055eeab0587c01e4e78a8e895cf927ebc6f18d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 780, + "historical_id": "fastbrain_001606", + "source": "sgd", + "query_hash": "66569760c459b164c5ac845c87055eeab0587c01e4e78a8e895cf927ebc6f18d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_001708", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9cde4c715d3d25a3542df6ce3211e766", + "normalized_query_sha256": "7b3f3e98e09c27be5c2a0aa13a71001093ed575e9648c7fb5bde2b74a17ceb17", + "query": "bration manuals and procedure guides to your email.\n\nIs there anything else I can help you with while you retrieve this information?\nuser: Thank you for the guidance. I'll contact our procurement team to retrieve the PO number for the HM-FP-3000-7741 unit. Once I have it, I'll provide both the Company ID (FPD-883) and the PO number to proceed with authentication. Please hold the request temporarily while I gather this information. I expect to return with the details within the next 30 minutes.\nassistant: I understand. I'll hold your request for the calibration manuals and procedure guides for your HM-FP-3000 Processor (serial HM-FP-3000-7741) temporarily while you retrieve the PO number.\n\nPlease take your time to contact your procurement team. I'll be ready to assist you as soon as you return with both:\n1. Company ID: FPD-883\n2. PO Number for the HM-FP-3000-7741 unit\n\nOnce you provide these details, I'll authenticate your company and proceed with retrieving and sending the technical documentation to your email.\n\nI'll be here when you're ready to continue.\nuser: I’ve retrieved the PO number from our procurement team. Here are the details: \n- **Company ID**: FPD-883 \n- **PO Number**: PO-482917 \n- **Equipment Serial**: HM-FP-3000-7741 \n\nPlease proceed with authentication and send the calibration manuals and procedure guides to our email. Once received, I’ll confirm the receipt for you.\nassistant: \ntool: \n\n{\"authenticated\": true, \"company_name\": \"FPD-883 Food Processing Co.\"}", + "target": "get_technical_document", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 955, + "historical_id": "fastbrain_001708", + "source": "nemotron_agentic", + "query_hash": "7b3f3e98e09c27be5c2a0aa13a71001093ed575e9648c7fb5bde2b74a17ceb17" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 955, + "historical_id": "fastbrain_001708", + "source": "nemotron_agentic", + "query_hash": "7b3f3e98e09c27be5c2a0aa13a71001093ed575e9648c7fb5bde2b74a17ceb17" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002022", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1986e4a10c2209dc2af8002ac4f7c5e8", + "normalized_query_sha256": "a6acbf244ca6f8635d5789935fc3a82a431ec562b26b0ae07136ac3fad6d8128", + "query": "play be warned by tech nine", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 276, + "historical_id": "semantic:validation:fastbrain_002022:present", + "source": "amazon_massive", + "query_hash": "a6acbf244ca6f8635d5789935fc3a82a431ec562b26b0ae07136ac3fad6d8128" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 619, + "historical_id": "semantic:validation:fastbrain_002022:absent", + "source": "amazon_massive", + "query_hash": "a6acbf244ca6f8635d5789935fc3a82a431ec562b26b0ae07136ac3fad6d8128" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002041", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dbac070127ec85db6cffb99e5eb8e4e9", + "normalized_query_sha256": "a59b7198433d126022665efa49fa36e50d1354f9fdaa1b2b70337ad0039ddafc", + "query": "I would like to find a doctor in Milpitas.", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 613, + "historical_id": "fastbrain_002041", + "source": "sgd", + "query_hash": "a59b7198433d126022665efa49fa36e50d1354f9fdaa1b2b70337ad0039ddafc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 613, + "historical_id": "fastbrain_002041", + "source": "sgd", + "query_hash": "a59b7198433d126022665efa49fa36e50d1354f9fdaa1b2b70337ad0039ddafc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002046", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6be97d4c62e6d70774f29845120e8706", + "normalized_query_sha256": "e5b03d404e08762e5aabdac2c03a4ea2695583c8cb29b7e1a81cd9d1e6992487", + "query": "i would like a cup of coffee", + "target": "iot_coffee", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 761, + "historical_id": "fastbrain_002046", + "source": "amazon_massive", + "query_hash": "e5b03d404e08762e5aabdac2c03a4ea2695583c8cb29b7e1a81cd9d1e6992487" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 761, + "historical_id": "fastbrain_002046", + "source": "amazon_massive", + "query_hash": "e5b03d404e08762e5aabdac2c03a4ea2695583c8cb29b7e1a81cd9d1e6992487" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002088", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:27484e3d96d9d694b70895c8b04139d4", + "normalized_query_sha256": "e626d07c450027cd69619a2eecfcf4b6044ebaa5a3ae7dfe41902c1b62bce09d", + "query": "Great make a reservation there", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 279, + "historical_id": "fastbrain_002088", + "source": "sgd", + "query_hash": "e626d07c450027cd69619a2eecfcf4b6044ebaa5a3ae7dfe41902c1b62bce09d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 279, + "historical_id": "fastbrain_002088", + "source": "sgd", + "query_hash": "e626d07c450027cd69619a2eecfcf4b6044ebaa5a3ae7dfe41902c1b62bce09d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002131", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9f2c6da6b9c5f9515da532e42cb61e09", + "normalized_query_sha256": "0ef39a4042a17d46af1b9d6ce6fd8f1c831593adaab4085450ad523ac7a7d4d6", + "query": "the timing parameters for these exercises to accommodate my needs? For example, extending the response window or allowing a pause feature? I’d greatly appreciate any flexibility you could offer.\nassistant: Hello! I'd be happy to help you with accessibility accommodations for your Parkinson's diagnosis. First, I need to verify your identity before proceeding.\n\nCould you please provide the email address associated with your account (User ID 10110101)? I'll send a verification code to that email to confirm your identity.\nuser: Sure, the email associated with my account is maria.gonzalez.premium@example.com. Please send the verification code there.\nassistant: \ntool: \n\n{\"success\": true, \"message\": \"Verification code sent to maria.gonzalez.premium@example.com. Please check your inbox and enter the code to continue.\"}\nassistant: I've sent a verification code to maria.gonzalez.premium@example.com. Please check your inbox and enter the verification code you received so I can authenticate your identity and assist you with your accessibility request.\nuser: \n\nI’ve received the verification code—thank you. The code is **482917**. Could you confirm this and proceed with adjusting the timing parameters for my speaking exercises? For clarity, I’d specifically benefit from: \n1. Extended response time (e.g., doubling the current limit) \n2. A pause/resume button during exercises \n3. Optional slower-paced mode for speech recognition\nassistant: \ntool: \n\n{\"authenticated\": true, \"locked\": false}", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 815, + "historical_id": "fastbrain_002131", + "source": "nemotron_agentic", + "query_hash": "0ef39a4042a17d46af1b9d6ce6fd8f1c831593adaab4085450ad523ac7a7d4d6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 815, + "historical_id": "fastbrain_002131", + "source": "nemotron_agentic", + "query_hash": "0ef39a4042a17d46af1b9d6ce6fd8f1c831593adaab4085450ad523ac7a7d4d6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002303", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2f70ffe99d42bfa5c334ca13d4051338", + "normalized_query_sha256": "6d3bd428411ab3149d4733e01a68711ecbe7eae3358d89cc6a775dce80739848", + "query": "Hey, I want to know the lyrics of the song \"Imagine\" by John Lennon.", + "target": "get_song_lyrics", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 318, + "historical_id": "fastbrain_002303", + "source": "glaive_v2", + "query_hash": "6d3bd428411ab3149d4733e01a68711ecbe7eae3358d89cc6a775dce80739848" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 318, + "historical_id": "fastbrain_002303", + "source": "glaive_v2", + "query_hash": "6d3bd428411ab3149d4733e01a68711ecbe7eae3358d89cc6a775dce80739848" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002357", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:52a0b7a772dd90941c7d329c2ee87f8a", + "normalized_query_sha256": "4107dcd0a3998f6bd4832ce6ac4a779b71af8357621d43b6589b257414aa96e9", + "query": "Look for SGID files and directories", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 199, + "historical_id": "fastbrain_002357", + "source": "nl2bash", + "query_hash": "4107dcd0a3998f6bd4832ce6ac4a779b71af8357621d43b6589b257414aa96e9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 199, + "historical_id": "fastbrain_002357", + "source": "nl2bash", + "query_hash": "4107dcd0a3998f6bd4832ce6ac4a779b71af8357621d43b6589b257414aa96e9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002524", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4d0ea347b90cdf4453235d248163dbda", + "normalized_query_sha256": "9019a268a9c263fe05050905db41b4609a859b4f45d1c931111fc86ac0add679", + "query": "please solve this math", + "target": "qa_maths", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 464, + "historical_id": "fastbrain_002524", + "source": "amazon_massive", + "query_hash": "9019a268a9c263fe05050905db41b4609a859b4f45d1c931111fc86ac0add679" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 464, + "historical_id": "fastbrain_002524", + "source": "amazon_massive", + "query_hash": "9019a268a9c263fe05050905db41b4609a859b4f45d1c931111fc86ac0add679" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002565", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0409b8e9e19afa957513246d9423b8eb", + "normalized_query_sha256": "d2b10c10ce8589389682db803f380e864431ef544b0c8cc00fe580183a4a1865", + "query": "i would like the bathroom lights set to yellow", + "target": "iot_hue_lightchange", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 987, + "historical_id": "fastbrain_002565", + "source": "amazon_massive", + "query_hash": "d2b10c10ce8589389682db803f380e864431ef544b0c8cc00fe580183a4a1865" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 987, + "historical_id": "fastbrain_002565", + "source": "amazon_massive", + "query_hash": "d2b10c10ce8589389682db803f380e864431ef544b0c8cc00fe580183a4a1865" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002621", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e4a2888dac50fe8df7cfb4163df09f60", + "normalized_query_sha256": "421947bc21f240006ef80b204b2ed40aca841b83ea2e97cdda4e9f62c2e689fc", + "query": "ability \n - **Critical principle**: Never answer questions or perform actions exceeding tool capabilities\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm a frequent flyer with Loyalty ID LTY6000 and currently have 12,000 points in my account. I need to check when my points will expire. Could you please confirm the expiration date for my account? I understand the policy might vary by region, so I'm happy to provide any necessary verification to access my profile details.\nassistant: Hello! I'd be happy to help you check your points expiration date. First, I need to verify your identity to access your profile details.\n\nCould you please provide either your email address or mobile number that's associated with your loyalty account? Once you provide that, I'll send a verification code to that contact method, and then you can provide the code back to me for authentication.\n\nThis is our standard security procedure to ensure we're protecting your account information.\nuser: Sure, my mobile number associated with the account is +1 (555) 123-4567. Please send the verification code there.\nassistant: \ntool: \n\n{\"success\": true}\nassistant: I've sent a verification code to your mobile number ending in 4567. Please provide the verification code you received, and I'll be able to authenticate your identity and access your profile details to check your points expiration date.\nuser: I received the verification code. It’s 347219. Please proceed with the verification.", + "target": "verify_guest_identity", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 577, + "historical_id": "fastbrain_002621", + "source": "nemotron_agentic", + "query_hash": "421947bc21f240006ef80b204b2ed40aca841b83ea2e97cdda4e9f62c2e689fc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 577, + "historical_id": "fastbrain_002621", + "source": "nemotron_agentic", + "query_hash": "421947bc21f240006ef80b204b2ed40aca841b83ea2e97cdda4e9f62c2e689fc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002701", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b6e440c09460d97b3ff583828ec94e36", + "normalized_query_sha256": "651441040f43db3ba5c46b3bc0d4106f8c342af83631c1eca5d885ee6dabd6be", + "query": "I also need a house for 2 with a 3.8 rating and laundry.", + "target": "SearchHouse", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 317, + "historical_id": "fastbrain_002701", + "source": "sgd", + "query_hash": "651441040f43db3ba5c46b3bc0d4106f8c342af83631c1eca5d885ee6dabd6be" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 317, + "historical_id": "fastbrain_002701", + "source": "sgd", + "query_hash": "651441040f43db3ba5c46b3bc0d4106f8c342af83631c1eca5d885ee6dabd6be" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002832", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c4f203dd1f89cd88f7b7b2fd6ab87cbc", + "normalized_query_sha256": "7ad889f0f6db1e4695187455ea8c8292985f805561ddd1fe077c7a281431263a", + "query": "Get a short term rental auto for me.", + "target": "GetCarsAvailable", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 661, + "historical_id": "fastbrain_002832", + "source": "sgd", + "query_hash": "7ad889f0f6db1e4695187455ea8c8292985f805561ddd1fe077c7a281431263a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 661, + "historical_id": "fastbrain_002832", + "source": "sgd", + "query_hash": "7ad889f0f6db1e4695187455ea8c8292985f805561ddd1fe077c7a281431263a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002867", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:da25d14b42e6a074ddc3c77ffb54a7da", + "normalized_query_sha256": "7620fff09ec27cfefb2994ec53e1491c2f0314160b926e07f115fded6264cef4", + "query": "Okay, I like that. Schedule me a visit for the 11th of March.", + "target": "ScheduleVisit", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 249, + "historical_id": "fastbrain_002867", + "source": "sgd", + "query_hash": "7620fff09ec27cfefb2994ec53e1491c2f0314160b926e07f115fded6264cef4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 249, + "historical_id": "fastbrain_002867", + "source": "sgd", + "query_hash": "7620fff09ec27cfefb2994ec53e1491c2f0314160b926e07f115fded6264cef4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002877", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4411d7bb91446064aaa38baa4944bad9", + "normalized_query_sha256": "de7fd7ec6e134ee6f57d9023a299ca7f746960d2cf031fee0773a54cd5122e17", + "query": "set an office meeting reminder for the next three days", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 435, + "historical_id": "fastbrain_002877", + "source": "amazon_massive", + "query_hash": "de7fd7ec6e134ee6f57d9023a299ca7f746960d2cf031fee0773a54cd5122e17" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 435, + "historical_id": "fastbrain_002877", + "source": "amazon_massive", + "query_hash": "de7fd7ec6e134ee6f57d9023a299ca7f746960d2cf031fee0773a54cd5122e17" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002887", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:61d4156c52a3e65cbab6eb5756f88eeb", + "normalized_query_sha256": "8c1e46502809df82bb690893293d1ffa8481d394eed3b5f3e280fb064eeabc7b", + "query": "I will be doing some travelling soon, so can you help me find some one-way flights?", + "target": "SearchOnewayFlight", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 309, + "historical_id": "fastbrain_002887", + "source": "sgd", + "query_hash": "8c1e46502809df82bb690893293d1ffa8481d394eed3b5f3e280fb064eeabc7b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 309, + "historical_id": "fastbrain_002887", + "source": "sgd", + "query_hash": "8c1e46502809df82bb690893293d1ffa8481d394eed3b5f3e280fb064eeabc7b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002898", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5ec3ef296f8c33ef40f80859c0957687", + "normalized_query_sha256": "c7353974ab5337758572daf2fbeb20f6773f5498ae4aba1f3982f2862830e878", + "query": "refresh my list and tell me what's on it", + "target": "lists_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 247, + "historical_id": "semantic:validation:fastbrain_002898:absent", + "source": "amazon_massive", + "query_hash": "c7353974ab5337758572daf2fbeb20f6773f5498ae4aba1f3982f2862830e878" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 314, + "historical_id": "semantic:validation:fastbrain_002898:present", + "source": "amazon_massive", + "query_hash": "c7353974ab5337758572daf2fbeb20f6773f5498ae4aba1f3982f2862830e878" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_002984", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:514ce7dd75f0f06631aaefdaaf887274", + "normalized_query_sha256": "ca2b02a8bf405f63df2b03ef4211a13026e97dc3908675cf5692fb19ada11f82", + "query": "where can i use this word", + "target": "qa_definition", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 413, + "historical_id": "semantic:validation:fastbrain_002984:present", + "source": "amazon_massive", + "query_hash": "ca2b02a8bf405f63df2b03ef4211a13026e97dc3908675cf5692fb19ada11f82" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 435, + "historical_id": "semantic:validation:fastbrain_002984:absent", + "source": "amazon_massive", + "query_hash": "ca2b02a8bf405f63df2b03ef4211a13026e97dc3908675cf5692fb19ada11f82" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_003223", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3a03beab8ee96e01146e61b319d17ca1", + "normalized_query_sha256": "a76c4c909724dd6236b5929446f7a02dd61d6ad2062e3aad38f87b5264f4823b", + "query": "Recursively copy all files and directories in current dir except \"foo\" to location specified by variable \"other\"", + "target": "rsync", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 881, + "historical_id": "fastbrain_003223", + "source": "nl2bash", + "query_hash": "a76c4c909724dd6236b5929446f7a02dd61d6ad2062e3aad38f87b5264f4823b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 881, + "historical_id": "fastbrain_003223", + "source": "nl2bash", + "query_hash": "a76c4c909724dd6236b5929446f7a02dd61d6ad2062e3aad38f87b5264f4823b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_003365", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:28ace1e22b834d3ba02ca58441065f02", + "normalized_query_sha256": "6313357c0a351caa93f50abf2379de63877a947352afca45a00ebbeb8b120d09", + "query": "set a reminder for my meeting today", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 66, + "historical_id": "fastbrain_003365", + "source": "amazon_massive", + "query_hash": "6313357c0a351caa93f50abf2379de63877a947352afca45a00ebbeb8b120d09" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 66, + "historical_id": "fastbrain_003365", + "source": "amazon_massive", + "query_hash": "6313357c0a351caa93f50abf2379de63877a947352afca45a00ebbeb8b120d09" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_003443", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dc6ba20246e14a83b1b8aa4a946c3ea8", + "normalized_query_sha256": "95f580bc7bd53d4f816b9df935a6a35d51bf5698583cbb62a9a1811e21e7abe0", + "query": "I am interested in a small rental car, and I need it on the 4th of March.", + "target": "GetCarsAvailable", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 53, + "historical_id": "fastbrain_003443", + "source": "sgd", + "query_hash": "95f580bc7bd53d4f816b9df935a6a35d51bf5698583cbb62a9a1811e21e7abe0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 53, + "historical_id": "fastbrain_003443", + "source": "sgd", + "query_hash": "95f580bc7bd53d4f816b9df935a6a35d51bf5698583cbb62a9a1811e21e7abe0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_003607", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2fd86f1dcce0a9c624899d622b0e2127", + "normalized_query_sha256": "ceb68bf80a99859229c0ebce3f53f1d0c50752466c81f5091c8285c7cf82648d", + "query": " your identity and found your customer profile (ID: CUST-789456). I can see you own the 2008 Bayliner 24ft with hull ID BAYL2008.\n\nFor the trade-in valuation process, you're correct that we need to start with the inspections. Here's how it works:\n\n**Step 1: Schedule Required Inspections**\nOur policy requires both:\n1. **Hull inspection** - For structural integrity and condition assessment\n2. **Engine diagnostics** - For mechanical performance evaluation\n\nYou'll need to schedule two service appointments:\n- One for \"hull\" service type\n- One for \"mechanical\" service type for engine diagnostics\n\n**Step 2: Bring Documentation**\nPlease bring proof of ownership (registration documents) to both appointments.\n\n**Step 3: Trade-in Evaluation**\nOnce both inspection reports are complete, we can process the trade-in evaluation using those reports along with market data for your 2008 Bayliner.\n\n**Important Notes:**\n- Trade-in valuations are only valid when applied toward a new boat purchase\n- The inspection reports must be recent (typically within 30 days)\n\nWould you like me to help you schedule these service appointments? I can create service tickets for both the hull inspection and mechanical diagnostics, then help you book appointments for each.\nuser: Yes, I'd like to schedule both the hull inspection and mechanical diagnostics. What dates and times are available for these appointments? Also, do you have any specific preparation instructions for either inspection that I should know about?", + "target": "create_service_ticket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 419, + "historical_id": "fastbrain_003607", + "source": "nemotron_agentic", + "query_hash": "ceb68bf80a99859229c0ebce3f53f1d0c50752466c81f5091c8285c7cf82648d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 419, + "historical_id": "fastbrain_003607", + "source": "nemotron_agentic", + "query_hash": "ceb68bf80a99859229c0ebce3f53f1d0c50752466c81f5091c8285c7cf82648d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_003779", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:936cdc6a451102248519b4d3b727169c", + "normalized_query_sha256": "0acc8f08a05dc2ab23477053814336f971280d8d9f24cf790f7c9f574b7888b7", + "query": "That is awesome. I want to get tickets to the show.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 379, + "historical_id": "fastbrain_003779", + "source": "sgd", + "query_hash": "0acc8f08a05dc2ab23477053814336f971280d8d9f24cf790f7c9f574b7888b7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 379, + "historical_id": "fastbrain_003779", + "source": "sgd", + "query_hash": "0acc8f08a05dc2ab23477053814336f971280d8d9f24cf790f7c9f574b7888b7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_003914", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0876fbb89618018a709b9bd8b0143393", + "normalized_query_sha256": "cde6c0807136a2e5782e84afb9cbf6d3d1aa74b401dcf0629ce424679ed8c908", + "query": "I'd like to watch at United Artists Stonestown Twin: what time can I watch it?", + "target": "GetTimesForMovie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 519, + "historical_id": "fastbrain_003914", + "source": "sgd", + "query_hash": "cde6c0807136a2e5782e84afb9cbf6d3d1aa74b401dcf0629ce424679ed8c908" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 519, + "historical_id": "fastbrain_003914", + "source": "sgd", + "query_hash": "cde6c0807136a2e5782e84afb9cbf6d3d1aa74b401dcf0629ce424679ed8c908" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_003955", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a9b194547054c7a3d2754a6573c3fd2b", + "normalized_query_sha256": "4111db541239aa5eac8918711ff3e774336021c71e798838e303e74cd11065a4", + "query": "Thank you! Could you also find me some 3d suspense movies shown at the California Theatre?", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 617, + "historical_id": "fastbrain_003955", + "source": "sgd", + "query_hash": "4111db541239aa5eac8918711ff3e774336021c71e798838e303e74cd11065a4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 617, + "historical_id": "fastbrain_003955", + "source": "sgd", + "query_hash": "4111db541239aa5eac8918711ff3e774336021c71e798838e303e74cd11065a4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_003997", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ae9b80f0dfd4b1b01e3573ab7c5574fa", + "normalized_query_sha256": "c2f974ecf8956311b02a5077ab0e4f06fe64eaa9cfb6b3a2912e78a6ae0e643c", + "query": "play back in black", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 934, + "historical_id": "fastbrain_003997", + "source": "amazon_massive", + "query_hash": "c2f974ecf8956311b02a5077ab0e4f06fe64eaa9cfb6b3a2912e78a6ae0e643c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 934, + "historical_id": "fastbrain_003997", + "source": "amazon_massive", + "query_hash": "c2f974ecf8956311b02a5077ab0e4f06fe64eaa9cfb6b3a2912e78a6ae0e643c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004068", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e6abb3662b70d41ab0f4031402d05516", + "normalized_query_sha256": "310fc2d4a4c8bf676504c3e58bc9e1ee4447f7caed1ba63fa2e220c26436469c", + "query": "I manage operations at FreshMart Supermarkets, and our aim is to implement RPA across our stores and online sales channel to improve efficiency. I request assistance with the following operational tasks: \n\n1. Update the inventory in real-time across our store with ID \"001\" whenever a sale of 3 units of product with ID \"A123\" occurs.\n\n2. Generate a purchase order automatically for product ID \"B456\"", + "target": "update_inventory_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 118, + "historical_id": "fastbrain_004068", + "source": "hermes_v1", + "query_hash": "310fc2d4a4c8bf676504c3e58bc9e1ee4447f7caed1ba63fa2e220c26436469c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 118, + "historical_id": "fastbrain_004068", + "source": "hermes_v1", + "query_hash": "310fc2d4a4c8bf676504c3e58bc9e1ee4447f7caed1ba63fa2e220c26436469c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004153", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:66b88b9c7e08ef2a391d152f4ae72eba", + "normalized_query_sha256": "9242e1443ef7f7522a7d4ccbf057ab26079d4831e545e4009000ab81b9e1bbed", + "query": "Hello! I'd like to find bus tickets. Can you help?", + "target": "FindBus", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 695, + "historical_id": "fastbrain_004153", + "source": "sgd", + "query_hash": "9242e1443ef7f7522a7d4ccbf057ab26079d4831e545e4009000ab81b9e1bbed" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 695, + "historical_id": "fastbrain_004153", + "source": "sgd", + "query_hash": "9242e1443ef7f7522a7d4ccbf057ab26079d4831e545e4009000ab81b9e1bbed" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004259", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:47e1e49163b64fced34f4c199d464cd0", + "normalized_query_sha256": "b36af5000d648be88861350c9ef257cdb9796f18567c629f24df9b713e1957e7", + "query": "play led zeppelin houses of the holy", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 384, + "historical_id": "fastbrain_004259", + "source": "amazon_massive", + "query_hash": "b36af5000d648be88861350c9ef257cdb9796f18567c629f24df9b713e1957e7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 384, + "historical_id": "fastbrain_004259", + "source": "amazon_massive", + "query_hash": "b36af5000d648be88861350c9ef257cdb9796f18567c629f24df9b713e1957e7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004524", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1f8f8c3c4f6d0904495552881a6c36cc", + "normalized_query_sha256": "c491f42847abdfb6460d2bc6c82c2cbb4c043c04d3878a3778590013d84adda7", + "query": "That sounds nice. I'd like a reservation at a restaurant there.", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 179, + "historical_id": "fastbrain_004524", + "source": "sgd", + "query_hash": "c491f42847abdfb6460d2bc6c82c2cbb4c043c04d3878a3778590013d84adda7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 179, + "historical_id": "fastbrain_004524", + "source": "sgd", + "query_hash": "c491f42847abdfb6460d2bc6c82c2cbb4c043c04d3878a3778590013d84adda7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004557", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:49d3dbd1f0fd23d1fbd3ab0ce196def7", + "normalized_query_sha256": "cad7ddac89d7eb21f2ca8233d9d2b93b01809c5e5a04ba407a5723f9fabb2f68", + "query": "Him, book it at 4:15 in the evening.", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 652, + "historical_id": "fastbrain_004557", + "source": "sgd", + "query_hash": "cad7ddac89d7eb21f2ca8233d9d2b93b01809c5e5a04ba407a5723f9fabb2f68" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 652, + "historical_id": "fastbrain_004557", + "source": "sgd", + "query_hash": "cad7ddac89d7eb21f2ca8233d9d2b93b01809c5e5a04ba407a5723f9fabb2f68" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004664", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4f6db90d299e5929dda7416ee1d19d97", + "normalized_query_sha256": "6d8018543716e56dbe34e93685dd0ae490112f327b61339ded70889f4f284a3a", + "query": "sound off", + "target": "audio_volume_mute", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 15, + "historical_id": "semantic:validation:fastbrain_004664:absent", + "source": "amazon_massive", + "query_hash": "6d8018543716e56dbe34e93685dd0ae490112f327b61339ded70889f4f284a3a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 430, + "historical_id": "semantic:validation:fastbrain_004664:present", + "source": "amazon_massive", + "query_hash": "6d8018543716e56dbe34e93685dd0ae490112f327b61339ded70889f4f284a3a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004665", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7505959a3d8d2cc803cc06e38792d0d6", + "normalized_query_sha256": "156a27a42796fea94eb84e13a4f83025fa61d477187abb217a2a938c24fd2a25", + "query": "e confirm if you received the pickup time SMS\"* before payment capture \n- After status change request: \n Ask *\"Please verify your updated prescription status\"* before next actions \n\n---\n\n### Transfer Protocol \nTransfer to pharmacist for: \n- Complex medication counseling \n- Prior authorization escalations \n- Prescription clarifications \n- Adverse reaction reports \n\n**Process**: \n1. Initiate pharmacist transfer \n2. Send message: **\"TRANSFERRING TO PHARMACIST - PLEASE DESCRIBE YOUR CONCERN\"** \n\n---\n\n### Agent Limitations \n- **Action Boundaries**: \n - Only perform tasks supported by available tools \n - Never override safety alerts (interactions/allergies) \n - Never suggest off-label medication uses \n - Never modify dosage instructions \n - Never process without valid prescription verification \n\n- **Verification Rules**: \n - Assume no action is completed until tool confirmation is received \n - Never claim verification without explicit tool output \n ...[TRUNCATED]\nuser: \n\nHello, I’m calling about a denied insurance claim for my cholesterol medication. My name is [Customer Name], and my Patient ID is P567890 with Rx ID R234567. I’m on Medicare Part D (Payer ID MD123456), and my medication was recently denied. Could you check the coverage status and see if there are more affordable alternatives within my policy? I’m on a fixed income, so cost is a big concern.\nassistant: \ntool: \n\n{\"coverage_status\": \"denied\", \"copay\": 125.50, \"requires_prior_auth\": true}", + "target": "check_prior_auth_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 958, + "historical_id": "fastbrain_004665", + "source": "nemotron_agentic", + "query_hash": "156a27a42796fea94eb84e13a4f83025fa61d477187abb217a2a938c24fd2a25" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 958, + "historical_id": "fastbrain_004665", + "source": "nemotron_agentic", + "query_hash": "156a27a42796fea94eb84e13a4f83025fa61d477187abb217a2a938c24fd2a25" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004689", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:898f427abc15c6bb5cec6064d2821003", + "normalized_query_sha256": "668df930d137a8c95e815608d58a426714f8335d64cc45ced9f005e987789f5c", + "query": "s enrolled with a Drop-In package. To send a verification code to your email, I need your guardian ID. Could you please provide your guardian ID?\n\nOnce I have that, I'll send the verification code and then we can check your drop-in count for this month and proceed with the request for tomorrow (9 AM to 12 PM).\nuser: My guardian ID is G12345. Please send the verification code to my email now.\nassistant: \ntool: \n\n{\n \"sent\": true\n}\nassistant: I've sent a verification code to your email. Please check your inbox and provide the 6-digit code you received.\n\nOnce you provide the code, I'll verify it and then we can check your drop-in count for this month and proceed with the drop-in request for tomorrow from 9 AM to 12 PM.\nuser: The verification code I received is 123456. Please confirm it.\nassistant: \ntool: \n\n{\n \"verified\": true\n}\nassistant: Perfect! Your verification code has been confirmed. \n\nNow, for your drop-in request tomorrow from 9 AM to 12 PM (3 hours), I need the exact date in YYYY-MM-DD format. For example, if tomorrow is December 15th, 2023, you would provide \"2023-12-15\".\n\nOnce you provide the date, I'll process your drop-in request. According to our policy:\n- Drop-in care requires 24-hour notice ($15/hour)\n- Maximum 8 instances monthly\n- Your child's current package allows up to 8 drop-ins per month\n\nThe system will check if this would be your 8th drop-in this month when processing the request.\nuser: The date for the drop-in is 2023-12-15. Please process the request.", + "target": "modify_schedule", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 848, + "historical_id": "fastbrain_004689", + "source": "nemotron_agentic", + "query_hash": "668df930d137a8c95e815608d58a426714f8335d64cc45ced9f005e987789f5c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 848, + "historical_id": "fastbrain_004689", + "source": "nemotron_agentic", + "query_hash": "668df930d137a8c95e815608d58a426714f8335d64cc45ced9f005e987789f5c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004720", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1350f59cec5dabd08adc20db6ad81b59", + "normalized_query_sha256": "779be14cadc8c7253afe1c364ee0b9e2b8db1bee4e9277340fbf33eb72259915", + "query": "if i have fifty dollars and spend twenty four how many dollars will i have left", + "target": "qa_maths", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 60, + "historical_id": "semantic:validation:fastbrain_004720:present", + "source": "amazon_massive", + "query_hash": "779be14cadc8c7253afe1c364ee0b9e2b8db1bee4e9277340fbf33eb72259915" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 532, + "historical_id": "semantic:validation:fastbrain_004720:absent", + "source": "amazon_massive", + "query_hash": "779be14cadc8c7253afe1c364ee0b9e2b8db1bee4e9277340fbf33eb72259915" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004732", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:81d4e5b1ecf65894c9a33c53d17d1dc8", + "normalized_query_sha256": "b262a0f4dd73c0be484440767c6d90f28cfff83d37c7a3c96c8293b434fad848", + "query": "where is stockton california", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 128, + "historical_id": "semantic:validation:fastbrain_004732:absent", + "source": "amazon_massive", + "query_hash": "b262a0f4dd73c0be484440767c6d90f28cfff83d37c7a3c96c8293b434fad848" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 848, + "historical_id": "semantic:validation:fastbrain_004732:present", + "source": "amazon_massive", + "query_hash": "b262a0f4dd73c0be484440767c6d90f28cfff83d37c7a3c96c8293b434fad848" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004780", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:779d406f4c249c88615002f85d6bfc24", + "normalized_query_sha256": "5d2f80fca6c381140f40f850c5411f8c625b677fc66b2c752816d10995cf1906", + "query": "create a meeting with friend", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 89, + "historical_id": "semantic:validation:fastbrain_004780:present", + "source": "amazon_massive", + "query_hash": "5d2f80fca6c381140f40f850c5411f8c625b677fc66b2c752816d10995cf1906" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 633, + "historical_id": "semantic:validation:fastbrain_004780:absent", + "source": "amazon_massive", + "query_hash": "5d2f80fca6c381140f40f850c5411f8c625b677fc66b2c752816d10995cf1906" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_004834", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3b5deb510436bdee848e33d25afb44b4", + "normalized_query_sha256": "ee1469ac26d1a57eedd235dd492c022f09d544b18fe21d01f8fad993e0326a11", + "query": "create an alarm for today at ten am", + "target": "alarm_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 164, + "historical_id": "semantic:validation:fastbrain_004834:present", + "source": "amazon_massive", + "query_hash": "ee1469ac26d1a57eedd235dd492c022f09d544b18fe21d01f8fad993e0326a11" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 251, + "historical_id": "semantic:validation:fastbrain_004834:absent", + "source": "amazon_massive", + "query_hash": "ee1469ac26d1a57eedd235dd492c022f09d544b18fe21d01f8fad993e0326a11" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005139", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fe42ff55fc2d965bd6e4d1dbb3da4bb9", + "normalized_query_sha256": "1f6763fcce2db06d09f4b4519dd046daa2d74fbe551e68b155df2bf94d74ad2e", + "query": "Can you find me the movie 'Inception'?", + "target": "search_movie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 212, + "historical_id": "fastbrain_005139", + "source": "glaive_v2", + "query_hash": "1f6763fcce2db06d09f4b4519dd046daa2d74fbe551e68b155df2bf94d74ad2e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 212, + "historical_id": "fastbrain_005139", + "source": "glaive_v2", + "query_hash": "1f6763fcce2db06d09f4b4519dd046daa2d74fbe551e68b155df2bf94d74ad2e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005222", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:baa6fc33cc00e1a9984b96c9ed94e572", + "normalized_query_sha256": "5db50a38d4ca9ba974340f7f1a61dc0875a5f6f61071b6f4d8432394dbbc88e2", + "query": "r verified academy-side failures \n\n---\n\n### Agent Limitations \n1. **Scope Boundaries**: \n - Only answer questions about supported tools/services \n - Do not provide musical advice/purchase recommendations \n - Never interpret complex music theory \n\n2. **Action Constraints**: \n - Verify student identity before profile access \n - Confirm subscription status before bookings \n - Execute tools for all verifications/actions \n - Never assume information is verified without tool confirmation \n\n3. **Prohibited Actions**: \n - Booking outside instructor availability \n - Disclosing instructor contact details \n - Overriding subscription deadlines \n - Issuing credits without approval \n\n---\n\n### Escalation Protocol \nTransfer to human agent for: \n- Emotional distress situations \n- Payment disputes >$100 \n- Technical failure compensation claims \n- Subscription term negotiations \n\nTransfer process: \n1. Use `transfer_to_human_agent` tool \n2. State: \"YOU ARE BEING TRANSFERRED TO A CUSTOMER SERVICE SPECIALIST.\" \n\n---\n\n### Error Handling \n- System errors: Offer retry OR callback option \n- Invalid requests: Explain violation with policy reference\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm a student with ID MM-8881 enrolled in the intermediate guitar program. I'm trying to access the practice videos but can't seem to find them. Could you please verify my enrollment and grant me access to the intermediate guitar resources?", + "target": "check_subscription_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 235, + "historical_id": "fastbrain_005222", + "source": "nemotron_agentic", + "query_hash": "5db50a38d4ca9ba974340f7f1a61dc0875a5f6f61071b6f4d8432394dbbc88e2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 235, + "historical_id": "fastbrain_005222", + "source": "nemotron_agentic", + "query_hash": "5db50a38d4ca9ba974340f7f1a61dc0875a5f6f61071b6f4d8432394dbbc88e2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005247", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f02e5e04a8f8afd57de3ba1f75527272", + "normalized_query_sha256": "5c236ffca9d75cd5b4f38271f73b0f4ce4a89fe1d3d54ed9154deaef7b1c27fc", + "query": "Can you help me find a good fantasy movie online?", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 348, + "historical_id": "fastbrain_005247", + "source": "sgd", + "query_hash": "5c236ffca9d75cd5b4f38271f73b0f4ce4a89fe1d3d54ed9154deaef7b1c27fc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 348, + "historical_id": "fastbrain_005247", + "source": "sgd", + "query_hash": "5c236ffca9d75cd5b4f38271f73b0f4ce4a89fe1d3d54ed9154deaef7b1c27fc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005252", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ced0933110bc63e9eaf64e061c3f4f2e", + "normalized_query_sha256": "6e836e40f61d483c9fb4be790572cf5e2c2220ab64ab2ab343f63caf98a65bbc", + "query": "I'd like to find a house to stay at.", + "target": "SearchHouse", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 327, + "historical_id": "fastbrain_005252", + "source": "sgd", + "query_hash": "6e836e40f61d483c9fb4be790572cf5e2c2220ab64ab2ab343f63caf98a65bbc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 327, + "historical_id": "fastbrain_005252", + "source": "sgd", + "query_hash": "6e836e40f61d483c9fb4be790572cf5e2c2220ab64ab2ab343f63caf98a65bbc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005287", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5366f8661c768b2ae3ea812db77d9b02", + "normalized_query_sha256": "48219b648a5f1a1619960d19b84ba849f9aed6aecb63fae2994e0b525be497a0", + "query": "Can you tell me how old someone born on 1990-05-20 would be today?", + "target": "calculate_age", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 603, + "historical_id": "fastbrain_005287", + "source": "glaive_v2", + "query_hash": "48219b648a5f1a1619960d19b84ba849f9aed6aecb63fae2994e0b525be497a0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 603, + "historical_id": "fastbrain_005287", + "source": "glaive_v2", + "query_hash": "48219b648a5f1a1619960d19b84ba849f9aed6aecb63fae2994e0b525be497a0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005439", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c10a978a973ad856bfe151cd79c7e554", + "normalized_query_sha256": "a88f60a8fb5b9dc83f159be348e263250e5debaf6594fc594aa0d8f76ec43dd2", + "query": "please show me weather on twenty seventh march two thousand seventeen", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 152, + "historical_id": "semantic:validation:fastbrain_005439:absent", + "source": "amazon_massive", + "query_hash": "a88f60a8fb5b9dc83f159be348e263250e5debaf6594fc594aa0d8f76ec43dd2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 240, + "historical_id": "semantic:validation:fastbrain_005439:present", + "source": "amazon_massive", + "query_hash": "a88f60a8fb5b9dc83f159be348e263250e5debaf6594fc594aa0d8f76ec43dd2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005442", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:94901f3124faf65b663bc167eaad6d05", + "normalized_query_sha256": "e2a8cc20ea851e5873488142ec69b365415401e0ea2437ad9c3783c5a43bd7a1", + "query": "s \n2. Never assume actions completed without tool verification: \n - Wait for `payment_processed` confirmation \n - Require `appointment_confirmed` status \n - Verify `ticket_closed` status before ending case \n3. Never diagnose electrical issues beyond basic safety checks \n4. Never override electrical code compliance \n5. Never commit to quotes without location verification\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I need to schedule a repair for a GFCI outlet in my bathroom at 1010 Fir Place. The outlet won’t reset after using the shower, and I suspect moisture exposure might be the cause. Can you book a standard service appointment for this?\nassistant: Hello! I'd be happy to help you schedule a repair service for your GFCI outlet. First, I'll need your customer ID to look up your account and create the service request.\n\nCould you please provide your BrightSpark customer ID?\n\nAlso, to help with scheduling, I need to confirm:\n1. Is the service address 1010 Fir Place?\n2. Will the breaker panel be accessible to our technician during the appointment?\nuser: My customer ID is 847291. Yes, the service address is 1010 Fir Place, and the breaker panel will be accessible. However, I should mention that the GFCI outlet has been exposed to moisture from the shower area multiple times, which might be causing the reset issue. Before any repair work, could you confirm the technician will test the grounding system to ensure it’s functioning properly?", + "target": "create_service_request", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 713, + "historical_id": "fastbrain_005442", + "source": "nemotron_agentic", + "query_hash": "e2a8cc20ea851e5873488142ec69b365415401e0ea2437ad9c3783c5a43bd7a1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 713, + "historical_id": "fastbrain_005442", + "source": "nemotron_agentic", + "query_hash": "e2a8cc20ea851e5873488142ec69b365415401e0ea2437ad9c3783c5a43bd7a1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005527", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c9e89f21be55cabe4984c80b089f3e33", + "normalized_query_sha256": "650e18ecb4cb8130512bfb4029a059534f6c3b90b9c7251daf93360ea9432343", + "query": "I would like do find a place where I can live.", + "target": "FindHomeByArea", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 259, + "historical_id": "fastbrain_005527", + "source": "sgd", + "query_hash": "650e18ecb4cb8130512bfb4029a059534f6c3b90b9c7251daf93360ea9432343" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 259, + "historical_id": "fastbrain_005527", + "source": "sgd", + "query_hash": "650e18ecb4cb8130512bfb4029a059534f6c3b90b9c7251daf93360ea9432343" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005617", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1bd7943bcd72c4a411149083dfa20798", + "normalized_query_sha256": "9d8a0514ac49788b0b128ce0fea687844648ffc13258196f0aa8d777c8899ea9", + "query": "change the color of lights", + "target": "iot_hue_lightchange", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 996, + "historical_id": "fastbrain_005617", + "source": "amazon_massive", + "query_hash": "9d8a0514ac49788b0b128ce0fea687844648ffc13258196f0aa8d777c8899ea9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 996, + "historical_id": "fastbrain_005617", + "source": "amazon_massive", + "query_hash": "9d8a0514ac49788b0b128ce0fea687844648ffc13258196f0aa8d777c8899ea9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005715", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e3f605dedcfb29a760b6806d07bbf3d7", + "normalized_query_sha256": "9fd1bf38495160cfd34c7a34fd9ebb4d97f4b6fd292db64b5e1098d33d76acab", + "query": "I run an e-commerce platform specializing in fashion, and I'm working on adding a feature to help customers find products similar to what they're looking for. Specifically, customers can upload a photo of a dress, and the system will search through our inventory to find visually similar dresses in terms of style and color. For example, a user might upload an image of a red cocktail dress, and we w", + "target": "generate_image_embedding", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 443, + "historical_id": "fastbrain_005715", + "source": "hermes_v1", + "query_hash": "9fd1bf38495160cfd34c7a34fd9ebb4d97f4b6fd292db64b5e1098d33d76acab" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 443, + "historical_id": "fastbrain_005715", + "source": "hermes_v1", + "query_hash": "9fd1bf38495160cfd34c7a34fd9ebb4d97f4b6fd292db64b5e1098d33d76acab" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005797", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dc9b6a1c955a83ae293ff507d07cca7a", + "normalized_query_sha256": "210cd7dc0e1f92d1232bfc7735c6b59a2307ef9ed1c16f2d519040ca2cb2833f", + "query": "update me", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 378, + "historical_id": "fastbrain_005797", + "source": "amazon_massive", + "query_hash": "210cd7dc0e1f92d1232bfc7735c6b59a2307ef9ed1c16f2d519040ca2cb2833f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 378, + "historical_id": "fastbrain_005797", + "source": "amazon_massive", + "query_hash": "210cd7dc0e1f92d1232bfc7735c6b59a2307ef9ed1c16f2d519040ca2cb2833f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_005883", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0d4dddb16082ea772905779ff0349ebc", + "normalized_query_sha256": "35bee4a47031d075611543c55a31aa2eb7d3aee8b0b8de912f78fccc102134d3", + "query": "I would like to make a hotel reservation at the Residence Inn Anaheim Resort/Convention Center.", + "target": "ReserveHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 835, + "historical_id": "fastbrain_005883", + "source": "sgd", + "query_hash": "35bee4a47031d075611543c55a31aa2eb7d3aee8b0b8de912f78fccc102134d3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 835, + "historical_id": "fastbrain_005883", + "source": "sgd", + "query_hash": "35bee4a47031d075611543c55a31aa2eb7d3aee8b0b8de912f78fccc102134d3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_006179", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e3d1e8a15874e7e8b123ba10bbdf6eb3", + "normalized_query_sha256": "8af1fc4056a32b798c29baf17630508c033d6ecef60770a2a9f20946a1097d3f", + "query": "when was born abraham lincoln", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 36, + "historical_id": "semantic:validation:fastbrain_006179:absent", + "source": "amazon_massive", + "query_hash": "8af1fc4056a32b798c29baf17630508c033d6ecef60770a2a9f20946a1097d3f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 375, + "historical_id": "semantic:validation:fastbrain_006179:present", + "source": "amazon_massive", + "query_hash": "8af1fc4056a32b798c29baf17630508c033d6ecef60770a2a9f20946a1097d3f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_006317", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7dec294c40d535479d77ca02367f7c55", + "normalized_query_sha256": "62098960acc0cefd2ccd1207f2d5f51ee29a19d3fa3872996828c7c85db5d81c", + "query": "every other wednesday remind me to go grocery shopping", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 425, + "historical_id": "semantic:validation:fastbrain_006317:absent", + "source": "amazon_massive", + "query_hash": "62098960acc0cefd2ccd1207f2d5f51ee29a19d3fa3872996828c7c85db5d81c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 712, + "historical_id": "semantic:validation:fastbrain_006317:present", + "source": "amazon_massive", + "query_hash": "62098960acc0cefd2ccd1207f2d5f51ee29a19d3fa3872996828c7c85db5d81c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_006395", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b9f21916a11bc63ea86b8816df900e9f", + "normalized_query_sha256": "9e3de688e6c5bbeb325e96ea72543201bd93cedfe52e954aa747265dca68f6f6", + "query": "Hey, I am thinking of watching a movie tonight. 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_007461", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:77cea29a57be7ac414a5b1353f556388", + "normalized_query_sha256": "872cb0c89c38d5803051f8d8e64f7471be334c428aa5762c4733ecb3807f67e5", + "query": "Let's buy tickets to an event please.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 790, + "historical_id": "fastbrain_007461", + "source": "sgd", + "query_hash": "872cb0c89c38d5803051f8d8e64f7471be334c428aa5762c4733ecb3807f67e5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 790, + "historical_id": "fastbrain_007461", + "source": "sgd", + "query_hash": "872cb0c89c38d5803051f8d8e64f7471be334c428aa5762c4733ecb3807f67e5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_007914", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b3c6ae193b57b3db4f4922519cf73356", + "normalized_query_sha256": "ead7fb28d8cc7c996b6faf80ab3dc881f4d990d862f660d4d97b7caa132d64b1", + "query": "installation?\nassistant: \ntool: \n\n{\"activation_status\": \"Sensor SENSOR-N774-01 activated successfully for FARM-GARLIC-01\", \"led_confirmation_required\": true}\nassistant: Excellent! I've successfully activated **Sensor SENSOR-N774-01** for your farm **FARM-GARLIC-01**.\n\n**Next Steps for Installation:**\n\n1. **Install the Sensor:**\n - Follow the manufacturer's installation instructions for the N-774 nutrient sensor\n - Ensure proper placement in the soil according to guidelines\n - Power on the device\n\n2. **Critical Verification Step:**\n - Within **5 minutes of power-up**, check the sensor's status LED\n - **Confirm the blue status LED is solid** (not blinking)\n - If the LED is blinking or off, there may be an installation issue\n\n3. **Post-Installation:**\n - Once you confirm the solid blue LED, report back to me\n - I'll then run the system verification to complete the activation process\n\n**Important Reminders:**\n- Your Advanced subscription now has 3 of 5 sensor slots used (2 original + this new one)\n- The sensor will begin data collection once verification is complete\n- Initial nutrient readings may take 24-48 hours to stabilize\n\nPlease proceed with installation and let me know once you see the **solid blue LED** on your N-774 sensor.\nuser: The N-774 sensor is installed and powered on. The blue LED is **solid** as instructed. I’ve confirmed the status. What’s next for verification? Do I need to wait for data to appear in the system, or should I check something else?", + "target": "verify_sensor_activation", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 537, + "historical_id": "fastbrain_007914", + "source": "nemotron_agentic", + "query_hash": "ead7fb28d8cc7c996b6faf80ab3dc881f4d990d862f660d4d97b7caa132d64b1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 537, + "historical_id": "fastbrain_007914", + "source": "nemotron_agentic", + "query_hash": "ead7fb28d8cc7c996b6faf80ab3dc881f4d990d862f660d4d97b7caa132d64b1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008043", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:988f7baf0b31464aa136dfbfb29b7df9", + "normalized_query_sha256": "9a92e6b457c8d2a023626dfaa0892de73afcc90aba636d02b4574580e644bc76", + "query": "what was george eliot's first november el", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 68, + "historical_id": "semantic:validation:fastbrain_008043:present", + "source": "amazon_massive", + "query_hash": "9a92e6b457c8d2a023626dfaa0892de73afcc90aba636d02b4574580e644bc76" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 996, + "historical_id": "semantic:validation:fastbrain_008043:absent", + "source": "amazon_massive", + "query_hash": "9a92e6b457c8d2a023626dfaa0892de73afcc90aba636d02b4574580e644bc76" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008104", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:52471573bf0d71ae48ec9f840a26081c", + "normalized_query_sha256": "dbc79fd0157a339fea385d54b5b3846a914343d9d269b53f6eb94e93985ecb93", + "query": "top news", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 526, + "historical_id": "fastbrain_008104", + "source": "amazon_massive", + "query_hash": "dbc79fd0157a339fea385d54b5b3846a914343d9d269b53f6eb94e93985ecb93" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 526, + "historical_id": "fastbrain_008104", + "source": "amazon_massive", + "query_hash": "dbc79fd0157a339fea385d54b5b3846a914343d9d269b53f6eb94e93985ecb93" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008183", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:61f257f257efb22d5e576f50a21cdb3d", + "normalized_query_sha256": "ab74ee8c743dc4a334f41df83d80171d051d382f901402ba797d51cc7f489c10", + "query": "what events are happening between two four p. m. today", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 326, + "historical_id": "fastbrain_008183", + "source": "amazon_massive", + "query_hash": "ab74ee8c743dc4a334f41df83d80171d051d382f901402ba797d51cc7f489c10" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 326, + "historical_id": "fastbrain_008183", + "source": "amazon_massive", + "query_hash": "ab74ee8c743dc4a334f41df83d80171d051d382f901402ba797d51cc7f489c10" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008186", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:49810a305c75776de0e6d1b611c36302", + "normalized_query_sha256": "a9033870e5da75089a7ffeecdee93b8fe39017acde3dbab1d089995d82ce37a9", + "query": "flag me an uber for ten p. m. tonight", + "target": "transport_taxi", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 132, + "historical_id": "semantic:validation:fastbrain_008186:present", + "source": "amazon_massive", + "query_hash": "a9033870e5da75089a7ffeecdee93b8fe39017acde3dbab1d089995d82ce37a9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 577, + "historical_id": "semantic:validation:fastbrain_008186:absent", + "source": "amazon_massive", + "query_hash": "a9033870e5da75089a7ffeecdee93b8fe39017acde3dbab1d089995d82ce37a9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008198", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:70a5741bf39b1e47dd3b693a6182ceea", + "normalized_query_sha256": "d389dc2016d386ec37295b541c2395d3b2382255a78bee8157dadcd134ef9d91", + "query": "explain what g. d. p. means", + "target": "qa_definition", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 153, + "historical_id": "fastbrain_008198", + "source": "amazon_massive", + "query_hash": "d389dc2016d386ec37295b541c2395d3b2382255a78bee8157dadcd134ef9d91" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 153, + "historical_id": "fastbrain_008198", + "source": "amazon_massive", + "query_hash": "d389dc2016d386ec37295b541c2395d3b2382255a78bee8157dadcd134ef9d91" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008298", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dee5bb4ef3b26f834c0b8ca2af6857a5", + "normalized_query_sha256": "06606570e9bc698c8f544c6d683a58f41d872686bdc2e792113d4e522f575f2d", + "query": "notify me when this meeting is close", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 242, + "historical_id": "semantic:validation:fastbrain_008298:absent", + "source": "amazon_massive", + "query_hash": "06606570e9bc698c8f544c6d683a58f41d872686bdc2e792113d4e522f575f2d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 874, + "historical_id": "semantic:validation:fastbrain_008298:present", + "source": "amazon_massive", + "query_hash": "06606570e9bc698c8f544c6d683a58f41d872686bdc2e792113d4e522f575f2d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008383", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5c8915f6ceb0dda6268cd1ab234127f6", + "normalized_query_sha256": "0365b078425802d5809ed509f89c54c14c203f4b349adbde4f5d23fa20f468fe", + "query": "my terminal says there are conflicts what do i do", + "target": "git_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 867, + "historical_id": "fastbrain_008383", + "source": "git_nl_commands", + "query_hash": "0365b078425802d5809ed509f89c54c14c203f4b349adbde4f5d23fa20f468fe" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 867, + "historical_id": "fastbrain_008383", + "source": "git_nl_commands", + "query_hash": "0365b078425802d5809ed509f89c54c14c203f4b349adbde4f5d23fa20f468fe" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008463", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:794e4074070376135b3a543aa4da0dd9", + "normalized_query_sha256": "cd21c03c5ab4338838a10ae43b5ef527d22a4e64ead3d70ae621b406fca3b682", + "query": "through mobile phones", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 195, + "historical_id": "semantic:validation:fastbrain_008463:absent", + "source": "amazon_massive", + "query_hash": "cd21c03c5ab4338838a10ae43b5ef527d22a4e64ead3d70ae621b406fca3b682" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 275, + "historical_id": "semantic:validation:fastbrain_008463:present", + "source": "amazon_massive", + "query_hash": "cd21c03c5ab4338838a10ae43b5ef527d22a4e64ead3d70ae621b406fca3b682" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008481", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4551b70e3ac15b0c5d8cf85920209ef6", + "normalized_query_sha256": "0ed72a685a6913c71f1eb54b1f62af77758b1403d9648bdcbd8e142513bf4707", + "query": "ou mentioned wanting to \"return the equipment 3 days before the scheduled pickup date.\" Since the rental hasn't started yet, I want to ensure I understand correctly:\n\n1. Are you looking to **shorten the rental period** (e.g., end on May 16 instead of May 18)?\n2. Or are you looking to **cancel the reservation entirely**?\n\n**Policy Context:**\n- Rental adjustments (±72 hours) are allowed within the modification window\n- Lighting equipment has a **1-day minimum rental period**\n- Modifications can adjust the rental end date, which would recalculate the total cost\n- Any cost difference would typically be credited to your account for future use\n\nCould you please clarify exactly how you'd like to adjust the rental period? For example:\n- \"I'd like to change the rental to end on May 16 instead of May 18\"\n- \"I want to cancel the reservation completely\"\n\nOnce I have this information, I can process the modification and provide details about any credit that would be applied to your account (IND-EVENT-99) for future film production services.\nuser: I’d like to **shorten the rental period** to end on **May 16, 2025, at 5:00 PM** instead of May 18. Since the rental hasn’t started yet and I’m within the modification window, I assume this adjustment is possible. Could you confirm the credit amount for the unused 2-day period and apply it directly to my account (IND-EVENT-99) for future services? I’d also appreciate a confirmation email with the updated reservation details and credit information.", + "target": "modify_reservation", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 903, + "historical_id": "fastbrain_008481", + "source": "nemotron_agentic", + "query_hash": "0ed72a685a6913c71f1eb54b1f62af77758b1403d9648bdcbd8e142513bf4707" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 903, + "historical_id": "fastbrain_008481", + "source": "nemotron_agentic", + "query_hash": "0ed72a685a6913c71f1eb54b1f62af77758b1403d9648bdcbd8e142513bf4707" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008596", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1bb33c468abe7bdc35a358caeb67e300", + "normalized_query_sha256": "f288ac001b4670b23a9cd3f57158a5e0323ef55566e473b21ea50304d87ecc38", + "query": "explicit tool output for every step \n3. **Data Integrity**: \n - Never override system validations \n - Reject modifications lacking authentication \n\n---\n\n### **Escalation Protocol** \nTransfer to human for: \n- Corporate gift programs \n- Point disputes \n- >3 shipment failures \n\n**Transfer Process**: \n1. State: *\"Transferring to human agent. Hold please.\"* \n2. Execute transfer tool\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I’m having an issue with my coffee subscription. My third delivery attempt failed again today, March 14, 2025. This has happened on February 15 and February 29 as well. My subscription ID is SUB-4411, and I’m on the Explorer plan. Can you tell me what the next steps are after three consecutive delivery failures? I’d like to resolve this as soon as possible.\nassistant: Hello! I'm sorry to hear about the repeated delivery failures. I understand this is frustrating, especially with three consecutive failed attempts.\n\nTo help you with this issue and access your subscription details, I'll need to verify your identity first. According to our policy, I need to send you an authentication code.\n\nPlease provide your email address or phone number associated with your subscription SUB-4411 so I can send you a verification code.\nuser: My email address associated with the subscription is emily.johnson@example.com. Please send the verification code there. I need to resolve this issue urgently as it’s now on my third failed delivery.", + "target": "send_authentication_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1016, + "historical_id": "fastbrain_008596", + "source": "nemotron_agentic", + "query_hash": "f288ac001b4670b23a9cd3f57158a5e0323ef55566e473b21ea50304d87ecc38" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1016, + "historical_id": "fastbrain_008596", + "source": "nemotron_agentic", + "query_hash": "f288ac001b4670b23a9cd3f57158a5e0323ef55566e473b21ea50304d87ecc38" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008889", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:92d01ffeb87ccf0fe5ead29d088e5713", + "normalized_query_sha256": "8bbd5e5856ead9fbd5d0425863942be4573267a75ebc7cc3d8247f2b835db3ca", + "query": "Hellboy sounds good. What are the times?", + "target": "GetTimesForMovie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 132, + "historical_id": "fastbrain_008889", + "source": "sgd", + "query_hash": "8bbd5e5856ead9fbd5d0425863942be4573267a75ebc7cc3d8247f2b835db3ca" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 132, + "historical_id": "fastbrain_008889", + "source": "sgd", + "query_hash": "8bbd5e5856ead9fbd5d0425863942be4573267a75ebc7cc3d8247f2b835db3ca" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_008920", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c925cde3855a59b75e012d1a9dae24b4", + "normalized_query_sha256": "e726049853a4fe144a3d84be16e30c01c6f4fdbdacc4a04b41e65f96c359ff19", + "query": "Delete all empty directories under root", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 373, + "historical_id": "fastbrain_008920", + "source": "nl2bash", + "query_hash": "e726049853a4fe144a3d84be16e30c01c6f4fdbdacc4a04b41e65f96c359ff19" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 373, + "historical_id": "fastbrain_008920", + "source": "nl2bash", + "query_hash": "e726049853a4fe144a3d84be16e30c01c6f4fdbdacc4a04b41e65f96c359ff19" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_009065", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5ddb1d5cce0fe10fcbc26bcb9070ed45", + "normalized_query_sha256": "7ca95d2cc9a595b5b9108001caa3046a108b0fc61b87a301bc07a46cba5fac66", + "query": "I have some chicken, broccoli, and cheese. Can you find me a keto recipe?", + "target": "search_recipes", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 887, + "historical_id": "fastbrain_009065", + "source": "glaive_v2", + "query_hash": "7ca95d2cc9a595b5b9108001caa3046a108b0fc61b87a301bc07a46cba5fac66" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 887, + "historical_id": "fastbrain_009065", + "source": "glaive_v2", + "query_hash": "7ca95d2cc9a595b5b9108001caa3046a108b0fc61b87a301bc07a46cba5fac66" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_009451", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:df4cb1cecfc3744bc33bbd01be73f344", + "normalized_query_sha256": "8572cd44fade75646176125e7b5064110fe9188f8f289e5b886d4cd166143399", + "query": "turn off smart plug socket", + "target": "iot_wemo_off", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 44, + "historical_id": "semantic:validation:fastbrain_009451:absent", + "source": "amazon_massive", + "query_hash": "8572cd44fade75646176125e7b5064110fe9188f8f289e5b886d4cd166143399" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 586, + "historical_id": "semantic:validation:fastbrain_009451:present", + "source": "amazon_massive", + "query_hash": "8572cd44fade75646176125e7b5064110fe9188f8f289e5b886d4cd166143399" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_009508", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:28e69ab1a98443902d14ba76adcd04ad", + "normalized_query_sha256": "cf26588498ee4b6c2dc202d9be0cb77fb1f584f9c141a5a2765fc37634f37306", + "query": "3-4444. Once that's confirmed, I'd like to proceed with the cancellation and schedule a new appointment after the storm. I'll have the vessel ID and problem description ready for rescheduling, but I'll need to determine the new date once weather conditions improve.\nassistant: \ntool: \n\n{\"sms_sent\": true}\nassistant: I've successfully sent an SMS verification code to your registered phone number (555) 333-4444.\n\n**To proceed with the cancellation, I need two things from you:**\n\n1. **The SMS verification code** you received\n2. **The current time** in ISO format (e.g., \"2024-01-15T14:30:00Z\") - this is required for the cancellation_time parameter to determine the refund eligibility\n\nOnce you provide both, I'll process the cancellation. Since you're within 24 hours of your appointment, the standard policy would be no refund, but as a Gold member, you'll be able to use your free reschedule benefit for your new appointment.\n\nAfter cancellation is complete, you can reschedule at your convenience by providing:\n- Vessel ID\n- Problem description \n- Desired date (with minimum 48-hour notice)\n- Time window preference (morning for vessels ≤30ft, afternoon for >30ft)\n\nPlease provide the SMS verification code and current time, and I'll process your cancellation.\nuser: The SMS verification code I received is 789456. The current time in ISO format is 2023-10-25T10:30:00Z. Please process the cancellation now. I'll follow up to reschedule once the weather clears and I can determine the new date.", + "target": "cancel_appointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 351, + "historical_id": "fastbrain_009508", + "source": "nemotron_agentic", + "query_hash": "cf26588498ee4b6c2dc202d9be0cb77fb1f584f9c141a5a2765fc37634f37306" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 351, + "historical_id": "fastbrain_009508", + "source": "nemotron_agentic", + "query_hash": "cf26588498ee4b6c2dc202d9be0cb77fb1f584f9c141a5a2765fc37634f37306" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_009795", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:39e2a6895d4a46565c7c822460d35940", + "normalized_query_sha256": "6eb14d8889ed82c88bf9ce110730deda4ad9ecb24cc98e784949b85b1ef1ffc0", + "query": "Thanks. Now I need to book a cab to take me there. I only need it for 1 person and a regular ride is fine.", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 557, + "historical_id": "fastbrain_009795", + "source": "sgd", + "query_hash": "6eb14d8889ed82c88bf9ce110730deda4ad9ecb24cc98e784949b85b1ef1ffc0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 557, + "historical_id": "fastbrain_009795", + "source": "sgd", + "query_hash": "6eb14d8889ed82c88bf9ce110730deda4ad9ecb24cc98e784949b85b1ef1ffc0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_009969", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1a8a5cd063cae86d05972ee4c3da00a0", + "normalized_query_sha256": "93d0206caa900707cb788cd46acb68d366d7db55a56f801419380ca513834529", + "query": "The title is \"Project Meeting\", start time is \"2022-04-15T09:00:00\", and end time is \"2022-04-15T10:00:00\".", + "target": "create_calendar_event", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 620, + "historical_id": "fastbrain_009969", + "source": "glaive_v2", + "query_hash": "93d0206caa900707cb788cd46acb68d366d7db55a56f801419380ca513834529" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 620, + "historical_id": "fastbrain_009969", + "source": "glaive_v2", + "query_hash": "93d0206caa900707cb788cd46acb68d366d7db55a56f801419380ca513834529" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_009983", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d22f6d838b8c8299e751147314d56816", + "normalized_query_sha256": "c6426f3c4e7664ea7df7cd237ba1255a9a591515bb4d6d2795983bc72adee0ce", + "query": "Hi, I would like to know the latest news about the Mars Rover.", + "target": "search_news", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 138, + "historical_id": "fastbrain_009983", + "source": "glaive_v2", + "query_hash": "c6426f3c4e7664ea7df7cd237ba1255a9a591515bb4d6d2795983bc72adee0ce" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 138, + "historical_id": "fastbrain_009983", + "source": "glaive_v2", + "query_hash": "c6426f3c4e7664ea7df7cd237ba1255a9a591515bb4d6d2795983bc72adee0ce" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_010010", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:854eb04113c3b88129b14ffb74048f24", + "normalized_query_sha256": "1899380cbd0987ae1538f5bf78a86577da29e298d73046d8fd6b97a47f02d8af", + "query": "please play the radio gaga song", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 146, + "historical_id": "fastbrain_010010", + "source": "amazon_massive", + "query_hash": "1899380cbd0987ae1538f5bf78a86577da29e298d73046d8fd6b97a47f02d8af" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 146, + "historical_id": "fastbrain_010010", + "source": "amazon_massive", + "query_hash": "1899380cbd0987ae1538f5bf78a86577da29e298d73046d8fd6b97a47f02d8af" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_010055", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fc6e8cc8e98029d49f45c965100e75c7", + "normalized_query_sha256": "5b5e5780ab51ff8b8909e1715662fe4f81237f4c4fadb3f8e360b19b8466907a", + "query": "That's great! 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_011493", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e080306cc15d165020a16101565b475f", + "normalized_query_sha256": "cdcd3020c0937ccf44235079ba9a273d04262fabc31a48d88da34981e4bd3a02", + "query": "what state is david lynch from", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 506, + "historical_id": "fastbrain_011493", + "source": "amazon_massive", + "query_hash": "cdcd3020c0937ccf44235079ba9a273d04262fabc31a48d88da34981e4bd3a02" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 506, + "historical_id": "fastbrain_011493", + "source": "amazon_massive", + "query_hash": "cdcd3020c0937ccf44235079ba9a273d04262fabc31a48d88da34981e4bd3a02" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_011572", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4a46de3109424b6b9db1d061997bda21", + "normalized_query_sha256": "8efd8d547c4c24dc31f4351c1f14d69a58a91a71dce7d32160e6712db309417e", + "query": "make some coffee", + "target": "iot_coffee", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 980, + "historical_id": "fastbrain_011572", + "source": "amazon_massive", + "query_hash": "8efd8d547c4c24dc31f4351c1f14d69a58a91a71dce7d32160e6712db309417e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 980, + "historical_id": "fastbrain_011572", + "source": "amazon_massive", + "query_hash": "8efd8d547c4c24dc31f4351c1f14d69a58a91a71dce7d32160e6712db309417e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_011713", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cc6a6435dbd618612c575c890e686eb8", + "normalized_query_sha256": "837c6c0ce64d7a5ad8f4aa2c6ea4a0383fb2c9d47eb50e7b716081369a774e0e", + "query": "is there any possibility of rain this week", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 167, + "historical_id": "fastbrain_011713", + "source": "amazon_massive", + "query_hash": "837c6c0ce64d7a5ad8f4aa2c6ea4a0383fb2c9d47eb50e7b716081369a774e0e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 167, + "historical_id": "fastbrain_011713", + "source": "amazon_massive", + "query_hash": "837c6c0ce64d7a5ad8f4aa2c6ea4a0383fb2c9d47eb50e7b716081369a774e0e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_011801", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f879885a6a50458202b13d93ceb26979", + "normalized_query_sha256": "5cf8ded0e768d09a07f97fbc1720d6ba95b6b6edab897d20d08f67feb46c02c0", + "query": "Hi, are there any cars available for rental on the 5th of this month?", + "target": "GetCarsAvailable", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 982, + "historical_id": "fastbrain_011801", + "source": "sgd", + "query_hash": "5cf8ded0e768d09a07f97fbc1720d6ba95b6b6edab897d20d08f67feb46c02c0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 982, + "historical_id": "fastbrain_011801", + "source": "sgd", + "query_hash": "5cf8ded0e768d09a07f97fbc1720d6ba95b6b6edab897d20d08f67feb46c02c0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_011897", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0483075a1249ed93e7681eb12e26a91a", + "normalized_query_sha256": "1795cee8c1ed6199124760e5c3ae9a81ab054712ea8522d1f202704c09ab1965", + "query": "I represent a cybersecurity research firm looking to leverage Quantum Computing as a Service (QCaaS) for groundbreaking simulations and data analyses. We prioritize secure cloud-based quantum solutions and require detailed comparisons of QCaaS providers that specialize in the cybersecurity field. It's essential for us to evaluate hardware specifications such as the qubit count and connectivity opt", + "target": "list_qcaas_providers", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 11, + "historical_id": "fastbrain_011897", + "source": "hermes_v1", + "query_hash": "1795cee8c1ed6199124760e5c3ae9a81ab054712ea8522d1f202704c09ab1965" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 11, + "historical_id": "fastbrain_011897", + "source": "hermes_v1", + "query_hash": "1795cee8c1ed6199124760e5c3ae9a81ab054712ea8522d1f202704c09ab1965" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_011918", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6c00b9d4fbdc10e33e027f3afe8be862", + "normalized_query_sha256": "302e63fc6640ecb03a8971b3fd9621a2fbd4753453cc2d03d679cb7ec52c6177", + "query": "tions \n- Permit extensions beyond 90 days \n- Artifact valuation inquiries \n- Unpublished research queries \n\n---\n\n## Agent Limitations \n- **Never answer** questions exceeding tool capabilities \n- **Never bypass** artifact handling/site access restrictions \n- **Never assume** identity/permit/safety compliance without explicit tool confirmation \n- **Never process** transactions outside documented scope \n- **Never speculate** beyond provided data \n\n---\n\n## Data Handling \n- Mask precise site coordinates (0.1° precision) \n- Encrypt artifact location metadata \n- Reveal permit details only to authenticated applicants\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm a retired nurse with a Basic membership, and I'm planning a group visit to Canyon de Chelly with six people. We don’t currently have any certifications. Could you clarify if safety training or first aid certification is required for our visit? I want to ensure we’re prepared for the site’s requirements.\nassistant: Hello! I'd be happy to help you with your questions about visiting Canyon de Chelly. First, I need to verify your identity to access your membership details and provide you with accurate site-specific information.\n\n**Please provide your Membership ID or registered email/full name** so I can send you a verification code and proceed with your inquiry.\nuser: Sure, my registered email is **johndoe@example.com** and full name is **John Doe**. Please send the verification code.", + "target": "send_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 805, + "historical_id": "fastbrain_011918", + "source": "nemotron_agentic", + "query_hash": "302e63fc6640ecb03a8971b3fd9621a2fbd4753453cc2d03d679cb7ec52c6177" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 805, + "historical_id": "fastbrain_011918", + "source": "nemotron_agentic", + "query_hash": "302e63fc6640ecb03a8971b3fd9621a2fbd4753453cc2d03d679cb7ec52c6177" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_011958", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0d3daa9f2305e63582bde3405f48586c", + "normalized_query_sha256": "b2e88b10fb91c0cfba7c12abfe5c3d38181f84a24c56249c330b0187c29cdde0", + "query": "stage the fix and amend it into the last commit", + "target": "git_commit", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 546, + "historical_id": "fastbrain_011958", + "source": "git_nl_commands", + "query_hash": "b2e88b10fb91c0cfba7c12abfe5c3d38181f84a24c56249c330b0187c29cdde0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 546, + "historical_id": "fastbrain_011958", + "source": "git_nl_commands", + "query_hash": "b2e88b10fb91c0cfba7c12abfe5c3d38181f84a24c56249c330b0187c29cdde0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012025", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8f2469c0d76de2fa7134a83289e625d9", + "normalized_query_sha256": "4821339000c2a38629be56385b34ccba309ba30e58ea2165ab7c07eaf24a1477", + "query": "do we have morgan's office's number", + "target": "email_querycontact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 427, + "historical_id": "semantic:validation:fastbrain_012025:present", + "source": "amazon_massive", + "query_hash": "4821339000c2a38629be56385b34ccba309ba30e58ea2165ab7c07eaf24a1477" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 892, + "historical_id": "semantic:validation:fastbrain_012025:absent", + "source": "amazon_massive", + "query_hash": "4821339000c2a38629be56385b34ccba309ba30e58ea2165ab7c07eaf24a1477" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012130", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ead27987b0ad6863f81be14629d95546", + "normalized_query_sha256": "2f919ec87c293dd1aaa18effe1285a1a8e761a1dab06591499a084ba760b3d03", + "query": "remind me of this meeting soon", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 171, + "historical_id": "semantic:validation:fastbrain_012130:absent", + "source": "amazon_massive", + "query_hash": "2f919ec87c293dd1aaa18effe1285a1a8e761a1dab06591499a084ba760b3d03" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 888, + "historical_id": "semantic:validation:fastbrain_012130:present", + "source": "amazon_massive", + "query_hash": "2f919ec87c293dd1aaa18effe1285a1a8e761a1dab06591499a084ba760b3d03" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012146", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2b3aadb72c8950b33112767ce4103002", + "normalized_query_sha256": "8beaa9f80a6cfda961243fd8914316a9095e9d606301752b74a2a59b5a9c092c", + "query": "Make directories and parents as needed to \"${raw_folder}\" and \"${split_folder}\"", + "target": "mkdir", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 702, + "historical_id": "fastbrain_012146", + "source": "nl2bash", + "query_hash": "8beaa9f80a6cfda961243fd8914316a9095e9d606301752b74a2a59b5a9c092c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 702, + "historical_id": "fastbrain_012146", + "source": "nl2bash", + "query_hash": "8beaa9f80a6cfda961243fd8914316a9095e9d606301752b74a2a59b5a9c092c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012239", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0137ea2de3852eaf8c8cbce6a9a1b6b2", + "normalized_query_sha256": "9b546fc8cbc93a1489e6406872dfff5887d35f9d0078d52eca04e3c71b5b0c5d", + "query": "I have a product that costs $100 and there's a 20% discount on it. Can you calculate the discounted price for me?", + "target": "calculate_discount", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 909, + "historical_id": "fastbrain_012239", + "source": "glaive_v2", + "query_hash": "9b546fc8cbc93a1489e6406872dfff5887d35f9d0078d52eca04e3c71b5b0c5d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 909, + "historical_id": "fastbrain_012239", + "source": "glaive_v2", + "query_hash": "9b546fc8cbc93a1489e6406872dfff5887d35f9d0078d52eca04e3c71b5b0c5d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012367", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e2b18096e0056cb89947d22488312762", + "normalized_query_sha256": "b7f29b78de392c002a399896c46be3edceb57b1d7d376729436f416f56cad673", + "query": "tery odds) \n\n2. **Verification Rules**: \n - Never assume document/case status without explicit tool confirmation \n - Never bypass expiration date validations \n\n3. **Advice Restrictions**: \n - Do not speculate on approval chances \n - Do not interpret regulations beyond approved FAQs \n\n4. **Escalation Protocol**: \n Transfer immediately for: \n - RFE responses \n - Medical emergency expedites \n - Visa overstay disclosures \n With message: *\"Transferring to an immigration specialist. Desc...[TRUNCATED]\nuser: \n\nHello, I'm calling about the $50 data correction fee I saw mentioned in my immigration application materials. Can you clarify what types of changes this fee covers? Also, I need to update my email address on file—would that be considered a biometric-impacting change or something else?\nassistant: Hello! I'd be happy to help clarify the $50 data correction fee and assist with updating your email address.\n\nFirst, I need to verify your identity for account security. Could you please provide:\n\n1. Your User ID\n2. Your Date of Birth (YYYY-MM-DD format)\n\nOnce I verify your identity, I can check your specific case details and provide accurate information about the data correction fee and email update process.\nuser: Sure, my User ID is **IM-48291** and my Date of Birth is **1985-03-17**. Let me know if you need anything else to verify my account. I’d also like to confirm if updating my email address would require the $50 fee or if that’s a separate process.", + "target": "authenticate_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 261, + "historical_id": "fastbrain_012367", + "source": "nemotron_agentic", + "query_hash": "b7f29b78de392c002a399896c46be3edceb57b1d7d376729436f416f56cad673" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 261, + "historical_id": "fastbrain_012367", + "source": "nemotron_agentic", + "query_hash": "b7f29b78de392c002a399896c46be3edceb57b1d7d376729436f416f56cad673" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012414", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f80baad8dd76607e80d77892883cad1b", + "normalized_query_sha256": "06ccc148557d6be5ea25b97119cc883ff2e9c1ead02c572c14dd0a1d44a196ff", + "query": "what events do i have for this coming week", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 612, + "historical_id": "fastbrain_012414", + "source": "amazon_massive", + "query_hash": "06ccc148557d6be5ea25b97119cc883ff2e9c1ead02c572c14dd0a1d44a196ff" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 612, + "historical_id": "fastbrain_012414", + "source": "amazon_massive", + "query_hash": "06ccc148557d6be5ea25b97119cc883ff2e9c1ead02c572c14dd0a1d44a196ff" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012469", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d4e291d54e9024e315e5c5403e79f758", + "normalized_query_sha256": "fe6808f454d497215b81fa2fea1d4d16ec9f16b893cf5a0afc653372f8a802c1", + "query": "where is the nearest smoke shop", + "target": "recommendation_locations", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 689, + "historical_id": "fastbrain_012469", + "source": "amazon_massive", + "query_hash": "fe6808f454d497215b81fa2fea1d4d16ec9f16b893cf5a0afc653372f8a802c1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 689, + "historical_id": "fastbrain_012469", + "source": "amazon_massive", + "query_hash": "fe6808f454d497215b81fa2fea1d4d16ec9f16b893cf5a0afc653372f8a802c1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012613", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:57583ee2bc2e888d8c62e27392f6ba5a", + "normalized_query_sha256": "5a4cda9c529e9b649e8114ac2d836960b76af273edabba98727b429831637881", + "query": "Perfect. Please find me a cab to go there.", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 788, + "historical_id": "fastbrain_012613", + "source": "sgd", + "query_hash": "5a4cda9c529e9b649e8114ac2d836960b76af273edabba98727b429831637881" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 788, + "historical_id": "fastbrain_012613", + "source": "sgd", + "query_hash": "5a4cda9c529e9b649e8114ac2d836960b76af273edabba98727b429831637881" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_012975", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7f672f13bd7c08a1c24ce7c8cd04f623", + "normalized_query_sha256": "e017b6ecfcb7202c679bf3a03f795f3c76412656bbe277856d77a075b8388fc1", + "query": "d security breaches \n- Potential data loss incidents \n- Contractual disputes \n- Multi-system outages \n\nProcess: \n1. Use transfer tool \n2. State: \"Transferring to a senior technician. Please describe the issue to them directly.\" \n\n## Agent Limitations\n- **Do not answer questions or perform actions outside defined capabilities** \n- Never assume verification without explicit tool confirmation \n- Never create warranties/contracts outside approved tools \n- Never override security protocols \n- Never share internal scripts or knowledge base content \n- Never speculate on breach causes without log verification \n\n## Security Constraints\n- Restrict remote access to: \n - VPN-connected corporate devices \n - Two-factor authenticated sessions \n- Never store credentials (use tokens only) \n- Log all actions with timestamp and user/company IDs \n\n## Compensation Policy\n- Service credits issued only for: \n - Timestamp-confirmed SLA violations \n - Repeated resolution failures (>3 attempts) \n - Incorrect parts shipments \n- Excludes user-induced damages and out-of-scope systems\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm reaching out to check the expiration date of our technical support contract. My company ID is NPO-2276, and my corporate email is director@nonprofit.org. Could you please verify our account so I can access the portal to view the contract details? We're preparing to discuss renewal options and need to confirm the timeline.", + "target": "authenticate_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 956, + "historical_id": "fastbrain_012975", + "source": "nemotron_agentic", + "query_hash": "e017b6ecfcb7202c679bf3a03f795f3c76412656bbe277856d77a075b8388fc1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 956, + "historical_id": "fastbrain_012975", + "source": "nemotron_agentic", + "query_hash": "e017b6ecfcb7202c679bf3a03f795f3c76412656bbe277856d77a075b8388fc1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013005", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a9ceeb8211b4313e092b96763dafb228", + "normalized_query_sha256": "d375b448ed25a60684f4f603ce5e0df616189fffc3e81770f67c87a1c21cf891", + "query": "switch off the lights", + "target": "iot_hue_lightoff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 166, + "historical_id": "fastbrain_013005", + "source": "amazon_massive", + "query_hash": "d375b448ed25a60684f4f603ce5e0df616189fffc3e81770f67c87a1c21cf891" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 166, + "historical_id": "fastbrain_013005", + "source": "amazon_massive", + "query_hash": "d375b448ed25a60684f4f603ce5e0df616189fffc3e81770f67c87a1c21cf891" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013138", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f9a3b2d23fb3a30754096767a354561a", + "normalized_query_sha256": "c78d864c8f446195d87df9c09f7b1230ef7145e0beb0ea2e8bd23facd3d033d7", + "query": "olly add lunch appointment with lisa to january second at twelve thirty p. m.", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 935, + "historical_id": "fastbrain_013138", + "source": "amazon_massive", + "query_hash": "c78d864c8f446195d87df9c09f7b1230ef7145e0beb0ea2e8bd23facd3d033d7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 935, + "historical_id": "fastbrain_013138", + "source": "amazon_massive", + "query_hash": "c78d864c8f446195d87df9c09f7b1230ef7145e0beb0ea2e8bd23facd3d033d7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013146", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8bf6fce199405e9bf74c509d40c8716d", + "normalized_query_sha256": "e540b0383681eb3a943f08150c4e6be973119c6553fae03f5e003a51cc23a15c", + "query": "hey olly play playlist tactics from music", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 204, + "historical_id": "semantic:validation:fastbrain_013146:absent", + "source": "amazon_massive", + "query_hash": "e540b0383681eb3a943f08150c4e6be973119c6553fae03f5e003a51cc23a15c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 325, + "historical_id": "semantic:validation:fastbrain_013146:present", + "source": "amazon_massive", + "query_hash": "e540b0383681eb3a943f08150c4e6be973119c6553fae03f5e003a51cc23a15c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013156", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cb8cfbb40b99d9d4436cde18416602bf", + "normalized_query_sha256": "cf3fecb8470297bef5427a69049e267c66187453c541a319a833ccecace4db8d", + "query": "Excellent. I'd like a bus ticket to the show. I should leave on Thursday next week.", + "target": "BuyBusTicket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 267, + "historical_id": "fastbrain_013156", + "source": "sgd", + "query_hash": "cf3fecb8470297bef5427a69049e267c66187453c541a319a833ccecace4db8d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 267, + "historical_id": "fastbrain_013156", + "source": "sgd", + "query_hash": "cf3fecb8470297bef5427a69049e267c66187453c541a319a833ccecace4db8d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013423", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dd3cfef81c48182985d9f9e8d4b608b5", + "normalized_query_sha256": "ae2ba05866a416b57b111600e6d9dde93fb6929ed627d49a80f94fc8e6bb2c1d", + "query": "Thank you very much. You've been very helpful. While waiting, please find me songs by Pavni Pandey from South Asia.", + "target": "LookupSong", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 224, + "historical_id": "fastbrain_013423", + "source": "sgd", + "query_hash": "ae2ba05866a416b57b111600e6d9dde93fb6929ed627d49a80f94fc8e6bb2c1d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 224, + "historical_id": "fastbrain_013423", + "source": "sgd", + "query_hash": "ae2ba05866a416b57b111600e6d9dde93fb6929ed627d49a80f94fc8e6bb2c1d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013440", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c2ef45ed6e8f0d3820734764c8d9ede3", + "normalized_query_sha256": "80b01578acb2dd1b9d5bcd72090fd29cbfb4985d9e75d89ad7950a0f4ac9ab57", + "query": "what is the latest controversy on donald trump", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 119, + "historical_id": "semantic:validation:fastbrain_013440:absent", + "source": "amazon_massive", + "query_hash": "80b01578acb2dd1b9d5bcd72090fd29cbfb4985d9e75d89ad7950a0f4ac9ab57" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 788, + "historical_id": "semantic:validation:fastbrain_013440:present", + "source": "amazon_massive", + "query_hash": "80b01578acb2dd1b9d5bcd72090fd29cbfb4985d9e75d89ad7950a0f4ac9ab57" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013651", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9580aab8edffd90330f72d6d65c750cb", + "normalized_query_sha256": "21dbedd8ee313541ae3879380509244e4f70358b672fad1616e77ce1d96e25af", + "query": "eclined payments freeze scheduling\n- Refunds only for unused packages ≤14 days after purchase\n\n### Skill-Level Rules\n- Beginner students: max 45-min lessons\n- Ensemble participation requires Level 3 proficiency\n\n## Lesson Transfers\nOnly permitted when:\n- Original instructor unavailable ≥3 weeks\n- Matching expertise available\n- Student consent obtained\n\n## Makeup Lessons\n- Only for documented emergencies\n- Require proof within 48 hours\n- Must be scheduled ≤14 days after absence\n- Max 2 per calendar year\n\n## Transfer Protocol\nUse `transfer_to_human_agent` for:\n1. Instructor consultations\n2. Payment disputes >$150\n3. Special accommodations\n4. Complex scheduling conflicts (>3 reschedules)\n\nThen send: \"Transferring you to a human agent. Please wait.\"\n\n## Agent Limitations\n- Only use provided tools for actions\n- Never answer questions beyond tool capabilities\n- Never disclose instructor contact details\n- Never assume actions completed without tool confirmation\n- Never override package expiration dates\n- Never approve lessons beyond paid duration\n- Never bypass authentication requirements\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm a professional musician preparing for a competition on August 28, 2025, and I urgently need to schedule a last-minute lesson. My Student ID is S34576, and my phone number is 555-2233. Are there any available instructors who can accommodate an emergency booking?\nassistant: \ntool: \n\n{\"success\": true, \"student_id\": \"S34576\"}", + "target": "get_student_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 238, + "historical_id": "fastbrain_013651", + "source": "nemotron_agentic", + "query_hash": "21dbedd8ee313541ae3879380509244e4f70358b672fad1616e77ce1d96e25af" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 238, + "historical_id": "fastbrain_013651", + "source": "nemotron_agentic", + "query_hash": "21dbedd8ee313541ae3879380509244e4f70358b672fad1616e77ce1d96e25af" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013781", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dc8933f75ff3e5c80aa00bc170affd33", + "normalized_query_sha256": "94c60b6c57779df9d2abdffea23020b2479f1c58f149d313e541db40a1a5d08a", + "query": "what events are on this month's schedule", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 167, + "historical_id": "semantic:validation:fastbrain_013781:present", + "source": "amazon_massive", + "query_hash": "94c60b6c57779df9d2abdffea23020b2479f1c58f149d313e541db40a1a5d08a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 826, + "historical_id": "semantic:validation:fastbrain_013781:absent", + "source": "amazon_massive", + "query_hash": "94c60b6c57779df9d2abdffea23020b2479f1c58f149d313e541db40a1a5d08a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_013964", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ef083d7dbc0704c802ae0322593dd58c", + "normalized_query_sha256": "cc29030c561ed6e4fec50faf2c90f45a5f99837c879c730bed53afd474ce4224", + "query": "Hi, I saw a dress that I really like. It's originally priced at $200 but it's on a 20% discount. Can you help me calculate how much it will cost after the discount?", + "target": "calculate_discount", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 251, + "historical_id": "fastbrain_013964", + "source": "glaive_v2", + "query_hash": "cc29030c561ed6e4fec50faf2c90f45a5f99837c879c730bed53afd474ce4224" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 251, + "historical_id": "fastbrain_013964", + "source": "glaive_v2", + "query_hash": "cc29030c561ed6e4fec50faf2c90f45a5f99837c879c730bed53afd474ce4224" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014064", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f7d8c2f52494526c64820c6d3e03979b", + "normalized_query_sha256": "b97a6750f65412913dcc5b24622fb5b29abc930d19f27b7eb8c048e6c0286537", + "query": "Limitations \n- Do not answer questions or perform actions outside defined tool capabilities \n- Never assume verification or action completion without explicit tool confirmation \n- Never create designs outside template library \n- Never promise unavailable materials \n- Never bypass address verification checks \n- Never provide design advice beyond documented options \n- Never commit to timelines without production_calendar verification \n\n## Transfer Protocol \nTransfer to human agent when: \n- Custom illustration requests \n- Venue map integration \n- Multi-wedding coordination \n- Complaints requiring managerial review \n- Legal documentation requests \n\nTransfer process: \n1. Use `transfer_to_design_consultant` tool \n2. Send message: \n \"YOU ARE BEING TRANSFERRED TO A STATIONERY DESIGN CONSULTANT FOR PERSONALIZED ASSISTANCE.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm reaching out regarding our order CREST11 under client HERALDIC for our September 5, 2025 wedding. We're currently in the first proof phase and need to integrate a family crest into our stationery. We have the crest artwork file ready to share, but before proceeding, could you please confirm our order authentication? Once verified, we’d appreciate being transferred to a design consultant to discuss custom illustration requirements for the crest integration.\nassistant: \ntool: \n\n{\"authentication_status\": true, \"client_id\": \"HERALDIC\", \"client_name\": \"Heraldic Traditions\"}", + "target": "transfer_to_design_consultant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 207, + "historical_id": "fastbrain_014064", + "source": "nemotron_agentic", + "query_hash": "b97a6750f65412913dcc5b24622fb5b29abc930d19f27b7eb8c048e6c0286537" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 207, + "historical_id": "fastbrain_014064", + "source": "nemotron_agentic", + "query_hash": "b97a6750f65412913dcc5b24622fb5b29abc930d19f27b7eb8c048e6c0286537" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014131", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b5f1b344e29b37e832b6cb05d67c1d1e", + "normalized_query_sha256": "4f4df518678e905e91f509d31955c8779c9c11e07593f16a7b5940cf46469f26", + "query": "Find all *.php (case insensitive) files and *.js files/directories (case insensitive) under /home/jul/here excluding /home/jul/here/exclude/* paths", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 893, + "historical_id": "fastbrain_014131", + "source": "nl2bash", + "query_hash": "4f4df518678e905e91f509d31955c8779c9c11e07593f16a7b5940cf46469f26" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 893, + "historical_id": "fastbrain_014131", + "source": "nl2bash", + "query_hash": "4f4df518678e905e91f509d31955c8779c9c11e07593f16a7b5940cf46469f26" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014160", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6f92d349f10481a1636205363fdcd181", + "normalized_query_sha256": "c70e07a0df15103c2bdd1dabd1b654547ba1df44edc11966b69f34f90b0c89c7", + "query": "add lunch with laura for thursday at one", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 332, + "historical_id": "semantic:validation:fastbrain_014160:present", + "source": "amazon_massive", + "query_hash": "c70e07a0df15103c2bdd1dabd1b654547ba1df44edc11966b69f34f90b0c89c7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 578, + "historical_id": "semantic:validation:fastbrain_014160:absent", + "source": "amazon_massive", + "query_hash": "c70e07a0df15103c2bdd1dabd1b654547ba1df44edc11966b69f34f90b0c89c7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014321", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9fcaff5c9bd65d3509c5ebda7c8e9f94", + "normalized_query_sha256": "b2b2fd38a216224173320a868b2144e06517bc5e907abdd218cf951f3d0e8eda", + "query": "i want to hear my playlist from that artist", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 133, + "historical_id": "semantic:validation:fastbrain_014321:absent", + "source": "amazon_massive", + "query_hash": "b2b2fd38a216224173320a868b2144e06517bc5e907abdd218cf951f3d0e8eda" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 455, + "historical_id": "semantic:validation:fastbrain_014321:present", + "source": "amazon_massive", + "query_hash": "b2b2fd38a216224173320a868b2144e06517bc5e907abdd218cf951f3d0e8eda" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014369", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bd3536f9ba57388f22acba8befbcb9cc", + "normalized_query_sha256": "02486d707d5237100fdc6333aff1ae5162f7d29eb6dd951746e604c1edd88988", + "query": "lower the lights in the room", + "target": "iot_hue_lightdim", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 252, + "historical_id": "semantic:validation:fastbrain_014369:absent", + "source": "amazon_massive", + "query_hash": "02486d707d5237100fdc6333aff1ae5162f7d29eb6dd951746e604c1edd88988" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 945, + "historical_id": "semantic:validation:fastbrain_014369:present", + "source": "amazon_massive", + "query_hash": "02486d707d5237100fdc6333aff1ae5162f7d29eb6dd951746e604c1edd88988" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014370", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fa3a0256658ccd7e65755e9c7e120609", + "normalized_query_sha256": "f1ca25f036fda2f324b7c80b32d1d7dab5ebf7e306fdd9a89ab004567799c8cc", + "query": "play enrique", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 102, + "historical_id": "semantic:validation:fastbrain_014370:present", + "source": "amazon_massive", + "query_hash": "f1ca25f036fda2f324b7c80b32d1d7dab5ebf7e306fdd9a89ab004567799c8cc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 754, + "historical_id": "semantic:validation:fastbrain_014370:absent", + "source": "amazon_massive", + "query_hash": "f1ca25f036fda2f324b7c80b32d1d7dab5ebf7e306fdd9a89ab004567799c8cc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014474", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0d63eaa9c1030707232e66a61c582747", + "normalized_query_sha256": "cce169d81f2a14f8e2f7fdd07383952a34ade4822f7f5e56b62f539da510fe8d", + "query": "Hello, can you find me a rental medium-sized car for use tomorrow in Portland?", + "target": "GetCarsAvailable", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 749, + "historical_id": "fastbrain_014474", + "source": "sgd", + "query_hash": "cce169d81f2a14f8e2f7fdd07383952a34ade4822f7f5e56b62f539da510fe8d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 749, + "historical_id": "fastbrain_014474", + "source": "sgd", + "query_hash": "cce169d81f2a14f8e2f7fdd07383952a34ade4822f7f5e56b62f539da510fe8d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014497", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:07b1b8d21115f87eeceaae30f37f2cb0", + "normalized_query_sha256": "8403d1eca3779fa9e05a6f5327d8d66f0d414432bb5edd506295acd012820261", + "query": "Hi, I need to find the factorial of 5. Can you help me with that?", + "target": "find_factorial", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 807, + "historical_id": "fastbrain_014497", + "source": "glaive_v2", + "query_hash": "8403d1eca3779fa9e05a6f5327d8d66f0d414432bb5edd506295acd012820261" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 807, + "historical_id": "fastbrain_014497", + "source": "glaive_v2", + "query_hash": "8403d1eca3779fa9e05a6f5327d8d66f0d414432bb5edd506295acd012820261" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014581", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:71d93ff7b914c71d9dd55af56bc43d15", + "normalized_query_sha256": "022b9308b230ab3072f6f2df1e8b1245baf693b0b06c0b0d9515805d22d3aaac", + "query": "remove events from calendar", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 403, + "historical_id": "semantic:validation:fastbrain_014581:present", + "source": "amazon_massive", + "query_hash": "022b9308b230ab3072f6f2df1e8b1245baf693b0b06c0b0d9515805d22d3aaac" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 921, + "historical_id": "semantic:validation:fastbrain_014581:absent", + "source": "amazon_massive", + "query_hash": "022b9308b230ab3072f6f2df1e8b1245baf693b0b06c0b0d9515805d22d3aaac" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014640", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f8ef0ef9b31c7115906fce904263e933", + "normalized_query_sha256": "1b7bed08b9c46a2b6ea971e5ab10f23ea24b652219cbb9be96ff3ad25a215e41", + "query": " = all services)\n- **Purchasing extra credits** is typically a pay-as-you-go option that might be more economical if you only occasionally exceed your 8-class limit\n- For exact pricing comparisons, you may want to contact our front desk directly\n\n**Regarding schedule flexibility:** Unfortunately, I cannot override tier restrictions or make exceptions to the 8-class monthly limit for Basic members - this is a system-enforced policy. However, I can check if the Morning Yoga on April 25th is available within your booking window.\n\n**Important note:** Basic members can book classes up to 3 days in advance. If April 25th is within the next 3 days, you might be able to book it (if you haven't used all 8 credits). If it's further out, you'd need to wait until it's within your booking window.\n\nTo check availability and explore your options, I'll need your member ID. Once I have that, I can:\n1. Check your current credit usage\n2. See if the Morning Yoga class is available for your tier\n3. Help you purchase extra credits if needed\n\nCould you please provide your member ID when you're ready?\nuser: My member ID is STU-2025-0425. Could you check if I’ve already used all 8 of my classes this month? Also, if purchasing a credit is the only option, can you confirm the cost upfront so I can decide if it’s feasible? I’d really prefer to stick to the morning slot if possible.\nassistant: \ntool: \n\n{\"membership_tier\": \"Basic\", \"class_credits\": 0, \"pt_credits\": 2, \"payment_methods\": [\"PM-2025-0425A\"]}", + "target": "get_class_availability", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 724, + "historical_id": "fastbrain_014640", + "source": "nemotron_agentic", + "query_hash": "1b7bed08b9c46a2b6ea971e5ab10f23ea24b652219cbb9be96ff3ad25a215e41" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 724, + "historical_id": "fastbrain_014640", + "source": "nemotron_agentic", + "query_hash": "1b7bed08b9c46a2b6ea971e5ab10f23ea24b652219cbb9be96ff3ad25a215e41" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_014710", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6aa0995780788690b20e6b886eba3a8e", + "normalized_query_sha256": "e89d38141ee044ceb80171c893bc7fa03c4344f54c63dce882850a0d75cdf252", + "query": "I'm planning a trip to Japan and would like to use Revolut's services to exchange currency before I go. I have 1500 USD that I'd like to convert to Japanese Yen (JPY). Could you please provide me with the current exchange rate from USD to JPY and calculate the total amount I will receive in JPY after the exchange? 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_015158", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:83dbfcb0711f45a4a717c802e0e361a4", + "normalized_query_sha256": "e6ed1e585d626f398127c7e70851746c85c6dc65e9592931704abeb767c9ed04", + "query": "please say to the email that sent me george i need the money now its urgent", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 988, + "historical_id": "fastbrain_015158", + "source": "amazon_massive", + "query_hash": "e6ed1e585d626f398127c7e70851746c85c6dc65e9592931704abeb767c9ed04" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 988, + "historical_id": "fastbrain_015158", + "source": "amazon_massive", + "query_hash": "e6ed1e585d626f398127c7e70851746c85c6dc65e9592931704abeb767c9ed04" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_015174", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7bc07dbfb8d43a55aadc81ee0cb01449", + "normalized_query_sha256": "d3b3c878e8651252bca00273e1e7c6d8f20909b393a75adb55af3177e79b3c7c", + "query": "what do i have scheduled for tomorrow i need to cancel", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 424, + "historical_id": "semantic:validation:fastbrain_015174:present", + "source": "amazon_massive", + "query_hash": "d3b3c878e8651252bca00273e1e7c6d8f20909b393a75adb55af3177e79b3c7c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 972, + "historical_id": "semantic:validation:fastbrain_015174:absent", + "source": "amazon_massive", + "query_hash": "d3b3c878e8651252bca00273e1e7c6d8f20909b393a75adb55af3177e79b3c7c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_015203", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f6bc8753c464f0c58ea6e35fc8fa6618", + "normalized_query_sha256": "992c66b06b9dfc1c672b8748b4555923772e1586dad6c3f9d7399165d668b0cf", + "query": "Hi, can you analyze this image for me? 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_015357", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f0ab1de3bf7c6861b8498e56d05f9036", + "normalized_query_sha256": "fabdd64896ea5af65525f26f25065479ff6c4d33dba683b5a0797002c83fdddf", + "query": "bring the changes from dev into here", + "target": "git_merge", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 29, + "historical_id": "fastbrain_015357", + "source": "git_nl_commands", + "query_hash": "fabdd64896ea5af65525f26f25065479ff6c4d33dba683b5a0797002c83fdddf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 29, + "historical_id": "fastbrain_015357", + "source": "git_nl_commands", + "query_hash": "fabdd64896ea5af65525f26f25065479ff6c4d33dba683b5a0797002c83fdddf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_015953", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0e6ccdcce3fd799ce86d9f606deb3322", + "normalized_query_sha256": "d2351790dc277b15e3b28bc188c05315942409aa4f58ce20d81f4361529f28ba", + "query": "reviews of the restaurants", + "target": "takeaway_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 673, + "historical_id": "semantic:validation:fastbrain_015953:present", + "source": "amazon_massive", + "query_hash": "d2351790dc277b15e3b28bc188c05315942409aa4f58ce20d81f4361529f28ba" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 756, + "historical_id": "semantic:validation:fastbrain_015953:absent", + "source": "amazon_massive", + "query_hash": "d2351790dc277b15e3b28bc188c05315942409aa4f58ce20d81f4361529f28ba" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_016292", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1cb63058a09b4d4da2e262265b18420b", + "normalized_query_sha256": "a4daf3dcbe114bc12da178d8c7e1d74a22f237ccd8252197bb4f5d09c27ca4a9", + "query": "what is the current weather in denver", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 300, + "historical_id": "semantic:validation:fastbrain_016292:present", + "source": "amazon_massive", + "query_hash": "a4daf3dcbe114bc12da178d8c7e1d74a22f237ccd8252197bb4f5d09c27ca4a9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 377, + "historical_id": "semantic:validation:fastbrain_016292:absent", + "source": "amazon_massive", + "query_hash": "a4daf3dcbe114bc12da178d8c7e1d74a22f237ccd8252197bb4f5d09c27ca4a9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_016294", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2e19e583965e220064a52f86f794ac9e", + "normalized_query_sha256": "f6302c34836d8c6890efac08868a5f0950ef2b912b5909ae3007186551e07ff6", + "query": "Hi, I need to calculate my monthly loan payment. 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I've successfully verified access to your order #EH787878, and I can see it's currently in \"processing\" status. I've also sent a verification code to admin@companyname.com for account authentication.\n\n**Next steps:**\n\n1. **Please check your email** for the verification code I just sent\n2. **Provide the verification code** you receive\n\nOnce you provide the code, I'll be able to:\n- Authenticate your account\n- Retrieve your profile information\n- Guide you through updating your account with tax-exempt status\n- Check the specific details of order #EH787878 to see what adjustments might be possible\n\nRegarding your question about retroactively applying tax exemption to your recent purchase: Since your order is in \"processing\" status, there may be some flexibility, but I'll need to check the exact order details and our system capabilities to provide accurate guidance.\n\nPlease provide the verification code when you receive it, and we'll proceed from there!\nuser: The verification code I received is 123456. Please proceed with authenticating my account.\nassistant: \ntool: \n\n{\"verified\": true, \"auth_token\": \"abcXYZ123\"}", + "target": "get_user_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 553, + "historical_id": "fastbrain_016623", + "source": "nemotron_agentic", + "query_hash": "ddc9b5980bf5ede363aa7ba6d86588e12030ea40d22a3fef34e80a2871413ec2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 553, + "historical_id": "fastbrain_016623", + "source": "nemotron_agentic", + "query_hash": "ddc9b5980bf5ede363aa7ba6d86588e12030ea40d22a3fef34e80a2871413ec2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_016627", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:43de90936057ed7886d89aa5d97de1f5", + "normalized_query_sha256": "8c73fbf4bc3070318731ae24dc1cde6e41437863c6eff9981b30b563ab1cf1dd", + "query": "Yes, I'd like a reservation for 17:30.", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 529, + "historical_id": "fastbrain_016627", + "source": "sgd", + "query_hash": "8c73fbf4bc3070318731ae24dc1cde6e41437863c6eff9981b30b563ab1cf1dd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 529, + "historical_id": "fastbrain_016627", + "source": "sgd", + "query_hash": "8c73fbf4bc3070318731ae24dc1cde6e41437863c6eff9981b30b563ab1cf1dd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_016732", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1e10e0660c7d8308c943aa45ff098d71", + "normalized_query_sha256": "4f0a99d0ccd1b91604d31fc84d92717cd7d07537dc731822e8d4c5b5812dec5f", + "query": "there are many important event in this month note down that", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 901, + "historical_id": "semantic:validation:fastbrain_016732:absent", + "source": "amazon_massive", + "query_hash": "4f0a99d0ccd1b91604d31fc84d92717cd7d07537dc731822e8d4c5b5812dec5f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 935, + "historical_id": "semantic:validation:fastbrain_016732:present", + "source": "amazon_massive", + "query_hash": "4f0a99d0ccd1b91604d31fc84d92717cd7d07537dc731822e8d4c5b5812dec5f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_016741", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d7281af573c93757cce8ecd62d4fb3ea", + "normalized_query_sha256": "43089678a9b4ce2e9d0bc7f562014845e03b4c2b1093da2a6944a8e08d3b80c7", + "query": "Make directory \"~/public_html\"", + "target": "mkdir", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 791, + "historical_id": "fastbrain_016741", + "source": "nl2bash", + "query_hash": "43089678a9b4ce2e9d0bc7f562014845e03b4c2b1093da2a6944a8e08d3b80c7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 791, + "historical_id": "fastbrain_016741", + "source": "nl2bash", + "query_hash": "43089678a9b4ce2e9d0bc7f562014845e03b4c2b1093da2a6944a8e08d3b80c7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_016827", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:27fca9051f5fc0b3a0e2d89e4779bb36", + "normalized_query_sha256": "13971284763dcec301f2815bb166fd26f4cfba41e6e19fff6e1a7598d783b778", + "query": "olly turn on any song by tupac", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 500, + "historical_id": "semantic:validation:fastbrain_016827:present", + "source": "amazon_massive", + "query_hash": "13971284763dcec301f2815bb166fd26f4cfba41e6e19fff6e1a7598d783b778" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 966, + "historical_id": "semantic:validation:fastbrain_016827:absent", + "source": "amazon_massive", + "query_hash": "13971284763dcec301f2815bb166fd26f4cfba41e6e19fff6e1a7598d783b778" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_016937", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:19e47cd41f2a85332fb340d81ca49562", + "normalized_query_sha256": "11ef3a240ae98a263c6f2c14a931f10ebd3978fe80769c912386bc5639368c31", + "query": "when is it going to snow next", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 655, + "historical_id": "fastbrain_016937", + "source": "amazon_massive", + "query_hash": "11ef3a240ae98a263c6f2c14a931f10ebd3978fe80769c912386bc5639368c31" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 655, + "historical_id": "fastbrain_016937", + "source": "amazon_massive", + "query_hash": "11ef3a240ae98a263c6f2c14a931f10ebd3978fe80769c912386bc5639368c31" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017059", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:116707aa9805a4e5eacf78c663a757e9", + "normalized_query_sha256": "3b5552181274d2635d600b3129b55a8857cd5052f1ae25171248707f322cc964", + "query": "what is jeff's number", + "target": "email_querycontact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 9, + "historical_id": "fastbrain_017059", + "source": "amazon_massive", + "query_hash": "3b5552181274d2635d600b3129b55a8857cd5052f1ae25171248707f322cc964" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 9, + "historical_id": "fastbrain_017059", + "source": "amazon_massive", + "query_hash": "3b5552181274d2635d600b3129b55a8857cd5052f1ae25171248707f322cc964" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017081", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8fa576d3a68056db23d1b5bfc7a424dd", + "normalized_query_sha256": "6b01631cfb48b64a38d06207b9d6068c2cb26efdd2f67ebb48eee5ffd5508255", + "query": "what is trump's approval rating", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 269, + "historical_id": "fastbrain_017081", + "source": "amazon_massive", + "query_hash": "6b01631cfb48b64a38d06207b9d6068c2cb26efdd2f67ebb48eee5ffd5508255" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 269, + "historical_id": "fastbrain_017081", + "source": "amazon_massive", + "query_hash": "6b01631cfb48b64a38d06207b9d6068c2cb26efdd2f67ebb48eee5ffd5508255" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017109", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:14a61b05ebdc2ad3ed3ebb52b93ec771", + "normalized_query_sha256": "65aa1990486a0e26d765dea4aa85afb2a6beb0320d4f10ee0adca686bcdbc3d5", + "query": "please check my gmail for new mail", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 523, + "historical_id": "semantic:validation:fastbrain_017109:present", + "source": "amazon_massive", + "query_hash": "65aa1990486a0e26d765dea4aa85afb2a6beb0320d4f10ee0adca686bcdbc3d5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1008, + "historical_id": "semantic:validation:fastbrain_017109:absent", + "source": "amazon_massive", + "query_hash": "65aa1990486a0e26d765dea4aa85afb2a6beb0320d4f10ee0adca686bcdbc3d5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017173", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f609ad9c01bb8a8a4639a914ca1223b6", + "normalized_query_sha256": "8c7fb5701c5bbba8c3e726b580a8114c8d79d1ad4c5006a02065300a171bee22", + "query": "I want to check the weather condition in particular area. Do you help me?", + "target": "GetWeather", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 914, + "historical_id": "fastbrain_017173", + "source": "sgd", + "query_hash": "8c7fb5701c5bbba8c3e726b580a8114c8d79d1ad4c5006a02065300a171bee22" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 914, + "historical_id": "fastbrain_017173", + "source": "sgd", + "query_hash": "8c7fb5701c5bbba8c3e726b580a8114c8d79d1ad4c5006a02065300a171bee22" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017249", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:102c786d94e1f365f295d617d477ed21", + "normalized_query_sha256": "aa552a44c8ecb48f99d46da2f9a4505fb3c4349c40850fff99ccc69e2a9c4994", + "query": "show the log with author names and relative dates in the format hash author date subject", + "target": "git_log", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 500, + "historical_id": "fastbrain_017249", + "source": "git_nl_commands", + "query_hash": "aa552a44c8ecb48f99d46da2f9a4505fb3c4349c40850fff99ccc69e2a9c4994" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 500, + "historical_id": "fastbrain_017249", + "source": "git_nl_commands", + "query_hash": "aa552a44c8ecb48f99d46da2f9a4505fb3c4349c40850fff99ccc69e2a9c4994" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017309", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bd49d77b48d70d73b46cca43409ddf61", + "normalized_query_sha256": "896dedfc51be5e88da60d30422ea6b43d5ba213da6a88397ac1260c816eb1d50", + "query": "what is the name of the music playing now", + "target": "music_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 399, + "historical_id": "fastbrain_017309", + "source": "amazon_massive", + "query_hash": "896dedfc51be5e88da60d30422ea6b43d5ba213da6a88397ac1260c816eb1d50" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 399, + "historical_id": "fastbrain_017309", + "source": "amazon_massive", + "query_hash": "896dedfc51be5e88da60d30422ea6b43d5ba213da6a88397ac1260c816eb1d50" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017379", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ebe27100ef3be7ed86c685b3f2e30f5c", + "normalized_query_sha256": "a87089d037f1f42282910de5cb32bb987e2d9111c02f1d7a93a11107b1b61238", + "query": "Thank you! Could you also help me search for a movie in that area for show on that day?", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1001, + "historical_id": "fastbrain_017379", + "source": "sgd", + "query_hash": "a87089d037f1f42282910de5cb32bb987e2d9111c02f1d7a93a11107b1b61238" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1001, + "historical_id": "fastbrain_017379", + "source": "sgd", + "query_hash": "a87089d037f1f42282910de5cb32bb987e2d9111c02f1d7a93a11107b1b61238" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017433", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2a6b9a68bd00c32b76b030af703daf3d", + "normalized_query_sha256": "363bc85c9827a57963af4c2b00ca080db8d0bf9e516a6bf2a94208a0585658df", + "query": "s \n- Free rescheduling during red weather warnings \n- Equipment substitutions for weather compatibility \n- Partial refunds for >50% activity cancellations \n\n## Payment Handling \n- Maximum $1000/transaction via chat \n- Security hold = 25% of equipment value \n- Credits expire after 18 months \n\n## Membership Benefits \n| Tier | Benefits | \n|------------|----------| \n| Explorer | 5% discount, basic equipment | \n| Adventurer | 15% discount, premium gear, priority rescheduling | \n| Pioneer | 25% discount, gear insurance, guaranteed guides | \n\n## Agent Limitations \n- Do not answer questions or perform actions outside defined tool capabilities \n- Never assume verification or action completion without explicit tool confirmation \n- Cannot predict weather beyond official advisories \n- Do not override certification requirements \n- Cannot validate medical clearance documents \n\n## Transfer Protocol \nImmediately escalate using `transfer_to_human_agent` for: \n- Altitude illness concerns \n- Lost equipment investigations \n- Insurance claims \n- ADA compliance requests \n- Payment disputes >$500 \n\nTransfer message: \n\"YOU ARE BEING TRANSFERRED TO...[TRUNCATED]\nuser: \n\nHello, I'm reaching out regarding my booking with ID B11222 (email: mid.refund@mail.com) for a trip departing on November 10, 2025. I want to confirm the refund policy for cancellations made 30-60 days prior to departure. I understand it’s 50%, but could you please verify this applies to my case?", + "target": "verify_identity", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 994, + "historical_id": "fastbrain_017433", + "source": "nemotron_agentic", + "query_hash": "363bc85c9827a57963af4c2b00ca080db8d0bf9e516a6bf2a94208a0585658df" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 994, + "historical_id": "fastbrain_017433", + "source": "nemotron_agentic", + "query_hash": "363bc85c9827a57963af4c2b00ca080db8d0bf9e516a6bf2a94208a0585658df" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017539", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1180b78300b3e1a9b330da2eb22d3c21", + "normalized_query_sha256": "12e37fbc94b116910e417efee62cda597631a77d87f28f673cadd492751ea6be", + "query": "ion \n\n## Payment Terms \n- 50% deposit required for production start \n- Balance due before shipment \n- Gold-tier clients: Net-30 terms (≥$5k order history) \n\n## Tool Interaction Rules \n1. Always verify order status via tools before modifications \n2. Confirm file compliance via tools before production \n3. Calculate rush fees dynamically \n4. One tool call at a time \n\n## Escalation Protocol \nTransfer to print specialist for: \n- Copyright infringement claims \n- Press equipment failures \n- Bulk orders (>10k units) \n\n## Agent Limitations \n- **Do NOT answer questions or perform actions outside defined tool capabilities** \n- **NEVER assume verification or action completion without explicit tool confirmation** \n- Never describe unverified technical specifications \n- Do not recommend paper stocks beyond catalog options \n- Cannot override minimum quantities or binding constraints \n- Do not suggest design software \n- Cannot promise delivery dates without carrier integration\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm reaching out as an election security specialist coordinating print production for a high-stakes ballot project. We require blockchain-verified print logs to ensure immutable audit trails for all production batches. Does your printing service offer cryptographic verification systems beyond standard order tracking? Specifically, do you implement hash-based logging or distributed ledger technology to secure print run metadata?", + "target": "transfer_to_print_specialist", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 321, + "historical_id": "fastbrain_017539", + "source": "nemotron_agentic", + "query_hash": "12e37fbc94b116910e417efee62cda597631a77d87f28f673cadd492751ea6be" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 321, + "historical_id": "fastbrain_017539", + "source": "nemotron_agentic", + "query_hash": "12e37fbc94b116910e417efee62cda597631a77d87f28f673cadd492751ea6be" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017600", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:660746f92468d85956d74a7d1c06ad78", + "normalized_query_sha256": "df3481d87cad43a6dab316989835f66aafadc5281d41644f9cd7c338abcf6c3c", + "query": "i would like to start this new audiobook", + "target": "play_audiobook", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1014, + "historical_id": "fastbrain_017600", + "source": "amazon_massive", + "query_hash": "df3481d87cad43a6dab316989835f66aafadc5281d41644f9cd7c338abcf6c3c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1014, + "historical_id": "fastbrain_017600", + "source": "amazon_massive", + "query_hash": "df3481d87cad43a6dab316989835f66aafadc5281d41644f9cd7c338abcf6c3c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017605", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f90dc8a23641cc71096328939c97d2da", + "normalized_query_sha256": "af8c8f5c440dce17fd213a2993f7665a4cb35808a4c60c5aee8727fbac45bcb3", + "query": "what food is good", + "target": "cooking_recipe", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 499, + "historical_id": "fastbrain_017605", + "source": "amazon_massive", + "query_hash": "af8c8f5c440dce17fd213a2993f7665a4cb35808a4c60c5aee8727fbac45bcb3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 499, + "historical_id": "fastbrain_017605", + "source": "amazon_massive", + "query_hash": "af8c8f5c440dce17fd213a2993f7665a4cb35808a4c60c5aee8727fbac45bcb3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017611", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c2a944a4ced916035a202ef2a4e01395", + "normalized_query_sha256": "a50845944015a2084ede74631233389355e95f9dbef5449704389c92ae8b22a2", + "query": "te inspection \n7. Avoid unapproved maintenance schedule changes\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I’m reaching out regarding a heritage hardscape project I’m working on—a 1920s fountain restoration. As a member of the historic society, I’m concerned about the $150 compaction test fee. Could you clarify if historical preservation projects qualify for exemptions? My zip code is 94103.\nassistant: Hello! I'd be happy to help you with your heritage hardscape project question. First, I need to verify your identity to access your account information and provide accurate assistance.\n\nYou've mentioned your zip code is 94103. Could you please provide one of the following authentication methods:\n\n1. **Client ID** (along with your zip code)\n2. **Full name and registered phone number**\n3. **Email address and the last 4 digits of your project reference number**\n\nAlternatively, I can send a verification code to your email or phone number for authentication.\n\nOnce verified, I can look into your specific project details and address your question about the $150 compaction test fee for historical preservation projects.\nuser: Sure, here’s my verification: **Jane Smith** with the registered phone number **(415) 555-0123** and zip code **94103**. I’d appreciate confirmation if historical preservation projects like this 1920s fountain restoration qualify for fee exemptions.\nassistant: \ntool: \n\n```json\n{\n \"authenticated\": true,\n \"client_id\": \"C123456\"\n}\n```", + "target": "get_client_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1017, + "historical_id": "fastbrain_017611", + "source": "nemotron_agentic", + "query_hash": "a50845944015a2084ede74631233389355e95f9dbef5449704389c92ae8b22a2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1017, + "historical_id": "fastbrain_017611", + "source": "nemotron_agentic", + "query_hash": "a50845944015a2084ede74631233389355e95f9dbef5449704389c92ae8b22a2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017648", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:03a738ac8cf87638532177cd6e1602c3", + "normalized_query_sha256": "1576365ce8282ed491a53560355b01331862e45fc530cb1a3f56717ed67dded7", + "query": "refresh my email", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 131, + "historical_id": "semantic:validation:fastbrain_017648:absent", + "source": "amazon_massive", + "query_hash": "1576365ce8282ed491a53560355b01331862e45fc530cb1a3f56717ed67dded7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 801, + "historical_id": "semantic:validation:fastbrain_017648:present", + "source": "amazon_massive", + "query_hash": "1576365ce8282ed491a53560355b01331862e45fc530cb1a3f56717ed67dded7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017657", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:27d10c10ebaf2d8911625cde91fe09a3", + "normalized_query_sha256": "6987ef24f64c7932a4a2c631c8eea9db80442f93c90fe0da0529d0830c8aa155", + "query": "tell me about donald trumps early life", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 873, + "historical_id": "fastbrain_017657", + "source": "amazon_massive", + "query_hash": "6987ef24f64c7932a4a2c631c8eea9db80442f93c90fe0da0529d0830c8aa155" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 873, + "historical_id": "fastbrain_017657", + "source": "amazon_massive", + "query_hash": "6987ef24f64c7932a4a2c631c8eea9db80442f93c90fe0da0529d0830c8aa155" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017703", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:81ae0d1d1326701b3e01077d92e04d61", + "normalized_query_sha256": "419abe601fea1e205a88a65be50289255f8034a5cc38b928dbc2b8d0989b5278", + "query": "That is fantastic. I need a dentist in Los Gatos.", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 211, + "historical_id": "fastbrain_017703", + "source": "sgd", + "query_hash": "419abe601fea1e205a88a65be50289255f8034a5cc38b928dbc2b8d0989b5278" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 211, + "historical_id": "fastbrain_017703", + "source": "sgd", + "query_hash": "419abe601fea1e205a88a65be50289255f8034a5cc38b928dbc2b8d0989b5278" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_017794", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1cdd769e505aaac8c02a586bc8949c1e", + "normalized_query_sha256": "ded1b9a4e924dc808c5fa6e3d51e324d2ab43df3ced4ca3f46cf14b6d17a950c", + "query": "olly will it rain on saturday", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 199, + "historical_id": "semantic:validation:fastbrain_017794:present", + "source": "amazon_massive", + "query_hash": "ded1b9a4e924dc808c5fa6e3d51e324d2ab43df3ced4ca3f46cf14b6d17a950c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 342, + "historical_id": "semantic:validation:fastbrain_017794:absent", + "source": "amazon_massive", + "query_hash": "ded1b9a4e924dc808c5fa6e3d51e324d2ab43df3ced4ca3f46cf14b6d17a950c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018035", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:93bc36aeb57efc042333f0a3c3e35891", + "normalized_query_sha256": "6cae9b58177a68683126fb3d0ef68c72e2405e6e706dd3b6e1d218a9f8045c10", + "query": "Find me an attraction I can go visit.", + "target": "FindAttractions", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 332, + "historical_id": "fastbrain_018035", + "source": "sgd", + "query_hash": "6cae9b58177a68683126fb3d0ef68c72e2405e6e706dd3b6e1d218a9f8045c10" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 332, + "historical_id": "fastbrain_018035", + "source": "sgd", + "query_hash": "6cae9b58177a68683126fb3d0ef68c72e2405e6e706dd3b6e1d218a9f8045c10" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018058", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a04979edab9f991c80b1077610691406", + "normalized_query_sha256": "446bb3d8f1ade271718d508f3b24d14f10f261c79edcc7b556009ef96933e5b3", + "query": "work may not be eligible until you upgrade to Premium insurance.\n\n## **Next Steps for Insurance Claim**\n\n1. **Breach Duration Verification:** You'll need to request continuous monitoring data to determine if the September 12 and 25 breaches exceeded 2 hours, which would trigger incident reports.\n\n2. **Insurance Upgrade:** To qualify for compensation, you must upgrade to Premium insurance coverage for this unit.\n\n3. **Compensation Eligibility:** If breaches exceeded 2 hours:\n - Temperature excursions >2 hours: Initiate incident report\n - Humidity breaches >3 hours: 10% monthly fee compensation\n - Credit calculation: (incident hours / 720) × monthly rate\n\nWould you like me to:\n1. Retrieve continuous monitoring data to check breach durations?\n2. Assist with upgrading your insurance to Premium tier?\n3. Check if incident reports were generated for these dates?\nuser: Thank you for the detailed logs and clarification. To proceed, I need the continuous monitoring data for the two temperature breaches on **2023-09-12** and **2023-09-25** to verify if the excursions exceeded 2 hours. Please retrieve this data to determine if incident reports were generated. \n\nAdditionally, I’d like to upgrade the insurance coverage to Premium for Unit ART-77. Could you guide me through the process and confirm the effective date of the upgrade? \n\nLastly, if the breaches meet the duration threshold, please provide the incident reports and clarify the compensation calculation steps for the client.", + "target": "get_environmental_logs", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1003, + "historical_id": "fastbrain_018058", + "source": "nemotron_agentic", + "query_hash": "446bb3d8f1ade271718d508f3b24d14f10f261c79edcc7b556009ef96933e5b3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1003, + "historical_id": "fastbrain_018058", + "source": "nemotron_agentic", + "query_hash": "446bb3d8f1ade271718d508f3b24d14f10f261c79edcc7b556009ef96933e5b3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018118", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d952d14d5669cb654307ec922dc4182a", + "normalized_query_sha256": "de70c4c8b3b8f2eec159d05194d5fa7558587cdc3d19b694ec9a3814aea1199c", + "query": "n:** Signal maintenance affecting **southbound** trains between 96th St and 86th St (5-10 minute delays until ~12:00 PM)\n2. **A train:** Track work affecting **southbound** trains between 59th St and 72nd St (3-5 minute delays until ~1:30 PM)\n\n**Trip Planning:**\n- **Direct route:** Take the 1 train uptown from 59th St-Columbus Circle to 96th St (0 transfers, ~35 minutes estimated)\n- Since the delays are on **southbound** tracks, your **northbound** trip to 96th St may experience minimal impact, but there could be ripple effects.\n\n**Alternative Routes to 96th St:**\nIf the 1 train has significant delays, consider:\n1. **Take the B or C train to 72nd St**, then transfer to the 1 train\n2. **Take the A train to 125th St**, then take a downtown 1 train back to 96th St\n3. **Local bus options** (M7, M11) can get you close to 96th St\n\n**Best Time to Leave:** \nGiven the signal maintenance is expected to clear by 12:00 PM, if you can travel after that time, you'll likely encounter fewer delays. If you need to go sooner, allow extra 10-15 minutes buffer.\n\nWould you like me to check real-time arrivals specifically for northbound 1 trains, or help you plan an alternative route with transfers?\nuser: Let’s check real-time arrivals for **northbound 1 trains** at 59th St-Columbus Circle first. If those look too delayed, I’d appreciate help mapping out the **B/C train transfer option** to 72nd St. Also, do you know if the **M7 or M11 buses** run 24/7, or should I check their schedules for today?", + "target": "get_real_time_arrivals", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 308, + "historical_id": "fastbrain_018118", + "source": "nemotron_agentic", + "query_hash": "de70c4c8b3b8f2eec159d05194d5fa7558587cdc3d19b694ec9a3814aea1199c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 308, + "historical_id": "fastbrain_018118", + "source": "nemotron_agentic", + "query_hash": "de70c4c8b3b8f2eec159d05194d5fa7558587cdc3d19b694ec9a3814aea1199c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018136", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4705de98dcc7c143664cf95be866dbe3", + "normalized_query_sha256": "609c8e603ff9885842607809299d7bc0e5828a3d052132a1548a0c10d60bd296", + "query": "Recursively change ownership of \"~/.npm\" to the current user", + "target": "chown", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 494, + "historical_id": "fastbrain_018136", + "source": "nl2bash", + "query_hash": "609c8e603ff9885842607809299d7bc0e5828a3d052132a1548a0c10d60bd296" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 494, + "historical_id": "fastbrain_018136", + "source": "nl2bash", + "query_hash": "609c8e603ff9885842607809299d7bc0e5828a3d052132a1548a0c10d60bd296" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018191", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:38b368b6d6fdbb581b69ac8f452d89b7", + "normalized_query_sha256": "cece05a8cc4d8beb17fde3282a5227a99cd219a0c939da0c2d3f09ad30c4cf30", + "query": "I like it and thanks. Now, help me to book the hotel that you have found for me from the date on next Tuesday.", + "target": "ReserveHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 575, + "historical_id": "fastbrain_018191", + "source": "sgd", + "query_hash": "cece05a8cc4d8beb17fde3282a5227a99cd219a0c939da0c2d3f09ad30c4cf30" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 575, + "historical_id": "fastbrain_018191", + "source": "sgd", + "query_hash": "cece05a8cc4d8beb17fde3282a5227a99cd219a0c939da0c2d3f09ad30c4cf30" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018219", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f31ba4dc18d5d2f7e346eea30a2c15c7", + "normalized_query_sha256": "0465866006bcdce6972c0f7cbe87e3effac4fea0f3a4848e82f3c366816cb503", + "query": "discard all my local modifications", + "target": "git_reset", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 312, + "historical_id": "fastbrain_018219", + "source": "git_nl_commands", + "query_hash": "0465866006bcdce6972c0f7cbe87e3effac4fea0f3a4848e82f3c366816cb503" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 312, + "historical_id": "fastbrain_018219", + "source": "git_nl_commands", + "query_hash": "0465866006bcdce6972c0f7cbe87e3effac4fea0f3a4848e82f3c366816cb503" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018267", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6ec33b533231d4cfa84d0bf9c5207e23", + "normalized_query_sha256": "0abdd864f703f04fc9dbe35ac1a6f3197cbc4e82fe8b62a952fa7cfe2764f047", + "query": "open remainder and find meeting time", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 47, + "historical_id": "semantic:validation:fastbrain_018267:present", + "source": "amazon_massive", + "query_hash": "0abdd864f703f04fc9dbe35ac1a6f3197cbc4e82fe8b62a952fa7cfe2764f047" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 66, + "historical_id": "semantic:validation:fastbrain_018267:absent", + "source": "amazon_massive", + "query_hash": "0abdd864f703f04fc9dbe35ac1a6f3197cbc4e82fe8b62a952fa7cfe2764f047" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018328", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d6d7ce7db37bcb7e7dd4531b08119c1f", + "normalized_query_sha256": "08e47d4086615eae64d0040eaaefee86fc31399d2976a9b7545c8f5028a68b9b", + "query": "72hrs before review: 50% \n- After filing: Non-refundable \n\n## Agent Limitations \n- Do NOT answer questions or perform actions outside defined tool capabilities \n- NEVER assume verification or action completion without explicit tool confirmation \n- Do NOT override jurisdictional requirements \n- Do NOT provide legal advice - state: \"Consult licensed counsel for interpretation\" \n\n## Transfer Protocol \nTransfer to human agent for: \n- Document interpretation requests \n- Dispute resolutions \n- Complex jurisdictional questions \n- Suspected fraudulent activities \n\nTransfer process: \n1. Execute `transfer_to_human_agent` tool \n2. Send message: \"YOU ARE BEING TRANSFERRED TO A DOCUMENT SPECIALIST. PLEASE DESCRIBE YOUR CONCERN.\" \n\n## Escalation Triggers \nTransfer required for: \n- Manual redlining requests \n- Confidentiality agreement breaches \n- Multi-jurisdictional conflicts \n- Urgent deadlines (<24hrs) with processing failures \n- Litigation-related requests\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm reaching out regarding Document ID INC-888 filed in Canada. I'm trying to understand if my current Basic subscription (User ID GLOBAL22) qualifies for international filing services. Could you clarify the subscription requirements for this?\nassistant: \ntool: \n\n{\"email\": \"global22@example.com\", \"phone\": \"+14155550123\", \"subscription_tier\": \"Basic\", \"payment_methods\": [\"credit_card_ending_in_1234\", \"PayPal_global22\"], \"document_count\": 1}", + "target": "get_document_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 664, + "historical_id": "fastbrain_018328", + "source": "nemotron_agentic", + "query_hash": "08e47d4086615eae64d0040eaaefee86fc31399d2976a9b7545c8f5028a68b9b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 664, + "historical_id": "fastbrain_018328", + "source": "nemotron_agentic", + "query_hash": "08e47d4086615eae64d0040eaaefee86fc31399d2976a9b7545c8f5028a68b9b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018334", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a9b025e9304ab4f9274a7cd298eb9548", + "normalized_query_sha256": "5ed3b464b124f543e5bea25b6d6029cfdd168163df7c43876d0c55d679142ba6", + "query": "y>\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I received a delivery of refrigerated milk (1/2 gallon) today, and the temperature read 45°F when I checked it. Since your policy allows instant refunds for temperature deviations without requiring evidence, I’d like to report this issue and request a refund. Can you process that for me?\nassistant: Hello! I'd be happy to help you with the temperature deviation issue for your refrigerated milk. First, I need to verify your identity to access your account and process the refund.\n\n**Authentication Steps:**\n1. **Please provide your contact information** - You can choose either:\n - Your Customer ID and PIN\n OR\n - Your registered phone number\n\n2. Once you provide this, I'll send a verification code if you choose the phone method, or verify your ID/PIN directly.\n\n3. After verification, I'll need your order ID to process the refund for the milk.\n\nCould you please share your preferred verification method and the necessary information?\nuser: My registered phone number is 555-123-4567. Please send the verification code. My order ID is #ORDER123456.\nassistant: \ntool: \n\n{\"success\": true}\nassistant: I've sent a verification code to your phone number 555-123-4567. Please provide the code you received so I can verify your identity and proceed with processing your refund for order #ORDER123456.\nuser: The verification code I received is 123456. I’ve double-checked my order ID: #ORDER123456. Please proceed with the refund.", + "target": "verify_user_identity", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 197, + "historical_id": "fastbrain_018334", + "source": "nemotron_agentic", + "query_hash": "5ed3b464b124f543e5bea25b6d6029cfdd168163df7c43876d0c55d679142ba6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 197, + "historical_id": "fastbrain_018334", + "source": "nemotron_agentic", + "query_hash": "5ed3b464b124f543e5bea25b6d6029cfdd168163df7c43876d0c55d679142ba6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018339", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f13f0a7fb6de1e9f70735cd3f30cfec7", + "normalized_query_sha256": "ac5c7e14d061dd027a44e3f21ef0eb2d0ea5276f3f0b1c83a58b5890324cccf9", + "query": "Yup, Please can you help me to find some good cheap priced Restaurant that will provide Soup & Salad?", + "target": "FindRestaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 650, + "historical_id": "fastbrain_018339", + "source": "sgd", + "query_hash": "ac5c7e14d061dd027a44e3f21ef0eb2d0ea5276f3f0b1c83a58b5890324cccf9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 650, + "historical_id": "fastbrain_018339", + "source": "sgd", + "query_hash": "ac5c7e14d061dd027a44e3f21ef0eb2d0ea5276f3f0b1c83a58b5890324cccf9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018527", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:15cc1de9fde3304273ee516997b8c897", + "normalized_query_sha256": "6f21de68ed53ae54f8b999e4a67098d4b1cacd339b0f815490efafff9c23638c", + "query": "I need you to call me a cab?", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 888, + "historical_id": "fastbrain_018527", + "source": "sgd", + "query_hash": "6f21de68ed53ae54f8b999e4a67098d4b1cacd339b0f815490efafff9c23638c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 888, + "historical_id": "fastbrain_018527", + "source": "sgd", + "query_hash": "6f21de68ed53ae54f8b999e4a67098d4b1cacd339b0f815490efafff9c23638c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_018679", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:13ea61850123a99a2172dee488cc4c2a", + "normalized_query_sha256": "8651b97893071c0f1b1eba363acc183f96702eb065c6a90f5095ba3dc1f7e7e2", + "query": "Execute /usr/bin/find with $* arguments where current directory is the first argument", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 850, + "historical_id": "fastbrain_018679", + "source": "nl2bash", + "query_hash": "8651b97893071c0f1b1eba363acc183f96702eb065c6a90f5095ba3dc1f7e7e2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 850, + "historical_id": "fastbrain_018679", + "source": "nl2bash", + "query_hash": "8651b97893071c0f1b1eba363acc183f96702eb065c6a90f5095ba3dc1f7e7e2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019002", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a7d0e56d9394bb30fe8734a248fae22b", + "normalized_query_sha256": "51a2307c323888d2e3368bf2973c6043f9caca55ab11db2a7d6d51e72f92b7b0", + "query": "fetch the latest changes from origin", + "target": "git_fetch", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 350, + "historical_id": "fastbrain_019002", + "source": "git_nl_commands", + "query_hash": "51a2307c323888d2e3368bf2973c6043f9caca55ab11db2a7d6d51e72f92b7b0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 350, + "historical_id": "fastbrain_019002", + "source": "git_nl_commands", + "query_hash": "51a2307c323888d2e3368bf2973c6043f9caca55ab11db2a7d6d51e72f92b7b0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019019", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5953a4975e85c87d3e931e21a6f09b43", + "normalized_query_sha256": "2057e5fccb327e3d601049f4d15c75cd4aab1affdf4e311513393987b9806227", + "query": "what is in the staging area right now", + "target": "git_diff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 625, + "historical_id": "fastbrain_019019", + "source": "git_nl_commands", + "query_hash": "2057e5fccb327e3d601049f4d15c75cd4aab1affdf4e311513393987b9806227" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 625, + "historical_id": "fastbrain_019019", + "source": "git_nl_commands", + "query_hash": "2057e5fccb327e3d601049f4d15c75cd4aab1affdf4e311513393987b9806227" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019077", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8660389d6ea2631ab3e14740b14b9aac", + "normalized_query_sha256": "bf034dcc8386894a0f40fa2cfb3bb1fc5e4bb11d43c1899c68b8fae533c87241", + "query": "\n## Compliance & Safety \n- Re-verify age every 120 days \n- Delivery hours: 9AM-7PM local time \n- No unattended drop-offs \n- Pre-authorize all payments \n\n---\n\n## Issue Resolution \n\n### Damaged Goods \n- Require unboxing video within 30min \n- Issue credits (not cash) \n- **Before refund processing**: \n *\"Please confirm the package seal was intact.\"* \n\n### Wrong Items \n- Replacements under $50 without return \n- Expedited shipping for errors \n\n---\n\n## Transfer Protocol \n**Immediately transfer to human agent for:** \n- Suspected fraud \n- Pairing recommendations \n- Custom import requests \n- Recurring temperature issues \n**Transfer message**: \"YOU ARE BEING TRANSFERRED TO A SPECIALIST AGENT. PLEASE DESCRIBE YOUR CONCERN.\" \n\n---\n\n## Agent Limitations \n1. **Only perform actions supported by available tools** \n2. **Never assume completion** - Require explicit tool confirmation for verifications/actions \n3. Never discuss food pairings or serving temperatures \n4. Never modify payments post-delivery \n5. Never bypass age verification \n6. Never share unverified delivery time estimates\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling to cancel my order #VD11223 with ZIP code 10001. The delivery was scheduled for November 20th, and I'm placing this request 50 hours in advance. I'd like to confirm if a full refund can be processed per your cancellation policy.\nassistant: \ntool: \n\n{\n \"authenticated\": true,\n \"user_id\": \"USER12345\"\n}", + "target": "cancel_order", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 605, + "historical_id": "fastbrain_019077", + "source": "nemotron_agentic", + "query_hash": "bf034dcc8386894a0f40fa2cfb3bb1fc5e4bb11d43c1899c68b8fae533c87241" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 605, + "historical_id": "fastbrain_019077", + "source": "nemotron_agentic", + "query_hash": "bf034dcc8386894a0f40fa2cfb3bb1fc5e4bb11d43c1899c68b8fae533c87241" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019385", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9befd52cfab1737a79fca8176acda50f", + "normalized_query_sha256": "e5a309181e16a4b74b7e59fca332c8489f53306c8b153983d44495341a9b79a1", + "query": "what is a good taco recipe", + "target": "cooking_recipe", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 280, + "historical_id": "semantic:validation:fastbrain_019385:absent", + "source": "amazon_massive", + "query_hash": "e5a309181e16a4b74b7e59fca332c8489f53306c8b153983d44495341a9b79a1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 508, + "historical_id": "semantic:validation:fastbrain_019385:present", + "source": "amazon_massive", + "query_hash": "e5a309181e16a4b74b7e59fca332c8489f53306c8b153983d44495341a9b79a1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019437", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:757a96c3c2c31fc98d08a7c0ffa0deb6", + "normalized_query_sha256": "5288dd9cfdecba6432438caa0f7909f38a5b46ad3be4857ddc674a322d812df3", + "query": "cation** \n- After membership change: \n   *\"Confirm your new benefits appear correctly in your account.\"* \n- Post-refund processing: \n   *\"Verify receipt within 5 business days.\"* \n\n---\n\n### **Refund Policy** \n- Weather cancellations: Full refund or 120% reschedule credit \n- Artist cancellations: Automatic refund processing \n- Non-refundable: Premium tickets, charity events \n\n---\n\n### **Merchandise** \n- Pre-orders: Must collect by intermission \n- Damaged items: Provide photo proof within 1 hour \n\n---\n\n### **Agent Limitations** \n- Never answer questions requiring unavailable data (e.g., artist contracts) \n- Never assume completion of actions without system confirmation \n- Never override system restrictions (age gates, transfer locks) \n- Only execute functions within defined capabilities \n- Never claim verification without explicit system validation \n\n---\n\n### **Escalation Protocol** \nTransfer to human agent for: \n1. Physical accessibility assessments \n2. Fraud investigations \n3. Group bookings >15 tick...[TRUNCATED]\nuser: \n\nHello, I need assistance transferring my tickets for the Oct 30 comedy night. My booking ID is HH135790 under Patel. I’d like to switch the attendee to a friend, but I don’t have their email on hand. Can you guide me through this process?\nassistant: \ntool: \n\n{\"authenticated\": true, \"guest_token\": \"GUEST-7X9B2LQ\"}\nassistant: \ntool: \n\n{\"event_ids\": [\"EVENT-1030-COMEDY\"], \"seats\": [\"A12\", \"A13\"], \"status\": \"Reserved\"}", + "target": "get_event_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 108, + "historical_id": "fastbrain_019437", + "source": "nemotron_agentic", + "query_hash": "5288dd9cfdecba6432438caa0f7909f38a5b46ad3be4857ddc674a322d812df3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 108, + "historical_id": "fastbrain_019437", + "source": "nemotron_agentic", + "query_hash": "5288dd9cfdecba6432438caa0f7909f38a5b46ad3be4857ddc674a322d812df3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019549", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:79c26894b6f908775e83992dca1dea93", + "normalized_query_sha256": "79e9080842fedce045c09d0da560d840c2057fd4ceb0f7c7805466ffc5e90c03", + "query": "hey olly turn off the light please", + "target": "iot_hue_lightoff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 118, + "historical_id": "semantic:validation:fastbrain_019549:present", + "source": "amazon_massive", + "query_hash": "79e9080842fedce045c09d0da560d840c2057fd4ceb0f7c7805466ffc5e90c03" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 209, + "historical_id": "semantic:validation:fastbrain_019549:absent", + "source": "amazon_massive", + "query_hash": "79e9080842fedce045c09d0da560d840c2057fd4ceb0f7c7805466ffc5e90c03" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019553", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0f228f71e4d04da4e065e27ec3126339", + "normalized_query_sha256": "67a5ed646d40c9f91a21cfc78dc02009a7723d382d7ec1fe1f6be004b584a2f2", + "query": "Thanks. Now let's place a reservation for this house.", + "target": "BookHouse", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 884, + "historical_id": "fastbrain_019553", + "source": "sgd", + "query_hash": "67a5ed646d40c9f91a21cfc78dc02009a7723d382d7ec1fe1f6be004b584a2f2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 884, + "historical_id": "fastbrain_019553", + "source": "sgd", + "query_hash": "67a5ed646d40c9f91a21cfc78dc02009a7723d382d7ec1fe1f6be004b584a2f2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019628", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:20777761e1dcc41b578d7686d3f7d970", + "normalized_query_sha256": "aa74de208613b8539313f2322df3f25e78890cd7052b377e5df676246b2ddcec", + "query": "show me my list", + "target": "lists_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 29, + "historical_id": "semantic:validation:fastbrain_019628:absent", + "source": "amazon_massive", + "query_hash": "aa74de208613b8539313f2322df3f25e78890cd7052b377e5df676246b2ddcec" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 682, + "historical_id": "semantic:validation:fastbrain_019628:present", + "source": "amazon_massive", + "query_hash": "aa74de208613b8539313f2322df3f25e78890cd7052b377e5df676246b2ddcec" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019701", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f080e8ca3a4574677f8e3918bf61b68d", + "normalized_query_sha256": "b34c2f94ab0f9a05a70505d95e129caecad8618551f798cf196424e92ebc21b5", + "query": "Can you help me extract queries from the following passage or insane, so we won't discuss it more here. For now, just realize that every set is either finite or infinite. \nYou might think, by the way, that there's no way to define an infinite set extensionally, since that would require infinite paper. This isn't true, though, if we creatively use an ellipsis: \n I = { 3 , 6 , 9 , 12 , 15 ", + "target": "ExpertQAExtractor", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 742, + "historical_id": "fastbrain_019701", + "source": "hermes_v1", + "query_hash": "b34c2f94ab0f9a05a70505d95e129caecad8618551f798cf196424e92ebc21b5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 742, + "historical_id": "fastbrain_019701", + "source": "hermes_v1", + "query_hash": "b34c2f94ab0f9a05a70505d95e129caecad8618551f798cf196424e92ebc21b5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019807", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:63939c1091c0e8b2155f5f175420d9ae", + "normalized_query_sha256": "2979ec98549b5c394814897d242cf45adec06171be605f86f8fba12bc8a69632", + "query": "or charge + restocking fee \n\n### Prohibited Actions \n- Scheduling without verified hangar availability \n- Overriding FAA inspection intervals \n- Approving non-conforming part installations \n- Issuing airworthiness certificates without tool verification \n\n### Agent Limitations \n- **Action Boundaries**: \n - Do not answer questions or perform actions outside defined tool capabilities \n - Never assume verification/action completion without explicit tool confirmation \n- **Verification Protocol**: \n - Require explicit proof (photos/logs) for claims \n - Document compliance checks via tool executions \n\n### Transfer Protocol \nTransfer to human agents for: \n- Accident investigations \n- Insurance claim coordination \n- Regulatory audits \nProcess: \n1. Use transfer_to_human_agents tool \n2. Send message: \"YOU ARE BEING TRANSFERRED TO A HUMAN TECHNICAL SPECIALIST. PLEASE HOLD ON.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm a fire protection specialist with Client ID FIREPROT for the private jet N777FP. I need to review the maintenance records for Fire System #SYS-444. Could you please provide the service history for this system, including the last hydrostatic test date? Additionally, I need to confirm if all technician certifications for this system meet current FAA compliance standards.\nassistant: \ntool: \n\n{\"authorization_token\": \"TOK-FP777\", \"client_name\": \"Aviation Fire Solutions Inc\", \"tail_numbers\": [\"N777FP\", \"N777FP-ALT\"]}", + "target": "retrieve_service_records", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 101, + "historical_id": "fastbrain_019807", + "source": "nemotron_agentic", + "query_hash": "2979ec98549b5c394814897d242cf45adec06171be605f86f8fba12bc8a69632" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 101, + "historical_id": "fastbrain_019807", + "source": "nemotron_agentic", + "query_hash": "2979ec98549b5c394814897d242cf45adec06171be605f86f8fba12bc8a69632" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_019916", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:80d4d25d9b2f43551a1291e664b0c903", + "normalized_query_sha256": "455f74b4ec9e4d7927a5110a7f6baf9329adcf0857418681b7ccc3860981dcb0", + "query": "Hi, I am looking for a book but I can't remember the name. The author's name is George Orwell and it's a dystopian novel.", + "target": "search_books", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 726, + "historical_id": "fastbrain_019916", + "source": "glaive_v2", + "query_hash": "455f74b4ec9e4d7927a5110a7f6baf9329adcf0857418681b7ccc3860981dcb0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 726, + "historical_id": "fastbrain_019916", + "source": "glaive_v2", + "query_hash": "455f74b4ec9e4d7927a5110a7f6baf9329adcf0857418681b7ccc3860981dcb0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_020033", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:42906accb883e943855025b0fd890a62", + "normalized_query_sha256": "2093b2441f804a5ba73e26bd2298051624dd03653cb743fdb28830409b54ac25", + "query": "owing the new due date.\" \n\nBefore redemption processing: \n> \"Have you verified your pawn ticket matches the item ID in our records?\" \n\n## Payment Processing \n- Allocation priority: \n 1. Late fees \n 2. Interest \n 3. Principal \n- Cash payments limited to $999/day \n\n## Escalation Protocol \nTransfer to human agent for: \n- Firearm redemptions \n- High-value disputes (>$5,000) \n- Suspected identity theft \n- Multi-state regulatory inquiries \n\nTransfer process: \n1. Activate transfer tool \n2. Display: \"TRANSFERRING YOU TO A HUMAN AGENT. PLEASE HOLD.\" \n\n## Agent Limitations \n- **Capability Boundaries** \n - Only use provided tools for all actions \n - Never perform actions outside defined tool functions \n - Decline investment/legal/tax advice requests \n\n- **Verification Protocol** \n - Assume NO action completed until tool confirmation received \n - Validate all information through retrieval tools \n - Never reference historical actions without explicit tool verification \n\n- **Interaction Boundaries** \n - Terminate conversations involving illegal activities \n - Cease interaction if unable to resolve after 3 attempts\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm a Gold tier jewelry store owner. I noticed your store is pricing item J-9900 at 70% of the guideline. Can you explain how you determine where in the 60-80% range you set prices? I'm seeing some competitors price it lower, so I want to understand your strategy better.", + "target": "get_item_appraisal", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 504, + "historical_id": "fastbrain_020033", + "source": "nemotron_agentic", + "query_hash": "2093b2441f804a5ba73e26bd2298051624dd03653cb743fdb28830409b54ac25" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 504, + "historical_id": "fastbrain_020033", + "source": "nemotron_agentic", + "query_hash": "2093b2441f804a5ba73e26bd2298051624dd03653cb743fdb28830409b54ac25" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_020068", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3205d9559ce065c6127e8b473bebbdf0", + "normalized_query_sha256": "02158f7cb6f61427585047374f754cd1b87ea4604f5ed039540d81bf2d9a6217", + "query": "Attempt to connect as root via ssh to host \"IP\" and copy /root/K to local machine, passing option \"StrictHostKeyChecking=no\" to the ssh backend - this will normally fail because ssh servers by default don't (and shouldn't) accept root logins.", + "target": "scp", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 277, + "historical_id": "fastbrain_020068", + "source": "nl2bash", + "query_hash": "02158f7cb6f61427585047374f754cd1b87ea4604f5ed039540d81bf2d9a6217" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 277, + "historical_id": "fastbrain_020068", + "source": "nl2bash", + "query_hash": "02158f7cb6f61427585047374f754cd1b87ea4604f5ed039540d81bf2d9a6217" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_020216", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7bd1cd2c4e1059c9bc4af2defc714918", + "normalized_query_sha256": "b904f54c915e3c12a22aeccf4b464307e90933e6b5ef847eda850ee96283e1d2", + "query": "cancel all events in the next two days", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 222, + "historical_id": "fastbrain_020216", + "source": "amazon_massive", + "query_hash": "b904f54c915e3c12a22aeccf4b464307e90933e6b5ef847eda850ee96283e1d2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 222, + "historical_id": "fastbrain_020216", + "source": "amazon_massive", + "query_hash": "b904f54c915e3c12a22aeccf4b464307e90933e6b5ef847eda850ee96283e1d2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_020286", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5589d0329217da8c7e4f6d7ae7180e27", + "normalized_query_sha256": "8ee111d82ade79531b01db40d850ea725765689b720ba46d1dade5e9a655f323", + "query": "Great. I need a cab to take me there.", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 4, + "historical_id": "fastbrain_020286", + "source": "sgd", + "query_hash": "8ee111d82ade79531b01db40d850ea725765689b720ba46d1dade5e9a655f323" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 4, + "historical_id": "fastbrain_020286", + "source": "sgd", + "query_hash": "8ee111d82ade79531b01db40d850ea725765689b720ba46d1dade5e9a655f323" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_020408", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e5768a5222cc73ceccbb45d8f25740c2", + "normalized_query_sha256": "a90fd2b0f2feb58c5991c3634903d439853b27bc52e2283757557a353b08ed6e", + "query": "I need to create an invoice for a customer named John Doe. He bought 2 units of Product A for $50 each and 3 units of Product B for $30 each.", + "target": "create_invoice", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 785, + "historical_id": "fastbrain_020408", + "source": "glaive_v2", + "query_hash": "a90fd2b0f2feb58c5991c3634903d439853b27bc52e2283757557a353b08ed6e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 785, + "historical_id": "fastbrain_020408", + "source": "glaive_v2", + "query_hash": "a90fd2b0f2feb58c5991c3634903d439853b27bc52e2283757557a353b08ed6e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_020480", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f812f9ff8d196b2711856541773f19eb", + "normalized_query_sha256": "5f9589548f83b7a08664b359a559abcd0652272138623485803d2b2ff773862d", + "query": "Hi, could you help me check the weather in Portland, OR please?", + "target": "GetWeather", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1006, + "historical_id": "fastbrain_020480", + "source": "sgd", + "query_hash": "5f9589548f83b7a08664b359a559abcd0652272138623485803d2b2ff773862d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1006, + "historical_id": "fastbrain_020480", + "source": "sgd", + "query_hash": "5f9589548f83b7a08664b359a559abcd0652272138623485803d2b2ff773862d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_020750", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3bbaa216c29dfea504386d14face6ca9", + "normalized_query_sha256": "cd04c0a46e6240490b4053ad7dcc1cc7259f57e5da6b8756c921a02b85011f6e", + "query": "I'm looking for three tickets to an event in San Francisco.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 554, + "historical_id": "fastbrain_020750", + "source": "sgd", + "query_hash": "cd04c0a46e6240490b4053ad7dcc1cc7259f57e5da6b8756c921a02b85011f6e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 554, + "historical_id": "fastbrain_020750", + "source": "sgd", + "query_hash": "cd04c0a46e6240490b4053ad7dcc1cc7259f57e5da6b8756c921a02b85011f6e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_020971", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4ab0d73273e575cf4af216233b239f35", + "normalized_query_sha256": "ab0b862c221889b95c9ab9377e351b6bf4d3f1f15a22334e229387998dbbebca", + "query": "stage the remaining changes interactively", + "target": "git_add", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 328, + "historical_id": "fastbrain_020971", + "source": "git_nl_commands", + "query_hash": "ab0b862c221889b95c9ab9377e351b6bf4d3f1f15a22334e229387998dbbebca" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 328, + "historical_id": "fastbrain_020971", + "source": "git_nl_commands", + "query_hash": "ab0b862c221889b95c9ab9377e351b6bf4d3f1f15a22334e229387998dbbebca" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021371", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c542c7f37319fcd9c94aa73498c343e3", + "normalized_query_sha256": "0bfd87d8c2fb1b0b33f955bfb80d17d833c256ce0d8325db3be3c841cd3d5308", + "query": "name the capital of nigeria", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 297, + "historical_id": "semantic:validation:fastbrain_021371:absent", + "source": "amazon_massive", + "query_hash": "0bfd87d8c2fb1b0b33f955bfb80d17d833c256ce0d8325db3be3c841cd3d5308" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 537, + "historical_id": "semantic:validation:fastbrain_021371:present", + "source": "amazon_massive", + "query_hash": "0bfd87d8c2fb1b0b33f955bfb80d17d833c256ce0d8325db3be3c841cd3d5308" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021468", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:13c723168ca17f4237d1126a16e3ccda", + "normalized_query_sha256": "f2c79258f50dacc805c196441a104ae4f3565eb456e7ed5d6323350b14416778", + "query": "uber me to the airport", + "target": "transport_taxi", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 147, + "historical_id": "semantic:validation:fastbrain_021468:present", + "source": "amazon_massive", + "query_hash": "f2c79258f50dacc805c196441a104ae4f3565eb456e7ed5d6323350b14416778" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 165, + "historical_id": "semantic:validation:fastbrain_021468:absent", + "source": "amazon_massive", + "query_hash": "f2c79258f50dacc805c196441a104ae4f3565eb456e7ed5d6323350b14416778" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021563", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0669a7b68acecbc94ed5f3bf09f3b415", + "normalized_query_sha256": "7c5196781aecf9f33f288f7816acda29907defa1d1cfca815df874bd49318475", + "query": "tell me what reminders i have for today", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 802, + "historical_id": "fastbrain_021563", + "source": "amazon_massive", + "query_hash": "7c5196781aecf9f33f288f7816acda29907defa1d1cfca815df874bd49318475" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 802, + "historical_id": "fastbrain_021563", + "source": "amazon_massive", + "query_hash": "7c5196781aecf9f33f288f7816acda29907defa1d1cfca815df874bd49318475" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021570", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3d8e52db05abacb43cb4d7cb894c207e", + "normalized_query_sha256": "16f416e80637594955dd91f993c162888da2b2b094f2728ab65529a44a72d063", + "query": "what reminders do i have pending", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 210, + "historical_id": "semantic:validation:fastbrain_021570:absent", + "source": "amazon_massive", + "query_hash": "16f416e80637594955dd91f993c162888da2b2b094f2728ab65529a44a72d063" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 825, + "historical_id": "semantic:validation:fastbrain_021570:present", + "source": "amazon_massive", + "query_hash": "16f416e80637594955dd91f993c162888da2b2b094f2728ab65529a44a72d063" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021659", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f29ce7164331f07d20f7e7dd3d42b3d4", + "normalized_query_sha256": "cdac4d242e2f5bcd47cd404fc77f882fa2e125ebd6ba2963b16f306b3d89b2fc", + "query": "Yes, on the 13th of March please.", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 35, + "historical_id": "fastbrain_021659", + "source": "sgd", + "query_hash": "cdac4d242e2f5bcd47cd404fc77f882fa2e125ebd6ba2963b16f306b3d89b2fc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 35, + "historical_id": "fastbrain_021659", + "source": "sgd", + "query_hash": "cdac4d242e2f5bcd47cd404fc77f882fa2e125ebd6ba2963b16f306b3d89b2fc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021722", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2ce7a6217932fea75f36b543fbea2308", + "normalized_query_sha256": "cd805f20e618da5e6ed32a30892bd5824afaa947e01c3bab92c532770e0e2cae", + "query": "i need all the local events for this area that are being held this weekend", + "target": "recommendation_events", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 604, + "historical_id": "semantic:validation:fastbrain_021722:absent", + "source": "amazon_massive", + "query_hash": "cd805f20e618da5e6ed32a30892bd5824afaa947e01c3bab92c532770e0e2cae" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 927, + "historical_id": "semantic:validation:fastbrain_021722:present", + "source": "amazon_massive", + "query_hash": "cd805f20e618da5e6ed32a30892bd5824afaa947e01c3bab92c532770e0e2cae" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021751", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ba843fa58d17e4a06735886497187256", + "normalized_query_sha256": "49bff204d522088bb7678c70e50bf036a1af5206ecf64aa024d02b4109a928e4", + "query": "Please search in New York City for kid-friendly places to visist. I would prefer to visit a historical spot.", + "target": "FindAttractions", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 96, + "historical_id": "fastbrain_021751", + "source": "sgd", + "query_hash": "49bff204d522088bb7678c70e50bf036a1af5206ecf64aa024d02b4109a928e4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 96, + "historical_id": "fastbrain_021751", + "source": "sgd", + "query_hash": "49bff204d522088bb7678c70e50bf036a1af5206ecf64aa024d02b4109a928e4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021774", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dda808da0ece05c7910f2619e8c2c904", + "normalized_query_sha256": "122db7047acd082084fb49197b8b96b4d64b909cbcc2796a24b5f984121facb6", + "query": "i like my robot to show me images or graphics", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 37, + "historical_id": "semantic:validation:fastbrain_021774:absent", + "source": "amazon_massive", + "query_hash": "122db7047acd082084fb49197b8b96b4d64b909cbcc2796a24b5f984121facb6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 768, + "historical_id": "semantic:validation:fastbrain_021774:present", + "source": "amazon_massive", + "query_hash": "122db7047acd082084fb49197b8b96b4d64b909cbcc2796a24b5f984121facb6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021779", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a4eb800df38f15c73266f9966d0f3f43", + "normalized_query_sha256": "8fe6a7c83d253baf0ce7b4bc3a4e3db6c8b31a6cad6a9954497eccb4e44262b9", + "query": "That sounds good to me, reserve the hotel you found with a check in date on Monday next week.", + "target": "ReserveHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 636, + "historical_id": "fastbrain_021779", + "source": "sgd", + "query_hash": "8fe6a7c83d253baf0ce7b4bc3a4e3db6c8b31a6cad6a9954497eccb4e44262b9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 636, + "historical_id": "fastbrain_021779", + "source": "sgd", + "query_hash": "8fe6a7c83d253baf0ce7b4bc3a4e3db6c8b31a6cad6a9954497eccb4e44262b9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021862", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5fd5354fc1a29a2af90f1ea5263de26f", + "normalized_query_sha256": "6feac72478fcf0baf962f496e0a112e7480534d4303f8008c189b5594230f44f", + "query": "ethodologies \n- Never extend credit expiration dates\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I’m reaching out regarding a discrepancy in our credit-to-hour conversion. Our contract states that 100 credits equal 100 hours, but we were recently charged 15 credits for a 10-hour B1-level language course (company ID: MFG-9872). Could you clarify why this course required 1.5 credits per hour instead of 1:1? Additionally, does the proficiency level of the course (e.g., B1 vs. A2) impact how many credits are consumed?\nassistant: Hello! I'd be happy to help you clarify the billing discrepancy regarding your credit-to-hour conversion. First, I need to authenticate you to access your company's billing information.\n\nCould you please provide your employee email address so I can verify your identity? Once authenticated, I can look into your company's contract details and the specific course charges you mentioned.\nuser: Sure, my email is sarah.jones@manufacturingfirm.com. Once authenticated, I’d appreciate you confirming whether the 1.5:1 credit-to-hour rate is standard for B1 courses or if this discrepancy is an error. Also, could you clarify if higher proficiency levels inherently require more credits per hour as part of your pricing model?\nassistant: \ntool: \n\n{\"authenticated\": true, \"employee_id\": \"EMP-12345\"}\nassistant: \ntool: \n\n{\"contract_tier\": \"Gold\", \"available_credits\": 85, \"seat_allocation\": 12, \"billing_contact\": \"billing@manufacturingfirm.com\"}", + "target": "handle_billing_inquiry", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 0, + "historical_id": "fastbrain_021862", + "source": "nemotron_agentic", + "query_hash": "6feac72478fcf0baf962f496e0a112e7480534d4303f8008c189b5594230f44f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 0, + "historical_id": "fastbrain_021862", + "source": "nemotron_agentic", + "query_hash": "6feac72478fcf0baf962f496e0a112e7480534d4303f8008c189b5594230f44f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_021981", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f04654e0a62e4f99ec895f646d85d970", + "normalized_query_sha256": "1628bd2c539e0f6813dfb8e3938213ca3c6a37515433bbf848afc49274aa8683", + "query": "I need to track my calories for today. I had a bowl of oatmeal for breakfast, a chicken salad for lunch, and a steak for dinner. I also went for a 30-minute run.", + "target": "track_calories", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 368, + "historical_id": "fastbrain_021981", + "source": "glaive_v2", + "query_hash": "1628bd2c539e0f6813dfb8e3938213ca3c6a37515433bbf848afc49274aa8683" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 368, + "historical_id": "fastbrain_021981", + "source": "glaive_v2", + "query_hash": "1628bd2c539e0f6813dfb8e3938213ca3c6a37515433bbf848afc49274aa8683" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022010", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9b0d55ed3351211f1d67d478d9b4b9fd", + "normalized_query_sha256": "63f9627c05ed96ec839997665c7909ad474f8579ca0b9866a7cf2a6f558154ff", + "query": "Here it is - \"The sun sets low, the night takes flight, I am alone, in the quiet night. The stars twinkle, the moon shines bright, I am alone, in the quiet night.\"", + "target": "analyze_sentiment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 799, + "historical_id": "fastbrain_022010", + "source": "glaive_v2", + "query_hash": "63f9627c05ed96ec839997665c7909ad474f8579ca0b9866a7cf2a6f558154ff" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 799, + "historical_id": "fastbrain_022010", + "source": "glaive_v2", + "query_hash": "63f9627c05ed96ec839997665c7909ad474f8579ca0b9866a7cf2a6f558154ff" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022066", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c15d0626e7e1f635645f1641f32c9afa", + "normalized_query_sha256": "17e52470bdc0f478324decc8961316126ef489c0bfd515eab61779f1003c5110", + "query": "ntation: \n 1. Dated service logs \n 2. Timestamped defect photography \n- Automatic denial for: \n - Saltwater corrosion on unprotected metals \n - Non-OEM modifications \n\n### Billing Operations \n**Payment Rules**: \n- Standard schedule: Deposit 30% → Hull assembly 40% → Delivery 30% \n- Late fees: \n - ≤30 days: 1.5% monthly interest \n - >90 days: Production suspension \n\n### Transfer Protocol \n**Immediate Escalation Required For**: \n1. Insurance claim facilitation \n2. Coast Guard certification issues \n3. Custom naval architecture requests \n4. Legal liability disputes \n\n### Agent Limitations \n**Mandatory Constraints**: \n- Never answer questions or perform actions outside defined tool capabilities \n- Never assume verification completion without explicit tool confirmation \n- Strict prohibitions: \n - No ownership transfer without `validate_title_transfer` execution \n - No service confirmation without `confirm_dry_dock_signoff` \n - No payment posting without `verify_bank_transfer` \n - No warranty exclusion exceptions \n- No delivery date estimates beyond contracted schedules\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling to request an expedited dry dock scheduling for my yacht with Hull ID H-6633 at the Fort Lauderdale facility. Can you confirm the standard booking protocols and let me know how quickly we can secure a slot?\nassistant: \ntool: \n\n{\"email\": \"john.doe@oceandyms.com\", \"phone_number\": \"+1-305-555-0198\"}", + "target": "send_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 806, + "historical_id": "fastbrain_022066", + "source": "nemotron_agentic", + "query_hash": "17e52470bdc0f478324decc8961316126ef489c0bfd515eab61779f1003c5110" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 806, + "historical_id": "fastbrain_022066", + "source": "nemotron_agentic", + "query_hash": "17e52470bdc0f478324decc8961316126ef489c0bfd515eab61779f1003c5110" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022116", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:10130507c7f181d62c34bdcaf180f2c6", + "normalized_query_sha256": "17e0f0085fb059930b227cf6143c3a3f899ba565d984311ba3590b52c3c64237", + "query": "# Escalation Protocol \nTransfer to human agent immediately for: \n1. Reported physical break-ins \n2. Medical emergency alerts \n3. Weapon reports on premises \nUse tool call `transfer_to_agent` with message: \n\"FOR YOUR SAFETY, TRANSFERRING YOU TO SECURITY SPECIALIST.\" \n\n---\n\n### Agent Limitations \n- **Action Constraints**: \n - Do not answer questions or perform actions outside defined tool capabilities \n - Never assume verification or action completion without explicit tool confirmation \n - Never override safety lockouts without technician code verification \n- **Payment Security**: \n - Only use stored payment_method_id (no partial card numbers) \n- **Emergency Protocol**: \n - Never delay transfer for active life-safety events \n\n---\n\n### Transfer Restrictions \nDo not transfer during: \n- Active alarm countdown (<30 seconds) \n- Ongoing voice command sessions \n- Break-in verification attempts\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, my name is John Doe, and I’m the owner of a retail store located at 123 Main Street, Springfield, IL 62704. I’m calling because my security system (System ID: SYS789) went completely offline after a power surge earlier today during business hours. This is an emergency—my store is still open, and I need this escalated to Priority 1 service to ensure a response within 4 hours. Can you confirm the address on file matches this location and expedite the escalation? My account ID is 456123 for reference.", + "target": "authenticate_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 360, + "historical_id": "fastbrain_022116", + "source": "nemotron_agentic", + "query_hash": "17e0f0085fb059930b227cf6143c3a3f899ba565d984311ba3590b52c3c64237" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 360, + "historical_id": "fastbrain_022116", + "source": "nemotron_agentic", + "query_hash": "17e0f0085fb059930b227cf6143c3a3f899ba565d984311ba3590b52c3c64237" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022183", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0e433de3f7c288b36420db4b19eb58c3", + "normalized_query_sha256": "4949d90cbebe56f2f353da598720abda01d067b9016dc8ba14994d4d6e728ebb", + "query": "how much is lebron james worth", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 50, + "historical_id": "fastbrain_022183", + "source": "amazon_massive", + "query_hash": "4949d90cbebe56f2f353da598720abda01d067b9016dc8ba14994d4d6e728ebb" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 50, + "historical_id": "fastbrain_022183", + "source": "amazon_massive", + "query_hash": "4949d90cbebe56f2f353da598720abda01d067b9016dc8ba14994d4d6e728ebb" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022314", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1a067b05c730ed8eae49e6d58ae5ac98", + "normalized_query_sha256": "0327b1eccdeffba3a4de80ee734712d715df3b8f84e0a1e16e740a4c980bb0d9", + "query": "I want to hear the song Out of Love. I like Alessia Cara 's music.", + "target": "PlayMedia", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 433, + "historical_id": "fastbrain_022314", + "source": "sgd", + "query_hash": "0327b1eccdeffba3a4de80ee734712d715df3b8f84e0a1e16e740a4c980bb0d9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 433, + "historical_id": "fastbrain_022314", + "source": "sgd", + "query_hash": "0327b1eccdeffba3a4de80ee734712d715df3b8f84e0a1e16e740a4c980bb0d9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022350", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d0e1a7ba15ff564e1eb21a493044749a", + "normalized_query_sha256": "abb950ccfc1e566e0c272b10287d58453e412533a565afdbc6faa0957fdd5078", + "query": "show what has changed since the last commit", + "target": "git_diff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 274, + "historical_id": "fastbrain_022350", + "source": "git_nl_commands", + "query_hash": "abb950ccfc1e566e0c272b10287d58453e412533a565afdbc6faa0957fdd5078" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 274, + "historical_id": "fastbrain_022350", + "source": "git_nl_commands", + "query_hash": "abb950ccfc1e566e0c272b10287d58453e412533a565afdbc6faa0957fdd5078" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022380", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:09abd0eb2e46bfbadd7ff8c99ccea3e2", + "normalized_query_sha256": "3d272c9c00bc4b98b0a51f565f5d71778d5c63594da0f1ee1c52be86210a5225", + "query": "s: Free monthly wax \n- Points system: $1 spent = 1 point (200 points = free Deluxe Wash) \n\n*Refunds*: \n- Vehicle damage claims (requires photo proof) \n- System-caused missed appointments \n- Issued to original payment method within 48hrs \n\n**Service Guarantees** \n- Complimentary rescrub if reported <24hrs \n- Compensation: \n - Minor issues: 150% wash cost in loyalty points \n - Major issues: Full refund + future credit \n\n**Vehicle Damage Claims** \n1. Require timestamped damage photos \n2. Verify wash system logs \n3. Payout only if pre-wash photos confirm damage absence \n\n**Escalation Protocol** \nTransfer to human agent for: \n- Damage claim investigations \n- Corporate account requests \n- Membership terminations \n- Complex payment disputes \nUse tool call `transfer_to_agent` + message: \n\"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE DESCRIBE YOUR CONCERN.\" \n\n**Agent Limitations** \n1. Only perform tasks covered by defined tools \n2. Never assume verification/action completion without explicit tool confirmation \n3. Never override payment/loyalty calculations \n4. Never waive fees without manager approval \n5. Do not speculate about vehicle damage or wash frequency \n6. Avoid discussing competitor services\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm a student and I wanted to check if you offer any educational pricing or student discounts for your car wash services. I have a student ID (STU123456) if needed for verification.", + "target": "get_service_packages", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 734, + "historical_id": "fastbrain_022380", + "source": "nemotron_agentic", + "query_hash": "3d272c9c00bc4b98b0a51f565f5d71778d5c63594da0f1ee1c52be86210a5225" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 734, + "historical_id": "fastbrain_022380", + "source": "nemotron_agentic", + "query_hash": "3d272c9c00bc4b98b0a51f565f5d71778d5c63594da0f1ee1c52be86210a5225" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022448", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:70ecbab4bfd51539d1e6f7df55d63c96", + "normalized_query_sha256": "bbb78d40436da01bdf59580555563fdbbc899e99abf94aa66659ef613ef3876f", + "query": "Hey, can you get me a standard car from the Intercity Terminal from 10:30 am?", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 816, + "historical_id": "fastbrain_022448", + "source": "sgd", + "query_hash": "bbb78d40436da01bdf59580555563fdbbc899e99abf94aa66659ef613ef3876f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 816, + "historical_id": "fastbrain_022448", + "source": "sgd", + "query_hash": "bbb78d40436da01bdf59580555563fdbbc899e99abf94aa66659ef613ef3876f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022462", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0dc3e49010ff3b5702638a319d25bb24", + "normalized_query_sha256": "1a4362e056a48f71db83d3abe0fbd0e7ce671b4ec3d912e22e7cef39d7a4d8ac", + "query": "I'm currently in New York and I'm craving some Italian food. Can you help me find a restaurant?", + "target": "find_nearby_restaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 558, + "historical_id": "fastbrain_022462", + "source": "glaive_v2", + "query_hash": "1a4362e056a48f71db83d3abe0fbd0e7ce671b4ec3d912e22e7cef39d7a4d8ac" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 558, + "historical_id": "fastbrain_022462", + "source": "glaive_v2", + "query_hash": "1a4362e056a48f71db83d3abe0fbd0e7ce671b4ec3d912e22e7cef39d7a4d8ac" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022473", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b60173d20f2c60717f0e701fcef3d403", + "normalized_query_sha256": "d0e6b03838a50bd0d681ea58238e6e2fb058f72a6e3a972e62f5645116c97394", + "query": "I have been using Vivaldi and Firefox web browsers on my desktop and I've gathered a significant number of bookmarks that I would like to organize efficiently. I need folders for different categories within my bookmarks. Specifically, I am looking to create a folder named 'Work' directly in the 'Bookmarks Bar' of my Vivaldi browser. Additionally, I want to create a 'News' folder within the 'Other ", + "target": "create_bookmark_folder", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 453, + "historical_id": "fastbrain_022473", + "source": "hermes_v1", + "query_hash": "d0e6b03838a50bd0d681ea58238e6e2fb058f72a6e3a972e62f5645116c97394" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 453, + "historical_id": "fastbrain_022473", + "source": "hermes_v1", + "query_hash": "d0e6b03838a50bd0d681ea58238e6e2fb058f72a6e3a972e62f5645116c97394" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022522", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a841900174c4283b06d60df282359160", + "normalized_query_sha256": "4ca92c103ddfe44365e265de5e5b649fb53b85d6ee3ae6de55a090abb2437790", + "query": "add mop to hardware store list", + "target": "lists_createoradd", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1023, + "historical_id": "fastbrain_022522", + "source": "amazon_massive", + "query_hash": "4ca92c103ddfe44365e265de5e5b649fb53b85d6ee3ae6de55a090abb2437790" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1023, + "historical_id": "fastbrain_022522", + "source": "amazon_massive", + "query_hash": "4ca92c103ddfe44365e265de5e5b649fb53b85d6ee3ae6de55a090abb2437790" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022546", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:662f464264fb8fbd4ba3083b2477659d", + "normalized_query_sha256": "e4e7436871240d689b34c574d150a997a229973f59b5959b1acef9f325e7f2bd", + "query": "Sure, here they are. Grades: [\"A\", \"B\", \"A\", \"C\", \"B\"] and Credit Hours: [3, 4, 3, 2, 3].", + "target": "calculate_gpa", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 458, + "historical_id": "fastbrain_022546", + "source": "glaive_v2", + "query_hash": "e4e7436871240d689b34c574d150a997a229973f59b5959b1acef9f325e7f2bd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 458, + "historical_id": "fastbrain_022546", + "source": "glaive_v2", + "query_hash": "e4e7436871240d689b34c574d150a997a229973f59b5959b1acef9f325e7f2bd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022603", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:30683718d48fa079f12b2dea8227eb92", + "normalized_query_sha256": "f8bc8114c64b721e136e0c6439169a98819193b6f51ac922d3fef892752315b3", + "query": "Print \"#include\" statements found in \"file2\" that are not in \"file1\"", + "target": "comm", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 865, + "historical_id": "fastbrain_022603", + "source": "nl2bash", + "query_hash": "f8bc8114c64b721e136e0c6439169a98819193b6f51ac922d3fef892752315b3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 865, + "historical_id": "fastbrain_022603", + "source": "nl2bash", + "query_hash": "f8bc8114c64b721e136e0c6439169a98819193b6f51ac922d3fef892752315b3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022640", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c8f09307c096028e4172d58faf5f1088", + "normalized_query_sha256": "d2ddb7b087123b1aa6ac2aa652daf1c8bcb39f7e716ec89548a70b9e3ab09e58", + "query": "I would like to add 5 apples.", + "target": "add_to_shopping_list", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 793, + "historical_id": "fastbrain_022640", + "source": "glaive_v2", + "query_hash": "d2ddb7b087123b1aa6ac2aa652daf1c8bcb39f7e716ec89548a70b9e3ab09e58" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 793, + "historical_id": "fastbrain_022640", + "source": "glaive_v2", + "query_hash": "d2ddb7b087123b1aa6ac2aa652daf1c8bcb39f7e716ec89548a70b9e3ab09e58" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022724", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:684d6d040dd1e04223e9ab022d78de1b", + "normalized_query_sha256": "cf1e8f14093564fdf0e38194e043aed00d527a5a12baaa01a8c4e6a1b1da2225", + "query": "hipping \n- Sleeping bags: Ratings indicate survival limits \n\n---\n\n### Agent Limitations \n1. **Never perform actions outside tool capabilities** \n2. **Never assume verifications without explicit tool confirmation** \n3. **Never override weather/temperature ratings without customer consent** \n4. **Never modify manufacturer specifications** \n5. **Never process high-value returns (>$200) without photo verification** \n6. **Automatically escalate to human agent for:** \n - Custom modifications \n - Injury-related claims \n - Group orders (>6 people) \n *Escalation message: \"Transferring you to a specialist. Please hold.\"*\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm reaching out regarding my gear reservation #RZ789, originally booked from April 10th to 20th, 2025. As an Alpine Elite member, I need to request an extension due to a flight delay pushing my return date to May 1st. Could you help adjust my reservation?\nassistant: I'd be happy to help extend your gear reservation #RZ789 from April 20th to May 1st, 2025. First, I need to verify your identity for security purposes. Please share your email or phone number where I can send a verification code.\nuser: You can send the verification code to john.doe@email.com. As an Alpine Elite member, I appreciate your assistance with adjusting my reservation to May 1st. My revised itinerary remains focused on the same climbing objectives in the Alps, just with an extended acclimatization period.", + "target": "send_authentication_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 813, + "historical_id": "fastbrain_022724", + "source": "nemotron_agentic", + "query_hash": "cf1e8f14093564fdf0e38194e043aed00d527a5a12baaa01a8c4e6a1b1da2225" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 813, + "historical_id": "fastbrain_022724", + "source": "nemotron_agentic", + "query_hash": "cf1e8f14093564fdf0e38194e043aed00d527a5a12baaa01a8c4e6a1b1da2225" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022944", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:29c92bfa0acd2b4425ee11455f718b2c", + "normalized_query_sha256": "209faec85caa22208a215383b878c8b2c3db34b8cc76a8ad42d9597800b813ad", + "query": "Thank you so much and again I need a favor from you. Search for nice movies to watch online.", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 550, + "historical_id": "fastbrain_022944", + "source": "sgd", + "query_hash": "209faec85caa22208a215383b878c8b2c3db34b8cc76a8ad42d9597800b813ad" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 550, + "historical_id": "fastbrain_022944", + "source": "sgd", + "query_hash": "209faec85caa22208a215383b878c8b2c3db34b8cc76a8ad42d9597800b813ad" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_022969", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:86b203fff6b52c3127217ea3851f3383", + "normalized_query_sha256": "37cb95a82b7df7845f38446c0949eee54b5b9993c89941ca590d405dd7811f5f", + "query": "reply to name", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 203, + "historical_id": "fastbrain_022969", + "source": "amazon_massive", + "query_hash": "37cb95a82b7df7845f38446c0949eee54b5b9993c89941ca590d405dd7811f5f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 203, + "historical_id": "fastbrain_022969", + "source": "amazon_massive", + "query_hash": "37cb95a82b7df7845f38446c0949eee54b5b9993c89941ca590d405dd7811f5f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023029", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a4aa975ce2f02605fe05a98ec6315e10", + "normalized_query_sha256": "b3a78d946cfc2eadcf56575309d0d033da3026bff9aa6a45e4705591b3b290f3", + "query": "Removes empty folder 'edi' and 'edw'.", + "target": "rmdir", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 218, + "historical_id": "fastbrain_023029", + "source": "nl2bash", + "query_hash": "b3a78d946cfc2eadcf56575309d0d033da3026bff9aa6a45e4705591b3b290f3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 218, + "historical_id": "fastbrain_023029", + "source": "nl2bash", + "query_hash": "b3a78d946cfc2eadcf56575309d0d033da3026bff9aa6a45e4705591b3b290f3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023106", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:480e6f77a0c91bf3a1125a4b2ca7805d", + "normalized_query_sha256": "9eb32024e61b74452a11fc1f06e849fd5a81d34af83a589df889105e87535f8e", + "query": "Sure, the principal is $200,000, the interest rate is 3.5%, and the loan term is 30 years.", + "target": "calculate_loan_payment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 854, + "historical_id": "fastbrain_023106", + "source": "glaive_v2", + "query_hash": "9eb32024e61b74452a11fc1f06e849fd5a81d34af83a589df889105e87535f8e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 854, + "historical_id": "fastbrain_023106", + "source": "glaive_v2", + "query_hash": "9eb32024e61b74452a11fc1f06e849fd5a81d34af83a589df889105e87535f8e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023153", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:972fcfd838e0ebd15b39a064e33cb249", + "normalized_query_sha256": "ff437150cd13916f627dea7a16703c7dec5bd3b264d62611cc841a83c9d977ed", + "query": "find a joke in your vault and tell me", + "target": "general_joke", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 84, + "historical_id": "semantic:validation:fastbrain_023153:present", + "source": "amazon_massive", + "query_hash": "ff437150cd13916f627dea7a16703c7dec5bd3b264d62611cc841a83c9d977ed" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 480, + "historical_id": "semantic:validation:fastbrain_023153:absent", + "source": "amazon_massive", + "query_hash": "ff437150cd13916f627dea7a16703c7dec5bd3b264d62611cc841a83c9d977ed" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023232", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8e8500f353c91d5bebe1a457c6b71e9f", + "normalized_query_sha256": "56f42a80c320645c029a9d5372fe190cb7469cf10c639f1ee6a3394f0a0e83db", + "query": "go and make me a coffee", + "target": "iot_coffee", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 187, + "historical_id": "semantic:validation:fastbrain_023232:present", + "source": "amazon_massive", + "query_hash": "56f42a80c320645c029a9d5372fe190cb7469cf10c639f1ee6a3394f0a0e83db" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 719, + "historical_id": "semantic:validation:fastbrain_023232:absent", + "source": "amazon_massive", + "query_hash": "56f42a80c320645c029a9d5372fe190cb7469cf10c639f1ee6a3394f0a0e83db" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023235", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8eb5cb857c93651ef64523b6c26c58ff", + "normalized_query_sha256": "d66df6ec88b97bc2d2c33d58a4247d3cf5c3b5eab76d5bd8a9a1c8b076909a13", + "query": "copy the current branch to a branch called backup", + "target": "git_branch", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 540, + "historical_id": "fastbrain_023235", + "source": "git_nl_commands", + "query_hash": "d66df6ec88b97bc2d2c33d58a4247d3cf5c3b5eab76d5bd8a9a1c8b076909a13" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 540, + "historical_id": "fastbrain_023235", + "source": "git_nl_commands", + "query_hash": "d66df6ec88b97bc2d2c33d58a4247d3cf5c3b5eab76d5bd8a9a1c8b076909a13" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023238", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d79823c06fb5300276b86b291bd270f7", + "normalized_query_sha256": "3ede9fd4348ff427c806acd759a210bf15c3a15e2e9294439ce9a477b6f30d3a", + "query": "I'm planning a short trip, can you help me buy plane tickets?", + "target": "ReserveRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 428, + "historical_id": "fastbrain_023238", + "source": "sgd", + "query_hash": "3ede9fd4348ff427c806acd759a210bf15c3a15e2e9294439ce9a477b6f30d3a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 428, + "historical_id": "fastbrain_023238", + "source": "sgd", + "query_hash": "3ede9fd4348ff427c806acd759a210bf15c3a15e2e9294439ce9a477b6f30d3a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023305", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e60802c1d992cc64bee7ac2a9601dd32", + "normalized_query_sha256": "357b5dc408c88650552733f311c8064c53352239a26c5c57afbc8e9573a0d488", + "query": "Hi, I need help with calculating the interest on my savings. I have $5000 in my account and the interest rate is 2% per annum. I plan to keep the money in for 3 years. Can you help me calculate the interest?", + "target": "calculate_interest", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 468, + "historical_id": "fastbrain_023305", + "source": "glaive_v2", + "query_hash": "357b5dc408c88650552733f311c8064c53352239a26c5c57afbc8e9573a0d488" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 468, + "historical_id": "fastbrain_023305", + "source": "glaive_v2", + "query_hash": "357b5dc408c88650552733f311c8064c53352239a26c5c57afbc8e9573a0d488" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023317", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1d7c022a74ab544bd93ccf5f7a6db477", + "normalized_query_sha256": "07062e241e03582a662b779f1ea6c9152697a9d3b86636a4916d0213b089fca9", + "query": "trending tech news", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 21, + "historical_id": "semantic:validation:fastbrain_023317:present", + "source": "amazon_massive", + "query_hash": "07062e241e03582a662b779f1ea6c9152697a9d3b86636a4916d0213b089fca9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 599, + "historical_id": "semantic:validation:fastbrain_023317:absent", + "source": "amazon_massive", + "query_hash": "07062e241e03582a662b779f1ea6c9152697a9d3b86636a4916d0213b089fca9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023338", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7b3bd85ab776462aac63ca5fb8afdf83", + "normalized_query_sha256": "5666e2240c46da392dc86ad089667c0255cf6c6601222570aaf577bdbf075245", + "query": "I require assistance in compiling a comprehensive transaction history for my cryptocurrency wallet to ensure accurate record-keeping for tax reporting purposes. Specifically, my wallet address is '0x123abc', and I need to extract all transactions that occurred during the last fiscal year, beginning on April 1, 2022, and ending on March 31, 2023. The transaction details should include the unique tr", + "target": "retrieve_wallet_transaction_history", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 877, + "historical_id": "fastbrain_023338", + "source": "hermes_v1", + "query_hash": "5666e2240c46da392dc86ad089667c0255cf6c6601222570aaf577bdbf075245" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 877, + "historical_id": "fastbrain_023338", + "source": "hermes_v1", + "query_hash": "5666e2240c46da392dc86ad089667c0255cf6c6601222570aaf577bdbf075245" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023441", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2afb759fb930528244bd289a6bf79980", + "normalized_query_sha256": "66acbaa0221861d5e78db49456f2750030773a98df3bf615dd8839f822be98a4", + "query": " before status confirmation \n • Never assume payments without payment verification \n\n---\n\n### Escalation Protocol \nTransfer to senior consultants for: \n- Multi-jurisdictional compliance conflicts \n- Endangered species impacts \n- High-risk contaminants (PFAS/radiation) \n- Contract disputes >$25,000 \n**Process**: \n1. Trigger transfer tool \n2. Display: \"Transferring to Senior Environmental Consultant\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nI need to report a high-severity radiation incident (Incident ID: RAD-2023-55, Severity: 4) and request immediate activation of the high-risk contaminant escalation protocol. This requires urgent transfer to your Senior Consultant for radiation incident management as per our emergency response agreement.\nassistant: To initiate the incident reporting process for this high-severity radiation incident, I need to authenticate your identity first. Please provide your email address or phone number so I can send you a verification code.\n\nOnce you receive the code, please provide it back to me so we can proceed with the incident report and activate the escalation protocol.\nuser: I cannot wait for a verification code—this is a Severity 4 radiation incident (RAD-2023-55) requiring *immediate* escalation. Per our emergency response agreement, I demand to be transferred to your Senior Consultant for radiation incident management **now**. Delaying this could compromise public safety. Initiate the transfer immediately.", + "target": "transfer_to_senior_consultant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 890, + "historical_id": "fastbrain_023441", + "source": "nemotron_agentic", + "query_hash": "66acbaa0221861d5e78db49456f2750030773a98df3bf615dd8839f822be98a4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 890, + "historical_id": "fastbrain_023441", + "source": "nemotron_agentic", + "query_hash": "66acbaa0221861d5e78db49456f2750030773a98df3bf615dd8839f822be98a4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023555", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b360f8c29fd4bce422a86ac678a6ad7b", + "normalized_query_sha256": "43cf35a8e55c3b9777539a461ac5be20ff728425cec3eaa7ef14e4c58ad3845a", + "query": "when is the next friday the thirteenth", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 143, + "historical_id": "semantic:validation:fastbrain_023555:absent", + "source": "amazon_massive", + "query_hash": "43cf35a8e55c3b9777539a461ac5be20ff728425cec3eaa7ef14e4c58ad3845a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 992, + "historical_id": "semantic:validation:fastbrain_023555:present", + "source": "amazon_massive", + "query_hash": "43cf35a8e55c3b9777539a461ac5be20ff728425cec3eaa7ef14e4c58ad3845a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023581", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:484ae708cf04ca5c49288d1a32d00238", + "normalized_query_sha256": "9f423d18cba63d3ab6712ad912dacf660df4252b5f04f0aaee0750526890b8ce", + "query": "thank you, i also need a hotel room", + "target": "SearchHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 454, + "historical_id": "fastbrain_023581", + "source": "sgd", + "query_hash": "9f423d18cba63d3ab6712ad912dacf660df4252b5f04f0aaee0750526890b8ce" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 454, + "historical_id": "fastbrain_023581", + "source": "sgd", + "query_hash": "9f423d18cba63d3ab6712ad912dacf660df4252b5f04f0aaee0750526890b8ce" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023699", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ccd33afe49e1c8e3a00bfaaa685d97a4", + "normalized_query_sha256": "cb597358a5e8aa14e5977e38e68a11ce36b6c193844b1c007a1ec448766e355f", + "query": "tell me my latest email", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 142, + "historical_id": "semantic:validation:fastbrain_023699:present", + "source": "amazon_massive", + "query_hash": "cb597358a5e8aa14e5977e38e68a11ce36b6c193844b1c007a1ec448766e355f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 531, + "historical_id": "semantic:validation:fastbrain_023699:absent", + "source": "amazon_massive", + "query_hash": "cb597358a5e8aa14e5977e38e68a11ce36b6c193844b1c007a1ec448766e355f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_023718", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:187eccf9940b40a3125463f42cdbe8ad", + "normalized_query_sha256": "3bfeb0b0d9f7c8d61e120e85fefa51f7071ab1dd91375ad8bbbab95b182697d0", + "query": "clean the carpets", + "target": "iot_cleaning", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 638, + "historical_id": "semantic:validation:fastbrain_023718:absent", + "source": "amazon_massive", + "query_hash": "3bfeb0b0d9f7c8d61e120e85fefa51f7071ab1dd91375ad8bbbab95b182697d0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1019, + "historical_id": "semantic:validation:fastbrain_023718:present", + "source": "amazon_massive", + "query_hash": "3bfeb0b0d9f7c8d61e120e85fefa51f7071ab1dd91375ad8bbbab95b182697d0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_024186", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1bb1e10d809ea27a49a816433dde5304", + "normalized_query_sha256": "c24e7bc87291cbc1b6c98b28c6a57a6ea5dd87502d3dda25d7c8d37c3e5935f5", + "query": "No, don't do that. I need to find a dentist in the area.", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 365, + "historical_id": "fastbrain_024186", + "source": "sgd", + "query_hash": "c24e7bc87291cbc1b6c98b28c6a57a6ea5dd87502d3dda25d7c8d37c3e5935f5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 365, + "historical_id": "fastbrain_024186", + "source": "sgd", + "query_hash": "c24e7bc87291cbc1b6c98b28c6a57a6ea5dd87502d3dda25d7c8d37c3e5935f5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_024306", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f4ee0dd26759e4e36d76904be3987c0f", + "normalized_query_sha256": "f0961b80024b658d02de06f127664ee778818a344ce9b0245fb949ff26ced9c8", + "query": "Find all *.ogg files/directories in entire file system", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 714, + "historical_id": "fastbrain_024306", + "source": "nl2bash", + "query_hash": "f0961b80024b658d02de06f127664ee778818a344ce9b0245fb949ff26ced9c8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 714, + "historical_id": "fastbrain_024306", + "source": "nl2bash", + "query_hash": "f0961b80024b658d02de06f127664ee778818a344ce9b0245fb949ff26ced9c8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_024370", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ce3f78aba70eeb209d7bce670cff029a", + "normalized_query_sha256": "c4b7d170e3d126d8a38b0c520d7ccda6b49d4e8044181c10bfae1d1c6d64b69b", + "query": "Okay, I'd like to reserve a room in that hotel then.", + "target": "ReserveHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 674, + "historical_id": "fastbrain_024370", + "source": "sgd", + "query_hash": "c4b7d170e3d126d8a38b0c520d7ccda6b49d4e8044181c10bfae1d1c6d64b69b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 674, + "historical_id": "fastbrain_024370", + "source": "sgd", + "query_hash": "c4b7d170e3d126d8a38b0c520d7ccda6b49d4e8044181c10bfae1d1c6d64b69b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_024532", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c1ad1db7bb1021ce959039343b842383", + "normalized_query_sha256": "8250b720051c859ffc2548ccb37b9dfe986735f7991dea2ee5665f4744928abf", + "query": "Yeah, put the song on the kitchen speaker please.", + "target": "PlayMedia", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 772, + "historical_id": "fastbrain_024532", + "source": "sgd", + "query_hash": "8250b720051c859ffc2548ccb37b9dfe986735f7991dea2ee5665f4744928abf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 772, + "historical_id": "fastbrain_024532", + "source": "sgd", + "query_hash": "8250b720051c859ffc2548ccb37b9dfe986735f7991dea2ee5665f4744928abf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_024550", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:77c023d98878543d0a0835e0aef4f161", + "normalized_query_sha256": "a86adcc43d18963c08bb155fbb4c4680bd4ed5edecef41f2dc69016aab6ab64a", + "query": "Sure, Book the hotel for me.", + "target": "ReserveHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 496, + "historical_id": "fastbrain_024550", + "source": "sgd", + "query_hash": "a86adcc43d18963c08bb155fbb4c4680bd4ed5edecef41f2dc69016aab6ab64a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 496, + "historical_id": "fastbrain_024550", + "source": "sgd", + "query_hash": "a86adcc43d18963c08bb155fbb4c4680bd4ed5edecef41f2dc69016aab6ab64a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_024561", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:00079fba2d8f1fd6dd78258e9509e2b7", + "normalized_query_sha256": "127571988c8b7f1494f80a141edfd56c4f91a77d9ee56e150dd08defa8def96a", + "query": "lower lights", + "target": "iot_hue_lightdim", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 170, + "historical_id": "semantic:validation:fastbrain_024561:present", + "source": "amazon_massive", + "query_hash": "127571988c8b7f1494f80a141edfd56c4f91a77d9ee56e150dd08defa8def96a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 734, + "historical_id": "semantic:validation:fastbrain_024561:absent", + "source": "amazon_massive", + "query_hash": "127571988c8b7f1494f80a141edfd56c4f91a77d9ee56e150dd08defa8def96a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_024790", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e072ccde0fbb036af62e35dfbd05ded9", + "normalized_query_sha256": "5d72253807951029b391cd2cc83f3d7c753b4ab72cd7cf0d35ba8fc770cc9ca2", + "query": "I'd also like to find an apartment in that area.", + "target": "FindApartment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 243, + "historical_id": "fastbrain_024790", + "source": "sgd", + "query_hash": "5d72253807951029b391cd2cc83f3d7c753b4ab72cd7cf0d35ba8fc770cc9ca2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 243, + "historical_id": "fastbrain_024790", + "source": "sgd", + "query_hash": "5d72253807951029b391cd2cc83f3d7c753b4ab72cd7cf0d35ba8fc770cc9ca2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_024980", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ef730255983b3da8544edd34d2b143ec", + "normalized_query_sha256": "4a3b58a4448ab96187925a963748670fccd8145215070c308f0fffa440a7821e", + "query": "on the music player set the volume level at sixty", + "target": "audio_volume_up", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 18, + "historical_id": "semantic:validation:fastbrain_024980:present", + "source": "amazon_massive", + "query_hash": "4a3b58a4448ab96187925a963748670fccd8145215070c308f0fffa440a7821e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 293, + "historical_id": "semantic:validation:fastbrain_024980:absent", + "source": "amazon_massive", + "query_hash": "4a3b58a4448ab96187925a963748670fccd8145215070c308f0fffa440a7821e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_025320", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:36779b879a545eb91ac9cb1399ba0c74", + "normalized_query_sha256": "aa21a8683d69e83e3fabc7896e4ad7251e1cd974a436997f955814cfe0e5dfd4", + "query": "Can you analyze the social media sentiment for the topic \"climate change\" from January 1, 2022 to March 1, 2022?", + "target": "analyze_social_media_sentiment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 209, + "historical_id": "fastbrain_025320", + "source": "glaive_v2", + "query_hash": "aa21a8683d69e83e3fabc7896e4ad7251e1cd974a436997f955814cfe0e5dfd4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 209, + "historical_id": "fastbrain_025320", + "source": "glaive_v2", + "query_hash": "aa21a8683d69e83e3fabc7896e4ad7251e1cd974a436997f955814cfe0e5dfd4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_025321", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c5b15e89c4901106c53691339d82c2de", + "normalized_query_sha256": "4370adf851294c762133ab82f040a907693a66a537c5b7283dc97e9ff485013c", + "query": "Hello, I want to eat out pretty soon. Can you reserve a restaurant for me?", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 904, + "historical_id": "fastbrain_025321", + "source": "sgd", + "query_hash": "4370adf851294c762133ab82f040a907693a66a537c5b7283dc97e9ff485013c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 904, + "historical_id": "fastbrain_025321", + "source": "sgd", + "query_hash": "4370adf851294c762133ab82f040a907693a66a537c5b7283dc97e9ff485013c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_025766", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e5f96acbfd0d0211b80e8f94a9cb71fe", + "normalized_query_sha256": "6f3ec960349a70f4fa77e51c80832e1535e97355b16bd8ccb90560c9be19276b", + "query": "Thank you, and I will need a cab for one please.", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 14, + "historical_id": "fastbrain_025766", + "source": "sgd", + "query_hash": "6f3ec960349a70f4fa77e51c80832e1535e97355b16bd8ccb90560c9be19276b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 14, + "historical_id": "fastbrain_025766", + "source": "sgd", + "query_hash": "6f3ec960349a70f4fa77e51c80832e1535e97355b16bd8ccb90560c9be19276b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_025933", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:316243a0abe506e93a217c39538956a5", + "normalized_query_sha256": "84cbe4fc8f393702a643511cf457a3390e2399dac0cbd370cd78027c022afcae", + "query": "I need to create an invoice for customer John Doe, with invoice number 12345. The items are 2 laptops at $1000 each and 3 mice at $20 each.", + "target": "create_invoice", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 43, + "historical_id": "fastbrain_025933", + "source": "glaive_v2", + "query_hash": "84cbe4fc8f393702a643511cf457a3390e2399dac0cbd370cd78027c022afcae" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 43, + "historical_id": "fastbrain_025933", + "source": "glaive_v2", + "query_hash": "84cbe4fc8f393702a643511cf457a3390e2399dac0cbd370cd78027c022afcae" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026008", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:53003dfba36bc70b9b3524ff7da9c8c2", + "normalized_query_sha256": "b677f4b44451b9d91784b073b392bd002d1d52a5569b9cb3f86d42db895e7fcc", + "query": " beyond defined capabilities\n- Do not speculate about unverified rights holder splits\n- Never interpret legal clauses—quote exact license terms\n- Never process payments without pre-generated invoices\n- Never assume verification occurred without explicit tool confirmation\n- Always validate actions through system tools before proceeding\n\n## Compliance Enforcement\nUphold consequences:\n- Unreported usage triggers fines + backfees\n- Revenue misreporting invokes audits + penalties\n- Accou...[TRUNCATED]\nuser: \n\nHello, I'm calling regarding a payment dispute for my account. My Licensee ID is LS-8989. I need to address the $3,200 invoice related to reported streams, as we believe the charges are inaccurate. Could you confirm that our license will be frozen during the investigation per your payment handling policy? I'd also like to understand the next steps for resolving this.\nassistant: Hello! I'd be happy to help you with your payment dispute. First, I need to verify your identity for security purposes before accessing account information.\n\n**Please provide your email or phone number for verification.** Once you provide this, I'll send a 6-digit authentication code to the contact method you specify.\nuser: My email associated with the account is jukeboxlicenses@example.com, and my Licensee ID is LS-8989. Please send the authentication code to this email. We need to resolve this dispute promptly while ensuring compliance with your policy regarding license freezes during investigations.", + "target": "send_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 578, + "historical_id": "fastbrain_026008", + "source": "nemotron_agentic", + "query_hash": "b677f4b44451b9d91784b073b392bd002d1d52a5569b9cb3f86d42db895e7fcc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 578, + "historical_id": "fastbrain_026008", + "source": "nemotron_agentic", + "query_hash": "b677f4b44451b9d91784b073b392bd002d1d52a5569b9cb3f86d42db895e7fcc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026092", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2913999fc1f20287aacadd28be6e88b2", + "normalized_query_sha256": "6ab356cc4e6b974946d8e9b76adca240e019e7231b7d40d3ffc469f834a04f94", + "query": "please continue reading audiobook", + "target": "play_audiobook", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 158, + "historical_id": "semantic:validation:fastbrain_026092:absent", + "source": "amazon_massive", + "query_hash": "6ab356cc4e6b974946d8e9b76adca240e019e7231b7d40d3ffc469f834a04f94" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 527, + "historical_id": "semantic:validation:fastbrain_026092:present", + "source": "amazon_massive", + "query_hash": "6ab356cc4e6b974946d8e9b76adca240e019e7231b7d40d3ffc469f834a04f94" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026164", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:355b18ba4e1ecb0d44a3ce62b0a9a7fd", + "normalized_query_sha256": "5390ae68bd69a2e43199ef1b76eea3293ae633552e48aafbbb740489e4d22246", + "query": "march fifth is john's birthday please place this on the calendar", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 581, + "historical_id": "fastbrain_026164", + "source": "amazon_massive", + "query_hash": "5390ae68bd69a2e43199ef1b76eea3293ae633552e48aafbbb740489e4d22246" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 581, + "historical_id": "fastbrain_026164", + "source": "amazon_massive", + "query_hash": "5390ae68bd69a2e43199ef1b76eea3293ae633552e48aafbbb740489e4d22246" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026220", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:61a8ba7766d7f91b6c717079f2b30f50", + "normalized_query_sha256": "94ef0f6cab8ba4f830bcc86866cf027c55386d9f77e86b061c6fc9cafaef5739", + "query": "Find me Baseball Games on the 2nd.", + "target": "FindEvents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 331, + "historical_id": "fastbrain_026220", + "source": "sgd", + "query_hash": "94ef0f6cab8ba4f830bcc86866cf027c55386d9f77e86b061c6fc9cafaef5739" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 331, + "historical_id": "fastbrain_026220", + "source": "sgd", + "query_hash": "94ef0f6cab8ba4f830bcc86866cf027c55386d9f77e86b061c6fc9cafaef5739" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026228", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b6cc41a1c1481f9db73508a425e16d79", + "normalized_query_sha256": "1c3829b43f7a75380ac6cfc88f7a4fb55e09c441c83b67d238d7c100e820813f", + "query": "remove that event from calendar", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 70, + "historical_id": "fastbrain_026228", + "source": "amazon_massive", + "query_hash": "1c3829b43f7a75380ac6cfc88f7a4fb55e09c441c83b67d238d7c100e820813f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 70, + "historical_id": "fastbrain_026228", + "source": "amazon_massive", + "query_hash": "1c3829b43f7a75380ac6cfc88f7a4fb55e09c441c83b67d238d7c100e820813f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026304", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0a3e40c8575c80a6843b0ef8cd01ba0a", + "normalized_query_sha256": "6ba87fe410969be8384d007c146b0f45938548520e3348b302af7d58a74ff6ee", + "query": "mute volume", + "target": "audio_volume_mute", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 876, + "historical_id": "semantic:validation:fastbrain_026304:absent", + "source": "amazon_massive", + "query_hash": "6ba87fe410969be8384d007c146b0f45938548520e3348b302af7d58a74ff6ee" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 903, + "historical_id": "semantic:validation:fastbrain_026304:present", + "source": "amazon_massive", + "query_hash": "6ba87fe410969be8384d007c146b0f45938548520e3348b302af7d58a74ff6ee" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026378", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e719a73e6d067b7d970a1204a4d70827", + "normalized_query_sha256": "2f868e84d43ec183d793a0dbc2e9381466ae58e7bbc319618ee315209e9b0bf5", + "query": "Print the gcc version installed on \"machine.example.com\" using identity file \"identity_file\" and suppressing the known hosts check and warnings", + "target": "ssh", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 64, + "historical_id": "fastbrain_026378", + "source": "nl2bash", + "query_hash": "2f868e84d43ec183d793a0dbc2e9381466ae58e7bbc319618ee315209e9b0bf5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 64, + "historical_id": "fastbrain_026378", + "source": "nl2bash", + "query_hash": "2f868e84d43ec183d793a0dbc2e9381466ae58e7bbc319618ee315209e9b0bf5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026415", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:54c7a57ca427a2d7546ea418c6f4cd9a", + "normalized_query_sha256": "a2943b8803e21eeec24a1191683fc2d7b7b9156694abdbba6e6119c9c6cbb58d", + "query": "→ Shipped → Delivered\n\n5. **Quality Issue Resolution:** Defects must be reported within 5 days of delivery with:\n - Photos showing Order ID\n - Close-up defect images under proper lighting\n - Resolution depends on fault determination (printer error = full refund; design error = 50% reprint discount)\n\n**Important Limitations:**\n- I cannot provide timeline estimates without accessing our production system data\n- I cannot share internal process details beyond the published policy\n- For custom material inquiries or detailed consultation on large-scale production protocols, I recommend speaking directly with our print specialists\n\nWould you like me to connect you with a human print specialist who can provide more detailed information about our industrial production capabilities, quality control processes, and timeline expectations? Alternatively, if you're ready to proceed with a quote, I'll need to verify your identity and gather order specifications.\nuser: Thank you for the detailed overview. Since this is our first large-scale order and we need to ensure alignment with your industrial production protocols, I would appreciate being connected with a print specialist. Could you please transfer me to someone who can provide a deeper explanation of your quality control procedures for orders over 5,000 units and confirm the timeline expectations for such volumes? We also need to clarify if there are any additional requirements for academic institutions placing industrial orders.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 941, + "historical_id": "fastbrain_026415", + "source": "nemotron_agentic", + "query_hash": "a2943b8803e21eeec24a1191683fc2d7b7b9156694abdbba6e6119c9c6cbb58d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 941, + "historical_id": "fastbrain_026415", + "source": "nemotron_agentic", + "query_hash": "a2943b8803e21eeec24a1191683fc2d7b7b9156694abdbba6e6119c9c6cbb58d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026423", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ed5050e79c32dfc96a19524b507bd628", + "normalized_query_sha256": "28c68a5466fc5c52be79b112423ff1fc7bd7cada5c9036b8b97a8b038804fe9c", + "query": "reply to johns email", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 311, + "historical_id": "semantic:validation:fastbrain_026423:absent", + "source": "amazon_massive", + "query_hash": "28c68a5466fc5c52be79b112423ff1fc7bd7cada5c9036b8b97a8b038804fe9c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 468, + "historical_id": "semantic:validation:fastbrain_026423:present", + "source": "amazon_massive", + "query_hash": "28c68a5466fc5c52be79b112423ff1fc7bd7cada5c9036b8b97a8b038804fe9c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026500", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:23026e3b4cd93cc0d70f191a505620c3", + "normalized_query_sha256": "95bb725036f6ae85f3308bbb0604972908a06f7a80c07ed311968c218c19b125", + "query": "rmation\"** which means:\n\n- **✅ Acceptable**: Approved electronic signature tools (DocuSign, Adobe Sign, HelloSign)\n- **❌ Not Acceptable**: Scanned PDF of physical signature\n- **Why**: E-signature tools provide audit trails, timestamp verification, and compliance with California Civil Code requirements for commercial lease modifications.\n\n## **Recommended Next Steps:**\n\n1. **For the Owner**: \n - Direct them to the owner portal to generate the verification token\n - If they encounter issues, they can contact owner support\n\n2. **For Your Signature**:\n - Use one of our integrated e-signature platforms\n - Ensure the signature includes your name, date, and tenant ID (T-7788)\n\n3. **Complete Submission**:\n Once you have both the owner verification token and your e-signature, you can proceed with the `modify_lease_agreement` tool submission.\n\nWould you like me to assist the property owner with identity verification, or do you need guidance on accessing the e-signature platform?\nuser: I've obtained the owner verification token from the property owner and completed the e-signature via DocuSign. Here are the details to proceed: \n- **Lease ID**: L-7788 \n- **Modification Type**: Sublease \n- **Confirmation Signature**: DocuSign e-signature (reference #DS-987654321) \n- **Owner Verification Token**: OV-2024-07-7788 \n\nCould you please process the sublease authorization using the `modify_lease_agreement` tool with these parameters? Let me know if additional validation is required.", + "target": "modify_lease_agreement", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 778, + "historical_id": "fastbrain_026500", + "source": "nemotron_agentic", + "query_hash": "95bb725036f6ae85f3308bbb0604972908a06f7a80c07ed311968c218c19b125" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 778, + "historical_id": "fastbrain_026500", + "source": "nemotron_agentic", + "query_hash": "95bb725036f6ae85f3308bbb0604972908a06f7a80c07ed311968c218c19b125" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026677", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:da3b551a9089829cd6a6b7e608978933", + "normalized_query_sha256": "5f46d6c4efabfd90b463e61ca29342e179abc5581810445d454b5b6221c470fd", + "query": "quirement to exhaust our coverage first before filing with your insurance provider. You can pursue both claims simultaneously if you wish.\n\n**To Initiate Your Claim**: I need two pieces of information:\n1. **Exact discharge timestamp**: Please provide the specific time (including timezone) when the container was discharged on February 28, 2025.\n2. **Documentation**: Once you've uploaded the signed delivery receipt, inspection report, and damage photos to your secure portal, please provide the document references or confirm they're ready for processing.\n\n**Important Notes**:\n- Our maximum liability remains $2,500 per container\n- Claims must be initiated within 48 hours of discharge (you're within this window)\n- The $700 excess beyond our liability would need to be addressed with your insurance provider\n\nPlease provide the exact discharge timestamp and confirm when your documentation is ready, and I'll initiate the damage claim process for container CNEU1234567.\nuser: The container was discharged at 3:45 PM PST on February 28, 2025. The documentation has been uploaded to the secure portal with reference numbers: \n- Delivery Receipt: DR-REF-DAM457-01 \n- Inspection Report: IR-REF-DAM457-02 \n- Damage Photos: DP-REF-DAM457-03 \n\nPlease confirm receipt of these documents and proceed with initiating the $2,500 liability claim. For the $700 excess, we will follow up directly with [Insurance Provider] as advised. Is there anything else required from our side to expedite this process?", + "target": "initiate_damage_claim", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 112, + "historical_id": "fastbrain_026677", + "source": "nemotron_agentic", + "query_hash": "5f46d6c4efabfd90b463e61ca29342e179abc5581810445d454b5b6221c470fd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 112, + "historical_id": "fastbrain_026677", + "source": "nemotron_agentic", + "query_hash": "5f46d6c4efabfd90b463e61ca29342e179abc5581810445d454b5b6221c470fd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026744", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:217643e78ebd672ce7c25c9fe11e8ed8", + "normalized_query_sha256": "67b6f398da29c32fd70bbb7ae4f7f80ef9a9cb9d66f8d5a9bc09a9247b1e4ef8", + "query": "let me know if i have new emails", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 736, + "historical_id": "semantic:validation:fastbrain_026744:present", + "source": "amazon_massive", + "query_hash": "67b6f398da29c32fd70bbb7ae4f7f80ef9a9cb9d66f8d5a9bc09a9247b1e4ef8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 959, + "historical_id": "semantic:validation:fastbrain_026744:absent", + "source": "amazon_massive", + "query_hash": "67b6f398da29c32fd70bbb7ae4f7f80ef9a9cb9d66f8d5a9bc09a9247b1e4ef8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026817", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6f83c19baeabf55de31189dfbe1d4c76", + "normalized_query_sha256": "59a3a7ec4ecf04f428e07cfed082d42ba4e56512fce5ed68e7fd4566288ce1d2", + "query": "I would like to locate a Dermatologist near Los Altos.", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1020, + "historical_id": "fastbrain_026817", + "source": "sgd", + "query_hash": "59a3a7ec4ecf04f428e07cfed082d42ba4e56512fce5ed68e7fd4566288ce1d2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1020, + "historical_id": "fastbrain_026817", + "source": "sgd", + "query_hash": "59a3a7ec4ecf04f428e07cfed082d42ba4e56512fce5ed68e7fd4566288ce1d2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026903", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:34d7ef302d716c4ae966cd8775b72dad", + "normalized_query_sha256": "e14fbeec306df77a21bce0af345c69da1f6eced19b036847a0151a088fe6ad87", + "query": "This sounds good, could you help me search for a bus ride there?", + "target": "FindBus", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 937, + "historical_id": "fastbrain_026903", + "source": "sgd", + "query_hash": "e14fbeec306df77a21bce0af345c69da1f6eced19b036847a0151a088fe6ad87" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 937, + "historical_id": "fastbrain_026903", + "source": "sgd", + "query_hash": "e14fbeec306df77a21bce0af345c69da1f6eced19b036847a0151a088fe6ad87" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_026981", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:56dbe170a6cbf15037fd699711aab403", + "normalized_query_sha256": "d87523c03f92417c3f1d8959655c773ca80e06e2a02f328552fd48d621f3fa48", + "query": "Archive \"source\" to \"destination\" via ssh on port \"PORT_NUMBER\"", + "target": "rsync", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 618, + "historical_id": "fastbrain_026981", + "source": "nl2bash", + "query_hash": "d87523c03f92417c3f1d8959655c773ca80e06e2a02f328552fd48d621f3fa48" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 618, + "historical_id": "fastbrain_026981", + "source": "nl2bash", + "query_hash": "d87523c03f92417c3f1d8959655c773ca80e06e2a02f328552fd48d621f3fa48" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027005", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9d5692e6dc39d7b18a9df74a2ab873b2", + "normalized_query_sha256": "0745e514662e4f13f507d1842df10cf02feed33a19c0395ffb19ab7a9c635847", + "query": "Can you help me find dates for events?", + "target": "GetEventDates", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 576, + "historical_id": "fastbrain_027005", + "source": "sgd", + "query_hash": "0745e514662e4f13f507d1842df10cf02feed33a19c0395ffb19ab7a9c635847" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 576, + "historical_id": "fastbrain_027005", + "source": "sgd", + "query_hash": "0745e514662e4f13f507d1842df10cf02feed33a19c0395ffb19ab7a9c635847" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027105", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:62b88f28f10c02dfbc34cca875361749", + "normalized_query_sha256": "ba72797c3e2d6aa6ffe6bc9875cd192ca5496885cb20767169beb1aa1b1a7ab6", + "query": "should i open the sun roof", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 315, + "historical_id": "semantic:validation:fastbrain_027105:absent", + "source": "amazon_massive", + "query_hash": "ba72797c3e2d6aa6ffe6bc9875cd192ca5496885cb20767169beb1aa1b1a7ab6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 781, + "historical_id": "semantic:validation:fastbrain_027105:present", + "source": "amazon_massive", + "query_hash": "ba72797c3e2d6aa6ffe6bc9875cd192ca5496885cb20767169beb1aa1b1a7ab6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027123", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e194c5455089d652963b1297b5db35fb", + "normalized_query_sha256": "5139fcf011d27389d9756f6b008d1706981faed12751128db288c105576303b1", + "query": "I am working on a dashboard for a financial exchanges data platform and need to display real-time currency conversion information to users. Specifically, I require the latest exchange rate from USD to EUR, and then I need to show how much 1000 USD would be in EUR using that rate. Please execute the necessary functions to retrieve the latest USD to EUR exchange rate and also convert an amount of 10", + "target": "get_latest_exchange_rate", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 183, + "historical_id": "fastbrain_027123", + "source": "hermes_v1", + "query_hash": "5139fcf011d27389d9756f6b008d1706981faed12751128db288c105576303b1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 183, + "historical_id": "fastbrain_027123", + "source": "hermes_v1", + "query_hash": "5139fcf011d27389d9756f6b008d1706981faed12751128db288c105576303b1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027370", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c502ecc8199650c2cd660532b6bd3eba", + "normalized_query_sha256": "b7d67aec0b5d40ad5c092a951efd741c42f141c5fd2e6b5d8ba32146cd533baf", + "query": "OK, that sounds good. I would like 1 ticket.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 681, + "historical_id": "fastbrain_027370", + "source": "sgd", + "query_hash": "b7d67aec0b5d40ad5c092a951efd741c42f141c5fd2e6b5d8ba32146cd533baf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 681, + "historical_id": "fastbrain_027370", + "source": "sgd", + "query_hash": "b7d67aec0b5d40ad5c092a951efd741c42f141c5fd2e6b5d8ba32146cd533baf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027409", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:282713176db25b8cba1c20d63ba5f855", + "normalized_query_sha256": "618b04149f029213b0d9bd97f977a2d786d427e770763ecf2bc151cf6c38e438", + "query": "hi google please remove the dinner plan on thursday", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 686, + "historical_id": "semantic:validation:fastbrain_027409:absent", + "source": "amazon_massive", + "query_hash": "618b04149f029213b0d9bd97f977a2d786d427e770763ecf2bc151cf6c38e438" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 944, + "historical_id": "semantic:validation:fastbrain_027409:present", + "source": "amazon_massive", + "query_hash": "618b04149f029213b0d9bd97f977a2d786d427e770763ecf2bc151cf6c38e438" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027531", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e0f552c8c57de66b87adedc1b6b888f6", + "normalized_query_sha256": "4a0523601c13326a2b3c2be2adee1a2bacda9350631f235a2d78ff45a966b5d3", + "query": "Could you help me find a good place to eat at that is not very costly and maybe has a diner atmosphere?", + "target": "FindRestaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 654, + "historical_id": "fastbrain_027531", + "source": "sgd", + "query_hash": "4a0523601c13326a2b3c2be2adee1a2bacda9350631f235a2d78ff45a966b5d3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 654, + "historical_id": "fastbrain_027531", + "source": "sgd", + "query_hash": "4a0523601c13326a2b3c2be2adee1a2bacda9350631f235a2d78ff45a966b5d3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027543", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:08586162021230877d1bcafec27a7d46", + "normalized_query_sha256": "37b2438c3247dc3a421d852a1296e082788787049853d47686c459b4bbad8cfd", + "query": "plicit reservation ID \n - Never waive fees outside policy guidelines \n\n2. **Verification Protocol** \n - Always confirm deposit holds via `check_deposit_status` before discussing releases \n - Never assume cancellations without weather advisory verification \n\n3. **Information Restrictions** \n - Do not advise on mechanical issues beyond diagnostic tools \n - Do not interpret insurance policies \n - Never suggest marine routes/navigation \n\n4. **Tool Dependency** \n - All actions require explicit tool execution \n - Confirm successful tool completion before notifying customer \n - Never claim action taken without tool confirmation\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling about an emergency with my sailboat's engine. Our Hunter 33.5 has maintenance ticket MTCE-2091 from two hours ago - the engine completely failed during our weekend trip, and we're stranded. We need same-day repair service as soon as possible.\nassistant: Hello! I understand this is an urgent situation with your sailboat's engine. First, I need to verify your identity to access your account and schedule emergency service.\n\nPlease provide the email address or phone number associated with your Coastal Haven Marina account so I can send you a verification code.\nuser: The phone number associated with our account is (555) 123-4567. Please send the verification code there. The engine failure is urgent - we're currently drifting in the bay with no power.", + "target": "send_auth_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 2, + "historical_id": "fastbrain_027543", + "source": "nemotron_agentic", + "query_hash": "37b2438c3247dc3a421d852a1296e082788787049853d47686c459b4bbad8cfd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 2, + "historical_id": "fastbrain_027543", + "source": "nemotron_agentic", + "query_hash": "37b2438c3247dc3a421d852a1296e082788787049853d47686c459b4bbad8cfd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027558", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0004fbd8a6bbbba31d270a0103c2b7cd", + "normalized_query_sha256": "9d50ffe4a3fd5c11e383a262e6a29eba75dfc31dc0a217f9e2a8e505bde26e23", + "query": "I need a room there for 1.", + "target": "SearchHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 319, + "historical_id": "fastbrain_027558", + "source": "sgd", + "query_hash": "9d50ffe4a3fd5c11e383a262e6a29eba75dfc31dc0a217f9e2a8e505bde26e23" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 319, + "historical_id": "fastbrain_027558", + "source": "sgd", + "query_hash": "9d50ffe4a3fd5c11e383a262e6a29eba75dfc31dc0a217f9e2a8e505bde26e23" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027641", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:29a653d0c6a656a38de941a14fb4cc22", + "normalized_query_sha256": "4f5a86c041fbe53e8a484f9fa6ff3db79411e2803dd51abfca2ec717d57a97db", + "query": "give me options for indian carry-out under dollars", + "target": "takeaway_order", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 471, + "historical_id": "fastbrain_027641", + "source": "amazon_massive", + "query_hash": "4f5a86c041fbe53e8a484f9fa6ff3db79411e2803dd51abfca2ec717d57a97db" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 471, + "historical_id": "fastbrain_027641", + "source": "amazon_massive", + "query_hash": "4f5a86c041fbe53e8a484f9fa6ff3db79411e2803dd51abfca2ec717d57a97db" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027713", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:def13070d7f9869c71b81061bf8aeef0", + "normalized_query_sha256": "69c3aca80e6ed468bb011fcd4f11025c147c0e0e0d319d288f59e0b5ce4782a4", + "query": "kill all background processes", + "target": "kill", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 808, + "historical_id": "fastbrain_027713", + "source": "nl2bash", + "query_hash": "69c3aca80e6ed468bb011fcd4f11025c147c0e0e0d319d288f59e0b5ce4782a4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 808, + "historical_id": "fastbrain_027713", + "source": "nl2bash", + "query_hash": "69c3aca80e6ed468bb011fcd4f11025c147c0e0e0d319d288f59e0b5ce4782a4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027724", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7b00b35b5a5a425042bc06ee39844cc9", + "normalized_query_sha256": "dbcb50e4efe22352fada7506091b022b4d450fd9daa597d9f053f5b669a65608", + "query": "check out the code as it was at the commit abc1234", + "target": "git_checkout", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 262, + "historical_id": "fastbrain_027724", + "source": "git_nl_commands", + "query_hash": "dbcb50e4efe22352fada7506091b022b4d450fd9daa597d9f053f5b669a65608" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 262, + "historical_id": "fastbrain_027724", + "source": "git_nl_commands", + "query_hash": "dbcb50e4efe22352fada7506091b022b4d450fd9daa597d9f053f5b669a65608" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_027728", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8e426127bbc17fbf6aa4711f1ece18af", + "normalized_query_sha256": "17c06478d02a83db66a42e55557f22fde40ad353fcad3bec9b0fbbd6eb99100f", + "query": "ord if you'd like me to access your account to provide more specific assistance.\nuser: I appreciate your clarity about the 12-hour rule—I understand the system’s limitations now. Since the session can’t be changed at this point, could you help me access the grief and anxiety resources you mentioned? I’d like to review them to feel more prepared for the video session. Also, if possible, could you guide me on how to schedule future sessions as text-based? I’m not comfortable sharing my user ID and password, but I’d be happy to take notes on the steps for future planning.\nassistant: \ntool: \n\n{\n \"resources\": [\n {\n \"content_id\": \"ART-101\",\n \"title\": \"Navigating Grief Through Mindfulness\",\n \"access_tier\": \"Premium\"\n },\n {\n \"content_id\": \"ART-102\",\n \"title\": \"Coping Strategies for Anxiety in Daily Life\",\n \"access_tier\": \"Basic\"\n },\n {\n \"content_id\": \"ART-105\",\n \"title\": \"Processing Complex Grief: A Self-Guided Approach\",\n \"access_tier\": \"Premium\"\n }\n ]\n}\nassistant: \ntool: \n\n{\n \"resources\": [\n {\n \"content_id\": \"MED-201\",\n \"title\": \"Guided Grief Meditation: Finding Peace in the Present\",\n \"access_tier\": \"Premium\"\n },\n {\n \"content_id\": \"MED-202\",\n \"title\": \"5-Minute Anxiety Relief Breathing Exercise\",\n \"access_tier\": \"Basic\"\n },\n {\n \"content_id\": \"MED-204\",\n \"title\": \"Combining Grief and Anxiety Management Through Mindfulness\",\n \"access_tier\": \"Plus\"\n }\n ]\n}", + "target": "access_resources", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 916, + "historical_id": "fastbrain_027728", + "source": "nemotron_agentic", + "query_hash": "17c06478d02a83db66a42e55557f22fde40ad353fcad3bec9b0fbbd6eb99100f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 916, + "historical_id": "fastbrain_027728", + "source": "nemotron_agentic", + "query_hash": "17c06478d02a83db66a42e55557f22fde40ad353fcad3bec9b0fbbd6eb99100f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028030", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:91ae58221b0e2a6b8b5a6998f37f99cd", + "normalized_query_sha256": "112c6210706c3a2aa7a86ce879efe521de3b8e3e84c140a3ab6b1d3f5a257252", + "query": "Yes great. make a reservation there", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 883, + "historical_id": "fastbrain_028030", + "source": "sgd", + "query_hash": "112c6210706c3a2aa7a86ce879efe521de3b8e3e84c140a3ab6b1d3f5a257252" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 883, + "historical_id": "fastbrain_028030", + "source": "sgd", + "query_hash": "112c6210706c3a2aa7a86ce879efe521de3b8e3e84c140a3ab6b1d3f5a257252" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028097", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cd02e8f02cfc28f983d77cf60e7c853c", + "normalized_query_sha256": "6e65b3dc51f2112d1163ade9ccb2e4d4aeeb68be6603f6381b2eeb147fb9021b", + "query": "there anything else I can help you with regarding non-hazardous shipments?\nuser: \n\nI understand your policy regarding hazardous materials. Since the package contains sulfuric acid, which is prohibited, I need to request immediate cancellation of my order. Could you assist with processing the cancellation and confirming there are no additional fees or steps required?\nassistant: I'd be happy to help you cancel your order. However, I need some information to verify your identity and locate your order first.\n\nTo proceed with cancellation, please provide:\n1. **Your order ID**\n2. **Identity verification** - Choose one of these options:\n - Your User ID + registered phone number\n OR\n - Your full name + pickup address\n\nOnce I have this information, I can verify your identity, check the order status, and process the cancellation according to our policies. The cancellation fees depend on whether the courier has already picked up the package.\n\nCould you please provide these details?\nuser: Here’s the information you requested: \n1. **Order ID**: ORD123456 \n2. **Identity verification**: Full name – John Doe, Pickup address – 123 Lab Street, Science University Campus \n\nCould you confirm if there are any cancellation fees associated with this order, and proceed with the cancellation? I’d also appreciate a confirmation email or message once it’s completed.\nassistant: \ntool: \n\n{\"verified\": true, \"user_id\": \"USER789\"}\nassistant: \ntool: \n\n{\"order_id\": \"ORD123456\", \"status\": \"pre_pickup\"}", + "target": "cancel_order", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 275, + "historical_id": "fastbrain_028097", + "source": "nemotron_agentic", + "query_hash": "6e65b3dc51f2112d1163ade9ccb2e4d4aeeb68be6603f6381b2eeb147fb9021b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 275, + "historical_id": "fastbrain_028097", + "source": "nemotron_agentic", + "query_hash": "6e65b3dc51f2112d1163ade9ccb2e4d4aeeb68be6603f6381b2eeb147fb9021b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028232", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:47f1305cd46074aa56b27ad2a0887fa2", + "normalized_query_sha256": "50422584c160c9aa67e7b22363c6d6ed87ba4cc6910f6f06f49dbf2404762687", + "query": "cook me some oats", + "target": "cooking_recipe", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 402, + "historical_id": "fastbrain_028232", + "source": "amazon_massive", + "query_hash": "50422584c160c9aa67e7b22363c6d6ed87ba4cc6910f6f06f49dbf2404762687" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 402, + "historical_id": "fastbrain_028232", + "source": "amazon_massive", + "query_hash": "50422584c160c9aa67e7b22363c6d6ed87ba4cc6910f6f06f49dbf2404762687" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028250", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6d43b74d8660015264344a9ec1aff935", + "normalized_query_sha256": "4a1c961cd5ce83b0bea4cd0b5a072679b81413090a816a90543d5e7c17a7c6b7", + "query": "tweet a complaint about potholes on road", + "target": "social_post", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 256, + "historical_id": "fastbrain_028250", + "source": "amazon_massive", + "query_hash": "4a1c961cd5ce83b0bea4cd0b5a072679b81413090a816a90543d5e7c17a7c6b7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 256, + "historical_id": "fastbrain_028250", + "source": "amazon_massive", + "query_hash": "4a1c961cd5ce83b0bea4cd0b5a072679b81413090a816a90543d5e7c17a7c6b7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028336", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:91fa5846fc4f808fb0c002388495da90", + "normalized_query_sha256": "f5361f8c309d9e6853266fc1aa7ea43c882984ae51220159351b077ed7f06cc2", + "query": "I need help buying tickets for this Outlaw Music Festival. I've been wanting to go there ever since I've heard that it is good. I would love to find it in NYC.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 685, + "historical_id": "fastbrain_028336", + "source": "sgd", + "query_hash": "f5361f8c309d9e6853266fc1aa7ea43c882984ae51220159351b077ed7f06cc2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 685, + "historical_id": "fastbrain_028336", + "source": "sgd", + "query_hash": "f5361f8c309d9e6853266fc1aa7ea43c882984ae51220159351b077ed7f06cc2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028353", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2ee5bf058139ec9b48ff27882cdd9dc7", + "normalized_query_sha256": "eec53a98b55d9c2488a98342394759bb1cdea09d77e4ad5671972cb481394532", + "query": "No. That's ok. What time is it playing.", + "target": "GetTimesForMovie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 25, + "historical_id": "fastbrain_028353", + "source": "sgd", + "query_hash": "eec53a98b55d9c2488a98342394759bb1cdea09d77e4ad5671972cb481394532" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 25, + "historical_id": "fastbrain_028353", + "source": "sgd", + "query_hash": "eec53a98b55d9c2488a98342394759bb1cdea09d77e4ad5671972cb481394532" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028476", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c6be13778475f70550655c353e5edca1", + "normalized_query_sha256": "a2898ef49e232a1f50e7414590b13b5521b4e8fbad5479ac30343891a2469560", + "query": "I need a rental car for pickup at 15:30.", + "target": "GetCarsAvailable", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 548, + "historical_id": "fastbrain_028476", + "source": "sgd", + "query_hash": "a2898ef49e232a1f50e7414590b13b5521b4e8fbad5479ac30343891a2469560" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 548, + "historical_id": "fastbrain_028476", + "source": "sgd", + "query_hash": "a2898ef49e232a1f50e7414590b13b5521b4e8fbad5479ac30343891a2469560" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028558", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d195483794bc34321b91fe1d32bbddf4", + "normalized_query_sha256": "595f36ace28cd3d4281c8323998a2380b250cf89250ca0d65cd3a16c6a948e1a", + "query": "I am interested in watching adventure movies in imax show.", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 697, + "historical_id": "fastbrain_028558", + "source": "sgd", + "query_hash": "595f36ace28cd3d4281c8323998a2380b250cf89250ca0d65cd3a16c6a948e1a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 697, + "historical_id": "fastbrain_028558", + "source": "sgd", + "query_hash": "595f36ace28cd3d4281c8323998a2380b250cf89250ca0d65cd3a16c6a948e1a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028587", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ff03dd39dfe2c4e394cdc7ce657183c0", + "normalized_query_sha256": "b137b8a4c9fe14d6a48e4a981adaa75647d47784d02ae13dfa9c06dec82cc281", + "query": "play my favorite game by name naughty dog", + "target": "play_game", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 222, + "historical_id": "semantic:validation:fastbrain_028587:absent", + "source": "amazon_massive", + "query_hash": "b137b8a4c9fe14d6a48e4a981adaa75647d47784d02ae13dfa9c06dec82cc281" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 816, + "historical_id": "semantic:validation:fastbrain_028587:present", + "source": "amazon_massive", + "query_hash": "b137b8a4c9fe14d6a48e4a981adaa75647d47784d02ae13dfa9c06dec82cc281" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028598", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:19bfbc64f51034f72df07892a7cbf35d", + "normalized_query_sha256": "4cf6ce20617f26f269a259fd18e454f13fb83802f447335d112389208809f7de", + "query": "where should i go for mexican food in my area", + "target": "recommendation_locations", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 103, + "historical_id": "semantic:validation:fastbrain_028598:present", + "source": "amazon_massive", + "query_hash": "4cf6ce20617f26f269a259fd18e454f13fb83802f447335d112389208809f7de" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 114, + "historical_id": "semantic:validation:fastbrain_028598:absent", + "source": "amazon_massive", + "query_hash": "4cf6ce20617f26f269a259fd18e454f13fb83802f447335d112389208809f7de" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028714", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:60b094e7ed6517336b7e77606fe1dbdb", + "normalized_query_sha256": "5b55f9b42cf5b65ff04d0b2ae642c4586109ac2a9d2728cf5328d369571f1d94", + "query": "Thank you so much. I want to listen a song", + "target": "LookupSong", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 216, + "historical_id": "fastbrain_028714", + "source": "sgd", + "query_hash": "5b55f9b42cf5b65ff04d0b2ae642c4586109ac2a9d2728cf5328d369571f1d94" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 216, + "historical_id": "fastbrain_028714", + "source": "sgd", + "query_hash": "5b55f9b42cf5b65ff04d0b2ae642c4586109ac2a9d2728cf5328d369571f1d94" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028914", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b1ff1ff615e392320004ab8eeb4df14f", + "normalized_query_sha256": "92605f942f2b85410fb17a2938a652b55dd067a380be9fcc56e127136d51ac4c", + "query": "I need a cab for 1, shared please.", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 157, + "historical_id": "fastbrain_028914", + "source": "sgd", + "query_hash": "92605f942f2b85410fb17a2938a652b55dd067a380be9fcc56e127136d51ac4c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 157, + "historical_id": "fastbrain_028914", + "source": "sgd", + "query_hash": "92605f942f2b85410fb17a2938a652b55dd067a380be9fcc56e127136d51ac4c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_028952", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2c84fbf15c3eee68c69f3ae15cbed393", + "normalized_query_sha256": "dac67bd8291abf8ffdd06dc7bf3284c667b87183dec25dd996a97607fb9cdfdd", + "query": "Thanks. Will you add an entry to my calendar? Use Property appointment as event title and three pm as event time.", + "target": "AddEvent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 63, + "historical_id": "fastbrain_028952", + "source": "sgd", + "query_hash": "dac67bd8291abf8ffdd06dc7bf3284c667b87183dec25dd996a97607fb9cdfdd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 63, + "historical_id": "fastbrain_028952", + "source": "sgd", + "query_hash": "dac67bd8291abf8ffdd06dc7bf3284c667b87183dec25dd996a97607fb9cdfdd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029048", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:437226149cf0cadf1d20976fc849212d", + "normalized_query_sha256": "c37f67789b546ec79a70b69c06e9c225eb28db5c97c37faefb022af781646ed8", + "query": "e check-in | 50% | \n| Peak/Holiday | ≥72 hours before check-in | 75% | \n\n### Check-In/Out \n- **Early Check-In**: \n - Before 1 PM: $25 fee (waived for Platinum guests if room ready) \n- **Late Check-Out**: \n - Up to 1 PM: Free \n - After 1 PM: $50/hour (max 3 hours) \n\n### Loyalty Benefits \n- **Room Upgrades**: \n - Silver: Subject to availability (10% chance) \n - Gold/Platinum: Complimentary if available ≥24 hours pre-check-in \n- **Point Redemption**: \n - 500 points = $10 dining credit \n - 2,000 points = 1 free night (Standard room only) \n\n### Compensation Protocol \n- **Cleaning Issues**: $50 credit/voucher \n- **Room Not Ready by 4 PM**: 10% refund of nightly rate \n- **Overbooking**: \n - Relocate to equal/higher category property \n - Provide $200 voucher \n\n## Escalation Protocol \nTransfer to human agent for: \n- Group bookings (8+ rooms) \n- ADA accessibility issues \n- Security incidents \n- Compensation requests over $250 \n\n**Process**: \n1. Use `transfer_to_human_agents` tool \n2. Send message: \"I'm transferring you to a human agent. Please hold.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm calling about my hotel reservation (RES01210) under Dean. The payment was declined because my card expired. I need to update my payment method to ensure the reservation stays active. Can you assist with securely updating my credit card information?\nassistant: \ntool: \n\n{\"guest_id\": \"G004821\", \"auth_token\": \"A3s9x7Lm2pQ0bR4nT8kZ\"}", + "target": "get_reservation_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 44, + "historical_id": "fastbrain_029048", + "source": "nemotron_agentic", + "query_hash": "c37f67789b546ec79a70b69c06e9c225eb28db5c97c37faefb022af781646ed8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 44, + "historical_id": "fastbrain_029048", + "source": "nemotron_agentic", + "query_hash": "c37f67789b546ec79a70b69c06e9c225eb28db5c97c37faefb022af781646ed8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029063", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:53e4572226bbeae26e1d2a58b8276430", + "normalized_query_sha256": "bf54dae873b2f443398383112907564065a6f8510c78eaf49db2aa48b2b6a615", + "query": "today's forecast", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 40, + "historical_id": "semantic:validation:fastbrain_029063:absent", + "source": "amazon_massive", + "query_hash": "bf54dae873b2f443398383112907564065a6f8510c78eaf49db2aa48b2b6a615" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 689, + "historical_id": "semantic:validation:fastbrain_029063:present", + "source": "amazon_massive", + "query_hash": "bf54dae873b2f443398383112907564065a6f8510c78eaf49db2aa48b2b6a615" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029132", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:00c8482ba067b20710f91f5f11861906", + "normalized_query_sha256": "a7055e2c2e663dce20c99b25ddcb7597060e9b1b15f057e052329d2fec2d283e", + "query": "find out if kfc delivers please", + "target": "takeaway_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 80, + "historical_id": "fastbrain_029132", + "source": "amazon_massive", + "query_hash": "a7055e2c2e663dce20c99b25ddcb7597060e9b1b15f057e052329d2fec2d283e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 80, + "historical_id": "fastbrain_029132", + "source": "amazon_massive", + "query_hash": "a7055e2c2e663dce20c99b25ddcb7597060e9b1b15f057e052329d2fec2d283e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029241", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cc02604d5aa20b2bb7ed8205b980c259", + "normalized_query_sha256": "c3df35048ef1cff7cdb022a2df1812c31126bf12c244ca6057f1a9ad7bc440a0", + "query": "Search the home directory for files whose names begin with \"test\"", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 929, + "historical_id": "fastbrain_029241", + "source": "nl2bash", + "query_hash": "c3df35048ef1cff7cdb022a2df1812c31126bf12c244ca6057f1a9ad7bc440a0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 929, + "historical_id": "fastbrain_029241", + "source": "nl2bash", + "query_hash": "c3df35048ef1cff7cdb022a2df1812c31126bf12c244ca6057f1a9ad7bc440a0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029242", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d7857b6e35ca04a23a3244c944991ff1", + "normalized_query_sha256": "d7e54ebcdc72c93e519cbabf2077744c34044ee4d83e1109537e63f643681d12", + "query": "derstand invoices should be treated as unpaid until system confirmation. Could you please verify the status and let me know if you need the payment receipt for a manual check?\nassistant: Hello! I'd be happy to help you verify the payment status for invoice INV-4477. First, I need to authenticate your account for security purposes.\n\nCould you please provide the email address or phone number associated with your TuneSync account? Once you provide this, I'll send you a verification code to confirm your identity.\nuser: Sure, the email associated with our account is **music.supervisor@realityshow.com**. Please send the verification code to that address. Once confirmed, I’d appreciate a prompt update on the payment status for **INV-4477** and whether you require the payment receipt for further verification.\nassistant: \ntool: \n\n{\"sent\": true, \"method\": \"email\"}\nassistant: I've sent a verification code to **music.supervisor@realityshow.com**. Please check your email and provide the code you received so I can authenticate your account.\n\nOnce verified, I'll immediately check the payment status for invoice INV-4477 and let you know if we need the payment receipt for further verification.\nuser: The verification code I received is **123456**. Please proceed with authenticating my account so I can get an update on the payment status for **INV-4477**. If the receipt is required, let me know and I’ll provide it immediately.\nassistant: \ntool: \n\n{\"authenticated\": true, \"user_id\": \"USR-987654\"}", + "target": "confirm_payment_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 524, + "historical_id": "fastbrain_029242", + "source": "nemotron_agentic", + "query_hash": "d7e54ebcdc72c93e519cbabf2077744c34044ee4d83e1109537e63f643681d12" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 524, + "historical_id": "fastbrain_029242", + "source": "nemotron_agentic", + "query_hash": "d7e54ebcdc72c93e519cbabf2077744c34044ee4d83e1109537e63f643681d12" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029314", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f1906433578a53424c9c0bb4621c63c2", + "normalized_query_sha256": "535f9f085a08412f0e6ce06432db3b0c9e8d128dd00cfb9698e6b1306cbdf616", + "query": "Thanks. Now please find me an apartment in the area.", + "target": "FindApartment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 974, + "historical_id": "fastbrain_029314", + "source": "sgd", + "query_hash": "535f9f085a08412f0e6ce06432db3b0c9e8d128dd00cfb9698e6b1306cbdf616" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 974, + "historical_id": "fastbrain_029314", + "source": "sgd", + "query_hash": "535f9f085a08412f0e6ce06432db3b0c9e8d128dd00cfb9698e6b1306cbdf616" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029534", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3fddd6d0ed13bb78da42b49229fa9aef", + "normalized_query_sha256": "381ed984b1b0e7915f42a8942b135fff285a5d836471801aca76cf3d69d593bd", + "query": "four p. m.", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 10, + "historical_id": "fastbrain_029534", + "source": "amazon_massive", + "query_hash": "381ed984b1b0e7915f42a8942b135fff285a5d836471801aca76cf3d69d593bd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 10, + "historical_id": "fastbrain_029534", + "source": "amazon_massive", + "query_hash": "381ed984b1b0e7915f42a8942b135fff285a5d836471801aca76cf3d69d593bd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029771", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e4a0b459e8c2ee1bf0e9c5e0b950bbcc", + "normalized_query_sha256": "2076d200c633c69a6d16096274eb1180f5221d83365b1dbae957f27d97ebeb06", + "query": "play nineties hip hop", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 489, + "historical_id": "semantic:validation:fastbrain_029771:absent", + "source": "amazon_massive", + "query_hash": "2076d200c633c69a6d16096274eb1180f5221d83365b1dbae957f27d97ebeb06" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 583, + "historical_id": "semantic:validation:fastbrain_029771:present", + "source": "amazon_massive", + "query_hash": "2076d200c633c69a6d16096274eb1180f5221d83365b1dbae957f27d97ebeb06" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029826", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c45e66477afa99a821e11f1070c257af", + "normalized_query_sha256": "7453a68c25b331c041422d815e9607912e52f5bc16ec488e44f48e7d77c8c24a", + "query": "what would be the easiest way to become a millionaire in less than five years", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 313, + "historical_id": "semantic:validation:fastbrain_029826:present", + "source": "amazon_massive", + "query_hash": "7453a68c25b331c041422d815e9607912e52f5bc16ec488e44f48e7d77c8c24a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 688, + "historical_id": "semantic:validation:fastbrain_029826:absent", + "source": "amazon_massive", + "query_hash": "7453a68c25b331c041422d815e9607912e52f5bc16ec488e44f48e7d77c8c24a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_029898", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1519544b77da1f8eaead2779857c12ff", + "normalized_query_sha256": "69e60bb80fee36e1c6fdebb19612f24fd75d0110442d5e6157eb0f6ce5a3e4c2", + "query": "you have to check out kfi he's the best", + "target": "music_likeness", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 321, + "historical_id": "semantic:validation:fastbrain_029898:present", + "source": "amazon_massive", + "query_hash": "69e60bb80fee36e1c6fdebb19612f24fd75d0110442d5e6157eb0f6ce5a3e4c2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 505, + "historical_id": "semantic:validation:fastbrain_029898:absent", + "source": "amazon_massive", + "query_hash": "69e60bb80fee36e1c6fdebb19612f24fd75d0110442d5e6157eb0f6ce5a3e4c2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030037", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d53da6d2e1d6e55b3b0be964670d8c91", + "normalized_query_sha256": "78bce65c2f680060a82e14a643d227fc94d1422bd1bb1da30ab97f997e72b7ee", + "query": "It is good. I wish to make a booking at a La Penca Azul restaurant around there for three people at one pm.", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 737, + "historical_id": "fastbrain_030037", + "source": "sgd", + "query_hash": "78bce65c2f680060a82e14a643d227fc94d1422bd1bb1da30ab97f997e72b7ee" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 737, + "historical_id": "fastbrain_030037", + "source": "sgd", + "query_hash": "78bce65c2f680060a82e14a643d227fc94d1422bd1bb1da30ab97f997e72b7ee" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030045", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3a9cf75c1953399f17d46196ca8f959d", + "normalized_query_sha256": "caa9c19c56759a1d38923249b87e881ab0ff2c84a400ce6ca033d8a477e8c8bd", + "query": "details to unverified contacts \n- **Never override** validation checks \n\n**Compliance Requirements** \n- Log all actions with patient ID/timestamp \n- Use HIPAA-compliant channels \n- Retain electronic PHI for 7 years \n- Destroy PHI after retention period\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm calling because I need to check if my new prescription for Sertraline will interact with my current medications, Warfarin and Metformin. I'd like a safety review before starting the antidepressant, as I want to ensure there are no dangerous conflicts. Could you help with that?\nassistant: Hello! I'd be happy to help you check for potential interactions between your new Sertraline prescription and your current medications. For medication safety reviews, I need to verify your identity first and then access your prescription information.\n\n**To proceed, I'll need:**\n1. Your Patient ID\n2. Your Date of Birth (YYYY-MM-DD)\n3. The prescription number (Rx number) for your new Sertraline prescription\n\nOnce I have this information, I can authenticate you and run a comprehensive interaction check against your current medications, including Warfarin and Metformin.\n\nCould you please provide these details?\nuser: My Patient ID is 123456, my date of birth is 1955-08-22, and the prescription number for my Sertraline is RX987654. Can you now check for interactions with my Warfarin and Metformin? I’m especially concerned about bleeding risks since I’m on anticoagulants.", + "target": "authenticate_patient", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 962, + "historical_id": "fastbrain_030045", + "source": "nemotron_agentic", + "query_hash": "caa9c19c56759a1d38923249b87e881ab0ff2c84a400ce6ca033d8a477e8c8bd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 962, + "historical_id": "fastbrain_030045", + "source": "nemotron_agentic", + "query_hash": "caa9c19c56759a1d38923249b87e881ab0ff2c84a400ce6ca033d8a477e8c8bd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030066", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9e7835968ed272e210328ab79eca1f4f", + "normalized_query_sha256": "84a40c989668576e8b21e804b23b2b4ad7b48f66f717500ee45d3195d5298f8f", + "query": "i want to see the news", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 197, + "historical_id": "semantic:validation:fastbrain_030066:present", + "source": "amazon_massive", + "query_hash": "84a40c989668576e8b21e804b23b2b4ad7b48f66f717500ee45d3195d5298f8f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 751, + "historical_id": "semantic:validation:fastbrain_030066:absent", + "source": "amazon_massive", + "query_hash": "84a40c989668576e8b21e804b23b2b4ad7b48f66f717500ee45d3195d5298f8f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030284", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4e9c1b643569209b4a1923b4f54d9425", + "normalized_query_sha256": "57e30d6ca6e0361bee7d89d0254ae2c470e17702117fa7a19d5388b9e12b6fdf", + "query": "Hi, I am looking for some vegan Italian recipes. Can you help me find some?", + "target": "search_recipes", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 475, + "historical_id": "fastbrain_030284", + "source": "glaive_v2", + "query_hash": "57e30d6ca6e0361bee7d89d0254ae2c470e17702117fa7a19d5388b9e12b6fdf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 475, + "historical_id": "fastbrain_030284", + "source": "glaive_v2", + "query_hash": "57e30d6ca6e0361bee7d89d0254ae2c470e17702117fa7a19d5388b9e12b6fdf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030516", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:39e36d0d87ef4e9911295bb987068560", + "normalized_query_sha256": "d47971682f1706d5a16f190f1ef463a99123bbcf48d371e4ff8c1c5e70808a2e", + "query": "i have a meeting tomorrow morning at ten please remind me", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 481, + "historical_id": "semantic:validation:fastbrain_030516:absent", + "source": "amazon_massive", + "query_hash": "d47971682f1706d5a16f190f1ef463a99123bbcf48d371e4ff8c1c5e70808a2e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 974, + "historical_id": "semantic:validation:fastbrain_030516:present", + "source": "amazon_massive", + "query_hash": "d47971682f1706d5a16f190f1ef463a99123bbcf48d371e4ff8c1c5e70808a2e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030602", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:737876fe1419e1bee9f22bf04d843823", + "normalized_query_sha256": "93544fa067721d4cbdd03bdc4d0f4aee7eb8fec0026b7f3134bcc160b2f51e14", + "query": "Hi, I need help with calculating a tip. My bill amount is $50 and I want to give a 20% tip.", + "target": "calculate_tip", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 325, + "historical_id": "fastbrain_030602", + "source": "glaive_v2", + "query_hash": "93544fa067721d4cbdd03bdc4d0f4aee7eb8fec0026b7f3134bcc160b2f51e14" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 325, + "historical_id": "fastbrain_030602", + "source": "glaive_v2", + "query_hash": "93544fa067721d4cbdd03bdc4d0f4aee7eb8fec0026b7f3134bcc160b2f51e14" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030629", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c82068098ca58f90f2ea1999d333df5f", + "normalized_query_sha256": "775be614c682794e2759cd0c4dcd7e5f09d8d9d2c702ed6c3ec14db83e310a9f", + "query": "\",\n \"team_id\": \"SPARROWS_12U\",\n \"family_id\": \"FAM_MARTINEZ_2024\"\n}\n```\nassistant: Great! Your identity has been successfully verified. I can see both Sofia (currently on Eagles) and Diego (currently on Sparrows) are in the U12 Competitive division.\n\nI checked the Falcons team details:\n- **Coach**: Linda Thompson\n- **Current roster**: 18 players (maximum capacity: 20)\n- **Available spots**: 2\n\nThis means there is space for both Sofia and Diego on the Falcons team.\n\n**Before proceeding, I need to ask:**\n1. **What is the reason for requesting this team reassignment?** (The league requires a valid reason such as relocation, scheduling conflicts, etc.)\n\n2. **Are you aware of the team placement priority system?** Returning players with the same coach have priority, and siblings in the same age group are considered for placement together when possible.\n\nOnce you provide the reason, I can proceed with the reassignment requests. Please note that each reassignment requires explicit confirmation from you before I can process it.\nuser: Thank you for checking! The reason for the reassignment is to allow both children to train and compete together as a team, which we believe will strengthen their development and family logistics. I’m aware of the priority system—since they’re siblings in the same age group, I understand the league considers unified placement when possible. Please proceed with the reassignment requests for Sofia and Diego. What are their player IDs? I’ll provide them now.", + "target": "assign_player_to_team", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 582, + "historical_id": "fastbrain_030629", + "source": "nemotron_agentic", + "query_hash": "775be614c682794e2759cd0c4dcd7e5f09d8d9d2c702ed6c3ec14db83e310a9f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 582, + "historical_id": "fastbrain_030629", + "source": "nemotron_agentic", + "query_hash": "775be614c682794e2759cd0c4dcd7e5f09d8d9d2c702ed6c3ec14db83e310a9f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030710", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a4aebcf22ab8c2239f284417b6b66d4b", + "normalized_query_sha256": "7c78779896344336c96f26c9debcf7b0c46fbbb2a5c01382afe65a1aebab29a9", + "query": "luding spam/junk folder) and provide the 6-digit code you received.\n\nOnce you provide the code, I'll validate it and we can proceed.\n\n**Regarding the confidentiality downgrade process:**\nAfter identity verification, I need to check if there are any translation orders associated with document DOC-EN-ES-2023-09-15. Confidentiality level changes typically need to be processed through the order management system, as document classification is tied to specific translation orders.\n\n**For manual deletion requests after 60 days:**\nThis would require escalation to a human agent, as our automated system only handles the standard 30-day auto-deletion. After identity verification, I can transfer you to a specialist who can:\n1. Document your 60-day retention requirement\n2. Set up a manual deletion request process\n3. Provide confirmation of deletion once completed\n\nPlease provide the verification code when you receive it, and let me know if there's a specific order ID associated with this document that you're aware of.\nuser: Verification code received: **487291**. \n\nThere is an associated translation order for this document: **ORDER-2023-EN-ES-0915A**. \n\nPlease proceed with the confidentiality downgrade verification and coordinate with the specialist for the manual deletion process. I’ll await your confirmation once the steps are completed.\nassistant: \ntool: \n\n{\n \"is_valid\": true,\n \"message\": \"Authentication code verified successfully. You may now proceed with the requested actions.\"\n}", + "target": "track_order_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 380, + "historical_id": "fastbrain_030710", + "source": "nemotron_agentic", + "query_hash": "7c78779896344336c96f26c9debcf7b0c46fbbb2a5c01382afe65a1aebab29a9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 380, + "historical_id": "fastbrain_030710", + "source": "nemotron_agentic", + "query_hash": "7c78779896344336c96f26c9debcf7b0c46fbbb2a5c01382afe65a1aebab29a9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030720", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:47cac3d83c09299d3596fb09a20da5cb", + "normalized_query_sha256": "d5724b801e3360bbaa2c866877efc0c3cb7495c3c23be42e7e9033dd05c08e80", + "query": "tyFlags\":[]}\nassistant: Hello! I can help you join a Virtual Queue. First, I've verified that your Ticket ID T262 is valid and you're currently in the park.\n\nHere's the step-by-step process to join a Virtual Queue:\n\n**Step 1: Choose an Attraction**\n- Look for attractions with \"Virtual Queue\" signs or check the park app\n- Each attraction has a unique ID (like \"DRAGON01\" or \"CASTLE02\")\n\n**Step 2: Check Party Size**\n- Virtual Queues allow groups of 1-8 people\n- If you have more than 8 people, you'll need to split into smaller groups\n\n**Step 3: Provide Information**\nTo join a Virtual Queue, I'll need:\n1. **Attraction ID** - Which ride you want to join\n2. **Party Size** - How many people in your group (including yourself)\n\n**Step 4: Join the Queue**\nOnce you give me these details, I can add you to the Virtual Queue. You'll receive:\n- An estimated wait time\n- A notification when it's your turn to ride\n- Instructions on where to go when called\n\n**Important Notes:**\n- You can have up to 2 concurrent Virtual Queues at once\n- You must have scanned your ticket at park entry (which you have)\n- Virtual Queue cancellations are free anytime\n- Groups larger than 8 must split into smaller parties\n\n**Ready to join?** Please tell me:\n1. Which attraction do you want to join? (Attraction ID)\n2. How many people are in your group?\nuser: Thank you for the clear steps! I’d like to join the Virtual Queue for the \"DRAGON01\" attraction with a party size of 2. Can you proceed with adding us to the queue?", + "target": "join_virtual_queue", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1013, + "historical_id": "fastbrain_030720", + "source": "nemotron_agentic", + "query_hash": "d5724b801e3360bbaa2c866877efc0c3cb7495c3c23be42e7e9033dd05c08e80" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1013, + "historical_id": "fastbrain_030720", + "source": "nemotron_agentic", + "query_hash": "d5724b801e3360bbaa2c866877efc0c3cb7495c3c23be42e7e9033dd05c08e80" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030745", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0ec4f182d2820c383cb810e33f75fdf0", + "normalized_query_sha256": "fd5c94843d37f2db4af3fd049780da2bf0bbb982dcfd97ec6612d4685dabb9cf", + "query": "tell me about the federal budget", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 25, + "historical_id": "semantic:validation:fastbrain_030745:absent", + "source": "amazon_massive", + "query_hash": "fd5c94843d37f2db4af3fd049780da2bf0bbb982dcfd97ec6612d4685dabb9cf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 581, + "historical_id": "semantic:validation:fastbrain_030745:present", + "source": "amazon_massive", + "query_hash": "fd5c94843d37f2db4af3fd049780da2bf0bbb982dcfd97ec6612d4685dabb9cf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030775", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d01473761e45a2bb72c396904f10d2b5", + "normalized_query_sha256": "05f271781819d1c87bbd53a11a4074838f43515c1492100fd0f1c1a4119c1b27", + "query": "what's the time in this time zone", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 666, + "historical_id": "semantic:validation:fastbrain_030775:absent", + "source": "amazon_massive", + "query_hash": "05f271781819d1c87bbd53a11a4074838f43515c1492100fd0f1c1a4119c1b27" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 956, + "historical_id": "semantic:validation:fastbrain_030775:present", + "source": "amazon_massive", + "query_hash": "05f271781819d1c87bbd53a11a4074838f43515c1492100fd0f1c1a4119c1b27" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_030851", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4db27110699e303d0f9afe92bb80d950", + "normalized_query_sha256": "db1427c05dcdfbd86ce71043047fd14fc4cd744b91b5168d7c2e4de5969008df", + "query": "Make directories a, b, c, ..., z under path2 as needed.", + "target": "mkdir", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 301, + "historical_id": "fastbrain_030851", + "source": "nl2bash", + "query_hash": "db1427c05dcdfbd86ce71043047fd14fc4cd744b91b5168d7c2e4de5969008df" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 301, + "historical_id": "fastbrain_030851", + "source": "nl2bash", + "query_hash": "db1427c05dcdfbd86ce71043047fd14fc4cd744b91b5168d7c2e4de5969008df" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031221", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:797b285f1c2e200d64d64a0c23f0862d", + "normalized_query_sha256": "8fd5f7ac3322ce34e03d3ed96af3905cef6569846779c92ec0d0f7d7ce252fc2", + "query": "what is the weather today", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 188, + "historical_id": "semantic:validation:fastbrain_031221:absent", + "source": "amazon_massive", + "query_hash": "8fd5f7ac3322ce34e03d3ed96af3905cef6569846779c92ec0d0f7d7ce252fc2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 282, + "historical_id": "semantic:validation:fastbrain_031221:present", + "source": "amazon_massive", + "query_hash": "8fd5f7ac3322ce34e03d3ed96af3905cef6569846779c92ec0d0f7d7ce252fc2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031280", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6ca8cf58bd82a61ac24a7c1e48052a01", + "normalized_query_sha256": "3c637360c89e77556a2ea9ddbf7de58a9b6ed927098521209c3dcb87a3f5d032", + "query": "Can you analyze the sentiment of this text for me? \"I had a wonderful day at the park, the weather was perfect and I felt so relaxed.\"", + "target": "analyze_sentiment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 649, + "historical_id": "fastbrain_031280", + "source": "glaive_v2", + "query_hash": "3c637360c89e77556a2ea9ddbf7de58a9b6ed927098521209c3dcb87a3f5d032" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 649, + "historical_id": "fastbrain_031280", + "source": "glaive_v2", + "query_hash": "3c637360c89e77556a2ea9ddbf7de58a9b6ed927098521209c3dcb87a3f5d032" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031308", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:71e9b61691fd7a7c535bdca3e19d5d5c", + "normalized_query_sha256": "0d232e747615e45d6b36b12cc034c9848b345b9bfefa714489bbf369fc856868", + "query": "Great! I'd like to purchase one ticket.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 383, + "historical_id": "fastbrain_031308", + "source": "sgd", + "query_hash": "0d232e747615e45d6b36b12cc034c9848b345b9bfefa714489bbf369fc856868" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 383, + "historical_id": "fastbrain_031308", + "source": "sgd", + "query_hash": "0d232e747615e45d6b36b12cc034c9848b345b9bfefa714489bbf369fc856868" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031343", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:38fdf0d93bf572deccf5c9de701d331e", + "normalized_query_sha256": "ba41f3b093aaaace8961ee6ec0aba78137bf162b95849fc9f5b39328e909d350", + "query": "where is the closest train depot", + "target": "transport_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 627, + "historical_id": "fastbrain_031343", + "source": "amazon_massive", + "query_hash": "ba41f3b093aaaace8961ee6ec0aba78137bf162b95849fc9f5b39328e909d350" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 627, + "historical_id": "fastbrain_031343", + "source": "amazon_massive", + "query_hash": "ba41f3b093aaaace8961ee6ec0aba78137bf162b95849fc9f5b39328e909d350" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031371", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c2806503f87af6fdf281f7d1e100d25a", + "normalized_query_sha256": "bd9ba5d8d1596d449a8b0abb2f1275c637bfd234ee8bcd6083156c8eb390fe3a", + "query": "Can you list Harris J songs in the album Salam?", + "target": "LookupMusic", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 902, + "historical_id": "fastbrain_031371", + "source": "sgd", + "query_hash": "bd9ba5d8d1596d449a8b0abb2f1275c637bfd234ee8bcd6083156c8eb390fe3a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 902, + "historical_id": "fastbrain_031371", + "source": "sgd", + "query_hash": "bd9ba5d8d1596d449a8b0abb2f1275c637bfd234ee8bcd6083156c8eb390fe3a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031406", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6bc2affb0df74f51e3d22b920050dd0b", + "normalized_query_sha256": "7817e4189d2b27c6e7fe3fbcf6b2836b5be5e5d2aeeffce5efc60c4af3736437", + "query": "ice account #5528. My name is [Name], and I’m a college student currently in a temporary housing lease. Unfortunately, my landlord canceled the lease early due to a broken housing contract, so I’ll need to terminate my service before the contract ends. I’m requesting a waiver for the early termination fee since this is out of my control. Could you check if this qualifies for an exception?\nassistant: Hello! I'd be happy to help you with your termination request. First, I need to verify your identity for security purposes.\n\nCould you please provide either your email address or phone number associated with account #5528? I'll send you an authentication code to verify your identity before accessing your account details.\nuser: Sure, I can provide my email. It’s student5528@example.com. Please send the code there. I really need to resolve this quickly since my housing situation is urgent.\nassistant: \ntool: \n\n{\"confirmation\": true}\nassistant: I've sent an authentication code to student5528@example.com. Please check your email and provide the code you received so I can verify your identity and access your account details. Once verified, I can help you with your termination request and discuss any potential fees or exceptions.\nuser: The code I received is 123456. Can you confirm it’s processed? I’d like to proceed with the termination request and clarify if my situation qualifies for a fee waiver due to the lease cancellation.\nassistant: \ntool: \n\n{\"valid\": true, \"customer_id\": \"5528\"}", + "target": "get_customer_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 315, + "historical_id": "fastbrain_031406", + "source": "nemotron_agentic", + "query_hash": "7817e4189d2b27c6e7fe3fbcf6b2836b5be5e5d2aeeffce5efc60c4af3736437" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 315, + "historical_id": "fastbrain_031406", + "source": "nemotron_agentic", + "query_hash": "7817e4189d2b27c6e7fe3fbcf6b2836b5be5e5d2aeeffce5efc60c4af3736437" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031488", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ca75f3d4478e108e02c1664a837b6a75", + "normalized_query_sha256": "533551306641c050168871613541514a466959b39fa132448b5abd1afa27c6ee", + "query": "make sure there is no events on my calendar", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 693, + "historical_id": "fastbrain_031488", + "source": "amazon_massive", + "query_hash": "533551306641c050168871613541514a466959b39fa132448b5abd1afa27c6ee" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 693, + "historical_id": "fastbrain_031488", + "source": "amazon_massive", + "query_hash": "533551306641c050168871613541514a466959b39fa132448b5abd1afa27c6ee" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031525", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0dc5d302a733fd899e96681828b1165d", + "normalized_query_sha256": "cd29192c1fc35a419d5c7f600407e44c7a0ab16627d00367bcaa5cc311d1b12d", + "query": "find me the nearest chinese for delivery", + "target": "takeaway_order", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 151, + "historical_id": "semantic:validation:fastbrain_031525:absent", + "source": "amazon_massive", + "query_hash": "cd29192c1fc35a419d5c7f600407e44c7a0ab16627d00367bcaa5cc311d1b12d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 465, + "historical_id": "semantic:validation:fastbrain_031525:present", + "source": "amazon_massive", + "query_hash": "cd29192c1fc35a419d5c7f600407e44c7a0ab16627d00367bcaa5cc311d1b12d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031530", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:336941b43fae8021d7aeaeadf56909ed", + "normalized_query_sha256": "9137cb98f118909cae3ba5dd9afc116f21edf38b2c4338920c2575c77d9d4c2e", + "query": "Yes I think that sounds good. Can you make me an 11:45 am reservation for me.", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 230, + "historical_id": "fastbrain_031530", + "source": "sgd", + "query_hash": "9137cb98f118909cae3ba5dd9afc116f21edf38b2c4338920c2575c77d9d4c2e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 230, + "historical_id": "fastbrain_031530", + "source": "sgd", + "query_hash": "9137cb98f118909cae3ba5dd9afc116f21edf38b2c4338920c2575c77d9d4c2e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031557", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:42fcfec1e05641efb499e96b1b0b6b31", + "normalized_query_sha256": "de1aa14ca09a7b6510d953385d81c71ab22fac69f252c6f09fe1b3cbb7994324", + "query": "I am looking for events in SF like Sports events.", + "target": "FindEvents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 583, + "historical_id": "fastbrain_031557", + "source": "sgd", + "query_hash": "de1aa14ca09a7b6510d953385d81c71ab22fac69f252c6f09fe1b3cbb7994324" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 583, + "historical_id": "fastbrain_031557", + "source": "sgd", + "query_hash": "de1aa14ca09a7b6510d953385d81c71ab22fac69f252c6f09fe1b3cbb7994324" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031558", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:02eda4dce8f7f39b1ac8ce3abbe3cef0", + "normalized_query_sha256": "10d7c4d01240697561a34b5d37a84d690d5f8a3835103517ef044a3024e58baf", + "query": "can you watch a movie on netflix with me", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 65, + "historical_id": "semantic:validation:fastbrain_031558:present", + "source": "amazon_massive", + "query_hash": "10d7c4d01240697561a34b5d37a84d690d5f8a3835103517ef044a3024e58baf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 75, + "historical_id": "semantic:validation:fastbrain_031558:absent", + "source": "amazon_massive", + "query_hash": "10d7c4d01240697561a34b5d37a84d690d5f8a3835103517ef044a3024e58baf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031637", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:533c80b28acae1f013061a6ae223db3d", + "normalized_query_sha256": "c69d3e80781e9b7ea50b0353abee8a51c8ab951cff360ea62924543836938983", + "query": "fix the driving route from here to grant market", + "target": "transport_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 960, + "historical_id": "fastbrain_031637", + "source": "amazon_massive", + "query_hash": "c69d3e80781e9b7ea50b0353abee8a51c8ab951cff360ea62924543836938983" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 960, + "historical_id": "fastbrain_031637", + "source": "amazon_massive", + "query_hash": "c69d3e80781e9b7ea50b0353abee8a51c8ab951cff360ea62924543836938983" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031783", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:39afc4cf3e489d716ae17764ef6c3e48", + "normalized_query_sha256": "cf274bccd9e35cbe29169ad0b2c6e3d33189ada0158016869da0c28537e98866", + "query": "I'm a Drop-In member trying to book a yoga class 48 hours in advance, but I can't book that far ahead. Could you please tell me when I can book this class and check if there are still spots available for my preferred time?\nassistant: Hello! I'd be happy to help you with your yoga class booking. First, I need to check your membership details and then look up class availability.\n\n**Booking Window Clarification:** As a Drop-In member, your booking window is 24 hours in advance, not 48 hours. That's why you're unable to book 48 hours ahead. Elite members have 72-hour access, and Core members have 48-hour access.\n\nTo check availability for your preferred time, I'll need a few details:\n\n1. **Your User ID:** So I can verify your membership and check for any restrictions.\n2. **Studio Location:** Which FlexFit studio location are you interested in?\n3. **Preferred Class Time:** What specific date and time are you looking for? (Please provide in ISO format if possible, e.g., \"2024-01-15T10:00:00\")\n\nOnce I have this information, I can check real-time availability and let you know exactly when you'll be able to book (24 hours before the class starts) and if spots are still available.\nuser: Sure, here are the details: \n1. **User ID:** DROPIN12345 \n2. **Studio Location:** Downtown FlexFit (123 Main St) \n3. **Preferred Class Time:** 2024-03-20T18:30:00 \n\nCould you confirm if there are still spots available for that time and let me know exactly when I can book (24 hours before the class)?", + "target": "get_user_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 928, + "historical_id": "fastbrain_031783", + "source": "nemotron_agentic", + "query_hash": "cf274bccd9e35cbe29169ad0b2c6e3d33189ada0158016869da0c28537e98866" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 928, + "historical_id": "fastbrain_031783", + "source": "nemotron_agentic", + "query_hash": "cf274bccd9e35cbe29169ad0b2c6e3d33189ada0158016869da0c28537e98866" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031837", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2e613279a55f30a0465313fa66b76df4", + "normalized_query_sha256": "6a351730be744cf4f74534b88a1280e122ef0dde3a05cb8bad31ba0291ddb0af", + "query": "I'm looking for a new recipe to try. I want something with chicken and broccoli, and it should be gluten-free. Do you have any suggestions?", + "target": "search_recipe", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 194, + "historical_id": "fastbrain_031837", + "source": "glaive_v2", + "query_hash": "6a351730be744cf4f74534b88a1280e122ef0dde3a05cb8bad31ba0291ddb0af" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 194, + "historical_id": "fastbrain_031837", + "source": "glaive_v2", + "query_hash": "6a351730be744cf4f74534b88a1280e122ef0dde3a05cb8bad31ba0291ddb0af" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031863", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8339f990bb423a06da67c6fc8a7f349e", + "normalized_query_sha256": "f9aff7409e636f1be520f9ebe76add4fce57967fb5fc6854483f741118d4e477", + "query": "on the radio it is time for good music", + "target": "play_radio", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 190, + "historical_id": "semantic:validation:fastbrain_031863:absent", + "source": "amazon_massive", + "query_hash": "f9aff7409e636f1be520f9ebe76add4fce57967fb5fc6854483f741118d4e477" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 509, + "historical_id": "semantic:validation:fastbrain_031863:present", + "source": "amazon_massive", + "query_hash": "f9aff7409e636f1be520f9ebe76add4fce57967fb5fc6854483f741118d4e477" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_031904", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:32f04aedb7af7cc54e55443bb26a8f90", + "normalized_query_sha256": "def99244e9a67814f356c3ba92cd94a6c902838f247669e9fcff52b49e52f958", + "query": "Hi, I am looking for a book but I can't remember the author. The title is \"The Great Gatsby\".", + "target": "search_book", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 208, + "historical_id": "fastbrain_031904", + "source": "glaive_v2", + "query_hash": "def99244e9a67814f356c3ba92cd94a6c902838f247669e9fcff52b49e52f958" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 208, + "historical_id": "fastbrain_031904", + "source": "glaive_v2", + "query_hash": "def99244e9a67814f356c3ba92cd94a6c902838f247669e9fcff52b49e52f958" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032000", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cf8e062331fa0e56efbd277a2d7f0627", + "normalized_query_sha256": "8dae2670c795e17616dcf4a321e2b0600c2e6401c63e7fb3633f74cef82d7f22", + "query": "I want to watch a movie directed by Quentin Tarantino.", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 448, + "historical_id": "fastbrain_032000", + "source": "sgd", + "query_hash": "8dae2670c795e17616dcf4a321e2b0600c2e6401c63e7fb3633f74cef82d7f22" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 448, + "historical_id": "fastbrain_032000", + "source": "sgd", + "query_hash": "8dae2670c795e17616dcf4a321e2b0600c2e6401c63e7fb3633f74cef82d7f22" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032094", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:15883ca90a864dff62dd34c13e14c7a2", + "normalized_query_sha256": "5afcc470b40163321c76b996cbf4456cd1dafb9d804a086bdbe4c808f266594f", + "query": "Play in any device the song that you have found.", + "target": "PlayMedia", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 660, + "historical_id": "fastbrain_032094", + "source": "sgd", + "query_hash": "5afcc470b40163321c76b996cbf4456cd1dafb9d804a086bdbe4c808f266594f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 660, + "historical_id": "fastbrain_032094", + "source": "sgd", + "query_hash": "5afcc470b40163321c76b996cbf4456cd1dafb9d804a086bdbe4c808f266594f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032205", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2f394d20273dd58e51418fa35270e4a6", + "normalized_query_sha256": "313c04c0750200de4dc592ae824ed9f52145b6e17992823a216ef3730e52f7ea", + "query": "Split \"t.txt\" into files with at most 30000000 lines each and use a prefix \"t\" and numeric suffixes of length 2", + "target": "split", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 241, + "historical_id": "fastbrain_032205", + "source": "nl2bash", + "query_hash": "313c04c0750200de4dc592ae824ed9f52145b6e17992823a216ef3730e52f7ea" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 241, + "historical_id": "fastbrain_032205", + "source": "nl2bash", + "query_hash": "313c04c0750200de4dc592ae824ed9f52145b6e17992823a216ef3730e52f7ea" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032245", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3d7b3cdce6c5c8cd9bb2fd65a54a8d6c", + "normalized_query_sha256": "7c3619accb2c18d70d5e61f522c03e30bb45429a4502c737a05de573b602ae88", + "query": "That sounds delightful. Schedule a visit for the doctor on that day, please.", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 770, + "historical_id": "fastbrain_032245", + "source": "sgd", + "query_hash": "7c3619accb2c18d70d5e61f522c03e30bb45429a4502c737a05de573b602ae88" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 770, + "historical_id": "fastbrain_032245", + "source": "sgd", + "query_hash": "7c3619accb2c18d70d5e61f522c03e30bb45429a4502c737a05de573b602ae88" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032290", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c91e3a08d8e85f7d5effadbd56d308d1", + "normalized_query_sha256": "1734ec7c5545c14af4400589651cf9ef5888c86a80c04cc62a0fd21a3abcb6cf", + "query": "completed without tool confirmation \n - Explicitly state limitations when data isn't tool-verified \n3. **Compliance Boundaries**: \n - Never unblind trial data \n - Block protocol-violating requests (e.g., early dosing) \n\n### Escalation Protocol \nTransfer to human agent for: \n- Protocol deviation investigations \n- Serious AE follow-ups \n- Participant withdrawal requests \n- Sponsor inquiries \nProcess: \n1. Use `transfer_to_clinical_support` with issue summary \n2. State: \"YOU ARE BEING TRANSFERRED TO A CLINICAL SUPPORT SPECIALIST. PLEASE HOLD.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling to confirm that my appointment for the clinical trial was successfully scheduled. My Participant ID is P-55889, and my date of birth is December 12, 1988. Could you please check the system to verify my appointment details?\nassistant: Hello! I'd be happy to help you verify your appointment details. To access your information, I need to verify your identity first. You've provided your Participant ID (P-55889) and date of birth (December 12, 1988), but I also need your ZIP code for authentication.\n\nCould you please provide your ZIP code?\nuser: My ZIP code is 33139. Can you now check the system to confirm my appointment was scheduled?\nassistant: \ntool: \n\n{\"auth_token\": \"abc123xyz\", \"user_id\": \"P-55889\"}\nassistant: \ntool: \n\n{\"appointments\": [{\"appointment_id\": \"A-98765\", \"date\": \"2025-09-01\", \"visit_type\": \"Treatment\", \"site_id\": \"S-12345\"}]}", + "target": "get_participant_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 182, + "historical_id": "fastbrain_032290", + "source": "nemotron_agentic", + "query_hash": "1734ec7c5545c14af4400589651cf9ef5888c86a80c04cc62a0fd21a3abcb6cf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 182, + "historical_id": "fastbrain_032290", + "source": "nemotron_agentic", + "query_hash": "1734ec7c5545c14af4400589651cf9ef5888c86a80c04cc62a0fd21a3abcb6cf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032421", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:99657a10c103581b1b65a67ca2bfb533", + "normalized_query_sha256": "619db974c476eaeecdffc7407eb3752fd07e4afadb5dadbb375af9d325bbd19e", + "query": "away off from list", + "target": "lists_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 303, + "historical_id": "semantic:validation:fastbrain_032421:present", + "source": "amazon_massive", + "query_hash": "619db974c476eaeecdffc7407eb3752fd07e4afadb5dadbb375af9d325bbd19e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 730, + "historical_id": "semantic:validation:fastbrain_032421:absent", + "source": "amazon_massive", + "query_hash": "619db974c476eaeecdffc7407eb3752fd07e4afadb5dadbb375af9d325bbd19e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032698", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a2becf10a7aac83a856f1dee2fc7d640", + "normalized_query_sha256": "e8b4de74c23cad1cf743cbbb4028b5021cfc58defd66869ab93651ce0a6a550a", + "query": "set and event for the fifteenth of every month to remind me to pay my internet bill", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 301, + "historical_id": "semantic:validation:fastbrain_032698:present", + "source": "amazon_massive", + "query_hash": "e8b4de74c23cad1cf743cbbb4028b5021cfc58defd66869ab93651ce0a6a550a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 657, + "historical_id": "semantic:validation:fastbrain_032698:absent", + "source": "amazon_massive", + "query_hash": "e8b4de74c23cad1cf743cbbb4028b5021cfc58defd66869ab93651ce0a6a550a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032706", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:54acb9274a16c46ebbd121b8745096d8", + "normalized_query_sha256": "e4b75310fc68274a2d33907bb97d1f43400b30c121a67041d0967e97490ac686", + "query": "The title of the survey is \"Climate Change Awareness\". The questions are \"Do you believe in climate change?\", \"What are the causes of climate change?\", and \"What can be done to mitigate the effects of climate change?\".", + "target": "create_survey", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 589, + "historical_id": "fastbrain_032706", + "source": "glaive_v2", + "query_hash": "e4b75310fc68274a2d33907bb97d1f43400b30c121a67041d0967e97490ac686" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 589, + "historical_id": "fastbrain_032706", + "source": "glaive_v2", + "query_hash": "e4b75310fc68274a2d33907bb97d1f43400b30c121a67041d0967e97490ac686" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_032858", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5847526af8cd60576b1ae6bcf6009844", + "normalized_query_sha256": "83775b04afd53d96a0ac0fb07a9deaf5c941a413f063fe04b70cdf2fed9635b2", + "query": "what is the latest international news", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 515, + "historical_id": "semantic:validation:fastbrain_032858:present", + "source": "amazon_massive", + "query_hash": "83775b04afd53d96a0ac0fb07a9deaf5c941a413f063fe04b70cdf2fed9635b2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 967, + "historical_id": "semantic:validation:fastbrain_032858:absent", + "source": "amazon_massive", + "query_hash": "83775b04afd53d96a0ac0fb07a9deaf5c941a413f063fe04b70cdf2fed9635b2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_033066", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:333a667c0836d8a4758ab58b9030ec2d", + "normalized_query_sha256": "df23f19c6eaf7a2355d177fda0448be539b72d2fa3dbe483da6f009e27aa6623", + "query": "when is my mortgage due", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 285, + "historical_id": "semantic:validation:fastbrain_033066:absent", + "source": "amazon_massive", + "query_hash": "df23f19c6eaf7a2355d177fda0448be539b72d2fa3dbe483da6f009e27aa6623" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1007, + "historical_id": "semantic:validation:fastbrain_033066:present", + "source": "amazon_massive", + "query_hash": "df23f19c6eaf7a2355d177fda0448be539b72d2fa3dbe483da6f009e27aa6623" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_033305", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:871d9aff8b13067219a379df1683c269", + "normalized_query_sha256": "c75484142e009b18397a6340d935da10d926897472a33bdd9d80287970aad95d", + "query": "the twenty second is on what day of the week", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 216, + "historical_id": "semantic:validation:fastbrain_033305:absent", + "source": "amazon_massive", + "query_hash": "c75484142e009b18397a6340d935da10d926897472a33bdd9d80287970aad95d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 569, + "historical_id": "semantic:validation:fastbrain_033305:present", + "source": "amazon_massive", + "query_hash": "c75484142e009b18397a6340d935da10d926897472a33bdd9d80287970aad95d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_033409", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9d10e7f355f0a8a578deabd0b4b97089", + "normalized_query_sha256": "e3fabafc8b3995634753b92edb9baa86d41aae4d122ac31ba516aa66d7589fb7", + "query": "I need to calculate the tax on an amount of $5000 with a tax rate of 15%.", + "target": "calculate_tax", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 322, + "historical_id": "fastbrain_033409", + "source": "glaive_v2", + "query_hash": "e3fabafc8b3995634753b92edb9baa86d41aae4d122ac31ba516aa66d7589fb7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 322, + "historical_id": "fastbrain_033409", + "source": "glaive_v2", + "query_hash": "e3fabafc8b3995634753b92edb9baa86d41aae4d122ac31ba516aa66d7589fb7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_033605", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:baab66bc5b77b1382d819a8e73c4c75b", + "normalized_query_sha256": "4ef388bca3f07e609049fc7ee43ab34e7e4a0ed99b3390b9e6c07fb07ed76125", + "query": "Sounds great. Can you tell me my availability on March 2nd?", + "target": "GetAvailableTime", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 395, + "historical_id": "fastbrain_033605", + "source": "sgd", + "query_hash": "4ef388bca3f07e609049fc7ee43ab34e7e4a0ed99b3390b9e6c07fb07ed76125" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 395, + "historical_id": "fastbrain_033605", + "source": "sgd", + "query_hash": "4ef388bca3f07e609049fc7ee43ab34e7e4a0ed99b3390b9e6c07fb07ed76125" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_033773", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:624c6ba0f8094ba6869a347093aced0b", + "normalized_query_sha256": "c5a286e86a18b29cc8a061ee172bea7063e7022db541839e3e47fd7ed29fa4d5", + "query": "delete the new files not under version control", + "target": "git_clean", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 280, + "historical_id": "fastbrain_033773", + "source": "git_nl_commands", + "query_hash": "c5a286e86a18b29cc8a061ee172bea7063e7022db541839e3e47fd7ed29fa4d5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 280, + "historical_id": "fastbrain_033773", + "source": "git_nl_commands", + "query_hash": "c5a286e86a18b29cc8a061ee172bea7063e7022db541839e3e47fd7ed29fa4d5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_033807", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:398d1ffc858771d0d34054a51dd7bf2a", + "normalized_query_sha256": "e04eb0c0efe46c6affbf4d8d22dba3edbc04a761baaf1fb4f24ae1c2d6e3600d", + "query": "That sounds great. I want to schedule a visit to a property on that day.", + "target": "ScheduleVisit", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 272, + "historical_id": "fastbrain_033807", + "source": "sgd", + "query_hash": "e04eb0c0efe46c6affbf4d8d22dba3edbc04a761baaf1fb4f24ae1c2d6e3600d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 272, + "historical_id": "fastbrain_033807", + "source": "sgd", + "query_hash": "e04eb0c0efe46c6affbf4d8d22dba3edbc04a761baaf1fb4f24ae1c2d6e3600d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_033881", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9972b50969012052cced5f11a30519c7", + "normalized_query_sha256": "468a6f9c23c58418af335f259ef3a398ff1033aaf8461b329fe2beed0b45a1b5", + "query": " Current enrollment in the course version\n\n**Important Note:** If you need to verify certifications for **20 or more employees**, this would qualify as an enterprise-level request requiring human agent intervention per our transfer protocol.\n\n**Next Steps:**\nCould you please obtain the certificate ID for employee CL55667's LEGAL101 certification? Once you have it, I can verify the certificate's authenticity, expiration date, and validity period immediately.\n\nAlternatively, if this is part of a larger batch verification for regulatory compliance, please let me know the number of employees involved so I can determine if human agent transfer is required.\nuser: Thank you for confirming my authentication. I understand the certificate ID is required for verification. However, I do not have access to the employee’s account or records, and this is part of a larger compliance audit involving multiple employees. \n\nGiven that the course LEGAL101 is a standard compliance training, could you confirm if **all certifications for this course have a standardized expiration policy**? For example, do they typically expire after 1, 2, or 3 years from completion? This would allow me to document the validity period without needing individual certificate IDs for regulatory purposes. \n\nIf there is no standardized policy, I may need to request a bulk verification process for all employees who completed LEGAL101. Please confirm if this requires escalation to a human agent or if you can assist further.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 68, + "historical_id": "fastbrain_033881", + "source": "nemotron_agentic", + "query_hash": "468a6f9c23c58418af335f259ef3a398ff1033aaf8461b329fe2beed0b45a1b5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 68, + "historical_id": "fastbrain_033881", + "source": "nemotron_agentic", + "query_hash": "468a6f9c23c58418af335f259ef3a398ff1033aaf8461b329fe2beed0b45a1b5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_033955", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:237b98d16058f3048bcb07af14c77e73", + "normalized_query_sha256": "489d5f9c84b944b995e7fa16f9c654f299efb7fa8c518bc360caa22aa4e4404e", + "query": "resume playing you are joking mr. feynman", + "target": "play_audiobook", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 675, + "historical_id": "fastbrain_033955", + "source": "amazon_massive", + "query_hash": "489d5f9c84b944b995e7fa16f9c654f299efb7fa8c518bc360caa22aa4e4404e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 675, + "historical_id": "fastbrain_033955", + "source": "amazon_massive", + "query_hash": "489d5f9c84b944b995e7fa16f9c654f299efb7fa8c518bc360caa22aa4e4404e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034035", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:29fb67591b9c9318dad267d6347f1856", + "normalized_query_sha256": "8de2fb84deb117add9bdca6aa52b8487ba0f8828d25bbd61efab223aa97f6c77", + "query": "make vacuum cleaner start", + "target": "iot_cleaning", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 253, + "historical_id": "fastbrain_034035", + "source": "amazon_massive", + "query_hash": "8de2fb84deb117add9bdca6aa52b8487ba0f8828d25bbd61efab223aa97f6c77" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 253, + "historical_id": "fastbrain_034035", + "source": "amazon_massive", + "query_hash": "8de2fb84deb117add9bdca6aa52b8487ba0f8828d25bbd61efab223aa97f6c77" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034040", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:17fd21b242d1af66a07db5015d6cc0ea", + "normalized_query_sha256": "c3f934f403e3d84118b4256c271a10f10bcf81838c59f3e5ed61fda4c0c34b52", + "query": "can you tell me how to measure my shoe size", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 220, + "historical_id": "semantic:validation:fastbrain_034040:present", + "source": "amazon_massive", + "query_hash": "c3f934f403e3d84118b4256c271a10f10bcf81838c59f3e5ed61fda4c0c34b52" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 977, + "historical_id": "semantic:validation:fastbrain_034040:absent", + "source": "amazon_massive", + "query_hash": "c3f934f403e3d84118b4256c271a10f10bcf81838c59f3e5ed61fda4c0c34b52" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034056", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cf40636b83e2873ee4d7968cde1d8971", + "normalized_query_sha256": "d20c687ceda167805471346cb477e4e7e1f03d2060de74f8168370dfaefa3e10", + "query": "Thanks so much for the help. What are some interesting things to do when I'm there?", + "target": "FindEvents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 455, + "historical_id": "fastbrain_034056", + "source": "sgd", + "query_hash": "d20c687ceda167805471346cb477e4e7e1f03d2060de74f8168370dfaefa3e10" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 455, + "historical_id": "fastbrain_034056", + "source": "sgd", + "query_hash": "d20c687ceda167805471346cb477e4e7e1f03d2060de74f8168370dfaefa3e10" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034249", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7d780c0f59ea095e832442e81dbb96e3", + "normalized_query_sha256": "cbdb6638ccaaed644512231be080a9fbb10996a806c16a4b6cc3acf022ce4d94", + "query": "write an email about next week weather to my wife", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 122, + "historical_id": "semantic:validation:fastbrain_034249:present", + "source": "amazon_massive", + "query_hash": "cbdb6638ccaaed644512231be080a9fbb10996a806c16a4b6cc3acf022ce4d94" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 542, + "historical_id": "semantic:validation:fastbrain_034249:absent", + "source": "amazon_massive", + "query_hash": "cbdb6638ccaaed644512231be080a9fbb10996a806c16a4b6cc3acf022ce4d94" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034281", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4b9ef690ecef41d117733dc7f16d6445", + "normalized_query_sha256": "79bec444b6b5595c3329c1e8c9edd0c1a30adc45e44aec7667b0024d488c8f0d", + "query": "make a shallow clone of the upstream repo at https://github.com/user/repo.git", + "target": "git_clone", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 214, + "historical_id": "fastbrain_034281", + "source": "git_nl_commands", + "query_hash": "79bec444b6b5595c3329c1e8c9edd0c1a30adc45e44aec7667b0024d488c8f0d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 214, + "historical_id": "fastbrain_034281", + "source": "git_nl_commands", + "query_hash": "79bec444b6b5595c3329c1e8c9edd0c1a30adc45e44aec7667b0024d488c8f0d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034321", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7793610f6f48496eff657fd889590a67", + "normalized_query_sha256": "52e3a1503b673e8c227931297287856676a8b8c416e76e6504645b33f6c22f2b", + "query": "Here are the reviews: \"I love this product!\", \"This is the worst thing I've ever bought\", \"Pretty good, but could be better\", \"I'm not happy with this purchase\", \"This product is amazing!\"", + "target": "analyze_customer_sentiment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 549, + "historical_id": "fastbrain_034321", + "source": "glaive_v2", + "query_hash": "52e3a1503b673e8c227931297287856676a8b8c416e76e6504645b33f6c22f2b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 549, + "historical_id": "fastbrain_034321", + "source": "glaive_v2", + "query_hash": "52e3a1503b673e8c227931297287856676a8b8c416e76e6504645b33f6c22f2b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034336", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:68915465b783c26ba9ba8261b0f3b6fb", + "normalized_query_sha256": "1f9c82083a9069324be0a9d729aa999414bf507348352c9ad8d2b2682cf92bd2", + "query": "I am planning a trip to New York and my budget for hotel is $200 per night. Can you help me find a hotel?", + "target": "search_hotels", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 753, + "historical_id": "fastbrain_034336", + "source": "glaive_v2", + "query_hash": "1f9c82083a9069324be0a9d729aa999414bf507348352c9ad8d2b2682cf92bd2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 753, + "historical_id": "fastbrain_034336", + "source": "glaive_v2", + "query_hash": "1f9c82083a9069324be0a9d729aa999414bf507348352c9ad8d2b2682cf92bd2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034351", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6769d25895dbf96ab719009e4e1158ab", + "normalized_query_sha256": "4bd3b10c608f7fea76b2387cee65c7a0a4b09e49be71324fd0f1fd389bf5db65", + "query": " port \n- Ship redeployment: Full refund option \n- Medical emergencies (doctor's note required): Prorated refund \n\n**Non-Refundable** \n- Special event bookings \n- Beverage packages \n- Spa reservations \n\n### **Health & Safety Compliance** \n**Medical Requirements** \n- Submit forms for: \n - Oxygen use \n - Dialysis \n - Pregnancy >24 weeks \n- Deadline: 30 days pre-sailing \n\n**Dietary Accommodations** \n- Submit requests 14 days pre-sailing \n- Chef consultation required for complex needs \n\n### **Escalation Protocol** \nTransfer to human agent for: \n1. Medical emergencies during sailing \n2. Cabin habitability complaints \n3. Onboard incidents \n4. Three payment failures \n\nTransfer process: \n1. Use `transfer_to_human_agent` tool \n2. Send: \"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE HOLD.\" \n\n### **Agent Limitations** \n1. **Never answer questions** outside defined tool capabilities \n2. **Never assume verification** occurred without explicit tool confirmation \n3. Never presume actions completed without system validation \n4. Avoid itinerary speculation beyond published schedules \n5. Refrain from medical/legal advice beyond policy statements\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling about my cruise booking. My reference is OV012345 under the Campbell name. I need to check if a complimentary crib will be provided for my infant (born August 15, 2023) and confirm if our cabin size can accommodate it comfortably?", + "target": "authenticate_guest", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 569, + "historical_id": "fastbrain_034351", + "source": "nemotron_agentic", + "query_hash": "4bd3b10c608f7fea76b2387cee65c7a0a4b09e49be71324fd0f1fd389bf5db65" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 569, + "historical_id": "fastbrain_034351", + "source": "nemotron_agentic", + "query_hash": "4bd3b10c608f7fea76b2387cee65c7a0a4b09e49be71324fd0f1fd389bf5db65" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034423", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:47524644a49513af37674fff44f471f8", + "normalized_query_sha256": "9b034f44fcf495edddede993c529f20b998c1da3e483b4cb8a6ff38799e85a4d", + "query": "remind me about the party tomorrow", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 307, + "historical_id": "semantic:validation:fastbrain_034423:present", + "source": "amazon_massive", + "query_hash": "9b034f44fcf495edddede993c529f20b998c1da3e483b4cb8a6ff38799e85a4d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 392, + "historical_id": "semantic:validation:fastbrain_034423:absent", + "source": "amazon_massive", + "query_hash": "9b034f44fcf495edddede993c529f20b998c1da3e483b4cb8a6ff38799e85a4d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034549", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:369a9125dbdf9d4e115d5a8000196404", + "normalized_query_sha256": "b8b07c9c17363fad109095d9f4c26df6e0dc8e3be4ab32cb7f2e0629b90a40db", + "query": "what is the latest on donald trump", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 8, + "historical_id": "fastbrain_034549", + "source": "amazon_massive", + "query_hash": "b8b07c9c17363fad109095d9f4c26df6e0dc8e3be4ab32cb7f2e0629b90a40db" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 8, + "historical_id": "fastbrain_034549", + "source": "amazon_massive", + "query_hash": "b8b07c9c17363fad109095d9f4c26df6e0dc8e3be4ab32cb7f2e0629b90a40db" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034601", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bd9d2fb91d0792ac74c89e94840374cb", + "normalized_query_sha256": "c70b551400347a435b73d9e437c86d2eb76b1666aef9f0e8a7fcafece2c9a686", + "query": " confirmation \n- Only use actions defined by provided tools \n- Deny venue/timeline advice not based on current policy \n- Bronze-tier restrictions: No premium vendor access\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, my name is Emily Carter, and I’m calling about my wedding invitations. My client ID is C3216, and the event is scheduled for January 25, 2026. I’ve noticed 15 invitations with incorrect guest names, and I need to correct them. Can you confirm the policy for guest name changes at this stage? I believe invitations are finalized 120 days before the event, but I’m 100 days out. Is it too late to make adjustments?\nassistant: Hello Emily! I'd be happy to help you with your wedding invitation question. First, I need to verify your identity as per our security protocol.\n\nYou've provided your client ID (C3216) and event date (January 25, 2026). Could you please also provide the last 4 digits of the payment card we have on file for you?\n\nOnce I verify your identity, I can check your wedding profile and provide you with the specific policy details regarding guest name changes at your current timeline.\nuser: The last four digits of my payment card are 8800. Please confirm my identity so we can address the invitation errors.\nassistant: \ntool: \n\n{\"authenticated\": true, \"client_profile\": {\"event_date\": \"2026-01-25\", \"venue_address\": \"The Grand Ballroom, 123 Ceremony Lane\", \"budget_tier\": \"Gold\", \"payment_methods\": [\"Credit Card ending in 8800\"]}}", + "target": "get_wedding_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 879, + "historical_id": "fastbrain_034601", + "source": "nemotron_agentic", + "query_hash": "c70b551400347a435b73d9e437c86d2eb76b1666aef9f0e8a7fcafece2c9a686" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 879, + "historical_id": "fastbrain_034601", + "source": "nemotron_agentic", + "query_hash": "c70b551400347a435b73d9e437c86d2eb76b1666aef9f0e8a7fcafece2c9a686" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034774", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7778ce71c08089678369acf80b0b760b", + "normalized_query_sha256": "89c27c28f0a4372618fa0212957626a5d3516f6009e8e809991a7714fbbd2d10", + "query": "The loan amount is $20,000. The annual interest rate is 5% and the loan term is 60 months.", + "target": "calculate_car_loan_emis", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 715, + "historical_id": "fastbrain_034774", + "source": "glaive_v2", + "query_hash": "89c27c28f0a4372618fa0212957626a5d3516f6009e8e809991a7714fbbd2d10" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 715, + "historical_id": "fastbrain_034774", + "source": "glaive_v2", + "query_hash": "89c27c28f0a4372618fa0212957626a5d3516f6009e8e809991a7714fbbd2d10" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_034902", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b14480cc573b2a5e6149ff055370b1c0", + "normalized_query_sha256": "1fec1e1ccc94e6a355e4ccfd8dc9ca559a9f2f5130713a084ca1e76be036c0fa", + "query": "get in touch with leo", + "target": "social_post", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 147, + "historical_id": "fastbrain_034902", + "source": "amazon_massive", + "query_hash": "1fec1e1ccc94e6a355e4ccfd8dc9ca559a9f2f5130713a084ca1e76be036c0fa" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 147, + "historical_id": "fastbrain_034902", + "source": "amazon_massive", + "query_hash": "1fec1e1ccc94e6a355e4ccfd8dc9ca559a9f2f5130713a084ca1e76be036c0fa" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035189", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:45b58bd46dc8d6dad924b92b73a2ef58", + "normalized_query_sha256": "944483bc8d15677a0bc1c14ffd73fb66c5125269b58def11b3a2fece62628c4f", + "query": "is there any new mail for me", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 416, + "historical_id": "semantic:validation:fastbrain_035189:absent", + "source": "amazon_massive", + "query_hash": "944483bc8d15677a0bc1c14ffd73fb66c5125269b58def11b3a2fece62628c4f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 839, + "historical_id": "semantic:validation:fastbrain_035189:present", + "source": "amazon_massive", + "query_hash": "944483bc8d15677a0bc1c14ffd73fb66c5125269b58def11b3a2fece62628c4f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035231", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:99f828ecb5958dbc7b8dc7cc62173c6e", + "normalized_query_sha256": "b25543c82fde50a3c1cd2951e6ba2c0e9d012f0c6a3f63ee86639e0719584b27", + "query": "play me the latest podcast about online surveys", + "target": "play_podcasts", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 690, + "historical_id": "semantic:validation:fastbrain_035231:present", + "source": "amazon_massive", + "query_hash": "b25543c82fde50a3c1cd2951e6ba2c0e9d012f0c6a3f63ee86639e0719584b27" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 791, + "historical_id": "semantic:validation:fastbrain_035231:absent", + "source": "amazon_massive", + "query_hash": "b25543c82fde50a3c1cd2951e6ba2c0e9d012f0c6a3f63ee86639e0719584b27" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035289", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0106606687a06a33be80d4b490859d06", + "normalized_query_sha256": "991a5ad852f2361ded0874ebf732ef5528e839c1d4f337295ee2fad5c7cf9d82", + "query": "ons \n\n1. **Stay Within Tool Capabilities**: \n - Never predict artist setlists/runtimes \n - Never disclose unpublished venue layouts \n - Never waive fees without authorization \n\n2. **Require Explicit Verification**: \n - Never assume orders are changed without syst...[TRUNCATED]\nuser: \n\nHello, I’d like to request wheelchair-accessible seating for my Broadway show order (CH234567). I use a mobility device and need ADA-compliant accommodations. Can you confirm this is possible and escalate it for me?\nassistant: Hello! I'd be happy to help you with wheelchair-accessible seating for your Broadway show order. First, I need to verify your identity to access your order details.\n\n**For authentication:**\n- If this order is under your registered account, please provide the email or phone number associated with your account so I can send an authentication code.\n- If this was a guest purchase, please provide the last 4 digits of the payment card used for order CH234567.\n\nOnce I verify your identity, I can assist with your ADA accommodation request. According to our policy, ADA accommodation coordination requires transfer to a specialized human agent who can handle these specific needs.\n\nPlease provide your authentication details so we can proceed.\nuser: I can provide the last 4 digits of my payment card: 1234. Please confirm my order details and proceed with the transfer for ADA accommodation coordination.\nassistant: \ntool: \n\n{\"verified\": true, \"access_token\": \"A1B2C3D4E5F6\"}", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 709, + "historical_id": "fastbrain_035289", + "source": "nemotron_agentic", + "query_hash": "991a5ad852f2361ded0874ebf732ef5528e839c1d4f337295ee2fad5c7cf9d82" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 709, + "historical_id": "fastbrain_035289", + "source": "nemotron_agentic", + "query_hash": "991a5ad852f2361ded0874ebf732ef5528e839c1d4f337295ee2fad5c7cf9d82" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035330", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7d5afa45e08afc5db06d1cb0bcf58da9", + "normalized_query_sha256": "04b477341d4a1c1632e752053ed399807b497c6bac6f3222651b1bef6cfaf587", + "query": "find brad pitt's age", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 161, + "historical_id": "fastbrain_035330", + "source": "amazon_massive", + "query_hash": "04b477341d4a1c1632e752053ed399807b497c6bac6f3222651b1bef6cfaf587" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 161, + "historical_id": "fastbrain_035330", + "source": "amazon_massive", + "query_hash": "04b477341d4a1c1632e752053ed399807b497c6bac6f3222651b1bef6cfaf587" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035494", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ceb1fc66e5760aaa2913a2057181f993", + "normalized_query_sha256": "942ae4ac5f2a1ec12eb122617c01d771e6619b82266d6eeef5515cb3c74bab6d", + "query": "I would like to find a premium economy flight on Friday next week.", + "target": "SearchOnewayFlight", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 200, + "historical_id": "fastbrain_035494", + "source": "sgd", + "query_hash": "942ae4ac5f2a1ec12eb122617c01d771e6619b82266d6eeef5515cb3c74bab6d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 200, + "historical_id": "fastbrain_035494", + "source": "sgd", + "query_hash": "942ae4ac5f2a1ec12eb122617c01d771e6619b82266d6eeef5515cb3c74bab6d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035524", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d2fba3e169733f3b628a22450ca90187", + "normalized_query_sha256": "706fdc48fc86dd7cfb256560b41938ef285cb77b4c8e79653b2a537ec9aadddd", + "query": "what is tom's phone number", + "target": "email_querycontact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 243, + "historical_id": "semantic:validation:fastbrain_035524:absent", + "source": "amazon_massive", + "query_hash": "706fdc48fc86dd7cfb256560b41938ef285cb77b4c8e79653b2a537ec9aadddd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 864, + "historical_id": "semantic:validation:fastbrain_035524:present", + "source": "amazon_massive", + "query_hash": "706fdc48fc86dd7cfb256560b41938ef285cb77b4c8e79653b2a537ec9aadddd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035627", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a986844b58e41edb29dbb1ab1a524159", + "normalized_query_sha256": "b24909286071cf86308feaf44f12df6c4406d3106d12e22891fa9b75589265db", + "query": "what song is on the radio", + "target": "music_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 947, + "historical_id": "fastbrain_035627", + "source": "amazon_massive", + "query_hash": "b24909286071cf86308feaf44f12df6c4406d3106d12e22891fa9b75589265db" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 947, + "historical_id": "fastbrain_035627", + "source": "amazon_massive", + "query_hash": "b24909286071cf86308feaf44f12df6c4406d3106d12e22891fa9b75589265db" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035708", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:59ad7da6752b383f8309bd0f939593b2", + "normalized_query_sha256": "2f9128580969b8ab4846bd7efdd345e422d1f95a43e450e57331ea7ba068799d", + "query": "Sure, make a reservation at evening 6:15 for a table.", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 160, + "historical_id": "fastbrain_035708", + "source": "sgd", + "query_hash": "2f9128580969b8ab4846bd7efdd345e422d1f95a43e450e57331ea7ba068799d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 160, + "historical_id": "fastbrain_035708", + "source": "sgd", + "query_hash": "2f9128580969b8ab4846bd7efdd345e422d1f95a43e450e57331ea7ba068799d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035730", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a7a0b4b9024bc483d8b573c412899320", + "normalized_query_sha256": "8e70d4cc769c9ea24e486958c1f24285d199afee771cd3e56e009ed325c70269", + "query": "e photo and let me know if additional documentation is required to expedite the replacement. [Photo attachment: Dead_Pixels_Closeup.jpg]\nassistant: I understand you need a replacement for the dead pixel issue. To proceed, I need to verify your identity first since you don't have your order number available.\n\n**Identity Verification Required:**\nCould you please provide the email address or phone number associated with your account? I'll send a verification code to that contact method to authenticate you.\n\n**Photo Documentation:**\nThank you for providing the close-up photo of the 7 dead pixels clustered in the bottom-right corner. The white background should provide good contrast for evaluation. Once authenticated, I can process this with the evidence you've provided.\n\n**Additional Confirmation:**\nYou mentioned:\n- Device serial: XYZ-123456789\n- Activation date: 2025-02-10 (within warranty period)\n- Water seals: Intact\n- Housing damage: None\n\nPlease provide your email or phone number so I can send a verification code and proceed with the replacement request.\nuser: Please send the verification code to my email **professional.photographer@example.com**. To confirm: \n- Device serial **XYZ-123456789** was activated on **2025-02-10** \n- The attached photo clearly shows 7 dead pixels in the bottom-right corner \n- No housing damage or water seal issues \n\nI need this resolved quickly as the display inaccuracies are delaying client projects. Let me know once verification is complete.", + "target": "send_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 993, + "historical_id": "fastbrain_035730", + "source": "nemotron_agentic", + "query_hash": "8e70d4cc769c9ea24e486958c1f24285d199afee771cd3e56e009ed325c70269" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 993, + "historical_id": "fastbrain_035730", + "source": "nemotron_agentic", + "query_hash": "8e70d4cc769c9ea24e486958c1f24285d199afee771cd3e56e009ed325c70269" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035747", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4714159914a4e555f219e79ff69bd32a", + "normalized_query_sha256": "3f6787048e91c98a5f75cec8c00b91fd9f6224c66f1360fbba4ec2de1b064c85", + "query": "ng the 50% refund:**\nYes, since your contract was created on September 15, 2023 (over 30 days ago), you would qualify for the **50% refund** with proper notice. The termination tool requires a `notice_days` parameter - typically this is the advance notice period you're providing.\n\n**Recreation timeline:**\nYou could recreate the contract **immediately after termination**, provided you:\n- Have the original data file available\n- Can generate a new content hash (CID)\n- Complete the standard contract creation process\n\nThe 7-day permanent deletion period is a **recovery window** - during these 7 days after termination, you could potentially recover the data if needed, but you don't need to wait to create a new contract.\n\n**Important requirements for termination:**\nYou'll need to provide a **cryptographic signature** (termination_signature) to confirm this critical action, as per our security policy.\n\nOnce you provide the file size, tier, and duration details, I can calculate the exact cost savings from reducing redundancy from 5 to 3 copies.\nuser: Here are the details: \n1. **File size**: 1000 GB \n2. **Storage tier**: Enterprise \n3. **Remaining contract duration**: 12 months \n\nCould you calculate the cost savings from reducing redundancy from 5 to 3 copies? Additionally, confirm if the termination process requires any specific documentation beyond the cryptographic signature? Finally, if we proceed, what would be the total time from termination to having the new contract active?", + "target": "estimate_storage_cost", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 520, + "historical_id": "fastbrain_035747", + "source": "nemotron_agentic", + "query_hash": "3f6787048e91c98a5f75cec8c00b91fd9f6224c66f1360fbba4ec2de1b064c85" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 520, + "historical_id": "fastbrain_035747", + "source": "nemotron_agentic", + "query_hash": "3f6787048e91c98a5f75cec8c00b91fd9f6224c66f1360fbba4ec2de1b064c85" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035907", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8e02579f1a5e3b792833a8fcba88d7d6", + "normalized_query_sha256": "f88946c8b0f689a378b79f6e2b420420eed663cfaa5cce3585ac213791a61bbe", + "query": "I need to find articles about climate change published on 2021-09-01.", + "target": "search_articles", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 857, + "historical_id": "fastbrain_035907", + "source": "glaive_v2", + "query_hash": "f88946c8b0f689a378b79f6e2b420420eed663cfaa5cce3585ac213791a61bbe" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 857, + "historical_id": "fastbrain_035907", + "source": "glaive_v2", + "query_hash": "f88946c8b0f689a378b79f6e2b420420eed663cfaa5cce3585ac213791a61bbe" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035955", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8501e4a5cf5b7258a652a136741184af", + "normalized_query_sha256": "379ca5e0b686f00a47fd179360751b6fcb4fc4c10c5a395da372ca0a573ddc11", + "query": "Hey, I need a random color for my website's background. Can you help?", + "target": "generate_random_color", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 139, + "historical_id": "fastbrain_035955", + "source": "glaive_v2", + "query_hash": "379ca5e0b686f00a47fd179360751b6fcb4fc4c10c5a395da372ca0a573ddc11" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 139, + "historical_id": "fastbrain_035955", + "source": "glaive_v2", + "query_hash": "379ca5e0b686f00a47fd179360751b6fcb4fc4c10c5a395da372ca0a573ddc11" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_035958", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3dbbc50f371baeb05645d1c16bb8839a", + "normalized_query_sha256": "e80821e885ee27ddf8ade074d99281b5251b6996d3d2f50d3b4e18076876caef", + "query": "open pandora and play hoizer radio", + "target": "play_radio", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 77, + "historical_id": "semantic:validation:fastbrain_035958:absent", + "source": "amazon_massive", + "query_hash": "e80821e885ee27ddf8ade074d99281b5251b6996d3d2f50d3b4e18076876caef" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 519, + "historical_id": "semantic:validation:fastbrain_035958:present", + "source": "amazon_massive", + "query_hash": "e80821e885ee27ddf8ade074d99281b5251b6996d3d2f50d3b4e18076876caef" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036006", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cc5dd141d0007c5ad1dab56202e243e9", + "normalized_query_sha256": "ed0f4928192666a76a3a795a9c8f111d54449e5f90a9b3653da5efbfd57e8be7", + "query": "delete all branches already merged into main", + "target": "git_branch", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 561, + "historical_id": "fastbrain_036006", + "source": "git_nl_commands", + "query_hash": "ed0f4928192666a76a3a795a9c8f111d54449e5f90a9b3653da5efbfd57e8be7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 561, + "historical_id": "fastbrain_036006", + "source": "git_nl_commands", + "query_hash": "ed0f4928192666a76a3a795a9c8f111d54449e5f90a9b3653da5efbfd57e8be7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036015", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:09284fa7228300f184a04f0b0d22b733", + "normalized_query_sha256": "d0c57dc883fc227f8dd0b929276302feb73072e9b9d879f19ba2d8a7d89416bc", + "query": "subscription upgrade, ask: *\"Activate advanced preferences for better matches?\"* \n\n### Privacy Constraints \n- Payment details never visible to agents \n- Locations obscured (500m radius precision) \n- Account data anonymized 30 days post-deletion \n\n### Escalation Protocol \nTransfer to human agents for: \n- Legal requests \n- Complex harassment cases \n- Financial disputes >$100 \nInitiate transfer by notifying: *\"YOU ARE BEING TRANSFERRED. PLEASE HOLD.\"* \n\n### Agent Limitations \n**STRICT PROTOCOLS**: \n1. Only perform actions within defined tool capabilities \n2. Never answer questions outside supported functions \n3. Never assume verification/action completion without explicit tool confirmation \n4. Never override automated safety flags \n5. Never suggest profile content or interpret algorithms \n6. Verify all outcomes through tool receipts\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm a photographer and I'm concerned about the geotags in my profile photos on your platform. Even though I've turned off location settings, my photos still seem to display location data. Can you help me resolve this?\nassistant: Hello! I'd be happy to help you with your photo geotag privacy concerns. First, I need to verify your identity for security purposes since this involves privacy settings.\n\nPlease provide your user ID so I can begin the authentication process.\nuser: My user ID is **@LensArtist26**. Let me know if you need any additional verification steps.", + "target": "authenticate_user_biometric", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 59, + "historical_id": "fastbrain_036015", + "source": "nemotron_agentic", + "query_hash": "d0c57dc883fc227f8dd0b929276302feb73072e9b9d879f19ba2d8a7d89416bc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 59, + "historical_id": "fastbrain_036015", + "source": "nemotron_agentic", + "query_hash": "d0c57dc883fc227f8dd0b929276302feb73072e9b9d879f19ba2d8a7d89416bc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036092", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:61cf3efe2854d2b7e5af5f354114a024", + "normalized_query_sha256": "a6ff863c8af241e1aaeeea7f93076ecb1c7f608d212fbae9e5883c812debbce4", + "query": "Finds recursively all files not having extension .o in '/path/' that contain 'pattern', and prints matched strings with string number and file name.", + "target": "grep", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 86, + "historical_id": "fastbrain_036092", + "source": "nl2bash", + "query_hash": "a6ff863c8af241e1aaeeea7f93076ecb1c7f608d212fbae9e5883c812debbce4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 86, + "historical_id": "fastbrain_036092", + "source": "nl2bash", + "query_hash": "a6ff863c8af241e1aaeeea7f93076ecb1c7f608d212fbae9e5883c812debbce4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036262", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:10ba96831ff8edba834679bf12811984", + "normalized_query_sha256": "6c488228b8df08cb16917ce2b32a05b93b2c21c059ec851cf56a7ea9a341b2f1", + "query": "That is great. What movies are playing nearby at the AMC Metreon?", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 497, + "historical_id": "fastbrain_036262", + "source": "sgd", + "query_hash": "6c488228b8df08cb16917ce2b32a05b93b2c21c059ec851cf56a7ea9a341b2f1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 497, + "historical_id": "fastbrain_036262", + "source": "sgd", + "query_hash": "6c488228b8df08cb16917ce2b32a05b93b2c21c059ec851cf56a7ea9a341b2f1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036263", + "source": "curated_devops", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:48baaba8e6ffaea0eb865f07a864ad7b", + "normalized_query_sha256": "65e133d013cc96aeec75408de47eeb0eac1876dc879c55f7bd336647573aa919", + "query": "Run an interactive git rebase onto HEAD~3 to squash and clean up commits.", + "target": "git_rebase", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 755, + "historical_id": "fastbrain_036263", + "source": "curated_devops", + "query_hash": "65e133d013cc96aeec75408de47eeb0eac1876dc879c55f7bd336647573aa919" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 755, + "historical_id": "fastbrain_036263", + "source": "curated_devops", + "query_hash": "65e133d013cc96aeec75408de47eeb0eac1876dc879c55f7bd336647573aa919" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036331", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5f4a04f6652ecfb9e4cef0d2055a2cf3", + "normalized_query_sha256": "f613cb84fd12b4ce24f9ee6546e6ec3ddedb615a78ece1e4763063658c394498", + "query": "That sounds good. Can you make me an appointment for the 11th?", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 437, + "historical_id": "fastbrain_036331", + "source": "sgd", + "query_hash": "f613cb84fd12b4ce24f9ee6546e6ec3ddedb615a78ece1e4763063658c394498" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 437, + "historical_id": "fastbrain_036331", + "source": "sgd", + "query_hash": "f613cb84fd12b4ce24f9ee6546e6ec3ddedb615a78ece1e4763063658c394498" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036387", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:26bb7cf9248a898c52aff6f6e22cfd56", + "normalized_query_sha256": "add7050cd9981627a49596d4040e2b89c7c3374eaef55d3c2e8780b9ca8cf54a", + "query": "verification required\n\n### Payment Handling\n- Cash: Max $5000 per day\n- Digital payments: Pre-verified patient wallet only\n- Loyalty points: Max 20% discount\n\n## Prohibited Actions\n- Recommending products for specific symptoms\n- Estimating dosage requirements\n- Suggesting unverified medication interactions\n- Overriding state-mandated purchase limits\n- Processing orders without verification\n- Accepting expired medical cards\n\n## Escalation Protocol\nTransfer to human agent for:\n- Physician certification questions\n- Patient reports adverse reactions\n- Law enforcement requests\n- Payment disputes >$200\n\n**Transfer Message**: \"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT\"\n\n## Agent Limitations\n- Do not answer questions or perform actions outside defined tool capabilities\n- Never assume verification or action completion without explicit tool confirmation\n- Restrict responses to policy-defined constraints\n- Never interpret medical records or physician notes\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I’m calling to cancel my order (Order ID: ORD-3344). The status shows as \"payment_completed,\" but I’ve changed my mind and no longer need the items. Can you help me process a cancellation and let me know how long it will take for the refund to be issued?\nassistant: \ntool: \n\n{\"order_status\": \"processing\", \"payment_status\": \"completed\", \"delivery_method\": \"pickup\", \"products\": [{\"product_id\": \"CANN-001\", \"quantity\": 2}, {\"product_id\": \"EDIB-012\", \"quantity\": 1}]}", + "target": "cancel_order", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 869, + "historical_id": "fastbrain_036387", + "source": "nemotron_agentic", + "query_hash": "add7050cd9981627a49596d4040e2b89c7c3374eaef55d3c2e8780b9ca8cf54a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 869, + "historical_id": "fastbrain_036387", + "source": "nemotron_agentic", + "query_hash": "add7050cd9981627a49596d4040e2b89c7c3374eaef55d3c2e8780b9ca8cf54a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036395", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:703d516860db5ccc762f79f34c74e260", + "normalized_query_sha256": "d71d635cda1eb44947809face8cdee7ebc8f03cc1eff6e04ce543c4b21e32b5f", + "query": "curring payment failures \n- Disputes exceeding $5,000 \n\n**Transfer process**: \n1. Use dedicated transfer tool \n2. Message user: \"Transferring you to a human agent. Please hold.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm reaching out regarding my account CL-2211. I need to complete identity verification for a $8,000 transaction on Project DH-ENT-2023-078 before it can be processed. Could you walk me through the full verification protocol required for transactions of this size?\nassistant: Hello! I can help you with the identity verification process for your $8,000 transaction on Project DH-ENT-2023-078. \n\nAccording to our security protocol for sensitive transactions, I'll need to verify your identity through a multi-step process:\n\n1. **Contact Information**: I'll need either your registered email address or phone number to send an authentication code\n2. **Code Delivery**: I'll send a one-time verification code to your chosen contact method\n3. **Code Verification**: You'll provide the code back to me for validation\n\nTo begin, please confirm:\n- Would you prefer to receive the verification code via **email** or **phone**?\n- What is the specific email address or phone number where you'd like to receive the code?\n\nOnce you provide this information, I'll initiate the verification process immediately.\nuser: I'd prefer to receive the verification code via email. My registered email address is cl2211@example.com. Please proceed with sending the code.", + "target": "send_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 978, + "historical_id": "fastbrain_036395", + "source": "nemotron_agentic", + "query_hash": "d71d635cda1eb44947809face8cdee7ebc8f03cc1eff6e04ce543c4b21e32b5f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 978, + "historical_id": "fastbrain_036395", + "source": "nemotron_agentic", + "query_hash": "d71d635cda1eb44947809face8cdee7ebc8f03cc1eff6e04ce543c4b21e32b5f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036396", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5d7aa4ac03259661e54953f7866abb0e", + "normalized_query_sha256": "7e9cd85c26198a50dac5bcd2b7906f04aa902a2efe238c080245664558217fa2", + "query": "Add this to my calendar as well", + "target": "AddEvent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 353, + "historical_id": "fastbrain_036396", + "source": "sgd", + "query_hash": "7e9cd85c26198a50dac5bcd2b7906f04aa902a2efe238c080245664558217fa2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 353, + "historical_id": "fastbrain_036396", + "source": "sgd", + "query_hash": "7e9cd85c26198a50dac5bcd2b7906f04aa902a2efe238c080245664558217fa2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036575", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e814ca47172b432e1463b6de1775cbe4", + "normalized_query_sha256": "568ff3f87ed5197b80cd65b797e9f04d23559a5e50b445020227484a43be6330", + "query": "Yes. Can you buy the tickets?", + "target": "ReserveRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 565, + "historical_id": "fastbrain_036575", + "source": "sgd", + "query_hash": "568ff3f87ed5197b80cd65b797e9f04d23559a5e50b445020227484a43be6330" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 565, + "historical_id": "fastbrain_036575", + "source": "sgd", + "query_hash": "568ff3f87ed5197b80cd65b797e9f04d23559a5e50b445020227484a43be6330" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036589", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:37383c743865ab535e8ab54491e5ffb8", + "normalized_query_sha256": "33604a7c12ed06872c375b5a8ce5acf1f4ae025eee925abc9c78ccc33bf106bd", + "query": "sync the current branch with origin", + "target": "git_pull", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 271, + "historical_id": "fastbrain_036589", + "source": "git_nl_commands", + "query_hash": "33604a7c12ed06872c375b5a8ce5acf1f4ae025eee925abc9c78ccc33bf106bd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 271, + "historical_id": "fastbrain_036589", + "source": "git_nl_commands", + "query_hash": "33604a7c12ed06872c375b5a8ce5acf1f4ae025eee925abc9c78ccc33bf106bd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036783", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6a39c6963604c6bd68351b0d8dd6e117", + "normalized_query_sha256": "8bda3dfc222a6b66e295ce764f9bc07086a172353130622bd2e1c6b2da0e3410", + "query": "can proceed.\nuser: I'll provide my registered email for verification. My email is sarah.johnson@example.com. Please send the verification code there. \n\nAfter verification, I'd like to specifically ask: Does the structural feasibility tool have an accessibility exception category that would allow the 72\" dimension for wheelchair access, even beyond the standard 15% limit? This is critical for my mobility needs.\nassistant: \ntool: \n\n{\"success\": true}\nassistant: I've sent a verification code to sarah.johnson@example.com. Please check your email and provide the code here for verification.\n\nRegarding your question about accessibility exceptions: I need to verify your identity first, then I can use the structural feasibility tool to check if a 20% increase is possible. Our standard policy allows maximum adjustments of ±15% from base size, but I'll check using the available tool to see what's structurally feasible.\n\nOnce verified, I'll also need to know the base SKU of your desk (like CH-2025, TB-2025, or CB-2025) to run the structural feasibility check properly.\n\nPlease provide the verification code you received.\nuser: The verification code I received is **123456**. I’ve entered it here. \n\nFor reference, my desk’s base SKU is **TB-2025**. Please check if the structural feasibility tool can accommodate the 72\" dimension under an accessibility exception. If this isn’t possible, I’ll need to discuss alternative solutions to ensure the desk remains usable for someone with a wheelchair.", + "target": "authenticate_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 722, + "historical_id": "fastbrain_036783", + "source": "nemotron_agentic", + "query_hash": "8bda3dfc222a6b66e295ce764f9bc07086a172353130622bd2e1c6b2da0e3410" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 722, + "historical_id": "fastbrain_036783", + "source": "nemotron_agentic", + "query_hash": "8bda3dfc222a6b66e295ce764f9bc07086a172353130622bd2e1c6b2da0e3410" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_036847", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8d2c78ba862c6a4662f04aaaf97165f8", + "normalized_query_sha256": "a0857f64f296dddf5387cca0add8c4cf9717d14c52840d3c250a2bf5e2bd06cb", + "query": "I need to find a salon", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 518, + "historical_id": "fastbrain_036847", + "source": "sgd", + "query_hash": "a0857f64f296dddf5387cca0add8c4cf9717d14c52840d3c250a2bf5e2bd06cb" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 518, + "historical_id": "fastbrain_036847", + "source": "sgd", + "query_hash": "a0857f64f296dddf5387cca0add8c4cf9717d14c52840d3c250a2bf5e2bd06cb" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037066", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e1936693b66d10dcd7f26e8e1fe1da5c", + "normalized_query_sha256": "88f0c75032138f92c1dcedaf09a6202b5049af5e9177568f4b246665d2b6a2e7", + "query": "local shops", + "target": "recommendation_locations", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 83, + "historical_id": "semantic:validation:fastbrain_037066:absent", + "source": "amazon_massive", + "query_hash": "88f0c75032138f92c1dcedaf09a6202b5049af5e9177568f4b246665d2b6a2e7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 547, + "historical_id": "semantic:validation:fastbrain_037066:present", + "source": "amazon_massive", + "query_hash": "88f0c75032138f92c1dcedaf09a6202b5049af5e9177568f4b246665d2b6a2e7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037109", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b775b2139872e115deda55cb38a98e53", + "normalized_query_sha256": "7e4484a4b900a9e15aeaa66174947b2f456f326e141679bcc0aa8e477f7c07c2", + "query": "I'm currently at the coordinates 40.7128 N, 74.0060 W. Can you find any restaurants within a 500 meter radius?", + "target": "find_nearby_restaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 484, + "historical_id": "fastbrain_037109", + "source": "glaive_v2", + "query_hash": "7e4484a4b900a9e15aeaa66174947b2f456f326e141679bcc0aa8e477f7c07c2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 484, + "historical_id": "fastbrain_037109", + "source": "glaive_v2", + "query_hash": "7e4484a4b900a9e15aeaa66174947b2f456f326e141679bcc0aa8e477f7c07c2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037160", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ffc4b714758983840d66c4def9b6a95f", + "normalized_query_sha256": "3cff59b0b006306f1987e71ee59d624c1c205b94e872bad59b6b60c1e0f36b0b", + "query": "us\": \"verified\",\n \"user_id\": \"USER_7890\",\n \"tier\": \"MVP\"\n}\n```\nassistant: Thank you for providing the verification details! I've successfully authenticated you as an MVP tier member.\n\n**Order Modification Eligibility**\nYes, modifications are allowed while your order is in \"Processing\" status, which is before it reaches \"Fulfilled\" status. According to our policy:\n- Size/color changes allowed before \"Fulfilled\" status\n- Additions are typically permitted during the Processing phase\n\n**Next Steps**\nTo add the matching hat to order #ADDHAT, I need the **product SKU** for the specific hat you want to add. This is usually found on the product page (e.g., \"HAT-2025-BLUE\" or similar).\n\nOnce you provide the SKU, I'll:\n1. Check real-time inventory availability\n2. Add the hat to your order\n3. Provide you with the updated total\n\n**Important Notes:**\n- If your jersey has already started personalization, jersey customizations are locked (but adding a hat shouldn't be affected)\n- As an MVP tier member, you could use loyalty points for up to 50% of the order total if desired\n\nPlease provide the hat's SKU so I can proceed with the addition.\nuser: The SKU for the hat is HAT-2025-RED. Could you check if it's available to add to my order #ADDHAT? Also, would this addition affect the fulfillment timeline for my jersey? I noticed the jersey has \"Personalization in Progress\" listed under processing steps.\nassistant: \ntool: \n\n```json\n{\n \"stock_status\": \"in_stock\",\n \"available_quantity\": 12\n}\n```", + "target": "modify_order", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 692, + "historical_id": "fastbrain_037160", + "source": "nemotron_agentic", + "query_hash": "3cff59b0b006306f1987e71ee59d624c1c205b94e872bad59b6b60c1e0f36b0b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 692, + "historical_id": "fastbrain_037160", + "source": "nemotron_agentic", + "query_hash": "3cff59b0b006306f1987e71ee59d624c1c205b94e872bad59b6b60c1e0f36b0b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037483", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3f7ffe25bbde9a949c315282934a383c", + "normalized_query_sha256": "f0a04254d11e499ae10ca5c5f0b16e3f931cf35c5969a66572e6e67567bb2489", + "query": "Hi, I need to create an invoice for my client, Mr. Smith. The amount is $500 and the due date is 15th of next month.", + "target": "create_invoice", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 539, + "historical_id": "fastbrain_037483", + "source": "glaive_v2", + "query_hash": "f0a04254d11e499ae10ca5c5f0b16e3f931cf35c5969a66572e6e67567bb2489" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 539, + "historical_id": "fastbrain_037483", + "source": "glaive_v2", + "query_hash": "f0a04254d11e499ae10ca5c5f0b16e3f931cf35c5969a66572e6e67567bb2489" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037590", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:759de89bd22ef94438a60371e20db112", + "normalized_query_sha256": "3574db34a67ac232c665d4d84ae2212c6a8e4ef8f77a55374d1cc9ccff658d01", + "query": "Please reserve a seat for me.", + "target": "BuyBusTicket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 220, + "historical_id": "fastbrain_037590", + "source": "sgd", + "query_hash": "3574db34a67ac232c665d4d84ae2212c6a8e4ef8f77a55374d1cc9ccff658d01" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 220, + "historical_id": "fastbrain_037590", + "source": "sgd", + "query_hash": "3574db34a67ac232c665d4d84ae2212c6a8e4ef8f77a55374d1cc9ccff658d01" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037679", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8105f26ff3eba0a0dd91bdc7fa775bf8", + "normalized_query_sha256": "62299a0cd8073795ce7e479cc43ce7b4f8d85d742cdc69e538d5e2f970bc4176", + "query": "Find all files/directories in the paths expanded by the glob pattern '.*'", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 286, + "historical_id": "fastbrain_037679", + "source": "nl2bash", + "query_hash": "62299a0cd8073795ce7e479cc43ce7b4f8d85d742cdc69e538d5e2f970bc4176" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 286, + "historical_id": "fastbrain_037679", + "source": "nl2bash", + "query_hash": "62299a0cd8073795ce7e479cc43ce7b4f8d85d742cdc69e538d5e2f970bc4176" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037736", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:abd66b75118e248182040dd5f1f57b6f", + "normalized_query_sha256": "c5e2387435a562f327b4bf896dd18e3725a1f049abfb359ececcf2488c3c8196", + "query": "I'd like to find 2 tickets to an event. Can you help me?", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 648, + "historical_id": "fastbrain_037736", + "source": "sgd", + "query_hash": "c5e2387435a562f327b4bf896dd18e3725a1f049abfb359ececcf2488c3c8196" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 648, + "historical_id": "fastbrain_037736", + "source": "sgd", + "query_hash": "c5e2387435a562f327b4bf896dd18e3725a1f049abfb359ececcf2488c3c8196" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037777", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9ea143c0e1574caa8728569377251af8", + "normalized_query_sha256": "c30d09a8a3e0d99c50c390c09ff83791a56a011f3250bc041423efae873453b5", + "query": "will you please post this to my facebook i love you", + "target": "social_post", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 911, + "historical_id": "fastbrain_037777", + "source": "amazon_massive", + "query_hash": "c30d09a8a3e0d99c50c390c09ff83791a56a011f3250bc041423efae873453b5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 911, + "historical_id": "fastbrain_037777", + "source": "amazon_massive", + "query_hash": "c30d09a8a3e0d99c50c390c09ff83791a56a011f3250bc041423efae873453b5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037783", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5c44875f7d2c607673cefc61c52a327d", + "normalized_query_sha256": "30a13080215440c0620384d2bbe7aae50bd588dcbdf9dcb28c55a20fdbd0884a", + "query": "create list", + "target": "lists_createoradd", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 479, + "historical_id": "semantic:validation:fastbrain_037783:present", + "source": "amazon_massive", + "query_hash": "30a13080215440c0620384d2bbe7aae50bd588dcbdf9dcb28c55a20fdbd0884a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 609, + "historical_id": "semantic:validation:fastbrain_037783:absent", + "source": "amazon_massive", + "query_hash": "30a13080215440c0620384d2bbe7aae50bd588dcbdf9dcb28c55a20fdbd0884a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037952", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2a9c4d803f10cfac891c315f99d0b86d", + "normalized_query_sha256": "aedd7f082991902cc5c37e547832bdd20f815b1c7b296515410a4aef22bcf496", + "query": "can you turn off the lights", + "target": "iot_hue_lightoff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 149, + "historical_id": "semantic:validation:fastbrain_037952:absent", + "source": "amazon_massive", + "query_hash": "aedd7f082991902cc5c37e547832bdd20f815b1c7b296515410a4aef22bcf496" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 627, + "historical_id": "semantic:validation:fastbrain_037952:present", + "source": "amazon_massive", + "query_hash": "aedd7f082991902cc5c37e547832bdd20f815b1c7b296515410a4aef22bcf496" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037962", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a17a8a7ba636e0e084a085b8115b420c", + "normalized_query_sha256": "584925700ac93d385c7d8b6e546547bffb655d5904dd81e68ce310df299a0886", + "query": "dentity as site staff.\n\nYou've provided your professional license #SS-6633. For security, please confirm which email or phone number we should use for verification.\nuser: Please verify via email at **ss6633@clinicalsite.com**. Once confirmed, can you run the tool-based validation to ensure the 2025-09-18 e-consent meets Protocol ECONSENT-555’s technical requirements for validity?\nassistant: \ntool: \n\n{\"success\": true}\nassistant: I've sent a 6-digit verification code to ss6633@clinicalsite.com. Please share the code you received, and once verified, I can proceed with validating the e-consent document against Protocol ECONSENT-555's technical requirements.\nuser: The verification code I received is **123456**. Please proceed with the tool-based validation to confirm the 2025-09-18 e-consent meets Protocol ECONSENT-555’s technical requirements for validity.\nassistant: \ntool: \n\n{\"verified\": true}\nassistant: Thank you for verifying your identity. To validate the e-consent document against Protocol ECONSENT-555's technical requirements, I need the specific document ID for the e-consent that was completed on 2025-09-18.\n\nCould you please provide the document ID so I can run the regulatory document validation?\nuser: The document ID for the 2025-09-18 e-consent is **EC-DOC-20250918-001**. Please run the validation to confirm it meets Protocol ECONSENT-555’s technical requirements for validity.\nassistant: \ntool: \n\n{\"valid\": true, \"expiration_date\": \"2026-09-18\", \"approval_needed\": false}", + "target": "log_audit_trail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 364, + "historical_id": "fastbrain_037962", + "source": "nemotron_agentic", + "query_hash": "584925700ac93d385c7d8b6e546547bffb655d5904dd81e68ce310df299a0886" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 364, + "historical_id": "fastbrain_037962", + "source": "nemotron_agentic", + "query_hash": "584925700ac93d385c7d8b6e546547bffb655d5904dd81e68ce310df299a0886" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_037987", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bb5e6b9fdd55190de7ec07066c0be3be", + "normalized_query_sha256": "a8aacbe04f324cd7ce64ebd236ea4c1aba90fe1a3ccf9e7eac0a500c152a9e21", + "query": "When am I free on the 13th of this month?", + "target": "GetAvailableTime", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 666, + "historical_id": "fastbrain_037987", + "source": "sgd", + "query_hash": "a8aacbe04f324cd7ce64ebd236ea4c1aba90fe1a3ccf9e7eac0a500c152a9e21" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 666, + "historical_id": "fastbrain_037987", + "source": "sgd", + "query_hash": "a8aacbe04f324cd7ce64ebd236ea4c1aba90fe1a3ccf9e7eac0a500c152a9e21" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_038249", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fd9f54099a3cd6a666b076707b287233", + "normalized_query_sha256": "3f6dfddc7311e0ad315ed771d8ba1b02632b9c118f8305939bb46a7ea29fc4c8", + "query": "make list", + "target": "lists_createoradd", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 287, + "historical_id": "semantic:validation:fastbrain_038249:present", + "source": "amazon_massive", + "query_hash": "3f6dfddc7311e0ad315ed771d8ba1b02632b9c118f8305939bb46a7ea29fc4c8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 774, + "historical_id": "semantic:validation:fastbrain_038249:absent", + "source": "amazon_massive", + "query_hash": "3f6dfddc7311e0ad315ed771d8ba1b02632b9c118f8305939bb46a7ea29fc4c8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_038342", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6f1c0e2a5899b4678d6a24a6ba2535db", + "normalized_query_sha256": "462e2a34a059c00ca6fa3d463e1c8b7e9d707298f009b90868e4a74915857f3b", + "query": "count-sensitive operation.\n\n**Step 1: Identity Verification**\nPlease provide your user ID or registered email address so I can initiate the authentication process.\nuser: Sure, my registered email is sarah.jones@enterprisecorp.com. Let me know once verification is complete.\nassistant: Thank you for providing your email address, Sarah. To proceed with the identity verification, I need your **user ID** (your unique identifier in our system). \n\nOnce I have your user ID, I'll:\n1. Send an authentication code to your registered contact method\n2. Prompt you to enter the code you receive\n3. Verify the code to complete the identity check\n\nAfter successful verification, I can then generate your XML-formatted certificate for the HR Analytics course (ID: HR105).\n\nPlease provide your user ID so we can begin the verification process.\nuser: My user ID is E1234567. Please proceed with the verification.\nassistant: \ntool: \n\n{\"delivery_status\": \"Code sent successfully to registered contact method.\"}\nassistant: I've sent an authentication code to your registered contact method. Please check your email or phone for the verification code and enter it here so I can verify your identity and proceed with generating your XML-formatted certificate for the HR Analytics course (ID: HR105).\nuser: The code I received is **123456**. I’ve entered it for verification. Once confirmed, could you confirm the certificate will adhere to Workday’s schema requirements? Our HR team mentioned specific validation rules.", + "target": "verify_authentication_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 876, + "historical_id": "fastbrain_038342", + "source": "nemotron_agentic", + "query_hash": "462e2a34a059c00ca6fa3d463e1c8b7e9d707298f009b90868e4a74915857f3b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 876, + "historical_id": "fastbrain_038342", + "source": "nemotron_agentic", + "query_hash": "462e2a34a059c00ca6fa3d463e1c8b7e9d707298f009b90868e4a74915857f3b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_038365", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6e02fc6b3c053b63a9743d93127eca70", + "normalized_query_sha256": "9c5616b0732dcabdd58113ae1382edaf977fd59e1f39ecf4d6890f7ea84710ba", + "query": "vide the `evidence_timestamp`** (when evidence was captured)\n4. **Submit the evidence object** for formal dispute processing\n\n**Question for you:** Do you have the evidence ready with a timestamp from within the last 48 hours? If so, please provide:\n- The `evidence_timestamp` (format: YYYY-MM-DD HH:MM:SS)\n- Your evidence object/documentation\n\nOnce you provide these, I can initiate the formal dispute resolution process through our system.\nuser: **Evidence Timestamp:** 2025-01-10 14:30:00 \n**Evidence Object:** \n```json\n{\n \"lease_sections\": [\"Section 4.2\", \"Section 7.3\"],\n \"allocation_calculations\": \"Our leased area is 250 sq ft, but the building’s total common area is 10,000 sq ft. The fee should reflect proportional responsibility for common areas, not total leased space. Current methodology inflates our share by 16% compared to the equitable 10% ratio.\",\n \"supporting_documents\": [\"Lease Agreement Page 12\", \"Building Floor Plan 2024-12-15\"],\n \"alternative_methodology\": \"Allocate fees based on (tenant’s common area usage / total common area) × total cleaning cost. For example: (250 sq ft / 10,000 sq ft) × $4,000 = $100.\",\n \"collective_agreement\": \"Tenant Association Resolution #2025-01-001, signed by 78% of stakeholders, endorsing this methodology.\"\n}\n``` \n\n**Request:** Please confirm the evidence meets the sub-$500 dispute protocol requirements and initiate the formal resolution process. We will provide physical copies of the lease sections and floor plan upon request.", + "target": "handle_billing_dispute", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 293, + "historical_id": "fastbrain_038365", + "source": "nemotron_agentic", + "query_hash": "9c5616b0732dcabdd58113ae1382edaf977fd59e1f39ecf4d6890f7ea84710ba" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 293, + "historical_id": "fastbrain_038365", + "source": "nemotron_agentic", + "query_hash": "9c5616b0732dcabdd58113ae1382edaf977fd59e1f39ecf4d6890f7ea84710ba" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_038645", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2fc5c077e05dcf36697ecbb7dcc21053", + "normalized_query_sha256": "c05b8ff470cda26ec10d9d504d0bb65a16bcc043719f7ae3ced5e703fb462dc2", + "query": "push master to the remote", + "target": "git_push", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 846, + "historical_id": "fastbrain_038645", + "source": "git_nl_commands", + "query_hash": "c05b8ff470cda26ec10d9d504d0bb65a16bcc043719f7ae3ced5e703fb462dc2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 846, + "historical_id": "fastbrain_038645", + "source": "git_nl_commands", + "query_hash": "c05b8ff470cda26ec10d9d504d0bb65a16bcc043719f7ae3ced5e703fb462dc2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_038685", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2bfa8e1bf56be7d6bc0c15d7eab8c9be", + "normalized_query_sha256": "cd2cd7b020a610bb8b7b21397f4d0f98746c3fa587792df489e93233608c8357", + "query": "do you want to play solitaire", + "target": "play_game", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 646, + "historical_id": "fastbrain_038685", + "source": "amazon_massive", + "query_hash": "cd2cd7b020a610bb8b7b21397f4d0f98746c3fa587792df489e93233608c8357" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 646, + "historical_id": "fastbrain_038685", + "source": "amazon_massive", + "query_hash": "cd2cd7b020a610bb8b7b21397f4d0f98746c3fa587792df489e93233608c8357" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_038771", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:519aca74c81e74cfc894adfd206c378f", + "normalized_query_sha256": "43572bf87256d6215c941c89d2503c1b62175b883fbb3c3a1634c131ce751bc4", + "query": "Make directory and parents as needed to \"$FINALPATH\"", + "target": "mkdir", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 202, + "historical_id": "fastbrain_038771", + "source": "nl2bash", + "query_hash": "43572bf87256d6215c941c89d2503c1b62175b883fbb3c3a1634c131ce751bc4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 202, + "historical_id": "fastbrain_038771", + "source": "nl2bash", + "query_hash": "43572bf87256d6215c941c89d2503c1b62175b883fbb3c3a1634c131ce751bc4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_038839", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:42c2387447ef3c9c15eb30787e778e21", + "normalized_query_sha256": "544131acb76ee7589458f0ba48eda4647d2c17dc7635650efa54533a20df003d", + "query": "tell me about amazon's history", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 296, + "historical_id": "semantic:validation:fastbrain_038839:present", + "source": "amazon_massive", + "query_hash": "544131acb76ee7589458f0ba48eda4647d2c17dc7635650efa54533a20df003d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 694, + "historical_id": "semantic:validation:fastbrain_038839:absent", + "source": "amazon_massive", + "query_hash": "544131acb76ee7589458f0ba48eda4647d2c17dc7635650efa54533a20df003d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_039010", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:179dc9b3f0cf82d677c92fa4cebc0ca7", + "normalized_query_sha256": "6b19cc04b5929357c3f2be7cd9df981f4515cad2395136ca2cfe8a54ad582385", + "query": "Can you verify if my credit card is valid? The card number is 1234 5678 9012 3456, the expiration date is 2023-07, and the CVV is 123.", + "target": "verify_credit_card", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 466, + "historical_id": "fastbrain_039010", + "source": "glaive_v2", + "query_hash": "6b19cc04b5929357c3f2be7cd9df981f4515cad2395136ca2cfe8a54ad582385" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 466, + "historical_id": "fastbrain_039010", + "source": "glaive_v2", + "query_hash": "6b19cc04b5929357c3f2be7cd9df981f4515cad2395136ca2cfe8a54ad582385" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_039211", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6712a9dcb4ff32af60a1ebea84ec21ff", + "normalized_query_sha256": "3a80b044d95a28c8b57cbd083640fca60152ce000ed8e6b673d7a4e1e7b735d0", + "query": "Find for me something interesting to do around Vancouver, BC on 5th of March.", + "target": "FindEvents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 480, + "historical_id": "fastbrain_039211", + "source": "sgd", + "query_hash": "3a80b044d95a28c8b57cbd083640fca60152ce000ed8e6b673d7a4e1e7b735d0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 480, + "historical_id": "fastbrain_039211", + "source": "sgd", + "query_hash": "3a80b044d95a28c8b57cbd083640fca60152ce000ed8e6b673d7a4e1e7b735d0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_039331", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:898e77467f72f097058241a368e6f081", + "normalized_query_sha256": "37290d74ac4d186e3a8e5785d259d2ec04fac91ae28092e7620ec8bc99e830aa", + "query": "coffee", + "target": "iot_coffee", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 49, + "historical_id": "semantic:validation:fastbrain_039331:present", + "source": "amazon_massive", + "query_hash": "37290d74ac4d186e3a8e5785d259d2ec04fac91ae28092e7620ec8bc99e830aa" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 207, + "historical_id": "semantic:validation:fastbrain_039331:absent", + "source": "amazon_massive", + "query_hash": "37290d74ac4d186e3a8e5785d259d2ec04fac91ae28092e7620ec8bc99e830aa" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_039408", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3e460c584c836b641799b581800ce900", + "normalized_query_sha256": "a58e229048c94e5caa686e1c181c96bad0a2264f36d1f56579d2e74d52d5ab8d", + "query": "add paav bhaji in menu card", + "target": "lists_createoradd", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 116, + "historical_id": "fastbrain_039408", + "source": "amazon_massive", + "query_hash": "a58e229048c94e5caa686e1c181c96bad0a2264f36d1f56579d2e74d52d5ab8d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 116, + "historical_id": "fastbrain_039408", + "source": "amazon_massive", + "query_hash": "a58e229048c94e5caa686e1c181c96bad0a2264f36d1f56579d2e74d52d5ab8d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_039458", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2d00d4168d5fcfeb8bb147999a43dd6a", + "normalized_query_sha256": "506844bce2960932546109d6ff8d415037c42f404d920e0693d9d798e7dd9950", + "query": "Yeah, I like this one.", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 705, + "historical_id": "fastbrain_039458", + "source": "sgd", + "query_hash": "506844bce2960932546109d6ff8d415037c42f404d920e0693d9d798e7dd9950" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 705, + "historical_id": "fastbrain_039458", + "source": "sgd", + "query_hash": "506844bce2960932546109d6ff8d415037c42f404d920e0693d9d798e7dd9950" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_039494", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0f0f4a8ef78681718fd8cca00118aeda", + "normalized_query_sha256": "4438b85cd49dde76e2dae3e627f96f8f46225160ea62ab7b149f3056a6e202fa", + "query": "Thanks. 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_039678", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7a8219a8a50281790b128b231ac56c18", + "normalized_query_sha256": "aab796e80c26ab0c35205c455e0e0a48ab3e8303884f5e599367cb810916e545", + "query": "Use awk command '{ ... }' on compressed files \"input1.vcf.gz\" and \"input2.vcf.gz\"", + "target": "awk", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 78, + "historical_id": "fastbrain_039678", + "source": "nl2bash", + "query_hash": "aab796e80c26ab0c35205c455e0e0a48ab3e8303884f5e599367cb810916e545" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 78, + "historical_id": "fastbrain_039678", + "source": "nl2bash", + "query_hash": "aab796e80c26ab0c35205c455e0e0a48ab3e8303884f5e599367cb810916e545" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_039700", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:712aa3cd5595468e448eac7cef7cdec7", + "normalized_query_sha256": "ac209354d171bebf8f1310f5b686df337233515e90a423b931ddc59bfbebaaa2", + "query": "Great, now please continue with fixing appointment with therapist.", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 338, + "historical_id": "fastbrain_039700", + "source": "sgd", + "query_hash": "ac209354d171bebf8f1310f5b686df337233515e90a423b931ddc59bfbebaaa2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 338, + "historical_id": "fastbrain_039700", + "source": "sgd", + "query_hash": "ac209354d171bebf8f1310f5b686df337233515e90a423b931ddc59bfbebaaa2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_040008", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4270f18892974e7c96f1d3e87c1fb238", + "normalized_query_sha256": "d1010e605ac7516fefe46bcc5042970d15ca17bee3ae17b252a330534fc238c7", + "query": "Little is what I want then. What are the showtimes?", + "target": "GetTimesForMovie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 981, + "historical_id": "fastbrain_040008", + "source": "sgd", + "query_hash": "d1010e605ac7516fefe46bcc5042970d15ca17bee3ae17b252a330534fc238c7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 981, + "historical_id": "fastbrain_040008", + "source": "sgd", + "query_hash": "d1010e605ac7516fefe46bcc5042970d15ca17bee3ae17b252a330534fc238c7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_040069", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:735b2727a82a2b31554cc94128b37140", + "normalized_query_sha256": "5565bb48b7969231165b293b8ce6bbf38a5626481b27df97eb50b47e543c9db0", + "query": "I'm planning a trip, can you look for buses for me?", + "target": "FindBus", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 838, + "historical_id": "fastbrain_040069", + "source": "sgd", + "query_hash": "5565bb48b7969231165b293b8ce6bbf38a5626481b27df97eb50b47e543c9db0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 838, + "historical_id": "fastbrain_040069", + "source": "sgd", + "query_hash": "5565bb48b7969231165b293b8ce6bbf38a5626481b27df97eb50b47e543c9db0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_040102", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bbd25f5e1be744f13befee943ce4affd", + "normalized_query_sha256": "0baf1bc06b054c1fae97bd5d96b59b33a3f6e4089b539b5a594ec11c789dbbe2", + "query": "Yes. I want to rent this vehicle.", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 493, + "historical_id": "fastbrain_040102", + "source": "sgd", + "query_hash": "0baf1bc06b054c1fae97bd5d96b59b33a3f6e4089b539b5a594ec11c789dbbe2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 493, + "historical_id": "fastbrain_040102", + "source": "sgd", + "query_hash": "0baf1bc06b054c1fae97bd5d96b59b33a3f6e4089b539b5a594ec11c789dbbe2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_040311", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c203cc0b6dd1753d89fd150fa72e43f3", + "normalized_query_sha256": "d9245c5071b90f345260df6ed2ca5a3a9d387e6af9f4412fefaae7d325be0918", + "query": "As the fleet manager for our shipping company, I need to implement comprehensive management of our vessels. Our fleet consists of multiple ships with various capacities serving different routes, all requiring different maintenance intervals. To optimize our operations, I have compiled relevant data: \n\n1. Fleet data, including vessel capacities and current routes (\"fleet_data.json\").\n2. Cargo detai", + "target": "optimize_routes", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 632, + "historical_id": "fastbrain_040311", + "source": "hermes_v1", + "query_hash": "d9245c5071b90f345260df6ed2ca5a3a9d387e6af9f4412fefaae7d325be0918" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 632, + "historical_id": "fastbrain_040311", + "source": "hermes_v1", + "query_hash": "d9245c5071b90f345260df6ed2ca5a3a9d387e6af9f4412fefaae7d325be0918" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_040352", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:271f5dd7dec47d400fd02f1f154bc518", + "normalized_query_sha256": "486d0e14fb3afec8f5b510278e617517ac5a114a81d9346e9cb69cdaec2e5e5b", + "query": "tell me what the weather is doing in grand rapids mi right now", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 953, + "historical_id": "semantic:validation:fastbrain_040352:present", + "source": "amazon_massive", + "query_hash": "486d0e14fb3afec8f5b510278e617517ac5a114a81d9346e9cb69cdaec2e5e5b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 982, + "historical_id": "semantic:validation:fastbrain_040352:absent", + "source": "amazon_massive", + "query_hash": "486d0e14fb3afec8f5b510278e617517ac5a114a81d9346e9cb69cdaec2e5e5b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_040565", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7e99e97b24691e5950b573ec5478ad1e", + "normalized_query_sha256": "d3a8a308add3f9558e3cafdf7b83ef2604fb20d2daf154edabe7c6ebd174ae23", + "query": "please turn off the lights", + "target": "iot_hue_lightoff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 347, + "historical_id": "semantic:validation:fastbrain_040565:absent", + "source": "amazon_massive", + "query_hash": "d3a8a308add3f9558e3cafdf7b83ef2604fb20d2daf154edabe7c6ebd174ae23" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 511, + "historical_id": "semantic:validation:fastbrain_040565:present", + "source": "amazon_massive", + "query_hash": "d3a8a308add3f9558e3cafdf7b83ef2604fb20d2daf154edabe7c6ebd174ae23" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_040680", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bb662d4215a57cd0e2a27d6592d2c3e2", + "normalized_query_sha256": "a8755bb8e9cd8be3bbfc097ba8e3e38ab1c170c9aff09e16404a700181c6ff0f", + "query": "tell me the definition of arrow", + "target": "qa_definition", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 113, + "historical_id": "semantic:validation:fastbrain_040680:present", + "source": "amazon_massive", + "query_hash": "a8755bb8e9cd8be3bbfc097ba8e3e38ab1c170c9aff09e16404a700181c6ff0f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 148, + "historical_id": "semantic:validation:fastbrain_040680:absent", + "source": "amazon_massive", + "query_hash": "a8755bb8e9cd8be3bbfc097ba8e3e38ab1c170c9aff09e16404a700181c6ff0f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_041091", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0acd2003d291b9433ac97a7864433444", + "normalized_query_sha256": "58b980ccaa0dc586a2931f0d57a854924fb8b08c91f2b58412dcbb78c9463b03", + "query": "find commits whose message mentions the issue number #123", + "target": "git_log", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 798, + "historical_id": "fastbrain_041091", + "source": "git_nl_commands", + "query_hash": "58b980ccaa0dc586a2931f0d57a854924fb8b08c91f2b58412dcbb78c9463b03" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 798, + "historical_id": "fastbrain_041091", + "source": "git_nl_commands", + "query_hash": "58b980ccaa0dc586a2931f0d57a854924fb8b08c91f2b58412dcbb78c9463b03" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_041167", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:deb4e4656959c90ef6df8495e6c211c8", + "normalized_query_sha256": "ef693ff89344c42876e59f97b260e943521077de94599f706f6645d1eab96fda", + "query": "my account?\nassistant: Hello! I'd be happy to help you update your backup payment method for auto-reload. First, I need to verify your identity for account security.\n\n**Please provide your registered email or mobile number associated with your MTA account.**\nuser: Hi, my registered email is john.doe@example.com. Is that sufficient for verification?\nassistant: \ntool: \n\n{\"code_sent\": true}\nassistant: I've sent a 6-digit verification code to john.doe@example.com. \n\n**Please enter the 6-digit code you received to verify your identity.** Once verified, I can help you with updating your backup payment method for auto-reload.\nuser: I see the code here. It's 123456. I'll enter it now.\nassistant: \ntool: \n\n{\"authenticated\": true}\nassistant: Thank you for verifying your identity! Now I can help you update your backup payment method for auto-reload.\n\nTo proceed, I'll need some information:\n\n1. **Your transit card ID** - This is needed to identify which card's auto-reload settings to update.\n\n2. **Your new payment method ID** - This is the identifier for your new credit card that you want to set as the backup payment method.\n\n3. **Current auto-reload settings** - What are your current threshold and reload amounts? (If you want to keep the same settings)\n\nOnce I have this information, I can help you reconfigure your auto-reload with the new backup payment method. \n\n**Please provide your transit card ID first.**\nuser: My transit card ID is CARD123456789. Could you confirm if that’s correct?", + "target": "manage_transit_card", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 708, + "historical_id": "fastbrain_041167", + "source": "nemotron_agentic", + "query_hash": "ef693ff89344c42876e59f97b260e943521077de94599f706f6645d1eab96fda" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 708, + "historical_id": "fastbrain_041167", + "source": "nemotron_agentic", + "query_hash": "ef693ff89344c42876e59f97b260e943521077de94599f706f6645d1eab96fda" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_041780", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ea677c5fbaf08029ac86fe83c6aecb2d", + "normalized_query_sha256": "0c817d2c5eb4063211498b0fc60d727e49c4c0aa937b369efdb19720095521c5", + "query": "Sounds great! Could you get me a rental car there please?", + "target": "GetCarsAvailable", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 642, + "historical_id": "fastbrain_041780", + "source": "sgd", + "query_hash": "0c817d2c5eb4063211498b0fc60d727e49c4c0aa937b369efdb19720095521c5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 642, + "historical_id": "fastbrain_041780", + "source": "sgd", + "query_hash": "0c817d2c5eb4063211498b0fc60d727e49c4c0aa937b369efdb19720095521c5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_041916", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a3a4c110591b02c475502884577d9c6c", + "normalized_query_sha256": "ba23309cfbe41a8aa226c759a737a30669144f0ad30066a781583e4d18aa9a4d", + "query": "check my inbox", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 861, + "historical_id": "fastbrain_041916", + "source": "amazon_massive", + "query_hash": "ba23309cfbe41a8aa226c759a737a30669144f0ad30066a781583e4d18aa9a4d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 861, + "historical_id": "fastbrain_041916", + "source": "amazon_massive", + "query_hash": "ba23309cfbe41a8aa226c759a737a30669144f0ad30066a781583e4d18aa9a4d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042008", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f463785d7913156611ff7b259d035ad4", + "normalized_query_sha256": "e7daed4051b0cea4184cd0a1adb68189258b7aca4f5e249f0effae792b4609de", + "query": "Print current shell using process ID", + "target": "ps", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 862, + "historical_id": "fastbrain_042008", + "source": "nl2bash", + "query_hash": "e7daed4051b0cea4184cd0a1adb68189258b7aca4f5e249f0effae792b4609de" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 862, + "historical_id": "fastbrain_042008", + "source": "nl2bash", + "query_hash": "e7daed4051b0cea4184cd0a1adb68189258b7aca4f5e249f0effae792b4609de" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042041", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:294bdd083de0e574d15a95a4468378e1", + "normalized_query_sha256": "9ef3ef8c60f70a1aff633c2510237be784b5ffb991073ffdd45850f95da1f618", + "query": "set reminder in calendar", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 512, + "historical_id": "fastbrain_042041", + "source": "amazon_massive", + "query_hash": "9ef3ef8c60f70a1aff633c2510237be784b5ffb991073ffdd45850f95da1f618" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 512, + "historical_id": "fastbrain_042041", + "source": "amazon_massive", + "query_hash": "9ef3ef8c60f70a1aff633c2510237be784b5ffb991073ffdd45850f95da1f618" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042116", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b80b5895f49cf723d65ed502d52513e8", + "normalized_query_sha256": "4f757c8c0ce5a1b24d024b6712a54b3612e21a88bc7a42edd448f639cb225071", + "query": "Sure, the title is \"Grocery Shopping\", the description is \"Buy fruits, vegetables, and bread\", and the due date is \"2022-03-15\".", + "target": "create_todo", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 190, + "historical_id": "fastbrain_042116", + "source": "glaive_v2", + "query_hash": "4f757c8c0ce5a1b24d024b6712a54b3612e21a88bc7a42edd448f639cb225071" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 190, + "historical_id": "fastbrain_042116", + "source": "glaive_v2", + "query_hash": "4f757c8c0ce5a1b24d024b6712a54b3612e21a88bc7a42edd448f639cb225071" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042118", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c170063003420db9ee45224ccffdb8cc", + "normalized_query_sha256": "cc3f385c982f8c39fa11157a783607aa9c377b16bb05b8e59dda9ea9f6f55d2c", + "query": "s \n- Absolute ban on processing Class A equipment orders without vessel registration# \n- Must use identity verification workflow when accessing customer records: \n 1. Request user's contact method (email/phone) \n 2. Initiate code delivery via verification tool \n 3. Collect user-provided code \n 4. Validate code through authentication tool\n\n\nTry to be helpful and alway...[TRUNCATED]\nuser: \n\nHello, I need to place an order for a bronze anchor (SKU ANCH-BRONZE-95) for my vessel EXPEDITION-7. The weight is 95kg, so I understand standard shipping applies. Could you confirm the shipping timeline and process for this order?\nassistant: Hello! I'd be happy to help you with your anchor order. First, I need to verify your identity to access your customer information and process your order securely.\n\nCould you please provide your preferred contact method for verification? I can send a verification code to either your email or phone number.\n\nOnce verified, I can check the product details for SKU ANCH-BRONZE-95 and provide you with shipping timeline information. I'll also need your customer ID and vessel registration number to proceed with the order.\n\nWhat contact method would you prefer for verification?\nuser: Please send the verification code to captain@expedition7.com. My customer ID is EXP-7-2023, and the vessel registration is EXPEDITION-7 as mentioned. Let me know if additional details are required to confirm the standard shipping process for the 95kg bronze anchor.", + "target": "initiate_verification", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 346, + "historical_id": "fastbrain_042118", + "source": "nemotron_agentic", + "query_hash": "cc3f385c982f8c39fa11157a783607aa9c377b16bb05b8e59dda9ea9f6f55d2c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 346, + "historical_id": "fastbrain_042118", + "source": "nemotron_agentic", + "query_hash": "cc3f385c982f8c39fa11157a783607aa9c377b16bb05b8e59dda9ea9f6f55d2c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042158", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fe20211d551aa541a90fddb514513ada", + "normalized_query_sha256": "3cc430d8842193be17404808a3f07865ae43197dfbe842719493882a0719119c", + "query": "can you give me a weather report", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 91, + "historical_id": "fastbrain_042158", + "source": "amazon_massive", + "query_hash": "3cc430d8842193be17404808a3f07865ae43197dfbe842719493882a0719119c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 91, + "historical_id": "fastbrain_042158", + "source": "amazon_massive", + "query_hash": "3cc430d8842193be17404808a3f07865ae43197dfbe842719493882a0719119c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042172", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e975abb07521a366d5b2775a4a04fe2f", + "normalized_query_sha256": "f0c6f7b4bcdc23b5973daa79f3ce248a9948b975cfd3157bb09940f7d1ba54d1", + "query": "Thanks. Will you find me some attractions there?", + "target": "FindAttractions", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 296, + "historical_id": "fastbrain_042172", + "source": "sgd", + "query_hash": "f0c6f7b4bcdc23b5973daa79f3ce248a9948b975cfd3157bb09940f7d1ba54d1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 296, + "historical_id": "fastbrain_042172", + "source": "sgd", + "query_hash": "f0c6f7b4bcdc23b5973daa79f3ce248a9948b975cfd3157bb09940f7d1ba54d1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042218", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:417e99b99db6989d88615bc103105a7f", + "normalized_query_sha256": "0bdf61f487ba51fa8c639472ce5b565b25b7667980bb5896be4e5fbb5a740d43", + "query": "can you add gloria to my list of clients", + "target": "lists_createoradd", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 594, + "historical_id": "semantic:validation:fastbrain_042218:present", + "source": "amazon_massive", + "query_hash": "0bdf61f487ba51fa8c639472ce5b565b25b7667980bb5896be4e5fbb5a740d43" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 911, + "historical_id": "semantic:validation:fastbrain_042218:absent", + "source": "amazon_massive", + "query_hash": "0bdf61f487ba51fa8c639472ce5b565b25b7667980bb5896be4e5fbb5a740d43" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042225", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:26ee384eafab5ccf5136e97c87370147", + "normalized_query_sha256": "29092d142cbd2ad545857329b5610e977e7086907bde64aac27aadae5f3290c7", + "query": "resume harry potter book four", + "target": "play_audiobook", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 684, + "historical_id": "fastbrain_042225", + "source": "amazon_massive", + "query_hash": "29092d142cbd2ad545857329b5610e977e7086907bde64aac27aadae5f3290c7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 684, + "historical_id": "fastbrain_042225", + "source": "amazon_massive", + "query_hash": "29092d142cbd2ad545857329b5610e977e7086907bde64aac27aadae5f3290c7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042367", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d3d934d2db1def2a5dca5688aed14cae", + "normalized_query_sha256": "90e848344484454d0c8f3f0fa80ccac1b972999003163159ee681b1c1719c907", + "query": "start my jazz playlist", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 432, + "historical_id": "fastbrain_042367", + "source": "amazon_massive", + "query_hash": "90e848344484454d0c8f3f0fa80ccac1b972999003163159ee681b1c1719c907" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 432, + "historical_id": "fastbrain_042367", + "source": "amazon_massive", + "query_hash": "90e848344484454d0c8f3f0fa80ccac1b972999003163159ee681b1c1719c907" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042488", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8e5699d01929654ed6720c5b6b0ab377", + "normalized_query_sha256": "2cd0399c37af18e3533061b46a4920580b499903476e7a5333520de7076d8ea7", + "query": "elections in up", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 198, + "historical_id": "semantic:validation:fastbrain_042488:absent", + "source": "amazon_massive", + "query_hash": "2cd0399c37af18e3533061b46a4920580b499903476e7a5333520de7076d8ea7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 891, + "historical_id": "semantic:validation:fastbrain_042488:present", + "source": "amazon_massive", + "query_hash": "2cd0399c37af18e3533061b46a4920580b499903476e7a5333520de7076d8ea7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042507", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:439ba360ed3e0dba91a622669bc28960", + "normalized_query_sha256": "17491db46c0a0f2d5622a2646fa9cfee16811473f52f7cd00a9eb66419374b1e", + "query": "d like to proceed with filing the claim and explore options for repair funding.\nassistant: \ntool: \n\n{\"authenticated\": true, \"user_id\": \"U123456789\"}\nassistant: Thank you for providing your information, Sarah. I've successfully verified your identity. To proceed with filing your claim and reviewing your coverage options, I'll need your policy number.\n\nOnce I have your policy number, I can:\n1. Retrieve your current policy details to confirm what coverage you have\n2. Help you file the claim for the deer collision\n3. Explain what options may be available to you\n\nRegarding your question about coverage for animal collisions:\n- **Liability coverage** typically only covers damage you cause to other people's property or injuries to others\n- Damage to your own vehicle from hitting a deer would generally fall under **comprehensive coverage** (or sometimes collision, depending on the specifics)\n- Policy adjustments for coverage changes apply to future incidents, not past events\n\nPlease provide your policy number so I can access your specific policy details and help you file the claim properly.\nuser: My policy number is **LIAB-THOM-2023-0315**. Given that I only have liability coverage, could you confirm if there’s any provision in my policy—such as an exception for wildlife collisions—that might allow coverage under collision or another rider? I understand this is typically under comprehensive, but I’d like to formally request that the repair costs be covered under collision if possible.", + "target": "get_policy_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 712, + "historical_id": "fastbrain_042507", + "source": "nemotron_agentic", + "query_hash": "17491db46c0a0f2d5622a2646fa9cfee16811473f52f7cd00a9eb66419374b1e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 712, + "historical_id": "fastbrain_042507", + "source": "nemotron_agentic", + "query_hash": "17491db46c0a0f2d5622a2646fa9cfee16811473f52f7cd00a9eb66419374b1e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042632", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:085f8da85c4ba3094bd689598069567b", + "normalized_query_sha256": "d4ddc7588afe2dd23173b39c6de91925ff4e8f191a6a0634c64275064199a8dd", + "query": "BN registration mandatory \n- Public domain verification enforced \n\n---\n\n### **Agent Limitations** \n1. **Tool Dependency**: \n - Only perform actions through available tools \n - Assume NO verification without explicit tool confirmation \n\n2. **Information Boundaries**: \n ...[TRUNCATED]\nuser: \n\nHello, I’d like to apply for an academic discount on my recent order (Order #SBP-9944 for the *Journal of Philosophy* single issue). Could you guide me through the verification process? I noticed I used a personal email address, so I assume I need to provide a .edu email instead. How do I proceed with that?\nassistant: Hello! I'd be happy to help you with academic discount verification for your order. First, I need to verify your identity to access your account and order details.\n\n**Identity Verification Required**\n\nCould you please provide either:\n1. Your registered email address or phone number (so I can send an authentication code), OR\n2. The last 4 digits of the payment card used for order #SBP-9944 (for guest verification)\n\nOnce I verify your identity, I can guide you through the academic verification process, which typically requires:\n- Providing a .edu email address for confirmation\n- Validating your institutional status\n- Checking if your order qualifies for academic discounts\n\nWhich verification method would you prefer to use?\nuser: I don’t have my payment card handy, but I can provide the last 4 digits. Let me check… it’s **4487**. Is that sufficient for verification?", + "target": "verify_guest_order", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 288, + "historical_id": "fastbrain_042632", + "source": "nemotron_agentic", + "query_hash": "d4ddc7588afe2dd23173b39c6de91925ff4e8f191a6a0634c64275064199a8dd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 288, + "historical_id": "fastbrain_042632", + "source": "nemotron_agentic", + "query_hash": "d4ddc7588afe2dd23173b39c6de91925ff4e8f191a6a0634c64275064199a8dd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042701", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fe1a84bbafa869fd2417cf3fdd200838", + "normalized_query_sha256": "6d3d46a0847be3c4701fef15dda8d51ee14214828f7700c62a5d498d4750b1c3", + "query": "All right, cool. Please find my party of 4 a house rated 4.4 stars or higher, that also offers laundry service.", + "target": "SearchHouse", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 33, + "historical_id": "fastbrain_042701", + "source": "sgd", + "query_hash": "6d3d46a0847be3c4701fef15dda8d51ee14214828f7700c62a5d498d4750b1c3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 33, + "historical_id": "fastbrain_042701", + "source": "sgd", + "query_hash": "6d3d46a0847be3c4701fef15dda8d51ee14214828f7700c62a5d498d4750b1c3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042721", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5e70984739ae7a4a1a4a510a1abd4e80", + "normalized_query_sha256": "ec57906c490dbf18d81d216bc3bba516ee0f3409b2e817bf37fc55e0aa9a1633", + "query": "please send the following email to uncle john yes i will be there", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 406, + "historical_id": "semantic:validation:fastbrain_042721:absent", + "source": "amazon_massive", + "query_hash": "ec57906c490dbf18d81d216bc3bba516ee0f3409b2e817bf37fc55e0aa9a1633" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 530, + "historical_id": "semantic:validation:fastbrain_042721:present", + "source": "amazon_massive", + "query_hash": "ec57906c490dbf18d81d216bc3bba516ee0f3409b2e817bf37fc55e0aa9a1633" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042836", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:852c4d793bc5c41bb8ea3a4d1def3d55", + "normalized_query_sha256": "017697281e070ee0d6cbf4c7a8259eb261441121c5fe8d7feec73e440d496cc0", + "query": "begin new list of lottery numbers", + "target": "lists_createoradd", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 463, + "historical_id": "semantic:validation:fastbrain_042836:present", + "source": "amazon_massive", + "query_hash": "017697281e070ee0d6cbf4c7a8259eb261441121c5fe8d7feec73e440d496cc0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 934, + "historical_id": "semantic:validation:fastbrain_042836:absent", + "source": "amazon_massive", + "query_hash": "017697281e070ee0d6cbf4c7a8259eb261441121c5fe8d7feec73e440d496cc0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042907", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5fc8cfdc744a5345c18f327fee21f462", + "normalized_query_sha256": "0d9bb178ef406008d671a4f197b4daaef330dcaa05bed471eb36d50752e21d14", + "query": "Do you know a place where I can get some food?", + "target": "FindRestaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 88, + "historical_id": "fastbrain_042907", + "source": "sgd", + "query_hash": "0d9bb178ef406008d671a4f197b4daaef330dcaa05bed471eb36d50752e21d14" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 88, + "historical_id": "fastbrain_042907", + "source": "sgd", + "query_hash": "0d9bb178ef406008d671a4f197b4daaef330dcaa05bed471eb36d50752e21d14" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_042963", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:37df1a2ae3d579bbf27d46de717cd244", + "normalized_query_sha256": "3195aa34ef61841e58f524172c875f2071cef8fd3508a3b9e19d80bf65dc6af7", + "query": "tell me what the word organism means", + "target": "qa_definition", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 104, + "historical_id": "semantic:validation:fastbrain_042963:present", + "source": "amazon_massive", + "query_hash": "3195aa34ef61841e58f524172c875f2071cef8fd3508a3b9e19d80bf65dc6af7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 685, + "historical_id": "semantic:validation:fastbrain_042963:absent", + "source": "amazon_massive", + "query_hash": "3195aa34ef61841e58f524172c875f2071cef8fd3508a3b9e19d80bf65dc6af7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_043120", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7a08a80dbbca27bbedf8a0107ba2deab", + "normalized_query_sha256": "c13062c448e9ba5460291e1e0f195bacdca5aac0c98774118e8deb54dcea43b0", + "query": "make a coffee", + "target": "iot_coffee", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 570, + "historical_id": "semantic:validation:fastbrain_043120:present", + "source": "amazon_massive", + "query_hash": "c13062c448e9ba5460291e1e0f195bacdca5aac0c98774118e8deb54dcea43b0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 720, + "historical_id": "semantic:validation:fastbrain_043120:absent", + "source": "amazon_massive", + "query_hash": "c13062c448e9ba5460291e1e0f195bacdca5aac0c98774118e8deb54dcea43b0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_043153", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8e19c37f18745ddf9e9da917515b0cea", + "normalized_query_sha256": "c0db039c4987f5726e50ad6b660a4d6867f68fcc2040ec9c22fcbfbd06be5c28", + "query": "delete my whole calendar for today", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 28, + "historical_id": "semantic:validation:fastbrain_043153:present", + "source": "amazon_massive", + "query_hash": "c0db039c4987f5726e50ad6b660a4d6867f68fcc2040ec9c22fcbfbd06be5c28" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 637, + "historical_id": "semantic:validation:fastbrain_043153:absent", + "source": "amazon_massive", + "query_hash": "c0db039c4987f5726e50ad6b660a4d6867f68fcc2040ec9c22fcbfbd06be5c28" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_043167", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6f457b1a07d56ff99efcc74fc2f3418e", + "normalized_query_sha256": "c7eca11825a3010e2235ccb7de929807a98b1f0627cfb95e85d9e7e0707e09a9", + "query": "I want to listen to some music. Can you play the song 'Bohemian Rhapsody' from the playlist 'Classic Rock'?", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 662, + "historical_id": "fastbrain_043167", + "source": "glaive_v2", + "query_hash": "c7eca11825a3010e2235ccb7de929807a98b1f0627cfb95e85d9e7e0707e09a9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 662, + "historical_id": "fastbrain_043167", + "source": "glaive_v2", + "query_hash": "c7eca11825a3010e2235ccb7de929807a98b1f0627cfb95e85d9e7e0707e09a9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_043439", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9df7bb95897b42421834d8ad2c44bb3a", + "normalized_query_sha256": "6bd7f4cc2c9eca3a4c1d85483fdb125e6a42fe081164bf69813b24073560de7c", + "query": "apabilities \n- **NEVER** assume verification or action completion without explicit tool confirmation \n- **NEVER** modify physician-ordered care plans \n- **NEVER** override dementia rating restrictions \n- **NEVER** disclose protected health information \n- Compensation offers require explicit manager approval \n- Strict adherence to tool-defined workflows only\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling regarding our client ENH135. I need to understand why your staff isn't authorized to modify specific physician-ordered care elements directly when adjustments seem necessary?\nassistant: Hello! I'd be happy to help you with questions about our care policies for client ENH135. First, I need to verify your identity to ensure I'm speaking with an authorized contact.\n\nCould you please provide your registered email address or phone number so I can send you a verification code?\nuser: Certainly, my phone number is 555-123-4567. Please send the verification code there.\nassistant: \ntool: \n\n{\"code_sent\": true}\nassistant: I've sent a verification code to your phone number 555-123-4567. Please provide the code you received so I can verify your identity and access client ENH135's information. Once verified, I can explain our policies regarding physician-ordered care plan modifications.\nuser: The verification code I received is 123456. Please proceed with verifying my identity.\nassistant: \ntool: \n\n{\"authenticated\": true, \"user_token\": \"abc123xyz\"}", + "target": "get_client_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 366, + "historical_id": "fastbrain_043439", + "source": "nemotron_agentic", + "query_hash": "6bd7f4cc2c9eca3a4c1d85483fdb125e6a42fe081164bf69813b24073560de7c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 366, + "historical_id": "fastbrain_043439", + "source": "nemotron_agentic", + "query_hash": "6bd7f4cc2c9eca3a4c1d85483fdb125e6a42fe081164bf69813b24073560de7c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_043453", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:672841b0985870423b027ae0a502bfd4", + "normalized_query_sha256": "d71831c02a98030d4091046bb391b568e1575a057f338d33946b18f50e567343", + "query": "I am looking for a movie but I can't remember the title. I know that Tom Hanks was in it.", + "target": "search_movie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 871, + "historical_id": "fastbrain_043453", + "source": "glaive_v2", + "query_hash": "d71831c02a98030d4091046bb391b568e1575a057f338d33946b18f50e567343" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 871, + "historical_id": "fastbrain_043453", + "source": "glaive_v2", + "query_hash": "d71831c02a98030d4091046bb391b568e1575a057f338d33946b18f50e567343" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_043789", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a4068720cd4f2f3c9ffd668410ec83f7", + "normalized_query_sha256": "41a0481c6878ab6e6e19626cd3638c928e48abdec15f28a16d9d99d565420d0c", + "query": "oed countries or sanctioned entities \n\n## Escalation Protocol \nTransfer to human agent immediately for: \n- Hazardous materials emergencies \n- >$50,000 claims \n- Regulatory compliance investigations \n- Repeated service failures \n\n**Transfer process**: \n1. Use `transfer_to_specialist` tool \n2. Send: \"YOU ARE BEING TRANSFERRED TO A LOGISTICS SPECIALIST. PLEASE PROVIDE YOUR CASE DETAILS.\" \n\n## Agent Limitations \n- **Answer only within tool capabilities**: Do...[TRUNCATED]\nuser: \n\nHello, I need urgent assistance with my shipment ID C44221. I’ve just realized we need to add three emergency spare parts SKUs to the consignment, but I’m not sure if this is possible after customs submission. Can you confirm if this can be accommodated quickly?\nassistant: Hello! I'd be happy to help you with your shipment C44221. First, I need to verify your identity to access the shipment details.\n\nTo authenticate using the shipment method, please provide the recipient's ZIP code for shipment C44221. Once verified, I can check the current status and let you know if consignment modifications are still possible.\nuser: The recipient's ZIP code for shipment C44221 is 90210. Please check if the three additional SKUs (SP-EMG-01, SP-EMG-02, SP-EMG-03) can still be incorporated. These are critical emergency parts needed for a client’s production line. If not, what alternatives do we have to expedite this?\nassistant: \ntool: \n\n{\"authenticated\": true, \"user_id\": \"user_12345\", \"service_tier\": \"Premium\"}", + "target": "get_shipment_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 510, + "historical_id": "fastbrain_043789", + "source": "nemotron_agentic", + "query_hash": "41a0481c6878ab6e6e19626cd3638c928e48abdec15f28a16d9d99d565420d0c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 510, + "historical_id": "fastbrain_043789", + "source": "nemotron_agentic", + "query_hash": "41a0481c6878ab6e6e19626cd3638c928e48abdec15f28a16d9d99d565420d0c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_043837", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:567c7e8b2de855d7f9bfbe97c77c931c", + "normalized_query_sha256": "e8d1a554d4b7616b373de38a986e28dfe904363d7f13aff3a73b5288dc7c476a", + "query": "Okay, I'd like to get a cab there. A shared ride is fine and it's only for 1 person.", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 843, + "historical_id": "fastbrain_043837", + "source": "sgd", + "query_hash": "e8d1a554d4b7616b373de38a986e28dfe904363d7f13aff3a73b5288dc7c476a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 843, + "historical_id": "fastbrain_043837", + "source": "sgd", + "query_hash": "e8d1a554d4b7616b373de38a986e28dfe904363d7f13aff3a73b5288dc7c476a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044015", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cb4596f04047d05180c269b56abfffc8", + "normalized_query_sha256": "5669d420fe7b96c0c3be2033e42ca9a97594d32a2e0fd87b73916f8014bd71c0", + "query": "Thanks a lot for the help. Can you show me any good attractions out there?", + "target": "FindAttractions", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 773, + "historical_id": "fastbrain_044015", + "source": "sgd", + "query_hash": "5669d420fe7b96c0c3be2033e42ca9a97594d32a2e0fd87b73916f8014bd71c0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 773, + "historical_id": "fastbrain_044015", + "source": "sgd", + "query_hash": "5669d420fe7b96c0c3be2033e42ca9a97594d32a2e0fd87b73916f8014bd71c0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044200", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d5db2a7cec1a9f60bbf546f4f3186587", + "normalized_query_sha256": "01c72904f70c0ed0ad533238d7e86c53811ab74707171cfa5e5795f53ac97521", + "query": "What's the current stock price of Apple?", + "target": "get_stock_price", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 989, + "historical_id": "fastbrain_044200", + "source": "glaive_v2", + "query_hash": "01c72904f70c0ed0ad533238d7e86c53811ab74707171cfa5e5795f53ac97521" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 989, + "historical_id": "fastbrain_044200", + "source": "glaive_v2", + "query_hash": "01c72904f70c0ed0ad533238d7e86c53811ab74707171cfa5e5795f53ac97521" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044216", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:be9da8204e4fb18a1a0c5c05c6e4c475", + "normalized_query_sha256": "a42b678cc6ca22def4a638869ecb80b6967fe6cf671ac455bc39d60a6e67e126", + "query": "Sure, the username is 'john_doe', email is 'john_doe@example.com', and password is 'password123'.", + "target": "create_user_account", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 12, + "historical_id": "fastbrain_044216", + "source": "glaive_v2", + "query_hash": "a42b678cc6ca22def4a638869ecb80b6967fe6cf671ac455bc39d60a6e67e126" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 12, + "historical_id": "fastbrain_044216", + "source": "glaive_v2", + "query_hash": "a42b678cc6ca22def4a638869ecb80b6967fe6cf671ac455bc39d60a6e67e126" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044370", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4d07cbd9afaa1928ba58589ecd23be85", + "normalized_query_sha256": "8811186cda42608733a725d88bda65711f676d4828c223ec554b92708369ff93", + "query": "tell the latest news about the trump", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 101, + "historical_id": "semantic:validation:fastbrain_044370:absent", + "source": "amazon_massive", + "query_hash": "8811186cda42608733a725d88bda65711f676d4828c223ec554b92708369ff93" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 814, + "historical_id": "semantic:validation:fastbrain_044370:present", + "source": "amazon_massive", + "query_hash": "8811186cda42608733a725d88bda65711f676d4828c223ec554b92708369ff93" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044389", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d79a2b1cc74ccc82c85b9c1016434b8c", + "normalized_query_sha256": "344cde149c8f3a0590e895c6b2c2687afe40a1afb8cf7ded2905f706e6410453", + "query": "Print the last 1000 lines of all files matching \"/var/spool/cron/*\"", + "target": "tail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 544, + "historical_id": "fastbrain_044389", + "source": "nl2bash", + "query_hash": "344cde149c8f3a0590e895c6b2c2687afe40a1afb8cf7ded2905f706e6410453" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 544, + "historical_id": "fastbrain_044389", + "source": "nl2bash", + "query_hash": "344cde149c8f3a0590e895c6b2c2687afe40a1afb8cf7ded2905f706e6410453" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044392", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c20a029fea3d890515c56240f790aa9b", + "normalized_query_sha256": "a61054047a26b776e4d377dd5b1c00e784a12ca3001552abf581da107f4a7949", + "query": "mation \n - Require explicit system validation before refunds/returns \n\n3. **Knowledge Constraints**: \n - Do not discuss unreleased products \n - Do not provide post-purchase competitor comparisons \n\n---\n\n### **Escalation Protocol** \nTransfer to human agent for: \n- Safety incidents \n- Legal/dispute inquiries \n- Complex warranty cases \nProcess: \n**\"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE HOLD.\"**\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling about the tutoring sessions I've been having. I have some sensory sensitivities and would like to request modifications to the session environment to make it more comfortable for me. My student ID is SM5556.\nassistant: I understand you'd like to request modifications for sensory sensitivities. First, I need to verify your identity. You've provided your student ID (SM5556). \n\nCould you please provide either:\n1. Your registered email or phone number, OR\n2. Your full name, date of birth, and parent/guardian contact information?\n\nThis is required before I can access your account and discuss session modifications.\nuser: Sure, I can provide my parent's contact information. Her name is Maria Gonzalez, and her phone number is (555) 123-4567. My full name is Samuel Mitchell, and my date of birth is March 15, 1998.", + "target": "verify_identity", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 339, + "historical_id": "fastbrain_044684", + "source": "nemotron_agentic", + "query_hash": "7c4add104654df7e090828567e14a06dae412a69798074f888f19a76da4916aa" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 339, + "historical_id": "fastbrain_044684", + "source": "nemotron_agentic", + "query_hash": "7c4add104654df7e090828567e14a06dae412a69798074f888f19a76da4916aa" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044796", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bf9237800a9a25e91928ec7764e30298", + "normalized_query_sha256": "dcc3909cb8e8d51732a8a7e7ef5b4e02b8bb4930d0c6b79e5b19a795e772d41e", + "query": "create a new list by tomorrow", + "target": "lists_createoradd", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 882, + "historical_id": "semantic:validation:fastbrain_044796:absent", + "source": "amazon_massive", + "query_hash": "dcc3909cb8e8d51732a8a7e7ef5b4e02b8bb4930d0c6b79e5b19a795e772d41e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 913, + "historical_id": "semantic:validation:fastbrain_044796:present", + "source": "amazon_massive", + "query_hash": "dcc3909cb8e8d51732a8a7e7ef5b4e02b8bb4930d0c6b79e5b19a795e772d41e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044863", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2fb4b66c79a2a4bade29043b914a3e75", + "normalized_query_sha256": "41d454a43876f959d7878c43c92369bf4a523fe71aab40719f3bd2afad6e5e9a", + "query": "what events near hear are happening this week", + "target": "recommendation_events", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 257, + "historical_id": "fastbrain_044863", + "source": "amazon_massive", + "query_hash": "41d454a43876f959d7878c43c92369bf4a523fe71aab40719f3bd2afad6e5e9a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 257, + "historical_id": "fastbrain_044863", + "source": "amazon_massive", + "query_hash": "41d454a43876f959d7878c43c92369bf4a523fe71aab40719f3bd2afad6e5e9a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044890", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ebac47dd6703b8459eea40a6658703ad", + "normalized_query_sha256": "60f9648cb75ee3b4d6f677e151df0d94731581713b8a373870edec0cc9674f17", + "query": "what meetings do i have next thursday", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 923, + "historical_id": "fastbrain_044890", + "source": "amazon_massive", + "query_hash": "60f9648cb75ee3b4d6f677e151df0d94731581713b8a373870edec0cc9674f17" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 923, + "historical_id": "fastbrain_044890", + "source": "amazon_massive", + "query_hash": "60f9648cb75ee3b4d6f677e151df0d94731581713b8a373870edec0cc9674f17" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_044974", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5cbf34704dceddb0b949a7c7bedd8fda", + "normalized_query_sha256": "1e54810c085effcd2f2865a4fc4f012d857575e7c9c234b85493daa4ed301f51", + "query": "send an email to all my company workers", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 327, + "historical_id": "semantic:validation:fastbrain_044974:absent", + "source": "amazon_massive", + "query_hash": "1e54810c085effcd2f2865a4fc4f012d857575e7c9c234b85493daa4ed301f51" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 726, + "historical_id": "semantic:validation:fastbrain_044974:present", + "source": "amazon_massive", + "query_hash": "1e54810c085effcd2f2865a4fc4f012d857575e7c9c234b85493daa4ed301f51" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045027", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fb69fa419a0e17aaa7267c904bc0cad6", + "normalized_query_sha256": "2ff78f943a6a8da82bb5b82592ad49b34524d3496edeef51444ef60c1c75197b", + "query": "what date is the fourth monday of march this year", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 174, + "historical_id": "fastbrain_045027", + "source": "amazon_massive", + "query_hash": "2ff78f943a6a8da82bb5b82592ad49b34524d3496edeef51444ef60c1c75197b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 174, + "historical_id": "fastbrain_045027", + "source": "amazon_massive", + "query_hash": "2ff78f943a6a8da82bb5b82592ad49b34524d3496edeef51444ef60c1c75197b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045044", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6845f101eb897a57417cba3b595da2cb", + "normalized_query_sha256": "60a6524cd96fc9c17808c6c96108eeedb2e5c7bc038d62afe55c1a94c1aaa7bf", + "query": "The recipient's email is boss@company.com. The subject is \"Project Update\". And in the body, I want to say \"Dear Boss, The project is on track and we are expecting to complete it by the end of this week. Regards, [User's Name]\".", + "target": "send_email", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 657, + "historical_id": "fastbrain_045044", + "source": "glaive_v2", + "query_hash": "60a6524cd96fc9c17808c6c96108eeedb2e5c7bc038d62afe55c1a94c1aaa7bf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 657, + "historical_id": "fastbrain_045044", + "source": "glaive_v2", + "query_hash": "60a6524cd96fc9c17808c6c96108eeedb2e5c7bc038d62afe55c1a94c1aaa7bf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045076", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9edf46d8dec9c6eb2807e5b93ac7f7eb", + "normalized_query_sha256": "58b0dff8bde67c1ad30ecde816a0f84300f422fe932a3883f78744e5db90974a", + "query": "what's the news on b. b. c. news", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 858, + "historical_id": "fastbrain_045076", + "source": "amazon_massive", + "query_hash": "58b0dff8bde67c1ad30ecde816a0f84300f422fe932a3883f78744e5db90974a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 858, + "historical_id": "fastbrain_045076", + "source": "amazon_massive", + "query_hash": "58b0dff8bde67c1ad30ecde816a0f84300f422fe932a3883f78744e5db90974a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045207", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6c530e379ea6e3988eda947f4e77d188", + "normalized_query_sha256": "9dc5379cfdd46f88dd979203fb33e12379757d530d5a180194ab90615a5b6e76", + "query": "ok fine. Can you find me flights there and back for 3 people? I have no preference over the airlines but need to be back on the 9th.", + "target": "SearchRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 131, + "historical_id": "fastbrain_045207", + "source": "sgd", + "query_hash": "9dc5379cfdd46f88dd979203fb33e12379757d530d5a180194ab90615a5b6e76" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 131, + "historical_id": "fastbrain_045207", + "source": "sgd", + "query_hash": "9dc5379cfdd46f88dd979203fb33e12379757d530d5a180194ab90615a5b6e76" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045270", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e8edbc1a19b5576a6d5f76e46b74c584", + "normalized_query_sha256": "2b282bcce649238cf1bf122e70a27c01524f5ec2b94b053784df333139df5966", + "query": "tion code via **email** or **phone**?\n\nOnce verified, I'll connect you with our estate planning specialist who can provide:\n- Exact fee breakdown\n- Expedited timeline confirmation\n- Required documentation checklist\n- Digital preview of the provenance records format\n\nPlease let me know your preferred contact method for authentication.\nuser: I’ll take the authentication code via **phone** for speed. Once verified, I’d like to confirm the specialist will handle all 15 artworks without requiring my coordination with other departments. Also, please clarify if the 50% VIP discount applies to the expedited processing as well. Let’s proceed.\nassistant: \ntool: \n\n{\"status\": \"Authentication code successfully sent to phone\"}\nassistant: I've successfully sent an authentication code to your phone. Please provide the code you received to complete the verification process.\n\nTo confirm your questions:\n1. **Yes**, the dedicated specialist will handle all 15 artworks end-to-end without requiring you to coordinate with other departments.\n2. **Yes**, the 50% VIP discount applies to both standard and expedited processing fees.\n\nOnce verified, I'll immediately connect you with our estate planning specialist who can provide the detailed fee breakdown and timeline options.\nuser: Authentication code received: **123456**. Please confirm the specialist is now assigned and will manage the full 15-artwork documentation package. I’ll await their contact for the detailed breakdown and timeline confirmation.", + "target": "verify_authentication_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 109, + "historical_id": "fastbrain_045270", + "source": "nemotron_agentic", + "query_hash": "2b282bcce649238cf1bf122e70a27c01524f5ec2b94b053784df333139df5966" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 109, + "historical_id": "fastbrain_045270", + "source": "nemotron_agentic", + "query_hash": "2b282bcce649238cf1bf122e70a27c01524f5ec2b94b053784df333139df5966" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045652", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b410e50dade2066632e8b6423c44a07e", + "normalized_query_sha256": "31e68a1cf24156dc1505fd2f39c9553c180ab99047d990b75a1f4af2496629ba", + "query": "ently moved and need to update the address for my mail-order prescription. The tracking number for my current 90-day supply is M778899, and it's being sent to my old address. My new address is 123 New Street, Cityville, ST 98765. Could you please let me know the protocol for correcting the shipping address according to your policy?\nassistant: Hello! I'd be happy to help you with updating your mail-order prescription shipping address. First, I need to verify your identity according to our security protocols.\n\n**To proceed, I'll need:**\n1. **Member ID**\n2. **Date of birth (MM/DD/YYYY format)**\n3. **Secondary verification:** Last 4 digits of your SSN OR your registered phone number\n\nAdditionally, could you provide the **prescription number (Rx number)** for your medication? The tracking number (M778899) is different from the Rx number I need to access your prescription details.\n\nOnce I have this information, I can authenticate you and check the status of your shipment to provide the correct protocol for address correction.\nuser: Sure, here’s the information you requested: \n- **Member ID:** 456789XYZ \n- **Date of Birth:** 05/15/1980 \n- **Secondary Verification:** Last 4 digits of SSN: 5678 \n- **Rx Number:** I’m not sure where to find this—can you guide me? \n\nI’m concerned about whether the package (Tracking# M778899) can be rerouted or if I’ll need to reorder. Let me know how to proceed!\nassistant: \ntool: \n\n{\"auth_token\": \"abc123xyz456\", \"expiration\": \"2025-11-25T01:44:54Z\"}", + "target": "get_member_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 206, + "historical_id": "fastbrain_045652", + "source": "nemotron_agentic", + "query_hash": "31e68a1cf24156dc1505fd2f39c9553c180ab99047d990b75a1f4af2496629ba" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 206, + "historical_id": "fastbrain_045652", + "source": "nemotron_agentic", + "query_hash": "31e68a1cf24156dc1505fd2f39c9553c180ab99047d990b75a1f4af2496629ba" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045696", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a9103c4894511b2cb37b9f9fa7a61a18", + "normalized_query_sha256": "82a5b4f0c6f20b77a83c50c90cd178bca7ff269de60d6bfc8f7f57a5486d511c", + "query": "do i need to apply sunburn cream when i go to golf club", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 111, + "historical_id": "semantic:validation:fastbrain_045696:present", + "source": "amazon_massive", + "query_hash": "82a5b4f0c6f20b77a83c50c90cd178bca7ff269de60d6bfc8f7f57a5486d511c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 961, + "historical_id": "semantic:validation:fastbrain_045696:absent", + "source": "amazon_massive", + "query_hash": "82a5b4f0c6f20b77a83c50c90cd178bca7ff269de60d6bfc8f7f57a5486d511c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045745", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:88d448f86460091d9599dc95e6a5d1a5", + "normalized_query_sha256": "1701a2f4a09bac577395d6f0a0abcafb3f0a50faa41b9cd8b5a23ca1c057a538", + "query": "Hi, I need help with calculating a tip. My bill amount is $85 and I want to give a 15% tip.", + "target": "calculate_tip", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 860, + "historical_id": "fastbrain_045745", + "source": "glaive_v2", + "query_hash": "1701a2f4a09bac577395d6f0a0abcafb3f0a50faa41b9cd8b5a23ca1c057a538" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 860, + "historical_id": "fastbrain_045745", + "source": "glaive_v2", + "query_hash": "1701a2f4a09bac577395d6f0a0abcafb3f0a50faa41b9cd8b5a23ca1c057a538" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_045831", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:11ca9c51d50459838dd9e5caed7285d1", + "normalized_query_sha256": "535cb84c82ef4dcdd8e8f76ed0a7a9ccd8e477a6c214dd466a567bcec20e6e8d", + "query": "oxes restricted \n - Hazardous materials handled per IATA regulations\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm reaching out from Toy World and our customer ID is TOYWORLD. We have five tracking numbers: 9400100000000000000023 through 9400100000000000000027. All these shipments are destined for the same warehouse. We'd like to consolidate these packages and inquire about potential volume discounts. Could you check if consolidation is possible and provide a cost estimate?\nassistant: \ntool: \n\n{\"authenticated\": true, \"user_name\": \"Toy World\"}\nassistant: \ntool: \n\n{\"origin\": \"123 Toy Lane, Springfield, IL 62704\", \"destination\": \"456 Warehouse Rd, Distribution Hub, CA 90001\", \"current_location\": \"Regional Sorting Center - Chicago\", \"status\": \"in_transit\", \"service_tier\": \"Economy\", \"insurance_value\": 250.00, \"estimated_delivery\": \"2025-02-01T10:00:00Z\"}\nassistant: \ntool: \n\n{\"origin\": \"123 Toy Lane, Springfield, IL 62704\", \"destination\": \"456 Warehouse Rd, Distribution Hub, CA 90001\", \"current_location\": \"Regional Sorting Center - Chicago\", \"status\": \"in_transit\", \"service_tier\": \"Economy\", \"insurance_value\": 280.00, \"estimated_delivery\": \"2025-02-01T12:30:00Z\"}\nassistant: \ntool: \n\n{\"addresses\": [\"456 Warehouse Rd, Distribution Hub, CA 90001\", \"123 Toy Lane, Springfield, IL 62704\"], \"payment_methods\": [\"Credit Card ending in 4567\", \"Account Credit Balance: $2,500.00\"], \"contact_preferences\": {\"email\": true, \"sms\": false}, \"special_handling\": []}", + "target": "get_shipment_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 729, + "historical_id": "fastbrain_045831", + "source": "nemotron_agentic", + "query_hash": "535cb84c82ef4dcdd8e8f76ed0a7a9ccd8e477a6c214dd466a567bcec20e6e8d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 729, + "historical_id": "fastbrain_045831", + "source": "nemotron_agentic", + "query_hash": "535cb84c82ef4dcdd8e8f76ed0a7a9ccd8e477a6c214dd466a567bcec20e6e8d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_046058", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ac70a50398d7a963895454227701deaa", + "normalized_query_sha256": "091584e493ba2843ff5bd673df6bc3c3df1a9518018c602e43c4e9a6bc309e3b", + "query": "amp: 2023-10-15T08:30:00 (ISO format)\n- Evidence:\n - Delivery Receipt: #DEL-8890\n - Inspection Report: #LAB-9876\n - Temperature Log: [awaiting your data]\n\n**Regarding the 48-hour window:** Based on your information (delivery 10/15/2023 8:30 AM, today is 10/17/2023), you're at the 48-hour mark. The dispute tool will validate the exact timing against the current system time. As long as we file before 10/17/2023 8:30 AM, you're within the window.\n\n**Important Policy Note:** Once filed, funds for this transaction will be frozen during the investigation, as per our dispute resolution policy.\n\nPlease provide the temperature log data so I can proceed with filing your claim.\nuser: Here is the temperature log data from the shipment (formatted as a string with timestamps and readings in Celsius): \n\n`\"2023-10-15T06:00:00, 4.5°C; 2023-10-15T08:00:00, 8.2°C; 2023-10-15T10:00:00, 12.7°C; 2023-10-15T12:00:00, 15.3°C; 2023-10-15T14:00:00, 14.9°C; 2023-10-15T16:00:00, 11.1°C; 2023-10-15T18:00:00, 5.8°C\"` \n\nThis shows a 4-hour period (8:00 AM–12:00 PM) where temperatures exceeded the safe range (above 8°C) during transit. The log is from our IoT sensors (Model AG-TempPro 3000). \n\n**48-hour confirmation:** We filed at 10/17/2023 9:15 AM (PST), so the window is still valid. \n\nPlease proceed with filing the dispute. If the system requires the log in a different format, I can provide it. Also, could you confirm the estimated timeline for resolution? We need to plan for replanting schedules.", + "target": "file_dispute", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1004, + "historical_id": "fastbrain_046058", + "source": "nemotron_agentic", + "query_hash": "091584e493ba2843ff5bd673df6bc3c3df1a9518018c602e43c4e9a6bc309e3b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1004, + "historical_id": "fastbrain_046058", + "source": "nemotron_agentic", + "query_hash": "091584e493ba2843ff5bd673df6bc3c3df1a9518018c602e43c4e9a6bc309e3b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_046123", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:82d9620ef15ac071919309734dce92d6", + "normalized_query_sha256": "b073b7b03f704bc0d7833707b18a542a828bb21217a5686a262bd26e4c70045b", + "query": "what time does the festival officially start", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 358, + "historical_id": "semantic:validation:fastbrain_046123:absent", + "source": "amazon_massive", + "query_hash": "b073b7b03f704bc0d7833707b18a542a828bb21217a5686a262bd26e4c70045b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 941, + "historical_id": "semantic:validation:fastbrain_046123:present", + "source": "amazon_massive", + "query_hash": "b073b7b03f704bc0d7833707b18a542a828bb21217a5686a262bd26e4c70045b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_046206", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b321bf9c56005fb5a5db011e31da0850", + "normalized_query_sha256": "53dc5264d0f2e1bb3222ad06a179f301655f556ef83f2903bb31be5ff97ee3d4", + "query": "Yes. Get me four tickets.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 341, + "historical_id": "fastbrain_046206", + "source": "sgd", + "query_hash": "53dc5264d0f2e1bb3222ad06a179f301655f556ef83f2903bb31be5ff97ee3d4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 341, + "historical_id": "fastbrain_046206", + "source": "sgd", + "query_hash": "53dc5264d0f2e1bb3222ad06a179f301655f556ef83f2903bb31be5ff97ee3d4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_046485", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:384b68231914389a9b3f30368d9e0a9d", + "normalized_query_sha256": "2118254cb2858abba7b29d1d4705aa37f8b52d2f325c96912ce672981dfe2e55", + "query": "Hi, I need to calculate the discount on a dress I want to buy. It's originally priced at $200 and there's a 20% discount on it.", + "target": "calculate_discount", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 572, + "historical_id": "fastbrain_046485", + "source": "glaive_v2", + "query_hash": "2118254cb2858abba7b29d1d4705aa37f8b52d2f325c96912ce672981dfe2e55" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 572, + "historical_id": "fastbrain_046485", + "source": "glaive_v2", + "query_hash": "2118254cb2858abba7b29d1d4705aa37f8b52d2f325c96912ce672981dfe2e55" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_046501", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:876412cbb91e44a482850a1ce7476fba", + "normalized_query_sha256": "6d4639a4d0cc0e4620f7113dd2930c8beabb92148337ef0d14fa187cb3892758", + "query": "Hi, I saw a dress that I really liked. It was originally priced at $200 but it's on sale with a 20% discount. 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Can you show me something cool to do there.", + "target": "FindAttractions", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 758, + "historical_id": "fastbrain_048654", + "source": "sgd", + "query_hash": "9622027c4e4496f0f926deaed230bbb19d90591c1909a6e776e1fb278ef08e8c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 758, + "historical_id": "fastbrain_048654", + "source": "sgd", + "query_hash": "9622027c4e4496f0f926deaed230bbb19d90591c1909a6e776e1fb278ef08e8c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_048696", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1febc05e62ba5a6c1044df830ab6d40b", + "normalized_query_sha256": "553c32c88e6a3b4733fe7a0f8c3f36d0d353563d2de3ae6d473d8c001505cb73", + "query": "I need to calculate the area of a rectangle. The base is 5 units and the height is 10 units.", + "target": "calculate_area", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 292, + "historical_id": "fastbrain_048696", + "source": "glaive_v2", + "query_hash": "553c32c88e6a3b4733fe7a0f8c3f36d0d353563d2de3ae6d473d8c001505cb73" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 292, + "historical_id": "fastbrain_048696", + "source": "glaive_v2", + "query_hash": "553c32c88e6a3b4733fe7a0f8c3f36d0d353563d2de3ae6d473d8c001505cb73" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_048731", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:65edf11c1c985b10838f072b870ba9b5", + "normalized_query_sha256": "fba59870632a0a7abe18f5f2ab3cdd2e6507981062ee809ba3bdb1595377a7ec", + "query": "It can be done later. Could you please help me in finding a bus to go there?", + "target": "FindBus", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 639, + "historical_id": "fastbrain_048731", + "source": "sgd", + "query_hash": "fba59870632a0a7abe18f5f2ab3cdd2e6507981062ee809ba3bdb1595377a7ec" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 639, + "historical_id": "fastbrain_048731", + "source": "sgd", + "query_hash": "fba59870632a0a7abe18f5f2ab3cdd2e6507981062ee809ba3bdb1595377a7ec" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_048785", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4c98b32368e2a2b0e49c200116001073", + "normalized_query_sha256": "939c61a07afa3b8e6c8c03dc47113edd2a1596f68bb0fc993301b63a3c5ad3fa", + "query": "training day", + "target": "recommendation_movies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 169, + "historical_id": "semantic:validation:fastbrain_048785:present", + "source": "amazon_massive", + "query_hash": "939c61a07afa3b8e6c8c03dc47113edd2a1596f68bb0fc993301b63a3c5ad3fa" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 478, + "historical_id": "semantic:validation:fastbrain_048785:absent", + "source": "amazon_massive", + "query_hash": "939c61a07afa3b8e6c8c03dc47113edd2a1596f68bb0fc993301b63a3c5ad3fa" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_048818", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9148bc0a644f5b52d82cd416e0f7ec76", + "normalized_query_sha256": "9b71b8a80c9cdc1f79780734c495d93b548141ab41f93b975f2979daed55fd7e", + "query": "I wish to check the weather in Nairobi on march 1st.", + "target": "GetWeather", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 392, + "historical_id": "fastbrain_048818", + "source": "sgd", + "query_hash": "9b71b8a80c9cdc1f79780734c495d93b548141ab41f93b975f2979daed55fd7e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 392, + "historical_id": "fastbrain_048818", + "source": "sgd", + "query_hash": "9b71b8a80c9cdc1f79780734c495d93b548141ab41f93b975f2979daed55fd7e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_048926", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cc5361254404786bf5609e29a7f00208", + "normalized_query_sha256": "0d763f38714deb1492dffb7b26aa0b0de1e6c6c0935b7d63d3ced82b7e574766", + "query": "To ensure our network's security, I'd like to establish a robust defense system against potential cyber threats. Firstly, I require a firewall configuration that strictly denies all incoming traffic by default, specifically blocking any IP address attempting to access the subnet 192.168.1.0/24. Additionally, I need to deploy an intrusion detection system with high-level monitoring for network traf", + "target": "configure_firewall", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 503, + "historical_id": "fastbrain_048926", + "source": "hermes_v1", + "query_hash": "0d763f38714deb1492dffb7b26aa0b0de1e6c6c0935b7d63d3ced82b7e574766" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 503, + "historical_id": "fastbrain_048926", + "source": "hermes_v1", + "query_hash": "0d763f38714deb1492dffb7b26aa0b0de1e6c6c0935b7d63d3ced82b7e574766" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049045", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3f955df903de6e7044132daf89588881", + "normalized_query_sha256": "3ddd00803dc32fd210f3e82e8c412aa0c1b9e490b813a6dfeb6d11423d6fa9de", + "query": "Okay, here they are. For English, I got an A and it was 3 credit hours. For Math, I got a B and it was 4 credit hours. For Science, I got a C and it was 3 credit hours.", + "target": "calculate_gpa", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 474, + "historical_id": "fastbrain_049045", + "source": "glaive_v2", + "query_hash": "3ddd00803dc32fd210f3e82e8c412aa0c1b9e490b813a6dfeb6d11423d6fa9de" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 474, + "historical_id": "fastbrain_049045", + "source": "glaive_v2", + "query_hash": "3ddd00803dc32fd210f3e82e8c412aa0c1b9e490b813a6dfeb6d11423d6fa9de" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049083", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5feb43f2089d83b4b4147b035eba0e88", + "normalized_query_sha256": "bfcaebc0d0c64abb89f6d1edd4631e1e65c0a9f8308a46ce7f47c27ad454d455", + "query": "please set alarm at five p. m.", + "target": "alarm_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 820, + "historical_id": "fastbrain_049083", + "source": "amazon_massive", + "query_hash": "bfcaebc0d0c64abb89f6d1edd4631e1e65c0a9f8308a46ce7f47c27ad454d455" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 820, + "historical_id": "fastbrain_049083", + "source": "amazon_massive", + "query_hash": "bfcaebc0d0c64abb89f6d1edd4631e1e65c0a9f8308a46ce7f47c27ad454d455" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049175", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f795a17f9cd32abfd7367f85431a345e", + "normalized_query_sha256": "c06b3be86be46cf1ca705f3628fefcb2100eec48dbd17fc240f11ee8e651f788", + "query": "us\": \"Booked\", \"service_tier\": \"Premium\", \"origin\": \"123 Old Oak St, Springfield\", \"destination\": \"456 Maple Ave, Riverside\"}\nassistant: Thank you for providing the verification code! I've successfully verified your identity. I can see your booking FAM778 is currently in \"Booked\" status with Premium service tier.\n\nYou're correct about the address change fee structure according to our policy:\n- **Address changes**: $100 fee + distance recalculation\n\nFor the 50-mile adjustment to your destination, the total cost would be:\n1. $100 fixed address change fee\n2. Recalculated distance charges based on the new total distance from your origin (123 Old Oak St, Springfield) to the new destination\n\nTo give you an exact total, I need to know:\n1. **What is the new destination address?**\n2. **When is your scheduled move date?** (If the move is within 72 hours and you need to change the date/time as well, there would be an additional $50 fee)\n\nAlso, could you confirm if this is purely an address change, or are you also changing the move date/time?\n\nOnce I have the new destination address, I can process the modification through our system which will provide the exact total including the distance recalculation.\nuser: The new destination address is 1234 Maple Lane, Riverside. The move date remains in 3 days as originally scheduled. This is only an address change—no adjustments to the move time or date. Can you now calculate the total cost including the $100 fee and recalculated distance charges?", + "target": "modify_booking", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 349, + "historical_id": "fastbrain_049175", + "source": "nemotron_agentic", + "query_hash": "c06b3be86be46cf1ca705f3628fefcb2100eec48dbd17fc240f11ee8e651f788" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 349, + "historical_id": "fastbrain_049175", + "source": "nemotron_agentic", + "query_hash": "c06b3be86be46cf1ca705f3628fefcb2100eec48dbd17fc240f11ee8e651f788" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049183", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bc81fed1973c94599f8e636cc09a6162", + "normalized_query_sha256": "7d918becad58104701fc9f148a72a06b1b6e17f867925a14e04fedef3aa5a59d", + "query": "c. n. n. headlines", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 226, + "historical_id": "semantic:validation:fastbrain_049183:present", + "source": "amazon_massive", + "query_hash": "7d918becad58104701fc9f148a72a06b1b6e17f867925a14e04fedef3aa5a59d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 652, + "historical_id": "semantic:validation:fastbrain_049183:absent", + "source": "amazon_massive", + "query_hash": "7d918becad58104701fc9f148a72a06b1b6e17f867925a14e04fedef3aa5a59d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049218", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:683cd6a2be9af9bca148b52dd70397ba", + "normalized_query_sha256": "b67a1294f42e3a30969200aacb336e1bef8d3fbed8c63d1ec9d76c87d6443b49", + "query": "undo the last commit keeping the files in the working tree", + "target": "git_reset", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 754, + "historical_id": "fastbrain_049218", + "source": "git_nl_commands", + "query_hash": "b67a1294f42e3a30969200aacb336e1bef8d3fbed8c63d1ec9d76c87d6443b49" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 754, + "historical_id": "fastbrain_049218", + "source": "git_nl_commands", + "query_hash": "b67a1294f42e3a30969200aacb336e1bef8d3fbed8c63d1ec9d76c87d6443b49" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049233", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:958065093f8236f331d0b59ab5152ed4", + "normalized_query_sha256": "6cc154504b34fc19ae90bb1b1645b3a657dbeaf1ee1f2741c1a4f2d3545c3d31", + "query": "please tweet a complaint to comcast for bad customer service", + "target": "social_post", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 418, + "historical_id": "semantic:validation:fastbrain_049233:present", + "source": "amazon_massive", + "query_hash": "6cc154504b34fc19ae90bb1b1645b3a657dbeaf1ee1f2741c1a4f2d3545c3d31" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 871, + "historical_id": "semantic:validation:fastbrain_049233:absent", + "source": "amazon_massive", + "query_hash": "6cc154504b34fc19ae90bb1b1645b3a657dbeaf1ee1f2741c1a4f2d3545c3d31" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049277", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d3b53592ee2103f81c04e238949cf830", + "normalized_query_sha256": "d707a81420662ce085708ac2a4e317c95a7a32eefecfcb192440772101c22120", + "query": "clear list things to do this weekend", + "target": "lists_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 748, + "historical_id": "fastbrain_049277", + "source": "amazon_massive", + "query_hash": "d707a81420662ce085708ac2a4e317c95a7a32eefecfcb192440772101c22120" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 748, + "historical_id": "fastbrain_049277", + "source": "amazon_massive", + "query_hash": "d707a81420662ce085708ac2a4e317c95a7a32eefecfcb192440772101c22120" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049401", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b4aba7e37f47b5b9e3510b687d512ea3", + "normalized_query_sha256": "c2d08348f9fcf9ad74dfa0074e569fd8e90b293cfe6e532c3e69a8ba68f98272", + "query": "I need to generate an invoice for customer John Doe. 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I really need to go to the gynecologist and that office is close to me!", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 347, + "historical_id": "fastbrain_049592", + "source": "sgd", + "query_hash": "8da36ed81986731273eee45a7c779197e457da6d17a6eb5741ec83878b7aa75b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 347, + "historical_id": "fastbrain_049592", + "source": "sgd", + "query_hash": "8da36ed81986731273eee45a7c779197e457da6d17a6eb5741ec83878b7aa75b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049620", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:36219f65a2ce90d2cffbd60fe2ba0dac", + "normalized_query_sha256": "74eda8cc0b347190816e19967323a0b74696562ed5c0ed356c29f359a60f1267", + "query": "show unstaged files only", + "target": "git_diff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 948, + "historical_id": "fastbrain_049620", + "source": "git_nl_commands", + "query_hash": "74eda8cc0b347190816e19967323a0b74696562ed5c0ed356c29f359a60f1267" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 948, + "historical_id": "fastbrain_049620", + "source": "git_nl_commands", + "query_hash": "74eda8cc0b347190816e19967323a0b74696562ed5c0ed356c29f359a60f1267" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049718", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8b5b12598a3935c370df6dbbf4b47865", + "normalized_query_sha256": "aa4e53185cea77b423aa8b30532c0c9c1893bad239f6445d0e2bfe6b7333e67e", + "query": "find world meaning in dictionary", + "target": "qa_definition", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 264, + "historical_id": "fastbrain_049718", + "source": "amazon_massive", + "query_hash": "aa4e53185cea77b423aa8b30532c0c9c1893bad239f6445d0e2bfe6b7333e67e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 264, + "historical_id": "fastbrain_049718", + "source": "amazon_massive", + "query_hash": "aa4e53185cea77b423aa8b30532c0c9c1893bad239f6445d0e2bfe6b7333e67e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049884", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7c1822b4ff31b54d96ce430de6925889", + "normalized_query_sha256": "90bf6ab8ce2b518a203c926a994b6bf041ce263e6ae2f3520d05a042a7a254c0", + "query": "Can you help me find an apartment that allows pets and has two baths?", + "target": "FindApartment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 811, + "historical_id": "fastbrain_049884", + "source": "sgd", + "query_hash": "90bf6ab8ce2b518a203c926a994b6bf041ce263e6ae2f3520d05a042a7a254c0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 811, + "historical_id": "fastbrain_049884", + "source": "sgd", + "query_hash": "90bf6ab8ce2b518a203c926a994b6bf041ce263e6ae2f3520d05a042a7a254c0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049931", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:25ec850ed58b5dac01061a4c09b3bc8c", + "normalized_query_sha256": "941316eca8a3c846f8ddcec170ece53d880b983a28a02294988592237a0c4f2b", + "query": "I have a dataset containing our company's historical sales records from January 2005 to December 2022. These records are pivotal for financial analysis and regulatory compliance owing to their sensitive nature. The data is meticulously organized in a CSV file named 'sales_records.csv' with detailed columns for 'Date', 'Product_ID', 'Quantity_Sold', 'Unit_Price', and 'Total_Revenue'. To ensure secu", + "target": "archive_data_to_cloud", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 837, + "historical_id": "fastbrain_049931", + "source": "hermes_v1", + "query_hash": "941316eca8a3c846f8ddcec170ece53d880b983a28a02294988592237a0c4f2b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 837, + "historical_id": "fastbrain_049931", + "source": "hermes_v1", + "query_hash": "941316eca8a3c846f8ddcec170ece53d880b983a28a02294988592237a0c4f2b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049938", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5e4bb8e4faa9d26f7d68b08666d346ec", + "normalized_query_sha256": "9b366140613092e1e36522ffdac9b8f546bf4cc0864d42d6bd6938e3fde7a08d", + "query": "Please tell me about the events on my calendar.", + "target": "GetEvents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 906, + "historical_id": "fastbrain_049938", + "source": "sgd", + "query_hash": "9b366140613092e1e36522ffdac9b8f546bf4cc0864d42d6bd6938e3fde7a08d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 906, + "historical_id": "fastbrain_049938", + "source": "sgd", + "query_hash": "9b366140613092e1e36522ffdac9b8f546bf4cc0864d42d6bd6938e3fde7a08d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049959", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a767d307a868110dab85adfc9464a32c", + "normalized_query_sha256": "9a9eaae4ef0d522b43c6ee3cef6804b3dc035697255c3edf6dca4ce7c88453af", + "query": "remind me to have launch with boss today at one p. m.", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 265, + "historical_id": "semantic:validation:fastbrain_049959:absent", + "source": "amazon_massive", + "query_hash": "9a9eaae4ef0d522b43c6ee3cef6804b3dc035697255c3edf6dca4ce7c88453af" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 419, + "historical_id": "semantic:validation:fastbrain_049959:present", + "source": "amazon_massive", + "query_hash": "9a9eaae4ef0d522b43c6ee3cef6804b3dc035697255c3edf6dca4ce7c88453af" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_049966", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e0e5a385a388588bfe7318a9f33253fd", + "normalized_query_sha256": "3b9189409a836bd628a2c151cc216633dcd654c0145ccc047c480740e6c5dc81", + "query": "Perfect. That works. I'd like to go ahead and reserve this rental!", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 375, + "historical_id": "fastbrain_049966", + "source": "sgd", + "query_hash": "3b9189409a836bd628a2c151cc216633dcd654c0145ccc047c480740e6c5dc81" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 375, + "historical_id": "fastbrain_049966", + "source": "sgd", + "query_hash": "3b9189409a836bd628a2c151cc216633dcd654c0145ccc047c480740e6c5dc81" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050053", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d68e4bb9201c8358eddd8317db2dccbc", + "normalized_query_sha256": "2d01c0841e75e6159a13d284b034ac3c3249de596aa31b7db104f344eaba494a", + "query": "read my upcoming events", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 178, + "historical_id": "semantic:validation:fastbrain_050053:present", + "source": "amazon_massive", + "query_hash": "2d01c0841e75e6159a13d284b034ac3c3249de596aa31b7db104f344eaba494a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1014, + "historical_id": "semantic:validation:fastbrain_050053:absent", + "source": "amazon_massive", + "query_hash": "2d01c0841e75e6159a13d284b034ac3c3249de596aa31b7db104f344eaba494a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050063", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:22dafc28d5a7a8308eb239de243a1551", + "normalized_query_sha256": "f7c1648caf92bfa833fb5c86a1f9a645816f93970b654e539a15a7bee9a1c5cb", + "query": "can you play the podcast from tomatojuicecom", + "target": "play_podcasts", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 156, + "historical_id": "semantic:validation:fastbrain_050063:absent", + "source": "amazon_massive", + "query_hash": "f7c1648caf92bfa833fb5c86a1f9a645816f93970b654e539a15a7bee9a1c5cb" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 460, + "historical_id": "semantic:validation:fastbrain_050063:present", + "source": "amazon_massive", + "query_hash": "f7c1648caf92bfa833fb5c86a1f9a645816f93970b654e539a15a7bee9a1c5cb" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050264", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8c42929a4f518bdf6b50f809cd875e06", + "normalized_query_sha256": "1f326e18753f16d1001e18f1976d57d85038237478b4d32b090fc97b5254c07c", + "query": "turn sound off", + "target": "audio_volume_mute", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 808, + "historical_id": "semantic:validation:fastbrain_050264:absent", + "source": "amazon_massive", + "query_hash": "1f326e18753f16d1001e18f1976d57d85038237478b4d32b090fc97b5254c07c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1006, + "historical_id": "semantic:validation:fastbrain_050264:present", + "source": "amazon_massive", + "query_hash": "1f326e18753f16d1001e18f1976d57d85038237478b4d32b090fc97b5254c07c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050370", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e1cdf7e68f85ee89ba7edcf8fe701dfd", + "normalized_query_sha256": "4a06b43c33f4488fabd4d4a91d8387cb2c124ea447fcb53ce0b6457b45883f48", + "query": "t\n- **screenshots**: Array of URLs to timestamped images showing the crash\n\n**Important:** Both processes require identity verification first.\n\n**To proceed:**\n1. Please provide your **user ID**\n2. Confirm whether you want to:\n - **Only reschedule** the match to a new time\n - **File a dispute** (with evidence submission)\n - **Both** (reschedule AND file a dispute)\n\nOnce I have this information, I'll initiate the verification process and guide you through the specific steps for your chosen action(s).\nuser: I would like to proceed with **both** rescheduling the match and filing a dispute. Here are my details: \n- **User ID**: ES3274 \n- **Preferred verification method**: Email \n\nFor the reschedule, I request the new time be set to **20:00 UTC** on the same day (original match date was [insert date if known]). \n\nFor the dispute, I will prepare the required evidence: \n1. **Replay files**: [Provide URLs for each affected player’s replay] \n2. **Network reports**: [Provide URLs to network diagnostics from the crash timestamp] \n3. **Timestamped screenshots**: [Provide URLs with timestamps showing the client crash for all four players] \n\nCould you confirm if the technical team will validate the crash logs (if included in the evidence) to ensure the issue was server/client-side and not player-specific? Once verified, I’ll complete the email verification and submit the evidence URLs.\nassistant: \ntool: \n\n{\n \"code_sent\": true,\n \"contact_address\": \"es3274_arena@esports.com\"\n}", + "target": "get_tournament_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 757, + "historical_id": "fastbrain_050370", + "source": "nemotron_agentic", + "query_hash": "4a06b43c33f4488fabd4d4a91d8387cb2c124ea447fcb53ce0b6457b45883f48" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 757, + "historical_id": "fastbrain_050370", + "source": "nemotron_agentic", + "query_hash": "4a06b43c33f4488fabd4d4a91d8387cb2c124ea447fcb53ce0b6457b45883f48" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050393", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a17f3906e8397efba1e76ac7231aa02d", + "normalized_query_sha256": "1f32d4ac464125c175fbbd113dbfed865c29d7300b2ec493369daba54582a35a", + "query": "Hi, I need a barcode for the data \"1234567890\".", + "target": "generate_barcode", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 972, + "historical_id": "fastbrain_050393", + "source": "glaive_v2", + "query_hash": "1f32d4ac464125c175fbbd113dbfed865c29d7300b2ec493369daba54582a35a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 972, + "historical_id": "fastbrain_050393", + "source": "glaive_v2", + "query_hash": "1f32d4ac464125c175fbbd113dbfed865c29d7300b2ec493369daba54582a35a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050550", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7b45832a8df265e6fa021f8c742beaf2", + "normalized_query_sha256": "d801e824c39cd788cb2857c6a19df9d2aa40e384f9abea4e7208a411aac55b73", + "query": "col**: Never assume action completion without tool confirmation \n- **Data Integrity**: Do not reference unverified analytics \n\n**Transfer Protocol** \nTransfer to human agent for: \n- Multi-platform sponsorship conflicts \n- Tax documentation disputes \n- Chargeback resolutions \nSteps: \n1. Use `transfer_to_human_agent` tool \n2. Display: \"TRANSFERRING YOU TO A HUMAN SPECIALIST. PLEASE HOLD.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I need to pause my campaign immediately—NEWS-AD-55 is running mid-roll ads during a sensitive news event, and it’s causing an emergency. Can you confirm this can be done without penalty? I’ll provide the Creator ID as needed.\nassistant: Hello, I understand this is an urgent situation regarding campaign NEWS-AD-55. To assist you properly, I'll need to verify your identity first.\n\nPlease provide your Creator ID along with either:\n1. Your registered email address, OR\n2. Your registered phone number\n\nOnce verified, I can help you address this emergency situation. For immediate campaign pausing due to sensitive content, we may need to escalate to a human specialist who can handle time-sensitive adjustments without penalties when justified by emergency circumstances.\nuser: My Creator ID is CREATOR-NEWS-001, and my registered email is newsbreakfastpodcast@example.com. This is urgent—can you confirm the pause will be processed immediately without penalty while we resolve the sensitive content issue? Every minute counts.", + "target": "verify_creator_email", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 819, + "historical_id": "fastbrain_050550", + "source": "nemotron_agentic", + "query_hash": "d801e824c39cd788cb2857c6a19df9d2aa40e384f9abea4e7208a411aac55b73" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 819, + "historical_id": "fastbrain_050550", + "source": "nemotron_agentic", + "query_hash": "d801e824c39cd788cb2857c6a19df9d2aa40e384f9abea4e7208a411aac55b73" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050570", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b32b306d2289308cf0dffecb543ac1cc", + "normalized_query_sha256": "301160cc61536f0e37f0f87d4ee08b9e4cfb5fca1df93daed3567ec144dbe156", + "query": " \"best\" for your paleontological work. I can only present the product information and hazard warnings.\n\nWould you like me to provide detailed specifications for any of these alternative knee pads to help you make an informed decision?\nuser: Thank you for the detailed overview. I'd appreciate seeing the full specifications for the **ProDig Knee Support System (SKU: KNE-350)** and the **RuggedFlex Ergonomic Knees (SKU: KNE-500)**. Specifically, could you clarify: \n1. The padding material and thickness for prolonged crouching support? \n2. How the straps adjust for uneven leg lengths or bulky fieldwork gear? \n3. If these models are tested for use in dusty/sandy environments (common in fossil sites)? \n\nThis will help assess their suitability for maintaining proper posture during extended excavation sessions.\nassistant: \ntool: \n\n{\"description\": \"ProDig Knee Support System with multi-density foam padding (1.5-inch thickness) for pressure distribution during extended crouching. Features 3-point adjustable straps with quick-release buckles to accommodate uneven leg lengths and bulky field gear. Test data shows performance in arid environments with sealed padding to resist sand infiltration.\", \"compatibility_notes\": \"Dust accumulation may affect strap mechanisms over time; recommended for use with knee sleeves in extreme conditions\", \"installation_requirements\": \"No tools required\", \"hazard_flags\": [\"Dust accumulation may affect strap mechanisms over time\"], \"weight_capacity\": 300}", + "target": "get_product_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 696, + "historical_id": "fastbrain_050570", + "source": "nemotron_agentic", + "query_hash": "301160cc61536f0e37f0f87d4ee08b9e4cfb5fca1df93daed3567ec144dbe156" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 696, + "historical_id": "fastbrain_050570", + "source": "nemotron_agentic", + "query_hash": "301160cc61536f0e37f0f87d4ee08b9e4cfb5fca1df93daed3567ec144dbe156" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050876", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5c9121ed0d1383eae834e46fdeda8c66", + "normalized_query_sha256": "c29586bca56b58a368aa88a8699c968567169227ab38cc9eb378c93b8aea6b67", + "query": "is there anything happening on jazz scene around edinburgh", + "target": "recommendation_events", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 363, + "historical_id": "semantic:validation:fastbrain_050876:absent", + "source": "amazon_massive", + "query_hash": "c29586bca56b58a368aa88a8699c968567169227ab38cc9eb378c93b8aea6b67" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 571, + "historical_id": "semantic:validation:fastbrain_050876:present", + "source": "amazon_massive", + "query_hash": "c29586bca56b58a368aa88a8699c968567169227ab38cc9eb378c93b8aea6b67" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050898", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:56e5875d84e69ed24b77b557fef562b5", + "normalized_query_sha256": "de3f61f855661ec24ce5c4973fbabab5152dc96579504b5c88f2ae532f17f4d9", + "query": "Thanks very much. I want to review my alarms now.", + "target": "GetAlarms", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 733, + "historical_id": "fastbrain_050898", + "source": "sgd", + "query_hash": "de3f61f855661ec24ce5c4973fbabab5152dc96579504b5c88f2ae532f17f4d9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 733, + "historical_id": "fastbrain_050898", + "source": "sgd", + "query_hash": "de3f61f855661ec24ce5c4973fbabab5152dc96579504b5c88f2ae532f17f4d9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050993", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c3bad73d234558a9c5a4149192810cba", + "normalized_query_sha256": "6d09a764e333055254c0f80c3a4e401cefa792873e6425fb5390c3a3408a6b23", + "query": "As part of my analysis on the quality of education within our region, I need to compile a detailed report showing a range of educational performance metrics from the previous academic year for all the high schools in our district. The metrics that need to be included are average test scores, graduation rates, and attendance rates. It is essential to compare this data to the state averages to get i", + "target": "get_education_statistics", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 89, + "historical_id": "fastbrain_050993", + "source": "hermes_v1", + "query_hash": "6d09a764e333055254c0f80c3a4e401cefa792873e6425fb5390c3a3408a6b23" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 89, + "historical_id": "fastbrain_050993", + "source": "hermes_v1", + "query_hash": "6d09a764e333055254c0f80c3a4e401cefa792873e6425fb5390c3a3408a6b23" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_050994", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cc580082ac096e904aa26b1c3bb2efa2", + "normalized_query_sha256": "88075ae847f84f3373ec0718e5f0ca3129b0806bc3c41ba74cce9b19ff78c26f", + "query": "search for files which are writable by either their owner or their group", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 398, + "historical_id": "fastbrain_050994", + "source": "nl2bash", + "query_hash": "88075ae847f84f3373ec0718e5f0ca3129b0806bc3c41ba74cce9b19ff78c26f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 398, + "historical_id": "fastbrain_050994", + "source": "nl2bash", + "query_hash": "88075ae847f84f3373ec0718e5f0ca3129b0806bc3c41ba74cce9b19ff78c26f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_051046", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:795d963e808d999a19d4d4e28ed1a3ea", + "normalized_query_sha256": "596715848405bbf51d4724759806f4d3c31fe2a1a772f1e036bae74e538df620", + "query": "I want to know the latest news in sports.", + "target": "get_news", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 656, + "historical_id": "fastbrain_051046", + "source": "glaive_v2", + "query_hash": "596715848405bbf51d4724759806f4d3c31fe2a1a772f1e036bae74e538df620" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 656, + "historical_id": "fastbrain_051046", + "source": "glaive_v2", + "query_hash": "596715848405bbf51d4724759806f4d3c31fe2a1a772f1e036bae74e538df620" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_051220", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:48f628811acf0e9671d293bd1b08cfec", + "normalized_query_sha256": "3402a3d016c040bbd3f5da0108ec7743a79dd02bd3f024c52f6df0eec720fd0a", + "query": "cation\"}\nassistant: I can see your booking is currently in \"pending_reverification\" status. For payment changes due to stolen/lost cards, our policy requires **full credit card re-verification**. This means:\n\n1. You need **both tokens**:\n - A payment authorization token (which you already have)\n - A separate **re-verification token** (for full card verification)\n\n2. The system does **not** handle this automatically - you must generate both tokens through our secure payment portal.\n\n**Process to generate the re-verification token:**\n- Log into your account on our payment portal\n- Select \"Emergency Card Replacement\" \n- Complete the full verification process (this generates the re-verification token)\n- Keep both tokens ready\n\nOnce you have both tokens, I can process the payment update using the `process_payment` tool with:\n- Booking reference: THEFT1\n- Payment token: [your payment authorization token]\n- Re-verification token: [your new re-verification token]\n\nWould you like me to proceed once you have both tokens, or do you need assistance with accessing the payment portal?\nuser: I can access the payment portal. Please proceed once I submit both tokens. I'll generate the re-verification token now. [After a brief pause] I've generated the re-verification token. My payment authorization token is \"PAY-789456\" and the re-verification token is \"RE-VERIFY-123456\". Can you process the update immediately?\nassistant: \ntool: \n\n{\"payment_id\": \"P-987654\", \"payment_status\": \"authorized\"}", + "target": "get_booking_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 522, + "historical_id": "fastbrain_051220", + "source": "nemotron_agentic", + "query_hash": "3402a3d016c040bbd3f5da0108ec7743a79dd02bd3f024c52f6df0eec720fd0a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 522, + "historical_id": "fastbrain_051220", + "source": "nemotron_agentic", + "query_hash": "3402a3d016c040bbd3f5da0108ec7743a79dd02bd3f024c52f6df0eec720fd0a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_051292", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6454f5d163bb46bdccef606e827fcf03", + "normalized_query_sha256": "134d96ccd9cefb73dbf7bd79eeb180b92e29c0ab4161abbdf86e2e09a8b2ca37", + "query": "I like the director Iain Softley, can you find me a movie he directed?", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 21, + "historical_id": "fastbrain_051292", + "source": "sgd", + "query_hash": "134d96ccd9cefb73dbf7bd79eeb180b92e29c0ab4161abbdf86e2e09a8b2ca37" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 21, + "historical_id": "fastbrain_051292", + "source": "sgd", + "query_hash": "134d96ccd9cefb73dbf7bd79eeb180b92e29c0ab4161abbdf86e2e09a8b2ca37" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_051558", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:43fe5dee0def1b77aa659e16e9f5d25a", + "normalized_query_sha256": "6ebc3248d21b97c823f86e3a7934c7ab97a7c7e55cabb06f7448646530f2eba7", + "query": "ails for booking **CERT-123**:\n\n- **Charter Dates**: April 20, 2025 (9:00 AM - 5:00 PM)\n- **Vessel Type**: Catamaran\n- **Deposit Status**: Paid in full (50% deposit + balance paid)\n- **Navigation Zones**: Santa Barbara Channel, Malibu Coast\n- **Client Tier**: Safety-Certified\n\nRegarding your request for an **authenticated completion certificate**: \n\nI need to be transparent about my capabilities. While I can retrieve and provide detailed booking information (as shown above), my current toolset does **not** include a function for generating or emailing signed completion certificates. The booking details I've provided could potentially serve as documentation for your corporate records.\n\nIf you require a formal, signed completion certificate, you may need to:\n1. Contact our administrative office directly during business hours\n2. Request to be transferred to a human agent who can process this type of documentation request\n\nWould you like me to:\n1. Provide any additional booking details from the system?\n2. Transfer you to a human agent for certificate generation?\n3. Or is the booking information I've provided sufficient for your records requirements?\nuser: Thank you for confirming the booking details. Since the system does not support generating the signed completion certificate directly, I would like to be transferred to a human agent who can process this documentation request. Please facilitate the transfer so I can obtain the authenticated certificate for our corporate records.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 809, + "historical_id": "fastbrain_051558", + "source": "nemotron_agentic", + "query_hash": "6ebc3248d21b97c823f86e3a7934c7ab97a7c7e55cabb06f7448646530f2eba7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 809, + "historical_id": "fastbrain_051558", + "source": "nemotron_agentic", + "query_hash": "6ebc3248d21b97c823f86e3a7934c7ab97a7c7e55cabb06f7448646530f2eba7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_051594", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:04ed8b768ea74df0bff90b264ab18b15", + "normalized_query_sha256": "0cbbbcbc95629ebc193e3e34974600c1322aa6a61bffa6e5286db0b01ec49c6b", + "query": "\n\n### Operational Constraints \n- No same-day pickups \n- Bulk pickup unavailable for commercial accounts \n- Hazardous collection prohibited during rain/wind >25mph \n- Eligibility limits: \n - E-Waste: Residential only (max 5 items/quarter) \n - Composting: Not for multi-unit buildings >8 units \n\n---\n\n### Identity Verification Protocol \nFor account-sensitive actions: \n1. Request user's email/phone \n2. Trigger code delivery tool \n3. Prompt: *\"Enter the 6-digit code sent to [contact method]\"* \n4. Validate code using verification tool \n\n---\n\n### Agent Limitations \n**Critical Restrictions**: \n- Only perform actions available in toolset \n- Never claim issue resolution without tool confirmation \n- Decline all unverifiable requests \n- Do not speculate beyond documented guidelines \n- Never assume action completion without explicit tool success status \n\n---\n\n### Escalation Protocol \nTransfer to human agent for: \n- Chemical emergencies \n- Commercial negotiations \n- ≥3 verified service failures \nTransfer process: \n1. Trigger transfer tool \n2. Notify: *\"Transferring you to a specialist. One moment please.\"*\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'm calling about my compost pickup at 8282 Gourmet Dr. My bin was rejected this week, and I’m confused about what went wrong. My Account ID is RES-3393. Could you explain why this happened and what your contamination policies are? I want to make sure I’m following the guidelines correctly.", + "target": "get_customer_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 591, + "historical_id": "fastbrain_051594", + "source": "nemotron_agentic", + "query_hash": "0cbbbcbc95629ebc193e3e34974600c1322aa6a61bffa6e5286db0b01ec49c6b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 591, + "historical_id": "fastbrain_051594", + "source": "nemotron_agentic", + "query_hash": "0cbbbcbc95629ebc193e3e34974600c1322aa6a61bffa6e5286db0b01ec49c6b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_051815", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:42d4a3e3b275a990baabb12a77afe780", + "normalized_query_sha256": "334dd388b3f570425bb4006c617c2eb768982c6aa92e387426235aeac69e64b5", + "query": "Perfect. I need to book a restaurant around there. the restaurant should be Chili's Grill for 4 people at 11:15 am", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 800, + "historical_id": "fastbrain_051815", + "source": "sgd", + "query_hash": "334dd388b3f570425bb4006c617c2eb768982c6aa92e387426235aeac69e64b5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 800, + "historical_id": "fastbrain_051815", + "source": "sgd", + "query_hash": "334dd388b3f570425bb4006c617c2eb768982c6aa92e387426235aeac69e64b5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_051905", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3a703a6b79f9b4117593ec8f7516b436", + "normalized_query_sha256": "1367744811b0957397d472eac9efc9562bde66cc4cdd11b0634091e37b6f8c00", + "query": "Assumption Prohibition**: \n - Never presume actions/verifications are complete without explicit tool confirmation \n - Example: Treat agreements as unsigned until digital signature confirmation \n - Example: Consider payments pending until clearance confirmation \n\n- **Response Restrictions**: \n - Never provide investment advice or valuation projections \n - Avoid subjective artistic assessments \n - Refrain from specialized conservation guidance\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I’m reaching out regarding my consigned Asian lacquerware piece valued at $9,500. As it’s highly sensitive to humidity fluctuations, I’d like to inquire about upgrading to your climate-controlled storage options. Could you please detail the enhanced storage tiers available and clarify the premium fees associated with this upgrade? I’d also appreciate reassurance that this service will adequately protect the item’s integrity.\nassistant: Hello! I'd be happy to help you with climate-controlled storage options for your consigned Asian lacquerware piece. First, I need to verify your identity for security purposes.\n\nCould you please provide your registered phone number or email address so I can send you an authentication code?\nuser: My registered email is elizabeth.vangogh@antiquelegacy.com. Please send the authentication code there. Given the piece’s value and sensitivity, I hope we can expedite this upgrade process to ensure optimal preservation conditions.", + "target": "send_authentication_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 511, + "historical_id": "fastbrain_051905", + "source": "nemotron_agentic", + "query_hash": "1367744811b0957397d472eac9efc9562bde66cc4cdd11b0634091e37b6f8c00" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 511, + "historical_id": "fastbrain_051905", + "source": "nemotron_agentic", + "query_hash": "1367744811b0957397d472eac9efc9562bde66cc4cdd11b0634091e37b6f8c00" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052050", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:354e9be6404451b586aedbd933b0d5e5", + "normalized_query_sha256": "d4d4a380b1713c531b3e5da5399313870c6a04123591129f26794424fc7cdeb9", + "query": "can you set up a meeting with me", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 334, + "historical_id": "semantic:validation:fastbrain_052050:present", + "source": "amazon_massive", + "query_hash": "d4d4a380b1713c531b3e5da5399313870c6a04123591129f26794424fc7cdeb9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 723, + "historical_id": "semantic:validation:fastbrain_052050:absent", + "source": "amazon_massive", + "query_hash": "d4d4a380b1713c531b3e5da5399313870c6a04123591129f26794424fc7cdeb9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052160", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3072fd8e700c39f3bf7187f507b13fcc", + "normalized_query_sha256": "3b0cd8089f33f40c1714a94ffecedb305fab065518e0ef3df2c0577d8c25b358", + "query": "recursively change owner and group of the directory /opt/antoniod/ to user and group antoniod", + "target": "chown", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 333, + "historical_id": "fastbrain_052160", + "source": "nl2bash", + "query_hash": "3b0cd8089f33f40c1714a94ffecedb305fab065518e0ef3df2c0577d8c25b358" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 333, + "historical_id": "fastbrain_052160", + "source": "nl2bash", + "query_hash": "3b0cd8089f33f40c1714a94ffecedb305fab065518e0ef3df2c0577d8c25b358" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052333", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0eb0873ccf5fcb5b6a8429e4ed7aa81d", + "normalized_query_sha256": "6d86d39cc2362701747b75d933999f78e39b87a036a6aca294902ff97ae8e6ba", + "query": "please set lunch meeting at twelve p. m. for every wednesday in march", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 54, + "historical_id": "semantic:validation:fastbrain_052333:present", + "source": "amazon_massive", + "query_hash": "6d86d39cc2362701747b75d933999f78e39b87a036a6aca294902ff97ae8e6ba" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 470, + "historical_id": "semantic:validation:fastbrain_052333:absent", + "source": "amazon_massive", + "query_hash": "6d86d39cc2362701747b75d933999f78e39b87a036a6aca294902ff97ae8e6ba" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052500", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9b4c7cc93c3c9294633578116a239ab4", + "normalized_query_sha256": "56d58698d46fe79e7f4933f601c51aa204e011bc9606b00ed8ec058919b3d453", + "query": "ccessfully verified your identity and membership status:\n\n**Verification Confirmed:**\n- Member ID: GVC-0956\n- Farm Name: Fungi Masters ✓\n- Membership Tier: **Premium** ✓\n- Farm Certification: Organic ✓\n- Payment Methods: ACH, cooperative credit\n\nHowever, I need to inform you that I don't have access to retrieve specific blockchain transaction IDs for payment settlements. My available tools are focused on:\n- Processing new payments\n- Checking member profiles and resource allocations\n- Managing crop sales and equipment rentals\n- Handling disputes and resource transfers\n\nFor your request regarding payment reference #PAY-8899, you would need to:\n1. Contact our cooperative accounting department directly\n2. Use the blockchain ledger portal (available to Premium members)\n3. Check your settlement confirmation email (Premium members receive 24-hour settlements)\n\n**What I can help you with instead:**\n- If you need to process a new payment, I can assist with that\n- If you have questions about your resource allocations or membership benefits\n- If you need to list crops or rent equipment\n\nWould you like assistance with any of these cooperative services?\nuser: Thank you for confirming my Premium status and the verification details. Since I specifically need the blockchain transaction ID for #PAY-8899, could you please transfer me to the cooperative accounting department or provide the direct process to access the blockchain ledger portal? Time is critical for our accounting reconciliation.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 81, + "historical_id": "fastbrain_052500", + "source": "nemotron_agentic", + "query_hash": "56d58698d46fe79e7f4933f601c51aa204e011bc9606b00ed8ec058919b3d453" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 81, + "historical_id": "fastbrain_052500", + "source": "nemotron_agentic", + "query_hash": "56d58698d46fe79e7f4933f601c51aa204e011bc9606b00ed8ec058919b3d453" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052583", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2ed9052fd0df092eb6b4be7e8ef3b8e7", + "normalized_query_sha256": "664c8004c548288fb1782f51f55fbcc124f76b073826ae119509a7bb1bbda59a", + "query": "ation code to your primary contact method and then check the drop-in count for this month.\n\nAccording to our policy, drop-in care requires:\n- 24-hour notice ($15/hour)\n- Maximum 8 instances monthly\n\nLet me know your ID so we can proceed!\nuser: My child's ID is C901. Please send the verification code to my email.\nassistant: \ntool: \n\n{\n \"profile\": {\n \"birthdate\": \"2018-05-15\",\n \"allergies\": [\"Peanuts\", \"Dairy\"],\n \"enrollment_status\": \"Active\",\n \"enrollment_details\": {\n \"enrollment_id\": \"E7721\",\n \"package_type\": \"Drop-In\",\n \"schedule\": {\n \"weekly_hours\": 15,\n \"drop_in_max\": 8\n },\n \"status\": \"Active\"\n }\n }\n}\nassistant: I can see your child C901 is enrolled with a Drop-In package. To send a verification code to your email, I need your guardian ID. Could you please provide your guardian ID?\n\nOnce I have that, I'll send the verification code and then we can check your drop-in count for this month and proceed with the request for tomorrow (9 AM to 12 PM).\nuser: My guardian ID is G12345. Please send the verification code to my email now.\nassistant: \ntool: \n\n{\n \"sent\": true\n}\nassistant: I've sent a verification code to your email. Please check your inbox and provide the 6-digit code you received.\n\nOnce you provide the code, I'll verify it and then we can check your drop-in count for this month and proceed with the drop-in request for tomorrow from 9 AM to 12 PM.\nuser: The verification code I received is 123456. Please confirm it.", + "target": "verify_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 821, + "historical_id": "fastbrain_052583", + "source": "nemotron_agentic", + "query_hash": "664c8004c548288fb1782f51f55fbcc124f76b073826ae119509a7bb1bbda59a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 821, + "historical_id": "fastbrain_052583", + "source": "nemotron_agentic", + "query_hash": "664c8004c548288fb1782f51f55fbcc124f76b073826ae119509a7bb1bbda59a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052718", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bd2bd5b9eb0cac21443e979aaf6ab0ae", + "normalized_query_sha256": "cd6f35249b704814aa2f9133bcb826238c7dc3ed2ab41b11d6af0412bc0e6a14", + "query": "increase the volume a bit", + "target": "audio_volume_up", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 441, + "historical_id": "fastbrain_052718", + "source": "amazon_massive", + "query_hash": "cd6f35249b704814aa2f9133bcb826238c7dc3ed2ab41b11d6af0412bc0e6a14" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 441, + "historical_id": "fastbrain_052718", + "source": "amazon_massive", + "query_hash": "cd6f35249b704814aa2f9133bcb826238c7dc3ed2ab41b11d6af0412bc0e6a14" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052757", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c4c4a371607cf8ae43f48f4b7f8606a4", + "normalized_query_sha256": "0f4034cc7c408db745dd5285141791e6e21d6c17240c06fc80073a8c573d9417", + "query": "can you delete this dinner party event", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 106, + "historical_id": "semantic:validation:fastbrain_052757:present", + "source": "amazon_massive", + "query_hash": "0f4034cc7c408db745dd5285141791e6e21d6c17240c06fc80073a8c573d9417" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 288, + "historical_id": "semantic:validation:fastbrain_052757:absent", + "source": "amazon_massive", + "query_hash": "0f4034cc7c408db745dd5285141791e6e21d6c17240c06fc80073a8c573d9417" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052904", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ef572defa261a45e11cadc04ea07410f", + "normalized_query_sha256": "6e9d6124651972e72aa52c0a055c49b41434ce5d6732312e5cfe10f23b928ea3", + "query": "instruct the coffee maker to make a filter coffee ready in ten minutes", + "target": "iot_coffee", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 818, + "historical_id": "fastbrain_052904", + "source": "amazon_massive", + "query_hash": "6e9d6124651972e72aa52c0a055c49b41434ce5d6732312e5cfe10f23b928ea3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 818, + "historical_id": "fastbrain_052904", + "source": "amazon_massive", + "query_hash": "6e9d6124651972e72aa52c0a055c49b41434ce5d6732312e5cfe10f23b928ea3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052965", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:567ced6bec970b12f56afaac6b639c7c", + "normalized_query_sha256": "626c6e40854501b87f7a80d6db5bc78a71712b9e4cc0fd44b2d1bc266e3aee39", + "query": "how is the current traffic between", + "target": "transport_traffic", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 565, + "historical_id": "semantic:validation:fastbrain_052965:present", + "source": "amazon_massive", + "query_hash": "626c6e40854501b87f7a80d6db5bc78a71712b9e4cc0fd44b2d1bc266e3aee39" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 813, + "historical_id": "semantic:validation:fastbrain_052965:absent", + "source": "amazon_massive", + "query_hash": "626c6e40854501b87f7a80d6db5bc78a71712b9e4cc0fd44b2d1bc266e3aee39" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_052966", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:feec60cede3cc31af41bad62e056520f", + "normalized_query_sha256": "a4ae901ce371a63595932aca2485973805d5ed75e40670de96d20ae929f69f57", + "query": "I'm planning on going to an event with my friend. Can you help me buy some tickets to it?", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 13, + "historical_id": "fastbrain_052966", + "source": "sgd", + "query_hash": "a4ae901ce371a63595932aca2485973805d5ed75e40670de96d20ae929f69f57" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 13, + "historical_id": "fastbrain_052966", + "source": "sgd", + "query_hash": "a4ae901ce371a63595932aca2485973805d5ed75e40670de96d20ae929f69f57" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_053100", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:37cde638cf2ab21267654ce6a87b6389", + "normalized_query_sha256": "30a10ec066e15a6e64b06aa6436df81321463cbb984176edf973a774598a460f", + "query": ": Great! I've successfully authenticated your account. I can see you're an intermediate rider with no medical alerts, weighing 145 lbs.\n\n**Regarding Your Rescheduling Request:**\n\n**Current Booking Status:**\n- Booking ID: L112233\n- Scheduled: July 22nd, 10:00 AM EDT\n- Current Time: July 20th, 11:34 PM EDT\n- **Time Until Lesson: ~36 hours**\n\n**Notice Period Implications:**\nSince you're within the 24-47 hour window:\n- If you were to **cancel**, you would receive a **50% refund**\n- For **rescheduling**, there may be administrative fees applied (typically similar to cancellation fees)\n\n**Rescheduling Options:**\nAs a regular member, you can reschedule up to **90 days in advance**. Available time slots depend on:\n1. Instructor availability\n2. Horse availability (considering your preferred horses H101 and H105)\n3. Horse daily ride limits (max 3 rides/day per horse)\n\n**To proceed with rescheduling, I need:**\n1. Your preferred new date and time (please provide in EDT format)\n2. Any horse preference for the new booking\n\nOnce you provide this information, I can check availability and process the rescheduling. Would you like to proceed with selecting a new time, or would you prefer to discuss cancellation options instead?\nuser: I’d like to reschedule to **July 24th at 10:00 AM EDT** if possible, and I’m happy to use the same horses (H101 or H105) if available. Could you check if this time works and confirm the administrative fee? I’d prefer to proceed with rescheduling rather than cancel.", + "target": "get_horse_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 61, + "historical_id": "fastbrain_053100", + "source": "nemotron_agentic", + "query_hash": "30a10ec066e15a6e64b06aa6436df81321463cbb984176edf973a774598a460f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 61, + "historical_id": "fastbrain_053100", + "source": "nemotron_agentic", + "query_hash": "30a10ec066e15a6e64b06aa6436df81321463cbb984176edf973a774598a460f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_053104", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:358663dd45b6c4567022bed08bf500b5", + "normalized_query_sha256": "c8f1a784054cd96418e36e67e12e3cfb52673a2e0e0d3a0f4678f3f0f0b35574", + "query": "Set timestamp of old_file.dat to specified timestamp.", + "target": "touch", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 836, + "historical_id": "fastbrain_053104", + "source": "nl2bash", + "query_hash": "c8f1a784054cd96418e36e67e12e3cfb52673a2e0e0d3a0f4678f3f0f0b35574" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 836, + "historical_id": "fastbrain_053104", + "source": "nl2bash", + "query_hash": "c8f1a784054cd96418e36e67e12e3cfb52673a2e0e0d3a0f4678f3f0f0b35574" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_053238", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bd0f91b4d0f9d81b9934cb0561ca4b43", + "normalized_query_sha256": "367fae2870588e556085a00f24770b9d2b8a1f3484f6ffd7284ec267f14d2809", + "query": "Hi, I would like to know the latest sports news in the US.", + "target": "get_news", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 20, + "historical_id": "fastbrain_053238", + "source": "glaive_v2", + "query_hash": "367fae2870588e556085a00f24770b9d2b8a1f3484f6ffd7284ec267f14d2809" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 20, + "historical_id": "fastbrain_053238", + "source": "glaive_v2", + "query_hash": "367fae2870588e556085a00f24770b9d2b8a1f3484f6ffd7284ec267f14d2809" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_053583", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0bf48376443aaa54809c6bdea2e4e593", + "normalized_query_sha256": "af06c64cfac254223e49c202753a98f363c83b083cc18e706a62830881753ade", + "query": "omewhere, but I'm not sure how strict that is. Could you clarify?\nassistant: I understand your concern about the catering menu changes. According to our policy, **menu changes must be finalized 14 days before the event**. Since your wedding is in 10 days, this deadline has passed and menu changes are no longer possible through our standard process.\n\nThe 14-day deadline is strict because catering vendors need sufficient time to:\n- Source ingredients\n- Prepare specialized menu items\n- Coordinate with their kitchen staff\n- Finalize quantities based on guest counts\n\n**Important note**: If you have urgent dietary accommodation needs (like allergies or medical restrictions), those can sometimes be addressed separately through our dietary flags system, but general menu changes are not possible at this point.\n\nTo better assist you, could you provide your event ID? I can check your current event details and see if there are any options available for your specific situation.\nuser: My event ID is 489217. Given the 14-day policy, I understand standard changes aren't possible, but I wanted to confirm if there's any flexibility for limited modifications through an emergency process. If the caterer allows this, what would the surcharge be for last-minute adjustments? I want to explore all options before finalizing anything.\nassistant: \ntool: \n\n{\"status\": \"Confirmed\", \"date\": \"2025-08-15T17:00\", \"budget\": 12000, \"venue\": \"V_30948\", \"hold_expiration\": \"2025-06-30T23:59\", \"deposit_paid\": true}", + "target": "get_guest_list_summary", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 196, + "historical_id": "fastbrain_053583", + "source": "nemotron_agentic", + "query_hash": "af06c64cfac254223e49c202753a98f363c83b083cc18e706a62830881753ade" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 196, + "historical_id": "fastbrain_053583", + "source": "nemotron_agentic", + "query_hash": "af06c64cfac254223e49c202753a98f363c83b083cc18e706a62830881753ade" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_053721", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6aad976ed88cd55c1313a219bcac5a04", + "normalized_query_sha256": "86561cd462708c34da93b69d9d158db5594873500725167562ce41ce2578ac77", + "query": "Hey, I need to add a new contact to my address book. The name is John Doe, email is johndoe@example.com and phone number is 1234567890.", + "target": "create_contact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 150, + "historical_id": "fastbrain_053721", + "source": "glaive_v2", + "query_hash": "86561cd462708c34da93b69d9d158db5594873500725167562ce41ce2578ac77" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 150, + "historical_id": "fastbrain_053721", + "source": "glaive_v2", + "query_hash": "86561cd462708c34da93b69d9d158db5594873500725167562ce41ce2578ac77" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_053811", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2d892474a25e06aa3d72e42ebdd4e7c1", + "normalized_query_sha256": "0f89fbc0cb7fa9e9e85825b56dd67dcb5afbbe75f4fa3162ba8985e6699513c1", + "query": "Hello, can you tell me what's the current time?", + "target": "get_current_time", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 436, + "historical_id": "fastbrain_053811", + "source": "glaive_v2", + "query_hash": "0f89fbc0cb7fa9e9e85825b56dd67dcb5afbbe75f4fa3162ba8985e6699513c1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 436, + "historical_id": "fastbrain_053811", + "source": "glaive_v2", + "query_hash": "0f89fbc0cb7fa9e9e85825b56dd67dcb5afbbe75f4fa3162ba8985e6699513c1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_053843", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8a796605dee7981e9eb67ca7076d9155", + "normalized_query_sha256": "1faccf64cdd113d252e83ffa138e7f26d571cf30b129c1f81158a0d06f1ad454", + "query": "check the traffic to our home", + "target": "transport_traffic", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 357, + "historical_id": "semantic:validation:fastbrain_053843:present", + "source": "amazon_massive", + "query_hash": "1faccf64cdd113d252e83ffa138e7f26d571cf30b129c1f81158a0d06f1ad454" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 868, + "historical_id": "semantic:validation:fastbrain_053843:absent", + "source": "amazon_massive", + "query_hash": "1faccf64cdd113d252e83ffa138e7f26d571cf30b129c1f81158a0d06f1ad454" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_054396", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:10010b09368695ca43cc0c13a5e22adc", + "normalized_query_sha256": "8c32c5a87d13b7d922db113500d8355151057e3ee758400bf2cc443687d488e5", + "query": "\n1. **Candidate ID**: What is the candidate's user ID or registered email?\n2. **Interview Details**: Do you have the specific interview ID for the final interview that was missed?\n3. **Previous Incidents**: Do you have records or references for the two prior no-show incidents?\n\nHowever, I should inform you that application disputes and suspension processes require escalation to our human support team for proper investigation and documentation. According to our transfer protocol, cases involving application disputes must be handled by a human agent to ensure proper due process and verification of all incidents.\n\nOnce you provide the candidate and interview details, I will initiate the transfer to a human agent who can:\n- Verify all three no-show incidents\n- Confirm the suspension eligibility\n- Process the 30-day application suspension\n- Notify the candidate according to platform procedures\n\nPlease provide the requested information so I can prepare the transfer with a complete summary of the situation.\nuser: Here are the details: \n1. **Candidate ID**: candidate123@jobseeker.com \n2. **Interview Details**: Final interview for Job ID #44556 scheduled on April 5th at 2:00 PM (confirmed via platform notification). \n3. **Previous Incidents**: \n - First no-show: Job ID #12345 on January 10th (no notice). \n - Second no-show: Job ID #67890 on February 15th (canceled 2 hours prior). \n\nPlease proceed with the transfer to the human agent to finalize the 30-day suspension process.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 217, + "historical_id": "fastbrain_054396", + "source": "nemotron_agentic", + "query_hash": "8c32c5a87d13b7d922db113500d8355151057e3ee758400bf2cc443687d488e5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 217, + "historical_id": "fastbrain_054396", + "source": "nemotron_agentic", + "query_hash": "8c32c5a87d13b7d922db113500d8355151057e3ee758400bf2cc443687d488e5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_054512", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bc455d587f66961aa0b98d3067ed1198", + "normalized_query_sha256": "5f8a7118eb16a58171e220fa2e094c5b37d84d298c79af8d9b18ab0e9d16631c", + "query": "Great. How about a Flexible bus ticket?", + "target": "FindBus", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 215, + "historical_id": "fastbrain_054512", + "source": "sgd", + "query_hash": "5f8a7118eb16a58171e220fa2e094c5b37d84d298c79af8d9b18ab0e9d16631c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 215, + "historical_id": "fastbrain_054512", + "source": "sgd", + "query_hash": "5f8a7118eb16a58171e220fa2e094c5b37d84d298c79af8d9b18ab0e9d16631c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_054537", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a07c3c1adc5304cbbc26a6c5b6ad0cea", + "normalized_query_sha256": "9857844046b67d017e6d2fd7ecebfc4069804b73dfa3595063dcb752aaf2eca0", + "query": "Thanks for the help. Now can you look for songs while we wait.", + "target": "LookupMusic", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 121, + "historical_id": "fastbrain_054537", + "source": "sgd", + "query_hash": "9857844046b67d017e6d2fd7ecebfc4069804b73dfa3595063dcb752aaf2eca0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 121, + "historical_id": "fastbrain_054537", + "source": "sgd", + "query_hash": "9857844046b67d017e6d2fd7ecebfc4069804b73dfa3595063dcb752aaf2eca0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_054561", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0e126c780808db108f24d5ce2a0e264c", + "normalized_query_sha256": "e4edfc6cf38f2958b7e09f271f3900dcdad5274b2ac1e2b55c40af1eab140037", + "query": "Can you help me find hotels in New York?", + "target": "SearchHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 949, + "historical_id": "fastbrain_054561", + "source": "sgd", + "query_hash": "e4edfc6cf38f2958b7e09f271f3900dcdad5274b2ac1e2b55c40af1eab140037" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 949, + "historical_id": "fastbrain_054561", + "source": "sgd", + "query_hash": "e4edfc6cf38f2958b7e09f271f3900dcdad5274b2ac1e2b55c40af1eab140037" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_055014", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dd7e0cbce219034878bbe19799e4bf06", + "normalized_query_sha256": "cb007ca8aadab78155f3ee1de149f0fe10934c3466a11184afdf87eb96707e24", + "query": "I need to book an apartment with 1 bath and 1 bedroom with fully furnished. 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The details are as follows: Expense date is April 15, 2023, for a business dinner category, costing $85.00 in USD, describ", + "target": "submit_expense", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 746, + "historical_id": "fastbrain_056226", + "source": "hermes_v1", + "query_hash": "3b84fc20991543a97beb3c7ca11bd67dc7252bd4d2b4835bd0853b8988132f2c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 746, + "historical_id": "fastbrain_056226", + "source": "hermes_v1", + "query_hash": "3b84fc20991543a97beb3c7ca11bd67dc7252bd4d2b4835bd0853b8988132f2c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_056337", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8f8a32728916fd87640a65eb8199465d", + "normalized_query_sha256": "45a54098990e854b1c694df9897571baefcdf5cdc5a0af61a02f7c1cb4cdea93", + "query": "could you describe that object please", + "target": "qa_definition", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 587, + "historical_id": "fastbrain_056337", + "source": "amazon_massive", + "query_hash": "45a54098990e854b1c694df9897571baefcdf5cdc5a0af61a02f7c1cb4cdea93" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 587, + "historical_id": "fastbrain_056337", + "source": "amazon_massive", + "query_hash": "45a54098990e854b1c694df9897571baefcdf5cdc5a0af61a02f7c1cb4cdea93" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_057057", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e8c8c5eaf24a058d8b4e27e990f26430", + "normalized_query_sha256": "8a6d96449c5c9d42055ad72289dc98d30f5e341dfd4f57ad7b585c061f40047a", + "query": "what time can i call from pennsylvania and it will be business hours in australia", + "target": "datetime_convert", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 742, + "historical_id": "semantic:validation:fastbrain_057057:present", + "source": "amazon_massive", + "query_hash": "8a6d96449c5c9d42055ad72289dc98d30f5e341dfd4f57ad7b585c061f40047a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 881, + "historical_id": "semantic:validation:fastbrain_057057:absent", + "source": "amazon_massive", + "query_hash": "8a6d96449c5c9d42055ad72289dc98d30f5e341dfd4f57ad7b585c061f40047a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_057536", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2cd43754c7c5b81edc439e8ac9fe3b04", + "normalized_query_sha256": "a8ad702012bad640db6d86a7de27f83bdf7b8433bcbc3437ec7c389309ac3204", + "query": "lower the volume please", + "target": "audio_volume_down", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 707, + "historical_id": "fastbrain_057536", + "source": "amazon_massive", + "query_hash": "a8ad702012bad640db6d86a7de27f83bdf7b8433bcbc3437ec7c389309ac3204" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 707, + "historical_id": "fastbrain_057536", + "source": "amazon_massive", + "query_hash": "a8ad702012bad640db6d86a7de27f83bdf7b8433bcbc3437ec7c389309ac3204" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_057649", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a25b9e75cf0b910556eb908ce9720817", + "normalized_query_sha256": "fb00ce58b4473d3049db08681a6b3440954b88433567e6e3d60ee3d2cf80fb3c", + "query": "Hi, I need to calculate the profit margin for a product. The cost price was $50 and I sold it for $75.", + "target": "calculate_profit_margin", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 963, + "historical_id": "fastbrain_057649", + "source": "glaive_v2", + "query_hash": "fb00ce58b4473d3049db08681a6b3440954b88433567e6e3d60ee3d2cf80fb3c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 963, + "historical_id": "fastbrain_057649", + "source": "glaive_v2", + "query_hash": "fb00ce58b4473d3049db08681a6b3440954b88433567e6e3d60ee3d2cf80fb3c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_057719", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5ce344d28e90db50d3cd16a5ad081aa2", + "normalized_query_sha256": "e6197be843048dc90bbc3fc85cb821e6fb9c98b77792fb408501c48ff5b26145", + "query": "ts)\n- Your 2:00 AM CST window **complies with policy** (outside business hours)\n\n**Available Alternatives:**\n\n1. **Immediate lower-intensity scan** - Medium/low intensity allowed during business hours\n2. **High-intensity scan after 6:00 PM CST today** - You could initiate this evening\n3. **Manual initiation at 2:00 AM CST** - Not feasible per your team availability\n\n**Escalation Path:**\nSince automated scheduling is required for your compliance protocols and isn't available through standard tools, I can transfer you to a SOC analyst for further assistance with policy exceptions or engineering solutions.\n\n**Would you like me to:**\nA) Initiate a medium/low intensity scan now on all 3 endpoints?\nB) Escalate to a SOC analyst for scheduling capability discussion?\nC) Wait until after 6:00 PM CST for a high-intensity scan?\n\nPlease advise on your preferred next step.\nuser: Given our compliance requirements and the unavailability of automated scheduling, I need to **escalate this to your engineering team** for a policy exception or technical solution. Our operational protocols mandate automated execution during off-peak hours, and manual initiation at 2:00 AM is not feasible. \n\nPlease **transfer me to a SOC analyst** to discuss either: \n1) A temporary policy exception for automated scheduling during off-peak hours, or \n2) Engineering intervention to enable scheduled scans per our endpoint management policies. \n\nThis is critical to maintaining compliance with our security framework.", + "target": "transfer_to_soc_analyst", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 343, + "historical_id": "fastbrain_057719", + "source": "nemotron_agentic", + "query_hash": "e6197be843048dc90bbc3fc85cb821e6fb9c98b77792fb408501c48ff5b26145" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 343, + "historical_id": "fastbrain_057719", + "source": "nemotron_agentic", + "query_hash": "e6197be843048dc90bbc3fc85cb821e6fb9c98b77792fb408501c48ff5b26145" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_057758", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a99eba7596dec45b67c5e2eda18cb3b7", + "normalized_query_sha256": "6e91cd8ce1a9d0987e36a1e7bbdea4836554d56e4f584f62f3e901607e140caf", + "query": "alexa i've got dollars for the month how much can i spend every day this month", + "target": "qa_maths", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 4, + "historical_id": "semantic:validation:fastbrain_057758:absent", + "source": "amazon_massive", + "query_hash": "6e91cd8ce1a9d0987e36a1e7bbdea4836554d56e4f584f62f3e901607e140caf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 767, + "historical_id": "semantic:validation:fastbrain_057758:present", + "source": "amazon_massive", + "query_hash": "6e91cd8ce1a9d0987e36a1e7bbdea4836554d56e4f584f62f3e901607e140caf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_057846", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:482608c4d5fbef9658c3223f61d700e4", + "normalized_query_sha256": "1a70f14c2ba9c24bb8b2d4f0469d04a8dc6ac588821a238af9b4cf475cb46855", + "query": "Hi, I need help with calculating my tax. My income is $50000 and my tax percentage is 20%.", + "target": "calculate_tax", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 645, + "historical_id": "fastbrain_057846", + "source": "glaive_v2", + "query_hash": "1a70f14c2ba9c24bb8b2d4f0469d04a8dc6ac588821a238af9b4cf475cb46855" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 645, + "historical_id": "fastbrain_057846", + "source": "glaive_v2", + "query_hash": "1a70f14c2ba9c24bb8b2d4f0469d04a8dc6ac588821a238af9b4cf475cb46855" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_057902", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:773353dc2cbd5dc5f25929e7e5c7a20c", + "normalized_query_sha256": "f42332249a2f49dc5fa199de66798b798af50834d95ec74520a41758c7914c4c", + "query": "i need a taxi to pick me up at the house and take me downtown", + "target": "transport_taxi", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 35, + "historical_id": "semantic:validation:fastbrain_057902:present", + "source": "amazon_massive", + "query_hash": "f42332249a2f49dc5fa199de66798b798af50834d95ec74520a41758c7914c4c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 267, + "historical_id": "semantic:validation:fastbrain_057902:absent", + "source": "amazon_massive", + "query_hash": "f42332249a2f49dc5fa199de66798b798af50834d95ec74520a41758c7914c4c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058084", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:86202d9f8ea042cb96587fb2d9af7661", + "normalized_query_sha256": "76ded1c4776055465f061b518c07077189f3df7c643dd183920ba3defe694240", + "query": "I also need a flight Leaving on the 1st and coming back Friday next week.", + "target": "SearchRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 329, + "historical_id": "fastbrain_058084", + "source": "sgd", + "query_hash": "76ded1c4776055465f061b518c07077189f3df7c643dd183920ba3defe694240" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 329, + "historical_id": "fastbrain_058084", + "source": "sgd", + "query_hash": "76ded1c4776055465f061b518c07077189f3df7c643dd183920ba3defe694240" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058140", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3e57ab386c7df9743162425141cb9ead", + "normalized_query_sha256": "6bc6f80c7625712816154bc53fc4c5e52b10fb61908dcb26111033d1bcad9b27", + "query": "Yes, start playing the movie for me.", + "target": "PlayMovie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 476, + "historical_id": "fastbrain_058140", + "source": "sgd", + "query_hash": "6bc6f80c7625712816154bc53fc4c5e52b10fb61908dcb26111033d1bcad9b27" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 476, + "historical_id": "fastbrain_058140", + "source": "sgd", + "query_hash": "6bc6f80c7625712816154bc53fc4c5e52b10fb61908dcb26111033d1bcad9b27" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058175", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0f969a1f7f23b43f92369ef45a8fdef0", + "normalized_query_sha256": "0f5fe998ac070b8830ef985662b8c5bde26da48a8a491573db54b1fa3d384b98", + "query": "Sure, here they are. I took Calculus with 4 credit hours and got a B. I took English with 3 credit hours and got an A. I took History with 3 credit hours and got a C.", + "target": "calculate_gpa", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 823, + "historical_id": "fastbrain_058175", + "source": "glaive_v2", + "query_hash": "0f5fe998ac070b8830ef985662b8c5bde26da48a8a491573db54b1fa3d384b98" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 823, + "historical_id": "fastbrain_058175", + "source": "glaive_v2", + "query_hash": "0f5fe998ac070b8830ef985662b8c5bde26da48a8a491573db54b1fa3d384b98" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058333", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5cec90035d27e825347d14c5a0190ea0", + "normalized_query_sha256": "18d43ab0112b147e1228462cfee5c7c157bae2e5d7ae57c24c91bc588f00eb6e", + "query": "hargy/loss of appetite \n- Post-surgical complications \n- Medication reactions \n- Behavioral emergencies \n\n**Compliance Note**: All interactions logged with timestamp/user ID.\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I need to update my pet's profile with a new zip code since I've recently moved. My account email is user@newdomain.com, and the new zip code is 75201 for my dog (pet ID DOG555). Can you help with this?\nassistant: I need to authenticate you first before I can help with any account updates. For authentication using email, I need your registered zip code (the one currently on file with your account).\n\nYou mentioned your new zip code is 75201, but I need the zip code currently associated with your account to verify your identity. Could you please provide that?\n\nOnce authenticated, I can help you understand what updates are possible for your pet's profile. Please note that zip code updates are typically for your account information, not your pet's profile, which includes weight, chronic conditions, and vaccination records.\nuser: I don't have access to my old zip code since I just moved. Can I authenticate using my phone number and birthdate instead? My phone number is 555-123-4567, and my birthdate is March 15, 1985. Once authenticated, I’d like to confirm the new zip code 75201 for my dog DOG555 to ensure future prescriptions are eligible for delivery to this address. Please ask for explicit confirmation before making any changes.", + "target": "authenticate_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 102, + "historical_id": "fastbrain_058333", + "source": "nemotron_agentic", + "query_hash": "18d43ab0112b147e1228462cfee5c7c157bae2e5d7ae57c24c91bc588f00eb6e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 102, + "historical_id": "fastbrain_058333", + "source": "nemotron_agentic", + "query_hash": "18d43ab0112b147e1228462cfee5c7c157bae2e5d7ae57c24c91bc588f00eb6e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058638", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:265f9200f93c67849ec7a5d1ca7666e1", + "normalized_query_sha256": "c3271424ef88e5aef4da6bc4be984f260c5044bbdbc0fe5875e8833e5cd9a988", + "query": "please lights less brighter", + "target": "iot_hue_lightdim", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 20, + "historical_id": "semantic:validation:fastbrain_058638:absent", + "source": "amazon_massive", + "query_hash": "c3271424ef88e5aef4da6bc4be984f260c5044bbdbc0fe5875e8833e5cd9a988" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 312, + "historical_id": "semantic:validation:fastbrain_058638:present", + "source": "amazon_massive", + "query_hash": "c3271424ef88e5aef4da6bc4be984f260c5044bbdbc0fe5875e8833e5cd9a988" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058740", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:48345d4a5bf2206eb732a2503b83c1ee", + "normalized_query_sha256": "123887243504c67e077cc78426fd17ec91c45cfe4d3c97deaea5cc7fcc4dccc3", + "query": "end the bisect session", + "target": "git_bisect", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 173, + "historical_id": "fastbrain_058740", + "source": "git_nl_commands", + "query_hash": "123887243504c67e077cc78426fd17ec91c45cfe4d3c97deaea5cc7fcc4dccc3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 173, + "historical_id": "fastbrain_058740", + "source": "git_nl_commands", + "query_hash": "123887243504c67e077cc78426fd17ec91c45cfe4d3c97deaea5cc7fcc4dccc3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058812", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:28590dfa5323f06e19121466b4adc18d", + "normalized_query_sha256": "1753525c7d606a3704832b86484c8a3186d13f033c202f2a0fc840b8727d08c0", + "query": "i want to do funny thing every day please set a reminder", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 615, + "historical_id": "fastbrain_058812", + "source": "amazon_massive", + "query_hash": "1753525c7d606a3704832b86484c8a3186d13f033c202f2a0fc840b8727d08c0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 615, + "historical_id": "fastbrain_058812", + "source": "amazon_massive", + "query_hash": "1753525c7d606a3704832b86484c8a3186d13f033c202f2a0fc840b8727d08c0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058832", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4b7984a68cab90055d623923209e0684", + "normalized_query_sha256": "828e30a0772c3b68eacbaa2a59e5a204f192ec0d00b0305601b50d3e2d39a375", + "query": "articipants access only: \n - Own visit summaries \n - PI-released lab results \n - Future appointments \n- Retrieve only current IRB-approved consent forms \n\n### Agent Limitations \n1. **Strict Scope Enforcement**: \n - Only perform actions within defined tool capabilities \n - Never diagnose conditions or interpret lab results \n - Never suggest treatment modifications \n2. **Verification Requirements**: \n - Never assume actions completed without tool confirmation \n - Explicitly state limitations when data isn't tool-verified \n3. **Compliance Boundaries**: \n - Never unblind trial data \n - Block protocol-violating requests (e.g., early dosing) \n\n### Escalation Protocol \nTransfer to human agent for: \n- Protocol deviation investigations \n- Serious AE follow-ups \n- Participant withdrawal requests \n- Sponsor inquiries \nProcess: \n1. Use `transfer_to_clinical_support` with issue summary \n2. State: \"YOU ARE BEING TRANSFERRED TO A CLINICAL SUPPORT SPECIALIST. PLEASE HOLD.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I’d like to schedule an appointment for my clinical trial participation, but I need to request a time outside the standard ±72 hour window. My Participant ID is P-76500, Date of Birth is 08/15/1992, and my ZIP Code is 75001. Could you help me identify the earliest available slot that fits the protocol requirements?\nassistant: \ntool: \n\n{\"auth_token\": \"a1b2c3d4-e5f6-7890-g1h2-i3j4k5l6m7n8\", \"user_id\": \"P-76500\"}", + "target": "get_participant_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 462, + "historical_id": "fastbrain_058832", + "source": "nemotron_agentic", + "query_hash": "828e30a0772c3b68eacbaa2a59e5a204f192ec0d00b0305601b50d3e2d39a375" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 462, + "historical_id": "fastbrain_058832", + "source": "nemotron_agentic", + "query_hash": "828e30a0772c3b68eacbaa2a59e5a204f192ec0d00b0305601b50d3e2d39a375" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058837", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6682ca59531927dd9851de5507f76b77", + "normalized_query_sha256": "3c85c564821534c2a6d6c05f73ea4e296c9f66f1bbaca844a5696e731e2dbd0e", + "query": "No, not at the moment. I want to find a restaurant near there first.", + "target": "FindRestaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 752, + "historical_id": "fastbrain_058837", + "source": "sgd", + "query_hash": "3c85c564821534c2a6d6c05f73ea4e296c9f66f1bbaca844a5696e731e2dbd0e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 752, + "historical_id": "fastbrain_058837", + "source": "sgd", + "query_hash": "3c85c564821534c2a6d6c05f73ea4e296c9f66f1bbaca844a5696e731e2dbd0e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058910", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c8c9d6a0d37b7391a8b00827f448cc6b", + "normalized_query_sha256": "eced8b30c9afbc3979f6850e8e7c8845a85f24ae60bb351215ba249927b33428", + "query": "delete all events and content from my calendar", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 467, + "historical_id": "fastbrain_058910", + "source": "amazon_massive", + "query_hash": "eced8b30c9afbc3979f6850e8e7c8845a85f24ae60bb351215ba249927b33428" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 467, + "historical_id": "fastbrain_058910", + "source": "amazon_massive", + "query_hash": "eced8b30c9afbc3979f6850e8e7c8845a85f24ae60bb351215ba249927b33428" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_058989", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e0624c413f25160b959b420e088832bb", + "normalized_query_sha256": "586bfc30357ab0fc781c7b7a0d34656227fadaf8e21e0872bdeab89ca51d41b8", + "query": "confirm that your identity has been successfully authenticated for the CORP-7721 account.\n\n**Regarding Your SOC 2 Report Request:**\n\nI need to inform you that as a **TalentBridge Recruitment Agent**, my capabilities are specifically focused on recruitment-related functions such as:\n- Job posting management\n- Candidate matching and interviews\n- Application processing\n- Client account management for recruitment purposes\n\nThe SOC 2 report and security compliance documentation fall outside my operational scope and tool capabilities. This type of request would typically be handled by your company's:\n- IT Security department\n- Compliance team\n- Administrative or legal support\n\n**Next Steps:**\n1. For security documentation requests, please contact your internal IT/security team directly\n2. If you have any recruitment-related needs for CORP-7721 (job postings, candidate searches, interview coordination, etc.), I'd be happy to assist you with those\n3. For urgent matters requiring human assistance, I can escalate you to a human agent using our transfer protocol\n\nIs there anything recruitment-related I can help you with today?\nuser: Thank you for confirming the authentication. Since the SOC 2 report request falls outside your scope, could you please **escalate this to the compliance/security team** or transfer me to the appropriate department that handles such documentation for CORP-7721? We need confirmation about the report's availability timeline as part of our due diligence process.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 840, + "historical_id": "fastbrain_058989", + "source": "nemotron_agentic", + "query_hash": "586bfc30357ab0fc781c7b7a0d34656227fadaf8e21e0872bdeab89ca51d41b8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 840, + "historical_id": "fastbrain_058989", + "source": "nemotron_agentic", + "query_hash": "586bfc30357ab0fc781c7b7a0d34656227fadaf8e21e0872bdeab89ca51d41b8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059070", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:931d38412d6cb53596b1fd7970fd795d", + "normalized_query_sha256": "7eb3d8b825d650d0e18bc77b3249a2c4497a36a8606854ce54e10fafbbc90832", + "query": "is my friend rohit online on facebook", + "target": "social_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 769, + "historical_id": "semantic:validation:fastbrain_059070:present", + "source": "amazon_massive", + "query_hash": "7eb3d8b825d650d0e18bc77b3249a2c4497a36a8606854ce54e10fafbbc90832" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 800, + "historical_id": "semantic:validation:fastbrain_059070:absent", + "source": "amazon_massive", + "query_hash": "7eb3d8b825d650d0e18bc77b3249a2c4497a36a8606854ce54e10fafbbc90832" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059162", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c6acfabbd98cfd3e91bf31784783936a", + "normalized_query_sha256": "1b7c9640358f1a0855eee8b6a872fd73660eb40a40cb9a377cfd75c0611d01e0", + "query": "go to the podcast and play the next episode in friends", + "target": "play_podcasts", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 721, + "historical_id": "fastbrain_059162", + "source": "amazon_massive", + "query_hash": "1b7c9640358f1a0855eee8b6a872fd73660eb40a40cb9a377cfd75c0611d01e0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 721, + "historical_id": "fastbrain_059162", + "source": "amazon_massive", + "query_hash": "1b7c9640358f1a0855eee8b6a872fd73660eb40a40cb9a377cfd75c0611d01e0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059283", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3b1b73f51597a8da6b64a69a822362e0", + "normalized_query_sha256": "a3199eccef93c8d1c20bcd462c6f46d74d5d156bc36c53d77d736a71ece4f533", + "query": "how is the news media reacting to trump's potential ties with russia", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 331, + "historical_id": "semantic:validation:fastbrain_059283:absent", + "source": "amazon_massive", + "query_hash": "a3199eccef93c8d1c20bcd462c6f46d74d5d156bc36c53d77d736a71ece4f533" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 411, + "historical_id": "semantic:validation:fastbrain_059283:present", + "source": "amazon_massive", + "query_hash": "a3199eccef93c8d1c20bcd462c6f46d74d5d156bc36c53d77d736a71ece4f533" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059424", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b91b5693057760aa71160ff15fa2ee53", + "normalized_query_sha256": "6df264f483191110379d4a61e936134166e9a5b6c6aaa250622a1890330b362d", + "query": "Hi, I need to book a flight from JFK to LAX on 2022-12-25.", + "target": "book_flight", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 162, + "historical_id": "fastbrain_059424", + "source": "glaive_v2", + "query_hash": "6df264f483191110379d4a61e936134166e9a5b6c6aaa250622a1890330b362d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 162, + "historical_id": "fastbrain_059424", + "source": "glaive_v2", + "query_hash": "6df264f483191110379d4a61e936134166e9a5b6c6aaa250622a1890330b362d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059660", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8faa6a4ce10e506d11d11b9c4f4f1b6d", + "normalized_query_sha256": "c2b7cf4cba4f5f05e0426df564cf9e0e8de31a826cc1d8a862bde8420257b296", + "query": "I'm searching for location to dine at, preferably ones not very costly for cost. Get me one in San Mateo.", + "target": "FindRestaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 825, + "historical_id": "fastbrain_059660", + "source": "sgd", + "query_hash": "c2b7cf4cba4f5f05e0426df564cf9e0e8de31a826cc1d8a862bde8420257b296" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 825, + "historical_id": "fastbrain_059660", + "source": "sgd", + "query_hash": "c2b7cf4cba4f5f05e0426df564cf9e0e8de31a826cc1d8a862bde8420257b296" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059733", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:adc7f878716a8a86f2f28976289083e2", + "normalized_query_sha256": "7abd9a00bda4e53063e464e70f7b1357a23c64bb9f058b0ab89ce569db64cb34", + "query": "wake me up at five am this week", + "target": "alarm_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 330, + "historical_id": "fastbrain_059733", + "source": "amazon_massive", + "query_hash": "7abd9a00bda4e53063e464e70f7b1357a23c64bb9f058b0ab89ce569db64cb34" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 330, + "historical_id": "fastbrain_059733", + "source": "amazon_massive", + "query_hash": "7abd9a00bda4e53063e464e70f7b1357a23c64bb9f058b0ab89ce569db64cb34" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059789", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:debace2a66b9c8fcd7ee0bad3184726b", + "normalized_query_sha256": "404de550bc1171a5c02246f49f1847421c66126d8aed30e697ad2a5790029ce7", + "query": "when is bank holiday", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1010, + "historical_id": "fastbrain_059789", + "source": "amazon_massive", + "query_hash": "404de550bc1171a5c02246f49f1847421c66126d8aed30e697ad2a5790029ce7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1010, + "historical_id": "fastbrain_059789", + "source": "amazon_massive", + "query_hash": "404de550bc1171a5c02246f49f1847421c66126d8aed30e697ad2a5790029ce7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059824", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cf88086fdf18492d176923ae2ea18b43", + "normalized_query_sha256": "c3629c10e1eebe176fd79415ef504685a6614d205c4636c67c2d80284f3fce1b", + "query": "The task is to prepare a presentation for the project meeting and the due date is next Monday.", + "target": "create_todo", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 717, + "historical_id": "fastbrain_059824", + "source": "glaive_v2", + "query_hash": "c3629c10e1eebe176fd79415ef504685a6614d205c4636c67c2d80284f3fce1b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 717, + "historical_id": "fastbrain_059824", + "source": "glaive_v2", + "query_hash": "c3629c10e1eebe176fd79415ef504685a6614d205c4636c67c2d80284f3fce1b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059834", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:93e24421a9b79bedfb178a4a8d734968", + "normalized_query_sha256": "3ee726f4dcdb5289dbc228b6db2b95bb557eee896b70b2f26b9a7a775a86c7db", + "query": "I want to make a reservation at a restaurant.", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 545, + "historical_id": "fastbrain_059834", + "source": "sgd", + "query_hash": "3ee726f4dcdb5289dbc228b6db2b95bb557eee896b70b2f26b9a7a775a86c7db" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 545, + "historical_id": "fastbrain_059834", + "source": "sgd", + "query_hash": "3ee726f4dcdb5289dbc228b6db2b95bb557eee896b70b2f26b9a7a775a86c7db" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059836", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:60bd413133b6d103d46eef5a06a65c57", + "normalized_query_sha256": "0b065bd41e745f2202a52fcac592cb0efa3cce353b05300fc7384b9736391e6a", + "query": "give me the list of available train tickets from edinburgh to leeds", + "target": "transport_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 401, + "historical_id": "fastbrain_059836", + "source": "amazon_massive", + "query_hash": "0b065bd41e745f2202a52fcac592cb0efa3cce353b05300fc7384b9736391e6a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 401, + "historical_id": "fastbrain_059836", + "source": "amazon_massive", + "query_hash": "0b065bd41e745f2202a52fcac592cb0efa3cce353b05300fc7384b9736391e6a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059872", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a7c7235bb7ccd3afe2b456da975f5ada", + "normalized_query_sha256": "b75107e033eefa1da8c9d95f462e517eb7367e47608fd24648bd550af9348074", + "query": "Thanks for the help. Now I need a cab to go there.", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 291, + "historical_id": "fastbrain_059872", + "source": "sgd", + "query_hash": "b75107e033eefa1da8c9d95f462e517eb7367e47608fd24648bd550af9348074" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 291, + "historical_id": "fastbrain_059872", + "source": "sgd", + "query_hash": "b75107e033eefa1da8c9d95f462e517eb7367e47608fd24648bd550af9348074" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_059943", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7d1e5b155d4c75428c311bda329ed852", + "normalized_query_sha256": "c780d6cce28f407e279e3555431174a651d343aab836f30452919b667102192c", + "query": "what new emails do i have", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 162, + "historical_id": "semantic:validation:fastbrain_059943:absent", + "source": "amazon_massive", + "query_hash": "c780d6cce28f407e279e3555431174a651d343aab836f30452919b667102192c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 453, + "historical_id": "semantic:validation:fastbrain_059943:present", + "source": "amazon_massive", + "query_hash": "c780d6cce28f407e279e3555431174a651d343aab836f30452919b667102192c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060115", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5f7056705ada7c5eafc542f292b7cf6c", + "normalized_query_sha256": "040614dc2b9f52e2a5486ddf9c4f612a9d607ee752ea9f939390f8af16826770", + "query": "start the oven", + "target": "iot_wemo_on", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 422, + "historical_id": "semantic:validation:fastbrain_060115:absent", + "source": "amazon_massive", + "query_hash": "040614dc2b9f52e2a5486ddf9c4f612a9d607ee752ea9f939390f8af16826770" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1018, + "historical_id": "semantic:validation:fastbrain_060115:present", + "source": "amazon_massive", + "query_hash": "040614dc2b9f52e2a5486ddf9c4f612a9d607ee752ea9f939390f8af16826770" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060350", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c3d8b7cf7c16fce35ee8989757741d84", + "normalized_query_sha256": "075f4ab854e2a33c8ac11c0758796af4ae994ec4e37e36b6f11b503492c68289", + "query": "traffic", + "target": "transport_traffic", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 210, + "historical_id": "fastbrain_060350", + "source": "amazon_massive", + "query_hash": "075f4ab854e2a33c8ac11c0758796af4ae994ec4e37e36b6f11b503492c68289" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 210, + "historical_id": "fastbrain_060350", + "source": "amazon_massive", + "query_hash": "075f4ab854e2a33c8ac11c0758796af4ae994ec4e37e36b6f11b503492c68289" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060366", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:69f1e7870e9b90db5c7675551d2d9a5d", + "normalized_query_sha256": "2188874c759452a65da3854bff660f3e08b925cd11a810680fa451b4b7588162", + "query": "have you added a meeting with tom tomorrow at nine p. m.", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 553, + "historical_id": "semantic:validation:fastbrain_060366:absent", + "source": "amazon_massive", + "query_hash": "2188874c759452a65da3854bff660f3e08b925cd11a810680fa451b4b7588162" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1010, + "historical_id": "semantic:validation:fastbrain_060366:present", + "source": "amazon_massive", + "query_hash": "2188874c759452a65da3854bff660f3e08b925cd11a810680fa451b4b7588162" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060411", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d1e977d5b6f58a1819be93464b7af8c1", + "normalized_query_sha256": "99642064ab29019ace4d9bc728fe5e2c5a3e84fbdeab66ce057d41ccd38820d3", + "query": "remove my morning alarm", + "target": "alarm_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 817, + "historical_id": "fastbrain_060411", + "source": "amazon_massive", + "query_hash": "99642064ab29019ace4d9bc728fe5e2c5a3e84fbdeab66ce057d41ccd38820d3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 817, + "historical_id": "fastbrain_060411", + "source": "amazon_massive", + "query_hash": "99642064ab29019ace4d9bc728fe5e2c5a3e84fbdeab66ce057d41ccd38820d3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060449", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f07e71d360ded52480ed606cc4fd4220", + "normalized_query_sha256": "118a7a44f82baa1e3a24805f99170ce97b956abe0a3248214644d1dbce224462", + "query": "and accuracy:** \n - Disclose fees and restrictions before confirmation \n - Double-check rules using tools (occupancy, availability) \n - Never reveal internal algorithms or speculate on future pricing\n\n\nT...[TRUNCATED]\nuser: \n\nHi, I noticed that my check-out date for reservation #RES2324 was changed from December 31 to December 29 without my authorization. I need to report this as a possible fraud. Can you assist me with this?\nassistant: \ntool: \n\n{\"current_time\": \"2025-12-29T05:50:43-06:00\"}\nassistant: I understand your concern about an unauthorized change to your reservation. To investigate this issue and access your reservation details, I first need to verify your identity.\n\nPlease provide:\n1. Your last name (as it appears on the reservation)\n2. Any other verification details you have (booking ID, loyalty number, or government ID)\n\nOnce verified, I can retrieve your reservation details and help you address this potential fraud issue.\nuser: My last name is Smith. Here’s my government ID number: [123-45-6789], and the registered email associated with the reservation is [j.smith@example.com]. The reservation number is RES2324. Can you confirm this was unauthorized and proceed with the investigation?\nassistant: \ntool: \n\n{\"verification_status\": true, \"guest_id\": \"GUEST12345\"}\nassistant: \ntool: \n\n{\"status\": \"Confirmed\", \"check_in\": \"2025-12-29\", \"check_out\": \"2025-12-29\", \"rate_plan\": \"Flexible\", \"room_type\": \"Deluxe\", \"view_code\": \"OV\", \"guest_id\": \"GUEST12345\"}", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 431, + "historical_id": "fastbrain_060449", + "source": "nemotron_agentic", + "query_hash": "118a7a44f82baa1e3a24805f99170ce97b956abe0a3248214644d1dbce224462" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 431, + "historical_id": "fastbrain_060449", + "source": "nemotron_agentic", + "query_hash": "118a7a44f82baa1e3a24805f99170ce97b956abe0a3248214644d1dbce224462" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060556", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fc08347d676094439f011aa9e01c82bb", + "normalized_query_sha256": "064300acd6a9e5123a720397badf249b1073475627fa5286d1abebc7137560eb", + "query": "Find all .rb files owned by root in the /apps/ folder and below that have been accessed in the last two minutes.", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 855, + "historical_id": "fastbrain_060556", + "source": "nl2bash", + "query_hash": "064300acd6a9e5123a720397badf249b1073475627fa5286d1abebc7137560eb" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 855, + "historical_id": "fastbrain_060556", + "source": "nl2bash", + "query_hash": "064300acd6a9e5123a720397badf249b1073475627fa5286d1abebc7137560eb" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060569", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:325e17b262732489770acb0477fd7a8f", + "normalized_query_sha256": "02ba3a903354853f870a9b08bea9c59f79ecbcb2c1bcde1bd91baac127ef9690", + "query": " (credit only), boarding (prorated), events (tiered) \n\n## Safety Protocols \n- Enforce helmet requirements for mounted activities \n- Adhere to medical restrictions (allergies, health conditions) \n- Verify safety equipment certifications \n\n## Escalation Procedure \nTransfer to human agent for: \n- Emergency situations \n- Complex insurance claims \n- Membership disputes \n- Legal inquiries \nEscalation Message: *\"Transferring you to a human agent for further assistance\"* \n\n## Agent Limitations \n**Action Boundaries** \n- Only perform tasks covered by available tools \n- Never bypass authentication protocols \n- Decline requests involving: \n - Veterinary advice \n - Training recommendations \n - Legal interpretations \n\n**Verification Protocol** \n- Never assume: \n - Identity verification without code confirmation \n - Action completion without tool validation \n - Policy exceptions without authorization \n\n**Information Boundaries** \n- Terminate attempts to override: \n - Safety restrictions \n - Medical protocols \n - Payment policies\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling regarding an overdue invoice for my account at your equestrian center. My invoice #INV-2024-882 is currently $300 overdue and 5 days late. I'd like to discuss setting up a payment plan to resolve this balance. Could you please transfer me to a human agent who handles financial arrangements? I need assistance creating a formal payment agreement.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 244, + "historical_id": "fastbrain_060569", + "source": "nemotron_agentic", + "query_hash": "02ba3a903354853f870a9b08bea9c59f79ecbcb2c1bcde1bd91baac127ef9690" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 244, + "historical_id": "fastbrain_060569", + "source": "nemotron_agentic", + "query_hash": "02ba3a903354853f870a9b08bea9c59f79ecbcb2c1bcde1bd91baac127ef9690" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060700", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:92fcb9b9539d1b7b150cc7b5f5909625", + "normalized_query_sha256": "ab10c8945e9648f9000f434d0d91a0067b92d7d1f50e421cba1bbbb066e316c4", + "query": "st inform you that according to Rutherford & Sons' consignment protocols, **reserve price changes are blocked less than 7 days pre-auction**. Since Auction A-775 is scheduled in 4 days, this modification is no longer permitted.\n\nThis policy is in place to ensure auction integrity and provide consistent information to all bidders during the final preview period. I understand your concerns about recent market shifts in Art Deco timepieces, but we cannot make exceptions to this rule.\n\n**Alternative options to consider:**\n1. **Post-auction consignment**: If the item does not meet its current reserve, you could consign it to a future auction with an adjusted reserve price that reflects current market conditions.\n2. **Withdrawal inquiry**: If you wish to explore withdrawal options, I can provide information on withdrawal deadlines and any associated fees (though withdrawals are also subject to timing restrictions).\n\nWould you like me to check the current status of your consignment or provide details about future auction schedules where you could submit this item with a revised reserve?\nuser: Thank you for confirming the policy restrictions. Given that the reserve cannot be adjusted, I’d like to explore the withdrawal options for CS-8821. What are the deadlines and fees for withdrawing the Art Deco clock from Auction A-775? If withdrawal isn’t feasible, could you provide details on the next available auction date where I could submit this consignment with the revised $9,500 reserve?", + "target": "get_auction_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 926, + "historical_id": "fastbrain_060700", + "source": "nemotron_agentic", + "query_hash": "ab10c8945e9648f9000f434d0d91a0067b92d7d1f50e421cba1bbbb066e316c4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 926, + "historical_id": "fastbrain_060700", + "source": "nemotron_agentic", + "query_hash": "ab10c8945e9648f9000f434d0d91a0067b92d7d1f50e421cba1bbbb066e316c4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060803", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:74a293984e21caabee2affbcb4a01c15", + "normalized_query_sha256": "050c118849da74f7c506fd72b12f95c3fe6e1ced2a8cd1723c4265b35c5e9903", + "query": "what is the boiling point of water", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 645, + "historical_id": "semantic:validation:fastbrain_060803:present", + "source": "amazon_massive", + "query_hash": "050c118849da74f7c506fd72b12f95c3fe6e1ced2a8cd1723c4265b35c5e9903" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 930, + "historical_id": "semantic:validation:fastbrain_060803:absent", + "source": "amazon_massive", + "query_hash": "050c118849da74f7c506fd72b12f95c3fe6e1ced2a8cd1723c4265b35c5e9903" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060903", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fe2688c10607bc4b68aab3143891df95", + "normalized_query_sha256": "9234b8a8b8a3ad6be1972cf9feba3dada144039a51f7e880dea83dd755093668", + "query": "Locate all *.csv files under the current directory tree", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 687, + "historical_id": "fastbrain_060903", + "source": "nl2bash", + "query_hash": "9234b8a8b8a3ad6be1972cf9feba3dada144039a51f7e880dea83dd755093668" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 687, + "historical_id": "fastbrain_060903", + "source": "nl2bash", + "query_hash": "9234b8a8b8a3ad6be1972cf9feba3dada144039a51f7e880dea83dd755093668" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060968", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f4ce40c9c0ed4ef595e3a8fe687df62f", + "normalized_query_sha256": "93498fc0010d2719b400740cf938cd51049a73a2ce11b59b970c57319d7412e8", + "query": "Hi, I just had a meal at a restaurant and the bill amount is $50. I want to leave a 20% tip. Can you calculate the tip amount for me?", + "target": "calculate_tip", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 22, + "historical_id": "fastbrain_060968", + "source": "glaive_v2", + "query_hash": "93498fc0010d2719b400740cf938cd51049a73a2ce11b59b970c57319d7412e8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 22, + "historical_id": "fastbrain_060968", + "source": "glaive_v2", + "query_hash": "93498fc0010d2719b400740cf938cd51049a73a2ce11b59b970c57319d7412e8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_060970", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8fa22e2705e96d1d01100c7cca30aed5", + "normalized_query_sha256": "3daa8d6ca49910d9819bd7889642a7690a78bb2e55e80b0a015c20cca9965ec5", + "query": "there is an email i need you to send my friend", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 80, + "historical_id": "semantic:validation:fastbrain_060970:present", + "source": "amazon_massive", + "query_hash": "3daa8d6ca49910d9819bd7889642a7690a78bb2e55e80b0a015c20cca9965ec5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 649, + "historical_id": "semantic:validation:fastbrain_060970:absent", + "source": "amazon_massive", + "query_hash": "3daa8d6ca49910d9819bd7889642a7690a78bb2e55e80b0a015c20cca9965ec5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061082", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:20517b22b9f2b3b9144dde1ceb200af7", + "normalized_query_sha256": "c0fd7abc60f58daecc807315e94fea185d03135b48bd3da8b6d158f3e472f2f0", + "query": "I'm looking for the movie \"Inception\".", + "target": "search_movie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 446, + "historical_id": "fastbrain_061082", + "source": "glaive_v2", + "query_hash": "c0fd7abc60f58daecc807315e94fea185d03135b48bd3da8b6d158f3e472f2f0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 446, + "historical_id": "fastbrain_061082", + "source": "glaive_v2", + "query_hash": "c0fd7abc60f58daecc807315e94fea185d03135b48bd3da8b6d158f3e472f2f0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061222", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2821fe8fc7b27668953ab3c8e6e2b15d", + "normalized_query_sha256": "8dd1bacc320d9dc7b9e20ca184246195f52517a689ff6c29adbb836cfd04ce44", + "query": "alexa reply to the mail with the following content hello you", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 659, + "historical_id": "fastbrain_061222", + "source": "amazon_massive", + "query_hash": "8dd1bacc320d9dc7b9e20ca184246195f52517a689ff6c29adbb836cfd04ce44" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 659, + "historical_id": "fastbrain_061222", + "source": "amazon_massive", + "query_hash": "8dd1bacc320d9dc7b9e20ca184246195f52517a689ff6c29adbb836cfd04ce44" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061229", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4752f4cdb2eefb54f0e77ae33e6e241f", + "normalized_query_sha256": "1329ade8926487893c575f2267a0357818910dcb82dfdc63c665c46521e18c2a", + "query": "erase shopping list", + "target": "lists_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 366, + "historical_id": "semantic:validation:fastbrain_061229:present", + "source": "amazon_massive", + "query_hash": "1329ade8926487893c575f2267a0357818910dcb82dfdc63c665c46521e18c2a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 745, + "historical_id": "semantic:validation:fastbrain_061229:absent", + "source": "amazon_massive", + "query_hash": "1329ade8926487893c575f2267a0357818910dcb82dfdc63c665c46521e18c2a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061248", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:82a1fc749c89d1089c055e51db074348", + "normalized_query_sha256": "5933a6543f7f11ddbad7511497b31297d72007faac9ba474832b6cc53481b552", + "query": "Split \"date.csv\" into files with at most 100 lines each", + "target": "split", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 119, + "historical_id": "fastbrain_061248", + "source": "nl2bash", + "query_hash": "5933a6543f7f11ddbad7511497b31297d72007faac9ba474832b6cc53481b552" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 119, + "historical_id": "fastbrain_061248", + "source": "nl2bash", + "query_hash": "5933a6543f7f11ddbad7511497b31297d72007faac9ba474832b6cc53481b552" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061354", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d4634ee23d0f7783c6b65bb0f2aa0e18", + "normalized_query_sha256": "f879009c4a97d16f2e4d70c370606105cb027c094ccbc9ef0c66983e0bd6b79d", + "query": "when will the match start", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 236, + "historical_id": "fastbrain_061354", + "source": "amazon_massive", + "query_hash": "f879009c4a97d16f2e4d70c370606105cb027c094ccbc9ef0c66983e0bd6b79d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 236, + "historical_id": "fastbrain_061354", + "source": "amazon_massive", + "query_hash": "f879009c4a97d16f2e4d70c370606105cb027c094ccbc9ef0c66983e0bd6b79d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061451", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a29312d2f7de5003b66ab3641cd2a76b", + "normalized_query_sha256": "a3ccb77891ea98b7cb524e30a87368ac54a13279b3221b47c3491fb076f4a765", + "query": "do i have a date on friday", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 31, + "historical_id": "semantic:validation:fastbrain_061451:present", + "source": "amazon_massive", + "query_hash": "a3ccb77891ea98b7cb524e30a87368ac54a13279b3221b47c3491fb076f4a765" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 906, + "historical_id": "semantic:validation:fastbrain_061451:absent", + "source": "amazon_massive", + "query_hash": "a3ccb77891ea98b7cb524e30a87368ac54a13279b3221b47c3491fb076f4a765" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061529", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:63a28a0baf8bfb116c85d8b80d3b014a", + "normalized_query_sha256": "e649f930ce843229c46fe35f083b431378a8637a941c69315ea18fa8f1a07007", + "query": "set lights to twenty percent", + "target": "iot_hue_lightchange", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 69, + "historical_id": "semantic:validation:fastbrain_061529:absent", + "source": "amazon_massive", + "query_hash": "e649f930ce843229c46fe35f083b431378a8637a941c69315ea18fa8f1a07007" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 863, + "historical_id": "semantic:validation:fastbrain_061529:present", + "source": "amazon_massive", + "query_hash": "e649f930ce843229c46fe35f083b431378a8637a941c69315ea18fa8f1a07007" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061753", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fe9d014c31e859675719908ce13e6f74", + "normalized_query_sha256": "587763cc8a13efffcb5498193047afb01561066a03ac047b905f4b45b04c2ee7", + "query": "I also want to get tickets for anything interesting going on in the area.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 826, + "historical_id": "fastbrain_061753", + "source": "sgd", + "query_hash": "587763cc8a13efffcb5498193047afb01561066a03ac047b905f4b45b04c2ee7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 826, + "historical_id": "fastbrain_061753", + "source": "sgd", + "query_hash": "587763cc8a13efffcb5498193047afb01561066a03ac047b905f4b45b04c2ee7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061900", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6d4d22340cedeeb6545211db847001fb", + "normalized_query_sha256": "2d80fc28ee8ccafee9348232cd79ba45689d938e7b010f7b0fb458092d135716", + "query": "This house seems nice. Can you tell me about buses that go there?", + "target": "FindBus", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 676, + "historical_id": "fastbrain_061900", + "source": "sgd", + "query_hash": "2d80fc28ee8ccafee9348232cd79ba45689d938e7b010f7b0fb458092d135716" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 676, + "historical_id": "fastbrain_061900", + "source": "sgd", + "query_hash": "2d80fc28ee8ccafee9348232cd79ba45689d938e7b010f7b0fb458092d135716" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061915", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3cf66e49b8b54321f4673dc29bcd3fcc", + "normalized_query_sha256": "2bb3862c7ce8a557daefb70530cb221283b514180ab13bb163d822901a0d6eae", + "query": "olly what are new drama movies opening this weekend", + "target": "recommendation_movies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 595, + "historical_id": "semantic:validation:fastbrain_061915:absent", + "source": "amazon_massive", + "query_hash": "2bb3862c7ce8a557daefb70530cb221283b514180ab13bb163d822901a0d6eae" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 907, + "historical_id": "semantic:validation:fastbrain_061915:present", + "source": "amazon_massive", + "query_hash": "2bb3862c7ce8a557daefb70530cb221283b514180ab13bb163d822901a0d6eae" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061921", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9437199e84814974d3247e27ded1e785", + "normalized_query_sha256": "23f6160ca8afbc867800ace5a0c3a07434b29779b1a0447780f5c6bffb3bba70", + "query": "Page through the output of running the specified PHP file.", + "target": "less", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 155, + "historical_id": "fastbrain_061921", + "source": "nl2bash", + "query_hash": "23f6160ca8afbc867800ace5a0c3a07434b29779b1a0447780f5c6bffb3bba70" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 155, + "historical_id": "fastbrain_061921", + "source": "nl2bash", + "query_hash": "23f6160ca8afbc867800ace5a0c3a07434b29779b1a0447780f5c6bffb3bba70" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_061939", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e3fc9feea5bb98b56f783e3374e79719", + "normalized_query_sha256": "38bc0d0817cf141abf7fea096ffa9cb400ded660fb2c9349a68d79262ed5bba8", + "query": "I am responsible for managing the logistics of oil & gas storage and transportation for our energy company. We are currently looking to optimize this process by utilizing our extensive data. We have precise data on our storage facilities, including their capacities, current inventory levels of oil & gas, and a detailed forecast of demand for the upcoming quarter. Our transportation network's sched", + "target": "optimize_storage_distribution", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 573, + "historical_id": "fastbrain_061939", + "source": "hermes_v1", + "query_hash": "38bc0d0817cf141abf7fea096ffa9cb400ded660fb2c9349a68d79262ed5bba8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 573, + "historical_id": "fastbrain_061939", + "source": "hermes_v1", + "query_hash": "38bc0d0817cf141abf7fea096ffa9cb400ded660fb2c9349a68d79262ed5bba8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062129", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b48e6872af932e3d4262b8140c3db178", + "normalized_query_sha256": "c819e4482d33e2bd78d45cb7e83badcf2b4bbffa1def75ef075b2a559b195ee3", + "query": "Yes, I'd like to book an appointment. The time would be 2:30 pm March 5th. Can you book it?", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 459, + "historical_id": "fastbrain_062129", + "source": "sgd", + "query_hash": "c819e4482d33e2bd78d45cb7e83badcf2b4bbffa1def75ef075b2a559b195ee3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 459, + "historical_id": "fastbrain_062129", + "source": "sgd", + "query_hash": "c819e4482d33e2bd78d45cb7e83badcf2b4bbffa1def75ef075b2a559b195ee3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062139", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:616668f81d795109465981cabe81d032", + "normalized_query_sha256": "3e157e749d7fa5cec341e43c6364f6e14805294d4e64895b21e9d39858a34822", + "query": "can you book a train ticket for me under dollars", + "target": "transport_ticket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 671, + "historical_id": "fastbrain_062139", + "source": "amazon_massive", + "query_hash": "3e157e749d7fa5cec341e43c6364f6e14805294d4e64895b21e9d39858a34822" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 671, + "historical_id": "fastbrain_062139", + "source": "amazon_massive", + "query_hash": "3e157e749d7fa5cec341e43c6364f6e14805294d4e64895b21e9d39858a34822" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062232", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:afcecfb9d893b2c4e559c211071c8b13", + "normalized_query_sha256": "088df98408b3da7a057ac77fd4715482e9ab5c8aeb7ec7d123a7306cc56a5e0c", + "query": "tell me the artist name", + "target": "music_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 495, + "historical_id": "semantic:validation:fastbrain_062232:present", + "source": "amazon_massive", + "query_hash": "088df98408b3da7a057ac77fd4715482e9ab5c8aeb7ec7d123a7306cc56a5e0c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 895, + "historical_id": "semantic:validation:fastbrain_062232:absent", + "source": "amazon_massive", + "query_hash": "088df98408b3da7a057ac77fd4715482e9ab5c8aeb7ec7d123a7306cc56a5e0c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062310", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:27e20e2b7e5fb103918790cbd3a523a2", + "normalized_query_sha256": "6a32ea3c8aa2d3ea93b5e06cc5d3e8455d065645c0ee029db4986e4376b167c0", + "query": "so clean news in past six hours is what", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 125, + "historical_id": "semantic:validation:fastbrain_062310:present", + "source": "amazon_massive", + "query_hash": "6a32ea3c8aa2d3ea93b5e06cc5d3e8455d065645c0ee029db4986e4376b167c0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 886, + "historical_id": "semantic:validation:fastbrain_062310:absent", + "source": "amazon_massive", + "query_hash": "6a32ea3c8aa2d3ea93b5e06cc5d3e8455d065645c0ee029db4986e4376b167c0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062341", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:443e3189aed5c172a21f1be7b37e9c22", + "normalized_query_sha256": "1ef1e244ab9cf3dffa8123c8fda3af4727ce4efb201e45fa481835098b43746b", + "query": "Yes, I'd like to rent a car.", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 796, + "historical_id": "fastbrain_062341", + "source": "sgd", + "query_hash": "1ef1e244ab9cf3dffa8123c8fda3af4727ce4efb201e45fa481835098b43746b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 796, + "historical_id": "fastbrain_062341", + "source": "sgd", + "query_hash": "1ef1e244ab9cf3dffa8123c8fda3af4727ce4efb201e45fa481835098b43746b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062350", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cd993008e0e94615431d473ed58e05f1", + "normalized_query_sha256": "34d5011822194f8faa50875bcd92a709a3c079031e7af45969c98ac5473106cf", + "query": "es\n\nIn general, our kitchen follows strict allergen safety protocols:\n- **Separate utensils, cutting boards, and cookware** designated for gluten-free preparation\n- **Dedicated cooking surfaces** when possible, with thorough cleaning between uses\n- **Segregated storage** of gluten-free ingredients in sealed, labeled containers\n- **Staff training** in allergen safety, including cross-contact prevention and emergency procedures\n\nAll allergy submissions require at least 48 hours' notice, so I recommend we process this request today to ensure everything is ready for your May 26 check-in.\n\nPlease provide your verification credentials so I can assist you further!\nuser: Hi, this is Emily Carter, Guest ID GH-222. I’d like to confirm my dietary requirements are on file. Following up on your kitchen protocols—could you clarify how you handle pre-made or processed foods that might contain gluten? For example, do you verify packaging for cross-contamination risks, or do you avoid such items altogether for guests with allergies? Also, how is the gluten-free menu communicated to kitchen staff to ensure compliance during peak hours?\nassistant: \ntool: \n\n```json\n{\n \"verified\": true,\n \"guest_id\": \"GH-222\",\n \"sustainability_tier\": \"Grove\"\n}\n```\nassistant: \ntool: \n\n```json\n{\n \"guest_id\": \"GH-222\",\n \"contact_info\": \"Email: emily.carter@example.com | Phone: (555) 123-4567\",\n \"accessibility_requirements\": [],\n \"dietary_restrictions\": [\"Gluten allergy\"],\n \"sustainability_tier\": \"Grove\"\n}\n```", + "target": "submit_dietary_request", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 127, + "historical_id": "fastbrain_062350", + "source": "nemotron_agentic", + "query_hash": "34d5011822194f8faa50875bcd92a709a3c079031e7af45969c98ac5473106cf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 127, + "historical_id": "fastbrain_062350", + "source": "nemotron_agentic", + "query_hash": "34d5011822194f8faa50875bcd92a709a3c079031e7af45969c98ac5473106cf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062518", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f1c06635382a93944d983b77c6d62b53", + "normalized_query_sha256": "5a1918743a2706b6b468b09a10e1b213249fbab54a192e16ddcad20cb6d1d545", + "query": "find all files that do not have execute permission to all", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 872, + "historical_id": "fastbrain_062518", + "source": "nl2bash", + "query_hash": "5a1918743a2706b6b468b09a10e1b213249fbab54a192e16ddcad20cb6d1d545" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 872, + "historical_id": "fastbrain_062518", + "source": "nl2bash", + "query_hash": "5a1918743a2706b6b468b09a10e1b213249fbab54a192e16ddcad20cb6d1d545" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062583", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a74e34f170ecc3272a6df27cd5789ada", + "normalized_query_sha256": "9f3f0d00ac1e95b52411f83746c77c94857eeb75f4f1ae3af97ad38ac2bb28d5", + "query": "i don't need light", + "target": "iot_hue_lightoff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 130, + "historical_id": "fastbrain_062583", + "source": "amazon_massive", + "query_hash": "9f3f0d00ac1e95b52411f83746c77c94857eeb75f4f1ae3af97ad38ac2bb28d5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 130, + "historical_id": "fastbrain_062583", + "source": "amazon_massive", + "query_hash": "9f3f0d00ac1e95b52411f83746c77c94857eeb75f4f1ae3af97ad38ac2bb28d5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062688", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:52af985f286c0e21540254d3e76b7e6e", + "normalized_query_sha256": "d988bf4cf1b094070e51834b931d8ddfd1451399923363c2236fe92e63cf295f", + "query": "my list please", + "target": "lists_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 986, + "historical_id": "fastbrain_062688", + "source": "amazon_massive", + "query_hash": "d988bf4cf1b094070e51834b931d8ddfd1451399923363c2236fe92e63cf295f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 986, + "historical_id": "fastbrain_062688", + "source": "amazon_massive", + "query_hash": "d988bf4cf1b094070e51834b931d8ddfd1451399923363c2236fe92e63cf295f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062725", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:21d9cd24fa9ca574f3afac77bb6bf083", + "normalized_query_sha256": "b755e8647a4788ee2cf8ad006f6bdb81b65a02acec4546988cb00a4c84aa363c", + "query": "how would you describe a ball", + "target": "qa_definition", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 441, + "historical_id": "semantic:validation:fastbrain_062725:present", + "source": "amazon_massive", + "query_hash": "b755e8647a4788ee2cf8ad006f6bdb81b65a02acec4546988cb00a4c84aa363c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 942, + "historical_id": "semantic:validation:fastbrain_062725:absent", + "source": "amazon_massive", + "query_hash": "b755e8647a4788ee2cf8ad006f6bdb81b65a02acec4546988cb00a4c84aa363c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_062956", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:25a2a543e797a6536bcb895334861dbf", + "normalized_query_sha256": "967ddc50d1fa3ddb2e3bf48d2c35bf98c7e3e8128da8203e758ec35277355ca4", + "query": "display a list of all java or jsp files in the current folders", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 991, + "historical_id": "fastbrain_062956", + "source": "nl2bash", + "query_hash": "967ddc50d1fa3ddb2e3bf48d2c35bf98c7e3e8128da8203e758ec35277355ca4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 991, + "historical_id": "fastbrain_062956", + "source": "nl2bash", + "query_hash": "967ddc50d1fa3ddb2e3bf48d2c35bf98c7e3e8128da8203e758ec35277355ca4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_063088", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a1df5d7d5ddb1e32879b3313133e6f6a", + "normalized_query_sha256": "d36b2398fff877fc41805a3b02d57d80d415eb9b13493faf0854ac4e02d8eed4", + "query": "urn/shipping policies \n Enforce purchase limits automatically\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm an active duty military member and I was wondering if your sports team merchandise offers a service member discount. Can you also let me know how the verification process works for online orders using a military ID?\nassistant: I'm unable to provide information about military discounts as this falls outside my current capabilities. The Chicago Hawks merchandise system I support focuses on loyalty program discounts based on fan tiers (Bronze, Silver, Gold) rather than military-specific discounts.\n\nFor verification processes using military ID, I don't have access to those systems or policies. You would need to contact our human customer support team directly for information about military verification procedures and any potential discounts available to service members.\n\nI can help you with other aspects of our merchandise system, such as:\n- Browsing available jerseys, apparel, and accessories\n- Managing orders and returns\n- Using loyalty points for discounts (5,000 points = $30 off jerseys)\n- Purchasing ticket bundles with merchandise inclusions\n\nWould you like assistance with any of these areas instead?\nuser: I understand you can't assist with military discounts directly. Could you please transfer me to a human agent who can provide details about service member discounts and the verification process for online orders using my military ID?", + "target": "transfer_to_human_support", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 304, + "historical_id": "fastbrain_063088", + "source": "nemotron_agentic", + "query_hash": "d36b2398fff877fc41805a3b02d57d80d415eb9b13493faf0854ac4e02d8eed4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 304, + "historical_id": "fastbrain_063088", + "source": "nemotron_agentic", + "query_hash": "d36b2398fff877fc41805a3b02d57d80d415eb9b13493faf0854ac4e02d8eed4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_063196", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ed7768249dc6cd93d013b84336a9474c", + "normalized_query_sha256": "e46b46006ff87019e432d7ddcf28dd2636ebe0a18422510cffb7988ec11d409d", + "query": "remind me of the meeting on tuesday", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 765, + "historical_id": "semantic:validation:fastbrain_063196:absent", + "source": "amazon_massive", + "query_hash": "e46b46006ff87019e432d7ddcf28dd2636ebe0a18422510cffb7988ec11d409d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 815, + "historical_id": "semantic:validation:fastbrain_063196:present", + "source": "amazon_massive", + "query_hash": "e46b46006ff87019e432d7ddcf28dd2636ebe0a18422510cffb7988ec11d409d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_063423", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7afc14d28f7c5251f82cc9011c097594", + "normalized_query_sha256": "4ea08db01626a03531827bf97dd87c072ed1af4cba28734cd842ac9b4a4dc63e", + "query": " \n- Date changes: >24h before start \n- Maximum 2 changes per reservation \n\n**4. Cancellations** \n| Time Before Start | Refund | \n|-------------------|--------| \n| >72h | Full | \n| 24-72h | 50% credit | \n| <24h | None | \n\n**5. Late Returns** \n- Hourly fee: 10% daily rate \n- Lost item: Full replacement after 72h \n\n**6. Insurance** \n- Basic waiver included \n- Premium: $10 (covers 50% damage) \n\n**7. Membership Benefits** \n- Bronze: Free shipping \n- Silver: 10% discount + express shipping \n- Gold: 20% discount + damage waiver \n\n---\n\n### Agent Limitations \n- Only perform actions within defined tool capabilities \n- Never assume actions are completed without explicit tool confirmation \n- Never bypass authentication steps \n- Decline requests requiring fashion advice \n- Never disclose internal assessment thresholds \n\n### Transfer Protocol \nTransfer to human agents for: \n- Replacement disputes >$500 \n- Custom design requests \n- Multiple unresolved claims \n- Security flags \n\nProcess: \n1. Use transfer_to_human_agent tool \n2. Send: \"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE WAIT MOMENTARILY.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm a Gold member and I need to report a lost rental item. My reservation ID is G9H2J4 for a Chanel handbag (SKU HB-772) that I've had for 80 hours. I understand the 72-hour lost item policy threshold has passed, so I'd like to confirm if I'm liable for the full $2,500 replacement value?", + "target": "get_reservation_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 394, + "historical_id": "fastbrain_063423", + "source": "nemotron_agentic", + "query_hash": "4ea08db01626a03531827bf97dd87c072ed1af4cba28734cd842ac9b4a4dc63e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 394, + "historical_id": "fastbrain_063423", + "source": "nemotron_agentic", + "query_hash": "4ea08db01626a03531827bf97dd87c072ed1af4cba28734cd842ac9b4a4dc63e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_063439", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5906940afc0dadafcca7864cd569616a", + "normalized_query_sha256": "c6246af0e39c170fc2f307f032247e0063ba7cb40d74e8bc76977c99b66642fd", + "query": "Look in the current directory and below for all files whose names begin with either \"my\" or \"qu\"", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 527, + "historical_id": "fastbrain_063439", + "source": "nl2bash", + "query_hash": "c6246af0e39c170fc2f307f032247e0063ba7cb40d74e8bc76977c99b66642fd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 527, + "historical_id": "fastbrain_063439", + "source": "nl2bash", + "query_hash": "c6246af0e39c170fc2f307f032247e0063ba7cb40d74e8bc76977c99b66642fd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_063730", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4b48c2c4b63675531f6c8bb4ba789691", + "normalized_query_sha256": "2726e5f4e96a3b2227f1d8e69117378af303497b93000a0d032ba8884e1d6b53", + "query": "is california between nevada and new york", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 283, + "historical_id": "fastbrain_063730", + "source": "amazon_massive", + "query_hash": "2726e5f4e96a3b2227f1d8e69117378af303497b93000a0d032ba8884e1d6b53" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 283, + "historical_id": "fastbrain_063730", + "source": "amazon_massive", + "query_hash": "2726e5f4e96a3b2227f1d8e69117378af303497b93000a0d032ba8884e1d6b53" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_063793", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2b3321eb9c3f120af213199878c508fb", + "normalized_query_sha256": "d25aa4d8f9f38db96d857b195003a7380d4cd1c93e40ea37338ebf890bb55400", + "query": "I've been feeling a bit sick. Can you book a doctor's visit for me?", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 87, + "historical_id": "fastbrain_063793", + "source": "sgd", + "query_hash": "d25aa4d8f9f38db96d857b195003a7380d4cd1c93e40ea37338ebf890bb55400" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 87, + "historical_id": "fastbrain_063793", + "source": "sgd", + "query_hash": "d25aa4d8f9f38db96d857b195003a7380d4cd1c93e40ea37338ebf890bb55400" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_064096", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b602228c87607bdceb79a64987c2a7b6", + "normalized_query_sha256": "50c9ea501a407aa79718041af8675f92787ac45c43a2a52c9156c02db915bbb0", + "query": "Sure, could you book it for me please?", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 72, + "historical_id": "fastbrain_064096", + "source": "sgd", + "query_hash": "50c9ea501a407aa79718041af8675f92787ac45c43a2a52c9156c02db915bbb0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 72, + "historical_id": "fastbrain_064096", + "source": "sgd", + "query_hash": "50c9ea501a407aa79718041af8675f92787ac45c43a2a52c9156c02db915bbb0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_064580", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a95d45f65941da766c7121bb3a495c3c", + "normalized_query_sha256": "479ac6e87ebd134e90dbf35171a2aa3ba56602c8cdfe53d98312a91618f5acde", + "query": "I do. 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_064898", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:377ead13860a6b4e7761a7fce2428084", + "normalized_query_sha256": "0769f1e8cbb52a1cd3335f6c1d6bc413bd53e9814780ff7f5010466180364822", + "query": "do you expect sun on sunday", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 155, + "historical_id": "semantic:validation:fastbrain_064898:absent", + "source": "amazon_massive", + "query_hash": "0769f1e8cbb52a1cd3335f6c1d6bc413bd53e9814780ff7f5010466180364822" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 771, + "historical_id": "semantic:validation:fastbrain_064898:present", + "source": "amazon_massive", + "query_hash": "0769f1e8cbb52a1cd3335f6c1d6bc413bd53e9814780ff7f5010466180364822" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_064957", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:215aabf032ad3334936cf84536dbca06", + "normalized_query_sha256": "f34b8298b43a500e261267c68e0326474775876f182ac33a84c303474b5bf558", + "query": "Can you look up some movie info. for me?", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 105, + "historical_id": "fastbrain_064957", + "source": "sgd", + "query_hash": "f34b8298b43a500e261267c68e0326474775876f182ac33a84c303474b5bf558" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 105, + "historical_id": "fastbrain_064957", + "source": "sgd", + "query_hash": "f34b8298b43a500e261267c68e0326474775876f182ac33a84c303474b5bf558" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_064961", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4d05531933b6b31d4cacb2840bc12033", + "normalized_query_sha256": "d26adb2898aaa12857da04234d3b4712f05c229c1b9bd5b404793dff4327c821", + "query": "I am working on a Databricks project titled \"Data Aggregation and ML Model Execution\" which requires processing voluminous datasets stored within an Azure Data Lake Storage Gen2 container located at `https://mydatalake.dfs.core.windows.net/lakehouse/data/`. The job entails two primary tasks: the first is to aggregate data, potentially using Apache Spark SQL queries for performance, and the second ", + "target": "create_job", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 629, + "historical_id": "fastbrain_064961", + "source": "hermes_v1", + "query_hash": "d26adb2898aaa12857da04234d3b4712f05c229c1b9bd5b404793dff4327c821" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 629, + "historical_id": "fastbrain_064961", + "source": "hermes_v1", + "query_hash": "d26adb2898aaa12857da04234d3b4712f05c229c1b9bd5b404793dff4327c821" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_064967", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:165aab3c0dc1230d0f29be9053fb35bc", + "normalized_query_sha256": "17391784a490090c9d7d95cf87f9686a2919c8dfcad4ef87a340d3fab33afb5a", + "query": "free up my schedule for the next week", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 739, + "historical_id": "fastbrain_064967", + "source": "amazon_massive", + "query_hash": "17391784a490090c9d7d95cf87f9686a2919c8dfcad4ef87a340d3fab33afb5a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 739, + "historical_id": "fastbrain_064967", + "source": "amazon_massive", + "query_hash": "17391784a490090c9d7d95cf87f9686a2919c8dfcad4ef87a340d3fab33afb5a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_064977", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:92bd7658d3d030d6c7084a6170665574", + "normalized_query_sha256": "4c0989306f29e9e21c96d08af75300a56a739685e1281f1e52578ba21fc95de6", + "query": "Not right now but can you reserve a hotel room for 4 days checking in on the 5th of March?", + "target": "ReserveHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 647, + "historical_id": "fastbrain_064977", + "source": "sgd", + "query_hash": "4c0989306f29e9e21c96d08af75300a56a739685e1281f1e52578ba21fc95de6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 647, + "historical_id": "fastbrain_064977", + "source": "sgd", + "query_hash": "4c0989306f29e9e21c96d08af75300a56a739685e1281f1e52578ba21fc95de6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065002", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cd60486f470663f2cc4d8b8b26b17b6f", + "normalized_query_sha256": "0714f968bc7307fc2c84139dea057967cf698eb6838b2474152a1c93cb26c911", + "query": "instruct me to cook", + "target": "cooking_recipe", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 19, + "historical_id": "fastbrain_065002", + "source": "amazon_massive", + "query_hash": "0714f968bc7307fc2c84139dea057967cf698eb6838b2474152a1c93cb26c911" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 19, + "historical_id": "fastbrain_065002", + "source": "amazon_massive", + "query_hash": "0714f968bc7307fc2c84139dea057967cf698eb6838b2474152a1c93cb26c911" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065015", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8117dd19ead6ec0538e9ebd944e2f185", + "normalized_query_sha256": "e77dbdc835eb0a0965c9864070ac5501ec6958a5dcf5f5346ea7d0d47d92e0cd", + "query": "what is happening in the world today", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 180, + "historical_id": "semantic:validation:fastbrain_065015:present", + "source": "amazon_massive", + "query_hash": "e77dbdc835eb0a0965c9864070ac5501ec6958a5dcf5f5346ea7d0d47d92e0cd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 995, + "historical_id": "semantic:validation:fastbrain_065015:absent", + "source": "amazon_massive", + "query_hash": "e77dbdc835eb0a0965c9864070ac5501ec6958a5dcf5f5346ea7d0d47d92e0cd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065251", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f14fa8bf093e81e6108936b6c30a328b", + "normalized_query_sha256": "86279198e25a7145efb8b6dc50a5e99f6389b8efaed765725d67252ae4907d51", + "query": "u to find the safest approach that balances your concerns with your puppy's medical needs.\n\n**Available Consultation Slots (EST times):**\n1. Wednesday, Oct 25: 10:00 AM (2:00 PM UTC)\n2. Thursday, Oct 26: 6:30 AM (10:30 AM UTC) \n3. Friday, Oct 27: 12:00 PM (4:00 PM UTC)\n4. Saturday, Oct 28: 7:15 AM (11:15 AM UTC) - Weekend hours\n5. Monday, Oct 30: 5:00 AM (9:00 AM UTC)\n\n**To Schedule:**\nI need your **owner ID** and we'll also need to complete the **pre-consultation symptom checklist**:\n1. Current temperature (if you have a pet thermometer)\n2. Gum color (pink, pale, white, etc.)\n3. Last meal time\n4. Recent vomiting/diarrhea episodes\n\n**Important:** For urgent triage levels 1-2, consultations must occur within 4 hours. Based on what you've shared, this seems like a routine (level 3-4) consultation.\n\nCould you please provide your owner ID and let me know which time slot works best for you? Also, if you can provide the symptom checklist information, I can help schedule the consultation for you.\nuser: Here’s the information you need: \n- **Owner ID**: VET-OWNER-3492 \n- **Preferred time slot**: Wednesday, Oct 25 at 10:00 AM (EST) \n- **Symptom checklist**: \n - Temperature: Don’t have a pet thermometer, but puppy seems normal \n - Gum color: Pink, no discoloration \n - Last meal: 3 hours ago (ate puppy food and water fine) \n - Vomiting/diarrhea: None reported \n\nI’ll be sure to explain my concerns about the clinic visit during the consultation. Can you confirm the appointment?", + "target": "schedule_consultation", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 252, + "historical_id": "fastbrain_065251", + "source": "nemotron_agentic", + "query_hash": "86279198e25a7145efb8b6dc50a5e99f6389b8efaed765725d67252ae4907d51" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 252, + "historical_id": "fastbrain_065251", + "source": "nemotron_agentic", + "query_hash": "86279198e25a7145efb8b6dc50a5e99f6389b8efaed765725d67252ae4907d51" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065257", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5c786c9654d72224835ed5e6cc0a9c97", + "normalized_query_sha256": "d62ce7885e5ba0f646c93d3eebd1b22eb6293595c49a8a18cdfc30bbedb2da5a", + "query": "I will check it out. I would need a house to rent when I get there. I want one with a rating around 3.4.", + "target": "SearchHouse", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 128, + "historical_id": "fastbrain_065257", + "source": "sgd", + "query_hash": "d62ce7885e5ba0f646c93d3eebd1b22eb6293595c49a8a18cdfc30bbedb2da5a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 128, + "historical_id": "fastbrain_065257", + "source": "sgd", + "query_hash": "d62ce7885e5ba0f646c93d3eebd1b22eb6293595c49a8a18cdfc30bbedb2da5a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065258", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6c353981e2f600e6852aac2fc084a415", + "normalized_query_sha256": "ab8bc35068ecc77db58616ea1bb88b1d4e35ddc127863dcb234f251c33a8dedf", + "query": "Okay, here they are: $50, $30, $20, $100, $75.", + "target": "track_expenses", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 670, + "historical_id": "fastbrain_065258", + "source": "glaive_v2", + "query_hash": "ab8bc35068ecc77db58616ea1bb88b1d4e35ddc127863dcb234f251c33a8dedf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 670, + "historical_id": "fastbrain_065258", + "source": "glaive_v2", + "query_hash": "ab8bc35068ecc77db58616ea1bb88b1d4e35ddc127863dcb234f251c33a8dedf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065336", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1296dd1713c9a7157c56808ff8668bc7", + "normalized_query_sha256": "86a5a36c08a9e7ea0d36387134f48efbb176df941138d0ca12af9cd602151594", + "query": "Hi, I need to calculate the speed. I traveled a distance of 100 miles in 2 hours. Can you help me with that?", + "target": "calculate_speed", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 324, + "historical_id": "fastbrain_065336", + "source": "glaive_v2", + "query_hash": "86a5a36c08a9e7ea0d36387134f48efbb176df941138d0ca12af9cd602151594" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 324, + "historical_id": "fastbrain_065336", + "source": "glaive_v2", + "query_hash": "86a5a36c08a9e7ea0d36387134f48efbb176df941138d0ca12af9cd602151594" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065410", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3ad2a41c51a1bd5f29d0f2a0201619a7", + "normalized_query_sha256": "de4364ecb6eb34641bdf2afb355ec39cd0ff7a06d9bcf88a346fb0e27705037f", + "query": "where is the south pole", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 608, + "historical_id": "fastbrain_065410", + "source": "amazon_massive", + "query_hash": "de4364ecb6eb34641bdf2afb355ec39cd0ff7a06d9bcf88a346fb0e27705037f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 608, + "historical_id": "fastbrain_065410", + "source": "amazon_massive", + "query_hash": "de4364ecb6eb34641bdf2afb355ec39cd0ff7a06d9bcf88a346fb0e27705037f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065432", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:23583d46ab1e296557c46a3d234a4160", + "normalized_query_sha256": "53e20a9c303b7f6c597017834ffe60a693849a5c3fa18e80b7e0993aef231e29", + "query": "ry/certification rules \n\n---\n\n### **Escalation Protocol** \nTransfer to human agent for: \n1. Custom farm visit requests \n2. Bulk orders (>20 boxes) \n3. Repeated delivery failures \n4. Allergic reaction reports \nTransfer process: \n1. Use `transfer_to_human_agents` tool \n2. Send: \"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE HOLD ON.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm reaching out about my subscription renewal. My subscription ID is FH-6069, and I'm located in ZIP code 98101 (renewal date: February 25, 2025). I wanted to confirm if there are any loyalty discounts or anniversary promotions available for long-term subscribers like myself?\nassistant: Hello! I'd be happy to help you with your subscription renewal and check for any available promotions. First, I need to verify your identity to access your subscription details.\n\nCould you please provide your registered email address or phone number along with your ZIP code 98101? This is required for authentication before I can look up your subscription information.\nuser: My registered email is sarah.jones@example.com, and my ZIP code is 98101. Could you confirm if there are any loyalty discounts or anniversary promotions available for my subscription (FH-6069) that renews on February 25, 2025? I’ve been a subscriber for several years and was hoping there might be a special offer.\nassistant: \ntool: \n\n{\"access_token\": \"abc123xyz\", \"auth_status\": \"verified\", \"user_id\": \"U-12345\"}", + "target": "get_user_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 420, + "historical_id": "fastbrain_065432", + "source": "nemotron_agentic", + "query_hash": "53e20a9c303b7f6c597017834ffe60a693849a5c3fa18e80b7e0993aef231e29" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 420, + "historical_id": "fastbrain_065432", + "source": "nemotron_agentic", + "query_hash": "53e20a9c303b7f6c597017834ffe60a693849a5c3fa18e80b7e0993aef231e29" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065496", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0c0a4d8d1321c07fa9acc2f42f6c21bc", + "normalized_query_sha256": "51213f1cd1b4efd387ae8a9a31f0c8c315bfc8f36eaf5a432f45fc70daa46c72", + "query": "urity purposes.\n\n**Please provide:**\n1. Your Customer ID\n2. Your 6-digit PIN\n\nIf you don't have your PIN available, I can use secondary authentication with your registered phone number or email address plus a security question answer.\n\nOnce authenticated, I'll need the diagnostic logs from your system for the false alarm incident, as these are required for all false alarm reports.\nuser: I don't have my Customer ID or PIN handy, so could we use the secondary authentication method with my registered email? Also, the diagnostic logs were automatically captured by the system within 30 minutes of the incident - I can share them if needed.\nassistant: I understand you'd like to use email-based secondary authentication. However, I still need your **Customer ID** to proceed with the authentication process.\n\n**Could you please provide:**\n1. Your Customer ID (you can find this on your billing statements or account dashboard)\n2. Your registered email address\n3. The answer to your security question\n\nOnce I have your Customer ID, I can authenticate you using the email security question method. After authentication, I'll help you submit the false alarm report with the diagnostic logs.\nuser: My Customer ID is CUST-789012, my registered email is sarah.johnson@example.com, and the answer to my security question (Mother's maiden name) is \"Anderson\". Please proceed with authentication. Once verified, I can immediately share the diagnostic logs from the SYS4455 system for the 10/29/2023 incident.", + "target": "authenticate_customer", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 507, + "historical_id": "fastbrain_065496", + "source": "nemotron_agentic", + "query_hash": "51213f1cd1b4efd387ae8a9a31f0c8c315bfc8f36eaf5a432f45fc70daa46c72" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 507, + "historical_id": "fastbrain_065496", + "source": "nemotron_agentic", + "query_hash": "51213f1cd1b4efd387ae8a9a31f0c8c315bfc8f36eaf5a432f45fc70daa46c72" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065508", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ed29d1f564f253a7b4092fa1b0cdbe0e", + "normalized_query_sha256": "eab762a03fd979a04cc4706e6536d382bc89d2d1356afcd054a16b2235ecd471", + "query": "finance", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 42, + "historical_id": "semantic:validation:fastbrain_065508:present", + "source": "amazon_massive", + "query_hash": "eab762a03fd979a04cc4706e6536d382bc89d2d1356afcd054a16b2235ecd471" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 306, + "historical_id": "semantic:validation:fastbrain_065508:absent", + "source": "amazon_massive", + "query_hash": "eab762a03fd979a04cc4706e6536d382bc89d2d1356afcd054a16b2235ecd471" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065512", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1b47c8f073a1db0f11e8e11bb93ec32a", + "normalized_query_sha256": "58b75ce828f2162317f5fb7680df85f8d260071d4cddd71bf477bc5aca1ebdd0", + "query": "Rename \"file.txt\" in directories \"v_1\", \"v_2\", and \"v_3\" each to \"v_1.txt\", \"v_2.txt\", and \"v_3.txt\" respectively and print the conversion", + "target": "rename", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 278, + "historical_id": "fastbrain_065512", + "source": "nl2bash", + "query_hash": "58b75ce828f2162317f5fb7680df85f8d260071d4cddd71bf477bc5aca1ebdd0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 278, + "historical_id": "fastbrain_065512", + "source": "nl2bash", + "query_hash": "58b75ce828f2162317f5fb7680df85f8d260071d4cddd71bf477bc5aca1ebdd0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065695", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2d82d499e607e09aa0ec2c4aa58aaa20", + "normalized_query_sha256": "c006b128688474484681e38154ef3d3a32e63d3aeec1b8ac2c3b211b43a4dd17", + "query": "delete my calendar", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 547, + "historical_id": "fastbrain_065695", + "source": "amazon_massive", + "query_hash": "c006b128688474484681e38154ef3d3a32e63d3aeec1b8ac2c3b211b43a4dd17" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 547, + "historical_id": "fastbrain_065695", + "source": "amazon_massive", + "query_hash": "c006b128688474484681e38154ef3d3a32e63d3aeec1b8ac2c3b211b43a4dd17" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065794", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:96a70f1b70d7a2045702597531f25f74", + "normalized_query_sha256": "c0f47fac93ee5c9ec693f2e4d30e15e30da4d9de367ba5e370e50af6946c04e5", + "query": "That's wonderful. I'd like to book tickets to the activity.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 477, + "historical_id": "fastbrain_065794", + "source": "sgd", + "query_hash": "c0f47fac93ee5c9ec693f2e4d30e15e30da4d9de367ba5e370e50af6946c04e5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 477, + "historical_id": "fastbrain_065794", + "source": "sgd", + "query_hash": "c0f47fac93ee5c9ec693f2e4d30e15e30da4d9de367ba5e370e50af6946c04e5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_065931", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:20911ea4bcdfeabe417a5b27541a3a35", + "normalized_query_sha256": "df82d8c449038e48fdebdc7332438575696dca304eba5253a9c1957aaba51c9d", + "query": "e warranty and to request a replacement. The purchase date was September 10, 2025. Can you let me know what steps I need to take?\nassistant: Hello! I'm sorry to hear about the scratched lenses on your glasses. I'd be happy to help you check your options for replacement.\n\nFirst, I need to verify your identity to access your order details and assist you further. According to our policy, I can authenticate you using one of these methods:\n\n**Option 1:** Customer ID and Date of Birth (YYYY-MM-DD)\n**Option 2:** Full name, Phone number, and ZIP code\n\nCould you please provide one of these sets of information? Once verified, I can look up your specific order from September 10, 2025, and explain what options are available based on our warranty and return policies.\n\nFor your reference, our standard policy states:\n- Eyeglasses have a 30-day return/exchange period from delivery date\n- Scratched lenses invalidate standard returns\n- Accidental damage may have different warranty coverage options\n\nPlease provide your authentication details, and I'll check your specific situation right away.\nuser: Thank you for your help. I’ll go with Option 2: My full name is Sarah Mitchell, my phone number is (555) 123-4567, and my ZIP code is 90210. Could you verify my order and confirm if the accidental drop qualifies for a replacement under the warranty? I’d appreciate details on the next steps if coverage applies.\nassistant: \ntool: \n\n```json\n{\n \"authenticated\": true,\n \"customer_id\": \"CUST-789456\"\n}\n```", + "target": "get_customer_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 584, + "historical_id": "fastbrain_065931", + "source": "nemotron_agentic", + "query_hash": "df82d8c449038e48fdebdc7332438575696dca304eba5253a9c1957aaba51c9d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 584, + "historical_id": "fastbrain_065931", + "source": "nemotron_agentic", + "query_hash": "df82d8c449038e48fdebdc7332438575696dca304eba5253a9c1957aaba51c9d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066131", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b55794c93b5e09d3e204182ace1711ef", + "normalized_query_sha256": "040154b0b49a0d690a2dc8439d6cfd6533aa283dabdb5d7daf5a4bf54d40b31a", + "query": "Thanks, are there any attractions near that area?", + "target": "FindAttractions", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 609, + "historical_id": "fastbrain_066131", + "source": "sgd", + "query_hash": "040154b0b49a0d690a2dc8439d6cfd6533aa283dabdb5d7daf5a4bf54d40b31a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 609, + "historical_id": "fastbrain_066131", + "source": "sgd", + "query_hash": "040154b0b49a0d690a2dc8439d6cfd6533aa283dabdb5d7daf5a4bf54d40b31a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066210", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0e46ee0170a09c0a5f242e3311da80c6", + "normalized_query_sha256": "d681d694d0e6802fea2b9f262d2f23c08fe1d6c2dcb2ef5679ccda0c5b3cbd89", + "query": "I need somewhere to take a friend out for dinner. I would prefer if it was To-go", + "target": "FindRestaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 491, + "historical_id": "fastbrain_066210", + "source": "sgd", + "query_hash": "d681d694d0e6802fea2b9f262d2f23c08fe1d6c2dcb2ef5679ccda0c5b3cbd89" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 491, + "historical_id": "fastbrain_066210", + "source": "sgd", + "query_hash": "d681d694d0e6802fea2b9f262d2f23c08fe1d6c2dcb2ef5679ccda0c5b3cbd89" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066251", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:77baba928aae81f70d1a164250b3f27f", + "normalized_query_sha256": "479056543bde244be5ebb0090ca7db5d1c1af7c847f25659886e4e81c6c018c9", + "query": "e you received to verify your identity before I can help update your account details.\nuser: I received the verification code. It’s 123456. Can you confirm this and proceed with updating my primary location to Northside?\nassistant: \ntool: \n\n{\"success\": true, \"user_id\": \"user_12345\"}\nassistant: \ntool: \n\n{\"membership_tier\": \"Platinum\", \"payment_methods\": [\"Credit Card ending in 4242\"], \"vehicle_count\": 2}\nassistant: Your identity has been verified! I can see you have a Platinum membership with 2 registered vehicles.\n\nRegarding your request to update your primary service location, our system doesn't store a default location as part of your profile. You can select your preferred location each time you book an appointment. The Northside location is available for bookings, and you can choose it when scheduling your next wash.\n\nIf you need to update your billing address or other account details that aren't related to service bookings, I'll need to transfer you to a human agent who can assist with those changes.\n\nWould you like to book a wash at the Northside location, or do you need assistance with other account updates?\nuser: I understand that the system doesn’t store a default location, but I’d still like to ensure my account reflects Northside as my primary service area for future reference. Can you confirm if this requires updating my billing address or if there’s another way to set it as my preferred location? If a human agent is needed for this, please go ahead and transfer me.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 355, + "historical_id": "fastbrain_066251", + "source": "nemotron_agentic", + "query_hash": "479056543bde244be5ebb0090ca7db5d1c1af7c847f25659886e4e81c6c018c9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 355, + "historical_id": "fastbrain_066251", + "source": "nemotron_agentic", + "query_hash": "479056543bde244be5ebb0090ca7db5d1c1af7c847f25659886e4e81c6c018c9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066281", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:438bbeb7916001c8525b843cd21bd34e", + "normalized_query_sha256": "05bf8aecf46a454a34cee8ba23df6ffe0a3d02dbcbe69e0b93134072d0fcb36b", + "query": "Find all files in the current directory and its sub-directories that have been modified sometime in the last 24 hours.", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 792, + "historical_id": "fastbrain_066281", + "source": "nl2bash", + "query_hash": "05bf8aecf46a454a34cee8ba23df6ffe0a3d02dbcbe69e0b93134072d0fcb36b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 792, + "historical_id": "fastbrain_066281", + "source": "nl2bash", + "query_hash": "05bf8aecf46a454a34cee8ba23df6ffe0a3d02dbcbe69e0b93134072d0fcb36b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066315", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e14e5a07ab90285fb42b1ee8dda76469", + "normalized_query_sha256": "b3a1d3f403beb36e5b3360b855cdfc483cd43efff51e4d493bcf6c7ff4e11c37", + "query": "s: Downgrade to Free tier immediately. \n- Proactively ask after payments: *\"Confirm the $24.99 charge appears correctly?\"* \n\n**Tutor Matching** \n- Tutors require native fluency certification and ≥4.8/5 rating. \n- Restrict same-language tutors for native speakers. \n\n---\n\n### Identity Verification \nFor sensitive actions: \n1. Ask for email/phone \n2. Send authentication code via tool \n3. Request user-provided code \n4. Verify code via tool before proceeding \n\n---\n\n### Agent Limitations \n- **Never** answer questions or perform actions outside defined tool capabilities. \n- **Never** assume verification occurred or actions completed without explicit tool confirmation. \n- **Never** override prerequisites, tier limits, or business rules. \n- **Never** disclose internal systems or policies. \n\n---\n\n### Escalation Protocol \nTransfer to human agents with mandatory message: \n\"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE HOLD ON.\" \n\n**Transfer triggers**: \n- Language proficiency disputes \n- Payment disputes >$100 \n- Three failed booking changes \n- Technical issues preventing course access\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm trying to reschedule my language lesson again. This is my third attempt, and I've had issues with the previous two. My session ID is JPN-TUT-221, and there are only 15 hours left. Could you please help me adjust the timing or confirm if there’s a protocol for escalating this after three failed attempts?", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 164, + "historical_id": "fastbrain_066315", + "source": "nemotron_agentic", + "query_hash": "b3a1d3f403beb36e5b3360b855cdfc483cd43efff51e4d493bcf6c7ff4e11c37" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 164, + "historical_id": "fastbrain_066315", + "source": "nemotron_agentic", + "query_hash": "b3a1d3f403beb36e5b3360b855cdfc483cd43efff51e4d493bcf6c7ff4e11c37" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066319", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9a7b23175b5a2d84ec4ce14fca787534", + "normalized_query_sha256": "2a0805008cb801753fa1b019d5b20bf8e796d19fd3997a55c2782db67e556b45", + "query": "Hi, I need to know my BMI. I weigh 70 kg and my height is 1.75 meters.", + "target": "calculate_bmi", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 832, + "historical_id": "fastbrain_066319", + "source": "glaive_v2", + "query_hash": "2a0805008cb801753fa1b019d5b20bf8e796d19fd3997a55c2782db67e556b45" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 832, + "historical_id": "fastbrain_066319", + "source": "glaive_v2", + "query_hash": "2a0805008cb801753fa1b019d5b20bf8e796d19fd3997a55c2782db67e556b45" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066500", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dd0168b7cf99e4d2677444d628c28133", + "normalized_query_sha256": "209e55789a1e25cdbd95d96c1d7141b40291b7a34bb1e92a868ffde30846dbc8", + "query": "Sounds good. Reserve the rental car for me.", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 559, + "historical_id": "fastbrain_066500", + "source": "sgd", + "query_hash": "209e55789a1e25cdbd95d96c1d7141b40291b7a34bb1e92a868ffde30846dbc8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 559, + "historical_id": "fastbrain_066500", + "source": "sgd", + "query_hash": "209e55789a1e25cdbd95d96c1d7141b40291b7a34bb1e92a868ffde30846dbc8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066510", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b01388f89f4ca0d19844ac5ff0a7423d", + "normalized_query_sha256": "49bc5edcec719c62539d59c0611eb4f2ebd92d9513e26b8238f72dd9bc4384a9", + "query": "can you change the light colors into dark in the house", + "target": "iot_hue_lightchange", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 260, + "historical_id": "semantic:validation:fastbrain_066510:present", + "source": "amazon_massive", + "query_hash": "49bc5edcec719c62539d59c0611eb4f2ebd92d9513e26b8238f72dd9bc4384a9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 653, + "historical_id": "semantic:validation:fastbrain_066510:absent", + "source": "amazon_massive", + "query_hash": "49bc5edcec719c62539d59c0611eb4f2ebd92d9513e26b8238f72dd9bc4384a9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066610", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8534b70868aacd5810fe45b53e9289d7", + "normalized_query_sha256": "2a482f9052c840cdf36ab06ce768873f2995443025bdcaac798cb288db5f2473", + "query": "ginal buyer liable for resale deficits \n\n---\n\n#### **Escalation Protocol** \nTransfer to human agent for: \n- Provenance verification \n- Insurance claims \n- Legal inquiries \n- Biometric ID checks (>$10k bids) \n\n**Transfer Message**: \n*\"Transferring you to a specialist. Please hold.\"* \n\n---\n\n#### **Agent Limitations** \n**Core Rules**: \n- **Do not answer questions or perform actions outside defined tool capabilities.** \n- **Never assume information was verified or actions completed without explicit tool confirmation.** \n\n**Specific Boundaries**: \n- Payments: Require explicit success confirmation from tool \n- Bids: Assume failure until bid_acknowledged receipt \n- Documents: Treat as invalid without authentication_tool validation \n- ...[TRUNCATED]\nuser: \n\nHello, I'm a registered bidder with the ID REG-3434. I'm calling to inquire if it's possible to withdraw my $7,000 bid for the antique desk three days before the auction. I recently found a similar item elsewhere at a lower price and would like to rescind my current bid if allowed under your pre-auction policies.\nassistant: Hello! I'd be happy to help you with your bid inquiry. First, I need to verify your identity for security purposes.\n\nPlease provide your email or phone number for verification. Once you provide this, I'll send you a verification code to confirm your identity before proceeding with any bid-related actions.\nuser: My registered email is reg3434@example.com. Please send the verification code there.", + "target": "send_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 111, + "historical_id": "fastbrain_066610", + "source": "nemotron_agentic", + "query_hash": "2a482f9052c840cdf36ab06ce768873f2995443025bdcaac798cb288db5f2473" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 111, + "historical_id": "fastbrain_066610", + "source": "nemotron_agentic", + "query_hash": "2a482f9052c840cdf36ab06ce768873f2995443025bdcaac798cb288db5f2473" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066620", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bc0cf6591e73536026a38d45a33b6b54", + "normalized_query_sha256": "4d026a288a7182229e3f1da69356656cc16ea61d62ec4455a345f1721a6a5ec1", + "query": "please mute yourself", + "target": "audio_volume_mute", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 358, + "historical_id": "fastbrain_066620", + "source": "amazon_massive", + "query_hash": "4d026a288a7182229e3f1da69356656cc16ea61d62ec4455a345f1721a6a5ec1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 358, + "historical_id": "fastbrain_066620", + "source": "amazon_massive", + "query_hash": "4d026a288a7182229e3f1da69356656cc16ea61d62ec4455a345f1721a6a5ec1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066788", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cc1a2cfc61c432092335afe7c01a208a", + "normalized_query_sha256": "d4458fec725466705720bb3eec535c49286c65e4a03d6221c5624ecf95ec2c01", + "query": "turn my audio higher", + "target": "audio_volume_up", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 320, + "historical_id": "semantic:validation:fastbrain_066788:present", + "source": "amazon_massive", + "query_hash": "d4458fec725466705720bb3eec535c49286c65e4a03d6221c5624ecf95ec2c01" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 502, + "historical_id": "semantic:validation:fastbrain_066788:absent", + "source": "amazon_massive", + "query_hash": "d4458fec725466705720bb3eec535c49286c65e4a03d6221c5624ecf95ec2c01" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066852", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4914de41a9e22406f87080e4e9f233b6", + "normalized_query_sha256": "78b6be58e180de80fe429fa3d5d1e432e361a51e8637c3a62b33ddf63d9336d8", + "query": "what is the weather like outside it is cloudy", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 52, + "historical_id": "fastbrain_066852", + "source": "amazon_massive", + "query_hash": "78b6be58e180de80fe429fa3d5d1e432e361a51e8637c3a62b33ddf63d9336d8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 52, + "historical_id": "fastbrain_066852", + "source": "amazon_massive", + "query_hash": "78b6be58e180de80fe429fa3d5d1e432e361a51e8637c3a62b33ddf63d9336d8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066909", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1c1f21e90ff9950c9436dcee5827df3b", + "normalized_query_sha256": "7f740908d538b96d38c1753f714bae98ce0d96eb373515388abe5b4400f9887c", + "query": "recent headlines", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 104, + "historical_id": "fastbrain_066909", + "source": "amazon_massive", + "query_hash": "7f740908d538b96d38c1753f714bae98ce0d96eb373515388abe5b4400f9887c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 104, + "historical_id": "fastbrain_066909", + "source": "amazon_massive", + "query_hash": "7f740908d538b96d38c1753f714bae98ce0d96eb373515388abe5b4400f9887c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_066979", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:888dbc6f19872c779ea031154c939520", + "normalized_query_sha256": "a1873254c816ecb91c874b0f2a20320baee733dacfc83550d9640452365ba0f8", + "query": ". Once confirmed, I’d like to move quickly to discuss the Platinum-tier options for deposit transfers—specifically, whether the full deposit is creditable toward a rescheduled event or a future booking, and if there’s a deadline to decide. Additionally, could you clarify what venue relocation assistance entails? For example, would your team help coordinate an indoor alternative, or do we need to secure a new venue independently while seeking reimbursement? The 75% rain chance is high, so time is critical.\nassistant: \ntool: \n\n{\"success\": true}\nassistant: I've sent a verification code to [HR.Manager@companydomain.com]. Please check your email and provide the verification code you received so I can verify your identity and access your event details. Once verified, I can help you with the weather emergency protocols for your Platinum-tier event, including deposit transfer options and venue relocation assistance.\nuser: The verification code is [123456]. Please confirm the deposit transfer policy for Platinum-tier clients—specifically, is the full deposit creditable toward a rescheduled event or a new booking? Also, what is the deadline to make this decision? Regarding venue relocation assistance, does your team coordinate an indoor alternative (e.g., venue recommendations, contract negotiation), or are we responsible for securing a new venue independently with potential reimbursement? We need clarity to act swiftly given the weather forecast.\nassistant: \ntool: \n\n{\"verified\": true}", + "target": "lookup_customer_by_contact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 868, + "historical_id": "fastbrain_066979", + "source": "nemotron_agentic", + "query_hash": "a1873254c816ecb91c874b0f2a20320baee733dacfc83550d9640452365ba0f8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 868, + "historical_id": "fastbrain_066979", + "source": "nemotron_agentic", + "query_hash": "a1873254c816ecb91c874b0f2a20320baee733dacfc83550d9640452365ba0f8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067046", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5f717f6ea59c4925d654fbf6cc7e0230", + "normalized_query_sha256": "51802270da2724a42ad54ac9dd682328cacccb9d09cbdf6537b832f971b73035", + "query": "what time is alarm set for", + "target": "alarm_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 145, + "historical_id": "semantic:validation:fastbrain_067046:absent", + "source": "amazon_massive", + "query_hash": "51802270da2724a42ad54ac9dd682328cacccb9d09cbdf6537b832f971b73035" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 343, + "historical_id": "semantic:validation:fastbrain_067046:present", + "source": "amazon_massive", + "query_hash": "51802270da2724a42ad54ac9dd682328cacccb9d09cbdf6537b832f971b73035" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067134", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:264497bf13961a25680501d03d0f50e8", + "normalized_query_sha256": "6bc5bc58b38949075a277e0f3ab482d07ca02a11106cbe0fe3a45503a37a45a4", + "query": "s \n\n**Verification Protocol** \n- No assumption of verification without explicit tool confirmation \n- Treat quality claims as unverified until batch analysis completes \n- Validate wholesale account status before applying pricing \n\n**Action Confirmation** \n- Never assume order modifications succeeded without tool validation \n- Require explicit eligibility checks for all refunds \n\n---\n\n### Escalation Protocol \nTransfer to human agent for: \n1. Legal/compliance inquiries \n2. Wholesale orders >50 cases \n3. Potential quality recalls \n4. Liability waiver requests \n\nTransfer ...[TRUNCATED]\nuser: \n\nHello, I'm calling about my order O-2024-909 to Florida. There's a hurricane warning in the area, and I'm concerned about shipping safety. Could you help delay the shipment until the weather clears? Also, I want to confirm if expedited shipping requirements would still apply in this situation, or if safety concerns take priority?\nassistant: \ntool: \n\n{\"items\": [{\"sku\": \"HH-BLUE-24\", \"quantity\": 6}, {\"sku\": \"HH-IPA-24\", \"quantity\": 6}], \"status\": \"Processing\", \"ship_location\": \"Miami, FL 33101\", \"temperature_log\": [{\"timestamp\": \"2024-08-20T14:30:00Z\", \"temp\": \"72°F\"}, {\"timestamp\": \"2024-08-21T08:15:00Z\", \"temp\": \"74°F\"}]}\nassistant: \ntool: \n\n{\"product_type\": \"Core Beer\", \"abv\": 6.2, \"packaging\": \"Can\", \"availability\": \"In Stock\", \"price_tier\": 1}\nassistant: \ntool: \n\n{\"product_type\": \"Seasonal Release\", \"abv\": 7.5, \"packaging\": \"Can\", \"availability\": \"In Stock\", \"price_tier\": 2}", + "target": "check_shipping_eligibility", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 57, + "historical_id": "fastbrain_067134", + "source": "nemotron_agentic", + "query_hash": "6bc5bc58b38949075a277e0f3ab482d07ca02a11106cbe0fe3a45503a37a45a4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 57, + "historical_id": "fastbrain_067134", + "source": "nemotron_agentic", + "query_hash": "6bc5bc58b38949075a277e0f3ab482d07ca02a11106cbe0fe3a45503a37a45a4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067350", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9dd0280d9a11dac7a8e0f2b5e2a2598d", + "normalized_query_sha256": "9122e9a82823d3e11ccf47c4233cf418991792248f4f5c338c806650e0633f21", + "query": "iggers inspection \n- **Mandatory checks**: \n - Confirm water shutoff location \n - Verify no electrical hazards \n\n## Escalation Protocol \nTransfer to human agent for: \n- Insurance claim coordination \n- Structural damage reports \n- Commercial account inquiries \n- Repeated service failures (≥3 visits) \n\nInitiate with `transfer_to_human_agent`, then send: \n\"YOU ARE BEING TRANSFERRED TO A PLUMBING SPECIALIST. PLEASE DESCRIBE YOUR ISSUE.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling regarding my plumbing service appointment (Ticket #TICK-4455) scheduled for May 8, 2025 at 10:00 AM. It's now 11:30 AM and the technician hasn't arrived. Could you please tell me what the protocol is when a technician exceeds the 2-hour window?\nassistant: Hello! I understand you're concerned about your technician not arriving within the scheduled window. First, I need to verify your identity to access your appointment details.\n\nCould you please provide one of the following:\n1. Your Customer ID and service address, OR\n2. Your full name and registered phone number?\n\nOnce verified, I can check the status of your appointment and explain our protocol for situations where a technician exceeds the arrival window.\nuser: Sure, my name is Sarah Thompson and my registered phone number is (555) 123-4567. Can you now tell me what the protocol is when a technician exceeds the 2-hour window for my plumbing appointment (Ticket #TICK-4455, scheduled 2025-05-08 10AM)?", + "target": "lookup_customer_by_contact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 311, + "historical_id": "fastbrain_067350", + "source": "nemotron_agentic", + "query_hash": "9122e9a82823d3e11ccf47c4233cf418991792248f4f5c338c806650e0633f21" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 311, + "historical_id": "fastbrain_067350", + "source": "nemotron_agentic", + "query_hash": "9122e9a82823d3e11ccf47c4233cf418991792248f4f5c338c806650e0633f21" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067380", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a8142cb802d8a7af22932566c82f9c85", + "normalized_query_sha256": "151763985c4ce295b6090db2ed43fc14f0598d8ee6043d234a41bbb497242c34", + "query": "Hi, i am planning for a trip, i want to find a rental car for till the 3rd, could you find it for me?", + "target": "GetCarsAvailable", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1007, + "historical_id": "fastbrain_067380", + "source": "sgd", + "query_hash": "151763985c4ce295b6090db2ed43fc14f0598d8ee6043d234a41bbb497242c34" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1007, + "historical_id": "fastbrain_067380", + "source": "sgd", + "query_hash": "151763985c4ce295b6090db2ed43fc14f0598d8ee6043d234a41bbb497242c34" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067406", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2caa01d02849e81137c879d57a496ec7", + "normalized_query_sha256": "89cbdee4e3d327ae3b3ef783f549072ad9c8678e539a0c5ff32cea05dd8721f4", + "query": "I want to find some online movies. Can you find me a Romantic movie directed by Cameron Crowe?", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 574, + "historical_id": "fastbrain_067406", + "source": "sgd", + "query_hash": "89cbdee4e3d327ae3b3ef783f549072ad9c8678e539a0c5ff32cea05dd8721f4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 574, + "historical_id": "fastbrain_067406", + "source": "sgd", + "query_hash": "89cbdee4e3d327ae3b3ef783f549072ad9c8678e539a0c5ff32cea05dd8721f4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067438", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9da15757980d6b534cea3fd59f720a59", + "normalized_query_sha256": "b81029183bff83ccd7c75d6040e2063cb3b10d94f8ac948afbd1fcae3167c132", + "query": "delete my meeting for tomorrow", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 159, + "historical_id": "semantic:validation:fastbrain_067438:absent", + "source": "amazon_massive", + "query_hash": "b81029183bff83ccd7c75d6040e2063cb3b10d94f8ac948afbd1fcae3167c132" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 487, + "historical_id": "semantic:validation:fastbrain_067438:present", + "source": "amazon_massive", + "query_hash": "b81029183bff83ccd7c75d6040e2063cb3b10d94f8ac948afbd1fcae3167c132" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067517", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:85b1c97ff901b575ccba3df99df835a0", + "normalized_query_sha256": "a3cd034011d540d4ac2e2cecfff460fc3d7f2602e6faac416d78c67e998bee0c", + "query": "can you tell me what time is it in new york", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 236, + "historical_id": "semantic:validation:fastbrain_067517:present", + "source": "amazon_massive", + "query_hash": "a3cd034011d540d4ac2e2cecfff460fc3d7f2602e6faac416d78c67e998bee0c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 678, + "historical_id": "semantic:validation:fastbrain_067517:absent", + "source": "amazon_massive", + "query_hash": "a3cd034011d540d4ac2e2cecfff460fc3d7f2602e6faac416d78c67e998bee0c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067526", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1b4a8cf44ef0e56a3c68541fb146b6b0", + "normalized_query_sha256": "de93f90c403fcc73fcf0ce56b847f877e4e2cbb655c412159921a5c16f5de420", + "query": "start house cleanup", + "target": "iot_cleaning", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 356, + "historical_id": "semantic:validation:fastbrain_067526:absent", + "source": "amazon_massive", + "query_hash": "de93f90c403fcc73fcf0ce56b847f877e4e2cbb655c412159921a5c16f5de420" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 973, + "historical_id": "semantic:validation:fastbrain_067526:present", + "source": "amazon_massive", + "query_hash": "de93f90c403fcc73fcf0ce56b847f877e4e2cbb655c412159921a5c16f5de420" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067605", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1dd0f28b56a71d6aa468f95225a430e5", + "normalized_query_sha256": "dbf76f580a0b07894a9bbb55af8534360ef1be932c94ee1288c24192fed73cd0", + "query": "Find all directories in the /path/to/base/dir tree", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 635, + "historical_id": "fastbrain_067605", + "source": "nl2bash", + "query_hash": "dbf76f580a0b07894a9bbb55af8534360ef1be932c94ee1288c24192fed73cd0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 635, + "historical_id": "fastbrain_067605", + "source": "nl2bash", + "query_hash": "dbf76f580a0b07894a9bbb55af8534360ef1be932c94ee1288c24192fed73cd0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_067718", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9d6d8f365836e62cb4faaa8e5abfa973", + "normalized_query_sha256": "7e9f391cb86059ef05867c09b1219780357db084a90b091c303ac7a8fd06fd63", + "query": "Hi, I need to calculate my BMI. 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_068063", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0dc02b87e069cc9497b7fc0438db0bf0", + "normalized_query_sha256": "475fdf9c028c37910b4591e15758006a7522ab670aa4f0388930c3c4fcbab232", + "query": "I would like to buy buy tickets.", + "target": "BuyBusTicket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 619, + "historical_id": "fastbrain_068063", + "source": "sgd", + "query_hash": "475fdf9c028c37910b4591e15758006a7522ab670aa4f0388930c3c4fcbab232" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 619, + "historical_id": "fastbrain_068063", + "source": "sgd", + "query_hash": "475fdf9c028c37910b4591e15758006a7522ab670aa4f0388930c3c4fcbab232" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_068182", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7edd5c9bf4381edf6e63c9cfef9132d0", + "normalized_query_sha256": "1dd46b20c64b422117303c53f0e9b48f3421b020aaae762279e8b4d73d709b52", + "query": "i want to find an apartment that accepts pets", + "target": "FindApartment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 833, + "historical_id": "fastbrain_068182", + "source": "sgd", + "query_hash": "1dd46b20c64b422117303c53f0e9b48f3421b020aaae762279e8b4d73d709b52" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 833, + "historical_id": "fastbrain_068182", + "source": "sgd", + "query_hash": "1dd46b20c64b422117303c53f0e9b48f3421b020aaae762279e8b4d73d709b52" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_068189", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:20a932f27d385cd8d3aff1df6332fd29", + "normalized_query_sha256": "6f3a42642c7a5b83676809967b9d7d8f5a66c1d158141d584fd70331071de386", + "query": "Looks nice, i want to go as soon as posssible. 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Get the curre", + "target": "get_current_weather", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 782, + "historical_id": "fastbrain_068361", + "source": "hermes_v1", + "query_hash": "8de70b2069f982539ed7e548eaabb24f1466db0946df07ed1d23481aba152231" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 782, + "historical_id": "fastbrain_068361", + "source": "hermes_v1", + "query_hash": "8de70b2069f982539ed7e548eaabb24f1466db0946df07ed1d23481aba152231" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_068517", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:915a2063c6ff4672856d2b5dbe1ab8bc", + "normalized_query_sha256": "35ac98f8fd0113ec40f2ddb5333664ad34be2ded053f41910842db1fe6b539f4", + "query": "i need some rap", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 436, + "historical_id": "semantic:validation:fastbrain_068517:absent", + "source": "amazon_massive", + "query_hash": "35ac98f8fd0113ec40f2ddb5333664ad34be2ded053f41910842db1fe6b539f4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 560, + "historical_id": "semantic:validation:fastbrain_068517:present", + "source": "amazon_massive", + "query_hash": "35ac98f8fd0113ec40f2ddb5333664ad34be2ded053f41910842db1fe6b539f4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_068643", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6a9816ffd59959aa1391e3fa46bc6768", + "normalized_query_sha256": "482128c89b81058a75f839fcbfa99b8f65f8eb7de8e332e5cbbf2d4014d190f6", + "query": "Help me find one way flights? 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Could you adjust the motion sensor sensitivity to exclude animals under 20 lbs? My garden is a wildlife haven, but these small critters shouldn't trigger alarms!\nassistant: Hello! I'd be happy to help you adjust the motion sensor sensitivity for your MS-777 device to reduce false alarms from wildlife. First, I need to verify your identity as required for device configuration changes.\n\n**Identity Verification Required**\n\nPlease provide your registered email or phone number along with your user ID so I can send you an authentication code.\nuser: Sure, my registered email is \"margaret.green.thumb@naturelovers.com\" and my user ID is \"GardenGuardian2023\". Please send the authentication code there.\nassistant: \ntool: \n\n{\"sent\": true, \"contact_method_used\": \"email\"}\nassistant: I've sent an authentication code to your registered email at margaret.green.thumb@naturelovers.com. \n\nPlease enter the code you received to verify your identity.\nuser: The code I received is 123456. I’ve entered it. Can you now adjust the sensitivity settings for device MS-777 to exclude motion under 20 lbs? The deer and raccoons are still foraging in my garden, and I’d love to avoid these alerts while keeping the system secure for actual threats.\nassistant: \ntool: \n\n{\"verified\": true, \"verification_method\": \"OTP\"}\nassistant: \ntool: \n\n{\"type\": \"MotionSensor\", \"location\": \"Garden area\", \"status\": \"Online\", \"battery_level\": 82, \"privacy_zones\": [], \"audio_recording_enabled\": false}", + "target": "get_system_id", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 237, + "historical_id": "fastbrain_068749", + "source": "nemotron_agentic", + "query_hash": "81401c0475f12e321f9b4b4ce00d61711a1392bf6ff9e9d08b2bb051ce4abab2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 237, + "historical_id": "fastbrain_068749", + "source": "nemotron_agentic", + "query_hash": "81401c0475f12e321f9b4b4ce00d61711a1392bf6ff9e9d08b2bb051ce4abab2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_068832", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5f6bb6bb318f38da3ffc89ea7968a863", + "normalized_query_sha256": "5cf46cd89515601e0d2a3cc601e9b853074d1908a938141a9ba650a02614079a", + "query": "erase", + "target": "lists_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 626, + "historical_id": "semantic:validation:fastbrain_068832:absent", + "source": "amazon_massive", + "query_hash": "5cf46cd89515601e0d2a3cc601e9b853074d1908a938141a9ba650a02614079a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 832, + "historical_id": "semantic:validation:fastbrain_068832:present", + "source": "amazon_massive", + "query_hash": "5cf46cd89515601e0d2a3cc601e9b853074d1908a938141a9ba650a02614079a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_068845", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2b79d1bd695a452b02e5653e41c893d2", + "normalized_query_sha256": "edf17e1098ac74c558e59b1c0ff0b17a8cf8bc79d4c546e1cb6cc6e5dfac54d5", + "query": "Sure, the recipient's email is john.doe@example.com. The subject is \"Project Update\" and the content is \"Hi John, The project is on track and we are meeting the deadlines as planned. Regards, User\".", + "target": "send_email", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 767, + "historical_id": "fastbrain_068845", + "source": "glaive_v2", + "query_hash": "edf17e1098ac74c558e59b1c0ff0b17a8cf8bc79d4c546e1cb6cc6e5dfac54d5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 767, + "historical_id": "fastbrain_068845", + "source": "glaive_v2", + "query_hash": "edf17e1098ac74c558e59b1c0ff0b17a8cf8bc79d4c546e1cb6cc6e5dfac54d5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_068939", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9c34b1c382caaf86113555d83fbb6a52", + "normalized_query_sha256": "e4cd861cb6a670e3dca3135a82e6b6d042ded5fb6b69a49cbc515dcc6a6a9e4e", + "query": "i would like some coffee now", + "target": "iot_coffee", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 885, + "historical_id": "semantic:validation:fastbrain_068939:present", + "source": "amazon_massive", + "query_hash": "e4cd861cb6a670e3dca3135a82e6b6d042ded5fb6b69a49cbc515dcc6a6a9e4e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 964, + "historical_id": "semantic:validation:fastbrain_068939:absent", + "source": "amazon_massive", + "query_hash": "e4cd861cb6a670e3dca3135a82e6b6d042ded5fb6b69a49cbc515dcc6a6a9e4e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069011", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1c4d8bc0b87e401d0b19e1f1d596fb9a", + "normalized_query_sha256": "babc13e8859d5d0cb47e2fe422c9615f0037d14470d27d0d9c53838c53bc82f1", + "query": "regarding your career coaching needs?\nuser: Thank you for confirming the session with Dr. Carter. Regarding the package expiration, I would like to **extend my current package** to ensure the session remains valid. Could you please process the extension and confirm the updated expiration date? Additionally, I’d appreciate it if you could verify that the extension covers the April 5 session. No other immediate requests at this time—thank you for your assistance.\nassistant: I can certainly help you extend your current package. To ensure your April 5 session remains valid, you'll need at least a 21-day extension (from March 15 to April 5). \n\nThe maximum extension allowed is **30 days**, which would give you coverage until approximately April 14, 2024. This would provide a buffer beyond your scheduled session.\n\n**Please confirm:** Would you like me to proceed with a **30-day extension** of your current PKG-EXEC-2023 package?\n\n**Important Note:** According to our policy, package extensions may involve credit fee calculations. The exact fee would be determined during the extension process.\n\nOnce confirmed, I'll process the extension and provide you with the updated expiration date.\nuser: Please proceed with the **30-day extension** for my PKG-EXEC-2023 package. I understand there may be a fee associated with this extension, but the buffer period until April 14 is acceptable to ensure full coverage for my session. Could you confirm the updated package expiration date once processed?", + "target": "extend_package", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 143, + "historical_id": "fastbrain_069011", + "source": "nemotron_agentic", + "query_hash": "babc13e8859d5d0cb47e2fe422c9615f0037d14470d27d0d9c53838c53bc82f1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 143, + "historical_id": "fastbrain_069011", + "source": "nemotron_agentic", + "query_hash": "babc13e8859d5d0cb47e2fe422c9615f0037d14470d27d0d9c53838c53bc82f1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069042", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:de31f214d93c0ef637ade9c4825e41e5", + "normalized_query_sha256": "e6e3358cd10af238aff56fbe02c84dd3402f0e51aafb9652f3a8e0928e8c6a8d", + "query": "please create a new contact information about jo", + "target": "email_addcontact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 335, + "historical_id": "fastbrain_069042", + "source": "amazon_massive", + "query_hash": "e6e3358cd10af238aff56fbe02c84dd3402f0e51aafb9652f3a8e0928e8c6a8d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 335, + "historical_id": "fastbrain_069042", + "source": "amazon_massive", + "query_hash": "e6e3358cd10af238aff56fbe02c84dd3402f0e51aafb9652f3a8e0928e8c6a8d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069088", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e49b5f9351159eb31330bcd00c9c858a", + "normalized_query_sha256": "adbcb274894b05a81834d4f74f49a326d7dc0a6571fc5bbfd5353d8422925973", + "query": "sitive data deletion requests \n4. Billing disputes exceeding $5,000 \n*Use transfer message: \"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT\"*\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling to challenge an invoice charge. My invoice number is INV-2023-6655 for $3,200. I need to verify this charge through your billing system and confirm my account details via email before we discuss resolution options. Can you assist with that?\nassistant: Hello! I'd be happy to help you verify and challenge this invoice charge. First, I need to authenticate your identity for security purposes before accessing any billing information.\n\n**Please provide your registered email or phone number** so I can send you a verification code. Once you receive the code, share it with me, and then I can proceed with looking up your invoice details.\n\nWhat is the email address or phone number associated with your TalentBridge account?\nuser: My registered email is john.doe@email.com. Please send the verification code there. I'll wait for it before proceeding.\nassistant: \ntool: \n\n{\"code_sent\": true}\nassistant: I've sent a verification code to john.doe@email.com. Please check your email (including spam folder) and share the code with me here. Once you provide the code, I can verify your identity and then help you with the invoice verification.\nuser: I've received the verification code. It's 4729. Please proceed with authenticating my account so we can review the invoice details.", + "target": "verify_authentication_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 294, + "historical_id": "fastbrain_069088", + "source": "nemotron_agentic", + "query_hash": "adbcb274894b05a81834d4f74f49a326d7dc0a6571fc5bbfd5353d8422925973" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 294, + "historical_id": "fastbrain_069088", + "source": "nemotron_agentic", + "query_hash": "adbcb274894b05a81834d4f74f49a326d7dc0a6571fc5bbfd5353d8422925973" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069181", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4903914d2375f9e03c9ff4d68f3c7594", + "normalized_query_sha256": "74e5b5f49b8b6d95184881796d2c2e8dd0c2cb90f2bcf27f6400d24602236613", + "query": "book me on a train leaving penn station tomorrow traveling to any city more than two hundred miles away", + "target": "transport_ticket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 449, + "historical_id": "semantic:validation:fastbrain_069181:present", + "source": "amazon_massive", + "query_hash": "74e5b5f49b8b6d95184881796d2c2e8dd0c2cb90f2bcf27f6400d24602236613" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 802, + "historical_id": "semantic:validation:fastbrain_069181:absent", + "source": "amazon_massive", + "query_hash": "74e5b5f49b8b6d95184881796d2c2e8dd0c2cb90f2bcf27f6400d24602236613" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069193", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:70dc6fb801d7c4fb86597834b421d768", + "normalized_query_sha256": "feee7f742f5cd188b205cfed8ea983dba6e3e99f061caf58fab27091a2e61166", + "query": "set reminder for tomorrow morning about my meeting", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 250, + "historical_id": "semantic:validation:fastbrain_069193:absent", + "source": "amazon_massive", + "query_hash": "feee7f742f5cd188b205cfed8ea983dba6e3e99f061caf58fab27091a2e61166" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 684, + "historical_id": "semantic:validation:fastbrain_069193:present", + "source": "amazon_massive", + "query_hash": "feee7f742f5cd188b205cfed8ea983dba6e3e99f061caf58fab27091a2e61166" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069306", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b777af80f07d1d11428c3da02a7c8adb", + "normalized_query_sha256": "d64e27f4bab79a7e03af0f18fc26aa71de6b208bbf9b34f100db406661b66332", + "query": "what are the lists i have", + "target": "lists_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 810, + "historical_id": "semantic:validation:fastbrain_069306:present", + "source": "amazon_massive", + "query_hash": "d64e27f4bab79a7e03af0f18fc26aa71de6b208bbf9b34f100db406661b66332" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 872, + "historical_id": "semantic:validation:fastbrain_069306:absent", + "source": "amazon_massive", + "query_hash": "d64e27f4bab79a7e03af0f18fc26aa71de6b208bbf9b34f100db406661b66332" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069563", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:91ff2aa3bf2c08fadf9e9fec01493702", + "normalized_query_sha256": "a3f2bc0c1cfeb990903f922ab9cb2d3962bd0b072515bd72da50e0dd8835dc3c", + "query": "please play the radio", + "target": "play_radio", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 462, + "historical_id": "semantic:validation:fastbrain_069563:absent", + "source": "amazon_massive", + "query_hash": "a3f2bc0c1cfeb990903f922ab9cb2d3962bd0b072515bd72da50e0dd8835dc3c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 540, + "historical_id": "semantic:validation:fastbrain_069563:present", + "source": "amazon_massive", + "query_hash": "a3f2bc0c1cfeb990903f922ab9cb2d3962bd0b072515bd72da50e0dd8835dc3c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069568", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6c8881ba9ef0536fa416353b2bc2e451", + "normalized_query_sha256": "f113a834b495a77e0e70f0af462e90033cc94b01ebd706a4fab2d73f8de53cfa", + "query": "ves the claim\n3. Once approved, we schedule repairs within 72 hours\n\n**For Your Safety**: Please keep the refrigeration unit powered off until repairs are completed, given the burning smell and visible damage you described.\n\nNow, I'm ready to submit your warranty claim. I have:\n- Verified client identity ✓\n- Project ID: PROJ-2023-REFRIG-045 ✓\n- Defect description: Refrigeration unit intermittently shutting off, burning smell, temperature fluctuations, visible discoloration and melted insulation on wires near main junction ✓\n\nI just need the **photo evidence in base64 format** to complete the submission. Once you provide that, I'll submit the claim immediately.\nuser: Understood, and I apologize for the oversight—I wasn’t aware of the base64 requirement. I’ll use an online converter to prepare the photos immediately. One moment while I process them. \n\n**Update**: I’ve converted the photos to base64 format. Here’s the encoded string for the first photo (discoloration and melted insulation): \n`/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEB...` \n\nAnd the second photo (burning smell area): \n`/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAMCAgMCAgMDAwMEAwMEBQgFBQQEB...` \n\nIs there a maximum length for the encoded string you can accept in one message? Also, should I split these into separate submissions or combine them? \n\nFor clarity, the 72-hour timeline is critical for us to avoid further food spoilage. Can you confirm our Premium tier ensures expedited review of the claim?", + "target": "submit_warranty_claim", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 600, + "historical_id": "fastbrain_069568", + "source": "nemotron_agentic", + "query_hash": "f113a834b495a77e0e70f0af462e90033cc94b01ebd706a4fab2d73f8de53cfa" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 600, + "historical_id": "fastbrain_069568", + "source": "nemotron_agentic", + "query_hash": "f113a834b495a77e0e70f0af462e90033cc94b01ebd706a4fab2d73f8de53cfa" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069570", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d15ecfe3ddb6784f772d4d2923b5014f", + "normalized_query_sha256": "8e7809b8891c61a4c16609ee1f3d880695a0661dc7cf0aa26b6c9bae38b523d4", + "query": "That sounds great to me. I would like to buy tickets.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 598, + "historical_id": "fastbrain_069570", + "source": "sgd", + "query_hash": "8e7809b8891c61a4c16609ee1f3d880695a0661dc7cf0aa26b6c9bae38b523d4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 598, + "historical_id": "fastbrain_069570", + "source": "sgd", + "query_hash": "8e7809b8891c61a4c16609ee1f3d880695a0661dc7cf0aa26b6c9bae38b523d4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069577", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:00b4fae3ff4bf06b13d467ea8bd589f0", + "normalized_query_sha256": "93f5795cb8742600a2f9aa8e1467e042bff10d660ba44e7c233556550b0cc5c0", + "query": "Can you find me a salon in Windsor, preferably a unisex salon?", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 789, + "historical_id": "fastbrain_069577", + "source": "sgd", + "query_hash": "93f5795cb8742600a2f9aa8e1467e042bff10d660ba44e7c233556550b0cc5c0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 789, + "historical_id": "fastbrain_069577", + "source": "sgd", + "query_hash": "93f5795cb8742600a2f9aa8e1467e042bff10d660ba44e7c233556550b0cc5c0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069631", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fb3c0f23c4eaebf38f88105b7d9295bd", + "normalized_query_sha256": "062298e4e9bb585cc26d61a027edf890fa79d61fe0b29af59361225147661328", + "query": "Great. Help me find an apartment in the area with two bedrooms and 1 bathroom please.", + "target": "FindApartment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1019, + "historical_id": "fastbrain_069631", + "source": "sgd", + "query_hash": "062298e4e9bb585cc26d61a027edf890fa79d61fe0b29af59361225147661328" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1019, + "historical_id": "fastbrain_069631", + "source": "sgd", + "query_hash": "062298e4e9bb585cc26d61a027edf890fa79d61fe0b29af59361225147661328" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069678", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:835c1c0f2871c712e07d45d8a7ecd967", + "normalized_query_sha256": "8f8f41e0bb5647742ac56dcf6c66f0a2eee728691e6c26a1a193be34fc667960", + "query": "Delete all broken symbolic links under '/usr/ports/packages' directory tree", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 560, + "historical_id": "fastbrain_069678", + "source": "nl2bash", + "query_hash": "8f8f41e0bb5647742ac56dcf6c66f0a2eee728691e6c26a1a193be34fc667960" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 560, + "historical_id": "fastbrain_069678", + "source": "nl2bash", + "query_hash": "8f8f41e0bb5647742ac56dcf6c66f0a2eee728691e6c26a1a193be34fc667960" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069694", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3ca8f82526c964186b36850f5e34226d", + "normalized_query_sha256": "0c1f249cc962f85afd7eef4691f4c328e07ddc8d67506f65d0d5ce5c729fcb4b", + "query": "tell me the weather for new york city", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 713, + "historical_id": "semantic:validation:fastbrain_069694:absent", + "source": "amazon_massive", + "query_hash": "0c1f249cc962f85afd7eef4691f4c328e07ddc8d67506f65d0d5ce5c729fcb4b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 784, + "historical_id": "semantic:validation:fastbrain_069694:present", + "source": "amazon_massive", + "query_hash": "0c1f249cc962f85afd7eef4691f4c328e07ddc8d67506f65d0d5ce5c729fcb4b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069775", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:715d4a20b36d6136dee8ddb073734fc0", + "normalized_query_sha256": "989fae0071c00df78d7a3a7017eca9d31439ff770b06a4fea28bf6d49637c56c", + "query": "olly i'm looking for a bar do you know a good one", + "target": "recommendation_locations", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 362, + "historical_id": "semantic:validation:fastbrain_069775:present", + "source": "amazon_massive", + "query_hash": "989fae0071c00df78d7a3a7017eca9d31439ff770b06a4fea28bf6d49637c56c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 917, + "historical_id": "semantic:validation:fastbrain_069775:absent", + "source": "amazon_massive", + "query_hash": "989fae0071c00df78d7a3a7017eca9d31439ff770b06a4fea28bf6d49637c56c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069793", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:90a626e136b2a5862ff799d23f7ba70c", + "normalized_query_sha256": "e9730214306751dc46579f1781033b9b7d48e698c14407f60b6bbb37356fe1c8", + "query": "% tolerance) \n- Strict dietary flag adherence \n- Never recommend: \n - Items above customer's price history \n - Limited editions without stock verification \n\n## Escalation Protocol \nTransfer to human agent for: \n1. Complex customizations (>3 ingredient changes) \n2. Allergy incidents \n3. Disputes over $200 \n4. Repeat contacts (3+ about same issue) \n**Mandatory Message**: \n\"YOU ARE BEING TRANSFERRED TO A CHOCOLATE SPECIALIST.\" \n\n## Agent Limitations \n1. **Tool Boundaries**: Never answer questions or perform actions outside defined tool capabilities. \n2. **No Assumptions**: Never imply verification or action completion without explicit tool confirmation. \n3. **Knowledge Restrictions**: Do not speculate on unverified topics: \n - Cocoa sourcing ethics \n - Future seasonal products \n - Competitor comparisons \n4. **Privacy Pr...[TRUNCATED]\nuser: \n\nHello, I’m calling about my order OC-20250822-66778, which was delivered today. The box arrived cracked, and the chocolates inside are damaged. I have a photo of the damaged packaging. Can you help me submit this as a quality issue and arrange a replacement?\nassistant: Hello! I'd be happy to help you with the damaged order. First, I need to verify your identity to proceed with the return request.\n\nPlease provide the last 4 digits of the payment card used for order OC-20250822-66778, and I'll authenticate your account.\nuser: The last four digits of my payment card are 4892. Please proceed with verifying my account.", + "target": "authenticate_customer", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 97, + "historical_id": "fastbrain_069793", + "source": "nemotron_agentic", + "query_hash": "e9730214306751dc46579f1781033b9b7d48e698c14407f60b6bbb37356fe1c8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 97, + "historical_id": "fastbrain_069793", + "source": "nemotron_agentic", + "query_hash": "e9730214306751dc46579f1781033b9b7d48e698c14407f60b6bbb37356fe1c8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_069882", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:61699ca6ec99bc2edf86fc336590a6f7", + "normalized_query_sha256": "bb7a9520fb40ee354614540dd8298f25292248ef4617e60e340c8a4a0dc653f2", + "query": "where can i find a preparation professional for under dollars", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 232, + "historical_id": "semantic:validation:fastbrain_069882:present", + "source": "amazon_massive", + "query_hash": "bb7a9520fb40ee354614540dd8298f25292248ef4617e60e340c8a4a0dc653f2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 407, + "historical_id": "semantic:validation:fastbrain_069882:absent", + "source": "amazon_massive", + "query_hash": "bb7a9520fb40ee354614540dd8298f25292248ef4617e60e340c8a4a0dc653f2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070039", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f02e18c345ea6c82954e05ce4544e938", + "normalized_query_sha256": "02f3a1cc4943c9420330c2b6a62845a41f992ccfae2fa9556a10983674621244", + "query": "I want to know the latest news about the Mars Rover from NASA.", + "target": "search_news", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 47, + "historical_id": "fastbrain_070039", + "source": "glaive_v2", + "query_hash": "02f3a1cc4943c9420330c2b6a62845a41f992ccfae2fa9556a10983674621244" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 47, + "historical_id": "fastbrain_070039", + "source": "glaive_v2", + "query_hash": "02f3a1cc4943c9420330c2b6a62845a41f992ccfae2fa9556a10983674621244" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070205", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7098d334f14f185d7b57d21c140ff77e", + "normalized_query_sha256": "0c05c5d82c2c3cfd7229e40776322ac3b0c1acff180e6251a54d514d9807a9b3", + "query": "ok google what is the weather going to be like on march twentieth two thousand and seventeen", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 174, + "historical_id": "semantic:validation:fastbrain_070205:present", + "source": "amazon_massive", + "query_hash": "0c05c5d82c2c3cfd7229e40776322ac3b0c1acff180e6251a54d514d9807a9b3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 186, + "historical_id": "semantic:validation:fastbrain_070205:absent", + "source": "amazon_massive", + "query_hash": "0c05c5d82c2c3cfd7229e40776322ac3b0c1acff180e6251a54d514d9807a9b3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070264", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:244f21ffde758ee334466b592b36f3a7", + "normalized_query_sha256": "c172edd82fbcaeb4d8ec42b5f764bb7c1f9eff82dd80e5f34c747065c995b768", + "query": "olly show me where i can find a hamburger", + "target": "recommendation_events", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 882, + "historical_id": "fastbrain_070264", + "source": "amazon_massive", + "query_hash": "c172edd82fbcaeb4d8ec42b5f764bb7c1f9eff82dd80e5f34c747065c995b768" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 882, + "historical_id": "fastbrain_070264", + "source": "amazon_massive", + "query_hash": "c172edd82fbcaeb4d8ec42b5f764bb7c1f9eff82dd80e5f34c747065c995b768" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070360", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a3f00b778b63205bcc3dae1e0b980c61", + "normalized_query_sha256": "4adbd6d0a67f02c4af7af1c0bd5237e4908164cfab30919c8d80714f06f3bbaf", + "query": "I am overseeing IT operations for our company and need to monitor the disk space on all computer systems in the network to prevent any storage issues. I require detailed disk space usage information for all types of drives, including local, removable, and network disks. For effective management, it's important to know the current free space, total size for each drive, the drive letters, as well as", + "target": "get_disk_space_usage", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 316, + "historical_id": "fastbrain_070360", + "source": "hermes_v1", + "query_hash": "4adbd6d0a67f02c4af7af1c0bd5237e4908164cfab30919c8d80714f06f3bbaf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 316, + "historical_id": "fastbrain_070360", + "source": "hermes_v1", + "query_hash": "4adbd6d0a67f02c4af7af1c0bd5237e4908164cfab30919c8d80714f06f3bbaf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070415", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7d622b4ee8360f7a6e8c212dba76893c", + "normalized_query_sha256": "aa2cb80cf312cc6584410e5e0ee553854e5544cdffa71de5531297b8c1ca0e03", + "query": " can verify success by:\n1. Checking your **Recent Posts tab** - archived posts may still appear there with a different status\n2. The post should no longer be publicly visible to subscribers or non-subscribers\n\n**To proceed with deletion or check your post's current status, I'll need to:**\n1. Authenticate you for this sensitive action\n2. Retrieve your post details\n\nCould you please provide:\n1. Your creator ID\n2. Your email or phone number for authentication (I'll send a verification code)\n\nOnce authenticated, I can help you check the post details or proceed with deletion if that's what you'd like to do.\nuser: My creator ID is CRE-4572, and my email is emma.johnson@musicstudio.com. Please send the verification code to my email. Once authenticated, I’d like to check the current status of post MUS-889 and confirm if it’s already archived or if I need to proceed with deletion.\nassistant: \ntool: \n\n{\n \"success\": true,\n \"message\": \"Authentication code successfully sent to emma.johnson@musicstudio.com. Please check inbox including spam folder.\"\n}\nassistant: I've sent an authentication code to emma.johnson@musicstudio.com. Please check your inbox (including spam folder) and enter the code you received here.\nuser: The code I received in my email is **VERIFY-7294X**. Please confirm it and check the current status of post MUS-889 for me. Is it already archived, or do I need to manually delete it to trigger archiving?\nassistant: \ntool: \n\n{\n \"success\": true,\n \"session_pin\": \"SP-9B3T5\"\n}", + "target": "get_content_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 354, + "historical_id": "fastbrain_070415", + "source": "nemotron_agentic", + "query_hash": "aa2cb80cf312cc6584410e5e0ee553854e5544cdffa71de5531297b8c1ca0e03" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 354, + "historical_id": "fastbrain_070415", + "source": "nemotron_agentic", + "query_hash": "aa2cb80cf312cc6584410e5e0ee553854e5544cdffa71de5531297b8c1ca0e03" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070446", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6c7276b2a421c811d4dc7c7d1215dd6d", + "normalized_query_sha256": "0efa8da029cef72404f3d394dc6ddc39c57a48bd814da0ac842ccf3f337c0326", + "query": "draft response to john's email", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 954, + "historical_id": "semantic:validation:fastbrain_070446:absent", + "source": "amazon_massive", + "query_hash": "0efa8da029cef72404f3d394dc6ddc39c57a48bd814da0ac842ccf3f337c0326" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 989, + "historical_id": "semantic:validation:fastbrain_070446:present", + "source": "amazon_massive", + "query_hash": "0efa8da029cef72404f3d394dc6ddc39c57a48bd814da0ac842ccf3f337c0326" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070475", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2e179212008bbff6d4ed691c8321289d", + "normalized_query_sha256": "0742edc8fdd51d260f6eaa0c8ee6f108d36bcae0a751ce1e16396a825b9f708e", + "query": "remind me to pay my bills at the start of every month", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 22, + "historical_id": "semantic:validation:fastbrain_070475:present", + "source": "amazon_massive", + "query_hash": "0742edc8fdd51d260f6eaa0c8ee6f108d36bcae0a751ce1e16396a825b9f708e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 129, + "historical_id": "semantic:validation:fastbrain_070475:absent", + "source": "amazon_massive", + "query_hash": "0742edc8fdd51d260f6eaa0c8ee6f108d36bcae0a751ce1e16396a825b9f708e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070574", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:de0dfce8f29e786b5fd519336232836f", + "normalized_query_sha256": "d4c055384f62a8ac8df5de0c2adbbb7228cc43404511c6ac4516fab0944c7c80", + "query": "please play some songs for kids", + "target": "play_radio", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 6, + "historical_id": "fastbrain_070574", + "source": "amazon_massive", + "query_hash": "d4c055384f62a8ac8df5de0c2adbbb7228cc43404511c6ac4516fab0944c7c80" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 6, + "historical_id": "fastbrain_070574", + "source": "amazon_massive", + "query_hash": "d4c055384f62a8ac8df5de0c2adbbb7228cc43404511c6ac4516fab0944c7c80" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070889", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d2ac3919beb700431b114414c73712d2", + "normalized_query_sha256": "22af5dd0358bacc906dd56db4d5cb5f69559f188f553de94ae623819be3cf744", + "query": "I am looking for some book recommendations. I enjoy mystery novels.", + "target": "recommend_books", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 250, + "historical_id": "fastbrain_070889", + "source": "glaive_v2", + "query_hash": "22af5dd0358bacc906dd56db4d5cb5f69559f188f553de94ae623819be3cf744" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 250, + "historical_id": "fastbrain_070889", + "source": "glaive_v2", + "query_hash": "22af5dd0358bacc906dd56db4d5cb5f69559f188f553de94ae623819be3cf744" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_070925", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7cbf7002a12d3b76dd0fe1ec0d0e4444", + "normalized_query_sha256": "a26dcd549795e31b17be3bae1c8bf94034154f1996005b5a2f556bcbb3b2730f", + "query": "-locked to Canada as it falls within permitted geographic distribution zones for this bundle.\"}\nassistant: Great! I've verified your identity and checked the availability of your DB-2025-XBOX digital bundle. Here's what I found:\n\n**Good news!** Your DB-2025-XBOX digital bundle **can be region-locked to Canada**. According to our system, Canada falls within the permitted geographic distribution zones for this specific bundle, so there are no region restrictions preventing you from updating your region.\n\n**Important Policy Information:**\nBased on Thunderstrike Esports policy, digital products have a **72-hour redemption window** from the time of purchase. Since you mentioned you haven't redeemed the bundle yet, you should still be within this window.\n\n**Next Steps:**\nTo proceed with updating your region and redeeming the bundle for Canada, I'll need to know:\n1. **Your current gaming platform** (Xbox, PlayStation, PC, etc.)\n2. **Confirmation that you're ready to redeem the bundle now**\n\nOnce you provide this information, I can help you process the digital product purchase with the updated Canada region settings.\n\n**Proactive Reminder:** Remember that digital products are **non-refundable after 15 minutes** of redemption, so please ensure you're ready to use the bundle before proceeding with redemption.\nuser: My current gaming platform is Xbox, and I'm ready to proceed with redeeming the bundle now. Could you confirm the steps to finalize the region update and redemption process?", + "target": "purchase_digital_product", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 405, + "historical_id": "fastbrain_070925", + "source": "nemotron_agentic", + "query_hash": "a26dcd549795e31b17be3bae1c8bf94034154f1996005b5a2f556bcbb3b2730f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 405, + "historical_id": "fastbrain_070925", + "source": "nemotron_agentic", + "query_hash": "a26dcd549795e31b17be3bae1c8bf94034154f1996005b5a2f556bcbb3b2730f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071202", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fd660516731f016de4c9930fef1c95a1", + "normalized_query_sha256": "fac49e38b2b467a6d8ec92fdedb1d14a21d9fd6723ad9b9f7cdd21d63259b754", + "query": "run game", + "target": "play_game", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 724, + "historical_id": "semantic:validation:fastbrain_071202:present", + "source": "amazon_massive", + "query_hash": "fac49e38b2b467a6d8ec92fdedb1d14a21d9fd6723ad9b9f7cdd21d63259b754" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 776, + "historical_id": "semantic:validation:fastbrain_071202:absent", + "source": "amazon_massive", + "query_hash": "fac49e38b2b467a6d8ec92fdedb1d14a21d9fd6723ad9b9f7cdd21d63259b754" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071230", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:def92b5e69682b1774dc6a3aaefd83a8", + "normalized_query_sha256": "ee38ea0ce01cac56d6bee6b197dbfcfd6ee8149542fab7a7a7f483e0eab80693", + "query": "I see, could you also make a reservation for 2 at the restaurant?", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 640, + "historical_id": "fastbrain_071230", + "source": "sgd", + "query_hash": "ee38ea0ce01cac56d6bee6b197dbfcfd6ee8149542fab7a7a7f483e0eab80693" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 640, + "historical_id": "fastbrain_071230", + "source": "sgd", + "query_hash": "ee38ea0ce01cac56d6bee6b197dbfcfd6ee8149542fab7a7a7f483e0eab80693" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071294", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:79df8009d6d199a92bc6c5bb4df818a2", + "normalized_query_sha256": "3eb3d60089ec98e62729f05a07483562f2716c327625808ca35dc263d034a608", + "query": "That's wonderful. I'd like to book this vehicle.", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 839, + "historical_id": "fastbrain_071294", + "source": "sgd", + "query_hash": "3eb3d60089ec98e62729f05a07483562f2716c327625808ca35dc263d034a608" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 839, + "historical_id": "fastbrain_071294", + "source": "sgd", + "query_hash": "3eb3d60089ec98e62729f05a07483562f2716c327625808ca35dc263d034a608" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071369", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:db0d259646b265b115aa4772f14fccb6", + "normalized_query_sha256": "178e9ed4bad8fec80d0e182b911a45fb8522f1a40bee2076a4d91e2d059b98bf", + "query": "I manage IT operations at our company, and we're implementing event-driven automation to streamline our processes. We've identified three key areas where RPA could significantly improve our workflow:\n\n1. For new sales orders received through our online sales platform, I need an RPA function that will automatically initiate an inventory check for the ordered items. This check should update the orde", + "target": "initiateInventoryCheck", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 273, + "historical_id": "fastbrain_071369", + "source": "hermes_v1", + "query_hash": "178e9ed4bad8fec80d0e182b911a45fb8522f1a40bee2076a4d91e2d059b98bf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 273, + "historical_id": "fastbrain_071369", + "source": "hermes_v1", + "query_hash": "178e9ed4bad8fec80d0e182b911a45fb8522f1a40bee2076a4d91e2d059b98bf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071518", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a4069df1832c874d944e484f40e01979", + "normalized_query_sha256": "db454ae47fd330df4475a4f3038abc825e879b9a2c8b16af70dc6ff13a01fbd2", + "query": "find today's wapo articles about trump", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 146, + "historical_id": "semantic:validation:fastbrain_071518:present", + "source": "amazon_massive", + "query_hash": "db454ae47fd330df4475a4f3038abc825e879b9a2c8b16af70dc6ff13a01fbd2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 231, + "historical_id": "semantic:validation:fastbrain_071518:absent", + "source": "amazon_massive", + "query_hash": "db454ae47fd330df4475a4f3038abc825e879b9a2c8b16af70dc6ff13a01fbd2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071634", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6ba5fac3ed2786d9a116d79d10eeb47c", + "normalized_query_sha256": "36238440cbfde244f676a1022e4b707862ffc7787617c31fc727a20087aeef0f", + "query": "display long listing of all files in the current directory whose size is 24 or 25 bytes.", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 601, + "historical_id": "fastbrain_071634", + "source": "nl2bash", + "query_hash": "36238440cbfde244f676a1022e4b707862ffc7787617c31fc727a20087aeef0f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 601, + "historical_id": "fastbrain_071634", + "source": "nl2bash", + "query_hash": "36238440cbfde244f676a1022e4b707862ffc7787617c31fc727a20087aeef0f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071660", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:731c67324899d7c5e35e3510c39f754a", + "normalized_query_sha256": "f3534d70e02b0763343a739cb256f6052797f51cb78d38c9dcf3bbf83a2ad267", + "query": "Can you please check my calendar and tell me when am I available?", + "target": "GetAvailableTime", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 460, + "historical_id": "fastbrain_071660", + "source": "sgd", + "query_hash": "f3534d70e02b0763343a739cb256f6052797f51cb78d38c9dcf3bbf83a2ad267" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 460, + "historical_id": "fastbrain_071660", + "source": "sgd", + "query_hash": "f3534d70e02b0763343a739cb256f6052797f51cb78d38c9dcf3bbf83a2ad267" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071792", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c905c5117b5c72b4a01d9c8aeabfc95b", + "normalized_query_sha256": "70ac93046955dbde234654a9581a0f5029f525ebf5c99f9ecb45361c4ccf088f", + "query": "current time in cpt", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 59, + "historical_id": "semantic:validation:fastbrain_071792:present", + "source": "amazon_massive", + "query_hash": "70ac93046955dbde234654a9581a0f5029f525ebf5c99f9ecb45361c4ccf088f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 233, + "historical_id": "semantic:validation:fastbrain_071792:absent", + "source": "amazon_massive", + "query_hash": "70ac93046955dbde234654a9581a0f5029f525ebf5c99f9ecb45361c4ccf088f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_071984", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b68fbf542864eb46b0a937d1b0402b3b", + "normalized_query_sha256": "800f3f54d5023046935237b800bb1574b5aab8bff8b321fd3ffc28cfb364a9a3", + "query": "do i have any reminders", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 87, + "historical_id": "semantic:validation:fastbrain_071984:absent", + "source": "amazon_massive", + "query_hash": "800f3f54d5023046935237b800bb1574b5aab8bff8b321fd3ffc28cfb364a9a3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1000, + "historical_id": "semantic:validation:fastbrain_071984:present", + "source": "amazon_massive", + "query_hash": "800f3f54d5023046935237b800bb1574b5aab8bff8b321fd3ffc28cfb364a9a3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072008", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fb279760f728238e5e7ac0e230f80041", + "normalized_query_sha256": "543d3ea85eb2d952718c98f4fcfd90e0a0712002457b72cafea33e06ac200524", + "query": "I like to search a film to watch online.", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 998, + "historical_id": "fastbrain_072008", + "source": "sgd", + "query_hash": "543d3ea85eb2d952718c98f4fcfd90e0a0712002457b72cafea33e06ac200524" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 998, + "historical_id": "fastbrain_072008", + "source": "sgd", + "query_hash": "543d3ea85eb2d952718c98f4fcfd90e0a0712002457b72cafea33e06ac200524" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072532", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:34948224a39eaa3c47dee86058832ecf", + "normalized_query_sha256": "cc70417c0d2e2a60027f0660669d2e77c948f3d7d658f91d0bb08a40259d9769", + "query": "find out more about the annual tea party", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 444, + "historical_id": "fastbrain_072532", + "source": "amazon_massive", + "query_hash": "cc70417c0d2e2a60027f0660669d2e77c948f3d7d658f91d0bb08a40259d9769" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 444, + "historical_id": "fastbrain_072532", + "source": "amazon_massive", + "query_hash": "cc70417c0d2e2a60027f0660669d2e77c948f3d7d658f91d0bb08a40259d9769" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072653", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a09fc7dbd030dfe38a5475127473868b", + "normalized_query_sha256": "77e1d3dc90ee54ed14dc4709cba5d568e0eaeac375d011989a079e2c2269785e", + "query": "Well then I'll take the bus ticket. There will be three of us.", + "target": "BuyBusTicket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 694, + "historical_id": "fastbrain_072653", + "source": "sgd", + "query_hash": "77e1d3dc90ee54ed14dc4709cba5d568e0eaeac375d011989a079e2c2269785e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 694, + "historical_id": "fastbrain_072653", + "source": "sgd", + "query_hash": "77e1d3dc90ee54ed14dc4709cba5d568e0eaeac375d011989a079e2c2269785e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072695", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:54e3625b0f7eaf67fb0ed49d551d69bf", + "normalized_query_sha256": "f449e2ac2f8270e9131a17651d87fe4f8e1b8522bbec8512d8f1e6c30fbf45d9", + "query": "\n- Never disclose backend systems \n- Avoid subjective travel advice\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling about my booking reference GA4445. I missed my connecting flight because of a delay, and now my next available flight is in 8 hours. I need to rebook under the flight disruption policy and would also like to request meal vouchers for the extended wait. Can you assist with this?\nassistant: Hello! I'm sorry to hear about your flight disruption. I can certainly help you with rebooking and meal vouchers under our disruption policy.\n\nFirst, I need to verify your identity to access your booking details. Please provide your email address or phone number for verification, and I'll send you an authentication code.\nuser: My email is passenger@example.com. I look forward to the verification code. Once verified, can you confirm the next steps for rebooking and issuing the meal vouchers as per your disruption policy?\nassistant: \ntool: \n\n{\"status\": \"sent\"}\nassistant: I've sent a verification code to passenger@example.com. Please check your email and enter the code here so I can verify your identity and access your booking details. Once verified, I can proceed with rebooking and issuing meal vouchers as per our disruption policy.\nuser: The verification code is 123456. Please proceed with rebooking my flight and issuing the meal vouchers as discussed.\nassistant: \ntool: \n\n{\"authenticated\": true, \"user_id\": \"user_12345\", \"user_tier\": \"Gold\"}", + "target": "get_booking_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 690, + "historical_id": "fastbrain_072695", + "source": "nemotron_agentic", + "query_hash": "f449e2ac2f8270e9131a17651d87fe4f8e1b8522bbec8512d8f1e6c30fbf45d9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 690, + "historical_id": "fastbrain_072695", + "source": "nemotron_agentic", + "query_hash": "f449e2ac2f8270e9131a17651d87fe4f8e1b8522bbec8512d8f1e6c30fbf45d9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072727", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:53d823d4960d0789f3b2070f92fe2c56", + "normalized_query_sha256": "640d4fd0f39fcd8adbc615066731d437c33d53ef2979cc973a3df36b4c9dc7a2", + "query": "I have recently completed a web design project for my client and I would like to utilize PayPal to send them an invoice for the agreed amount of $500.00. Since my client prefers PayPal for transactions, could you assist me by generating a PayPal payment request addressed to my client's email client@example.com? I need to include a short description stating that it's for web design services provide", + "target": "create_paypal_payment_request", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 133, + "historical_id": "fastbrain_072727", + "source": "hermes_v1", + "query_hash": "640d4fd0f39fcd8adbc615066731d437c33d53ef2979cc973a3df36b4c9dc7a2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 133, + "historical_id": "fastbrain_072727", + "source": "hermes_v1", + "query_hash": "640d4fd0f39fcd8adbc615066731d437c33d53ef2979cc973a3df36b4c9dc7a2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072771", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:21f8686d09c47845b012705786eb66a1", + "normalized_query_sha256": "b1a2dc9c2d3a5fd5fc1dfdb6dcc7856c2774cf89493ac6c48e9b875b7e70c13c", + "query": "send an email to grandma saying we are dropping by on saturday see you then", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 58, + "historical_id": "fastbrain_072771", + "source": "amazon_massive", + "query_hash": "b1a2dc9c2d3a5fd5fc1dfdb6dcc7856c2774cf89493ac6c48e9b875b7e70c13c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 58, + "historical_id": "fastbrain_072771", + "source": "amazon_massive", + "query_hash": "b1a2dc9c2d3a5fd5fc1dfdb6dcc7856c2774cf89493ac6c48e9b875b7e70c13c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072782", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b4e88180242a6e4257d0f9035f68c9fc", + "normalized_query_sha256": "3c2f1ef8863a2012bcc474fa3c07e7cf1e9ae1471237e1f94d4d12ece28a029a", + "query": "remind me about my schedule for the afternoon", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 423, + "historical_id": "semantic:validation:fastbrain_072782:present", + "source": "amazon_massive", + "query_hash": "3c2f1ef8863a2012bcc474fa3c07e7cf1e9ae1471237e1f94d4d12ece28a029a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 794, + "historical_id": "semantic:validation:fastbrain_072782:absent", + "source": "amazon_massive", + "query_hash": "3c2f1ef8863a2012bcc474fa3c07e7cf1e9ae1471237e1f94d4d12ece28a029a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072868", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:46f2b4a7792d580b72eb558ba3e15c88", + "normalized_query_sha256": "1ab58898b58205920f04930d8d7c8d2b6750abf689f2b69e2cd893336fa53444", + "query": "set my alarm for six am", + "target": "alarm_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 90, + "historical_id": "fastbrain_072868", + "source": "amazon_massive", + "query_hash": "1ab58898b58205920f04930d8d7c8d2b6750abf689f2b69e2cd893336fa53444" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 90, + "historical_id": "fastbrain_072868", + "source": "amazon_massive", + "query_hash": "1ab58898b58205920f04930d8d7c8d2b6750abf689f2b69e2cd893336fa53444" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_072897", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d7c2db57cc70e2995aa99904dca35b51", + "normalized_query_sha256": "d4b7f7bc7e66931095d7b5accf9cf2befb5354a0db25a6cde75450fea2acd82a", + "query": "any high priority emails", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 630, + "historical_id": "semantic:validation:fastbrain_072897:present", + "source": "amazon_massive", + "query_hash": "d4b7f7bc7e66931095d7b5accf9cf2befb5354a0db25a6cde75450fea2acd82a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 939, + "historical_id": "semantic:validation:fastbrain_072897:absent", + "source": "amazon_massive", + "query_hash": "d4b7f7bc7e66931095d7b5accf9cf2befb5354a0db25a6cde75450fea2acd82a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_073044", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a492559c1cce3264cdb6e0e630d8befc", + "normalized_query_sha256": "6afaa3604fa3307c4bbdcfee0aeac016f2541a6fbdf3dc6ec0a80116897f62ab", + "query": "do i have any notifications from social media", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 219, + "historical_id": "fastbrain_073044", + "source": "amazon_massive", + "query_hash": "6afaa3604fa3307c4bbdcfee0aeac016f2541a6fbdf3dc6ec0a80116897f62ab" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 219, + "historical_id": "fastbrain_073044", + "source": "amazon_massive", + "query_hash": "6afaa3604fa3307c4bbdcfee0aeac016f2541a6fbdf3dc6ec0a80116897f62ab" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_073132", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0ad50dd1f70ddbe50b49b915bdc18d8b", + "normalized_query_sha256": "baca7c1cd2b4e013b9487f48caf24bc2b8fcb0dfbe32ea779ca197d5f3658b01", + "query": "delete shopping from my list", + "target": "lists_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 850, + "historical_id": "semantic:validation:fastbrain_073132:absent", + "source": "amazon_massive", + "query_hash": "baca7c1cd2b4e013b9487f48caf24bc2b8fcb0dfbe32ea779ca197d5f3658b01" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 904, + "historical_id": "semantic:validation:fastbrain_073132:present", + "source": "amazon_massive", + "query_hash": "baca7c1cd2b4e013b9487f48caf24bc2b8fcb0dfbe32ea779ca197d5f3658b01" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_073288", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e0def7f70f1c5d968bacfbee6e35a99a", + "normalized_query_sha256": "b99f0268cd0123fbcf57e177540a4575e26fdee37888cd15fcb411db341db24b", + "query": "hey olly do i have any emails from bob about the bulldog", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 134, + "historical_id": "semantic:validation:fastbrain_073288:absent", + "source": "amazon_massive", + "query_hash": "b99f0268cd0123fbcf57e177540a4575e26fdee37888cd15fcb411db341db24b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 890, + "historical_id": "semantic:validation:fastbrain_073288:present", + "source": "amazon_massive", + "query_hash": "b99f0268cd0123fbcf57e177540a4575e26fdee37888cd15fcb411db341db24b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_073588", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a766f84d6a3bb9aceb389c2658d20b17", + "normalized_query_sha256": "acb8e71c3a8263d56106d1058ec3828a0234cd250065ae4daee7afd158180189", + "query": "wedding. First, I need to check an important detail: **weddings require a minimum of 45 calendar days' notice** for booking. \n\nSince December 10th is approaching quickly, could you let me know today's date? This will help me determine if we can proceed with the booking.\n\nIf the date does work within our policy, I'll need the following information to create your booking:\n1. Your customer ID (if you've booked with us before)\n2. The full venue address\n3. Your preferred menu package ID\n4. Final guest count (you mentioned 120 guests)\n\nAlso, please note:\n- A 10% non-refundable deposit is required for all new bookings\n- Guest count variance is limited to ±10% from the initial estimate\n- We automatically apply discounts: 15% for Tuesday-Thursday events (I'll check which day December 10th falls on)\n\nCould you provide the above details so I can assist you further?\nuser: Thank you for the information. Today's date is indeed November 10th, so I understand the 45-day policy puts us 15 days short. That said, I'm fully aware of the risks and would like to proceed with an immediate booking. I can pay the 10% non-refundable deposit right now to secure the December 10th date. \n\nCould you check availability for that day? If it's possible, I'll need help finalizing the details - my venue is The Frostwood Manor at 123 Snowflake Lane, and we'd prefer the \"Winter Elegance\" menu package (ID: WEM-2023). The guest count is confirmed at 120. Is there any way to make this work with an expedited process?", + "target": "get_menu_package", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 17, + "historical_id": "fastbrain_073588", + "source": "nemotron_agentic", + "query_hash": "acb8e71c3a8263d56106d1058ec3828a0234cd250065ae4daee7afd158180189" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 17, + "historical_id": "fastbrain_073588", + "source": "nemotron_agentic", + "query_hash": "acb8e71c3a8263d56106d1058ec3828a0234cd250065ae4daee7afd158180189" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_073589", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c9b0db5747926e3546793c5c9af930f1", + "normalized_query_sha256": "e8a99410acc63c409f6cb6f6bd49827fe790e39c29ea4876fd52ae16ef4093e0", + "query": "I am thankful. I want to have a cab to go there, so get me one.", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 700, + "historical_id": "fastbrain_073589", + "source": "sgd", + "query_hash": "e8a99410acc63c409f6cb6f6bd49827fe790e39c29ea4876fd52ae16ef4093e0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 700, + "historical_id": "fastbrain_073589", + "source": "sgd", + "query_hash": "e8a99410acc63c409f6cb6f6bd49827fe790e39c29ea4876fd52ae16ef4093e0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_073889", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ef63724fe2336b45c37b50913d179580", + "normalized_query_sha256": "690b925808dcd080a713dbe59195c48b653bcf063dde261f609d9e8a6a75bb8d", + "query": "Hi, I need help calculating the tip for my bill. The total bill is $50 and I want to leave a 20% tip.", + "target": "calculate_tip", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 204, + "historical_id": "fastbrain_073889", + "source": "glaive_v2", + "query_hash": "690b925808dcd080a713dbe59195c48b653bcf063dde261f609d9e8a6a75bb8d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 204, + "historical_id": "fastbrain_073889", + "source": "glaive_v2", + "query_hash": "690b925808dcd080a713dbe59195c48b653bcf063dde261f609d9e8a6a75bb8d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_073911", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e7fa2905adf6096d50f77cc55b25c3d4", + "normalized_query_sha256": "3fc1cc143dfcbd377b56dace202bc937cb2c8f58405a88291542d5518395e80f", + "query": " bilingual customer service roles. How can I set that up? Also, could you confirm if my 1 year of experience and bilingual skills meet the requirements for this role?\nassistant: To confirm your qualifications: Based on what you've shared, **yes, you meet the minimum requirements** for the Bilingual Customer Support Agent role (CS1002):\n- ✅ **Experience**: 1 year of customer service experience (meets the 1+ years requirement)\n- ✅ **Language Skills**: Fluent in Spanish and English (matches the required language skills)\n\nNow, let's set up a job alert for similar bilingual customer service roles. I'll need a few details:\n\n1. **Keywords**: What specific terms would you like me to include? I suggest: [\"bilingual\", \"customer service\", \"Spanish\", \"English\", \"support agent\"]\n\n2. **Frequency**: Free tier users can receive up to 3 alerts per day. How often would you like notifications?\n - Daily\n - Every other day\n - Weekly\n\n3. **Alert Name**: Do you have a preferred name for this alert? (e.g., \"Bilingual Customer Service Roles\")\n\nOnce you provide these details, I'll create the alert for you. Remember, free tier users can have up to 5 active alerts.\nuser: Please use the suggested keywords: \"bilingual,\" \"customer service,\" \"Spanish,\" \"English,\" and \"support agent.\" I’d like the alert to be **daily** since new roles might post frequently. For the alert name, let’s use \"Bilingual Customer Service Roles.\" Also, could you remind me again about the expiration date for the CS1002 listing?", + "target": "manage_job_alert", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 599, + "historical_id": "fastbrain_073911", + "source": "nemotron_agentic", + "query_hash": "3fc1cc143dfcbd377b56dace202bc937cb2c8f58405a88291542d5518395e80f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 599, + "historical_id": "fastbrain_073911", + "source": "nemotron_agentic", + "query_hash": "3fc1cc143dfcbd377b56dace202bc937cb2c8f58405a88291542d5518395e80f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_073978", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1b94c381a4e145731e36e649df6eb5e6", + "normalized_query_sha256": "a3138924e2060efa7122c912f15340636b5d5f4ea136382945c1dd8f0b1118ce", + "query": "let me know the morning of my doctor appointment on the twelfth", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 248, + "historical_id": "semantic:validation:fastbrain_073978:absent", + "source": "amazon_massive", + "query_hash": "a3138924e2060efa7122c912f15340636b5d5f4ea136382945c1dd8f0b1118ce" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 614, + "historical_id": "semantic:validation:fastbrain_073978:present", + "source": "amazon_massive", + "query_hash": "a3138924e2060efa7122c912f15340636b5d5f4ea136382945c1dd8f0b1118ce" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_074183", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0caf344f2184e60364aa3fde794135f3", + "normalized_query_sha256": "c05b12f4f0b122d9e5c79e0f7239588009b0f2a1c6c1c309bcfe049179d826fd", + "query": "show what is staged for the next commit", + "target": "git_diff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 303, + "historical_id": "fastbrain_074183", + "source": "git_nl_commands", + "query_hash": "c05b12f4f0b122d9e5c79e0f7239588009b0f2a1c6c1c309bcfe049179d826fd" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 303, + "historical_id": "fastbrain_074183", + "source": "git_nl_commands", + "query_hash": "c05b12f4f0b122d9e5c79e0f7239588009b0f2a1c6c1c309bcfe049179d826fd" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_074232", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e06ef870312fd58c1864a8a0a2e3fa6b", + "normalized_query_sha256": "291f4d4c52b4e5bffafd716fdaa5e92d4d98d51c2d39f970d70ca1ff95153cc0", + "query": "fun things to do today", + "target": "recommendation_events", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 282, + "historical_id": "fastbrain_074232", + "source": "amazon_massive", + "query_hash": "291f4d4c52b4e5bffafd716fdaa5e92d4d98d51c2d39f970d70ca1ff95153cc0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 282, + "historical_id": "fastbrain_074232", + "source": "amazon_massive", + "query_hash": "291f4d4c52b4e5bffafd716fdaa5e92d4d98d51c2d39f970d70ca1ff95153cc0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_074247", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8f1c4ca2c14e7e9c70bab124eae4d486", + "normalized_query_sha256": "8158b5700cc88a656bf09bcc3ff361767671c81378f758153ce73ec8c4b7be98", + "query": "Create an empty file (or update timestamp of file) specified by variable \"correctFilePathAndName\"", + "target": "touch", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 513, + "historical_id": "fastbrain_074247", + "source": "nl2bash", + "query_hash": "8158b5700cc88a656bf09bcc3ff361767671c81378f758153ce73ec8c4b7be98" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 513, + "historical_id": "fastbrain_074247", + "source": "nl2bash", + "query_hash": "8158b5700cc88a656bf09bcc3ff361767671c81378f758153ce73ec8c4b7be98" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_074319", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:672b5ac01443379f2802251ec36769ab", + "normalized_query_sha256": "aaed82bcfcb53aeefaed30311baf4b86fed9b51761c45b64e536ecedd50b0b9e", + "query": "I also am in need of finding a round trip flight to go there. I will be returning on 11th of March 2019.", + "target": "SearchRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 122, + "historical_id": "fastbrain_074319", + "source": "sgd", + "query_hash": "aaed82bcfcb53aeefaed30311baf4b86fed9b51761c45b64e536ecedd50b0b9e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 122, + "historical_id": "fastbrain_074319", + "source": "sgd", + "query_hash": "aaed82bcfcb53aeefaed30311baf4b86fed9b51761c45b64e536ecedd50b0b9e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_074346", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7ce199de10d061daefcd8e1e524b67fa", + "normalized_query_sha256": "1b64e2206a27ffd4184d61e48adde6fa61a1a1fc5f847501037b80cc2bc325b5", + "query": "tell me the name of the song", + "target": "music_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 225, + "historical_id": "semantic:validation:fastbrain_074346:present", + "source": "amazon_massive", + "query_hash": "1b64e2206a27ffd4184d61e48adde6fa61a1a1fc5f847501037b80cc2bc325b5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 544, + "historical_id": "semantic:validation:fastbrain_074346:absent", + "source": "amazon_massive", + "query_hash": "1b64e2206a27ffd4184d61e48adde6fa61a1a1fc5f847501037b80cc2bc325b5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_074471", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:24fa7bd99b19f609e0c191139eaff7a7", + "normalized_query_sha256": "031416f0e9a08831cb9efdd7f81f137143d99f23fd3e86bedf99f877fc437ca0", + "query": "I appreciate it very much. Can you tell me about the alarms I have set?", + "target": "GetAlarms", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 725, + "historical_id": "fastbrain_074471", + "source": "sgd", + "query_hash": "031416f0e9a08831cb9efdd7f81f137143d99f23fd3e86bedf99f877fc437ca0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 725, + "historical_id": "fastbrain_074471", + "source": "sgd", + "query_hash": "031416f0e9a08831cb9efdd7f81f137143d99f23fd3e86bedf99f877fc437ca0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_074832", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:33c344444f24209c9db512621ffbfd41", + "normalized_query_sha256": "6fd49ee07a0553ac1557df6c80c0d507e4aeedf1ad9933b52945f8b8c8156fd9", + "query": "wipe calendar clean", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 306, + "historical_id": "fastbrain_074832", + "source": "amazon_massive", + "query_hash": "6fd49ee07a0553ac1557df6c80c0d507e4aeedf1ad9933b52945f8b8c8156fd9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 306, + "historical_id": "fastbrain_074832", + "source": "amazon_massive", + "query_hash": "6fd49ee07a0553ac1557df6c80c0d507e4aeedf1ad9933b52945f8b8c8156fd9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_074986", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3d55e89f5caca46e066c746dc15b20fb", + "normalized_query_sha256": "157eae6848b682d1cc3b70f71ef4c7ff9cc5cba25e8d06b51d8f4ceb4122e290", + "query": "reply to robert's email of today morning", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 393, + "historical_id": "semantic:validation:fastbrain_074986:present", + "source": "amazon_massive", + "query_hash": "157eae6848b682d1cc3b70f71ef4c7ff9cc5cba25e8d06b51d8f4ceb4122e290" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 400, + "historical_id": "semantic:validation:fastbrain_074986:absent", + "source": "amazon_massive", + "query_hash": "157eae6848b682d1cc3b70f71ef4c7ff9cc5cba25e8d06b51d8f4ceb4122e290" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075006", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b874f877c893a0243b55304dfc464396", + "normalized_query_sha256": "9386b508fe412b30fc6ccb887c0ede09a2cf577761cc1b249b9773c2ee596fca", + "query": "please provide world hot news", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 117, + "historical_id": "semantic:validation:fastbrain_075006:present", + "source": "amazon_massive", + "query_hash": "9386b508fe412b30fc6ccb887c0ede09a2cf577761cc1b249b9773c2ee596fca" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 137, + "historical_id": "semantic:validation:fastbrain_075006:absent", + "source": "amazon_massive", + "query_hash": "9386b508fe412b30fc6ccb887c0ede09a2cf577761cc1b249b9773c2ee596fca" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075022", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d76c048e6c8b5529c480777263af5189", + "normalized_query_sha256": "0db52f4076c082518412afd3dd3576e2cb0c63703fd7fed5e23ade60efef31d9", + "query": "answer", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1022, + "historical_id": "fastbrain_075022", + "source": "amazon_massive", + "query_hash": "0db52f4076c082518412afd3dd3576e2cb0c63703fd7fed5e23ade60efef31d9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1022, + "historical_id": "fastbrain_075022", + "source": "amazon_massive", + "query_hash": "0db52f4076c082518412afd3dd3576e2cb0c63703fd7fed5e23ade60efef31d9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075057", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5fad071b270c597e766245f531948fd4", + "normalized_query_sha256": "54bd919897c50512b3306c0519b36d221b13997681c86300cdade0e2586352c2", + "query": "e status of your public domain verification for \"Pride and Prejudice\" under Project ID CP2025-44.\n\nAlso, to use the validation tool, I'll need:\n1. The specific content/text you're planning to print\n2. Whether you have an ISBN registered for this custom printing project\n\nPlease provide your contact information first, and then we can proceed with the copyright verification.\nuser: My email address is customprints@example.com. Please send the authentication code there. \n\nFor the validation tool, we plan to print the full text of *Pride and Prejudice* as the content, and we do not currently have an ISBN registered for this project. Let me know once verification is complete!\nassistant: \ntool: \n\n{\"sent\": true}\nassistant: I've sent an authentication code to customprints@example.com. Please check your email and provide the code you received so I can verify your identity.\n\nOnce verified, I can proceed with validating the copyright status for \"Pride and Prejudice\" using our validation tool. As you mentioned, you don't have an ISBN registered for this project yet, which is something we'll need to address since valid ISBN registration is mandatory for custom printing according to our policy.\nuser: I've received the authentication code. It is **8XK9L2**. Please verify this so we can proceed with the copyright validation for *Pride and Prejudice* under Project ID CP2025-44. Once confirmed, could you clarify the ISBN registration process for custom printing? We’ll need to address this next.", + "target": "confirm_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 844, + "historical_id": "fastbrain_075057", + "source": "nemotron_agentic", + "query_hash": "54bd919897c50512b3306c0519b36d221b13997681c86300cdade0e2586352c2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 844, + "historical_id": "fastbrain_075057", + "source": "nemotron_agentic", + "query_hash": "54bd919897c50512b3306c0519b36d221b13997681c86300cdade0e2586352c2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075077", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7d3580b786ccd5bf595bd112d2acb3f0", + "normalized_query_sha256": "3ec7c0984a5019ebeeca9e21922a58e208660126134efcd10d2e80295c41aae0", + "query": "how much is a train ticket to san fransisco", + "target": "transport_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 510, + "historical_id": "semantic:validation:fastbrain_075077:absent", + "source": "amazon_massive", + "query_hash": "3ec7c0984a5019ebeeca9e21922a58e208660126134efcd10d2e80295c41aae0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 714, + "historical_id": "semantic:validation:fastbrain_075077:present", + "source": "amazon_massive", + "query_hash": "3ec7c0984a5019ebeeca9e21922a58e208660126134efcd10d2e80295c41aae0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075095", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cf9c4edff48ba47d5498755eb4efdf22", + "normalized_query_sha256": "0b0b1bc4c12ac350ca67f60b8543bcc183e6f621f981ee7b4ca9e1982292ef98", + "query": "You know, this is perfect. Let's make reservations please.", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 163, + "historical_id": "fastbrain_075095", + "source": "sgd", + "query_hash": "0b0b1bc4c12ac350ca67f60b8543bcc183e6f621f981ee7b4ca9e1982292ef98" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 163, + "historical_id": "fastbrain_075095", + "source": "sgd", + "query_hash": "0b0b1bc4c12ac350ca67f60b8543bcc183e6f621f981ee7b4ca9e1982292ef98" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075166", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b18f368ec054e58b129f4482bea6b0fb", + "normalized_query_sha256": "8b7a692c568b4b5e78b3b0262baaf0216a662ca2a3b84e6d3f04992d8dabfc83", + "query": "Hi, I need to track my package. The tracking number is 123456789 and the carrier is UPS.", + "target": "track_package", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 94, + "historical_id": "fastbrain_075166", + "source": "glaive_v2", + "query_hash": "8b7a692c568b4b5e78b3b0262baaf0216a662ca2a3b84e6d3f04992d8dabfc83" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 94, + "historical_id": "fastbrain_075166", + "source": "glaive_v2", + "query_hash": "8b7a692c568b4b5e78b3b0262baaf0216a662ca2a3b84e6d3f04992d8dabfc83" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075235", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e02901bb49d85de3596ba950e0ccccb8", + "normalized_query_sha256": "8b6bfe00f00345950da703a9a970d891d328b08f378c186dec232133ef62af19", + "query": "plants", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 292, + "historical_id": "semantic:validation:fastbrain_075235:present", + "source": "amazon_massive", + "query_hash": "8b6bfe00f00345950da703a9a970d891d328b08f378c186dec232133ef62af19" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 759, + "historical_id": "semantic:validation:fastbrain_075235:absent", + "source": "amazon_massive", + "query_hash": "8b6bfe00f00345950da703a9a970d891d328b08f378c186dec232133ef62af19" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075293", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:703d87944ef1e3712e2e45b640ac6d2d", + "normalized_query_sha256": "45d082f839e0d49677832423d8c56dd8a02460d517ed57bfb5da70758c0a663e", + "query": "ou clarify why this happened and initiate an investigation to review the status advancement per the documented feedback rules? I’d like to ensure this aligns with the proper process.\nassistant: Hello! I'd be happy to help you investigate the status of your application for role R55667. To look into this properly, I'll need some information from you first.\n\n**Please provide:**\n1. Your candidate ID (so I can verify your identity and access your application details)\n2. Your application ID (if you have it)\n\nOnce I have this information, I can:\n- Check your current application status and stage\n- Review whether the rejection was processed according to our documented feedback rules\n- Initiate appropriate next steps based on what I find\n\nAccording to our policy, applications should only be advanced or rejected after documented interview feedback. If your application was rejected before the interview stage without proper documentation, this may require escalation to a human specialist for investigation.\n\nPlease share your candidate ID and application ID so I can begin the investigation.\nuser: My candidate ID is CAND-987654 and my application ID is APP-321098. I’d like to confirm that my application for R55667 was marked \"Rejected\" before the interview stage, and I request an investigation to ensure this aligns with your documented feedback rules. Please let me know if you need further details.\nassistant: \ntool: \n\n{\"current_stage\": \"rejected\", \"last_update_time\": \"2023-09-15T14:32:18Z\"}", + "target": "get_candidate_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 486, + "historical_id": "fastbrain_075293", + "source": "nemotron_agentic", + "query_hash": "45d082f839e0d49677832423d8c56dd8a02460d517ed57bfb5da70758c0a663e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 486, + "historical_id": "fastbrain_075293", + "source": "nemotron_agentic", + "query_hash": "45d082f839e0d49677832423d8c56dd8a02460d517ed57bfb5da70758c0a663e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075294", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b3c58e747d89285e622497f6c1513ed0", + "normalized_query_sha256": "3f53a1943e730c42c8c7d525682bed0e90ebf5e649d3d3ccc12a2a1ab82d1c35", + "query": "nvoices as unpaid until payment confirmation \n- Consider disputes unresolved until status tool execution \n- Never store payment details post-transaction \n- Log royalty disclosures per protocol\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm reaching out regarding a modification to our license LX-2024-177. We made an update yesterday to add Singapore as a territory, and I need to confirm that this change is reflected in the audit logs. Could you verify the modification history for this license ID?\nassistant: Hello! I'd be happy to help you verify license modifications. First, I need to authenticate you according to our security protocol.\n\nPlease provide your registered email or phone number so I can send you a verification code. Once authenticated, I can assist you with license-related inquiries.\n\nRegarding your question about modification history audit logs, I should mention that I don't have direct access to view modification history logs. However, I can help you verify the current status of your license and confirm that Singapore has been added as a territory. Would that be helpful?\nuser: To proceed with authentication, I can provide my registered email: **training.developer@corporateco.com**. Please send the verification code. Once authenticated, could you also guide me on how to access the dashboard to verify the audit trail for License ID LX-2024-177? The user specifically needs to confirm Singapore’s addition in the modification history.", + "target": "send_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 776, + "historical_id": "fastbrain_075294", + "source": "nemotron_agentic", + "query_hash": "3f53a1943e730c42c8c7d525682bed0e90ebf5e649d3d3ccc12a2a1ab82d1c35" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 776, + "historical_id": "fastbrain_075294", + "source": "nemotron_agentic", + "query_hash": "3f53a1943e730c42c8c7d525682bed0e90ebf5e649d3d3ccc12a2a1ab82d1c35" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075333", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cc8edcf569834e7920106b3673849f3c", + "normalized_query_sha256": "f5bb4e001321b56d60ac403d02ae4a902404dfe7cadb0b9588da3aec2fcc9eb7", + "query": "dentity theft incidents \n\n---\n\n### Agent Limitations \n**Mandatory Constraints** \n1. **Action Boundaries**: \n - Only use available tools for all actions \n - Never answer questions beyond agent capabilities \n2. **Verification Protocol**: \n - Never assume successful identity/document validation without tool confirmation \n - Do not proceed if any document status ≠ \"approved\" \n3. **Compliance Restrictions**: \n - Reject filings failing IRS validation rules \n - Never process payments exceeding authorized limits \n4. **Termination Conditions**: \n Immediately end conversations requesting: \n - Tax evasion strategies \n - Backdated submissions \n - Fictitious deductions \n5. **Confirmation Principle**: \n - Never state any action is completed without explicit tool execution confirmation\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I need help resetting my account access after I changed my phone number. My client ID is SR123, and my new phone number ends in 4567. Could you guide me through the verification process?\nassistant: Hello! I'd be happy to help you reset your account access with your new phone number. To proceed with the verification process, I'll need your complete phone number to send you an authentication code.\n\nCould you please provide your full phone number (including area code) so I can send the verification code?\nuser: My full phone number is (212) 555-4567. Please send the authentication code to this number.", + "target": "send_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 682, + "historical_id": "fastbrain_075333", + "source": "nemotron_agentic", + "query_hash": "f5bb4e001321b56d60ac403d02ae4a902404dfe7cadb0b9588da3aec2fcc9eb7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 682, + "historical_id": "fastbrain_075333", + "source": "nemotron_agentic", + "query_hash": "f5bb4e001321b56d60ac403d02ae4a902404dfe7cadb0b9588da3aec2fcc9eb7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075405", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9bb26c57db98e5b3ccadd8959a53b858", + "normalized_query_sha256": "8e084c68d629c47d63b2d8a93e5d85a801ba7c1437996b597508e1d88ce669b1", + "query": "good afternoon how are you", + "target": "general_greet", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 154, + "historical_id": "semantic:validation:fastbrain_075405:present", + "source": "amazon_massive", + "query_hash": "8e084c68d629c47d63b2d8a93e5d85a801ba7c1437996b597508e1d88ce669b1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 196, + "historical_id": "semantic:validation:fastbrain_075405:absent", + "source": "amazon_massive", + "query_hash": "8e084c68d629c47d63b2d8a93e5d85a801ba7c1437996b597508e1d88ce669b1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075490", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f04d271cf5fd467c2b58193957043977", + "normalized_query_sha256": "017e53f02505eb164635e48e6f0576a3bcf18401f7b8d236ca983bc7f300e93d", + "query": "show me the time in new york city", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 927, + "historical_id": "fastbrain_075490", + "source": "amazon_massive", + "query_hash": "017e53f02505eb164635e48e6f0576a3bcf18401f7b8d236ca983bc7f300e93d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 927, + "historical_id": "fastbrain_075490", + "source": "amazon_massive", + "query_hash": "017e53f02505eb164635e48e6f0576a3bcf18401f7b8d236ca983bc7f300e93d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_075540", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ff22aaa497827b2072455f18a39cf36b", + "normalized_query_sha256": "ebef3bfc893c9c33b0f678f0c03224a71a3671fb4bb3f71d2d62be4ce8a3f28c", + "query": "what is the forecast", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 109, + "historical_id": "semantic:validation:fastbrain_075540:absent", + "source": "amazon_massive", + "query_hash": "ebef3bfc893c9c33b0f678f0c03224a71a3671fb4bb3f71d2d62be4ce8a3f28c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 115, + "historical_id": "semantic:validation:fastbrain_075540:present", + "source": "amazon_massive", + "query_hash": "ebef3bfc893c9c33b0f678f0c03224a71a3671fb4bb3f71d2d62be4ce8a3f28c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076023", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:60abe47440b6883d16b68916ad9fae03", + "normalized_query_sha256": "cad6c2c88f3e8bfa8bb07300c4de9b272d6b0cecc931c2d20758a38eb5ad6cb4", + "query": "apabilities** \n - Decline requests outside defined tool scope \n - Do not provide non-garment advice (e.g., style trends) \n\n2. **Verification Protocol** \n - Never assume validation without explicit system checks: \n - Measurements → Verify expiration date \n - Fabric stock → Confirm availability \n - Appointments → Check booking confirmation \n\n3. **Knowledge Boundaries** \n - Never speculate on delivery timelines beyond system data \n - Avoid medical interpretations of measurements \n\n4. **Action Constraints** \n - Always require tool execution for commitments: \n - \"Fabric available\" → Verified via stock check \n - \"Appointment booked\" → Confirmed via scheduling tool \n - Never bypass minimum fabric requirements \n\n---\n\n## Escalation Protocol \nTransfer to master tailor when encountering: \n- Complex redesigns \n- Measurement asymmetry >2cm \n- Orders ≥$5,000 requiring cancellation \n- Repeated fit dissatisfaction \n\nInform customer: \n\"YOU ARE BEING TRANSFERRED TO A MASTER TAILOR FOR PERSONALIZED ASSISTANCE\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I’m urgently reaching out about my bespoke morning suit order (SR-3267) for a wedding on October 20th. The current status shows as “Fabric Cut,” but I need daily updates moving forward. I’m prepared to pay rush fees, but I require an absolute delivery guarantee by October 18th—no exceptions. Can you confirm this is possible and outline the production timeline?", + "target": "check_order_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 76, + "historical_id": "fastbrain_076023", + "source": "nemotron_agentic", + "query_hash": "cad6c2c88f3e8bfa8bb07300c4de9b272d6b0cecc931c2d20758a38eb5ad6cb4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 76, + "historical_id": "fastbrain_076023", + "source": "nemotron_agentic", + "query_hash": "cad6c2c88f3e8bfa8bb07300c4de9b272d6b0cecc931c2d20758a38eb5ad6cb4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076048", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c96e454bd7c7aee54b2befb60d9c0f43", + "normalized_query_sha256": "c614f110d553d96f839c14a5cabd278709365f6c93349eec1d5e89536d9d4e0a", + "query": "I have some vacation time coming up. Please search for something interesting for me to do.", + "target": "FindEvents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 932, + "historical_id": "fastbrain_076048", + "source": "sgd", + "query_hash": "c614f110d553d96f839c14a5cabd278709365f6c93349eec1d5e89536d9d4e0a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 932, + "historical_id": "fastbrain_076048", + "source": "sgd", + "query_hash": "c614f110d553d96f839c14a5cabd278709365f6c93349eec1d5e89536d9d4e0a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076092", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:11cd43c153b27aa3afbf95337130177a", + "normalized_query_sha256": "a5fb5e501df956c38ade531d07d476839d943f9cca9308cec9c925835ffcf2db", + "query": "nly perform actions supported by available tools** \n2. **Never assume completion** - Require explicit tool confirmation for verifications/actions \n3. Never discuss food pairings or serving temperatures \n4. Never modify payments post-delivery \n5. Never bypass age verification \n6. Never share unverified delivery time estimates\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling to cancel my wine delivery order #VD20231027, which is scheduled for October 15th. I noticed the delivery is in 36 hours and I need to cancel last-minute. Could you confirm what refund percentage applies for cancellations made between 24-48 hours before delivery? I believe the policy mentions 50% refunds for this window.\nassistant: Hello! I'd be happy to help you cancel order #VD20231027. First, I need to verify your identity for security purposes.\n\n**Please provide the delivery ZIP code associated with this order.**\n\nOnce I have that, I can authenticate your account and check the exact refund amount. You're correct about our cancellation policy: for cancellations made 24-48 hours before delivery, a 50% refund applies. Since you mentioned 36 hours before delivery, that would fall into the 50% refund tier.\nuser: The ZIP code for the delivery is 10001. Please proceed with canceling the order and processing the 50% refund. Could you confirm how long it will take for the refund to reflect in my account?\nassistant: \ntool: \n\n{\"authenticated\": true, \"user_id\": \"USR123456\"}", + "target": "cancel_order", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 771, + "historical_id": "fastbrain_076092", + "source": "nemotron_agentic", + "query_hash": "a5fb5e501df956c38ade531d07d476839d943f9cca9308cec9c925835ffcf2db" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 771, + "historical_id": "fastbrain_076092", + "source": "nemotron_agentic", + "query_hash": "a5fb5e501df956c38ade531d07d476839d943f9cca9308cec9c925835ffcf2db" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076120", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:291e22293d927962d07772f2f05cd264", + "normalized_query_sha256": "11ca338c02f8635e42f6770a52003da5bf3ededec9f450340243e3cb649f493e", + "query": "t offer.\nassistant: Hello! I'd be happy to help you with your career coaching needs. First, I need to verify your identity to access your account.\n\n**Authentication Required:** Please provide the verification code that was sent to your email or phone number associated with client ID PRT456.\n\nRegarding your request for help with negotiating a part-time arrangement with your employer, I need to inform you that according to our policy, complex compensation negotiations require transfer to a human career specialist. This ensures you receive the most appropriate and comprehensive guidance for your specific situation.\n\nOnce you provide the verification code, I can:\n1. Authenticate your account\n2. Check your subscription status\n3. Transfer you to a career specialist who can help with your negotiation needs\n\nPlease provide your verification code to proceed.\nuser: Here is my verification code: 7XK9L. Once verified, could you please transfer me to a specialist and also suggest any alternative sessions or resources you might have available for negotiating part-time arrangements? I’d appreciate structured guidance that aligns with my subscription.\nassistant: \ntool: \n\n{\"verified\": true, \"session_token\": \"A1B2C3D4E5F6G7H8I9J0\"}\nassistant: \ntool: \n\n{\"career_field\": \"Unspecified\", \"experience_level\": \"Mid-Level\", \"subscription_tier\": \"Basic\", \"career_goals\": \"Flexible work arrangement negotiation\", \"resume_version\": \"V3.2\"}\nassistant: \ntool: \n\n{\"valid\": true, \"expiration_date\": \"2024-12-31\"}", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 451, + "historical_id": "fastbrain_076120", + "source": "nemotron_agentic", + "query_hash": "11ca338c02f8635e42f6770a52003da5bf3ededec9f450340243e3cb649f493e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 451, + "historical_id": "fastbrain_076120", + "source": "nemotron_agentic", + "query_hash": "11ca338c02f8635e42f6770a52003da5bf3ededec9f450340243e3cb649f493e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076182", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3ddaf60b0dc7592b7f45cdcdd27d04b1", + "normalized_query_sha256": "b19f484860e3bd877a8ac91317f218cb8cc97b74ef45c183e29a78132decf865", + "query": "I am looking for books by J.K. Rowling. Can you help me find some?", + "target": "search_books", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 915, + "historical_id": "fastbrain_076182", + "source": "glaive_v2", + "query_hash": "b19f484860e3bd877a8ac91317f218cb8cc97b74ef45c183e29a78132decf865" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 915, + "historical_id": "fastbrain_076182", + "source": "glaive_v2", + "query_hash": "b19f484860e3bd877a8ac91317f218cb8cc97b74ef45c183e29a78132decf865" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076209", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b4553834eb57aa33214ca322a7d85c88", + "normalized_query_sha256": "e530537833a72336c91e9d5abf6204e225c58617ee82f58ba55b38c8f9a6290c", + "query": "I am looking for songs, preferably Latin", + "target": "LookupSong", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 65, + "historical_id": "fastbrain_076209", + "source": "sgd", + "query_hash": "e530537833a72336c91e9d5abf6204e225c58617ee82f58ba55b38c8f9a6290c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 65, + "historical_id": "fastbrain_076209", + "source": "sgd", + "query_hash": "e530537833a72336c91e9d5abf6204e225c58617ee82f58ba55b38c8f9a6290c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076782", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ea7b5e30e337f298e741027952e66c09", + "normalized_query_sha256": "e95416c452560c93eefcfa50195d92386ee23981d7bf4ec0ba406969d87f1a68", + "query": ": \n\n{\"membership_tier\": \"VIP\", \"payment_methods\": [\"****4321\"], \"favorite_players\": [\"Kael\", \"Zara\"]}\nassistant: Thank you for providing the verification details. I can confirm you are a verified VIP member with payment method ending in 4321.\n\nBefore I process the refund for your defective tournament hoodie (order #M55667), I need your explicit confirmation to proceed. According to our policy, defective merchandise is eligible for refunds.\n\nPlease confirm with \"yes\" if you'd like me to process the refund for this defective item.\nuser: Before confirming, could you please use the item_inspection tool to verify the defect in the photo I provided? Once confirmed, I'll say \"yes\" to proceed with the refund.\nassistant: I understand you'd like me to verify the defect using the item_inspection tool. To do this, I need a few more details:\n\n1. **Item ID**: The specific ID of the tournament hoodie from your order #M55667\n2. **Packaging condition**: Can you describe the condition of the original packaging? (e.g., \"original packaging intact,\" \"packaging damaged,\" etc.)\n\nOnce I have this information, I can use the item_inspection tool to verify the defect based on the photo you've provided, and then we can proceed with the refund confirmation.\nuser: The Item ID for the tournament hoodie in my order is ITEM-12345 (from order #M55667). The original packaging was intact when I received it, so the damage must have occurred prior to shipping. I’ll email the photo to your team now for inspection.", + "target": "item_inspection", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 910, + "historical_id": "fastbrain_076782", + "source": "nemotron_agentic", + "query_hash": "e95416c452560c93eefcfa50195d92386ee23981d7bf4ec0ba406969d87f1a68" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 910, + "historical_id": "fastbrain_076782", + "source": "nemotron_agentic", + "query_hash": "e95416c452560c93eefcfa50195d92386ee23981d7bf4ec0ba406969d87f1a68" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076794", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:89a5e3be7de66877e6cdd1dfaf9ad5c7", + "normalized_query_sha256": "e13616a4d91ba61687822cf0bd72cb5ca0ec1802c6d6346f17334b6f97586e0f", + "query": "Find files starting with the word \"file\" in the current directory tree, ignoring the case", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 171, + "historical_id": "fastbrain_076794", + "source": "nl2bash", + "query_hash": "e13616a4d91ba61687822cf0bd72cb5ca0ec1802c6d6346f17334b6f97586e0f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 171, + "historical_id": "fastbrain_076794", + "source": "nl2bash", + "query_hash": "e13616a4d91ba61687822cf0bd72cb5ca0ec1802c6d6346f17334b6f97586e0f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_076850", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:573680a7d052c0195dd86a50c791be8f", + "normalized_query_sha256": "a60042301e5e5deb78f53967566f215cbf18decd62cb2561d363be8f0167e49e", + "query": "from my play list filter devotional songs and play right now for me", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 474, + "historical_id": "semantic:validation:fastbrain_076850:present", + "source": "amazon_massive", + "query_hash": "a60042301e5e5deb78f53967566f215cbf18decd62cb2561d363be8f0167e49e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 608, + "historical_id": "semantic:validation:fastbrain_076850:absent", + "source": "amazon_massive", + "query_hash": "a60042301e5e5deb78f53967566f215cbf18decd62cb2561d363be8f0167e49e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; 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After successful validation, we'll assist with recovering your client credentials.\"}\nassistant: I've sent a verification code to email@example.com. Please check your inbox (including spam/junk folder) and enter the code here.\n\nRegarding your question about additional verification steps:\n\n**After email verification:**\n1. **For account holders** (which seems to be your case): I'll need your Client ID and 4-digit security PIN to fully authenticate your account\n2. **If you don't have these credentials**: We may need to verify additional information or potentially escalate to a human agent for credential recovery, depending on our security protocols\n\n**For guest bookings** (if you only have a booking but not a full account): I would need your booking confirmation number, last name, and phone number.\n\nOnce you provide the verification code from your email, I can guide you through the specific next steps for recovering your client credentials.\nuser: I've received the verification code and entered it here: **[123456]**. Now that my email is verified, I need to clarify that I **don't have my Client ID or 4-digit security PIN** on hand. As mentioned earlier, I'm trying to recover these credentials. What specific information can I provide to prove my account ownership instead? For example, do you need my full name, date of birth, or details about my first booking? I want to ensure I can complete this process securely.", + "target": "validate_verification_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 672, + "historical_id": "fastbrain_077734", + "source": "nemotron_agentic", + "query_hash": "1ca196ecd8fd662792f892fc51b71efc71a1300acb4850c85afd0a97b4569992" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 672, + "historical_id": "fastbrain_077734", + "source": "nemotron_agentic", + "query_hash": "1ca196ecd8fd662792f892fc51b71efc71a1300acb4850c85afd0a97b4569992" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_077834", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:977647b944f2077f1d8a6cdcfac53060", + "normalized_query_sha256": "b9035df4428db63eaa33018297e4643e606d2017b6935a0fd34b1eb8a78383f6", + "query": "Hi, I need a QR code for my website. The URL is www.mywebsite.com. I want the size to be 500 pixels and the error correction level to be high.", + "target": "generate_qr_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 48, + "historical_id": "fastbrain_077834", + "source": "glaive_v2", + "query_hash": "b9035df4428db63eaa33018297e4643e606d2017b6935a0fd34b1eb8a78383f6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 48, + "historical_id": "fastbrain_077834", + "source": "glaive_v2", + "query_hash": "b9035df4428db63eaa33018297e4643e606d2017b6935a0fd34b1eb8a78383f6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_077963", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8e486aa5b4cdc5d2b423ef8278edadfe", + "normalized_query_sha256": "ea4a7d844fcc1a8d8874bc475939d9c0783875136ab2d4d78060902831a6d3b5", + "query": " your question: Yes, same-day Essential services are available, but they are limited to Essential services only (such as mowing, trimming, basic cleanup). I can help you schedule this once your identity is verified.\n\nPlease provide the verification code you received.\nuser: Code received and entered: 48291. I need the earliest available time today—can you confirm the crew can start within the next 4 hours? The tenant’s move-in depends on this.\nassistant: \ntool: \n\n{\"verified\": true, \"account_id\": \"acc_12345\", \"properties\": [\"prop_67890\"]}\nassistant: Thank you for verifying your identity! I can now help you schedule same-day Essential services for your rental property.\n\nEssential services include: basic lawn mowing, edging, trimming, and debris removal. For more extensive work like irrigation, aeration, or tree pruning over 15ft, those would require assessments and may not be available same-day.\n\nTo schedule the service, I need to know your preferred time slot for today. What time would work best for you? Please provide a specific time (e.g., \"2:00 PM\") so I can check availability and book the appointment.\n\nAlso, could you confirm that you only need the basic Essential services mentioned above?\nuser: I need the **earliest available time today**—ideally within the next 4 hours. Confirm the crew can start by 10 AM. And yes, I only require the Essential services you listed (mowing, edging, debris removal). No optional upgrades—this is strictly for tenant readiness. Schedule it now.", + "target": "schedule_service", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 411, + "historical_id": "fastbrain_077963", + "source": "nemotron_agentic", + "query_hash": "ea4a7d844fcc1a8d8874bc475939d9c0783875136ab2d4d78060902831a6d3b5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 411, + "historical_id": "fastbrain_077963", + "source": "nemotron_agentic", + "query_hash": "ea4a7d844fcc1a8d8874bc475939d9c0783875136ab2d4d78060902831a6d3b5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_077969", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:883a60f318a454dd699fd8f8f235ec86", + "normalized_query_sha256": "d7ee7fa0332d68ea35ff2e0d55948c2fd0199175d3fb880705735dc29633a30d", + "query": "please turn off right side of light", + "target": "iot_hue_lightoff", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 647, + "historical_id": "semantic:validation:fastbrain_077969:absent", + "source": "amazon_massive", + "query_hash": "d7ee7fa0332d68ea35ff2e0d55948c2fd0199175d3fb880705735dc29633a30d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 997, + "historical_id": "semantic:validation:fastbrain_077969:present", + "source": "amazon_massive", + "query_hash": "d7ee7fa0332d68ea35ff2e0d55948c2fd0199175d3fb880705735dc29633a30d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_078167", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:675abd1ebc39309a83f89d753575f2e1", + "normalized_query_sha256": "247ce6c8201e0b3fac8c3812e8b93fbea5c9f305076e8c861f021b154685b6a2", + "query": "what is my astrology report for today", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 795, + "historical_id": "fastbrain_078167", + "source": "amazon_massive", + "query_hash": "247ce6c8201e0b3fac8c3812e8b93fbea5c9f305076e8c861f021b154685b6a2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 795, + "historical_id": "fastbrain_078167", + "source": "amazon_massive", + "query_hash": "247ce6c8201e0b3fac8c3812e8b93fbea5c9f305076e8c861f021b154685b6a2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_078500", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4ef468d11ec6d7d52eb51daa7a5247be", + "normalized_query_sha256": "365a6f64c2e51329b5ea0dd0100a2901b88a62343fed0aacd98932a8c5af3fd1", + "query": "remind me about the march sixteen meeting", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 254, + "historical_id": "semantic:validation:fastbrain_078500:absent", + "source": "amazon_massive", + "query_hash": "365a6f64c2e51329b5ea0dd0100a2901b88a62343fed0aacd98932a8c5af3fd1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 857, + "historical_id": "semantic:validation:fastbrain_078500:present", + "source": "amazon_massive", + "query_hash": "365a6f64c2e51329b5ea0dd0100a2901b88a62343fed0aacd98932a8c5af3fd1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_078750", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b750bcb25aaec9af05c9a7578572fac2", + "normalized_query_sha256": "bf4c234471c60632c9ddc1558be8123b6a9be3d9a28bc5427ace9a2227458b95", + "query": "The image URL is \"http://example.com/image.jpg\". The top text should be \"When you code all night\" and the bottom text should be \"And forget to save your work\".", + "target": "generate_meme", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 363, + "historical_id": "fastbrain_078750", + "source": "glaive_v2", + "query_hash": "bf4c234471c60632c9ddc1558be8123b6a9be3d9a28bc5427ace9a2227458b95" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 363, + "historical_id": "fastbrain_078750", + "source": "glaive_v2", + "query_hash": "bf4c234471c60632c9ddc1558be8123b6a9be3d9a28bc5427ace9a2227458b95" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_078925", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:17e1360d9aed23b3e8c3a0c59d28464f", + "normalized_query_sha256": "0ee676441e74583e3be9415b2c073df3aecd37799fb9e4d2956ecf5d9a4adac4", + "query": "put a mail to sandy", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 338, + "historical_id": "semantic:validation:fastbrain_078925:absent", + "source": "amazon_massive", + "query_hash": "0ee676441e74583e3be9415b2c073df3aecd37799fb9e4d2956ecf5d9a4adac4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 467, + "historical_id": "semantic:validation:fastbrain_078925:present", + "source": "amazon_massive", + "query_hash": "0ee676441e74583e3be9415b2c073df3aecd37799fb9e4d2956ecf5d9a4adac4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_079376", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f8a75bc373375b7de28e0d746d09c2d4", + "normalized_query_sha256": "7dffc0a8a0508730c9c6e16daebf371e5a20de463326954cbb8a8104c59591cf", + "query": "length of traffic", + "target": "transport_traffic", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 618, + "historical_id": "semantic:validation:fastbrain_079376:present", + "source": "amazon_massive", + "query_hash": "7dffc0a8a0508730c9c6e16daebf371e5a20de463326954cbb8a8104c59591cf" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 884, + "historical_id": "semantic:validation:fastbrain_079376:absent", + "source": "amazon_massive", + "query_hash": "7dffc0a8a0508730c9c6e16daebf371e5a20de463326954cbb8a8104c59591cf" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_079607", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:32a5e8931333fa18f46ebf84255ba244", + "normalized_query_sha256": "49da9a3c4b9126d1d0fdd5b6b2985c05c25784dab8a9a98460417bcc46698fe1", + "query": "play the program where i left off yesterday", + "target": "play_audiobook", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 899, + "historical_id": "fastbrain_079607", + "source": "amazon_massive", + "query_hash": "49da9a3c4b9126d1d0fdd5b6b2985c05c25784dab8a9a98460417bcc46698fe1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 899, + "historical_id": "fastbrain_079607", + "source": "amazon_massive", + "query_hash": "49da9a3c4b9126d1d0fdd5b6b2985c05c25784dab8a9a98460417bcc46698fe1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_079773", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ec30596a1ea47f3143be5138fb63f9d9", + "normalized_query_sha256": "0965c6e8150b39560bc5db435dca64ab35b6a12b3c5968f32eab951b2a6d7572", + "query": "Hi, I need help calculating my gross salary. My basic salary is $5000, allowances are $2000 and deductions are $1000.", + "target": "calculate_gross_salary", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 623, + "historical_id": "fastbrain_079773", + "source": "glaive_v2", + "query_hash": "0965c6e8150b39560bc5db435dca64ab35b6a12b3c5968f32eab951b2a6d7572" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 623, + "historical_id": "fastbrain_079773", + "source": "glaive_v2", + "query_hash": "0965c6e8150b39560bc5db435dca64ab35b6a12b3c5968f32eab951b2a6d7572" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_079823", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dbb48c1a7917aff5f1f03259881947a4", + "normalized_query_sha256": "5cbdf2c044e1bdcf4d944f1cab3fdf37cd21eca4580862fc03a61c4959a683c7", + "query": "i need a ticket on the train to new york city tomorrow", + "target": "transport_ticket", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 772, + "historical_id": "semantic:validation:fastbrain_079823:absent", + "source": "amazon_massive", + "query_hash": "5cbdf2c044e1bdcf4d944f1cab3fdf37cd21eca4580862fc03a61c4959a683c7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 978, + "historical_id": "semantic:validation:fastbrain_079823:present", + "source": "amazon_massive", + "query_hash": "5cbdf2c044e1bdcf4d944f1cab3fdf37cd21eca4580862fc03a61c4959a683c7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_079855", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:af33f4e698351e1f760d25f3a29bf31b", + "normalized_query_sha256": "90a7b210a6669e4ba760cd60212300924869f58215efef9f441d641ee533692f", + "query": "Yes make a reservation for two people on March 7th at 13:00.", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 461, + "historical_id": "fastbrain_079855", + "source": "sgd", + "query_hash": "90a7b210a6669e4ba760cd60212300924869f58215efef9f441d641ee533692f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 461, + "historical_id": "fastbrain_079855", + "source": "sgd", + "query_hash": "90a7b210a6669e4ba760cd60212300924869f58215efef9f441d641ee533692f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080065", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8d5fffbedf2687d5982df502f6a4f366", + "normalized_query_sha256": "42dda1ac49c44c81694b7a291ef48e714a3fad4c9da3c3e1649ca1b7fa89fe70", + "query": "Require explicit confirmation for financial transactions \n - Validate VIN before processing warranty claims \n - Escalate when requests exceed capabilities \n\n---\n\n### Escalation Protocol \n\nTransfer to human agent for: \n1. Insurance claim coordination \n2. Multi-point inspection disputes \n3. Complex electrical diagnostics \n4. Three consecutive appointment changes \n5. Safety-related concerns \n\nTransfer process: \n1. Use `transfer_to_shop_advisor` tool \n2. Send message: \"YOU ARE BEING TRANSFERRED TO A SHOP ADVISOR. PLEASE DESCRIBE YOUR CONCERN TO THEM.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, my name is [Customer Name], and I'm calling about my vehicle with VIN 3FA6P0G3XHR123456. My customer ID is R678901. I need to request a loaner vehicle because my 2020 Ford Fusion is scheduled for a 6-hour repair. While I understand your policy allows loaners for repairs exceeding 8 hours, I’m a full-time ride-share driver and this downtime will cost me significant income. Can you help make an exception in my case?\nassistant: \ntool: \n\n{\"loyalty_status\": \"Silver\", \"vehicles\": [{\"vin\": \"3FA6P0G3XHR123456\", \"make\": \"Ford\", \"model\": \"Fusion\", \"year\": 2020}], \"payment_methods\": [{\"id\": \"PM12345\", \"type\": \"credit_card\", \"last_four\": \"4444\"}], \"service_history\": [{\"repair_id\": \"RX7890123456\", \"date\": \"2023-09-15\", \"service_code\": \"OIL_CHANGE\"}]}\nassistant: \ntool: \n\n{\"warranty_status\": \"Expired\", \"expiration_mileage\": 60000, \"deductible_required\": true}", + "target": "transfer_to_shop_advisor", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 901, + "historical_id": "fastbrain_080065", + "source": "nemotron_agentic", + "query_hash": "42dda1ac49c44c81694b7a291ef48e714a3fad4c9da3c3e1649ca1b7fa89fe70" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 901, + "historical_id": "fastbrain_080065", + "source": "nemotron_agentic", + "query_hash": "42dda1ac49c44c81694b7a291ef48e714a3fad4c9da3c3e1649ca1b7fa89fe70" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080178", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ca3d5129913cdb6b48cb6e9e26c5ac47", + "normalized_query_sha256": "db7f5824ee43eea2f77792c561fc03fd0c97e4399d51b6dc31c8b7858da08fd8", + "query": "I am planning to catch a movie with my friend later. Can you recommend a 3d movie?", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 562, + "historical_id": "fastbrain_080178", + "source": "sgd", + "query_hash": "db7f5824ee43eea2f77792c561fc03fd0c97e4399d51b6dc31c8b7858da08fd8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 562, + "historical_id": "fastbrain_080178", + "source": "sgd", + "query_hash": "db7f5824ee43eea2f77792c561fc03fd0c97e4399d51b6dc31c8b7858da08fd8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080270", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cfb0a717a4979c501589c20a25542618", + "normalized_query_sha256": "64a6f70cac88c294b373617efc808a9950d14b129ad57d40317aadde7d6a83d3", + "query": "I am arranging a business meeting at the Liberty Business Center in New York on May 15th, 2023. The meeting is scheduled to begin at 9:00 AM and will end at 1:00 PM. There will be approximately 20 attendees. We need a conference-style seating arrangement to facilitate discussion. Additionally, we require audio-visual equipment for presentations. Light refreshments should be provided as well. Pleas", + "target": "reserveConferenceRoom", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 631, + "historical_id": "fastbrain_080270", + "source": "hermes_v1", + "query_hash": "64a6f70cac88c294b373617efc808a9950d14b129ad57d40317aadde7d6a83d3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 631, + "historical_id": "fastbrain_080270", + "source": "hermes_v1", + "query_hash": "64a6f70cac88c294b373617efc808a9950d14b129ad57d40317aadde7d6a83d3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080403", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2ed8cda2cd56b02141381c6b908a6cee", + "normalized_query_sha256": "ae9106a01dd99157d0d164e92b9e869e1ed94736a2fa4193d7c675a99ffbb420", + "query": "set alarm tomorrow at six am", + "target": "alarm_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 498, + "historical_id": "semantic:validation:fastbrain_080403:present", + "source": "amazon_massive", + "query_hash": "ae9106a01dd99157d0d164e92b9e869e1ed94736a2fa4193d7c675a99ffbb420" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 830, + "historical_id": "semantic:validation:fastbrain_080403:absent", + "source": "amazon_massive", + "query_hash": "ae9106a01dd99157d0d164e92b9e869e1ed94736a2fa4193d7c675a99ffbb420" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080406", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cc80a78ade0d3c93f57f97a93559acd5", + "normalized_query_sha256": "36cf66e81870d9778460ff810525d1ec2a3bae000dd67a03c5b7e24a1b21b4b7", + "query": "is it currently raining in new york city", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 74, + "historical_id": "fastbrain_080406", + "source": "amazon_massive", + "query_hash": "36cf66e81870d9778460ff810525d1ec2a3bae000dd67a03c5b7e24a1b21b4b7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 74, + "historical_id": "fastbrain_080406", + "source": "amazon_massive", + "query_hash": "36cf66e81870d9778460ff810525d1ec2a3bae000dd67a03c5b7e24a1b21b4b7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080456", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:818782f8f3c22b6694188248ad1bfb5f", + "normalized_query_sha256": "3911c02ced9c9ec382fc9e0f572c0c5c81b7dcf948189df0d013bd13e9a36eb9", + "query": "Hi, I am planning to visit New York on 15th of next month. Can you help me find some events happening there on that day?", + "target": "search_events", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 920, + "historical_id": "fastbrain_080456", + "source": "glaive_v2", + "query_hash": "3911c02ced9c9ec382fc9e0f572c0c5c81b7dcf948189df0d013bd13e9a36eb9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 920, + "historical_id": "fastbrain_080456", + "source": "glaive_v2", + "query_hash": "3911c02ced9c9ec382fc9e0f572c0c5c81b7dcf948189df0d013bd13e9a36eb9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080464", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b01b4f4bc391e9f283cb7f309beabbc2", + "normalized_query_sha256": "1899a51c1e6a0f238f9e8bc07f4df194858ecd14a7294eaa1af2199cbb147c6b", + "query": "system. When you paid the $2,000 deposit, a payment method ID would have been created and associated with your profile. You can typically find this on your previous payment confirmation emails or invoices.\n\n**For the outstanding balance:**\nYes, you can pay the $1,500 via credit card now. If you don't have your payment method ID, we have a few options:\n\n1. You can provide the last 4 digits of the credit card you used for the deposit, and I can help locate your payment method\n2. You can use a new credit card, and I'll need the card details to process the payment\n\nHowever, before proceeding with payment, would you like me to check if any additional services were added to your reservation that might explain the price increase? I can also review your reservation details more thoroughly.\n\nAdditionally, if you believe there's been an error in the pricing, I can transfer you to a human agent who can review the booking history and pricing details more comprehensively.\nuser: Let’s start with the pricing discrepancy—can you show me a detailed breakdown of how the total increased from $2,500 to $3,500? I never received any communication about additional fees or service changes. Also, I don’t have the payment method ID on hand. I’d prefer to use a new credit card for the $1,500, but I need clarity on the total before proceeding. Can you provide an itemized invoice of all charges? If there’s no explanation for the $1,000 increase, I’ll need to escalate this to a human agent for resolution.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 39, + "historical_id": "fastbrain_080464", + "source": "nemotron_agentic", + "query_hash": "1899a51c1e6a0f238f9e8bc07f4df194858ecd14a7294eaa1af2199cbb147c6b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 39, + "historical_id": "fastbrain_080464", + "source": "nemotron_agentic", + "query_hash": "1899a51c1e6a0f238f9e8bc07f4df194858ecd14a7294eaa1af2199cbb147c6b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080664", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c5cc2a391ae4944f03bb5c9dc5845e68", + "normalized_query_sha256": "7d3c7fa8ecb2ddfa130fa3a7ba854e34ed33ceeed9b70c44d10960dc4dae89de", + "query": "This flight sounds great. Please can you look for round trip also? I would be travelling in Economy class?", + "target": "SearchRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 740, + "historical_id": "fastbrain_080664", + "source": "sgd", + "query_hash": "7d3c7fa8ecb2ddfa130fa3a7ba854e34ed33ceeed9b70c44d10960dc4dae89de" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 740, + "historical_id": "fastbrain_080664", + "source": "sgd", + "query_hash": "7d3c7fa8ecb2ddfa130fa3a7ba854e34ed33ceeed9b70c44d10960dc4dae89de" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080783", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:11c63076f6fa2417cac03b1f6a91deaf", + "normalized_query_sha256": "18c8d760e51a388eb67c72a82ddbd270844aae994f70d7dbcce73bb11b739053", + "query": "start a game and play with me", + "target": "play_game", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 494, + "historical_id": "semantic:validation:fastbrain_080783:present", + "source": "amazon_massive", + "query_hash": "18c8d760e51a388eb67c72a82ddbd270844aae994f70d7dbcce73bb11b739053" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 824, + "historical_id": "semantic:validation:fastbrain_080783:absent", + "source": "amazon_massive", + "query_hash": "18c8d760e51a388eb67c72a82ddbd270844aae994f70d7dbcce73bb11b739053" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_080960", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e80c960d3a4c24c60f8b966b99c45e7a", + "normalized_query_sha256": "9322a900726c0d991d87026b671ffe2ab87d0b30a7a4f58875dabe467ee470c2", + "query": "what are the things make a woman happy", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 396, + "historical_id": "semantic:validation:fastbrain_080960:present", + "source": "amazon_massive", + "query_hash": "9322a900726c0d991d87026b671ffe2ab87d0b30a7a4f58875dabe467ee470c2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 420, + "historical_id": "semantic:validation:fastbrain_080960:absent", + "source": "amazon_massive", + "query_hash": "9322a900726c0d991d87026b671ffe2ab87d0b30a7a4f58875dabe467ee470c2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081074", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:36ed3f1336b8573e531275d6e4e69504", + "normalized_query_sha256": "b252b360b901faf3c0631e2f7a3b10c6e1761763acdb58f29e9f40a36f9dbee9", + "query": "I need an Economy round trip flight.", + "target": "SearchRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 265, + "historical_id": "fastbrain_081074", + "source": "sgd", + "query_hash": "b252b360b901faf3c0631e2f7a3b10c6e1761763acdb58f29e9f40a36f9dbee9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 265, + "historical_id": "fastbrain_081074", + "source": "sgd", + "query_hash": "b252b360b901faf3c0631e2f7a3b10c6e1761763acdb58f29e9f40a36f9dbee9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081097", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0d7b52a9d5db2ca6897686044570c138", + "normalized_query_sha256": "7018214c32ab73bad349b0e5455c613d615e7a0a9f8e0370556bae06a71a6756", + "query": "open the new york times app", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 727, + "historical_id": "fastbrain_081097", + "source": "amazon_massive", + "query_hash": "7018214c32ab73bad349b0e5455c613d615e7a0a9f8e0370556bae06a71a6756" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 727, + "historical_id": "fastbrain_081097", + "source": "amazon_massive", + "query_hash": "7018214c32ab73bad349b0e5455c613d615e7a0a9f8e0370556bae06a71a6756" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081121", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3adf0197e1483c4f32e236d2caafb6d0", + "normalized_query_sha256": "184c7a08d9f4614303484ee025c7ce3c87735bf1bb4cf1aa86b2b2bf70bf5a59", + "query": "send an email to sarah about brunch today", + "target": "email_sendemail", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 404, + "historical_id": "fastbrain_081121", + "source": "amazon_massive", + "query_hash": "184c7a08d9f4614303484ee025c7ce3c87735bf1bb4cf1aa86b2b2bf70bf5a59" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 404, + "historical_id": "fastbrain_081121", + "source": "amazon_massive", + "query_hash": "184c7a08d9f4614303484ee025c7ce3c87735bf1bb4cf1aa86b2b2bf70bf5a59" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081144", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2b8fc4974f3ee4b6f51e05388cd820c9", + "normalized_query_sha256": "b2c58cdcb778470d83a96d34a88e87229eca9509665501d4cebf671b2dc67849", + "query": "Help me to find flights, please.", + "target": "SearchRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 853, + "historical_id": "fastbrain_081144", + "source": "sgd", + "query_hash": "b2c58cdcb778470d83a96d34a88e87229eca9509665501d4cebf671b2dc67849" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 853, + "historical_id": "fastbrain_081144", + "source": "sgd", + "query_hash": "b2c58cdcb778470d83a96d34a88e87229eca9509665501d4cebf671b2dc67849" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081278", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:28bc660ceefaefd872d6b7a2a7ce34ec", + "normalized_query_sha256": "77f16298f2973ab2274f860741c38e596c4b5cc41f13e8acdf8c3a135e19f90e", + "query": "ones 3-5) \n- Organic fertilizers required near water features \n\n#### **Billing & Payments** \n- Deposits: Design 30%, Installation 50% \n- Balance due within 15 days of completion \n- Late fees: 5% after 15 days, 2% monthly \n**Proactive Action**: After invoice updates, ask: \n*\"Verify charges match your approved modifications before payment?\"* \n\n### Sustainability Constraints \n- Drip irrigation mandatory on slopes >15° \n- Spray irrigation prohibited in areas <10 sq ft \n- Tropical plants forbidden in zone 6 or lower \n\n### Escalation Protocol \nTransfer for: \n- Hardscape engineering questions \n- Custom irrigation designs \n- Billing disputes >$500 \n- Native species permits \nPrompt: \"YOU ARE BEING TRANSFERRED TO A SPECIALIST. DESCRIBE YOUR NEEDS.\" \n\n### Agent Limitations \n1. **Never perform actions outside defined tool capabilities** \n2. **Never assume verification/actions occurred without explicit tool confirmation** \n3. Never suggest plant species outside verified inventory \n4. Refuse scheduling violating 48-hour policy \n5. Reject cost estimates not generated by quote tool \n6. Deflect troubleshooting requiring on-site inspection \n7. Avoid unapproved maintenance schedule changes\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm client C-7722 with the zip code 90210. I need to schedule a consultation regarding the mandatory drip irrigation requirement for my 18° slope as mentioned in the survey. Can you help me with the next steps?", + "target": "authenticate_client", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 738, + "historical_id": "fastbrain_081278", + "source": "nemotron_agentic", + "query_hash": "77f16298f2973ab2274f860741c38e596c4b5cc41f13e8acdf8c3a135e19f90e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 738, + "historical_id": "fastbrain_081278", + "source": "nemotron_agentic", + "query_hash": "77f16298f2973ab2274f860741c38e596c4b5cc41f13e8acdf8c3a135e19f90e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081299", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:33e626ac014e4c6b142d9c1ef110dc28", + "normalized_query_sha256": "bc97a2b8b6490ee06d96f7124914e91d8adceea22bf8beb2632658bfa80aa531", + "query": "please remove my alarm", + "target": "alarm_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 473, + "historical_id": "semantic:validation:fastbrain_081299:absent", + "source": "amazon_massive", + "query_hash": "bc97a2b8b6490ee06d96f7124914e91d8adceea22bf8beb2632658bfa80aa531" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 968, + "historical_id": "semantic:validation:fastbrain_081299:present", + "source": "amazon_massive", + "query_hash": "bc97a2b8b6490ee06d96f7124914e91d8adceea22bf8beb2632658bfa80aa531" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081307", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ba47a79b180418a7cc399a314f1790f8", + "normalized_query_sha256": "5d4ef964c72d099cd859259cc0c9202c5ddb13bba92f8f9b1615a6e0179986c3", + "query": "Hey, can you help me split the bill among my friends? We had a total bill of $200, we want to give a tip of 15% and we are 4 people.", + "target": "calculate_tip_split", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 165, + "historical_id": "fastbrain_081307", + "source": "glaive_v2", + "query_hash": "5d4ef964c72d099cd859259cc0c9202c5ddb13bba92f8f9b1615a6e0179986c3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 165, + "historical_id": "fastbrain_081307", + "source": "glaive_v2", + "query_hash": "5d4ef964c72d099cd859259cc0c9202c5ddb13bba92f8f9b1615a6e0179986c3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081312", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:00f59bce9da00c90a2a616526344cb73", + "normalized_query_sha256": "c3e705a4fd88b434731131435ac8bcab69eb7e8036cfdb8737cd9da5d9026d61", + "query": "push to a new remote repository for the first time", + "target": "git_push", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 234, + "historical_id": "fastbrain_081312", + "source": "git_nl_commands", + "query_hash": "c3e705a4fd88b434731131435ac8bcab69eb7e8036cfdb8737cd9da5d9026d61" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 234, + "historical_id": "fastbrain_081312", + "source": "git_nl_commands", + "query_hash": "c3e705a4fd88b434731131435ac8bcab69eb7e8036cfdb8737cd9da5d9026d61" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081591", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:87c7f4a726d49747324e6d0ac2548f6c", + "normalized_query_sha256": "c9a9ea2478d1b0bf547ee0c614f507dbf4bc326447455449695e12dee5fd2986", + "query": "look up in contacts", + "target": "email_querycontact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 52, + "historical_id": "semantic:validation:fastbrain_081591:present", + "source": "amazon_massive", + "query_hash": "c9a9ea2478d1b0bf547ee0c614f507dbf4bc326447455449695e12dee5fd2986" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 546, + "historical_id": "semantic:validation:fastbrain_081591:absent", + "source": "amazon_massive", + "query_hash": "c9a9ea2478d1b0bf547ee0c614f507dbf4bc326447455449695e12dee5fd2986" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081600", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ded1495431b67ff60958e755ea3b93d4", + "normalized_query_sha256": "35151ce4fb43235bf50bbb6d7211e5ca26d0052b733ef3a82ab8f4aa574ac0ec", + "query": "play my workout playlist", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 217, + "historical_id": "semantic:validation:fastbrain_081600:present", + "source": "amazon_massive", + "query_hash": "35151ce4fb43235bf50bbb6d7211e5ca26d0052b733ef3a82ab8f4aa574ac0ec" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 987, + "historical_id": "semantic:validation:fastbrain_081600:absent", + "source": "amazon_massive", + "query_hash": "35151ce4fb43235bf50bbb6d7211e5ca26d0052b733ef3a82ab8f4aa574ac0ec" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081607", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dfc42ecf9b85e748eafb6fb4adcc2814", + "normalized_query_sha256": "62eb12b83615079c13f11fcf8646edc68b502af5efa06d7fbf1fc03f9b5c6187", + "query": "Can you calculate the area of a square with a side length of 5?", + "target": "calculate_area", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 438, + "historical_id": "fastbrain_081607", + "source": "glaive_v2", + "query_hash": "62eb12b83615079c13f11fcf8646edc68b502af5efa06d7fbf1fc03f9b5c6187" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 438, + "historical_id": "fastbrain_081607", + "source": "glaive_v2", + "query_hash": "62eb12b83615079c13f11fcf8646edc68b502af5efa06d7fbf1fc03f9b5c6187" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081668", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:caa7813fa0363613ac7683d553f1f366", + "normalized_query_sha256": "0f4d63b8485ab93ae4425f688333c35aa264f80cc0a35a903c09ac68a5ad750a", + "query": "get rid of my current alarms", + "target": "alarm_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 214, + "historical_id": "semantic:validation:fastbrain_081668:present", + "source": "amazon_massive", + "query_hash": "0f4d63b8485ab93ae4425f688333c35aa264f80cc0a35a903c09ac68a5ad750a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 683, + "historical_id": "semantic:validation:fastbrain_081668:absent", + "source": "amazon_massive", + "query_hash": "0f4d63b8485ab93ae4425f688333c35aa264f80cc0a35a903c09ac68a5ad750a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_081901", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bac946a8dd1d87766145bba18ea28321", + "normalized_query_sha256": "c113df147f6ab2288201d0573802747280df277f68c54191f90dd8e4aa93cb12", + "query": "I need to create a todo list for my day. I need to do grocery shopping, pick up my dry cleaning, and finish my work report.", + "target": "create_todo_list", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 263, + "historical_id": "fastbrain_081901", + "source": "glaive_v2", + "query_hash": "c113df147f6ab2288201d0573802747280df277f68c54191f90dd8e4aa93cb12" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 263, + "historical_id": "fastbrain_081901", + "source": "glaive_v2", + "query_hash": "c113df147f6ab2288201d0573802747280df277f68c54191f90dd8e4aa93cb12" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082120", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7863ad63f348da69f8fe4cd8440eb074", + "normalized_query_sha256": "ecc1c93c60187d38645683eab2038139c9fa080d0ce349fc6e852331d6106f8e", + "query": "are there any email updates", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 14, + "historical_id": "semantic:validation:fastbrain_082120:absent", + "source": "amazon_massive", + "query_hash": "ecc1c93c60187d38645683eab2038139c9fa080d0ce349fc6e852331d6106f8e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 464, + "historical_id": "semantic:validation:fastbrain_082120:present", + "source": "amazon_massive", + "query_hash": "ecc1c93c60187d38645683eab2038139c9fa080d0ce349fc6e852331d6106f8e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082133", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3aa547988baa6c94507c3af628285124", + "normalized_query_sha256": "047b1458b0a2eed165b77cbba95ac584f8d94c971e458e452f1052ec65deff37", + "query": "The title is \"Project Update\", it should start at 10:00 and end at 11:00. The attendees are john.doe@example.com, jane.doe@example.com, and boss@example.com.", + "target": "schedule_meeting", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 964, + "historical_id": "fastbrain_082133", + "source": "glaive_v2", + "query_hash": "047b1458b0a2eed165b77cbba95ac584f8d94c971e458e452f1052ec65deff37" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 964, + "historical_id": "fastbrain_082133", + "source": "glaive_v2", + "query_hash": "047b1458b0a2eed165b77cbba95ac584f8d94c971e458e452f1052ec65deff37" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082228", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fcef423fbdf335c8cfd4c5a445d60187", + "normalized_query_sha256": "27819238bc0d9e2afe797f96e6d17933e823b8994074e17ff9e4b13396f49d90", + "query": "olly change the lighting to blue", + "target": "iot_hue_lightchange", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 422, + "historical_id": "fastbrain_082228", + "source": "amazon_massive", + "query_hash": "27819238bc0d9e2afe797f96e6d17933e823b8994074e17ff9e4b13396f49d90" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 422, + "historical_id": "fastbrain_082228", + "source": "amazon_massive", + "query_hash": "27819238bc0d9e2afe797f96e6d17933e823b8994074e17ff9e4b13396f49d90" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082289", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:91438903944a31bbf4e9126b921bc4b8", + "normalized_query_sha256": "37bedbc43fe5adb0b8488374177048eb33a7830653c0643dd1c61b1beae7615f", + "query": "set an alarm for eight p. m.", + "target": "alarm_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 48, + "historical_id": "semantic:validation:fastbrain_082289:absent", + "source": "amazon_massive", + "query_hash": "37bedbc43fe5adb0b8488374177048eb33a7830653c0643dd1c61b1beae7615f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 437, + "historical_id": "semantic:validation:fastbrain_082289:present", + "source": "amazon_massive", + "query_hash": "37bedbc43fe5adb0b8488374177048eb33a7830653c0643dd1c61b1beae7615f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082376", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:33c991aeef9f540e2f564b34e13a1148", + "normalized_query_sha256": "d2943a90f59f74ae08fa8745eba736455ed5f87ecbd80e9d42623740681804ed", + "query": "ak/generator swaps after departure \n- Equipment failure: Hourly pro-rated refund \n\n### Fuel Policies \n- Boats/RVs depart with full tanks \n- $50 refuel penalty + fuel cost if returned <¾ tank \n- Generator fuel: Prepaid in 5-gallon increments \n- Propane refunds calculated by onboard meter \n\n## Refunds & Compensation \n- Rental period shortening: Pro-rated refund \n- Equipment malfunctions: Daily credit \n- Early returns: No refund \n- Compensation limited to: \n - Reservation system errors \n - Unannounced maintenance \n - Failure to provide reserved item \n - (Verify facts before commitment) \n\n**Agent Limitations**: \n- **Do not answer questions or perform actions outside defined tool capabilities** \n- **Never assume verification or action completion without explicit tool confirmation** \n- Do not override age restrictions or insurance requirements \n- Never promise specific vehicles/locations without availability verification \n\n**Transfer Protocol**: \nUse `transfer_to_human_agents` for: \n- Multi-item accident reports \n- On-road/on-water emergencies \n- Insurance disputes over $1000 \n- Corporate account requests \nTransfer message:...[TRUNCATED]\nuser: \n\nHello, I’d like to add a generator to my RV rental reservation, but I missed the cutoff time. My DL ID is RI9900115 and confirmation code is RES223355. Is there any way to add this as a late add-on? I understand the policy requires 48-hour advance notice, but I just wanted to check if you can accommodate this.", + "target": "authenticate_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 829, + "historical_id": "fastbrain_082376", + "source": "nemotron_agentic", + "query_hash": "d2943a90f59f74ae08fa8745eba736455ed5f87ecbd80e9d42623740681804ed" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 829, + "historical_id": "fastbrain_082376", + "source": "nemotron_agentic", + "query_hash": "d2943a90f59f74ae08fa8745eba736455ed5f87ecbd80e9d42623740681804ed" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082482", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7fa9f129bded8c7277ae0bc1158bce4c", + "normalized_query_sha256": "2b582a343c8ce3f96d3a0f158b654ef62f819cf883439477205718f9dab9409a", + "query": "finish bisecting and return to my branch", + "target": "git_bisect", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 415, + "historical_id": "fastbrain_082482", + "source": "git_nl_commands", + "query_hash": "2b582a343c8ce3f96d3a0f158b654ef62f819cf883439477205718f9dab9409a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 415, + "historical_id": "fastbrain_082482", + "source": "git_nl_commands", + "query_hash": "2b582a343c8ce3f96d3a0f158b654ef62f819cf883439477205718f9dab9409a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082615", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:87e333af3f9ad7562a765075e7a4fe9a", + "normalized_query_sha256": "214a4180e997bd33465eaa295304df1dfc8181cf1e8586c342840131b0b39c69", + "query": "what could be the time in india", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 168, + "historical_id": "semantic:validation:fastbrain_082615:present", + "source": "amazon_massive", + "query_hash": "214a4180e997bd33465eaa295304df1dfc8181cf1e8586c342840131b0b39c69" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 735, + "historical_id": "semantic:validation:fastbrain_082615:absent", + "source": "amazon_massive", + "query_hash": "214a4180e997bd33465eaa295304df1dfc8181cf1e8586c342840131b0b39c69" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082713", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:07434b531d787fd077369ba209dbbdab", + "normalized_query_sha256": "8bf2d4489e2aeb2039223dac63e4cdc9e97d3f1760d3b063175dfa629bad5027", + "query": "i need a recipe for chicken thighs for my pressure cooker", + "target": "cooking_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 575, + "historical_id": "semantic:validation:fastbrain_082713:present", + "source": "amazon_massive", + "query_hash": "8bf2d4489e2aeb2039223dac63e4cdc9e97d3f1760d3b063175dfa629bad5027" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 994, + "historical_id": "semantic:validation:fastbrain_082713:absent", + "source": "amazon_massive", + "query_hash": "8bf2d4489e2aeb2039223dac63e4cdc9e97d3f1760d3b063175dfa629bad5027" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082732", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:42e9702f1c859e0abdd4cd80bdae2b10", + "normalized_query_sha256": "dd65ce5cf6db48390973ef58e894899859ea46625dfe472f912337dc5b9a0e2a", + "query": "That would work. Could you also find me a round trip flight? I'd like to return on the 14th of this month. I'd like to travel in Economy and want to leave on the 1st.", + "target": "SearchRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 498, + "historical_id": "fastbrain_082732", + "source": "sgd", + "query_hash": "dd65ce5cf6db48390973ef58e894899859ea46625dfe472f912337dc5b9a0e2a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 498, + "historical_id": "fastbrain_082732", + "source": "sgd", + "query_hash": "dd65ce5cf6db48390973ef58e894899859ea46625dfe472f912337dc5b9a0e2a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082836", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:80c795dac5ab8c71c6d5e7666f5000e4", + "normalized_query_sha256": "6b6bf2a4fda6e2a672bb9adc666d6756fc4c9243c8607f7eb57bbe3e218d5465", + "query": "ur information. If you don't have it handy, you might check previous emails from Premier Motors, your account paperwork, or you could contact our dealership directly to retrieve it.\n\n**Registration Documentation:** As a chatbot agent, I'm not authorized to provide specific legal advice about what forms of identification are acceptable for vehicle registration. This includes interpreting whether a passport, student ID, or other documents would suffice. Our dealership specialists who handle registration paperwork have the expertise to guide you through the exact documentation requirements for your specific situation.\n\n**Reservation Process:** Once you have your customer ID, here's what we'll need to reserve the Camry XLE:\n1. Stock number: TAC2024C\n2. Price confirmation: $33,800 (must be explicitly confirmed)\n3. Hold duration: Up to 24 hours maximum for certified vehicles\n4. $500 refundable deposit\n\nCould you please retrieve your customer ID so we can proceed with the reservation? After the reservation is placed, our registration specialists will be available to answer all your documentation questions in detail.\nuser: I understand the need for my customer ID, but I’m currently unable to locate it. Is there any way to proceed with the reservation temporarily while I retrieve it? Also, could you please connect me to a registration specialist to confirm if a passport would suffice for my daughter’s identification? I want to ensure we don’t face delays when finalizing the paperwork.", + "target": "transfer_to_human_agents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 704, + "historical_id": "fastbrain_082836", + "source": "nemotron_agentic", + "query_hash": "6b6bf2a4fda6e2a672bb9adc666d6756fc4c9243c8607f7eb57bbe3e218d5465" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 704, + "historical_id": "fastbrain_082836", + "source": "nemotron_agentic", + "query_hash": "6b6bf2a4fda6e2a672bb9adc666d6756fc4c9243c8607f7eb57bbe3e218d5465" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082935", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a061728c12bc6c157dc6037731ee8b1f", + "normalized_query_sha256": "ce4c9c5c360e2175ee3466f20819c8192d0c0b4aaeeacb9c26b5cb717efbe466", + "query": "is there a food festival in the area", + "target": "recommendation_events", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 0, + "historical_id": "semantic:validation:fastbrain_082935:present", + "source": "amazon_massive", + "query_hash": "ce4c9c5c360e2175ee3466f20819c8192d0c0b4aaeeacb9c26b5cb717efbe466" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 931, + "historical_id": "semantic:validation:fastbrain_082935:absent", + "source": "amazon_massive", + "query_hash": "ce4c9c5c360e2175ee3466f20819c8192d0c0b4aaeeacb9c26b5cb717efbe466" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082936", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:85c8f72d5e1468da3ece19c91b32268a", + "normalized_query_sha256": "a9f12440ba5a5c1a61947391a8e12371b0f1c3676d99fcdcd3a4dc8561277594", + "query": "show me today all train times of next two hours at jodhpur to pali", + "target": "transport_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 141, + "historical_id": "semantic:validation:fastbrain_082936:present", + "source": "amazon_massive", + "query_hash": "a9f12440ba5a5c1a61947391a8e12371b0f1c3676d99fcdcd3a4dc8561277594" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 412, + "historical_id": "semantic:validation:fastbrain_082936:absent", + "source": "amazon_massive", + "query_hash": "a9f12440ba5a5c1a61947391a8e12371b0f1c3676d99fcdcd3a4dc8561277594" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_082966", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:aa93c6d0b66dd75b8f729a06ca42417c", + "normalized_query_sha256": "a2e5bcb038d6dc5423f927c3f82ba7bc4b935e9c901568066d84ff165d072ddb", + "query": "Can you help me schedule a meeting for tomorrow at 10:00 AM with John and Sarah?", + "target": "schedule_meeting", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 551, + "historical_id": "fastbrain_082966", + "source": "glaive_v2", + "query_hash": "a2e5bcb038d6dc5423f927c3f82ba7bc4b935e9c901568066d84ff165d072ddb" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 551, + "historical_id": "fastbrain_082966", + "source": "glaive_v2", + "query_hash": "a2e5bcb038d6dc5423f927c3f82ba7bc4b935e9c901568066d84ff165d072ddb" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083118", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3013d9d44c7a6c9f88110a695f892dca", + "normalized_query_sha256": "2df32f787a7a7968668a6d0decbbe768afe68f620eae8cf30acc772900f3f739", + "query": "set the alarm for five o'clock in the morning", + "target": "alarm_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 213, + "historical_id": "semantic:validation:fastbrain_083118:absent", + "source": "amazon_massive", + "query_hash": "2df32f787a7a7968668a6d0decbbe768afe68f620eae8cf30acc772900f3f739" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 360, + "historical_id": "semantic:validation:fastbrain_083118:present", + "source": "amazon_massive", + "query_hash": "2df32f787a7a7968668a6d0decbbe768afe68f620eae8cf30acc772900f3f739" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083268", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ff62c54955943031012f1b0ee49e5a6b", + "normalized_query_sha256": "9ee3b1bc185095febee0f0d69da6cc5aedd0041dad043fb18d71eb4cb5b2f897", + "query": "Hi, I want to know how much I can save if I buy a dress that costs $100 with a 20% discount.", + "target": "calculate_discount", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 228, + "historical_id": "fastbrain_083268", + "source": "glaive_v2", + "query_hash": "9ee3b1bc185095febee0f0d69da6cc5aedd0041dad043fb18d71eb4cb5b2f897" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 228, + "historical_id": "fastbrain_083268", + "source": "glaive_v2", + "query_hash": "9ee3b1bc185095febee0f0d69da6cc5aedd0041dad043fb18d71eb4cb5b2f897" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083505", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e37737d36fb0c6b10db366cf70a7dce4", + "normalized_query_sha256": "becb2e38d5bc99d25c4fab2de835cb80db38eb06b611162ab86cd844668c4acb", + "query": "I am responsible for the data accuracy in our company's marketing database. We have a CSV file named \"customer_information.csv\" stored on our secure server, located at \"path/to/customer_dataset.csv\". The important fields in this dataset that need validation are email addresses, postal codes, and opt-in statuses. I need to ensure that all email addresses adhere to a standard format, postal codes ar", + "target": "validate_customer_data", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 567, + "historical_id": "fastbrain_083505", + "source": "hermes_v1", + "query_hash": "becb2e38d5bc99d25c4fab2de835cb80db38eb06b611162ab86cd844668c4acb" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 567, + "historical_id": "fastbrain_083505", + "source": "hermes_v1", + "query_hash": "becb2e38d5bc99d25c4fab2de835cb80db38eb06b611162ab86cd844668c4acb" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083544", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c1eace25e2d3edb85470394eb3014a6c", + "normalized_query_sha256": "03545c4cb3c716aba72a95e7dd247e8ebabd14a440f094ccc78a128fca4fd06e", + "query": "Sure. Please can you schedule an appointment for 3rd of this month at morning 11?", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 952, + "historical_id": "fastbrain_083544", + "source": "sgd", + "query_hash": "03545c4cb3c716aba72a95e7dd247e8ebabd14a440f094ccc78a128fca4fd06e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 952, + "historical_id": "fastbrain_083544", + "source": "sgd", + "query_hash": "03545c4cb3c716aba72a95e7dd247e8ebabd14a440f094ccc78a128fca4fd06e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083775", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ae4a1af88abc2f51178f9964d7da78c6", + "normalized_query_sha256": "a2d49ac0537583846fff6c891e1b4f4b6c1f7e9985e197122e807dc852d60c6b", + "query": "ns and exchanges are tier-based:\n\n- Bronze: 15%\n- **Silver: 10%** (as you were charged)\n- Gold: Free\n\nSilver membership benefits include free shipping, but restocking fees still apply at the 10% rate for both returns and exchanges. The charge appears to be correctly applied based on our policy.\n\nIf you'd like me to review the specific exchange transaction, please provide the order ID so I can check the details.\nuser: Thank you for confirming the policy, but I’d like to request the 10% fee be reversed regardless. As a retired firefighter, I’ve supported this company for years, and I believe the policy may have been miscommunicated. Could you manually reverse the charge as a courtesy?\nassistant: I understand your request and appreciate your long-term support as a retired firefighter. However, as an automated support agent, I don't have the capability to manually reverse charges or make exceptions to the published policy. The 10% restocking fee for Silver members was correctly applied according to our current terms.\n\nIf you believe there's been a genuine error or miscommunication that requires further review, I can transfer you to a human agent who may have more flexibility to address your concern. Would you like me to escalate this to our human support team?\nuser: Yes, please transfer me to a human agent. I’d like to speak with someone who can review my case and determine if the fee can be waived, especially considering my status as a retired firefighter and long-time customer.", + "target": "transfer_to_human_agents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 907, + "historical_id": "fastbrain_083775", + "source": "nemotron_agentic", + "query_hash": "a2d49ac0537583846fff6c891e1b4f4b6c1f7e9985e197122e807dc852d60c6b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 907, + "historical_id": "fastbrain_083775", + "source": "nemotron_agentic", + "query_hash": "a2d49ac0537583846fff6c891e1b4f4b6c1f7e9985e197122e807dc852d60c6b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083779", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4107dbed705968197bf02ffa1f8ff461", + "normalized_query_sha256": "f96285289169f21018fc3502fb87061a10f2d20ac6de7e2b5caaca58b2fadef8", + "query": "Hi, can you get me the latest news?", + "target": "get_news", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 314, + "historical_id": "fastbrain_083779", + "source": "glaive_v2", + "query_hash": "f96285289169f21018fc3502fb87061a10f2d20ac6de7e2b5caaca58b2fadef8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 314, + "historical_id": "fastbrain_083779", + "source": "glaive_v2", + "query_hash": "f96285289169f21018fc3502fb87061a10f2d20ac6de7e2b5caaca58b2fadef8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083792", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:10665c4a297f48366b04e41e2cc33de0", + "normalized_query_sha256": "dc274ee4474b4fc85f170eccd4b17a2d2839332fc65843f262b631c9e387f850", + "query": " (UN3481)\" in the special items, which is correct. However, this needs to be properly included in the commercial invoice.\n\n2. **MSDS Sheet Requirements**: **Yes, you need to provide MSDS sheets for all lithium-ion battery types in your shipment.** Singapore customs requires Material Safety Data Sheets for hazardous materials like UN3481 lithium-ion batteries. For 40kg of batteries, you should provide MSDS for each battery model/power type.\n\n**Next Steps:**\nSince customs documents haven't been submitted yet, I can generate new APAC customs documents (commercial invoice + packing list) with the correct UN3481 classification included. Would you like me to generate these documents now?\n\n**Important Notes:**\n- APAC shipments require both commercial invoice and packing list\n- The UN3481 classification must appear on both documents\n- You'll need to attach MSDS sheets separately when submitting to customs\n- Lithium-ion batteries must be properly packaged according to IATA/IMDG regulations\n\nWould you like me to proceed with generating the customs documents for shipment S66666?\nuser: Yes, please proceed to generate the APAC customs documents (commercial invoice + packing list) with the correct UN3481 classification for shipment S66666. I will ensure the MSDS sheets for all lithium-ion battery types in the 21m³ cargo are attached separately as required by Singapore customs. Can you confirm the updated documents will include the UN3481 classification on both the invoice and packing list?", + "target": "generate_customs_documents", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 509, + "historical_id": "fastbrain_083792", + "source": "nemotron_agentic", + "query_hash": "dc274ee4474b4fc85f170eccd4b17a2d2839332fc65843f262b631c9e387f850" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 509, + "historical_id": "fastbrain_083792", + "source": "nemotron_agentic", + "query_hash": "dc274ee4474b4fc85f170eccd4b17a2d2839332fc65843f262b631c9e387f850" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083802", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:faebc73e4ee32e0676e793a7afb2b467", + "normalized_query_sha256": "cbaedbb849c3df6d07412bb2cbe976153d2c6ac5a798521a5b309bf373de31d7", + "query": "can i have the ten day forecast for me area", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 273, + "historical_id": "semantic:validation:fastbrain_083802:present", + "source": "amazon_massive", + "query_hash": "cbaedbb849c3df6d07412bb2cbe976153d2c6ac5a798521a5b309bf373de31d7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 655, + "historical_id": "semantic:validation:fastbrain_083802:absent", + "source": "amazon_massive", + "query_hash": "cbaedbb849c3df6d07412bb2cbe976153d2c6ac5a798521a5b309bf373de31d7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_083977", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:279c723bfe3b6286bf9afa0a8530d016", + "normalized_query_sha256": "29117fa033827c4edb9bf00d960507bb7a5c5eaefc2993c81f4d3c7b97262cd9", + "query": "tell me international news", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 270, + "historical_id": "semantic:validation:fastbrain_083977:absent", + "source": "amazon_massive", + "query_hash": "29117fa033827c4edb9bf00d960507bb7a5c5eaefc2993c81f4d3c7b97262cd9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 676, + "historical_id": "semantic:validation:fastbrain_083977:present", + "source": "amazon_massive", + "query_hash": "29117fa033827c4edb9bf00d960507bb7a5c5eaefc2993c81f4d3c7b97262cd9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_084158", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e6cb7cbdc1ac4b5a19080dd15e2193f8", + "normalized_query_sha256": "011e2048639ace0babcbe3c2834ba30b529a2af3049ce52900d2bf1f1fc11736", + "query": "s without signed liability waiver \n\n## Identity Verification Protocol \nWhen authentication is required: \n1. Request user's email or phone number \n2. Call `send_verification_code` to deliver authentication code \n3. Prompt user: \"Please share the code sent to your contact method\" \n4. Validate code via `verify_authentication_code` before proceeding \n\n## Agent Limitations \n1. **Action Boundaries**: \n - Only use provided tools; never perform non-supported actions \n - Do not answer questions outside tool capabilities \n2. **Verification Rules**: \n - Never assume completion without explicit tool confirmation \n - Treat all actions as incomplete until system verification \n3. **Information Boundaries**: \n - No speculation on property value impact \n - Never override package constraints without approval \n\n## Transfer Protocol \nImmediately escalate: \n- Custom furniture requests \n- Insurance claim negotiations \n- Damage assessment disputes \n- Complex financing arrangements \nExecute: `escalate_to_human(\"Issue summary\")` + transfer notification\n\n\nTry to be helpful and alw...[TRUNCATED]\nuser: \n\nHello, I'm a real estate agent managing a probate sale and need to reschedule the home staging service due to an upcoming probate hearing. Could you please provide the official documentation required to request a fee waiver for the rescheduling? I’ll need to submit this to the court for approval, and I want to ensure the process is handled promptly. Thank you!", + "target": "escalate_to_human", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 951, + "historical_id": "fastbrain_084158", + "source": "nemotron_agentic", + "query_hash": "011e2048639ace0babcbe3c2834ba30b529a2af3049ce52900d2bf1f1fc11736" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 951, + "historical_id": "fastbrain_084158", + "source": "nemotron_agentic", + "query_hash": "011e2048639ace0babcbe3c2834ba30b529a2af3049ce52900d2bf1f1fc11736" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_084531", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dfba619e5677d4b5bfa8e210ae592da6", + "normalized_query_sha256": "d8d237ccad9afbc42f3a89433af8b70a0e42864d250d7c68a91f1819c3bd875e", + "query": "is it raining outside", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 259, + "historical_id": "semantic:validation:fastbrain_084531:absent", + "source": "amazon_massive", + "query_hash": "d8d237ccad9afbc42f3a89433af8b70a0e42864d250d7c68a91f1819c3bd875e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 786, + "historical_id": "semantic:validation:fastbrain_084531:present", + "source": "amazon_massive", + "query_hash": "d8d237ccad9afbc42f3a89433af8b70a0e42864d250d7c68a91f1819c3bd875e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085086", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8c9013fb32068ce50527faaa3dd1b6fb", + "normalized_query_sha256": "c6273fdc291547d6af3b6e0a01d7b91ce9cec2c4666dcc762414b517ea5de265", + "query": "could you please display the videos for cooking italian food", + "target": "cooking_recipe", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 54, + "historical_id": "fastbrain_085086", + "source": "amazon_massive", + "query_hash": "c6273fdc291547d6af3b6e0a01d7b91ce9cec2c4666dcc762414b517ea5de265" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 54, + "historical_id": "fastbrain_085086", + "source": "amazon_massive", + "query_hash": "c6273fdc291547d6af3b6e0a01d7b91ce9cec2c4666dcc762414b517ea5de265" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085171", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ff8b1d734ef62705e888ad4789d65dca", + "normalized_query_sha256": "86ad613831c0402e90b8e01ff1e2a16660f652824a77fbca5f29106ebc1cb4da", + "query": "Yes, I would like to go ahead and book it.", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 154, + "historical_id": "fastbrain_085171", + "source": "sgd", + "query_hash": "86ad613831c0402e90b8e01ff1e2a16660f652824a77fbca5f29106ebc1cb4da" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 154, + "historical_id": "fastbrain_085171", + "source": "sgd", + "query_hash": "86ad613831c0402e90b8e01ff1e2a16660f652824a77fbca5f29106ebc1cb4da" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085196", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f0a2a7ff8dfdde708821f5983ad17227", + "normalized_query_sha256": "0617941cbb4c78502a982abbc204c7caaed00bac637d73a4851b25f887ee8c8e", + "query": "Hi, I have a product that costs $100 and I want to apply a 20% discount. Can you help me calculate the discounted price?", + "target": "calculate_discount", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 15, + "historical_id": "fastbrain_085196", + "source": "glaive_v2", + "query_hash": "0617941cbb4c78502a982abbc204c7caaed00bac637d73a4851b25f887ee8c8e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 15, + "historical_id": "fastbrain_085196", + "source": "glaive_v2", + "query_hash": "0617941cbb4c78502a982abbc204c7caaed00bac637d73a4851b25f887ee8c8e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085405", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2cf9ee406d625c013e5d2c8f7b336965", + "normalized_query_sha256": "138e2764ffa6b72f738d6f230b06ef69fa6ce3bcf7e1226564c897f5cab61f1d", + "query": "mi to London Heathrow in Economy class. I have a suitcase that weighs 28kg, and I need to confirm the excess baggage fee for a second bag exceeding the 23kg limit. Is the charge $100 for this?\nassistant: \ntool: \n\n{\"baggage_allowance\": \"1 × 23kg\", \"flight_number\": \"SJ404\", \"departure_time\": \"2024-03-20T10:30:00Z\", \"fare_class\": \"Economy\", \"seat_assigned\": false}\nassistant: Hello! I can help you with baggage fee information for your booking BAG28 on flight SJ404.\n\nFirst, I need to verify your identity for security purposes. Could you please provide your SkyJet ID?\n\nAlso, to clarify your baggage situation:\n- Your Economy fare includes **1 free bag up to 23kg**.\n- You mentioned a suitcase weighing 28kg. Is this your **first bag** (which would be overweight) or a **second bag** beyond your free allowance?\n\nAccording to SkyJet policy:\n- **Economy class**: 1 × 23kg free bag\n- **Excess bags**: $100 per additional bag (up to 32kg)\n- **Oversized bags** (>32kg): Require cargo transfer\n\nYour 28kg bag is under the 32kg oversized limit, so if it's a second bag, the fee would be $100. If it's your first bag but overweight, please clarify so I can check the exact overweight charges.\n\nOnce I have your SkyJet ID and clarification, I can provide precise fee information and help you add the baggage if needed.\nuser: My SkyJet ID is **SJ123456**, and the 28kg suitcase is my **second bag**. Could you confirm the $100 excess fee for this Economy class booking? I’d like to proceed with paying it now.", + "target": "add_baggage", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 880, + "historical_id": "fastbrain_085405", + "source": "nemotron_agentic", + "query_hash": "138e2764ffa6b72f738d6f230b06ef69fa6ce3bcf7e1226564c897f5cab61f1d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 880, + "historical_id": "fastbrain_085405", + "source": "nemotron_agentic", + "query_hash": "138e2764ffa6b72f738d6f230b06ef69fa6ce3bcf7e1226564c897f5cab61f1d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085430", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:738145411d5cd4212ca59b1efcfb866b", + "normalized_query_sha256": "2fbf2f00a0b5ca05f7ab913cd56efc8d932331ab2f3c3ff91f923925020b8668", + "query": " staff for: \n- On-site incidents \n- Refunds >$100 \n- Group bookings (>20 tickets) \n- Voucher redemption issues \n\nTransfer process: \n1. Use `transfer_to_human_agent` tool \n2. Send: \"YOU ARE BEING TRANSFERRED TO CINEMA STAFF. PLEASE HOLD ON.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHi, I'd like to request a seat change from row K to row D in the Standard tier. Can you check if there are available seats in row D for my event?\nassistant: Hello! I'd be happy to help you with your seat change request. To check seat availability in row D and process the relocation, I'll need some information from you.\n\nFirst, I need to verify your identity. Could you please provide:\n1. Your User ID and registered phone number **OR**\n2. Your booking confirmation code and the last 4 digits of the payment card used\n\nAdditionally, I'll need:\n- Your ticket ID\n- The screening ID for your event\n\nOnce I have this information, I can check the available seats in row D (Standard tier) and help you with the relocation. Please note that seat changes can only be made within the same price tier, which you've already indicated (Standard tier).\n\nWhat authentication method would you prefer to use?\nuser: I'll use option 2 for authentication. My booking confirmation code is **BK482719**, the last 4 digits of my payment card are **5683**, ticket ID is **TIX-STD-9274**, and the screening ID is **SCR-345-THU**. Could you check if there are open seats in row D (Standard tier) for my event?", + "target": "authenticate_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 864, + "historical_id": "fastbrain_085430", + "source": "nemotron_agentic", + "query_hash": "2fbf2f00a0b5ca05f7ab913cd56efc8d932331ab2f3c3ff91f923925020b8668" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 864, + "historical_id": "fastbrain_085430", + "source": "nemotron_agentic", + "query_hash": "2fbf2f00a0b5ca05f7ab913cd56efc8d932331ab2f3c3ff91f923925020b8668" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085438", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5967ae118a55d75a160f6e58afd411a8", + "normalized_query_sha256": "16c4ae940e2bc6f26ef0e8d028ef0e8b43c58b53ab730edd0315e4ca573d82f4", + "query": "What type of attractions at in the area?", + "target": "FindAttractions", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 388, + "historical_id": "fastbrain_085438", + "source": "sgd", + "query_hash": "16c4ae940e2bc6f26ef0e8d028ef0e8b43c58b53ab730edd0315e4ca573d82f4" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 388, + "historical_id": "fastbrain_085438", + "source": "sgd", + "query_hash": "16c4ae940e2bc6f26ef0e8d028ef0e8b43c58b53ab730edd0315e4ca573d82f4" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085476", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:35eb6dbd694056e4a8ec162bee36295f", + "normalized_query_sha256": "04bbcc781f44f65fefc047bc4ed6719e259eabe9e0df53058751724bb1794857", + "query": "set reminder for meeting at three p. m. tomorrow", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 831, + "historical_id": "fastbrain_085476", + "source": "amazon_massive", + "query_hash": "04bbcc781f44f65fefc047bc4ed6719e259eabe9e0df53058751724bb1794857" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 831, + "historical_id": "fastbrain_085476", + "source": "amazon_massive", + "query_hash": "04bbcc781f44f65fefc047bc4ed6719e259eabe9e0df53058751724bb1794857" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085592", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:44333bd2c7e6ae04aee0a6e210c7aa2b", + "normalized_query_sha256": "15bc93b81fb0ddce19d76f64d60f5bea709b41ca3d42554cdae5e1f6a54c4c85", + "query": "me issue that was supposedly fixed in repair on August 20, 2025 (Ticket RETURN2025). Noise persists immediately after repair completion.\"\n\n**Important Notes:**\n- Warranty claims require a mandatory inspection within 7 days of submission\n- Common denial reasons include: lack of maintenance records, improper use, modifications not approved by manufacturer, or damage from external causes\n- For manufacturer defect claims that require complex assessment, we may need to escalate to a human agent\n\nWould you like to proceed with submitting the warranty claim now? If so, please provide the incident timestamp and photo URLs.\nuser: I’ll provide the requested details now. The incident timestamp is **2025-08-21T09:15:00** (first noticed the knocking noise the day after the repair). Here are the **photo URLs**: \n1. https://photos.example.com/engine_compartment_1.jpg \n2. https://photos.example.com/engine_compartment_2.jpg \n\nFor the **detailed description**, I’ll reiterate: \"The boat’s engine produces a loud knocking noise under load, identical to the issue that was repaired on August 20, 2025 (Ticket RETURN2025). The noise persists immediately after the repair was completed, suggesting the fix was ineffective. Photos show the components that were supposedly addressed during the repair.\" \n\nCan you confirm these details are sufficient to submit the warranty claim? Also, will the inspection within 7 days be scheduled automatically, or should I contact the dealership directly to arrange it?", + "target": "submit_warranty", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 517, + "historical_id": "fastbrain_085592", + "source": "nemotron_agentic", + "query_hash": "15bc93b81fb0ddce19d76f64d60f5bea709b41ca3d42554cdae5e1f6a54c4c85" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 517, + "historical_id": "fastbrain_085592", + "source": "nemotron_agentic", + "query_hash": "15bc93b81fb0ddce19d76f64d60f5bea709b41ca3d42554cdae5e1f6a54c4c85" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085675", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:92a6649fd94e4cfddfcc49458fb4cf11", + "normalized_query_sha256": "e97f43a8f73778312dada133b984445ea49c9b08286ea86649487db8ad9ad416", + "query": "i have to done it on the post important music file", + "target": "music_likeness", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 552, + "historical_id": "semantic:validation:fastbrain_085675:absent", + "source": "amazon_massive", + "query_hash": "e97f43a8f73778312dada133b984445ea49c9b08286ea86649487db8ad9ad416" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 936, + "historical_id": "semantic:validation:fastbrain_085675:present", + "source": "amazon_massive", + "query_hash": "e97f43a8f73778312dada133b984445ea49c9b08286ea86649487db8ad9ad416" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_085749", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:85c824c928add59bf91015963b320c28", + "normalized_query_sha256": "623f60bce08bbda0abb426a266d3d3cf3d584a16aeaf9a4fc04f2355d2148c07", + "query": "are there any emails from the school about my conference", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 526, + "historical_id": "semantic:validation:fastbrain_085749:present", + "source": "amazon_massive", + "query_hash": "623f60bce08bbda0abb426a266d3d3cf3d584a16aeaf9a4fc04f2355d2148c07" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 705, + "historical_id": "semantic:validation:fastbrain_085749:absent", + "source": "amazon_massive", + "query_hash": "623f60bce08bbda0abb426a266d3d3cf3d584a16aeaf9a4fc04f2355d2148c07" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_086080", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:21c928d1b12af7e3d7f18c71697b748f", + "normalized_query_sha256": "18fb7b20f48ff7b3d893c86f1879ff1a50ef9ef557e74ac2b5a426e4ecd6392b", + "query": "remind me about my lunch date for monday", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 447, + "historical_id": "fastbrain_086080", + "source": "amazon_massive", + "query_hash": "18fb7b20f48ff7b3d893c86f1879ff1a50ef9ef557e74ac2b5a426e4ecd6392b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 447, + "historical_id": "fastbrain_086080", + "source": "amazon_massive", + "query_hash": "18fb7b20f48ff7b3d893c86f1879ff1a50ef9ef557e74ac2b5a426e4ecd6392b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_086234", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c68029601c719870d6de5dba4b9a7ec2", + "normalized_query_sha256": "93ddc49cebe0f8cd252946c888818acc935456fabcc903179ae804130e44c186", + "query": "create new contact with email", + "target": "email_addcontact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 930, + "historical_id": "fastbrain_086234", + "source": "amazon_massive", + "query_hash": "93ddc49cebe0f8cd252946c888818acc935456fabcc903179ae804130e44c186" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 930, + "historical_id": "fastbrain_086234", + "source": "amazon_massive", + "query_hash": "93ddc49cebe0f8cd252946c888818acc935456fabcc903179ae804130e44c186" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_086285", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5750b37d4bc19221221f7a2fbfa198ac", + "normalized_query_sha256": "fa4b7364b0f72adad39d02b125c5beb3bf45bae0bfc3d01e218a77234977988c", + "query": "have i gotten any emails lately", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 472, + "historical_id": "semantic:validation:fastbrain_086285:absent", + "source": "amazon_massive", + "query_hash": "fa4b7364b0f72adad39d02b125c5beb3bf45bae0bfc3d01e218a77234977988c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 764, + "historical_id": "semantic:validation:fastbrain_086285:present", + "source": "amazon_massive", + "query_hash": "fa4b7364b0f72adad39d02b125c5beb3bf45bae0bfc3d01e218a77234977988c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_086359", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e38fa0db26dce9acf45594bb5b418901", + "normalized_query_sha256": "c91b5084bc1156684ed0afa1eb56294a8df435fe02f2e7d2a197ea3a7dfdbcf8", + "query": "i would like to listen to some down tempo", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 401, + "historical_id": "semantic:validation:fastbrain_086359:absent", + "source": "amazon_massive", + "query_hash": "c91b5084bc1156684ed0afa1eb56294a8df435fe02f2e7d2a197ea3a7dfdbcf8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 529, + "historical_id": "semantic:validation:fastbrain_086359:present", + "source": "amazon_massive", + "query_hash": "c91b5084bc1156684ed0afa1eb56294a8df435fe02f2e7d2a197ea3a7dfdbcf8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_086429", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ce32d49076f836273b40db3208885fa5", + "normalized_query_sha256": "ad9c8120fe00733fad33fb8033d2c58161dc2b33ce7d3a840e4e120503257aba", + "query": "I looking for an event in SF on March 13th.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1009, + "historical_id": "fastbrain_086429", + "source": "sgd", + "query_hash": "ad9c8120fe00733fad33fb8033d2c58161dc2b33ce7d3a840e4e120503257aba" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1009, + "historical_id": "fastbrain_086429", + "source": "sgd", + "query_hash": "ad9c8120fe00733fad33fb8033d2c58161dc2b33ce7d3a840e4e120503257aba" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_086576", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d9d806b62ab0a58c10183c66508ce4a6", + "normalized_query_sha256": "51605f80226f20b36dcb61317e813f0be56a9d7d1c05eea6fe52bf4a2c3c726e", + "query": "please check my inbox and tell me if there are any new arrivals", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 299, + "historical_id": "semantic:validation:fastbrain_086576:absent", + "source": "amazon_massive", + "query_hash": "51605f80226f20b36dcb61317e813f0be56a9d7d1c05eea6fe52bf4a2c3c726e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 976, + "historical_id": "semantic:validation:fastbrain_086576:present", + "source": "amazon_massive", + "query_hash": "51605f80226f20b36dcb61317e813f0be56a9d7d1c05eea6fe52bf4a2c3c726e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_086658", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:07497a6fcf03bf1420bd13d65908a753", + "normalized_query_sha256": "1614b4a8a9a04c957d7659b0bfbe3cf4663f90db94f97e360770848a39f15653", + "query": "Perhaps later. 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Rowling?", + "target": "search_books", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 310, + "historical_id": "fastbrain_086850", + "source": "glaive_v2", + "query_hash": "14767f14bbc4d6eb4696de34d890d0f87bab6a8c69a8ca8c696e20e2c96c8b6d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 310, + "historical_id": "fastbrain_086850", + "source": "glaive_v2", + "query_hash": "14767f14bbc4d6eb4696de34d890d0f87bab6a8c69a8ca8c696e20e2c96c8b6d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_086932", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7b4d18dbe8d4ac2eb3543a06c90c3f84", + "normalized_query_sha256": "77b88734f5e387cd00b6ec2d60a1fe6083a5ecd7f7c00b9a511b61e873f66b5b", + "query": "show us red", + "target": "iot_hue_lightchange", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 241, + "historical_id": "semantic:validation:fastbrain_086932:present", + "source": "amazon_massive", + "query_hash": "77b88734f5e387cd00b6ec2d60a1fe6083a5ecd7f7c00b9a511b61e873f66b5b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 858, + "historical_id": "semantic:validation:fastbrain_086932:absent", + "source": "amazon_massive", + "query_hash": "77b88734f5e387cd00b6ec2d60a1fe6083a5ecd7f7c00b9a511b61e873f66b5b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087009", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ae46924f15e6f319a827d83f0c5a88ec", + "normalized_query_sha256": "80a0f138cdd874cb5185146a3c9984c9c67a07ef0bc82d28601b061dd879c474", + "query": "ions \n1. **Action Boundaries**: \n - Do not answer questions or perform actions outside defined tool capabilities \n - Never assume verification/action completion without explicit tool confirmation \n\n2. **Verification Protocol**: \n - Require Customer ID + delivery ZIP code for account access \n - Validate through tool execution before: \n - Ingredient substitutions \n - Delivery revisions \n - Payment changes \n\n---\n\n### Compliance Requirements \n- **Allergen Disclosure**: \n Always warn of cross-contamination risks for peanut/tree nut items \n- **Payment Security**: \n Mask card numbers as ****-****-****-1234 \n- **Temperature Validation**: \n Require thermal sensor proof for perishable claims \n\n---\n\n### Transfer Protocol \nTransfer to human agent for: \n- Medical emergency allergen exposures \n- Subscription billing disputes >$100 \n- Third-party delivery complaints \n**Process**: \n1. Use `transfer_to_human_agent` tool \n2. Send message: \n \"YOU ARE BEING TRANSFERRED TO A DEDICATED SUPPORT AGENT. PLEASE DESCRIBE YOUR CONCERN TO THEM.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm interested in planning a special meal for Valentine's Day. Do you offer themed menus for February 14th? I'd love something romantic and elegant, but I also need options that accommodate both a vegetarian and gluten-free preference. Could you let me know if your seasonal menu for this holiday aligns with your published calendar offerings?", + "target": "get_weekly_menu", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 84, + "historical_id": "fastbrain_087009", + "source": "nemotron_agentic", + "query_hash": "80a0f138cdd874cb5185146a3c9984c9c67a07ef0bc82d28601b061dd879c474" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 84, + "historical_id": "fastbrain_087009", + "source": "nemotron_agentic", + "query_hash": "80a0f138cdd874cb5185146a3c9984c9c67a07ef0bc82d28601b061dd879c474" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087079", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:793f342bd0057c5495129e0e89d6c495", + "normalized_query_sha256": "e9abe124038c528610ddce1ef108e27e15d6d757f296539b02ddb2f280dce967", + "query": "remind me every monday to take out the trash", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 219, + "historical_id": "semantic:validation:fastbrain_087079:absent", + "source": "amazon_massive", + "query_hash": "e9abe124038c528610ddce1ef108e27e15d6d757f296539b02ddb2f280dce967" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 865, + "historical_id": "semantic:validation:fastbrain_087079:present", + "source": "amazon_massive", + "query_hash": "e9abe124038c528610ddce1ef108e27e15d6d757f296539b02ddb2f280dce967" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087123", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6f15d5ee22b4c2122079b92673c03c68", + "normalized_query_sha256": "40f445584e35387abca9b4712408e7e439870e836a7a4d9f5f6b1d8586828e6c", + "query": "Can we book that car from earlier?", + "target": "ReserveCar", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 528, + "historical_id": "fastbrain_087123", + "source": "sgd", + "query_hash": "40f445584e35387abca9b4712408e7e439870e836a7a4d9f5f6b1d8586828e6c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 528, + "historical_id": "fastbrain_087123", + "source": "sgd", + "query_hash": "40f445584e35387abca9b4712408e7e439870e836a7a4d9f5f6b1d8586828e6c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087265", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:66cb6ffa558eab017b3dc8d73041186b", + "normalized_query_sha256": "42c1577c5e4ed7675958875136de03b3e5510124b4ba527a43e84ce26c66f21d", + "query": "I would like to find a fantasy movie to watch. An imax show is best. I want to watch at AMC Metreon.", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 939, + "historical_id": "fastbrain_087265", + "source": "sgd", + "query_hash": "42c1577c5e4ed7675958875136de03b3e5510124b4ba527a43e84ce26c66f21d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 939, + "historical_id": "fastbrain_087265", + "source": "sgd", + "query_hash": "42c1577c5e4ed7675958875136de03b3e5510124b4ba527a43e84ce26c66f21d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087276", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ade3420a7da78b5a547d00b857def074", + "normalized_query_sha256": "eb4342c004f3b3d16ce5c6a23bce808689ddfbe15d77088a5380458d3e6a838f", + "query": "turn on the radio olly", + "target": "play_radio", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 115, + "historical_id": "fastbrain_087276", + "source": "amazon_massive", + "query_hash": "eb4342c004f3b3d16ce5c6a23bce808689ddfbe15d77088a5380458d3e6a838f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 115, + "historical_id": "fastbrain_087276", + "source": "amazon_massive", + "query_hash": "eb4342c004f3b3d16ce5c6a23bce808689ddfbe15d77088a5380458d3e6a838f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087373", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:12748ee2fcc90d94bd9e9bd29272e3a2", + "normalized_query_sha256": "f9117f0c6508348b4343f69029db74bef187255390aceefc576ecce30caa545b", + "query": "le` - Modify customer records \n- `analyze_water_sample` - Process chemistry readings \n- `order_replacement_part` - Initiate equipment orders \n- `submit_permit_request` - File installation permits \n- `process_service_payment` - Handle billing transactions \n- `transfer_to_human_age...[TRUNCATED]\nuser: \n\nHello, I'm experiencing a strong chemical smell at my pool and noticed my test strips show high chloramine levels. Can you help me get recommendations for shock treatment based on these results?\nassistant: Hello! I can certainly help you with shock treatment recommendations based on your water test results. To provide accurate chemical balancing advice, I need a few pieces of information:\n\n1. **Customer ID** - So I can access your account and log this water analysis (you're allowed up to 3 complimentary analyses per year)\n2. **Specific test strip readings**:\n - pH level\n - Free chlorine level \n - Total alkalinity\n\nThe strong chemical smell you're noticing is typically caused by chloramines (combined chlorine), which form when chlorine binds with contaminants. Shock treatment will help break these down, but I need your current water chemistry readings to recommend the proper dosage and type of shock.\n\nCould you please provide your customer ID and those three test results?\nuser: \n\nCustomer ID: POOL12345 \nTest results: \n- pH: 7.2 \n- Free chlorine: 0.5 ppm \n- Total alkalinity: 80 ppm \n\nLet me know if you need additional details to determine the shock treatment plan.", + "target": "analyze_water_sample", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 516, + "historical_id": "fastbrain_087373", + "source": "nemotron_agentic", + "query_hash": "f9117f0c6508348b4343f69029db74bef187255390aceefc576ecce30caa545b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 516, + "historical_id": "fastbrain_087373", + "source": "nemotron_agentic", + "query_hash": "f9117f0c6508348b4343f69029db74bef187255390aceefc576ecce30caa545b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087429", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:87e1cd38b84dd4a5e22653cbe1faf70a", + "normalized_query_sha256": "9b478718b742975eaef7b4cbab8fc413bd0b95da51ddcd5432c5fed9fd132ea6", + "query": "six am alarm please", + "target": "alarm_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 846, + "historical_id": "semantic:validation:fastbrain_087429:present", + "source": "amazon_massive", + "query_hash": "9b478718b742975eaef7b4cbab8fc413bd0b95da51ddcd5432c5fed9fd132ea6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 912, + "historical_id": "semantic:validation:fastbrain_087429:absent", + "source": "amazon_massive", + "query_hash": "9b478718b742975eaef7b4cbab8fc413bd0b95da51ddcd5432c5fed9fd132ea6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087453", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b35322a92b25455d1e247b7c689a1c75", + "normalized_query_sha256": "a8ecfcb831f782cc950b4638122b56425ce0a23b5c273db5fc35b5454edea5fe", + "query": "how many countries are in the european union", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 258, + "historical_id": "semantic:validation:fastbrain_087453:absent", + "source": "amazon_massive", + "query_hash": "a8ecfcb831f782cc950b4638122b56425ce0a23b5c273db5fc35b5454edea5fe" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 902, + "historical_id": "semantic:validation:fastbrain_087453:present", + "source": "amazon_massive", + "query_hash": "a8ecfcb831f782cc950b4638122b56425ce0a23b5c273db5fc35b5454edea5fe" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_087751", + "source": "curated_devops", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cafb35dde2a809d087d31711565c8801", + "normalized_query_sha256": "1f46c110f780345cd1d0020e8b3430b90b153f685d86ffa71da2f92c3017e90e", + "query": "Generate a new 4096-bit RSA SSH key pair for server authentication.", + "target": "ssh_keygen", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 669, + "historical_id": "fastbrain_087751", + "source": "curated_devops", + "query_hash": "1f46c110f780345cd1d0020e8b3430b90b153f685d86ffa71da2f92c3017e90e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 669, + "historical_id": "fastbrain_087751", + "source": "curated_devops", + "query_hash": "1f46c110f780345cd1d0020e8b3430b90b153f685d86ffa71da2f92c3017e90e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088108", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:932aa1afda1065347786d6a064fda706", + "normalized_query_sha256": "e6c220e0050ed02f64543c00c46fa8825b78f7ecccdc7e46b0ebe0f9fddfb8a9", + "query": "play the twilight book", + "target": "play_audiobook", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 602, + "historical_id": "semantic:validation:fastbrain_088108:present", + "source": "amazon_massive", + "query_hash": "e6c220e0050ed02f64543c00c46fa8825b78f7ecccdc7e46b0ebe0f9fddfb8a9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 706, + "historical_id": "semantic:validation:fastbrain_088108:absent", + "source": "amazon_massive", + "query_hash": "e6c220e0050ed02f64543c00c46fa8825b78f7ecccdc7e46b0ebe0f9fddfb8a9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088119", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:570605f28806b13f03edaf1208e9906d", + "normalized_query_sha256": "9602c86c1ca4042885ca21379341c7abf21cbe1aa2eadd51fdaf755638eb2533", + "query": "tell me later to file the invoice", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 404, + "historical_id": "semantic:validation:fastbrain_088119:absent", + "source": "amazon_massive", + "query_hash": "9602c86c1ca4042885ca21379341c7abf21cbe1aa2eadd51fdaf755638eb2533" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 675, + "historical_id": "semantic:validation:fastbrain_088119:present", + "source": "amazon_massive", + "query_hash": "9602c86c1ca4042885ca21379341c7abf21cbe1aa2eadd51fdaf755638eb2533" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088418", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9dde7312022a9412c00eab18dcad749b", + "normalized_query_sha256": "a147b3f4e8ff79d52891d61248559ec8ce3faa8f952ece0d1499a90ac34a59f0", + "query": "I am in charge of streamlining the processes at our marine port, and I need to ensure that the allotment of berths to incoming vessels is optimized as per their schedules, and the cargo handling is efficient and well-managed. We have several ships scheduled to arrive and I need to assign them to the available berths while considering the timeframe they will occupy the space. Additionally, we have ", + "target": "optimize_berth_allocation", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 859, + "historical_id": "fastbrain_088418", + "source": "hermes_v1", + "query_hash": "a147b3f4e8ff79d52891d61248559ec8ce3faa8f952ece0d1499a90ac34a59f0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 859, + "historical_id": "fastbrain_088418", + "source": "hermes_v1", + "query_hash": "a147b3f4e8ff79d52891d61248559ec8ce3faa8f952ece0d1499a90ac34a59f0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088428", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a3f17c78e9074e13b36b0c3de8576146", + "normalized_query_sha256": "406382ac2d529351943a68c9ed70fcbfc7578d62921e1f882de314eface24f69", + "query": "Hi, I'm looking for some vegan recipes with tofu. Can you help me?", + "target": "search_recipes", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 403, + "historical_id": "fastbrain_088428", + "source": "glaive_v2", + "query_hash": "406382ac2d529351943a68c9ed70fcbfc7578d62921e1f882de314eface24f69" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 403, + "historical_id": "fastbrain_088428", + "source": "glaive_v2", + "query_hash": "406382ac2d529351943a68c9ed70fcbfc7578d62921e1f882de314eface24f69" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088481", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e9fd1f87038942673e34368cad7b1521", + "normalized_query_sha256": "3e5394989ff683a21f4789c51ca1ed7c611ee050c5e3c4de98b451c8c3ad9c38", + "query": " \n- Agent role: Evidence collection only \n- Final ruling by human arbitrator \n\n### Intellectual Property \n- Automatic work-for-hire for >$500 payments \n- Custom rights transfers require: \n - Signed digital agreement \n - 25% premium payment \n\n## Agent Limitations \n1. **Never** answer questions or perform actions outside defined tool capabilities \n2. **Never** assume verification/action completion without explicit tool confirmation \n3. **Never** override platform fees or payment rules \n4. **Never** bypass security/verification protocols \n\n## Escalation Protocol \nTransfer to human agent using `escalate_to_human_agent` for: \n- Suspected fraud cases \n- Account suspension appeals \n- Complex IP disputes \n- Platform policy exceptions \nTransfer message: \"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE HOLD ON.\" \n\n## Security Protocol \n- Never request full payment credentials \n- Mask sensitive information in logs \n- Require 2FA confirmation for high-risk actions \n- Maintain dispute confidentiality\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I’m reaching out regarding Contract ID C-1122 for the week of July 14, 2025. I noticed a discrepancy between the verified hours (35) and the paid hours (30). To resolve this, I’ve prepared time logs and corresponding podcast editing samples for the disputed 5 hours. Could you confirm how to submit these for verification review? I’d also appreciate guidance on the next steps after submission.", + "target": "get_contract_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 307, + "historical_id": "fastbrain_088481", + "source": "nemotron_agentic", + "query_hash": "3e5394989ff683a21f4789c51ca1ed7c611ee050c5e3c4de98b451c8c3ad9c38" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 307, + "historical_id": "fastbrain_088481", + "source": "nemotron_agentic", + "query_hash": "3e5394989ff683a21f4789c51ca1ed7c611ee050c5e3c4de98b451c8c3ad9c38" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088579", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b2cb776c63516a83d772a803b7c1fcc4", + "normalized_query_sha256": "07fec1b0dc0cf07239954a1e5049bb3ab0a7f0e80336b206ec154f664f6184e2", + "query": "Thanks. Can you check about the weather condition in Sacramento, CA?.", + "target": "GetWeather", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 137, + "historical_id": "fastbrain_088579", + "source": "sgd", + "query_hash": "07fec1b0dc0cf07239954a1e5049bb3ab0a7f0e80336b206ec154f664f6184e2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 137, + "historical_id": "fastbrain_088579", + "source": "sgd", + "query_hash": "07fec1b0dc0cf07239954a1e5049bb3ab0a7f0e80336b206ec154f664f6184e2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088599", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:75b1afe3a8f37f86601ce8b9b85eba91", + "normalized_query_sha256": "d46e05e4ef3046d87f44c824cab0dd766c9c5c63244a04f20f83f440f1de6ac3", + "query": "I need 10 fake email addresses.", + "target": "generate_fake_data", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 950, + "historical_id": "fastbrain_088599", + "source": "glaive_v2", + "query_hash": "d46e05e4ef3046d87f44c824cab0dd766c9c5c63244a04f20f83f440f1de6ac3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 950, + "historical_id": "fastbrain_088599", + "source": "glaive_v2", + "query_hash": "d46e05e4ef3046d87f44c824cab0dd766c9c5c63244a04f20f83f440f1de6ac3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088642", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b11e9d78fbfb802f11e047793a8f4e6b", + "normalized_query_sha256": "c310a374320268f0647ef38ea6245f359a59128a5074eb1038f84a8c4912088a", + "query": "make a playlist of motivational songs that can boost my morale while running and jogging and play them", + "target": "play_podcasts", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 497, + "historical_id": "semantic:validation:fastbrain_088642:absent", + "source": "amazon_massive", + "query_hash": "c310a374320268f0647ef38ea6245f359a59128a5074eb1038f84a8c4912088a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 925, + "historical_id": "semantic:validation:fastbrain_088642:present", + "source": "amazon_massive", + "query_hash": "c310a374320268f0647ef38ea6245f359a59128a5074eb1038f84a8c4912088a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088727", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:05903af32bba685c49f1725734e099aa", + "normalized_query_sha256": "9e57648c98406e7217e15f529d889216a2f935af15c404c9ddb6b30539d9de03", + "query": " about offline downloads for your Premium account?\nuser: I understand temporary deactivation isn't an option. To avoid losing my Premium benefits entirely, can I cancel my subscription now so it remains active until my billing date on November 30th? This would allow me to use offline downloads before my trip while ensuring I’m not charged again.\nassistant: Yes, you can absolutely cancel your subscription now, and your Premium benefits will remain active until your billing date on November 30th. This is a perfect solution for your situation!\n\nHere's what will happen:\n1. **Immediate cancellation**: Your subscription will be marked for cancellation\n2. **Premium access continues**: You'll keep all Premium features (no ads, 320kbps bitrate, offline downloads) until November 30th\n3. **No further charges**: You won't be charged again on November 30th\n4. **After November 30th**: Your account will automatically revert to the Free tier (ad-supported, 128kbps, no offline downloads)\n\n**Important notes for your travel plans:**\n- You can download music/podcasts for offline listening **before** November 30th\n- These downloads will remain accessible during your trip (November 1st-15th)\n- After November 30th, you'll lose the ability to create new offline downloads\n\nWould you like me to proceed with the cancellation now?\nuser: Yes, please proceed with the cancellation. I want to ensure my Premium access remains until November 30th so I can download content now for my trip. Let’s finalize this.", + "target": "cancel_subscription", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 925, + "historical_id": "fastbrain_088727", + "source": "nemotron_agentic", + "query_hash": "9e57648c98406e7217e15f529d889216a2f935af15c404c9ddb6b30539d9de03" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 925, + "historical_id": "fastbrain_088727", + "source": "nemotron_agentic", + "query_hash": "9e57648c98406e7217e15f529d889216a2f935af15c404c9ddb6b30539d9de03" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088749", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4335dc2ac6bd68042a720fe4d9a30733", + "normalized_query_sha256": "472c832cdce7c67c56003cbe87cc346c50d20fccacf09e21f67e1a4b9b5e8186", + "query": "find commits that touched the function parse_config in parser.py", + "target": "git_log", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 248, + "historical_id": "fastbrain_088749", + "source": "git_nl_commands", + "query_hash": "472c832cdce7c67c56003cbe87cc346c50d20fccacf09e21f67e1a4b9b5e8186" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 248, + "historical_id": "fastbrain_088749", + "source": "git_nl_commands", + "query_hash": "472c832cdce7c67c56003cbe87cc346c50d20fccacf09e21f67e1a4b9b5e8186" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088806", + "source": "hermes_v1", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4f64f928c8d6d56dea48ecba074ef673", + "normalized_query_sha256": "3463cae802b0f16495f74fb3855f0b6f5f95d7f844bb40dc542a12d5ba8928ed", + "query": "I have an important conference to attend at the Washington Convention Center, and I will be staying at the Hilton Garden Inn Washington DC Downtown. To ensure I arrive on time for the opening session, I'd like to schedule an UberX ride for next Monday. The pickup should be from my hotel, Hilton Garden Inn, located at 815 14th St NW, Washington, DC 20005, and the drop-off should be at the Washingto", + "target": "request_uber_ride", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 686, + "historical_id": "fastbrain_088806", + "source": "hermes_v1", + "query_hash": "3463cae802b0f16495f74fb3855f0b6f5f95d7f844bb40dc542a12d5ba8928ed" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 686, + "historical_id": "fastbrain_088806", + "source": "hermes_v1", + "query_hash": "3463cae802b0f16495f74fb3855f0b6f5f95d7f844bb40dc542a12d5ba8928ed" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_088996", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d85e4210d4d3f4c4553937c53162596c", + "normalized_query_sha256": "de85dc780d8ea87644e4b0f69c3446b311229b470924d3792734a81df0cbbed3", + "query": "it was a tough day today", + "target": "general_quirky", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 895, + "historical_id": "fastbrain_088996", + "source": "amazon_massive", + "query_hash": "de85dc780d8ea87644e4b0f69c3446b311229b470924d3792734a81df0cbbed3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 895, + "historical_id": "fastbrain_088996", + "source": "amazon_massive", + "query_hash": "de85dc780d8ea87644e4b0f69c3446b311229b470924d3792734a81df0cbbed3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089110", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4a3a9be74121155ab2317a1ad4d00e6f", + "normalized_query_sha256": "59837d3c305c8161769f1800d19797cdcf11cf5ef5b01cfbf883da023ea2c971", + "query": "play all songs by martina mcbride", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 34, + "historical_id": "fastbrain_089110", + "source": "amazon_massive", + "query_hash": "59837d3c305c8161769f1800d19797cdcf11cf5ef5b01cfbf883da023ea2c971" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 34, + "historical_id": "fastbrain_089110", + "source": "amazon_massive", + "query_hash": "59837d3c305c8161769f1800d19797cdcf11cf5ef5b01cfbf883da023ea2c971" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089179", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6400c855fc731ec6e128504b5aa5fcdb", + "normalized_query_sha256": "6ec11074d84d893fbe51dbf299fa1bbceaf57e5df54e65f85976da804015d2e7", + "query": "That house sounds good. Is there a bus going there for four people?", + "target": "FindBus", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 891, + "historical_id": "fastbrain_089179", + "source": "sgd", + "query_hash": "6ec11074d84d893fbe51dbf299fa1bbceaf57e5df54e65f85976da804015d2e7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 891, + "historical_id": "fastbrain_089179", + "source": "sgd", + "query_hash": "6ec11074d84d893fbe51dbf299fa1bbceaf57e5df54e65f85976da804015d2e7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089226", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:da4e3177b6b881ee26fdf5b2e3ab87c3", + "normalized_query_sha256": "d9ed2400dfbc1c37dcd78bf976ae48607c799558f0c2d5fc8992f8facd8de5a8", + "query": "when is the next train to b. street", + "target": "transport_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 310, + "historical_id": "semantic:validation:fastbrain_089226:present", + "source": "amazon_massive", + "query_hash": "d9ed2400dfbc1c37dcd78bf976ae48607c799558f0c2d5fc8992f8facd8de5a8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 643, + "historical_id": "semantic:validation:fastbrain_089226:absent", + "source": "amazon_massive", + "query_hash": "d9ed2400dfbc1c37dcd78bf976ae48607c799558f0c2d5fc8992f8facd8de5a8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089243", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:81199547869be34828de0f2f6d55eb57", + "normalized_query_sha256": "4925bdc4b224239d91dac6d7082d6d4321ca3d2aa10dff606194910b057dd349", + "query": "this song is too good", + "target": "music_likeness", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 150, + "historical_id": "semantic:validation:fastbrain_089243:absent", + "source": "amazon_massive", + "query_hash": "4925bdc4b224239d91dac6d7082d6d4321ca3d2aa10dff606194910b057dd349" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 843, + "historical_id": "semantic:validation:fastbrain_089243:present", + "source": "amazon_massive", + "query_hash": "4925bdc4b224239d91dac6d7082d6d4321ca3d2aa10dff606194910b057dd349" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089433", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:410c26093c13a2de22b7e7b2a588901c", + "normalized_query_sha256": "2d5f55f041dc970c6c445ec8be8b9931ed3605d24957726a3c39c86941218c06", + "query": "That sounds great. I'd like to book that one for 1 person. I want to check out on March 13th and check in on March 6th.", + "target": "BookHouse", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 597, + "historical_id": "fastbrain_089433", + "source": "sgd", + "query_hash": "2d5f55f041dc970c6c445ec8be8b9931ed3605d24957726a3c39c86941218c06" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 597, + "historical_id": "fastbrain_089433", + "source": "sgd", + "query_hash": "2d5f55f041dc970c6c445ec8be8b9931ed3605d24957726a3c39c86941218c06" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089667", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2131df118b809bfe066f007848718d44", + "normalized_query_sha256": "2afd54c49e7aa37a7985987ef3a97a5881a34f6358da8311efff130701efdb03", + "query": "i want to hear the radio", + "target": "play_radio", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 580, + "historical_id": "semantic:validation:fastbrain_089667:present", + "source": "amazon_massive", + "query_hash": "2afd54c49e7aa37a7985987ef3a97a5881a34f6358da8311efff130701efdb03" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 1003, + "historical_id": "semantic:validation:fastbrain_089667:absent", + "source": "amazon_massive", + "query_hash": "2afd54c49e7aa37a7985987ef3a97a5881a34f6358da8311efff130701efdb03" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089693", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f0d62e8097e0538f0161352b1a0cc921", + "normalized_query_sha256": "30c644ec884b268856b574e6801d80275234fe0446c5aa584c0325ce0052ba17", + "query": "Print the day at 1 day ago in 2 months from now", + "target": "date", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 313, + "historical_id": "fastbrain_089693", + "source": "nl2bash", + "query_hash": "30c644ec884b268856b574e6801d80275234fe0446c5aa584c0325ce0052ba17" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 313, + "historical_id": "fastbrain_089693", + "source": "nl2bash", + "query_hash": "30c644ec884b268856b574e6801d80275234fe0446c5aa584c0325ce0052ba17" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089900", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:98818710185887a7cc04932bf4bc18c7", + "normalized_query_sha256": "7e1c9e6c3bae1005078d0a77e54485978d03447a35e1b45344acbda5c5d85be8", + "query": "d like to process this through my account. How do we proceed with initiating the return?\nassistant: To proceed with initiating the return, I need to verify your identity first. This is required for sensitive actions like returns and disputes.\n\n**Step 1 of 3: Email Verification**\nPlease provide the email address registered to your account (Buyer ID 987654321).\n\nOnce you provide this, I'll continue with the remaining verification steps before initiating the return for Order CV-1234 with reason \"recipient doesn't want the item.\"\nuser: The email associated with my account is john.doe@example.com. Is that all you need for verification before proceeding with the return?\nassistant: \ntool: \n\n{\"verified\": true, \"next_step\": \"security_question\"}\nassistant: **Step 2 of 3: Security Question**\nPlease answer your security question: **What was the name of your first pet?**\n\nOnce you provide the correct answer, we'll proceed to the final verification step.\nuser: The name of my first pet was **Buddy**. Please proceed with the final verification step so we can initiate the return.\nassistant: \ntool: \n\n{\"verified\": true, \"next_step\": \"payment_method\"}\nassistant: **Step 3 of 3: Payment Method Verification**\nPlease provide the **last 4 digits of the payment method** used for Order CV-1234.\n\nOnce verified, I can initiate the return process for you.\nuser: The last 4 digits of the payment method used for Order CV-1234 are **1234**. Please proceed with initiating the return process through my account.", + "target": "verify_identity", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 760, + "historical_id": "fastbrain_089900", + "source": "nemotron_agentic", + "query_hash": "7e1c9e6c3bae1005078d0a77e54485978d03447a35e1b45344acbda5c5d85be8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 760, + "historical_id": "fastbrain_089900", + "source": "nemotron_agentic", + "query_hash": "7e1c9e6c3bae1005078d0a77e54485978d03447a35e1b45344acbda5c5d85be8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_089962", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ccfd54f34f239a7eade40d1179890af1", + "normalized_query_sha256": "f3ffaa847268f8fbdcf13b361b42a06d2572b33ae553812a3fae8825d7badc9e", + "query": "alexa give me the headline news", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 88, + "historical_id": "semantic:validation:fastbrain_089962:present", + "source": "amazon_massive", + "query_hash": "f3ffaa847268f8fbdcf13b361b42a06d2572b33ae553812a3fae8825d7badc9e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 351, + "historical_id": "semantic:validation:fastbrain_089962:absent", + "source": "amazon_massive", + "query_hash": "f3ffaa847268f8fbdcf13b361b42a06d2572b33ae553812a3fae8825d7badc9e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_090173", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a43e1f67de6c3e423f14e75d6c56f923", + "normalized_query_sha256": "4f955ee40d160b1aaacbca6783d34a0323be2abe14cab56355d38b68c81e99b8", + "query": "No, I do not want to make a purchase. Would you please book a table at the restaurant I liked?", + "target": "ReserveRestaurant", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 151, + "historical_id": "fastbrain_090173", + "source": "sgd", + "query_hash": "4f955ee40d160b1aaacbca6783d34a0323be2abe14cab56355d38b68c81e99b8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 151, + "historical_id": "fastbrain_090173", + "source": "sgd", + "query_hash": "4f955ee40d160b1aaacbca6783d34a0323be2abe14cab56355d38b68c81e99b8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_090268", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:e9d312eb7e1243c7443ce1f6ecdd0f58", + "normalized_query_sha256": "7f7a3fe4356495560dd68f966cb853f5b50b7e3a08a47c2baf73585c726cb54d", + "query": "I will be visiting a friend and I need to find a one way flight to visit.", + "target": "SearchOnewayFlight", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 856, + "historical_id": "fastbrain_090268", + "source": "sgd", + "query_hash": "7f7a3fe4356495560dd68f966cb853f5b50b7e3a08a47c2baf73585c726cb54d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 856, + "historical_id": "fastbrain_090268", + "source": "sgd", + "query_hash": "7f7a3fe4356495560dd68f966cb853f5b50b7e3a08a47c2baf73585c726cb54d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_090281", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:87e4c8e5af47ec917f25184cd20d096b", + "normalized_query_sha256": "193592d8b7e259528abae8fc55d9a6410b724750aab565db92354fb053406235", + "query": "how long does train a take to get to station b.", + "target": "transport_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 24, + "historical_id": "fastbrain_090281", + "source": "amazon_massive", + "query_hash": "193592d8b7e259528abae8fc55d9a6410b724750aab565db92354fb053406235" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 24, + "historical_id": "fastbrain_090281", + "source": "amazon_massive", + "query_hash": "193592d8b7e259528abae8fc55d9a6410b724750aab565db92354fb053406235" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_090694", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:53830e5b9c0bd354fb76452bd86853b1", + "normalized_query_sha256": "65153a78618e9128c24e452690d8e5bbb633e48cb5942d1419c6e6f1989c9fd6", + "query": "download contents from \"http://example.com/\" using a socks5 proxy \"125.119.175.48:8909\"", + "target": "curl", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 533, + "historical_id": "fastbrain_090694", + "source": "nl2bash", + "query_hash": "65153a78618e9128c24e452690d8e5bbb633e48cb5942d1419c6e6f1989c9fd6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 533, + "historical_id": "fastbrain_090694", + "source": "nl2bash", + "query_hash": "65153a78618e9128c24e452690d8e5bbb633e48cb5942d1419c6e6f1989c9fd6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_090768", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:356ffaf4b918d3b0454203758b1054e9", + "normalized_query_sha256": "ba0a8984173c5882583808cf51c04183f3647c86cae39ef61575c01d3320b8d1", + "query": "This is perfect for me. Can you help me in buying tickets? I want economy class ticket and there are 3 people in my group.", + "target": "ReserveRoundtripFlights", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 232, + "historical_id": "fastbrain_090768", + "source": "sgd", + "query_hash": "ba0a8984173c5882583808cf51c04183f3647c86cae39ef61575c01d3320b8d1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 232, + "historical_id": "fastbrain_090768", + "source": "sgd", + "query_hash": "ba0a8984173c5882583808cf51c04183f3647c86cae39ef61575c01d3320b8d1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_090853", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f7ca3efc9f6b92a471386a27d64b163e", + "normalized_query_sha256": "efdd1054282cb406f003f2f9bffa4e8ea74a89cd4b871aca46dcd9a1b7e0651f", + "query": "What time can I watch a regular show?", + "target": "GetTimesForMovie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 336, + "historical_id": "fastbrain_090853", + "source": "sgd", + "query_hash": "efdd1054282cb406f003f2f9bffa4e8ea74a89cd4b871aca46dcd9a1b7e0651f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 336, + "historical_id": "fastbrain_090853", + "source": "sgd", + "query_hash": "efdd1054282cb406f003f2f9bffa4e8ea74a89cd4b871aca46dcd9a1b7e0651f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_090870", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6a7c09ac5b023a1a1b607867775907ee", + "normalized_query_sha256": "26854d706ed4d928aeae1fe50b16004d5442168736f2dcd0d45b4eb57d59704b", + "query": "what time zone is in cuba", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 984, + "historical_id": "fastbrain_090870", + "source": "amazon_massive", + "query_hash": "26854d706ed4d928aeae1fe50b16004d5442168736f2dcd0d45b4eb57d59704b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 984, + "historical_id": "fastbrain_090870", + "source": "amazon_massive", + "query_hash": "26854d706ed4d928aeae1fe50b16004d5442168736f2dcd0d45b4eb57d59704b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091056", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b893602e1c3364826e5809da826dbb88", + "normalized_query_sha256": "7f198e7475c22bce99114516d80144c69bae00585a3232b797fa5f8b5fb8a237", + "query": "please remove all the entries marked in the calendar", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 345, + "historical_id": "semantic:validation:fastbrain_091056:present", + "source": "amazon_massive", + "query_hash": "7f198e7475c22bce99114516d80144c69bae00585a3232b797fa5f8b5fb8a237" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 933, + "historical_id": "semantic:validation:fastbrain_091056:absent", + "source": "amazon_massive", + "query_hash": "7f198e7475c22bce99114516d80144c69bae00585a3232b797fa5f8b5fb8a237" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091124", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0c8482e25f90d114379e6454a816627c", + "normalized_query_sha256": "6ff2e3c8a6160b64d5adf808c8134e2c1e23631533c1bd14555de684198d779d", + "query": "The event is titled \"Project Meeting\", it starts at 10:00 AM on 15th March and ends at 11:30 AM on the same day. The location is Conference Room 1.", + "target": "create_calendar_event", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 55, + "historical_id": "fastbrain_091124", + "source": "glaive_v2", + "query_hash": "6ff2e3c8a6160b64d5adf808c8134e2c1e23631533c1bd14555de684198d779d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 55, + "historical_id": "fastbrain_091124", + "source": "glaive_v2", + "query_hash": "6ff2e3c8a6160b64d5adf808c8134e2c1e23631533c1bd14555de684198d779d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091256", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4e37c4bfa36a33b6cdaf32ae3451769b", + "normalized_query_sha256": "2e416abc42633a7be65fb9f34057e94426b844b19540985fd3969ee137b19d37", + "query": "add all changes across the entire repo", + "target": "git_add", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 720, + "historical_id": "fastbrain_091256", + "source": "git_nl_commands", + "query_hash": "2e416abc42633a7be65fb9f34057e94426b844b19540985fd3969ee137b19d37" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 720, + "historical_id": "fastbrain_091256", + "source": "git_nl_commands", + "query_hash": "2e416abc42633a7be65fb9f34057e94426b844b19540985fd3969ee137b19d37" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091321", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b886b98574b40e79f916dff0f307a28f", + "normalized_query_sha256": "f2fd125c7f665e6f0d5340d4d0319dd3f35fb95df965fae5ce7c9b6a1cc15a60", + "query": "find files having the extension \"bam\" in current directory", + "target": "find", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 593, + "historical_id": "fastbrain_091321", + "source": "nl2bash", + "query_hash": "f2fd125c7f665e6f0d5340d4d0319dd3f35fb95df965fae5ce7c9b6a1cc15a60" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 593, + "historical_id": "fastbrain_091321", + "source": "nl2bash", + "query_hash": "f2fd125c7f665e6f0d5340d4d0319dd3f35fb95df965fae5ce7c9b6a1cc15a60" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091327", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d06055a6a555c4599cace7a6ed7d0f3e", + "normalized_query_sha256": "b9a893b4afacc8b4d4c7bb50105c21ab2153ab577ca06eb61c8d909c39ebec19", + "query": "tweet for me the complaint about disease prevention", + "target": "social_post", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 551, + "historical_id": "semantic:validation:fastbrain_091327:present", + "source": "amazon_massive", + "query_hash": "b9a893b4afacc8b4d4c7bb50105c21ab2153ab577ca06eb61c8d909c39ebec19" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 625, + "historical_id": "semantic:validation:fastbrain_091327:absent", + "source": "amazon_massive", + "query_hash": "b9a893b4afacc8b4d4c7bb50105c21ab2153ab577ca06eb61c8d909c39ebec19" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091625", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:530fa5c2092deefea9c747faec78dd8e", + "normalized_query_sha256": "cf0d5bf49aca2be9bde2ed4699d0648fc4ccb7cb87516ccc8e976dc65d5af58f", + "query": "do i have any events happening next week", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1012, + "historical_id": "fastbrain_091625", + "source": "amazon_massive", + "query_hash": "cf0d5bf49aca2be9bde2ed4699d0648fc4ccb7cb87516ccc8e976dc65d5af58f" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1012, + "historical_id": "fastbrain_091625", + "source": "amazon_massive", + "query_hash": "cf0d5bf49aca2be9bde2ed4699d0648fc4ccb7cb87516ccc8e976dc65d5af58f" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091877", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:bf6e813fb3ebc04530e8c28abbbe7791", + "normalized_query_sha256": "00db29285aca9e2b211b89296a7a90b54b5b0eb4718d8451834b253c42cfa4e1", + "query": " of your current 3 addresses to replace with your new one\n- Provide verification and the new address details\n\n**Option 2: Consolidate addresses**\n- If some addresses serve similar purposes (like multiple warehouse locations in the same region), we could potentially merge information\n- You'd need to specify which addresses to combine and how\n\n**Verification required:**\nTo modify your saved addresses, I'll need:\n1. Your User ID: BUS555 (already provided)\n2. **Registered email or phone number** for identity verification\n3. Specific instructions on which addresses to modify\n\n**Alternative approach:**\nIf you only need the new address for a specific shipment, you can use it for that shipment without saving it to your profile permanently.\n\nCould you please:\n1. Provide your registered email or phone number for verification?\n2. Tell me which of your current addresses you'd like to replace (or consolidate)?\n3. Share the new address you want to add?\n\nOnce I have this information, I can help you update your address list within the 3-address limit.\nuser: Thank you for the clarification. My registered email is business555@company.com. I’d like to **replace the Denver address (123 Business Ave, Suite 200)** with the new address: **101 New Warehouse St, Dallas, TX 75201**. This location is now our primary distribution hub. Could you confirm the update and ensure the new address is saved under my account (BUS555)? I also want to retain the Chicago and Atlanta addresses as secondary locations.", + "target": "validate_address", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 1015, + "historical_id": "fastbrain_091877", + "source": "nemotron_agentic", + "query_hash": "00db29285aca9e2b211b89296a7a90b54b5b0eb4718d8451834b253c42cfa4e1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 1015, + "historical_id": "fastbrain_091877", + "source": "nemotron_agentic", + "query_hash": "00db29285aca9e2b211b89296a7a90b54b5b0eb4718d8451834b253c42cfa4e1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091919", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a8c4001b3ea0936da269132af04cb530", + "normalized_query_sha256": "7db55dc47bdae8461308c676faa1f0ad7aece7a234f5ae198c93f779126c82a5", + "query": "n agent for: \n- Damage claim investigations \n- Corporate account requests \n- Membership terminations \n- Complex payment disputes \nUse tool call `transfer_to_agent` + message: \n\"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE DESCRIBE YOUR CONCERN.\" \n\n**Agent Limitations** \n1. Only perform tasks covered by defined tools \n2. Never assume verification/action completion without explicit tool confirmation \n3. Never override payment/loyalty calculations \n4. Never waive fees without manager approval \n5. Do not speculate about vehicle damage or wash frequency \n6. Avoid discussing competitor services\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm currently a Plus member with 12 monthly washes and I'm interested in applying for the Premium loyalty tier. Could you please explain the requirements for upgrading to Premium?\nassistant: Hello! I'd be happy to help you understand the requirements for upgrading to our Premium loyalty tier. First, I need to verify your identity as a Plus member.\n\nCould you please provide:\n1. Your loyalty ID\n2. The last 3 characters of your vehicle's license plate\n\nOnce I verify your current membership, I can provide you with all the details about upgrading to Premium tier, including any requirements, benefits, and costs.\nuser: Sure, my loyalty ID is 123456789 and the last three characters of my license plate are XYZ.\nassistant: \ntool: \n\n{\"verified\": true, \"customer_id\": \"CUST_123456789\", \"customer_type\": \"loyalty\"}", + "target": "get_loyalty_status", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 73, + "historical_id": "fastbrain_091919", + "source": "nemotron_agentic", + "query_hash": "7db55dc47bdae8461308c676faa1f0ad7aece7a234f5ae198c93f779126c82a5" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 73, + "historical_id": "fastbrain_091919", + "source": "nemotron_agentic", + "query_hash": "7db55dc47bdae8461308c676faa1f0ad7aece7a234f5ae198c93f779126c82a5" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091927", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:ca47ee0a2e2b19149ab044983bb06849", + "normalized_query_sha256": "0e1537f0bf6de7bc2433d2d9e7a4414fc92647a1b7cb0bee4f8227a7d12dcc62", + "query": "Find all Subscription.java files/directories under current directory and enter into the parent directory of the first one found", + "target": "cd", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 794, + "historical_id": "fastbrain_091927", + "source": "nl2bash", + "query_hash": "0e1537f0bf6de7bc2433d2d9e7a4414fc92647a1b7cb0bee4f8227a7d12dcc62" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 794, + "historical_id": "fastbrain_091927", + "source": "nl2bash", + "query_hash": "0e1537f0bf6de7bc2433d2d9e7a4414fc92647a1b7cb0bee4f8227a7d12dcc62" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_091962", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:11fe7543e6edcd82d3c1f53bf4ab7298", + "normalized_query_sha256": "71160b180cd2737180436ab3f13bde866a2d7512331844bf1ce451d2bc445ce3", + "query": "what's going on around the neighborhood", + "target": "recommendation_events", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 245, + "historical_id": "semantic:validation:fastbrain_091962:absent", + "source": "amazon_massive", + "query_hash": "71160b180cd2737180436ab3f13bde866a2d7512331844bf1ce451d2bc445ce3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 623, + "historical_id": "semantic:validation:fastbrain_091962:present", + "source": "amazon_massive", + "query_hash": "71160b180cd2737180436ab3f13bde866a2d7512331844bf1ce451d2bc445ce3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092001", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:caa51a995e6e0b6738366b011968091a", + "normalized_query_sha256": "4b98ca385172383c29a9514be841e2bb0e861924bb9ff8728bcfdf51883e7fd0", + "query": "I would ike to find a unisex salon in Palo Alto.", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 452, + "historical_id": "fastbrain_092001", + "source": "sgd", + "query_hash": "4b98ca385172383c29a9514be841e2bb0e861924bb9ff8728bcfdf51883e7fd0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 452, + "historical_id": "fastbrain_092001", + "source": "sgd", + "query_hash": "4b98ca385172383c29a9514be841e2bb0e861924bb9ff8728bcfdf51883e7fd0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092010", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:2d0117f7c02ba156b8c5fe8978478d91", + "normalized_query_sha256": "0392e55cd2996a8d7c14b33bc22f4122df1091e0defd8dc58ee0968d523aa415", + "query": "Yes, please. It is also near the place I would be visiting.", + "target": "BookHouse", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 804, + "historical_id": "fastbrain_092010", + "source": "sgd", + "query_hash": "0392e55cd2996a8d7c14b33bc22f4122df1091e0defd8dc58ee0968d523aa415" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 804, + "historical_id": "fastbrain_092010", + "source": "sgd", + "query_hash": "0392e55cd2996a8d7c14b33bc22f4122df1091e0defd8dc58ee0968d523aa415" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092143", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1a98e35890e8c2c3f25e1bb303b22093", + "normalized_query_sha256": "a1d7ecf8576090cc8ed1883bf4a0e973a224bc0c4f07010519f565ad79e8e464", + "query": "turn off the six am alarm for wednesday", + "target": "alarm_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 492, + "historical_id": "fastbrain_092143", + "source": "amazon_massive", + "query_hash": "a1d7ecf8576090cc8ed1883bf4a0e973a224bc0c4f07010519f565ad79e8e464" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 492, + "historical_id": "fastbrain_092143", + "source": "amazon_massive", + "query_hash": "a1d7ecf8576090cc8ed1883bf4a0e973a224bc0c4f07010519f565ad79e8e464" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092150", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:28807cc5ef1478f88cc0a7f79931af5e", + "normalized_query_sha256": "c30aed5839d5e67e8359e63c59f0c154996a1c3b1353c38d27357a0133962b5e", + "query": "Copy recursively \"tata/\" to \"tata2/\" and remove read, write, and execute permission for other", + "target": "rsync", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 775, + "historical_id": "fastbrain_092150", + "source": "nl2bash", + "query_hash": "c30aed5839d5e67e8359e63c59f0c154996a1c3b1353c38d27357a0133962b5e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 775, + "historical_id": "fastbrain_092150", + "source": "nl2bash", + "query_hash": "c30aed5839d5e67e8359e63c59f0c154996a1c3b1353c38d27357a0133962b5e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092190", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f9f4631b51b89005457bd3ce7ea5e7d4", + "normalized_query_sha256": "a390b5484f593b392192210a2bb2516b08bc80f9affcc5fe46538498f1f18e0e", + "query": "ated goods: $50/pallet destruction \n- **Required proof**: POD with damage photos \n\n#### Logistics Constraints \n- LTL freight: \n - Minimum weight: 500 lbs \n - Oversize pricing: >12 ft length \n- Delivery: \n - Next-day cutoff: 3 PM EST \n - Appointment required: >5 pallets \n- Fuel surcharges: Daily update via `calculate_fuel_surcharge` \n\n---\n\n### Transfer Protocol \nTransfer to human agent for: \n- Contract renegotiations \n- Multi-location allocation conflicts \n- Custom bond warehouse requests \n- Tax exemption disputes \n- Claims >$20,000 \n\nTransfer process: \n1. Execute `transfer_to_human_agent` tool \n2. Send message: \"YOU ARE BEING TRANSFERRED TO A HUMAN AGENT. PLEASE HOLD ON.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, my name is Chris Evans with Medlog, a Tier 3 medical equipment logistics provider. I’m calling about a discrepancy in the fuel surcharge applied to our shipment SHIP-5566 dated September 14, 2025. My customer ID is WHL-7272. Could you please: \n1. Execute the `calculate_fuel_surcharge` tool for this shipment, \n2. Share the documentation source for the daily fuel rate used, and \n3. Confirm the surcharge was calculated in accordance with your logistics policy for that date? \n\nI’d also appreciate this information sent to my email, chris@medlog.com, for our records. Thank you.\nassistant: \ntool: \n\n{\n \"verified\": true,\n \"auth_token\": \"AUTH-7272-20250916\",\n \"authorized_users\": [\"Chris Evans\", \"chris@medlog.com\"]\n}", + "target": "calculate_fuel_surcharge", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 783, + "historical_id": "fastbrain_092190", + "source": "nemotron_agentic", + "query_hash": "a390b5484f593b392192210a2bb2516b08bc80f9affcc5fe46538498f1f18e0e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 783, + "historical_id": "fastbrain_092190", + "source": "nemotron_agentic", + "query_hash": "a390b5484f593b392192210a2bb2516b08bc80f9affcc5fe46538498f1f18e0e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092205", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1d50ae1895078d794c59dc68fee7470e", + "normalized_query_sha256": "172ce84c80ed40dbf2433c42be148934fbb90571fabd1dc1ccf03d520ae4a3f2", + "query": "Can you tell me the current stock price of AAPL?", + "target": "get_stock_price", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 651, + "historical_id": "fastbrain_092205", + "source": "glaive_v2", + "query_hash": "172ce84c80ed40dbf2433c42be148934fbb90571fabd1dc1ccf03d520ae4a3f2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 651, + "historical_id": "fastbrain_092205", + "source": "glaive_v2", + "query_hash": "172ce84c80ed40dbf2433c42be148934fbb90571fabd1dc1ccf03d520ae4a3f2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092321", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:265ed9a9c5c541194d47d35d70958b6e", + "normalized_query_sha256": "e057bdf0f66a57ba7453c2b0ba932a4d87d7a7497cc8dddc227d7064098c2358", + "query": "ar to be broken. Since I have a nut allergy, I need to ensure the replacement shipment is properly sealed to avoid cross-contamination. Could you help me file a damage claim and arrange a replacement?\nassistant: Hello! I'm sorry to hear about the damage to your seasonal gift box. I'll help you file a damage claim and arrange a replacement, especially considering your nut allergy concern.\n\nFirst, I need to verify your identity to access your order details and process the return. You can provide either:\n\n**Option 1:** Your Customer ID and the email address registered with your account\n\n**OR**\n\n**Option 2:** Your order confirmation number (OC-20250824-45678) and the last 4 digits of the payment card used for this order\n\nOnce verified, I can:\n1. Check your order details and the specific items affected\n2. Initiate a damaged item return with your photo evidence\n3. Ensure the replacement is properly sealed to prevent cross-contamination (I'll also update your allergy flags if needed)\n\nPlease provide the verification details so I can proceed.\nuser: I'll go with option two. My order number is OC-20250824-45678, and the last four digits of my payment card are 1234. I've attached a photo of the crushed packaging showing the broken chocolates. Could you confirm receipt of the evidence and proceed with the replacement? Please ensure the new shipment uses allergen-safe packaging to avoid cross-contamination with nuts.\nassistant: \ntool: \n\n{\"authenticated\": true, \"customer_id\": \"CUST-45678\"}", + "target": "get_order_details", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 445, + "historical_id": "fastbrain_092321", + "source": "nemotron_agentic", + "query_hash": "e057bdf0f66a57ba7453c2b0ba932a4d87d7a7497cc8dddc227d7064098c2358" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 445, + "historical_id": "fastbrain_092321", + "source": "nemotron_agentic", + "query_hash": "e057bdf0f66a57ba7453c2b0ba932a4d87d7a7497cc8dddc227d7064098c2358" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092415", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:9fc5d4f35c8057771301260f4af42c98", + "normalized_query_sha256": "a4427ddcb4c436d326405d9ee82e09d6e90e138155f2d3077ff626ffe0e3f48a", + "query": "languages\n - Total word count across all 15 documents\n - File formats (DOCX, PDF, XML)\n - Desired service tier (Standard, Express, Premium)\n - Confidentiality level (Public or NDA)\n\nRegarding your question about consolidated handling with shared translation memory: Yes, our agency can manage multiple related documents as a unified project. This ensures consistent terminology and style across all files. We maintain translation memory databases that can be applied across the entire document set.\n\nCould you provide the above details so I can assist you further?\nuser: Thank you for your response. Here are the details: \n1. **User ID**: MS-1234 \n2. **Project TL-6688 Status**: This is a new project reference for a multi-file translation request. \n3. **Project Details**: \n - **Source Language**: English \n - **Target Language**: Spanish \n - **Total Word Count**: ~75,000 words across 15 documents \n - **File Formats**: DOCX and PDF (editable versions provided) \n - **Service Tier**: Premium (with 48-hour turnaround) \n - **Confidentiality**: NDA required (maritime technical specifications) \n\nAs mentioned, we strongly prefer consolidated handling with a shared translation memory to maintain consistency in technical terminology (e.g., cargo specifications, safety protocols, and regulatory compliance terms). Could you confirm your process for implementing this, and whether there are any additional steps or costs for consolidating the files into a single project?", + "target": "get_user_profile", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 968, + "historical_id": "fastbrain_092415", + "source": "nemotron_agentic", + "query_hash": "a4427ddcb4c436d326405d9ee82e09d6e90e138155f2d3077ff626ffe0e3f48a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 968, + "historical_id": "fastbrain_092415", + "source": "nemotron_agentic", + "query_hash": "a4427ddcb4c436d326405d9ee82e09d6e90e138155f2d3077ff626ffe0e3f48a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092426", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:18339407e567fa4eb5a3d6c955f9fa7d", + "normalized_query_sha256": "90233864fa1efcfc36befcf1f9f0e9699d5e7c14ad21c2fbdf11a1411d5acc5d", + "query": "te your profile. For security reasons, I need to verify your identity before proceeding with any account changes.\n\nTo use the email verification method, I require your unique user ID. This is different from your email address and is typically found in your account settings or in confirmation emails from HeartLink Connect.\n\nCould you please:\n1. Check your email (temporarybreak@pause.com) for any HeartLink Connect communications - your user ID is usually included in welcome emails or account notifications\n2. Log into the HeartLink Connect app or website and look in your account settings for your user ID\n3. Alternatively, if you have biometric authentication enabled on your device, I can use biometric verification instead, but I'll still need your user ID for that method\n\nOnce you provide your user ID, I can send a verification code to your email and proceed with the temporary deactivation process.\nuser: I found my user ID in an old welcome email. It's HLC-12345. Please send the verification code to temporarybreak@pause.com so I can complete the temporary deactivation process.\nassistant: \ntool: \n\n{\"code_sent\": true}\nassistant: I've sent a 6-digit verification code to temporarybreak@pause.com. Please enter the code you received to verify your identity and proceed with the temporary deactivation process.\nuser: I've received the verification code. It's 123456. Please proceed with the temporary deactivation of my profile while preserving all my data. How long will this process take?", + "target": "verify_security_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 606, + "historical_id": "fastbrain_092426", + "source": "nemotron_agentic", + "query_hash": "90233864fa1efcfc36befcf1f9f0e9699d5e7c14ad21c2fbdf11a1411d5acc5d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 606, + "historical_id": "fastbrain_092426", + "source": "nemotron_agentic", + "query_hash": "90233864fa1efcfc36befcf1f9f0e9699d5e7c14ad21c2fbdf11a1411d5acc5d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092698", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c4694579973aef1f75d4886639ab99fb", + "normalized_query_sha256": "05e062445af21e267d1201d1125c66481a9d0f5758bb55b2296ac2f3da06ca07", + "query": "can i find the cheapest train ticket for this destination", + "target": "transport_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 515, + "historical_id": "fastbrain_092698", + "source": "amazon_massive", + "query_hash": "05e062445af21e267d1201d1125c66481a9d0f5758bb55b2296ac2f3da06ca07" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 515, + "historical_id": "fastbrain_092698", + "source": "amazon_massive", + "query_hash": "05e062445af21e267d1201d1125c66481a9d0f5758bb55b2296ac2f3da06ca07" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_092870", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a7ab1d3140cf3bb01abc98ed08dedb30", + "normalized_query_sha256": "3db32b709bcb75be43792326074ede1db805a819b564811154fb2e872168bef1", + "query": "Help me to find a good dentist in Gilroy.", + "target": "FindProvider", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 284, + "historical_id": "fastbrain_092870", + "source": "sgd", + "query_hash": "3db32b709bcb75be43792326074ede1db805a819b564811154fb2e872168bef1" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 284, + "historical_id": "fastbrain_092870", + "source": "sgd", + "query_hash": "3db32b709bcb75be43792326074ede1db805a819b564811154fb2e872168bef1" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_093082", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0c6d8e8060970c9003f4c67efbe72bd4", + "normalized_query_sha256": "c0d2469406731c97570303039f501e980bbbcf41e5f9d4b8bf3b9c40f99e021b", + "query": "Find me a place to eat in Berkeley", + "target": "FindRestaurants", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 38, + "historical_id": "fastbrain_093082", + "source": "sgd", + "query_hash": "c0d2469406731c97570303039f501e980bbbcf41e5f9d4b8bf3b9c40f99e021b" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 38, + "historical_id": "fastbrain_093082", + "source": "sgd", + "query_hash": "c0d2469406731c97570303039f501e980bbbcf41e5f9d4b8bf3b9c40f99e021b" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_093090", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c6bf7e94c8584d2432438f51cd93df14", + "normalized_query_sha256": "b2e7120849a272543377239e7c9c3ce2f6b379cb4f58bdc6caf4f87ec7197aaa", + "query": "Yes, book me an appointment there please.", + "target": "BookAppointment", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 170, + "historical_id": "fastbrain_093090", + "source": "sgd", + "query_hash": "b2e7120849a272543377239e7c9c3ce2f6b379cb4f58bdc6caf4f87ec7197aaa" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 170, + "historical_id": "fastbrain_093090", + "source": "sgd", + "query_hash": "b2e7120849a272543377239e7c9c3ce2f6b379cb4f58bdc6caf4f87ec7197aaa" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; 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semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_093421", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:b15cad8fe04741f6a8a4dfa8837d82cb", + "normalized_query_sha256": "2197257ca051ac9d0bfe228af974d5e3547bc15e940e412f951639d0a6172f68", + "query": "within the past three months how many meetings did i have with mr. richards", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 831, + "historical_id": "semantic:validation:fastbrain_093421:present", + "source": "amazon_massive", + "query_hash": "2197257ca051ac9d0bfe228af974d5e3547bc15e940e412f951639d0a6172f68" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 957, + "historical_id": "semantic:validation:fastbrain_093421:absent", + "source": "amazon_massive", + "query_hash": "2197257ca051ac9d0bfe228af974d5e3547bc15e940e412f951639d0a6172f68" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_093527", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:14590f3277b21aaec06427d6fdf3a999", + "normalized_query_sha256": "eccf9300124d54e1ffb17f739d8916c94ed9a2a15803e769faf63cb51cace088", + "query": "daily news from source", + "target": "news_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 574, + "historical_id": "semantic:validation:fastbrain_093527:present", + "source": "amazon_massive", + "query_hash": "eccf9300124d54e1ffb17f739d8916c94ed9a2a15803e769faf63cb51cace088" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 732, + "historical_id": "semantic:validation:fastbrain_093527:absent", + "source": "amazon_massive", + "query_hash": "eccf9300124d54e1ffb17f739d8916c94ed9a2a15803e769faf63cb51cace088" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_093720", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:c06dadf6dfb4ad6322b6d0110a2cedd3", + "normalized_query_sha256": "cd8ce3d09acc70e165239fcb521248f6838619e7f7086b8e9b207ec63ac04255", + "query": "Resolve symbolic link of file \"/foo/bar/baz\"", + "target": "readlink", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 830, + "historical_id": "fastbrain_093720", + "source": "nl2bash", + "query_hash": "cd8ce3d09acc70e165239fcb521248f6838619e7f7086b8e9b207ec63ac04255" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 830, + "historical_id": "fastbrain_093720", + "source": "nl2bash", + "query_hash": "cd8ce3d09acc70e165239fcb521248f6838619e7f7086b8e9b207ec63ac04255" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_093871", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:82888f5ec70e7dbcaeb90f2e62657a92", + "normalized_query_sha256": "1a6175f07a44bea38c7f6ae2a399c1ce66a99d6b2deb03cc308c02f4fa0f3107", + "query": "Can you get me a pool ride to 1734 El Camino Real?", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 225, + "historical_id": "fastbrain_093871", + "source": "sgd", + "query_hash": "1a6175f07a44bea38c7f6ae2a399c1ce66a99d6b2deb03cc308c02f4fa0f3107" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 225, + "historical_id": "fastbrain_093871", + "source": "sgd", + "query_hash": "1a6175f07a44bea38c7f6ae2a399c1ce66a99d6b2deb03cc308c02f4fa0f3107" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_093935", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4e1c8bc0db34d49a19a1d492baf7a77f", + "normalized_query_sha256": "7b8b19304dc7a8be42d093fa7a32d9dd246a4c427ef57fac94c88b4233dcae8e", + "query": "Now, tell me about weather on March 3rd.", + "target": "GetWeather", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 781, + "historical_id": "fastbrain_093935", + "source": "sgd", + "query_hash": "7b8b19304dc7a8be42d093fa7a32d9dd246a4c427ef57fac94c88b4233dcae8e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 781, + "historical_id": "fastbrain_093935", + "source": "sgd", + "query_hash": "7b8b19304dc7a8be42d093fa7a32d9dd246a4c427ef57fac94c88b4233dcae8e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_093982", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5210eb000dda2e33f010b315b657fc9b", + "normalized_query_sha256": "9cbb78704721853d2de2c90177a8f67ece3d9fc75bdffac668b76d6e5edcda04", + "query": "ask them to remember always to lock the door and keep a careful watch on their keys", + "target": "alarm_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 390, + "historical_id": "fastbrain_093982", + "source": "amazon_massive", + "query_hash": "9cbb78704721853d2de2c90177a8f67ece3d9fc75bdffac668b76d6e5edcda04" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 390, + "historical_id": "fastbrain_093982", + "source": "amazon_massive", + "query_hash": "9cbb78704721853d2de2c90177a8f67ece3d9fc75bdffac668b76d6e5edcda04" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_094027", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:cb36d812a2b19b7801f0d67faeb5f73c", + "normalized_query_sha256": "9dfd9358025d81e25e61678df733b61305e4c4c8f70e25310a9551ecabdd3789", + "query": "Hi, I want to know if the product with id 'P1234' is available in stock?", + "target": "check_stock_availability", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 30, + "historical_id": "fastbrain_094027", + "source": "glaive_v2", + "query_hash": "9dfd9358025d81e25e61678df733b61305e4c4c8f70e25310a9551ecabdd3789" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 30, + "historical_id": "fastbrain_094027", + "source": "glaive_v2", + "query_hash": "9dfd9358025d81e25e61678df733b61305e4c4c8f70e25310a9551ecabdd3789" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_094091", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:4edce602f9200038bd8b6a29cd00d009", + "normalized_query_sha256": "56fdce645f813f354a4316f25490ee34d04876898fd6c1cee123dec35035dc12", + "query": " to marina staff - please hold\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling about my marina fuel card that was declined at the pump. My reservation ID is FUELCARD2024 for slip PUMP1. This is my first time using the card, so I'm not sure what might be causing the authorization failure. Can you help me resolve this?\nassistant: Hello! I'd be happy to help you with the fuel card authorization issue. First, I need to verify your identity for security purposes, as this involves payment authorization.\n\nCould you please provide your user ID? Once I have that, I'll need to send you an authentication code to your registered email or phone to confirm your identity.\n\nAlso, to help troubleshoot the authorization failure, could you tell me:\n1. What type of fuel were you trying to purchase (diesel or gasoline)?\n2. How many gallons were you attempting to get?\n3. Were you at pump PUMP1 specifically?\n\nThis information will help me understand what might have caused the decline and assist you with a new authorization if needed.\nuser: My user ID is BOAT2024USER. The authentication code was sent to my email—let me check it. \n\nRegarding the fuel: I was trying to purchase diesel, approximately 100 gallons, at pump PUMP1 as listed on my reservation. The card declined before any fuel was dispensed. Let me confirm the code... it’s 478291. \n\nCould you also check if my card was activated properly? I received it in the mail a week ago but haven’t used it before.", + "target": "verify_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 595, + "historical_id": "fastbrain_094091", + "source": "nemotron_agentic", + "query_hash": "56fdce645f813f354a4316f25490ee34d04876898fd6c1cee123dec35035dc12" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 595, + "historical_id": "fastbrain_094091", + "source": "nemotron_agentic", + "query_hash": "56fdce645f813f354a4316f25490ee34d04876898fd6c1cee123dec35035dc12" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_094178", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8c132156eef36f92ccbbac874ba1b23d", + "normalized_query_sha256": "e7495bdcea7f11be5aa7d351901a7333862205e0477ef249fe4ecbc9b6493233", + "query": "schedule a meeting with the v. p. of the department on the twenty first of march two thousand and seventeen", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 31, + "historical_id": "fastbrain_094178", + "source": "amazon_massive", + "query_hash": "e7495bdcea7f11be5aa7d351901a7333862205e0477ef249fe4ecbc9b6493233" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 31, + "historical_id": "fastbrain_094178", + "source": "amazon_massive", + "query_hash": "e7495bdcea7f11be5aa7d351901a7333862205e0477ef249fe4ecbc9b6493233" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_094266", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:25d5733ad69599d7efc135516fc14c78", + "normalized_query_sha256": "fddba7eced74ca35623150003e32af235b5d7146b228b7ef64ef0b3d2708d739", + "query": "turn on the t. v. socket when i arrive home", + "target": "iot_wemo_on", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 184, + "historical_id": "fastbrain_094266", + "source": "amazon_massive", + "query_hash": "fddba7eced74ca35623150003e32af235b5d7146b228b7ef64ef0b3d2708d739" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 184, + "historical_id": "fastbrain_094266", + "source": "amazon_massive", + "query_hash": "fddba7eced74ca35623150003e32af235b5d7146b228b7ef64ef0b3d2708d739" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_094277", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f0645b96d09958894f8d6f3c930340c8", + "normalized_query_sha256": "24cdeb680ab8124350a6bda52b397942c0ced6b8a101cdf361c72ee9088ba133", + "query": "A Faithful Man sounds good. What time is it screening.", + "target": "GetTimesForMovie", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 822, + "historical_id": "fastbrain_094277", + "source": "sgd", + "query_hash": "24cdeb680ab8124350a6bda52b397942c0ced6b8a101cdf361c72ee9088ba133" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 822, + "historical_id": "fastbrain_094277", + "source": "sgd", + "query_hash": "24cdeb680ab8124350a6bda52b397942c0ced6b8a101cdf361c72ee9088ba133" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_094488", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dc63b858f3db56ec7cad15adb9e42425", + "normalized_query_sha256": "6fa561344edfd4b39e63aa6a9c2b08314bf0dbec91c482ee235e94221b0a3bc6", + "query": "closer by nine inch nails", + "target": "play_music", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 237, + "historical_id": "semantic:validation:fastbrain_094488:present", + "source": "amazon_massive", + "query_hash": "6fa561344edfd4b39e63aa6a9c2b08314bf0dbec91c482ee235e94221b0a3bc6" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 696, + "historical_id": "semantic:validation:fastbrain_094488:absent", + "source": "amazon_massive", + "query_hash": "6fa561344edfd4b39e63aa6a9c2b08314bf0dbec91c482ee235e94221b0a3bc6" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_094593", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0054f22d2bf364796e89a4426318dffe", + "normalized_query_sha256": "c5687570c637adbc4aa6a3c12196b448428404e81891f717fa79ad011b93157c", + "query": "what is the name of this song and who sings it", + "target": "music_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 508, + "historical_id": "fastbrain_094593", + "source": "amazon_massive", + "query_hash": "c5687570c637adbc4aa6a3c12196b448428404e81891f717fa79ad011b93157c" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 508, + "historical_id": "fastbrain_094593", + "source": "amazon_massive", + "query_hash": "c5687570c637adbc4aa6a3c12196b448428404e81891f717fa79ad011b93157c" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_094599", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:be703000846c2dfb320efa2dde508539", + "normalized_query_sha256": "d6c731440775c0042b4c7e8df9803de401188ca216ca33392ec461bc2753336a", + "query": "withdrawal requests \n- Sponsor inquiries \nProcess: \n1. Use `transfer_to_clinical_support` with issue summary \n2. State: \"YOU ARE BEING TRANSFERRED TO A CLINICAL SUPPORT SPECIALIST. PLEASE HOLD.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm a site staff member from SITE-555 with staff credential CO-2222. I'm calling about participant PT-3330 in trial NEU-2024-022 who reported a headache. Could you confirm if this symptom is considered trial-related?\nassistant: Hello! I'd be happy to help you with the participant's reported symptom. First, I need to authenticate you as site staff. You've provided your site ID (SITE-555) and staff credential (CO-2222), but I also need your two-factor authentication code to complete the verification.\n\nOnce authenticated, I can assist you with the adverse event reporting process. Regarding your question about whether the headache is trial-related, I should clarify that as a clinical trial support agent, I cannot speculate on diagnoses or causality assessments. However, I can help you submit a formal adverse event report which includes a causality assessment field (Unrelated, Likely, or Definitive).\n\nCould you please provide your two-factor authentication code?\nuser: My two-factor authentication code is 789456. Once authenticated, I’d like to clarify the protocol for determining if the headache in PT-3330 is trial-related. Can you guide me on how to proceed with the adverse event report and causality assessment?", + "target": "authenticate_user", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 372, + "historical_id": "fastbrain_094599", + "source": "nemotron_agentic", + "query_hash": "d6c731440775c0042b4c7e8df9803de401188ca216ca33392ec461bc2753336a" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 372, + "historical_id": "fastbrain_094599", + "source": "nemotron_agentic", + "query_hash": "d6c731440775c0042b4c7e8df9803de401188ca216ca33392ec461bc2753336a" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095237", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:d2435d26d9047d2da014984ebde97b92", + "normalized_query_sha256": "393beadcf177622c06ff058ccbcb9ebf75b50846c0b14f8a63ceb6819f05fb06", + "query": "Can you find me a Halsey song?", + "target": "LookupMusic", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 542, + "historical_id": "fastbrain_095237", + "source": "sgd", + "query_hash": "393beadcf177622c06ff058ccbcb9ebf75b50846c0b14f8a63ceb6819f05fb06" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 542, + "historical_id": "fastbrain_095237", + "source": "sgd", + "query_hash": "393beadcf177622c06ff058ccbcb9ebf75b50846c0b14f8a63ceb6819f05fb06" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095291", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8f5d0bf714c0f43431faeb087da1d878", + "normalized_query_sha256": "a484f52c666c75456fddc0e88db0dd4bc7946308829c49dd3b6f5a0bd53a5191", + "query": "meeting please set reminder", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 71, + "historical_id": "fastbrain_095291", + "source": "amazon_massive", + "query_hash": "a484f52c666c75456fddc0e88db0dd4bc7946308829c49dd3b6f5a0bd53a5191" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 71, + "historical_id": "fastbrain_095291", + "source": "amazon_massive", + "query_hash": "a484f52c666c75456fddc0e88db0dd4bc7946308829c49dd3b6f5a0bd53a5191" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095399", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f0d03c5ced4dcda9f70720d9b086bb80", + "normalized_query_sha256": "e52b184016ebb842ba0c8502e0933b8b9c10dada5b6a4e8f92870d8125055432", + "query": "e design advice beyond specifications \n - Schedule inspections without pre-checks \n - Promise timelines exceeding vendor capacity\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm calling to cancel our bathroom renovation project. My name is [Customer Name], and our Contract ID is CTR-8899. We'd like to proceed with canceling the project and understanding the refund process for our $7,500 deposit. Can you assist with this?\nassistant: Hello [Customer Name]. I can help you with the cancellation process for contract CTR-8899. \n\nFirst, I need to verify your identity for contract access. You've provided your name, but I also need to confirm your property address. Could you please provide:\n1. Your property address (street, city, state, and ZIP code) for verification\n2. Or if you have a project PIN, you can provide that instead\n\nAdditionally, to determine the refund amount for your $7,500 deposit, I need to know the current stage of your project:\n- **Pre-construction**: Project hasn't started yet\n- **Post-permitting**: Permits have been obtained but construction hasn't begun\n- **Active construction**: Work has already started on-site\n\nWhich stage best describes your project's current status?\nuser: Our project is in the **Pre-construction** stage—no work has begun yet. For verification, here’s our project PIN: **CTR-8899**. Our property address is 123 Maple Street, New York, NY 10001. Can you confirm the refund amount after the $350 cancellation fee?", + "target": "verify_contract_access", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 590, + "historical_id": "fastbrain_095399", + "source": "nemotron_agentic", + "query_hash": "e52b184016ebb842ba0c8502e0933b8b9c10dada5b6a4e8f92870d8125055432" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 590, + "historical_id": "fastbrain_095399", + "source": "nemotron_agentic", + "query_hash": "e52b184016ebb842ba0c8502e0933b8b9c10dada5b6a4e8f92870d8125055432" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095438", + "source": "git_nl_commands", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7665535ead09187583b3866ec1464441", + "normalized_query_sha256": "92729da8dd65df7d3f514e9b7758f937733a2766c848ff0dd700b49c5b100330", + "query": "publish this release tag v1.0.0", + "target": "git_push", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 429, + "historical_id": "fastbrain_095438", + "source": "git_nl_commands", + "query_hash": "92729da8dd65df7d3f514e9b7758f937733a2766c848ff0dd700b49c5b100330" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 429, + "historical_id": "fastbrain_095438", + "source": "git_nl_commands", + "query_hash": "92729da8dd65df7d3f514e9b7758f937733a2766c848ff0dd700b49c5b100330" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095603", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fbf4a3c28dfd044eeeefe3106b87aa85", + "normalized_query_sha256": "5c8ed3284410a8fb3aef12aeb49e9517ce5192dc0d04b4489fa2e80116af7cec", + "query": "Yes, can you help me find some good Dancehall music from The Gaza Don album?", + "target": "LookupMusic", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 668, + "historical_id": "fastbrain_095603", + "source": "sgd", + "query_hash": "5c8ed3284410a8fb3aef12aeb49e9517ce5192dc0d04b4489fa2e80116af7cec" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 668, + "historical_id": "fastbrain_095603", + "source": "sgd", + "query_hash": "5c8ed3284410a8fb3aef12aeb49e9517ce5192dc0d04b4489fa2e80116af7cec" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095615", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f7139b4ddd8ee8c883e43d5f4622ab58", + "normalized_query_sha256": "dd22637489d34df0ad91edff71e9ac7963ebeae89672ba3a2e5212d0417356dc", + "query": "clear my events search data", + "target": "calendar_remove", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 605, + "historical_id": "semantic:validation:fastbrain_095615:absent", + "source": "amazon_massive", + "query_hash": "dd22637489d34df0ad91edff71e9ac7963ebeae89672ba3a2e5212d0417356dc" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 606, + "historical_id": "semantic:validation:fastbrain_095615:present", + "source": "amazon_massive", + "query_hash": "dd22637489d34df0ad91edff71e9ac7963ebeae89672ba3a2e5212d0417356dc" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095633", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:32ed472a06bc0dc72e112f3426a498bf", + "normalized_query_sha256": "159b2b9ca23d30033b4c74dc953adc903cd87961b5f99cac1ebd2e922e70f5ea", + "query": "do i have any appointments this weekend", + "target": "calendar_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 795, + "historical_id": "semantic:validation:fastbrain_095633:absent", + "source": "amazon_massive", + "query_hash": "159b2b9ca23d30033b4c74dc953adc903cd87961b5f99cac1ebd2e922e70f5ea" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 879, + "historical_id": "semantic:validation:fastbrain_095633:present", + "source": "amazon_massive", + "query_hash": "159b2b9ca23d30033b4c74dc953adc903cd87961b5f99cac1ebd2e922e70f5ea" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095635", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:7271b008c26a07f8ce652e827a6ea60d", + "normalized_query_sha256": "f9bb4d208132d7da04fde9c2720ed1577944990b501d81010977cd39d18d5dcb", + "query": "don't talk", + "target": "audio_volume_mute", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 445, + "historical_id": "semantic:validation:fastbrain_095635:present", + "source": "amazon_massive", + "query_hash": "f9bb4d208132d7da04fde9c2720ed1577944990b501d81010977cd39d18d5dcb" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 486, + "historical_id": "semantic:validation:fastbrain_095635:absent", + "source": "amazon_massive", + "query_hash": "f9bb4d208132d7da04fde9c2720ed1577944990b501d81010977cd39d18d5dcb" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095656", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:f60879cc09f0265e33fd75cadb0381f4", + "normalized_query_sha256": "902964648b7da74b5077227cb5a58b58f031fe32b30eb9fc492aaa9ef2c8bd56", + "query": "alexa play pdr on the am band radio", + "target": "play_radio", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 42, + "historical_id": "fastbrain_095656", + "source": "amazon_massive", + "query_hash": "902964648b7da74b5077227cb5a58b58f031fe32b30eb9fc492aaa9ef2c8bd56" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 42, + "historical_id": "fastbrain_095656", + "source": "amazon_massive", + "query_hash": "902964648b7da74b5077227cb5a58b58f031fe32b30eb9fc492aaa9ef2c8bd56" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095669", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:01a0b0c609ed2c32e0e8ec8934435bb3", + "normalized_query_sha256": "0037c5d06330a9368db26f1ee91baede823896eccaddf4b41f8382296e73b4b0", + "query": "Can you find me oneway flights for my upcoming trip?", + "target": "SearchOnewayFlight", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 389, + "historical_id": "fastbrain_095669", + "source": "sgd", + "query_hash": "0037c5d06330a9368db26f1ee91baede823896eccaddf4b41f8382296e73b4b0" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 389, + "historical_id": "fastbrain_095669", + "source": "sgd", + "query_hash": "0037c5d06330a9368db26f1ee91baede823896eccaddf4b41f8382296e73b4b0" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095682", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1941409169427261020d7a1f396d9926", + "normalized_query_sha256": "fed59cac2c8d7dfc986ff0ac0fab54d0e84ed8876906797d547642131096c9a9", + "query": "do not talk", + "target": "audio_volume_mute", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 503, + "historical_id": "semantic:validation:fastbrain_095682:absent", + "source": "amazon_massive", + "query_hash": "fed59cac2c8d7dfc986ff0ac0fab54d0e84ed8876906797d547642131096c9a9" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 804, + "historical_id": "semantic:validation:fastbrain_095682:present", + "source": "amazon_massive", + "query_hash": "fed59cac2c8d7dfc986ff0ac0fab54d0e84ed8876906797d547642131096c9a9" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095716", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:46eff02b7217145bce6e1bcdbadac124", + "normalized_query_sha256": "0ee6c6e4092a6cfd93827c114ba9263c14de18dec7f6b861807f59cce3cc9dff", + "query": "Fine, get tickets to the event.", + "target": "BuyEventTickets", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 16, + "historical_id": "fastbrain_095716", + "source": "sgd", + "query_hash": "0ee6c6e4092a6cfd93827c114ba9263c14de18dec7f6b861807f59cce3cc9dff" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 16, + "historical_id": "fastbrain_095716", + "source": "sgd", + "query_hash": "0ee6c6e4092a6cfd93827c114ba9263c14de18dec7f6b861807f59cce3cc9dff" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095732", + "source": "nl2bash", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:5552589aa19d7719945572156643a236", + "normalized_query_sha256": "7f83825cb150dd1dd7b4bc0f19cb5cdd8ae381668d33dac2a177f5780abf5e4e", + "query": "forcibly and verbosely create a symbolic link named \"target\" to file \"source\"", + "target": "ln", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 456, + "historical_id": "fastbrain_095732", + "source": "nl2bash", + "query_hash": "7f83825cb150dd1dd7b4bc0f19cb5cdd8ae381668d33dac2a177f5780abf5e4e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 456, + "historical_id": "fastbrain_095732", + "source": "nl2bash", + "query_hash": "7f83825cb150dd1dd7b4bc0f19cb5cdd8ae381668d33dac2a177f5780abf5e4e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095739", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3eaab58050460fcfb5dbd3beab1b16da", + "normalized_query_sha256": "4327deac7eb83f6e606f165e76a9820de2c684c743138e4787a3422325676a58", + "query": " in Program Files or where you installed it)\n - Select the LinguaLearn executable file\n - Make sure both \"Private\" and \"Public\" boxes are checked\n - Click \"Add\"\n\n4. **Specific Port Exception (if needed)**:\n - In the same \"Allowed apps\" window, click \"Advanced settings\"\n - In the left pane, click \"Inbound Rules\"\n - Click \"New Rule\" in the right pane\n - Select \"Port\" and click Next\n - Select \"TCP\" and enter \"8080\" in \"Specific local ports\"\n - Click Next, select \"Allow the connection\"\n - Click Next, check all three profiles (Domain, Private, Public)\n - Name the rule \"LinguaLearn Sync Port\" and click Finish\n\n**Alternative Quick Check**:\n- Temporarily disable Windows Firewall to test if this resolves the sync issue:\n - In Windows Security > Firewall & network protection\n - Click on your active network (Private or Public)\n - Toggle \"Microsoft Defender Firewall\" to Off (temporarily)\n - Try syncing again\n - Remember to turn it back on after testing\n\nWould you like me to guide you through any of these specific steps, or would you prefer to be transferred to a technical specialist who can provide more detailed, real-time assistance with your Windows configuration?\nuser: I think I'll go ahead and be transferred to a technical specialist. The firewall settings seem a bit too technical for me to handle confidently, and I want to ensure the issue is resolved properly without risking my system's security. Could you connect me with someone who can assist with this?", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 945, + "historical_id": "fastbrain_095739", + "source": "nemotron_agentic", + "query_hash": "4327deac7eb83f6e606f165e76a9820de2c684c743138e4787a3422325676a58" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 945, + "historical_id": "fastbrain_095739", + "source": "nemotron_agentic", + "query_hash": "4327deac7eb83f6e606f165e76a9820de2c684c743138e4787a3422325676a58" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_095952", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:3cd8acfa4871eeab107560f0bb3802c6", + "normalized_query_sha256": "9a6df174e7793eb9ecba0daa761a9c2c334ad87438853cfd90af3e4c3b8e8889", + "query": "r ($50/year) ✓\n- **Owned Gear:** Carabiner, Ice Axe, Harness (serial numbers on file)\n\n**For Gym Membership Applications:**\nYou'll need to obtain personal liability insurance through:\n1. **Specialty climbing insurance providers** (like the American Alpine Club, British Mountaineering Council, etc.)\n2. **Personal insurance companies** offering adventure sports coverage\n3. **Homeowner's/renter's insurance** with adventure sports riders\n\n**What PeakGrip Can Do:**\n- Verify your gear ownership records\n- Confirm your certification status\n- Provide purchase history for equipment value documentation\n- Assist with equipment-specific insurance for future purchases\n\nWould you like me to:\n1. Check the recall status of your current gear?\n2. Verify certification compatibility for any new equipment?\n3. Provide details about equipment insurance for future purchases?\n\nOr would you prefer I transfer you to a human agent for further guidance on third-party insurance requirements?\nuser: Thank you for clarifying the limitations of PeakGrip's insurance coverage. I understand now that personal liability insurance isn't included in my membership. Since the gym specifically requires third-party insurance verification for their application, could you please transfer me to a human agent who can help me identify appropriate insurance providers or generate alternative documentation that confirms my gear ownership and certification status? I need this information to proceed with their application process.", + "target": "transfer_to_human_agent", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 736, + "historical_id": "fastbrain_095952", + "source": "nemotron_agentic", + "query_hash": "9a6df174e7793eb9ecba0daa761a9c2c334ad87438853cfd90af3e4c3b8e8889" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 736, + "historical_id": "fastbrain_095952", + "source": "nemotron_agentic", + "query_hash": "9a6df174e7793eb9ecba0daa761a9c2c334ad87438853cfd90af3e4c3b8e8889" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096118", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:72b17d49d11df821f66f0fa5c0017cdd", + "normalized_query_sha256": "e618cc34133cc35d5607de2df3d2f2c1b09159dc45c7d3404f9610277f4b9487", + "query": "what's his number", + "target": "email_querycontact", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 944, + "historical_id": "fastbrain_096118", + "source": "amazon_massive", + "query_hash": "e618cc34133cc35d5607de2df3d2f2c1b09159dc45c7d3404f9610277f4b9487" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 944, + "historical_id": "fastbrain_096118", + "source": "amazon_massive", + "query_hash": "e618cc34133cc35d5607de2df3d2f2c1b09159dc45c7d3404f9610277f4b9487" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096307", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0ae3f5e386482b656edc572a74f2352b", + "normalized_query_sha256": "3ffeeb94b1f710ebffffc86d93783c37a7bc9c9609e5019b955492ef460ccf2e", + "query": "I'm planning my next business trip and I need to book a hotel room for 8 days.", + "target": "ReserveHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 766, + "historical_id": "fastbrain_096307", + "source": "sgd", + "query_hash": "3ffeeb94b1f710ebffffc86d93783c37a7bc9c9609e5019b955492ef460ccf2e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 766, + "historical_id": "fastbrain_096307", + "source": "sgd", + "query_hash": "3ffeeb94b1f710ebffffc86d93783c37a7bc9c9609e5019b955492ef460ccf2e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096315", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:90a9c6c4469d10d17ca5a21d65a63c69", + "normalized_query_sha256": "94ec6eb0922fc41be2a1712a426072a0b1812a44cd8d71f064d4d99c67829772", + "query": "fined system capabilities\n - Never bypass certification or insurance verifications\n - Never modify deposits without system validation\n \n- **Verification Principles**:\n - Assume NO action completed without explicit tool confirmation\n - Assume NO information verified without system validation\n \n- **Knowledge Boundaries**:\n - Decline filmmaking advice or equipment recommendations\n - Avoid subjective assessments\n - Reject non-rental service inquiries (e.g., script consulting)\n\n**Fundamental Rule**: All actions require explicit system confirmation. Never proceed without tool verification.\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm part of a documentary crew with reservation #DOC123. We need to confirm if our delivery driver will have access to a government facility requiring background checks. Can you clarify what site-specific safety assessments or documentation are required for delivery access to secure locations?\nassistant: Hello! I'd be happy to help you with your delivery question for reservation #DOC123. First, I need to verify your identity to access your reservation details and provide specific information about your delivery.\n\nCould you please provide your email or phone number so I can send you a verification code? Once you receive the code, please provide it back to me so I can authenticate your account and access your reservation information.\nuser: Sure, here's my email: doccrew@example.com. Please send the verification code.", + "target": "initiate_verification", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 834, + "historical_id": "fastbrain_096315", + "source": "nemotron_agentic", + "query_hash": "94ec6eb0922fc41be2a1712a426072a0b1812a44cd8d71f064d4d99c67829772" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 834, + "historical_id": "fastbrain_096315", + "source": "nemotron_agentic", + "query_hash": "94ec6eb0922fc41be2a1712a426072a0b1812a44cd8d71f064d4d99c67829772" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096505", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:dc51010ab1e6c75437eb7629f2d8ebc1", + "normalized_query_sha256": "e598ec947b4ab35874bc8b335b3c0ab66b80d2f7a3c4e1fa37f8b989d6cf83d2", + "query": "did anybody email me today", + "target": "email_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 308, + "historical_id": "semantic:validation:fastbrain_096505:absent", + "source": "amazon_massive", + "query_hash": "e598ec947b4ab35874bc8b335b3c0ab66b80d2f7a3c4e1fa37f8b989d6cf83d2" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 915, + "historical_id": "semantic:validation:fastbrain_096505:present", + "source": "amazon_massive", + "query_hash": "e598ec947b4ab35874bc8b335b3c0ab66b80d2f7a3c4e1fa37f8b989d6cf83d2" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096511", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:75d29561d33bc6b0dd866ad113adc762", + "normalized_query_sha256": "fb70bc68ea4fda399daff83f41c0e4156e1ab7bbeb581034681cf21f70b63391", + "query": " details:\n\n**Validation ID:** `HZ-987654-20231115`\n**Approval Status:** Approved\n**Volume Compliance:** Confirmed (your 5-gallon containers are within the 10-gallon maximum limit)\n\n**Next Steps for Scheduling Pickup:**\nNow you can schedule your hazardous materials pickup using the validation ID. You'll need to provide:\n\n1. **Pickup Date**: In YYYY-MM-DD format\n2. **Container Type**: Choose from Bin, Truck, or Cage\n3. **Material Description**: \"lead-based paint debris\" (as declared)\n4. **Weight in Pounds**: Total weight of all materials\n\n**Proactive Confirmation (per policy):**\nPlease confirm your items comply with all limits before I finalize scheduling:\n- Maximum 10 gallons per container ✓ (you confirmed 5-gallon containers)\n- Proper packaging and labeling ✓ (you confirmed compliance)\n- Safety data sheets submitted ✓ (completed)\n\n**Ready to Schedule:**\nPlease provide your preferred pickup date (YYYY-MM-DD format), container type, and total weight in pounds, and I'll schedule the hazardous materials pickup for you using your validation ID.\nuser: Here are the details for scheduling: \n1. **Pickup Date**: 2023-11-20 \n2. **Container Type**: Bin \n3. **Total Weight**: 50 lbs \n\nPlease schedule the hazardous materials pickup using the validation ID `HZ-987654-20231115`. I’ve confirmed all EPA compliance requirements per your guidance and the EPA’s RRP program guidelines. \n\n**Final Request**: Can you confirm the pickup is scheduled and provide a reference number for this booking?", + "target": "schedule_pickup", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 995, + "historical_id": "fastbrain_096511", + "source": "nemotron_agentic", + "query_hash": "fb70bc68ea4fda399daff83f41c0e4156e1ab7bbeb581034681cf21f70b63391" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 995, + "historical_id": "fastbrain_096511", + "source": "nemotron_agentic", + "query_hash": "fb70bc68ea4fda399daff83f41c0e4156e1ab7bbeb581034681cf21f70b63391" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096572", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:55b192fdf1b7ef9c25a38c32b1ad2e43", + "normalized_query_sha256": "df655bdcbf47eac87e4b5a0b2fe2c905552f07c276cc48ae72d89273b5c5f944", + "query": "how old is brad pitt", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 402, + "historical_id": "semantic:validation:fastbrain_096572:absent", + "source": "amazon_massive", + "query_hash": "df655bdcbf47eac87e4b5a0b2fe2c905552f07c276cc48ae72d89273b5c5f944" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 458, + "historical_id": "semantic:validation:fastbrain_096572:present", + "source": "amazon_massive", + "query_hash": "df655bdcbf47eac87e4b5a0b2fe2c905552f07c276cc48ae72d89273b5c5f944" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096586", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:1fb533d4d6bb80bad7649114d1279382", + "normalized_query_sha256": "5ade8744be4a7f1336bd49269f3e98b00ea6180ecd7fed2a23602f7c04027402", + "query": "display current time", + "target": "datetime_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 39, + "historical_id": "semantic:validation:fastbrain_096586:absent", + "source": "amazon_massive", + "query_hash": "5ade8744be4a7f1336bd49269f3e98b00ea6180ecd7fed2a23602f7c04027402" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 909, + "historical_id": "semantic:validation:fastbrain_096586:present", + "source": "amazon_massive", + "query_hash": "5ade8744be4a7f1336bd49269f3e98b00ea6180ecd7fed2a23602f7c04027402" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096790", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:fa86121616456da35bd004b0edea7992", + "normalized_query_sha256": "d7bcf584914299801c35f05be81f7bcb3f0e1c201b1f65a7c13a10e02c8a39a7", + "query": "how much is the average house", + "target": "qa_factoid", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 49, + "historical_id": "fastbrain_096790", + "source": "amazon_massive", + "query_hash": "d7bcf584914299801c35f05be81f7bcb3f0e1c201b1f65a7c13a10e02c8a39a7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 49, + "historical_id": "fastbrain_096790", + "source": "amazon_massive", + "query_hash": "d7bcf584914299801c35f05be81f7bcb3f0e1c201b1f65a7c13a10e02c8a39a7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096792", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:6125f0a787234236d92647fecf3dcd5a", + "normalized_query_sha256": "7809e7646c9ed55af9cc77e44368e664dda58532d590a8ddc992b52e0cbc2356", + "query": "Okay..fine..what about the weather there on day after tomorrow?", + "target": "GetWeather", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 751, + "historical_id": "fastbrain_096792", + "source": "sgd", + "query_hash": "7809e7646c9ed55af9cc77e44368e664dda58532d590a8ddc992b52e0cbc2356" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 751, + "historical_id": "fastbrain_096792", + "source": "sgd", + "query_hash": "7809e7646c9ed55af9cc77e44368e664dda58532d590a8ddc992b52e0cbc2356" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096813", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:17c7cbf6874bcc9f17ed9b7939b79b02", + "normalized_query_sha256": "1bc344c840822ddd684e04dc9d6bcf54cb058b58d25bb15d9a4a1d4eddfe1344", + "query": "remind me to go to the gym every monday and tuesday", + "target": "calendar_set", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 896, + "historical_id": "semantic:validation:fastbrain_096813:absent", + "source": "amazon_massive", + "query_hash": "1bc344c840822ddd684e04dc9d6bcf54cb058b58d25bb15d9a4a1d4eddfe1344" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 932, + "historical_id": "semantic:validation:fastbrain_096813:present", + "source": "amazon_massive", + "query_hash": "1bc344c840822ddd684e04dc9d6bcf54cb058b58d25bb15d9a4a1d4eddfe1344" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_096944", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:a10eacd3807be076db589999d8a182e9", + "normalized_query_sha256": "9d69d8480af3b9711db9b3706493099e090e9c3742816edd465ff4c77c71abc8", + "query": "how long do i roast chicken breasts", + "target": "cooking_recipe", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 861, + "historical_id": "semantic:validation:fastbrain_096944:present", + "source": "amazon_massive", + "query_hash": "9d69d8480af3b9711db9b3706493099e090e9c3742816edd465ff4c77c71abc8" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 958, + "historical_id": "semantic:validation:fastbrain_096944:absent", + "source": "amazon_massive", + "query_hash": "9d69d8480af3b9711db9b3706493099e090e9c3742816edd465ff4c77c71abc8" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_097083", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:eca4bb47249edbb2d40e5a83119652c5", + "normalized_query_sha256": "089d0f645bda5227bf681f415c389f75cd08acff5e098a021545d9de23b3fabe", + "query": "Thanks a lot for everything. Could you also find me a hotel in the area?", + "target": "SearchHotel", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 469, + "historical_id": "fastbrain_097083", + "source": "sgd", + "query_hash": "089d0f645bda5227bf681f415c389f75cd08acff5e098a021545d9de23b3fabe" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 469, + "historical_id": "fastbrain_097083", + "source": "sgd", + "query_hash": "089d0f645bda5227bf681f415c389f75cd08acff5e098a021545d9de23b3fabe" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_097158", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:70a1eb6830b33c58453cecd3a1a8f13b", + "normalized_query_sha256": "9d925ee4720d5debc234c5f2ff4b0565ccd3f677379c048e9a0ff4c5a015f43d", + "query": "Will you be able to book a cab for me to go there?", + "target": "GetRide", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 918, + "historical_id": "fastbrain_097158", + "source": "sgd", + "query_hash": "9d925ee4720d5debc234c5f2ff4b0565ccd3f677379c048e9a0ff4c5a015f43d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 918, + "historical_id": "fastbrain_097158", + "source": "sgd", + "query_hash": "9d925ee4720d5debc234c5f2ff4b0565ccd3f677379c048e9a0ff4c5a015f43d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_097232", + "source": "glaive_v2", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0917cc5ce18ca928ebcd5efa721147f9", + "normalized_query_sha256": "38cc1cfcfd7783dfbd47d84598e7f1f3e0c0dfe5e24f29315540e7bdcdf3025e", + "query": "The subject is \"Project Update\" and the content is \"Hi John, The project is on track and we are meeting the deadlines as planned. Regards, Alex\".", + "target": "send_email", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 205, + "historical_id": "fastbrain_097232", + "source": "glaive_v2", + "query_hash": "38cc1cfcfd7783dfbd47d84598e7f1f3e0c0dfe5e24f29315540e7bdcdf3025e" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 205, + "historical_id": "fastbrain_097232", + "source": "glaive_v2", + "query_hash": "38cc1cfcfd7783dfbd47d84598e7f1f3e0c0dfe5e24f29315540e7bdcdf3025e" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_097284", + "source": "amazon_massive", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:0f23feac090cf347ac21c969d84d2c87", + "normalized_query_sha256": "79e42c703e188ceb9fa17fd86d6e6f655b29d09775d1e156f17cfce8aaa2d387", + "query": "tell me the humidity level downtown today", + "target": "weather_query", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 63, + "historical_id": "semantic:validation:fastbrain_097284:absent", + "source": "amazon_massive", + "query_hash": "79e42c703e188ceb9fa17fd86d6e6f655b29d09775d1e156f17cfce8aaa2d387" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/coverage_validation.jsonl.gz", + "file_row": 85, + "historical_id": "semantic:validation:fastbrain_097284:present", + "source": "amazon_massive", + "query_hash": "79e42c703e188ceb9fa17fd86d6e6f655b29d09775d1e156f17cfce8aaa2d387" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_097321", + "source": "sgd", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:03c836da0a3737c052fffcb40bcbbdf3", + "normalized_query_sha256": "9c3652f847c675b13a2e8d63d1e9063c43d9a381dfa19c29d821f4da186e874d", + "query": "I'd like to find some movies online directed by Hiroyasu Ishida.", + "target": "FindMovies", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 386, + "historical_id": "fastbrain_097321", + "source": "sgd", + "query_hash": "9c3652f847c675b13a2e8d63d1e9063c43d9a381dfa19c29d821f4da186e874d" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 386, + "historical_id": "fastbrain_097321", + "source": "sgd", + "query_hash": "9c3652f847c675b13a2e8d63d1e9063c43d9a381dfa19c29d821f4da186e874d" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_097398", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:8fd80bfabbb234384794d2c27175e46d", + "normalized_query_sha256": "f3fe71dc2fe854f0035de91721e837c58918c7b37b70ed6f1ff19187a2da48e7", + "query": "x($50, insurance_deductible)` \n- Appeal denials via `initiate_insurance_appeal` \n\n## Agent Limitations \n- **Do not answer questions or perform actions outside defined tool capabilities** \n- **Never assume verification or action completion without explicit tool confirmation** \n- Never diagnose conditions or interpret lab results \n- Never override prescription contraindications \n- Only recommend first-aid treatments \n- Deny off-hours emergencies (redirect to `find_emergency_clinic`) \n- Terminate animal cruelty discussions \n\n## Compensation \nService credits for: \n- Technical failures during consultation \n- Provider delays >15 minutes \n- Incorrect prescriptions \n- Credit value: 120% of service cost \n\n## Transfer Protocol \nTransfer to licensed vet for: \n- Vomiting/lethargy/loss of appetite \n- Post-surgical complications \n- Medication reactions \n- Behavioral emergencies \n\n**Compliance Note**: All interactions logged with timestamp/user ID.\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I'm concerned about my 10-year-old dog Charlie. Last night there were loud fireworks nearby, and this morning he's completely ignoring commands or any sounds. He’s usually responsive, but now it’s like he can’t hear anything. Could this be sudden deafness? I need guidance on whether this requires immediate veterinary attention. My email is deafnesshelp@email.com and zip code is 90035.\nassistant: \ntool: \n\n{\"authenticated\": true, \"user_id\": \"user_12345\"}", + "target": "transfer_to_licensed_vet", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 352, + "historical_id": "fastbrain_097398", + "source": "nemotron_agentic", + "query_hash": "f3fe71dc2fe854f0035de91721e837c58918c7b37b70ed6f1ff19187a2da48e7" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 352, + "historical_id": "fastbrain_097398", + "source": "nemotron_agentic", + "query_hash": "f3fe71dc2fe854f0035de91721e837c58918c7b37b70ed6f1ff19187a2da48e7" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_097430", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:655c94ef1d95f3b298c9c496bf186796", + "normalized_query_sha256": "a75241ec39a654bb9c6bd25e3524c502a1ce803885d337ded50e000471ea3e05", + "query": " - Future inventory speculation \n\n### **Transfer Protocol** \nEscalate to human agent for: \n- Complex damage claims (>3 bottles) \n- Business/wholesale inquiries \n- Regulatory compliance issues \n- Upon explicit customer request \nTransfer message: \"YOU ARE BEING TRANSFERRED TO A VINE SELECT SPECIALIST.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello, I’m trying to update the wine preferences for a gift subscription I purchased (VS-GIFT-555). As the purchaser with an active account, can I make these changes directly, or do I need the recipient’s permission first?\nassistant: Hello! I'd be happy to help you update the wine preferences for your gift subscription. First, I need to authenticate your account to ensure security since subscription changes are account-sensitive operations.\n\nCould you please provide your email address or phone number so I can send you an authentication code?\n\nOnce authenticated, I can help you with the subscription changes. Also, could you clarify what specific changes you'd like to make to the wine preferences? Are you looking to swap wines within the subscription, or make other adjustments?\nuser: Sure, my email is sarah.johnson@example.com. Please send the authentication code there. \n\nFor the changes, I’d like to replace the \"Barolo Riserva\" bottle with a \"Château Margaux\" selection in the next delivery. Also, does the recipient need to approve these changes, or can I update the preferences directly as the purchaser?", + "target": "send_authentication_code", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 381, + "historical_id": "fastbrain_097430", + "source": "nemotron_agentic", + "query_hash": "a75241ec39a654bb9c6bd25e3524c502a1ce803885d337ded50e000471ea3e05" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 381, + "historical_id": "fastbrain_097430", + "source": "nemotron_agentic", + "query_hash": "a75241ec39a654bb9c6bd25e3524c502a1ce803885d337ded50e000471ea3e05" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + }, + { + "canonical_id": "fastbrain_097433", + "source": "nemotron_agentic", + "original_split": "train", + "legacy_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "legacy_query_parent_id": "legacy_query:47f491f2441111805b3a3a1847e7328c", + "normalized_query_sha256": "4226146061c9ff77f19e0e7146a08f1c1da4a330403129c518c86f87965097a3", + "query": "t \n- Consistent design theme required \n- Separate shipment fees apply \n\n### Cultural Customizations \n- Religious symbols: Signed approval required \n- Non-Latin characters: +$50 fee \n- Double-sided translations: +30% fee \n\n## Agent Limitations \n- Do not answer questions or perform actions outside defined tool capabilities \n- Never assume verification or action completion without explicit tool confirmation \n- Never create designs outside template library \n- Never promise unavailable materials \n- Never bypass address verification checks \n- Never provide design advice beyond documented options \n- Never commit to timelines without production_calendar verification \n\n## Transfer Protocol \nTransfer to human agent when: \n- Custom illustration requests \n- Venue map integration \n- Multi-wedding coordination \n- Complaints requiring managerial review \n- Legal documentation requests \n\nTransfer process: \n1. Use `transfer_to_design_consultant` tool \n2. Send message: \n \"YOU ARE BEING TRANSFERRED TO A STATIONERY DESIGN CONSULTANT FOR PERSONALIZED ASSISTANCE.\"\n\n\nTry to be helpful and always follow the policy.\nuser: \n\nHello! I'm reaching out about my wedding stationery order #STARS44 (Client ID: COSMIC). I'd like to discuss incorporating a custom star chart for our September 22, 2025 wedding date. Since this requires specialized design work, could I please be connected with a design consultant? \n\n[Authentication requested: Client ID & Order # already provided]", + "target": "authenticate_client", + "released_v2_split": null, + "eligible_for_frozen_v3_validation_by_membership": true, + "historical_memberships": [ + { + "historical_split": "validation", + "file": "scratch/fastbrain_next/augmented/validation.jsonl.gz", + "file_row": 142, + "historical_id": "fastbrain_097433", + "source": "nemotron_agentic", + "query_hash": "4226146061c9ff77f19e0e7146a08f1c1da4a330403129c518c86f87965097a3" + }, + { + "historical_split": "validation", + "file": "scratch/fastbrain_retention/data/retention_validation.jsonl.gz", + "file_row": 142, + "historical_id": "fastbrain_097433", + "source": "nemotron_agentic", + "query_hash": "4226146061c9ff77f19e0e7146a08f1c1da4a330403129c518c86f87965097a3" + } + ], + "caveat": "Membership preservation only: the row belonged to original default/train, so this is not a newly unseen evaluation example; semantic/source/parent checks still apply." + } + ] +} diff --git a/curation/v3/legacy_repair_decisions.json b/curation/v3/legacy_repair_decisions.json new file mode 100644 index 0000000000000000000000000000000000000000..1c324e16a8fc19b6c97680dc3148a6108c1c8e80 --- /dev/null +++ b/curation/v3/legacy_repair_decisions.json @@ -0,0 +1,23292 @@ +{ + "description_updates": { + "git_nl_commands": { + "git_rm": "Remove tracked paths from the index and usually the working tree; with --cached, stop tracking while keeping local files.", + "git_push": "Update remote branches or tags, transferring required objects; deletion options can remove remote branches or tags.", + "git_clean": "Remove untracked files and optional directories; options can include ignored paths or restrict removal to ignored paths.", + "git_commit": "Create or amend a commit from staged changes; supported options can also stage modified or deleted tracked files.", + "git_config": "Read, set, unset, or edit Git configuration.", + "git_reset": "Reset index entries or move HEAD; selected modes also update the working tree.", + "git_stash": "Save, list, inspect, apply, pop, or discard stashed working-tree changes.", + "git_reflog": "Inspect or manage local reference-update history, including expiring or deleting entries.", + "git_reset_hard": "Reset HEAD, the index, and tracked working-tree paths to a specified commit, discarding tracked changes; this is not a general cleanup of untracked files." + }, + "curated_devops": { + "git_rm": "Remove tracked paths from the index and usually the working tree; with --cached, stop tracking while keeping local files.", + "git_push": "Update remote branches or tags, transferring required objects; deletion options can remove remote branches or tags.", + "git_clean": "Remove untracked files and optional directories; options can include ignored paths or restrict removal to ignored paths.", + "git_commit": "Create or amend a commit from staged changes; supported options can also stage modified or deleted tracked files.", + "git_config": "Read, set, unset, or edit Git configuration.", + "git_reset": "Reset index entries or move HEAD; selected modes also update the working tree.", + "git_stash": "Save, list, inspect, apply, pop, or discard stashed working-tree changes.", + "git_reflog": "Inspect or manage local reference-update history, including expiring or deleting entries.", + "git_reset_hard": "Reset HEAD, the index, and tracked working-tree paths to a specified commit, discarding tracked changes; this is not a general cleanup of untracked files." + }, + "nl2bash": { + "comm": "Compare two files sorted by line, reporting lines unique to either file or common to both.", + "join": "Join lines from two suitably sorted files on a matching field.", + "more": "Display text interactively one screenful at a time.", + "rm": "Remove files and, with recursive options, directory trees.", + "rmdir": "Remove empty directories." + }, + "amazon_massive": { + "alarm_query": "Ask about saved alarms, their times, or whether an alarm is active.", + "alarm_remove": "Cancel or delete a saved alarm, or snooze an alarm.", + "alarm_set": "Set or enable an alarm for a specified time or recurrence.", + "audio_volume_down": "Lower the volume of speech or audio playback.", + "audio_volume_mute": "Mute speech or audio output.", + "audio_volume_other": "Access audio-volume settings or request a volume adjustment without a clear increase, decrease, or mute action.", + "audio_volume_up": "Raise the volume of speech or audio playback.", + "calendar_query": "Ask about scheduled events, appointments, or reminders.", + "calendar_remove": "Cancel or delete calendar events, appointments, or reminders.", + "calendar_set": "Create a calendar event, appointment, or scheduled reminder.", + "datetime_convert": "Convert a clock time between time zones, compare time offsets, or change the selected time zone.", + "datetime_query": "Ask for a date, day of the week, or current time in a location.", + "email_addcontact": "Add an email address or a new entry to the contact list.", + "email_query": "Find, check, or read received email messages.", + "email_querycontact": "Look up a person or their saved contact details, such as a phone number, address, or email address.", + "email_sendemail": "Compose, send, or reply to an email message.", + "general_greet": "Exchange greetings or ask how the assistant is doing.", + "general_joke": "Ask for a joke or something funny.", + "general_quirky": "Engage in open-ended conversation, personal remarks, opinions, or broad reflective questions.", + "iot_cleaning": "Start or control a connected cleaning device.", + "iot_coffee": "Start or control a connected coffee maker to prepare coffee.", + "iot_hue_lightchange": "Change the color of connected lights.", + "iot_hue_lightdim": "Reduce the brightness of connected lights.", + "iot_hue_lightoff": "Turn connected lights off.", + "iot_hue_lighton": "Turn connected lights on.", + "iot_hue_lightup": "Increase the brightness of connected lights.", + "iot_wemo_off": "Turn a connected smart plug or its attached appliance off.", + "iot_wemo_on": "Turn a connected smart plug or its attached appliance on.", + "lists_createoradd": "Create a list or add an item to an existing list.", + "lists_query": "Show saved lists or read the items on a list.", + "lists_remove": "Delete a list or remove an item from a list.", + "music_dislikeness": "Express dislike for a song or ask that it not be played again.", + "music_likeness": "Express a positive music preference or save a song to favorites, a playlist, or a music collection.", + "music_query": "Ask for information about a song, artist, recording, or music currently playing.", + "music_settings": "Change music playback settings or controls, such as shuffle, repeat, or skipping a song.", + "news_query": "Request news headlines or news on a topic.", + "play_audiobook": "Play or resume an audiobook.", + "play_game": "Start or play a game.", + "play_music": "Play music, a song, an album, an artist, or a playlist.", + "play_podcasts": "Play a podcast or move between podcast episodes when podcast context is available.", + "play_radio": "Play or tune to a radio station.", + "qa_definition": "Ask for the meaning or definition of a word, phrase, or concept.", + "qa_factoid": "Ask a factual question about people, places, events, objects, or general knowledge.", + "qa_maths": "Calculate an arithmetic result or answer a mathematical question.", + "recommendation_events": "Find or recommend events and activities to attend.", + "recommendation_locations": "Find or recommend places to visit, shops, restaurants, or nearby services.", + "recommendation_movies": "Find or recommend movies to watch or ask what is showing.", + "social_post": "Publish a post, status update, or message on social media.", + "social_query": "Read social-media updates, notifications, activity, or account information.", + "takeaway_order": "Request a takeaway or food-delivery order.", + "takeaway_query": "Ask about takeaway or food-delivery availability, providers, or delivery details.", + "transport_query": "Ask for routes, travel times, public-transport schedules, or navigation information.", + "transport_taxi": "Request or book a taxi or ride-hailing car.", + "transport_ticket": "Find or book a ticket or seat for a train, flight, or other scheduled transport.", + "transport_traffic": "Ask about traffic conditions or congestion on a route.", + "weather_query": "Ask about current weather, forecasts, or expected weather conditions." + } + }, + "quarantine_by_id": { + "fastbrain_073902": "The entire request is a seat reservation confirmation; both bus-ticket purchase and one-way-flight reservation are offered. The service/dialogue context that distinguishes them was not recovered.", + "fastbrain_001980": "The request asks for things to do and dates of events. FindEvents and GetEventDates are both candidates; the missing service state does not establish a unique action.", + "fastbrain_095250": "Get me a one-way flight does not uniquely distinguish searching from reserving in the exposed SearchOnewayFlight/ReserveOnewayFlight choice set.", + "fastbrain_036223": "The user requests a reminder tomorrow, while create_todo only promises creating a todo item. No recovered native schema establishes timed-reminder support.", + "fastbrain_006573": "Let us call it Trip to Hawaii identifies a name but not the object or action; the earlier conversation needed to justify create_todo_list is absent.", + "fastbrain_047068": "The request combines filesystem search and FTP upload; matching native commands invoke curl inside find -exec. The exposed find capability alone does not certify the complete operation, and multiple native rows remain matched.", + "fastbrain_066823": "The same native query maps both to a full comm/sort/grep process-substitution command and to corrupt comm -23 < <. Preserve both matches; neither first-match selection nor description rewriting recovers a unique executable source command.", + "fastbrain_018257": "The native command is git rm -r --cached build/ followed by appending build/ to .gitignore. git_rm alone cannot perform the requested ignore-file edit.", + "fastbrain_001956": "The request explicitly names mypy and flake8 but the target is an undefined pylint_typecheck wrapper. No implementation/native schema establishes that it invokes the requested tools.", + "fastbrain_093872": "The request covers all pods in a namespace, while k8s_rollout_restart describes a single deployment. The missing cluster/controller state does not establish equivalence.", + "fastbrain_052056": "The nature of artificial intelligence is labeled general_quirky. Its boundary with factual/definition questions is not established by an authoritative ontology contract; retain the native label as evidence, not a repaired unique action.", + "fastbrain_060517": "What are jalapenos is natively labeled cooking_recipe, but it asks for an ingredient definition. No automatic relabeling or expansion of recipe to all food questions is justified.", + "fastbrain_041207": "How many hours ahead is NYC is natively datetime_query; retained training examples also place offset questions in datetime_convert. Do not invent an exclusive boundary or silently relabel.", + "fastbrain_085213": "scope_finance: Calculate the payment on a loan from principal, interest rate, and loan term.", + "fastbrain_053065": "extract_files_by_extension creates and populates Compliance_Audit_2023, then generate_file_report reads that folder. The user also says parallel, but the visible dependency and contradictory timing prevent certifying the co-emitted set as ready to run in parallel.", + "fastbrain_082954": "list_container_services receives cluster=\"web-app-service\", although the user provided a service name and task definition, not its cluster. Passing the native schema does not ground this argument.", + "fastbrain_085403": "scope_finance: Send a USD peer-to-peer payment through Cash App.", + "fastbrain_019803": "scope_finance: Transfer Bitcoin between blockchain wallets.", + "fastbrain_000663": "Go back is labeled play_podcasts but no podcast/media context is present.", + "fastbrain_001094": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_003615": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_003814": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_004588": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_006314": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_006908": "The only input is olly, labeled recommendation_events; no event-discovery request is visible.", + "fastbrain_007278": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_007548": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_009396": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_009620": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_010650": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_011831": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_014223": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_014395": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_018409": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_018951": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_019650": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_019701": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_019814": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_021009": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_022512": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_023841": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_025221": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_025527": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_026912": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_030148": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_030224": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_031415": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_032263": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_032458": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_033907": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_034935": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_035025": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_035668": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_037269": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_038171": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_038452": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_038478": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_038731": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_040564": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_041338": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_041747": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_041953": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_042460": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_042788": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_042858": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_044134": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_044345": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_047863": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_048021": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_048283": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_048341": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_048703": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_049032": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_052279": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_052623": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_052847": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_054312": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_054919": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_056956": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_057143": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_057350": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_058529": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_059264": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_060192": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_064217": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_064337": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_065901": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_066260": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_066817": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_066989": "The only input is fast, labeled audio_volume_up; no volume-increase intent is established.", + "fastbrain_067124": "Lights out is labeled iot_hue_lighton. Its ordinary reading does not justify turning lights on; isolate without silently flipping the label.", + "fastbrain_067177": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_068616": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_069052": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_069738": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_069842": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_069917": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_070360": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_071601": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_072216": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_072541": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_072710": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_073565": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_074038": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_074847": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_075874": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_076673": "Change my alarm is labeled alarm_remove, but the request does not distinguish deletion, snoozing, or editing. Do not redefine remove as all alarm changes.", + "fastbrain_077729": "Send a text to Alice is labeled email_querycontact; contact lookup may be a prerequisite, but no native dialogue/tool state establishes it as the unique next action.", + "fastbrain_080319": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_081597": "Weird things people want to know is labeled cooking_recipe without any visible cooking request. Do not expand a recipe description to cover it.", + "fastbrain_083328": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_084352": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_084852": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_086394": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_086791": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_087260": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_089369": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_090206": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_090223": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_090519": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_090984": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_091373": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_092413": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_092661": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_097266": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_097324": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_099783": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_101452": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_102619": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_102781": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_102876": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_103111": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_105430": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_107019": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_001166": "scope_finance: Initiate a USD transfer to a PayPal recipient.", + "fastbrain_002492": "scope_finance: Create an actual Venmo peer-to-peer payment.", + "fastbrain_012497": "scope_finance: Approve a consumer loan and compute its payments and amortization schedule.", + "fastbrain_022513": "scope_finance: Initiate a credit-card payment through a payment gateway; this is an explicit payment API operation, not merely a shopping-price mention.", + "fastbrain_023338": "scope_finance: Retrieve cryptocurrency-wallet financial transaction history.", + "fastbrain_023893": "scope_finance: Retrieve PayPal financial transaction history.", + "fastbrain_029254": "scope_finance: Transfer USD funds between e-wallet users.", + "fastbrain_036850": "scope_finance: Send a Venmo peer-to-peer payment.", + "fastbrain_039428": "scope_finance: Link a credit card to Coinbase as the first action of a cryptocurrency-purchase workflow.", + "fastbrain_056780": "scope_finance: Initiate a Revolut international money transfer.", + "fastbrain_063295": "scope_finance: Purchase Bitcoin through Coinbase.", + "fastbrain_064277": "scope_finance: Create a cryptocurrency wallet for asset management and payments.", + "fastbrain_064932": "scope_finance: Create a Klarna buy-now-pay-later installment-payment session; the requested operation is credit/payment processing.", + "fastbrain_067688": "scope_finance: Transfer funds between checking and savings bank accounts.", + "fastbrain_072727": "scope_finance: Create a PayPal payment request; distinguish this explicit payment-network action from ordinary invoice document generation.", + "fastbrain_084822": "scope_finance: Create a brokerage account and update an investment profile for securities trading.", + "fastbrain_084975": "scope_finance: Link a checking bank account to PayPal as a funding source.", + "fastbrain_085346": "scope_finance: Retrieve PayPal transaction history and account balance.", + "fastbrain_101949": "scope_finance: Purchase Bitcoin through Cash App and query the USD/BTC exchange rate." + }, + "primary_evidence": { + "schema_version": "legacy_repair_research_v1", + "date": "2026-10-14", + "scope": "Research decisions for a new explicitly limited legacy replay and Hermes observed-action release. No original/released file, HF repository, or model was changed by this script.", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "reviewed_release_revision": "11e6f657f6d8830da230d4e49c65d0f67f106585", + "description_updates": { + "git_nl_commands": { + "git_rm": "Remove tracked paths from the index and usually the working tree; with --cached, stop tracking while keeping local files.", + "git_push": "Update remote branches or tags, transferring required objects; deletion options can remove remote branches or tags.", + "git_commit": "Create or amend a commit from staged changes; supported options can also stage modified or deleted tracked files.", + "git_clean": "Remove untracked files and optional directories; options can include ignored paths or restrict removal to ignored paths.", + "git_config": "Read, set, unset, or edit Git configuration.", + "git_reset": "Reset index entries or move HEAD; selected modes also update the working tree.", + "git_stash": "Save, list, inspect, apply, pop, or discard stashed working-tree changes.", + "git_reflog": "Inspect or manage local reference-update history, including expiring or deleting entries." + }, + "curated_devops": { + "git_commit": "Create or amend a commit from staged changes; supported options can also stage modified or deleted tracked files.", + "git_reset_hard": "Reset HEAD, the index, and tracked working-tree paths to a specified commit, discarding tracked changes; this is not a general cleanup of untracked files." + }, + "nl2bash": { + "comm": "Compare two files sorted by line, reporting lines unique to either file or common to both.", + "join": "Join lines from two suitably sorted files on a matching field.", + "more": "Display text interactively one screenful at a time.", + "rm": "Remove files and, with recursive options, directory trees.", + "rmdir": "Remove empty directories." + }, + "amazon_massive": { + "alarm_query": "Ask about saved alarms, their times, or whether an alarm is active.", + "alarm_remove": "Cancel or delete a saved alarm, or snooze an alarm.", + "alarm_set": "Set or enable an alarm for a specified time or recurrence.", + "audio_volume_down": "Lower the volume of speech or audio playback.", + "audio_volume_mute": "Mute speech or audio output.", + "audio_volume_other": "Access audio-volume settings or request a volume adjustment without a clear increase, decrease, or mute action.", + "audio_volume_up": "Raise the volume of speech or audio playback.", + "calendar_query": "Ask about scheduled events, appointments, or reminders.", + "calendar_remove": "Cancel or delete calendar events, appointments, or reminders.", + "calendar_set": "Create a calendar event, appointment, or scheduled reminder.", + "datetime_convert": "Convert a clock time between time zones, compare time offsets, or change the selected time zone.", + "datetime_query": "Ask for a date, day of the week, or current time in a location.", + "email_addcontact": "Add an email address or a new entry to the contact list.", + "email_query": "Find, check, or read received email messages.", + "email_querycontact": "Look up a person or their saved contact details, such as a phone number, address, or email address.", + "email_sendemail": "Compose, send, or reply to an email message.", + "general_greet": "Exchange greetings or ask how the assistant is doing.", + "general_joke": "Ask for a joke or something funny.", + "general_quirky": "Engage in open-ended conversation, personal remarks, opinions, or broad reflective questions.", + "iot_cleaning": "Start or control a connected cleaning device.", + "iot_coffee": "Start or control a connected coffee maker to prepare coffee.", + "iot_hue_lightchange": "Change the color of connected lights.", + "iot_hue_lightdim": "Reduce the brightness of connected lights.", + "iot_hue_lightoff": "Turn connected lights off.", + "iot_hue_lighton": "Turn connected lights on.", + "iot_hue_lightup": "Increase the brightness of connected lights.", + "iot_wemo_off": "Turn a connected smart plug or its attached appliance off.", + "iot_wemo_on": "Turn a connected smart plug or its attached appliance on.", + "lists_createoradd": "Create a list or add an item to an existing list.", + "lists_query": "Show saved lists or read the items on a list.", + "lists_remove": "Delete a list or remove an item from a list.", + "music_dislikeness": "Express dislike for a song or ask that it not be played again.", + "music_likeness": "Express a positive music preference or save a song to favorites, a playlist, or a music collection.", + "music_query": "Ask for information about a song, artist, recording, or music currently playing.", + "music_settings": "Change music playback settings or controls, such as shuffle, repeat, or skipping a song.", + "news_query": "Request news headlines or news on a topic.", + "play_audiobook": "Play or resume an audiobook.", + "play_game": "Start or play a game.", + "play_music": "Play music, a song, an album, an artist, or a playlist.", + "play_podcasts": "Play a podcast or move between podcast episodes when podcast context is available.", + "play_radio": "Play or tune to a radio station.", + "qa_definition": "Ask for the meaning or definition of a word, phrase, or concept.", + "qa_factoid": "Ask a factual question about people, places, events, objects, or general knowledge.", + "qa_maths": "Calculate an arithmetic result or answer a mathematical question.", + "recommendation_events": "Find or recommend events and activities to attend.", + "recommendation_locations": "Find or recommend places to visit, shops, restaurants, or nearby services.", + "recommendation_movies": "Find or recommend movies to watch or ask what is showing.", + "social_post": "Publish a post, status update, or message on social media.", + "social_query": "Read social-media updates, notifications, activity, or account information.", + "takeaway_order": "Request a takeaway or food-delivery order.", + "takeaway_query": "Ask about takeaway or food-delivery availability, providers, or delivery details.", + "transport_query": "Ask for routes, travel times, public-transport schedules, or navigation information.", + "transport_taxi": "Request or book a taxi or ride-hailing car.", + "transport_ticket": "Find or book a ticket or seat for a train, flight, or other scheduled transport.", + "transport_traffic": "Ask about traffic conditions or congestion on a route.", + "weather_query": "Ask about current weather, forecasts, or expected weather conditions." + } + }, + "quarantine_by_id": { + "fastbrain_000663": "Go back is labeled play_podcasts but no podcast/media context is present.", + "fastbrain_001094": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_001956": "The request explicitly names mypy and flake8 but the target is an undefined pylint_typecheck wrapper. No implementation/native schema establishes that it invokes the requested tools.", + "fastbrain_001980": "The request asks for things to do and dates of events. FindEvents and GetEventDates are both candidates; the missing service state does not establish a unique action.", + "fastbrain_003615": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_003814": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_004588": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_006314": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_006573": "Let us call it Trip to Hawaii identifies a name but not the object or action; the earlier conversation needed to justify create_todo_list is absent.", + "fastbrain_006908": "The only input is olly, labeled recommendation_events; no event-discovery request is visible.", + "fastbrain_007278": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_007548": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_009396": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_009620": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_010650": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_011831": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_014223": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_014395": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_018257": "The native command is git rm -r --cached build/ followed by appending build/ to .gitignore. git_rm alone cannot perform the requested ignore-file edit.", + "fastbrain_018409": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_018951": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_019650": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_019701": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_019814": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_021009": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_022512": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_023841": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_025221": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_025527": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_026912": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_030148": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_030224": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_031415": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_032263": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_032458": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_033907": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_034935": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_035025": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_035668": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_036223": "The user requests a reminder tomorrow, while create_todo only promises creating a todo item. No recovered native schema establishes timed-reminder support.", + "fastbrain_037269": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_038171": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_038452": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_038478": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_038731": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_040564": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_041207": "How many hours ahead is NYC is natively datetime_query; retained training examples also place offset questions in datetime_convert. Do not invent an exclusive boundary or silently relabel.", + "fastbrain_041338": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_041747": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_041953": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_042460": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_042788": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_042858": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_044134": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_044345": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_047068": "The request combines filesystem search and FTP upload; matching native commands invoke curl inside find -exec. The exposed find capability alone does not certify the complete operation, and multiple native rows remain matched.", + "fastbrain_047863": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_048021": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_048283": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_048341": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_048703": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_049032": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_052056": "The nature of artificial intelligence is labeled general_quirky. Its boundary with factual/definition questions is not established by an authoritative ontology contract; retain the native label as evidence, not a repaired unique action.", + "fastbrain_052279": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_052623": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_052847": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_053065": "extract_files_by_extension creates and populates Compliance_Audit_2023, then generate_file_report reads that folder. The user also says parallel, but the visible dependency and contradictory timing prevent certifying the co-emitted set as ready to run in parallel.", + "fastbrain_054312": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_054919": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_056956": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_057143": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_057350": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_058529": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_059264": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_060192": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_060517": "What are jalapenos is natively labeled cooking_recipe, but it asks for an ingredient definition. No automatic relabeling or expansion of recipe to all food questions is justified.", + "fastbrain_064217": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_064337": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_065901": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_066260": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_066817": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_066823": "The same native query maps both to a full comm/sort/grep process-substitution command and to corrupt comm -23 < <. Preserve both matches; neither first-match selection nor description rewriting recovers a unique executable source command.", + "fastbrain_066989": "The only input is fast, labeled audio_volume_up; no volume-increase intent is established.", + "fastbrain_067124": "Lights out is labeled iot_hue_lighton. Its ordinary reading does not justify turning lights on; isolate without silently flipping the label.", + "fastbrain_067177": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_068616": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_069052": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_069738": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_069842": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_069917": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_070360": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_071601": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_072216": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_072541": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_072710": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_073565": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_073902": "The entire request is a seat reservation confirmation; both bus-ticket purchase and one-way-flight reservation are offered. The service/dialogue context that distinguishes them was not recovered.", + "fastbrain_074038": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_074847": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_075874": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_076673": "Change my alarm is labeled alarm_remove, but the request does not distinguish deletion, snoozing, or editing. Do not redefine remove as all alarm changes.", + "fastbrain_077729": "Send a text to Alice is labeled email_querycontact; contact lookup may be a prerequisite, but no native dialogue/tool state establishes it as the unique next action.", + "fastbrain_080319": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_081597": "Weird things people want to know is labeled cooking_recipe without any visible cooking request. Do not expand a recipe description to cover it.", + "fastbrain_082954": "list_container_services receives cluster=\"web-app-service\", although the user provided a service name and task definition, not its cluster. Passing the native schema does not ground this argument.", + "fastbrain_083328": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_084352": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_084852": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_086394": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_086791": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_087260": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_089369": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_090206": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_090223": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_090519": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_090984": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_091373": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_092413": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_092661": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_093872": "The request covers all pods in a namespace, while k8s_rollout_restart describes a single deployment. The missing cluster/controller state does not establish equivalence.", + "fastbrain_095250": "Get me a one-way flight does not uniquely distinguish searching from reserving in the exposed SearchOnewayFlight/ReserveOnewayFlight choice set.", + "fastbrain_097266": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_097324": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_099783": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_101452": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_102619": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_102781": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_102876": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_103111": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_105430": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "fastbrain_107019": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments." + }, + "candidate_repairs_by_id": { + "fastbrain_084743": { + "source": "nl2bash", + "released_split": "train", + "released_parquet_row": 3316, + "target_unchanged": "more", + "prior_acceptable_targets": [ + "more", + "less" + ], + "remove_candidate_name": "less", + "replacement_candidate": { + "name": "wc", + "description": "Count lines, words, and bytes in files." + }, + "policy": "Preserve candidate count and position. Replace the alternative action; do not invent a capability difference between more and less.", + "evidence": "Both page the given text interactively. The pinned more manual also permits backwards paging and documents less emulation; never claim more cannot go backwards.", + "legacy_query": "Display the content of YourFile.txt, waiting for user input at each page.", + "old_choices": [ + { + "description": "Compress or expand files", + "name": "gzip" + }, + { + "description": "View file contents page by page", + "name": "less" + }, + { + "description": "Change file permission bits", + "name": "chmod" + }, + { + "description": "View file contents page by page", + "name": "more" + }, + { + "description": "List directory contents", + "name": "ls" + } + ] + }, + "fastbrain_085213": { + "source": "glaive_v2", + "released_split": "train", + "released_parquet_row": 80363, + "target_unchanged": "calculate_loan_payment", + "prior_acceptable_targets": [ + "calculate_loan_payment", + "calculateLoanPayment" + ], + "remove_candidate_name": "calculateLoanPayment", + "replacement_candidate": { + "name": "check_palindrome", + "description": "Check whether a string reads the same forwards and backwards." + }, + "policy": "Finance exclusion takes precedence. Apply this candidate repair only if the row is retained under the final finance policy.", + "evidence": "The candidate descriptions are identical and describe the same loan-payment action; spelling is not a semantic distinction.", + "legacy_query": "The principal amount is $200,000. The interest rate is 3.5% and the loan term is 30 years.", + "old_choices": [ + { + "description": "Calculate the monthly payment for a loan", + "name": "calculate_loan_payment" + }, + { + "description": "Roll a dice and return the result", + "name": "roll_dice" + }, + { + "description": "Calculate the monthly payment for a loan", + "name": "calculateLoanPayment" + }, + { + "description": "Get the conversion rate between two currencies", + "name": "get_conversion_rate" + }, + { + "description": "Create a new event in the calendar", + "name": "create_event" + } + ] + } + }, + "finance_exclude_by_id": { + "fastbrain_001166": "Initiate a USD transfer to a PayPal recipient.", + "fastbrain_002492": "Create an actual Venmo peer-to-peer payment.", + "fastbrain_012497": "Approve a consumer loan and compute its payments and amortization schedule.", + "fastbrain_019803": "Transfer Bitcoin between blockchain wallets.", + "fastbrain_022513": "Initiate a credit-card payment through a payment gateway; this is an explicit payment API operation, not merely a shopping-price mention.", + "fastbrain_023338": "Retrieve cryptocurrency-wallet financial transaction history.", + "fastbrain_023893": "Retrieve PayPal financial transaction history.", + "fastbrain_029254": "Transfer USD funds between e-wallet users.", + "fastbrain_036850": "Send a Venmo peer-to-peer payment.", + "fastbrain_039428": "Link a credit card to Coinbase as the first action of a cryptocurrency-purchase workflow.", + "fastbrain_056780": "Initiate a Revolut international money transfer.", + "fastbrain_063295": "Purchase Bitcoin through Coinbase.", + "fastbrain_064277": "Create a cryptocurrency wallet for asset management and payments.", + "fastbrain_064932": "Create a Klarna buy-now-pay-later installment-payment session; the requested operation is credit/payment processing.", + "fastbrain_067688": "Transfer funds between checking and savings bank accounts.", + "fastbrain_072727": "Create a PayPal payment request; distinguish this explicit payment-network action from ordinary invoice document generation.", + "fastbrain_084822": "Create a brokerage account and update an investment profile for securities trading.", + "fastbrain_084975": "Link a checking bank account to PayPal as a funding source.", + "fastbrain_085213": "Calculate the payment on a loan from principal, interest rate, and loan term.", + "fastbrain_085346": "Retrieve PayPal transaction history and account balance.", + "fastbrain_085403": "Send a USD peer-to-peer payment through Cash App.", + "fastbrain_101949": "Purchase Bitcoin through Cash App and query the USD/BTC exchange rate." + }, + "frozen_validation_source_ids_by_source": { + "amazon_massive": [ + "fastbrain_000114", + "fastbrain_000480", + "fastbrain_001075", + "fastbrain_001079", + "fastbrain_002022", + "fastbrain_002046", + "fastbrain_002524", + "fastbrain_002565", + "fastbrain_002877", + "fastbrain_002898", + "fastbrain_002984", + "fastbrain_003365", + "fastbrain_003997", + "fastbrain_004259", + "fastbrain_004664", + "fastbrain_004720", + "fastbrain_004732", + "fastbrain_004780", + "fastbrain_004834", + "fastbrain_005439", + "fastbrain_005617", + "fastbrain_005797", + "fastbrain_006179", + "fastbrain_006317", + "fastbrain_006473", + "fastbrain_006564", + "fastbrain_007205", + "fastbrain_007254", + "fastbrain_007439", + "fastbrain_007658", + "fastbrain_008043", + "fastbrain_008104", + "fastbrain_008183", + "fastbrain_008186", + "fastbrain_008198", + "fastbrain_008298", + "fastbrain_008463", + "fastbrain_009451", + "fastbrain_010010", + "fastbrain_011009", + "fastbrain_011062", + "fastbrain_011156", + "fastbrain_011199", + "fastbrain_011493", + "fastbrain_011572", + "fastbrain_011713", + "fastbrain_012025", + "fastbrain_012130", + "fastbrain_012414", + "fastbrain_012469", + "fastbrain_013005", + "fastbrain_013138", + "fastbrain_013146", + "fastbrain_013440", + "fastbrain_013781", + "fastbrain_014160", + "fastbrain_014321", + "fastbrain_014369", + "fastbrain_014370", + "fastbrain_014581", + "fastbrain_014999", + "fastbrain_015158", + "fastbrain_015174", + "fastbrain_015282", + "fastbrain_015616", + "fastbrain_015735", + "fastbrain_015748", + "fastbrain_015825", + "fastbrain_015931", + "fastbrain_015953", + "fastbrain_016292", + "fastbrain_016391", + "fastbrain_016732", + "fastbrain_016827", + "fastbrain_016937", + "fastbrain_017059", + "fastbrain_017081", + "fastbrain_017109", + "fastbrain_017309", + "fastbrain_017600", + "fastbrain_017605", + "fastbrain_017648", + "fastbrain_017657", + "fastbrain_017794", + "fastbrain_018267", + "fastbrain_019385", + "fastbrain_019549", + "fastbrain_019628", + "fastbrain_020216", + "fastbrain_021371", + "fastbrain_021468", + "fastbrain_021563", + "fastbrain_021570", + "fastbrain_021722", + "fastbrain_021774", + "fastbrain_022183", + "fastbrain_022522", + "fastbrain_022969", + "fastbrain_023153", + "fastbrain_023232", + "fastbrain_023317", + "fastbrain_023555", + "fastbrain_023699", + "fastbrain_023718", + "fastbrain_024561", + "fastbrain_024980", + "fastbrain_026092", + "fastbrain_026164", + "fastbrain_026228", + "fastbrain_026304", + "fastbrain_026423", + "fastbrain_026744", + "fastbrain_027105", + "fastbrain_027409", + "fastbrain_027641", + "fastbrain_028232", + "fastbrain_028250", + "fastbrain_028587", + "fastbrain_028598", + "fastbrain_029063", + "fastbrain_029132", + "fastbrain_029534", + "fastbrain_029771", + "fastbrain_029826", + "fastbrain_029898", + "fastbrain_030066", + "fastbrain_030516", + "fastbrain_030745", + "fastbrain_030775", + "fastbrain_031221", + "fastbrain_031343", + "fastbrain_031488", + "fastbrain_031525", + "fastbrain_031558", + "fastbrain_031637", + "fastbrain_031863", + "fastbrain_032421", + "fastbrain_032698", + "fastbrain_032858", + "fastbrain_033066", + "fastbrain_033305", + "fastbrain_033955", + "fastbrain_034035", + "fastbrain_034040", + "fastbrain_034249", + "fastbrain_034423", + "fastbrain_034549", + "fastbrain_034902", + "fastbrain_035189", + "fastbrain_035231", + "fastbrain_035330", + "fastbrain_035524", + "fastbrain_035627", + "fastbrain_035958", + "fastbrain_037066", + "fastbrain_037777", + "fastbrain_037783", + "fastbrain_037952", + "fastbrain_038249", + "fastbrain_038685", + "fastbrain_038839", + "fastbrain_039331", + "fastbrain_039408", + "fastbrain_039666", + "fastbrain_040352", + "fastbrain_040565", + "fastbrain_040680", + "fastbrain_041916", + "fastbrain_042041", + "fastbrain_042158", + "fastbrain_042218", + "fastbrain_042225", + "fastbrain_042367", + "fastbrain_042488", + "fastbrain_042721", + "fastbrain_042836", + "fastbrain_042963", + "fastbrain_043120", + "fastbrain_043153", + "fastbrain_044370", + "fastbrain_044796", + "fastbrain_044863", + "fastbrain_044890", + "fastbrain_044974", + "fastbrain_045027", + "fastbrain_045076", + "fastbrain_045696", + "fastbrain_046123", + "fastbrain_046615", + "fastbrain_046899", + "fastbrain_047182", + "fastbrain_047225", + "fastbrain_047330", + "fastbrain_047850", + "fastbrain_047957", + "fastbrain_048102", + "fastbrain_048225", + "fastbrain_048233", + "fastbrain_048581", + "fastbrain_048785", + "fastbrain_049083", + "fastbrain_049183", + "fastbrain_049233", + "fastbrain_049277", + "fastbrain_049573", + "fastbrain_049718", + "fastbrain_049959", + "fastbrain_050053", + "fastbrain_050063", + "fastbrain_050264", + "fastbrain_050876", + "fastbrain_052050", + "fastbrain_052333", + "fastbrain_052718", + "fastbrain_052757", + "fastbrain_052904", + "fastbrain_052965", + "fastbrain_053843", + "fastbrain_055059", + "fastbrain_055337", + "fastbrain_055754", + "fastbrain_055961", + "fastbrain_055967", + "fastbrain_056337", + "fastbrain_057057", + "fastbrain_057536", + "fastbrain_057758", + "fastbrain_057902", + "fastbrain_058638", + "fastbrain_058812", + "fastbrain_058910", + "fastbrain_059070", + "fastbrain_059162", + "fastbrain_059283", + "fastbrain_059733", + "fastbrain_059789", + "fastbrain_059836", + "fastbrain_059943", + "fastbrain_060115", + "fastbrain_060350", + "fastbrain_060366", + "fastbrain_060411", + "fastbrain_060803", + "fastbrain_060970", + "fastbrain_061222", + "fastbrain_061229", + "fastbrain_061354", + "fastbrain_061451", + "fastbrain_061529", + "fastbrain_061915", + "fastbrain_062139", + "fastbrain_062232", + "fastbrain_062310", + "fastbrain_062583", + "fastbrain_062688", + "fastbrain_062725", + "fastbrain_063196", + "fastbrain_063730", + "fastbrain_064739", + "fastbrain_064890", + "fastbrain_064898", + "fastbrain_064967", + "fastbrain_065002", + "fastbrain_065015", + "fastbrain_065410", + "fastbrain_065508", + "fastbrain_065695", + "fastbrain_066510", + "fastbrain_066620", + "fastbrain_066788", + "fastbrain_066852", + "fastbrain_066909", + "fastbrain_067046", + "fastbrain_067438", + "fastbrain_067517", + "fastbrain_067526", + "fastbrain_067836", + "fastbrain_068016", + "fastbrain_068348", + "fastbrain_068517", + "fastbrain_068832", + "fastbrain_068939", + "fastbrain_069042", + "fastbrain_069181", + "fastbrain_069193", + "fastbrain_069306", + "fastbrain_069563", + "fastbrain_069694", + "fastbrain_069775", + "fastbrain_069882", + "fastbrain_070205", + "fastbrain_070264", + "fastbrain_070446", + "fastbrain_070475", + "fastbrain_070574", + "fastbrain_071202", + "fastbrain_071518", + "fastbrain_071792", + "fastbrain_071984", + "fastbrain_072532", + "fastbrain_072771", + "fastbrain_072782", + "fastbrain_072868", + "fastbrain_072897", + "fastbrain_073044", + "fastbrain_073132", + "fastbrain_073288", + "fastbrain_073978", + "fastbrain_074232", + "fastbrain_074346", + "fastbrain_074832", + "fastbrain_074986", + "fastbrain_075006", + "fastbrain_075022", + "fastbrain_075077", + "fastbrain_075235", + "fastbrain_075405", + "fastbrain_075490", + "fastbrain_075540", + "fastbrain_076850", + "fastbrain_076928", + "fastbrain_076998", + "fastbrain_077143", + "fastbrain_077379", + "fastbrain_077395", + "fastbrain_077969", + "fastbrain_078167", + "fastbrain_078500", + "fastbrain_078925", + "fastbrain_079376", + "fastbrain_079607", + "fastbrain_079823", + "fastbrain_080403", + "fastbrain_080406", + "fastbrain_080783", + "fastbrain_080960", + "fastbrain_081097", + "fastbrain_081121", + "fastbrain_081299", + "fastbrain_081591", + "fastbrain_081600", + "fastbrain_081668", + "fastbrain_082120", + "fastbrain_082228", + "fastbrain_082289", + "fastbrain_082615", + "fastbrain_082713", + "fastbrain_082935", + "fastbrain_082936", + "fastbrain_083118", + "fastbrain_083802", + "fastbrain_083977", + "fastbrain_084531", + "fastbrain_085086", + "fastbrain_085476", + "fastbrain_085675", + "fastbrain_085749", + "fastbrain_086080", + "fastbrain_086234", + "fastbrain_086285", + "fastbrain_086359", + "fastbrain_086576", + "fastbrain_086690", + "fastbrain_086718", + "fastbrain_086932", + "fastbrain_087079", + "fastbrain_087276", + "fastbrain_087429", + "fastbrain_087453", + "fastbrain_088108", + "fastbrain_088119", + "fastbrain_088642", + "fastbrain_088996", + "fastbrain_089110", + "fastbrain_089226", + "fastbrain_089243", + "fastbrain_089667", + "fastbrain_089962", + "fastbrain_090281", + "fastbrain_090870", + "fastbrain_091056", + "fastbrain_091327", + "fastbrain_091625", + "fastbrain_091962", + "fastbrain_092143", + "fastbrain_092698", + "fastbrain_093379", + "fastbrain_093421", + "fastbrain_093527", + "fastbrain_093982", + "fastbrain_094178", + "fastbrain_094266", + "fastbrain_094488", + "fastbrain_094593", + "fastbrain_095291", + "fastbrain_095615", + "fastbrain_095633", + "fastbrain_095635", + "fastbrain_095656", + "fastbrain_095682", + "fastbrain_096118", + "fastbrain_096505", + "fastbrain_096572", + "fastbrain_096586", + "fastbrain_096790", + "fastbrain_096813", + "fastbrain_096944", + "fastbrain_097284" + ], + "curated_devops": [ + "fastbrain_036263", + "fastbrain_087751" + ], + "git_nl_commands": [ + "fastbrain_000937", + "fastbrain_008383", + "fastbrain_011958", + "fastbrain_015357", + "fastbrain_017249", + "fastbrain_018219", + "fastbrain_019002", + "fastbrain_019019", + "fastbrain_020971", + "fastbrain_022350", + "fastbrain_023235", + "fastbrain_027724", + "fastbrain_033773", + "fastbrain_034281", + "fastbrain_036006", + "fastbrain_036589", + "fastbrain_038645", + "fastbrain_041091", + "fastbrain_047032", + "fastbrain_048485", + "fastbrain_048534", + "fastbrain_049218", + "fastbrain_049620", + "fastbrain_056204", + "fastbrain_058740", + "fastbrain_074183", + "fastbrain_081312", + "fastbrain_082482", + "fastbrain_088749", + "fastbrain_091256", + "fastbrain_095438" + ], + "glaive_v2": [ + "fastbrain_000372", + "fastbrain_002303", + "fastbrain_005139", + "fastbrain_005287", + "fastbrain_006395", + "fastbrain_006760", + "fastbrain_009065", + "fastbrain_009969", + "fastbrain_009983", + "fastbrain_010237", + "fastbrain_012239", + "fastbrain_013964", + "fastbrain_014497", + "fastbrain_015203", + "fastbrain_015804", + "fastbrain_016294", + "fastbrain_019916", + "fastbrain_020408", + 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Reconstructed view exclusions must not return any held-out ID or request sibling to training.", + "nemotron_legacy_crop": "The 164 historical Nemotron validation IDs are recorded for protection, outside the non-Nemotron replay restoration scope. Do not reintroduce the cropped Nemotron legacy view." + }, + "hermes": { + "mapping_summary": { + "train": { + "canonical_rows": 1015, + "unique_source_recovered": 1015, + "cropped_query": 1001, + "cropped_target_description": 23, + "multi_call_source_rows": 539, + "first_round_schema_valid": 914, + "first_round_schema_invalid": 101, + "schema_valid_multi_call": 513, + "schema_valid_single_call": 401 + }, + "test": { + "canonical_rows": 132, + "unique_source_recovered": 132, + "cropped_query": 130, + "cropped_target_description": 5, + "multi_call_source_rows": 71, + "first_round_schema_valid": 124, + "first_round_schema_invalid": 8, + "schema_valid_multi_call": 69, + "schema_valid_single_call": 55 + } + }, + "source": { + "repo": "NousResearch/hermes-function-calling-v1", + "revision": "dae3e1d28cfbcf4b915c04ea1e072030529b4bda", + "file": "func-calling-singleturn.json", + "url": 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Additionally, the conce", + "target": "analyze_batch_purity", + "legacy_query_parent_id": "legacy_query:c14e31d047a0b01cfb2a977e9ae8ac42", + "normalized_query_sha256": "27283c9035850dca7e62e330fca385588c815e493f07a696ead085dbc66fff93", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:fb4e0d36-f6fa-4405-8879-c53700a2708e", + "source_record_id": "fb4e0d36-f6fa-4405-8879-c53700a2708e", + "request_group_id": "hermes_request:0eabff209d0692d935ad86322e64da87a3583dadf11b8fc1519782d17c1c703b", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_084686", + "observed_call_count": 3, + "observed_call_names": [ + "analyze_batch_purity", + "verify_batch_concentration", + "check_expiration_dates" + ], + "source_order_preserved": true, + "source_order_is_unique_gold": false, + "all_emitted_calls_are_certified_parallel": false + }, + "fastbrain_095305": { + "relationship": "separate_retrievals_with_later_playback_dependency", + "explanation": "Episode retrieval and topic search can use their respective supplied inputs. The requested playback needs the retrieved episode identifier and is not a first-round emitted call.", + "limitation": "The observed first set is not proof that the whole user request was completed.", + "canonical_id": "fastbrain_095305", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'm developing a feature for an Android mobile application that involves integrating podcast functionality. Specifically, I want to access the latest episodes from the \"Gone Mobile\" podcast series, with a cap at retrieving the last 5 episodes. Moreover, I'm keen on playing the most current episode for listening directly within the app. Also, it would be beneficial to have a list of podcasts that t", + "target": "get_podcast_episodes", + "legacy_query_parent_id": "legacy_query:cb0d22c9925ef0a681a7d218278be65b", + "normalized_query_sha256": "341b9044c14abd6b60346ccadd66262315dd93cc7fdd35183a59db3874ba68a2", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:40770cc3-af26-4898-8fbf-0e1e9083fe2a", + "source_record_id": "40770cc3-af26-4898-8fbf-0e1e9083fe2a", + "request_group_id": "hermes_request:45a80b590f7f82d886ce96418e7f19efebc3de79822bbd0a9ccd86ccbd1d4151", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_095305", + "observed_call_count": 2, + "observed_call_names": [ + "get_podcast_episodes", + "search_podcasts" + ], + "source_order_preserved": true, + "source_order_is_unique_gold": false, + "all_emitted_calls_are_certified_parallel": false + }, + "fastbrain_025446": { + "relationship": "shared_store_write_dependency_not_resolved", + "explanation": "Adding and updating patient records and whole-directory deduplication can affect shared state. The user says Firstly, but source co-emission does not establish a safe concurrent schedule.", + "limitation": "Keep observed source order and call set only; parallel independence is unknown.", + "canonical_id": "fastbrain_025446", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am overseeing the operations of a healthcare clinic and need to perform some updates on our electronic health records system. Firstly, I must add a new patient record for Jane Rogers, born on June 10, 1982, living at 456 Elm Street, Lakeside, LN. She has had past treatments for asthma and is currently on Ventolin. She's allergic to penicillin. Additionally, we need to update the record for patie", + "target": "add_patient_record", + "legacy_query_parent_id": "legacy_query:9b1ba7b9f455827f9ba1e5ede2769216", + "normalized_query_sha256": "86c5f4cdf4fde0d5fe63287c269c1e4eea787c84471502aa0e1ea4903e31547b", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:46e8e543-b119-4daf-84d6-d8b1a84091a7", + "source_record_id": "46e8e543-b119-4daf-84d6-d8b1a84091a7", + "request_group_id": "hermes_request:b06e42df0d84d14d8f33d0fb83214e7f09349ef174d4bb5aa7c2145f7519aa73", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_025446", + "observed_call_count": 3, + "observed_call_names": [ + "add_patient_record", + "update_patient_record", + "remove_duplicate_records" + ], + "source_order_preserved": true, + "source_order_is_unique_gold": false, + "all_emitted_calls_are_certified_parallel": false + }, + "fastbrain_013518": { + "relationship": "no_visible_output_dependency_but_runtime_independence_unverified", + "explanation": "Presentation sharing and revision tracking both use the supplied presentation ID. No returned value from one is passed to the other.", + "limitation": "Shared presentation state and implementation effects were not executed or verified.", + "canonical_id": "fastbrain_013518", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Our team is finalizing the presentation 'Annual Tech Innovations' for the upcoming conference, and we aim to foster a collaborative environment for its completion. To facilitate this process, I would like to share the Google Slides presentation, which has the unique identifier '1a2b3c4d5e6f', with my team members whose email addresses are user@example.com and collaborator@example.com. I want to en", + "target": "share_presentation", + "legacy_query_parent_id": "legacy_query:b73454a7dfc69e2014c167e94dca7335", + "normalized_query_sha256": "f99046bbcc14ebeca28ec5c75d6cbc1d01d66ae583b171e1e638e143a2ccc79a", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:3c6db2a0-2fa1-43ea-b368-cd3ce78909ef", + "source_record_id": "3c6db2a0-2fa1-43ea-b368-cd3ce78909ef", + "request_group_id": "hermes_request:d42d4ffc462c595b9ccf3f20acf846eee1bed096123160dcfd541beeb93fd1eb", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_013518", + "observed_call_count": 2, + "observed_call_names": [ + "share_presentation", + "track_revision_history" + ], + "source_order_preserved": true, + "source_order_is_unique_gold": false, + "all_emitted_calls_are_certified_parallel": false + }, + "fastbrain_053065": { + "relationship": "visible_read_after_create_and_populate_dependency", + "explanation": "extract_files_by_extension creates and populates Compliance_Audit_2023, then generate_file_report reads that folder. The user also says parallel, but the visible dependency and contradictory timing prevent certifying the co-emitted set as ready to run in parallel.", + "limitation": "Quarantine the co-emitted gold; no ordering repair was synthesized.", + "canonical_id": "fastbrain_053065", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "As we prepare for the upcoming audit, I require assistance with gathering and organizing all compliance-related documents stored on our company's Dropbox. Specifically, please extract all files with the .pdf extension located within the '/Compliance' folder. These files are essential for adhering to our compliance standards and will include various policy documents, compliance reports, and records", + "target": "extract_files_by_extension", + "legacy_query_parent_id": "legacy_query:eb70b12e3b92a93797f6756f6b2ea3f6", + "normalized_query_sha256": "00b8aea557638b0ebd2a7ab2ab66f82647f3326c24d1911c60facb9aa734aece", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:c1714874-1e2a-45b5-bf5e-e55306046abc", + "source_record_id": "c1714874-1e2a-45b5-bf5e-e55306046abc", + "request_group_id": "hermes_request:6f544a67601704560f3e370d47d6833ee1afd2ffbc5f21679d9d85e47a01411d", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_053065", + "observed_call_count": 2, + "observed_call_names": [ + "extract_files_by_extension", + "generate_file_report" + ], + "source_order_preserved": true, + "source_order_is_unique_gold": false, + "all_emitted_calls_are_certified_parallel": false + }, + "fastbrain_082954": { + "relationship": "ungrounded_required_argument", + "explanation": "list_container_services receives cluster=\"web-app-service\", although the user provided a service name and task definition, not its cluster. Passing the native schema does not ground this argument.", + "limitation": "Quarantine; do not guess the cluster identifier.", + "canonical_id": "fastbrain_082954", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently working on deploying a web application on AWS and require specific compute services that can efficiently handle scalability, security, and reliability. For this purpose, I need to obtain detailed information on EC2 instances, including instance types such as 't2.micro' located in the 'us-west-2a' availability zone, and their running states. Additionally, I want a list of container s", + "target": "describe_ec2_instances", + "legacy_query_parent_id": "legacy_query:ca1eab6f2fa5679a2e72715abd61a4c9", + "normalized_query_sha256": "6a87d27efba080bdfdfdbddc3990171ecc0778d5473a6f07040a5e0fdf17ba2c", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:6ee8e53b-8934-47e7-8c86-ab301eb52995", + "source_record_id": "6ee8e53b-8934-47e7-8c86-ab301eb52995", + "request_group_id": "hermes_request:9f9abdacdf2911ef52650cd2e5933c1eeb48abee27a058577dde5c688b8c7467", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_082954", + "observed_call_count": 3, + "observed_call_names": [ + "describe_ec2_instances", + "list_container_services", + "get_serverless_options" + ], + "source_order_preserved": true, + "source_order_is_unique_gold": false, + "all_emitted_calls_are_certified_parallel": false + } + }, + "limitations": [ + "Exact prefix+name+description matching recovers source identity; it does not certify correct supervision.", + "JSON-schema validation uses native schemas without format assertions. It cannot verify argument grounding, API effects, task completion, or unique next-action correctness.", + "Single native calls with at least two native tools are observed-choice candidates, not human-verified unique gold. Do not add unrelated distractors to singleton catalogs merely to manufacture a choice task.", + "No first-call collapse of multi-call source rows is permitted. Do not impose source order as the only valid execution schedule." + ] + }, + "quarantine_records": [ + { + "canonical_id": "fastbrain_001094", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently browsing a website that has a list of tutorials, and I need to quickly register for two of them that I am particularly interested in. The webpage has several tutorial listings, and each has a 'Sign Up' button next to it. I have identified two tutorials that I want to enroll in; the first is titled \"Advanced Web Scraping Techniques,\" and the second is \"Interactive Web Automation.\" Co", + "target": "click_button_by_text", + "legacy_query_parent_id": "legacy_query:fd9a8d8bc546495a9dbc56ea9206917d", + "normalized_query_sha256": "34d3f6320cd17f16031ae1d88adb45be1e3470f0bbf64ad16e849732f8499379", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:8753af14-10cd-4450-bb06-84a0ddd441ae", + "source_record_id": "8753af14-10cd-4450-bb06-84a0ddd441ae", + "request_group_id": "hermes_request:31c6620084e3b4adf93c1c41f810bae7e2033c7cd51bfed65e474acafadb1be9", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_001094", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "button_text" + ], + "validator": "enum", + "detail": "'Sign Up' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "page_content" + ], + "validator": "enum", + "detail": "'Advanced Web Scraping Techniques automation tools like Selenium and Puppeteer' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "button_text" + ], + "validator": "enum", + "detail": "'Sign Up' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "page_content" + ], + "validator": "enum", + "detail": "'Interactive Web Automation engaging dynamically with web pages in real time' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_003615", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I've recently started using an iPhone and am working on organizing my schedule more efficiently. I need to check my calendar for the upcoming week to see all my scheduled events. Additionally, I want to add a new event for an important project deadline that I have next week. The details for the new event are as follows: The event should be titled \"Project Deadline,\" and it needs to be scheduled fo", + "target": "get_calendar_events", + "legacy_query_parent_id": "legacy_query:e1e67c5e2514a7caffb0584306a683b1", + "normalized_query_sha256": "a5e4f18cd83de22b86739266002c8d63a7692aad3b1d78d54ef1ec3f76c0fcb0", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:0ad64227-30e8-40da-809c-34887fc21415", + "source_record_id": "0ad64227-30e8-40da-809c-34887fc21415", + "request_group_id": "hermes_request:42899b3518c763e6ccfb0d02fd5bfeffa7bbf963d319bfe2b4d6d75dfa153142", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_003615", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "start_date" + ], + "validator": "enum", + "detail": "'2023-04-09' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "end_date" + ], + "validator": "enum", + "detail": "'2023-04-16' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "calendar_id" + ], + "validator": "enum", + "detail": "'primary' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_003814", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I have composed an important email intended for one of our key clients and it needs to be dispatched through my Gmail account via Outlook. This email is not only crucial in terms of content but also in the timing of its delivery. Therefore, it's paramount that it is tagged with high importance to ensure immediate attention. The email includes an attachment which is a detailed proposal document out", + "target": "send_email_with_attachment", + "legacy_query_parent_id": "legacy_query:a0ae7bff7de31c3737e9dc535162e044", + "normalized_query_sha256": "b259fea29203c059a865eba916a8390b755024fae7932eb3bca8bbe6d96f2f52", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:fd749ec8-00cb-4808-9800-fe5a367f2b1f", + "source_record_id": "fd749ec8-00cb-4808-9800-fe5a367f2b1f", + "request_group_id": "hermes_request:d42039f3d0c276776471a1826ff40acc9a908493292351ac6330d9f624555d9e", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_003814", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "from_account" + ], + "validator": "enum", + "detail": "'mygmailaccount@gmail.com' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "subject" + ], + "validator": "enum", + "detail": "'Exclusive Proposal for Partnership Expansion' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "body" + ], + "validator": "enum", + "detail": "'Dear Valued Partner,\\n\\nI am pleased to present to you our detailed proposal document outlining our new services and the benefits they hold for our esteemed clients like yourselves. We trust this proposal will uphold our commitment to excellence and reinforce our continued partnership. We look forward to discussing this exciting opportunity with you further.\\n\\nBest regards,\\n[Your Name]' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_004588", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage through it. Do you worry excessively about writing assignments? Do they make you feel uneasy or agitated? Do you have negative feelings about certain types of writing? If you answered yes to any of these questions, you might be dealing with writing anxiety. Writing anxiety simply means that a writer is experiencing negative feeli", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:f3161159d2e240b1972f1a8d5cd514b2", + "normalized_query_sha256": "473dcf0512f83c49fddb202c0a875dd95e820dc9ffbe0c61da65c4e72fc868f8", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:2a57bf04-3917-4831-999a-4878ddef3e8b", + "source_record_id": "2a57bf04-3917-4831-999a-4878ddef3e8b", + "request_group_id": "hermes_request:4cc9b05b0bad4fb8af597a0bcae5ac6fdc003435de452fb4ee5c14762fa6b817", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_004588", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_006314", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage : A directed graph. \n weighted, by the way. If a pair of vertices in such a graph is attached \"both ways,\" then each of the two edges will have its own weight. \n Washington, DC \n Fredericksburg \n Richmond \n Virginia Beach \n 50 \n 60 100 \n 150 \n Figure 5.3: A weighted (and undirected) graph. \n**adjacent.** If two vertices have an e", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:8c79291fd21abb04910384575832c414", + "normalized_query_sha256": "edd1bcbbede6015e862d90c39610ea8197e223db06b94103453ce49710015c6b", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:9388b796-196c-4766-8ce0-4409eabf1ed1", + "source_record_id": "9388b796-196c-4766-8ce0-4409eabf1ed1", + "request_group_id": "hermes_request:d00faae5d14faa6ba9bd436db5ed6dbaff33a3549ce83790c17ea3d20fc0cbf4", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_006314", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_007278", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am administering a server that is currently handling multiple user processes, and I need to perform a couple of key tasks to ensure optimal performance. First, I need to obtain a full list of all active processes. This list should include the Process ID (PID), the amount of memory each process is using, and their current operational state (such as running, sleeping, etc.). It's vital to monitor ", + "target": "list_active_processes", + "legacy_query_parent_id": "legacy_query:c73e2788744a09727c094ddce494a26c", + "normalized_query_sha256": "1ccd61b35cd7be27e78ae58aa0b68c4f37191482faed2dd100e820ab6f0f81c6", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:415cd276-6764-4a61-ae5f-104be0239040", + "source_record_id": "415cd276-6764-4a61-ae5f-104be0239040", + "request_group_id": "hermes_request:4b0dd003ed1f901d1f98965c47aceb2a40a52999ff7f203533fd1e21796d9500", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_007278", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "include_memory_usage" + ], + "validator": "type", + "detail": "None is not of type 'boolean'" + }, + { + "type": "argument_schema", + "path": [ + "include_state" + ], + "validator": "type", + "detail": "None is not of type 'boolean'" + }, + { + "type": "argument_schema", + "path": [ + "threshold" + ], + "validator": "enum", + "detail": "90 is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_007548", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am working with a dataset of 10,000 images of various flower species, and I need it properly divided for a deep learning task. The images are labeled with the species and I need to maintain these proportions when splitting into subsets. I would like to perform a stratified split of my dataset to form the training, validation, and test sets. Specifically, the training set should contain 70% of th", + "target": "stratified_dataset_split", + "legacy_query_parent_id": "legacy_query:0aed7466dacaa432cb8cb7aac4e56633", + "normalized_query_sha256": "214edd57c913ad8f09d630cb1363710d28f60dbdb24bc1a43195a9ac6a080e9a", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:1c870a2e-42a8-4e28-bc2d-930bbdf3c721", + "source_record_id": "1c870a2e-42a8-4e28-bc2d-930bbdf3c721", + "request_group_id": "hermes_request:212ff92d67b98715628e5b245b38d6479bc389c553fe28128d07518738b6ac2a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_007548", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "dataset" + ], + "validator": "enum", + "detail": "'/images/flower_images.csv' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "labels" + ], + "validator": "enum", + "detail": "'/images/flower_labels.csv' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "train_size" + ], + "validator": "enum", + "detail": "0.7 is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "validation_size" + ], + "validator": "enum", + "detail": "0.15 is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "test_size" + ], + "validator": "enum", + "detail": "0.15 is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_009396", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage of them occurs. You might first think that we'd add the two probabilities for the two events and be done with it, but a glance at the diagram tells you this means trouble. Pr(U ) is .4, and Pr(R) is .7. Even if we weren't very smart, we'd know something was wrong as soon as we added .4 + .7 = 1. 1 to get a probability of over 1 a", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:5f7a5241de6392cbd888ad5f5be2f364", + "normalized_query_sha256": "f3f5b0dc9bf8dab65b85167028fb6f690c828e1f266bf60cae5b00f317d59d7d", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:8bed96d9-04b9-429e-ae85-abeda950ce11", + "source_record_id": "8bed96d9-04b9-429e-ae85-abeda950ce11", + "request_group_id": "hermes_request:50beecbd499d749d963120a0b7d141bc00098cd344f1d80d062ce475b7eec7e5", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_009396", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json", + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_009620", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I have been working on a new authentication feature for our web application, and I have committed all my code to a branch named 'feature-auth'. Now, I need to incorporate these updates into our mainline development. I would like to switch to our 'development' branch, which is the integration branch we use for all features before they are deployed. After switching, I need to merge the changes from ", + "target": "git_checkout_branch", + "legacy_query_parent_id": "legacy_query:605e1de391e4543fc7fa81d78ecca46b", + "normalized_query_sha256": "1f6239a766476cc4c102b3909761b1981fd2fda283d9c7d1db9137c86a5c796f", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:92b2f2b9-b691-4b5d-8866-5679ef853672", + "source_record_id": "92b2f2b9-b691-4b5d-8866-5679ef853672", + "request_group_id": "hermes_request:faf293ead2a6027d6f1cd3907919ba3bbaf2bd962c28a7dfb626ab1606de7d3a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_009620", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "branch_name" + ], + "validator": "enum", + "detail": "'development' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "source_branch" + ], + "validator": "enum", + "detail": "'feature-auth' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "destination_branch" + ], + "validator": "enum", + "detail": "'development' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "branch_name" + ], + "validator": "enum", + "detail": "'development' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "remote_name" + ], + "validator": "enum", + "detail": "'origin' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_010650", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am looking for engaging gamification apps that can be utilized to foster team building and enhance educational experiences within my team. Specifically, I'm interested in apps that provide scavenger hunt activities and other challenges that can be completed in a group setting, which may include tasks such as photo taking, quizzes, and GPS check-ins to encourage teamwork and problem-solving. Addi", + "target": "find_gamification_apps", + "legacy_query_parent_id": "legacy_query:3ec3de1b4a8b8f64acce72115b623a86", + "normalized_query_sha256": "e43fa4db16e0cb8c6de2fdf7b5f4ea9be8a5072724680401b5fadb11db6e59a2", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:3da05b9c-2695-4e9e-848e-8652c6a3820b", + "source_record_id": "3da05b9c-2695-4e9e-848e-8652c6a3820b", + "request_group_id": "hermes_request:29754747a5f4b76b217e92d658c4be19b2f71b6abba3e4ba127df50d871e0696", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_010650", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "category" + ], + "validator": "enum", + "detail": "'Team Building' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "category" + ], + "validator": "type", + "detail": "None is not of type 'string'" + } + ] + }, + { + "canonical_id": "fastbrain_011831", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "As the inventory manager at Seaport Logistics, I'm overseeing inventory for a diverse range of products including anchor chains, navigational equipment, container latches, and fuel supplies. Our inventory database, `seaport_inventory.json`, tracks item IDs, current stock levels, restock thresholds, and storage locations. Today, I'd like to perform three tasks: first, monitor current stock levels f", + "target": "monitor_port_inventory", + "legacy_query_parent_id": "legacy_query:80821c8db7e32e7f34fe63984ff002fc", + "normalized_query_sha256": "e3e16174c26ad45f0831e847d3c3ce633b87fac43741fefc23f05386f6100ce6", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:28498356-ac78-4170-8fb8-7d1b08a70a06", + "source_record_id": "28498356-ac78-4170-8fb8-7d1b08a70a06", + "request_group_id": "hermes_request:2929e0a8b4c41bdcd415b7559b0a9c6bcf8bd3de88fea3d7dace41ebebcb17cd", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_011831", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "shipment_details" + ], + "validator": "required", + "detail": "'shipment_type' is a required property" + }, + { + "type": "argument_schema", + "path": [ + "shipment_details" + ], + "validator": "required", + "detail": "'shipment_details' is a required property" + }, + { + "type": "argument_schema", + "path": [ + "shipment_details" + ], + "validator": "required", + "detail": "'shipment_type' is a required property" + }, + { + "type": "argument_schema", + "path": [ + "shipment_details" + ], + "validator": "required", + "detail": "'shipment_details' is a required property" + } + ] + }, + { + "canonical_id": "fastbrain_014223", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'm interested in purchasing a smartphone online. I have a budget of $100 to $1000 and I'm open to different brands. I'd like to see a list of available smartphones within this price range. Once I have the list, I would appreciate help in comparing the features of two specific models that catch my interest. After making my decision, I'll need assistance in adding my selected smartphone to my shopp", + "target": "list_available_smartphones", + "legacy_query_parent_id": "legacy_query:c08e28df9d37073fe61e0a3164321c80", + "normalized_query_sha256": "a729559f214038e9417b99ace759496be536f6fc4b9cf961f9b7c34753eb3e86", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:89454ea8-9382-4035-9968-5dad228e7782", + "source_record_id": "89454ea8-9382-4035-9968-5dad228e7782", + "request_group_id": "hermes_request:3b28daccd2e75d0012ea82aee4f1378665d8d7c45f6ff3ff05eee6dc3e595ced", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_014223", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "filters" + ], + "validator": "required", + "detail": "'brand' is a required property" + } + ] + }, + { + "canonical_id": "fastbrain_014395", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am developing an Android app for note-taking and need to integrate functionality for users to create, view, and search for notes. Specifically, I'd like to add a feature that allows users to create a note with a title and detailed content. For instance, I want to create a note titled 'Meeting Notes' detailing our discussion about project milestones and deadlines. Additionally, I need to be able ", + "target": "createNote", + "legacy_query_parent_id": "legacy_query:b00f7a28302fa93014d3c1820813588b", + "normalized_query_sha256": "ef9710b048efdc49a9dfd07c882502eb6498011d49d7f3df416e28332b131e87", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:50a9bc6c-e241-4ce5-8bef-5de681dce55c", + "source_record_id": "50a9bc6c-e241-4ce5-8bef-5de681dce55c", + "request_group_id": "hermes_request:e6d49ca5836d39788e093263ca3771bfa05233c32b10b60ac1f238ee1dd84a38", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_014395", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "title" + ], + "validator": "enum", + "detail": "'Meeting Notes' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "content" + ], + "validator": "enum", + "detail": "'Discuss project milestones and deadlines.' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "title" + ], + "validator": "enum", + "detail": "'Meeting' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_018409", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage have encountered. This will be especially important when you are asked to construct proofs whose conclusion states that two objects are equal. If you have an electronic copy of the book, such as the PDF version, searching on \"Definition CVE\" can be an instructive exercise. See how often, and where, the definition is employed. OK,", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:bb93ae44cb5b915c0d3b1205cde005d3", + "normalized_query_sha256": "5ac73ae97bc17f5da09a385cd1297f5f852be7a0a11e5f0580a50787c70438a7", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:8c250196-c23d-4c91-88be-b578d7afd181", + "source_record_id": "8c250196-c23d-4c91-88be-b578d7afd181", + "request_group_id": "hermes_request:75bfc52208e94b7debd567d06c690c28f3e6ce19efb82592ebfc0a8a3c033b95", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_018409", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_018951", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to automate the assignment of new leads to our sales representatives in Salesforce. The automation should be based on the geographic location of each lead, ensuring that leads are matched with reps who manage the corresponding regions. Please use the 'LeadAddress' field from the lead records to determine the location. The assignment rules should be as follows: leads from North America", + "target": "assign_leads_to_sales_reps", + "legacy_query_parent_id": "legacy_query:595461a148e01028be8cc607fc71b32d", + "normalized_query_sha256": "37bf5a2125536ec9e3504457295f9e103b6b0e02bb9a0bfbae0ae35828f4d2f2", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:e9f08e76-2079-47d9-b337-d943421f32fe", + "source_record_id": "e9f08e76-2079-47d9-b337-d943421f32fe", + "request_group_id": "hermes_request:adb5e212f09121bf49a05a50e6b0650c9f5b2512b306bb18c7188c1fc3103204", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_018951", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "lead_criteria" + ], + "validator": "required", + "detail": "'assignment_rules' is a required property" + } + ] + }, + { + "canonical_id": "fastbrain_019650", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage t C o u r s e i n L i n e a r A l g e b r a 40 \nsentence is pretty vague, so let us be more precise. Using our notation for the sets D and F from the reduced row-echelon form (Definition RREF): xdi = [B]i,n+1 , 1 <= i <= r xfi = 0, 1 <= i <= n - r These values for the variables make the equations represented by the first r rows o", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:d38b0c655a1cc1fa2204740ac8c1b5d9", + "normalized_query_sha256": "8fef3554b7d50a7857d7e15a1764671f7425b630637fddc36f0976731dd60351", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:116b0a9c-d7a2-4efb-bfd9-10470728798b", + "source_record_id": "116b0a9c-d7a2-4efb-bfd9-10470728798b", + "request_group_id": "hermes_request:913550eef32a9c0b614b342263547e283d1240cbb3727080ad88f7c7ec9d34ed", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_019650", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_019701", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage or insane, so we won't discuss it more here. For now, just realize that every set is either finite or infinite. \nYou might think, by the way, that there's no way to define an infinite set extensionally, since that would require infinite paper. This isn't true, though, if we creatively use an ellipsis: \n I = { 3 , 6 , 9 , 12 , 15 ", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:f080e8ca3a4574677f8e3918bf61b68d", + "normalized_query_sha256": "b34c2f94ab0f9a05a70505d95e129caecad8618551f798cf196424e92ebc21b5", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:92c2216d-471b-4b98-aa93-f2d9c19799d7", + "source_record_id": "92c2216d-471b-4b98-aa93-f2d9c19799d7", + "request_group_id": "hermes_request:c46f1d6a3cef851dfb2f6f4e8332f598d765fbedc170d3ce4d978d9ab2a881c2", + "protected_effective_split": "validation", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_019701", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_019814", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently managing a project through Microsoft Planner, and I have a particular task, identified by the task ID \"task456\", for which I need to set up a notification system. I want to create a webhook that triggers when this task is marked as complete. Could you please establish this webhook for my Planner with the planner ID \"abc123\"? Additionally, once the task is completed, I need an automa", + "target": "create_task_completed_webhook", + "legacy_query_parent_id": "legacy_query:197635d4f67e222310edd7b033d385a7", + "normalized_query_sha256": "dc749458bf2ad036ccef76f9716e3e340c436853819ba870d529f51542e8937a", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:1e40ffdc-cb01-4d93-b8a5-28fe2f9a04da", + "source_record_id": "1e40ffdc-cb01-4d93-b8a5-28fe2f9a04da", + "request_group_id": "hermes_request:8408a3d7240c0d0e85a33d60a821c97ed1384b08b059ca7234a65a9c6b67d378", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_019814", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "planner_id" + ], + "validator": "enum", + "detail": "'abc123' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "task_id" + ], + "validator": "enum", + "detail": "'task456' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "channel_id" + ], + "validator": "enum", + "detail": "'C01B237K2' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "message_text" + ], + "validator": "enum", + "detail": "'A task has been marked as complete in Microsoft Planner.' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_021009", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 'r'^ } (Definition RREF). There are four steps in the proof, and the first three are about showing that B and C have the same number of pivot columns, in the same places. In other words, the \"primed\" symbols are a necessary fiction. First Step. Suppose that d 1 < d' 1. Then 1 = [B] 1 d 1 Definition RREF \n = \n ^ m \n k=1 \n d 1 k [C", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:813a98d995ea646b58aef114421ce436", + "normalized_query_sha256": "0ec7e9467b354e3e1d029f66c4fdd5ceccb502a8830ff9c894cd4d9c83e61abb", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:77095778-9e4e-46ae-b97c-1e84c726b4a9", + "source_record_id": "77095778-9e4e-46ae-b97c-1e84c726b4a9", + "request_group_id": "hermes_request:410e87568a80bd8d362dd1fd7c9a7fdbe9e9796a9b30d7786358c87b4c186ff5", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_021009", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_022512", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I've recently purchased an Oculus Rift headset and am interested in finding VR applications that can offer engaging and interactive experiences. Specifically, I'm looking for entertainment apps that have high popularity and user engagement. I would appreciate a curated list of the top 5 entertainment VR apps that are suitable for my Oculus Rift. Also, could you seamlessly launch one of those apps ", + "target": "get_compatible_vr_apps", + "legacy_query_parent_id": "legacy_query:b918a4e89f6b3c49f697363f0e7a5e67", + "normalized_query_sha256": "37cb19da2024b7f08e80c65e398ffc1d230f78c284e609c594a94cbe426347ef", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:e642d8fa-6e0f-4053-98eb-6553b7000a10", + "source_record_id": "e642d8fa-6e0f-4053-98eb-6553b7000a10", + "request_group_id": "hermes_request:b134011da37b088f0c0386a36b257768c8e2ccfc49b6a9e5dddbfeb07e08d89f", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_022512", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "headset" + ], + "validator": "enum", + "detail": "'Oculus Rift' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "category" + ], + "validator": "enum", + "detail": "'Entertainment' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "limit" + ], + "validator": "enum", + "detail": "5 is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_023841", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently exploring various technology stacks used in desktop applications, particularly focusing on functionalities within social media apps that allow users to engage with posts. I would like to perform four specific actions using the respective functions: \n\n1. Retrieve the latest two posts from Twitter.\n2. Post a new update on Twitter with the content: \"Excited to learn about tech stacks f", + "target": "retrieve_latest_posts", + "legacy_query_parent_id": "legacy_query:68529c027e9ecd662d12ff59a28eb953", + "normalized_query_sha256": "58ac120118f56b27971d0d54348129aaa927042eba104d6599a040113e8d2e49", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:adb40093-0217-47d6-b790-2af48b093613", + "source_record_id": "adb40093-0217-47d6-b790-2af48b093613", + "request_group_id": "hermes_request:72d8ead88535053fd5b7b8d21c03060801113f496858f95700cc305d3ddc2bbd", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_023841", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "platform" + ], + "validator": "enum", + "detail": "'Twitter' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "count" + ], + "validator": "enum", + "detail": "2 is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "authentication_token" + ], + "validator": "type", + "detail": "None is not of type 'string'" + }, + { + "type": "argument_schema", + "path": [ + "platform" + ], + "validator": "enum", + "detail": "'Twitter' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "content" + ], + "validator": "enum", + "detail": "'Excited to learn about tech stacks for desktop applications! #TechStacks #DesktopDev' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "authentication_token" + ], + "validator": "type", + "detail": "None is not of type 'string'" + }, + { + "type": "argument_schema", + "path": [ + "platform" + ], + "validator": "enum", + "detail": "'Twitter' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "post_id" + ], + "validator": "enum", + "detail": "'1234567890' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "authentication_token" + ], + "validator": "type", + "detail": "None is not of type 'string'" + }, + { + "type": "argument_schema", + "path": [ + "platform" + ], + "validator": "enum", + "detail": "'Twitter' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "post_id" + ], + "validator": "enum", + "detail": "'123456789' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "comment" + ], + "validator": "enum", + "detail": "'This is a fascinating post about tech stacks!' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "authentication_token" + ], + "validator": "type", + "detail": "None is not of type 'string'" + } + ] + }, + { + "canonical_id": "fastbrain_025221", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 3. (Step 1) Now compute 1838 mod 16. This gives us the value 14, which is of course a base 10 number. The equivalent hex digit is E. So we now write down E to the left of the 3: \nE3 \n4. (Step 2) Dividing 1838 by 16 and taking the floor gives us \n114. Since this is again not zero, we perform step 2b: make 114 our new value", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:e7ac917268dcc45ecc08f5787304f9cb", + "normalized_query_sha256": "7e5568ade2dab0e781da1a0600dc89d17b92c5f64cddf0751f0045b36e93ef9e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:f8acf133-90f4-47cf-b2c0-ef69480f8893", + "source_record_id": "f8acf133-90f4-47cf-b2c0-ef69480f8893", + "request_group_id": "hermes_request:f1695b89fcaa37e4859cef4fbc389f19e3d699c792b2c8ddef99c18c67bf8333", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_025221", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_025527", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage ounced \"oh-of-n\"), it means that the time it takes to execute the algorithm is _proportional to the number of nodes._ This doesn't imply any specific number of milliseconds or anything -- that is highly dependent on the type of computer hardware, you have, the programming language, and a myriad of other things. But what we _can_ ", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:5fbc2460f6c8a89db11df254a9f58a64", + "normalized_query_sha256": "cb2ba505027094f6cefe73b3b4c09b9fb716d2b261464e9255857e28c12bb1be", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:18556c45-f8ec-4d01-b43e-eafda339660b", + "source_record_id": "18556c45-f8ec-4d01-b43e-eafda339660b", + "request_group_id": "hermes_request:c03130267f8200b64c070e589ceacbe5deb2349b3b2f1276a4a143edeccd9bf2", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_025527", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_026912", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage y Kelly is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted. \n#### WORKS CITED \n241 COLLECTED WORKS \n###### CHAPTER 30. \n## GIVING CREDIT WHERE IT'S DUE \n Why and How to Cite Your Sources LIZ DELF Using outside sources in your paper is a great move. Doing outside research enric", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:f51827573276e8b2ba64bc796946bbd5", + "normalized_query_sha256": "3d860ac0d8fb20a234bd9d8d64bd76846067aea9508bcd4d867d740b32863051", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:a8b2710b-2d5b-4e7f-bd21-319fddb3d726", + "source_record_id": "a8b2710b-2d5b-4e7f-bd21-319fddb3d726", + "request_group_id": "hermes_request:09c47b884f70f26bd8f4ca9a0ae011fc66b910402b03812b763d8ec9dd9991fe", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_026912", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_030148", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am coordinating a marketing campaign for our company's upcoming product release and require an organized schedule for emailing our subscribers. We have a prepared list of subscribers in a CSV file along with a collection of pre-designed email templates stored in a specific directory. I need a function that will automatically send the first promotional email on April 15th, 2023, to introduce the ", + "target": "schedule_email_campaign", + "legacy_query_parent_id": "legacy_query:5dbf83a36b5632c8e3676e41abee57da", + "normalized_query_sha256": "a1fb59782e7238010c4d153ff392d355f9e9a5b9d83a51632223f3e0d4297750", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:24cb5642-7f64-4572-8a24-a3ca0fb03197", + "source_record_id": "24cb5642-7f64-4572-8a24-a3ca0fb03197", + "request_group_id": "hermes_request:582bb04940304dc941c418da4f72fbb42f21cf00f34f904573eeecb7f1c8dfcf", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_030148", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "subscriber_list_csv" + ], + "validator": "enum", + "detail": "'path/to/subscriber_list.csv' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "email_templates_directory" + ], + "validator": "enum", + "detail": "'path/to/email_templates' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "campaign_start_date" + ], + "validator": "enum", + "detail": "'2023-04-15' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "campaign_end_date" + ], + "validator": "enum", + "detail": "'2023-05-15' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_030224", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I have two specific tasks that I need assistance with on my Windows desktop. First, I made an error and deleted a couple of important work-related files named 'work_presentation.pptx' and 'annual_report.xlsx' from my desktop, and I need to restore them from the Recycle Bin back to their original locations. Second, to clear up storage space on my external USB drive, which is assigned the letter 'E:", + "target": "restore_files", + "legacy_query_parent_id": "legacy_query:4b9ad744f4deb4a72f32580cef1fd433", + "normalized_query_sha256": "207a7c7c58c0ba4ee35bc17500e1c8080655edf9c7df92ae222c00e4875c5ce5", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:13d64a90-9f09-4b22-bba4-1b4bff250fa3", + "source_record_id": "13d64a90-9f09-4b22-bba4-1b4bff250fa3", + "request_group_id": "hermes_request:d0de98b671f38152ecdc03a6ec343625c9f617694d4ac6ba1945b8bd4e5e71d1", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_030224", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "source" + ], + "validator": "type", + "detail": "None is not of type 'string'" + }, + { + "type": "argument_schema", + "path": [ + "drive" + ], + "validator": "type", + "detail": "None is not of type 'string'" + } + ] + }, + { + "canonical_id": "fastbrain_031415", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage The table below summarizes the types of secondary sources in four tiers. All sources have their legitimate uses, but the top-tier ones are preferable for citation in an academic context. \n#### AH, THE RESEARCH PAPER \n#### SOME SOURCES ARE BETTER THAN OTHERS \n A DAM GOOD ARGUMENT 154 \n Table 20.1 Analyzing the quality of secondary", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:7b1c44adf30d2e00fda52d63fdd7556b", + "normalized_query_sha256": "739de279ca0b813b2cf864a9a1452f1179b69e8379bd7d2cb2a92a95ace835ff", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:5044d7b8-3c83-43ba-9df2-361cd550b89b", + "source_record_id": "5044d7b8-3c83-43ba-9df2-361cd550b89b", + "request_group_id": "hermes_request:4d52a00d7171ae2e5a9e1640d147442caabf78aef86bc617a8170832429417b5", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_031415", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_032263", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am developing a sentiment analysis tool to understand customer opinions about our services. To accomplish this, I've collected a batch of recent customer feedback comments and I would like to classify each as either positive or negative sentiment. I intend to use a TensorFlow Hub model capable of text classification, specifically the one available at `https://tfhub.dev/google/tf2-preview/gnews-s", + "target": "load_text_embedding_model", + "legacy_query_parent_id": "legacy_query:deb1fbcd10ef2acac8b18de4f4a69487", + "normalized_query_sha256": "25a7033ded00c909055ee1b963696c6de86debd135d0d30eac5ac8bbdd8b3ad5", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:21c3d195-3a9d-41f9-9ac8-031a5f40fad0", + "source_record_id": "21c3d195-3a9d-41f9-9ac8-031a5f40fad0", + "request_group_id": "hermes_request:9b7b26c794cc73fc4298b6d497e0ccbb75e55cd2ed8615206a14cb661b1ebedb", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_032263", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "model_url" + ], + "validator": "enum", + "detail": "'https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_032458", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage propositions are. Such an expression is called a **tautology** : it's always true. The word \"tautology\" has a negative connotation in regular English usage: it refers to a statement so obvious as to not tell you anything, like \"all triangles have three sides,\" or \"the fatal overdose was deadly.\" But in logic, tautologies are quit", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:8c4aa0229153b0ee86ec39f0a55ffe1d", + "normalized_query_sha256": "123975273d665d25497148962ded9f8b82c89674cf5e3d0679b292854531de10", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:e950908c-58f0-4a80-9af9-b3421845f798", + "source_record_id": "e950908c-58f0-4a80-9af9-b3421845f798", + "request_group_id": "hermes_request:ecff466e2a32f37a99f353871dd500f2318af2feb6e21087b72dc995080784d1", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_032458", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_033907", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'm a beginner eager to learn JavaScript and I'm searching for a coding app that's particularly friendly to new programmers. I need an app that offers interactive tutorials to help me grasp the basics effectively. From my research, I see 'Encode' might be a good fit, as it is reputed for its user-friendly interface and supportive learning environment for JavaScript beginners. I'd like to find an a", + "target": "find_coding_app", + "legacy_query_parent_id": "legacy_query:ed4af756b0d929120b29e2a124e387d0", + "normalized_query_sha256": "8d0fda5b57e3408b4d7141ab6dafff3b9713ba2027f73ef4f91d784e1a862d0e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:24dd6f9f-b67a-48d7-8261-73461fcbb2a9", + "source_record_id": "24dd6f9f-b67a-48d7-8261-73461fcbb2a9", + "request_group_id": "hermes_request:a59712e5745617f2d1a883a5318ffbee2bc982a1df67c47f06f39c2ef739ce44", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_033907", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "language" + ], + "validator": "enum", + "detail": "'JavaScript' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_034935", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage for five years, which they call Midwestern University, do not help facilitate social mobility. Frequently, the students who entered college best prepared were those who were already middle or upper class, meaning the opportunities the workingand lower-class students received were more limited (Armstrong and Hamilton 1-26). When y", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:15f46a8f51d79ff8588abe4aeea57282", + "normalized_query_sha256": "67a3cf8d74387c5d8a0743b854f18d70d854283ceb00caccd71986e499ab87f8", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:a630342f-0940-4225-b281-d92387000ae9", + "source_record_id": "a630342f-0940-4225-b281-d92387000ae9", + "request_group_id": "hermes_request:78d84ed5a5244a0e4fbb4c544a4a741cd3abea39ec65c17c1e3025c92ccb8f29", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_034935", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_035025", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage answers, higher stakes, very specific \n##### Health \n Are people in the United States more obese than they used to be? Too straightforward, not specific enough Have obesity rates increased in the United States over the last one hundred years? More specific Is there a correlation between obesity rates and economic instability in t", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:a15f874168219f07d952817b39997d59", + "normalized_query_sha256": "4513dc6b96042106b40f3eed5d28e40135f34a9269c24e611f9f2dd39ea3c9c3", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:5f906392-bad2-4313-a5dc-355f0856330c", + "source_record_id": "5f906392-bad2-4313-a5dc-355f0856330c", + "request_group_id": "hermes_request:e8f5a9b8c3974f8ef13e73f788ea7db269ec1c4a9e5030f9492d8a7f509e17c1", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_035025", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_035668", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "The IT department of our enterprise has initiated a data migration project to transition our substantial IT infrastructure, consisting of multiple petabytes of data, from its current on-premises setup to a cloud computing environment. Our objective is to minimize downtime, specifically limiting it to a window of 2 hours, while also ensuring the integrity of data during the migration process. To fa", + "target": "generate_migration_plan", + "legacy_query_parent_id": "legacy_query:4d8bb1f57c9537e6406ae3f7febf47dc", + "normalized_query_sha256": "b9971f370b169eafcaa5123d80546a0f7f70205e5ed2df415d5f44332fde6197", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:34ea0eea-4c02-4253-b30a-31a2237255df", + "source_record_id": "34ea0eea-4c02-4253-b30a-31a2237255df", + "request_group_id": "hermes_request:9529cdb08aac6a43fe6037eedbfb2ddee061458f333b39079f6061d699c1d825", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_035668", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "data_volume" + ], + "validator": "enum", + "detail": "'multiple petabytes' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "source_environment" + ], + "validator": "enum", + "detail": "'on-premises' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "target_environment" + ], + "validator": "enum", + "detail": "'cloud' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "downtime_tolerance" + ], + "validator": "enum", + "detail": "'minimal' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_037269", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am developing a travel guide section within my Android application focused on Hungarian destinations. As part of the user experience, I want to provide a feature that allows users to translate common English phrases to Hungarian directly within the app. The phrases to translate include greetings and essential questions a traveler might need, such as \"Hello\", \"Thank you\", \"How much does it cost?\"", + "target": "translate_phrases", + "legacy_query_parent_id": "legacy_query:437582d54b78eec3f4deb9424708710d", + "normalized_query_sha256": "c2806e03bf8900b255ed6601ae09ca7641ff26e53bc9b7c9c14eaf12d6f242bc", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:1343117d-7519-4c36-8aa0-c053eb490b47", + "source_record_id": "1343117d-7519-4c36-8aa0-c053eb490b47", + "request_group_id": "hermes_request:f81e7dbb152303d9f74697fdc821ab53208a47b5cf5cb4523f9e4694f1af886a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_037269", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "source_language" + ], + "validator": "enum", + "detail": "'en' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "target_language" + ], + "validator": "enum", + "detail": "'hu' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_038171", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently developing a desktop application using MonoDevelop, and I'm looking to integrate Git for version control in my Visual Studio Code environment to better manage the project. The project is located at the following path on my system: `/Users/myusername/projects/my-application`. I need to initialize a Git repository in this project directory. \n\nOnce the repository is set up, I would lik", + "target": "initialize_git_repository", + "legacy_query_parent_id": "legacy_query:f0039fef6176c78753f032140b36efd4", + "normalized_query_sha256": "c79a355715fb2c12f8883908515eddac11d96b96fcc87015ef039a22c8e0c1b5", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:43069657-c694-4e1e-bdd2-8253b57afb21", + "source_record_id": "43069657-c694-4e1e-bdd2-8253b57afb21", + "request_group_id": "hermes_request:aed83475c712877c761a9cd5415ab687d093c77d7582bb5c79debac10e44fd69", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_038171", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "project_path" + ], + "validator": "enum", + "detail": "'/Users/myusername/projects/my-application' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "editor" + ], + "validator": "enum", + "detail": "'Visual Studio Code' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "branch_name" + ], + "validator": "enum", + "detail": "'feature-branch' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "checkout" + ], + "validator": "enum", + "detail": "True is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_038452", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "To enhance our company's efficiency in supply chain management, we have identified four critical areas that require optimization: inventory analysis, demand forecasting, transportation route optimization, and delivery scheduling. As part of our efforts to streamline our supply chain logistics, we have collected extensive data that we wish to analyze and utilize for improvement.\n\nFor inventory anal", + "target": "analyze_current_inventory", + "legacy_query_parent_id": "legacy_query:35d863624dc308641712f958cd77eb3e", + "normalized_query_sha256": "a4d6b2af27cf4d109ad26a56abc49ee2daa89dc3859f9e230538d8bbf18764b8", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:cac519de-7fe8-4c23-af9c-40d37760390f", + "source_record_id": "cac519de-7fe8-4c23-af9c-40d37760390f", + "request_group_id": "hermes_request:8428358f0483bf52f89cd007f40caf06cb6b7ad79dea01d094c689c17e0b6ccc", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_038452", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "cost_constraints", + "max_cost" + ], + "validator": "type", + "detail": "None is not of type 'number'" + } + ] + }, + { + "canonical_id": "fastbrain_038478", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 6. Is this graph weighted? \n No. (No numbers annotating the edges.) \n7. Is it a tree? \n No. (A tree must be connected, and must also have no cycles, which this graph clearly does: e.g. , B-to-A-to-E-to-B.) \n8. Is it a DAG? \n Not remotely: it is neither directed nor acyclic. \n9. If this graph represented an en- dorelation, how", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:3661de9745d3c76ede3226de7542cfb5", + "normalized_query_sha256": "f217414759d39d7e6e856f5826b4bc9c435e033f0953cd427ebd90d8142d630e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:1737bbd7-9e80-4133-bb34-1095efc8d1a1", + "source_record_id": "1737bbd7-9e80-4133-bb34-1095efc8d1a1", + "request_group_id": "hermes_request:14f643478a9e90a66c9ec6daee50d3bac26843aad5321b7b55ae0c170d3a3343", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_038478", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_038731", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage students not simply think about the \"so what\" of their writing but also consider and analyze the prompt more carefully. I've found that students go through a layered understanding of a prompt, at first understanding the basic concept and then looking more carefully at the prompt's specific requirements. But what makes their paper", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:1464a361c9d70b9cce873812a066e051", + "normalized_query_sha256": "e71d52cb1e6928b0eb0ded2e7fe53e1e3b632a23ab391eafefaa33eb8c3996f2", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:0a9ed99a-4cda-45ad-9bfe-74652befffd7", + "source_record_id": "0a9ed99a-4cda-45ad-9bfe-74652befffd7", + "request_group_id": "hermes_request:bc0e31a82e4f501e3d33919b99f7aa82e25ff69960a2fb05c324d238c13f054c", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_038731", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_040564", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage a teacher your task is usually to explain what you are still engaged in trying to understand to someone who understands it better. (255) A DAM GOOD ARGUMENT 8 \n Don't be scared whenever you are given an assignment. Professors know what it was like to be in college and write all kinds of papers. They aren't trying to make your liv", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:bf279b5610c6752d5a7b188de5cbf231", + "normalized_query_sha256": "213a6fd2b502f8905c703553cfb34ab141dfb7d52d723589e3250489e8a787c3", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:d798b1c3-b698-4284-a25f-d9684477c25b", + "source_record_id": "d798b1c3-b698-4284-a25f-d9684477c25b", + "request_group_id": "hermes_request:4bf0c60266a3e3a2ddf1016c158a711c670740cbfa8d5c467abe7e2a95814502", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_040564", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_041338", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage .2. PERMUTATIONS 151 \n R S K I R K I S R K S I I R S K I R K S I S R K I S K R I K R S I K S R S R I K * * * \nThen, for the 5-letter word: \n B R I S K B R I K S B R S I K B R S K I B R K I S B R K S I B I R S K B I R K S * * * \n**Partial permutations** \nSometimes we want to count the permutations of a set, but only want to choose", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:2634f6f0f61c71820563684b59f8a169", + "normalized_query_sha256": "da436321eaf5912c7d553e65307ad3322eca1246c0de6eaa93b0d5ea23514ba4", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:4a2e1536-a29a-48fd-9204-e33f17e412ea", + "source_record_id": "4a2e1536-a29a-48fd-9204-e33f17e412ea", + "request_group_id": "hermes_request:201771607b08eb452fc4bd59fa77b7a1f878b3d889f1d14c5d5b0c24ade72ab6", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_041338", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_041747", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am interested in experiencing the capabilities of 1Password on my iPhone, particularly its integration with iOS features. For this purpose, I would like to set up a trial account that includes specific functionalities such as biometric login for secure access, secure password sharing options for collaboration, and dark web monitoring for enhanced security surveillance. As I plan to extensively t", + "target": "create_trial_account", + "legacy_query_parent_id": "legacy_query:6a8364266123d3efc89861f39c49a768", + "normalized_query_sha256": "ba758db4c8d64037967354418cca6d9b06215dbf62908afae588078ccd9969cf", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:41d37a42-2aa2-455a-a364-29c437c7bfc5", + "source_record_id": "41d37a42-2aa2-455a-a364-29c437c7bfc5", + "request_group_id": "hermes_request:9ab01d61fbb6ad859ae86562a50f6fd8c4fb8b761f020b3bd9b5679ef667fb5b", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_041747", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "app_name" + ], + "validator": "enum", + "detail": "'1Password' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "app_name" + ], + "validator": "enum", + "detail": "'1Password' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_041953", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage = - 3 x + y - z = 0 \nC50+^ A three-digit number has two properties. The tens-digit and the ones-digit add up to 5. If the number is written with the digits in the reverse order, and then subtracted \nSSS S L E B e e z e r : A F i r s t C o u r s e i n L i n e a r A l g e b r a 16 \nfrom the original number, the result is 792. Use a", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:f4b5f4fabd4ce2c3fda28cc0fc24a8d5", + "normalized_query_sha256": "1b057dc0101b3f29b3834685493b29f5e884d41c58784792be64d7d85b76b27d", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:07fb2128-e9c1-477c-9171-cedc3a2d17b6", + "source_record_id": "07fb2128-e9c1-477c-9171-cedc3a2d17b6", + "request_group_id": "hermes_request:53a5b5ba0289780fa674056321ded3feeaeb5a369b0986004ff4f6ab98676035", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_041953", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_042460", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently working on a project where I need to download a large set of podcast transcripts for analysis. These transcripts are available on the website located at `http://example.com/podcasts/transcripts/` and are named sequentially, starting from 'episode1.txt' all the way up to 'episode600.txt'. To ensure that I comply with the website's terms of service and do not overload their server, I ", + "target": "check_robots_txt", + "legacy_query_parent_id": "legacy_query:5be40358ac5f4432dd51d895976156a1", + "normalized_query_sha256": "053253127d054af3dca614a3bdee316038bea4330ac8cd46afb457262659fce3", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:8c00a0e5-d3c9-4498-a724-3030e0d3ab12", + "source_record_id": "8c00a0e5-d3c9-4498-a724-3030e0d3ab12", + "request_group_id": "hermes_request:68d3d9a7c0237047bdfec656caa5064806ade5fe14c4c9fa3478dbabf929e027", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_042460", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "website_url" + ], + "validator": "enum", + "detail": "'http://example.com' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_042788", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently analyzing a dataset that contains details about various properties, including their area in square feet and total price. To enhance my machine learning model's ability to predict property prices effectively, I aim to create a new feature that calculates the price per square foot for each property entry. Additionally, to ensure that my model is not influenced by extreme values, I nee", + "target": "calculate_price_per_square_foot", + "legacy_query_parent_id": "legacy_query:78a6071c96ad678312bad8be74e0f2e8", + "normalized_query_sha256": "40bff27b9c6798847ceb6a1491e358f7ea37f36ef2a0f0ad603c588ce9a7b551", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:f3c55da9-8a96-4fa9-b03e-c6ba52d7196f", + "source_record_id": "f3c55da9-8a96-4fa9-b03e-c6ba52d7196f", + "request_group_id": "hermes_request:aa0c0ad3335b5c50b41029b57877db0e7b230686a34ca201396c2c74001ccb93", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_042788", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "area_column" + ], + "validator": "enum", + "detail": "'area (sq ft)' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "price_column" + ], + "validator": "enum", + "detail": "'total_price (USD)' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "data_column" + ], + "validator": "enum", + "detail": "'total_price (USD)' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "threshold" + ], + "validator": "enum", + "detail": "1.5 is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_042858", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Our team is currently refining the final draft of our project report, which necessitates collaborative editing from various members to incorporate their insights and finalize the content. To facilitate this, I would like to request the opening of our document named 'Project_Report.docx' located within our shared folder path at '/team_shared/project_reports/'. Additionally, we need to enable change", + "target": "open_shared_document", + "legacy_query_parent_id": "legacy_query:0846b9c14e71d55df36e1da6ef252e5c", + "normalized_query_sha256": "30589a2c6e099d6152c4cd77b11d89871d41e362a188cee77fccb84fc07a8156", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:bbaeee5f-0484-4c78-8c5b-9fac1bd587b6", + "source_record_id": "bbaeee5f-0484-4c78-8c5b-9fac1bd587b6", + "request_group_id": "hermes_request:d0bd82ca8764dd43756af022b5bdac983e44412dc362898c141718670dff039b", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_042858", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "document_name" + ], + "validator": "enum", + "detail": "'Project_Report.docx' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "shared_folder_path" + ], + "validator": "enum", + "detail": "'/team_shared/project_reports/' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "document_id" + ], + "validator": "enum", + "detail": "'doc_12345' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_044134", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently analyzing a set of wildlife images obtained from a recent expedition. These images are stored in a secure cloud bucket and feature various animal species in their natural habitats. For my research, I need to detect and classify the animals present in each image. I have chosen to utilize the 'fasterrcnn_resnet50_fpn' model, a well-known pre-trained object detection model from Torch H", + "target": "load_pretrained_model", + "legacy_query_parent_id": "legacy_query:7de2235f8b62f69d1b8dc25bced59a72", + "normalized_query_sha256": "0ea52282a0f4d105bcae0674b4c5a6fea2a864e126a13c0fa675b84f1a14b38b", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:b979520e-583f-4fd4-8e0f-f8d4aa32e24b", + "source_record_id": "b979520e-583f-4fd4-8e0f-f8d4aa32e24b", + "request_group_id": "hermes_request:2ce35fbbcd9f3f1f7b283576c9b1206a21ee5894c4b6584edc8975cc3a112fbd", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_044134", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "model_name" + ], + "validator": "enum", + "detail": "'fasterrcnn_resnet50_fpn' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "pretrained" + ], + "validator": "enum", + "detail": "True is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "model" + ], + "validator": "type", + "detail": "None is not of type 'string'" + } + ] + }, + { + "canonical_id": "fastbrain_044345", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage previous piece of writing. Yes, that means it is appropriate to cite yourself if you want to reference your own writing in a new context! Whew! That feels like a lot, to the point where including sources might start to feel like a landmine of potential mistakes. But so long as you (1) pay careful attention to where your sources c", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:1a6ff9b8c3514ed19a6e0ca2fee31b17", + "normalized_query_sha256": "9d8df049c4396acfe9ac881ada96f759a6b89897139f910ab14e30044ba86c28", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:34ccf09b-04a4-4d0c-970d-343474307ad5", + "source_record_id": "34ccf09b-04a4-4d0c-970d-343474307ad5", + "request_group_id": "hermes_request:05e09cdece74640f2c475538533f0a9bbf4e9f66082183c16d130b77f80e2a8c", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_044345", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_047863", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage to sway us. Even the most stoic individuals have some emotional threshold over which no logic can pass. For example, we can seldom be reasonable when faced with a crime against a loved one, a betrayal, or the face of an adorable baby. The easiest way to differentiate the appeals is to imagine selling a product based on them. Unti", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:0a478907c09b2663cbf87dec710cbcce", + "normalized_query_sha256": "8064e42129dbd8a33f004a696fa7bf6460ecd403e4ba617794b4343be3307a0d", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:0a4cc5c7-da18-41d5-8b6f-5fd60197d121", + "source_record_id": "0a4cc5c7-da18-41d5-8b6f-5fd60197d121", + "request_group_id": "hermes_request:01ff09b28aa4e4523542709938892326cc21f154acf2f809913184e790cd4a0e", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_047863", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_048021", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I manage the database for our company's sales department, and I need to obtain the most recent sales figures for analysis. Specifically, I am looking for the sales data from the 'QuarterlySales' table for our current fiscal quarter. Here is the precise SQL query I need to execute on our database named 'SalesDB':\n\n```sql\nSELECT * FROM QuarterlySales WHERE Quarter = (SELECT QUARTER(CURRENT_DATE()) F", + "target": "get_current_quarter", + "legacy_query_parent_id": "legacy_query:6ef9edb666d8b1c8395cec06a66f412e", + "normalized_query_sha256": "f96bac68e80fd2c8bd8bfdff26a0ea4638897a3c5e7efec923a7a353a3b80600", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:4833674a-8706-4acf-909e-398490182af0", + "source_record_id": "4833674a-8706-4acf-909e-398490182af0", + "request_group_id": "hermes_request:0c4913255d847cf70003dfdf0d78a63d4c309247c67012a3d3d18d79014b73dd", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_048021", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "date" + ], + "validator": "type", + "detail": "None is not of type 'string'" + } + ] + }, + { + "canonical_id": "fastbrain_048283", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'm planning a special weekend getaway in New York City and I'm on the lookout for the best local deals to enhance my experience. Specifically, I would like to dine at some fabulous Italian restaurants that offer great deals and coupons. Additionally, I'm interested in exploring the city's vibrant culture through live music events, and I would appreciate any deals related to them as well. My exact", + "target": "search_local_deals", + "legacy_query_parent_id": "legacy_query:e0235495742ef714795ab27ae331a7e2", + "normalized_query_sha256": "adcab9ba433157beb35cf2dcb97ff9b0842d9013535d48a44b3487c93c104da3", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:d8d7e5a8-4e69-4f9b-ae6a-b611f18e9904", + "source_record_id": "d8d7e5a8-4e69-4f9b-ae6a-b611f18e9904", + "request_group_id": "hermes_request:8db991aa9b1ebba77b368ef7ee0d7aba530fd1c71531a92e44ba4e051dc2705a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_048283", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "preferences" + ], + "validator": "required", + "detail": "'event_type' is a required property" + }, + { + "type": "argument_schema", + "path": [ + "preferences" + ], + "validator": "required", + "detail": "'cuisine' is a required property" + } + ] + }, + { + "canonical_id": "fastbrain_048341", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage it again? \n#### ACTIVITIES \n119 COLLECTED WORKS \n**1.** For more about the relationships between revision, writing experience, and writing processes, see Alice Horning and Anne Becker's _Revision: History,_ _Theory, and Practice_ (Parlor Press) and Linda Adler-Kassner and Elizabeth Wardle's _Naming What We Know: Thres", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:f73ba7fb5a3a03910f1b4cdee4b6d0d9", + "normalized_query_sha256": "b2c4a60f5cd9ec4ad355cad2733abc60c59dd0045ea848218969d142967c0a35", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:67c3c4f6-02a4-4d6c-a65f-fb3eb49ece80", + "source_record_id": "67c3c4f6-02a4-4d6c-a65f-fb3eb49ece80", + "request_group_id": "hermes_request:ffd1c7f5f9e3fdbfbfe287d157f953f32a85707334a5fa9c36a424158c5cafc1", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_048341", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_048703", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage audience to see how their _other_ latent beliefs (in this case, tacit acceptance of inhumane treatment of their country's less well-off) are not only wrong but immoral. That's a rather extreme example, but we can see how it applies to the roommate example as well. The roommates share a respect for the history that shapes the visi", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:7d78089eba12b02dbb00b7b13b2c06a9", + "normalized_query_sha256": "cec6993b07d323470855fbea8846789b4e4aad5e68c4b4407d2fc0a2d5785ae2", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:0a84b63a-e32d-4583-9fe7-3c45f516cc56", + "source_record_id": "0a84b63a-e32d-4583-9fe7-3c45f516cc56", + "request_group_id": "hermes_request:9b8a1569666c421ea540a367c4943c298bee57141a484ec991708d5f902fe799", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_048703", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_049032", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I have a collection of music on my iPhone that is currently quite disorganized. I'm in need of a mobile application that would enable me to efficiently manage and categorize my music library. Specifically, I'm looking for features that include automatic genre categorization, the ability to sort music by artist and album, and the functionality to create and maintain custom playlists. Ideally, the a", + "target": "search_music_organizer_apps", + "legacy_query_parent_id": "legacy_query:8017cba8d10f7522edfb481ab03d1e8d", + "normalized_query_sha256": "8cab331f783708c6c4852adfa87034a982963a23ba2f89cf479867810961fa94", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:c6093d66-768d-49dd-99c4-29c8ff6653c9", + "source_record_id": "c6093d66-768d-49dd-99c4-29c8ff6653c9", + "request_group_id": "hermes_request:38b3064717e1ee0b6fc5caf4d9d63c6ca0bf116fe81e73d17f5ebd47096787c3", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_049032", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "category" + ], + "validator": "type", + "detail": "None is not of type 'string'" + } + ] + }, + { + "canonical_id": "fastbrain_052279", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 4 2 4 - 1 - 7 4 \n \nC31+^ Find the null space of the matrix B, N (B). \n B = \n \n - 6 4 - 36 6 2 - 1 10 - 1 - 3 2 - 18 3 \n \nM45 Without doing any computations, and without examining any solutions, say as much as possible about the form of the solution set for corresponding homogeneous system of equations of each archetype tha", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:184bef289a2f345b5a42dcf8f22f78af", + "normalized_query_sha256": "5f5594fbfae76fd92a25d9387d598fb4ae0f048e6ece899ec1325cfd3a2ebb15", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:181a4420-1d5a-4304-bbc5-a5c297766807", + "source_record_id": "181a4420-1d5a-4304-bbc5-a5c297766807", + "request_group_id": "hermes_request:4909fe5d033714238ed3af307e7fd5886c93bf3a21ec7e5b9f4a6c3cd31ea3c7", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_052279", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_052623", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage # A First Course in Linear Algebra \n## Robert A. Beezer \n## University of Puget Sound \n## Version 3.40 \n## Congruent Press \nRobert A. Beezer is a Professor of Mathematics at the University of Puget Sound, where he has been on the faculty since 1984. He received a B.S. in Mathematics (with an Emphasis in Computer Science) from the", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:3bdf99f0223f60d94565e83f9a12c6d2", + "normalized_query_sha256": "ac73e0c6a358e094ac566b48d43821bc1ae67c1ecb4e708eb941ab885601e63f", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:e68a4eb3-9e56-4cab-897e-80be2d5c63a8", + "source_record_id": "e68a4eb3-9e56-4cab-897e-80be2d5c63a8", + "request_group_id": "hermes_request:2aa21c76867ee8c00ebe952d265ca1312b4a92ba53029c62222af27fe4925f0d", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_052623", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_052847", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage is an argument about the effects of global warming rhetoric, not global warming itself. It would break the standpoint rule to argue that the writer/ speaker does not believe in global warming. This is not the issue at hand. Similar to #3, this rule assures that the evidence you use must actually relate to your standpoint. Let's s", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:53b2a5fc3024fe621c99005486c88758", + "normalized_query_sha256": "13e5b6c890dc94d69a0ad8db4a33cbdb3a32944397661b23c1c3e707e04555f6", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:50613121-ffa0-4bde-97e7-be5391e11077", + "source_record_id": "50613121-ffa0-4bde-97e7-be5391e11077", + "request_group_id": "hermes_request:09c7e89c7cd74b8b1510dbc7f1240735572a8a7d0153092c585b2332e32c649c", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_052847", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_054312", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I need to update my colleague on the progress of our project and share the latest report with them. I want to send a text message directly to their desktop application to keep them in the loop. The message should include a summary of the recent milestones we've achieved and any upcoming deadlines. Along with the text update, I need to attach the latest project report in PDF format, which outlines ", + "target": "sendMessageWithAttachment", + "legacy_query_parent_id": "legacy_query:9627d3a373a043e9f7c28118eb6a7313", + "normalized_query_sha256": "862c83061cfbf566d8c5ae6c210c9a9fcdc77db71c75d9f280b36a6f5e2d0c41", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:cfdee00a-6fc9-4326-8552-bf87a37ed04b", + "source_record_id": "cfdee00a-6fc9-4326-8552-bf87a37ed04b", + "request_group_id": "hermes_request:d0355d13d498f21d2776fc98eb20c5cca4c62814fe8148bb3f45b5c116a465d2", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_054312", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "recipient" + ], + "validator": "enum", + "detail": "'colleague@example.com' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "subject" + ], + "validator": "enum", + "detail": "'Project Update - Milestones and Next Steps' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "body" + ], + "validator": "enum", + "detail": "\"Hi team, please find the attached project report detailing our recent progress and the upcoming tasks we need to focus on. Let's aim to discuss this in our next meeting.\" is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "attachmentPath" + ], + "validator": "enum", + "detail": "'C:/Users/MyName/Documents/ProjectReport.pdf' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_054919", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage a great variety of emotions to support their argument, from fear, passion, and joy to pity, kinship, and rage. By playing on the audience's feelings, writers can increase the impact of their arguments. There are two especially effective techniques for cultivating pathos: \n**1.** **_Make the audience aware of the issue's relevance", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:ce254165e7c7d6734e3d6f7560857e5d", + "normalized_query_sha256": "7428f378f3ab6148ecc8a1bbb89e52fd286f47a3843498835ca4d57de71717f0", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:d579b07a-bc0a-4786-9206-aac8bdb3d7d2", + "source_record_id": "d579b07a-bc0a-4786-9206-aac8bdb3d7d2", + "request_group_id": "hermes_request:b22bd46971f8c604e5c2b6b56e8be4353ed7ce153ac3f91d5d181ac2c4f1e4d6", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_054919", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_056956", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I've recently had a productive meeting with my project team where we discussed the important timeline and deliverables for our upcoming project. I would like to create a new note in my desktop application titled 'Meeting Summary' and populate it with the details from this discussion so that I can reference them later. Additionally, I have an existing note with the ID '12345', which needs to be upd", + "target": "create_note", + "legacy_query_parent_id": "legacy_query:6084826b605ec7eb2472b2627bcd9c7d", + "normalized_query_sha256": "383eb889d55ceb3cddf9ce32571d7384fcce71cae1c7e4eefe4626bb1630f761", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:ebd8b80e-1673-4393-9ee1-25310990dd6a", + "source_record_id": "ebd8b80e-1673-4393-9ee1-25310990dd6a", + "request_group_id": "hermes_request:bd1c7ea29adc9bb356f55b36b5dd8e1a985aaa38ae0ad7a71973986e94180413", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_056956", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "title" + ], + "validator": "enum", + "detail": "'Meeting Summary' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "content" + ], + "validator": "enum", + "detail": "\"This note summarizes the key points from the project team's recent meeting. The important timeline and deliverables discussed for the upcoming project are outlined in detail to serve as a reference for future work.\" is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_057143", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage Let's look at the **weak form** first. It says: \n1. _If_ a predicate is true for a certain number, \n2. _and_ its being true for some number would reliably mean that it's also true for the next number ( _i.e._ , one number greater), \n3. _then_ it's true for all numbers. \nAll you have to do is prove those two things, and you've", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:1fd4d7737a35b35dca52bb72293d74b6", + "normalized_query_sha256": "0f9a90cca0a969e5925b1e2cabf386b3361f351cdec2a18facd2672781779808", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:c3348b81-bedd-4f20-a873-8cc099b979d6", + "source_record_id": "c3348b81-bedd-4f20-a873-8cc099b979d6", + "request_group_id": "hermes_request:f1a425e29309abffada2b77a52c2f1e05a68e477e7498079ad1e32b7cd007acb", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_057143", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_057350", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Our company has recently concluded an employee satisfaction survey, collecting feedback from various departments. I have collated the results, including identifiable data, department allocations, specific ratings for the work environment and management, as well as any remarks provided by the staff. I wish to delve into this data to extract insights on the overall employee sentiment towards their w", + "target": "analyzeEmployeeSurvey", + "legacy_query_parent_id": "legacy_query:38bf6586b8f2ec9dd82acd1bbc245e0b", + "normalized_query_sha256": "9861f2509dd5bfb00ab365b15ac52211c5d04f71677dadd936e87d0bcf9b3f38", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:282d3700-8f16-44b1-bfa0-46d4bbc5817a", + "source_record_id": "282d3700-8f16-44b1-bfa0-46d4bbc5817a", + "request_group_id": "hermes_request:8c5e22b098eda19570ff2f4dd049380f05dbd005fe3ca4e56cbab889e493a051", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_057350", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "analysis_results" + ], + "validator": "required", + "detail": "'average_ratings' is a required property" + } + ] + }, + { + "canonical_id": "fastbrain_058529", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage . It's like saying, \"I've noticed you are very concerned about X; I have a thought about that subject too.\" If she only included those somewhat vague references to other writers, then the introduction would be weak, but the quotation from Downie and Schudson introduces a specific pressing question that others feel must be answere", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:9e6515fc8715861dc86df4bebad60a8f", + "normalized_query_sha256": "496d1866b531c5fc1c4d0826dacdabd673c8a4cd1502c9340a280a60437b3c7c", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:27235d1b-226b-4cd3-80ee-a15543b9b30b", + "source_record_id": "27235d1b-226b-4cd3-80ee-a15543b9b30b", + "request_group_id": "hermes_request:f962272ba6111345e96f0ced009303b35ed928900073b5170932e64625528532", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_058529", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_059264", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 1 2 1 2 1 1 8 1 1 0 5 ] and work to reduced row-echelon form, first with j = 1, - 2 R 1 +R 2 ------- [ 1 - 1 2 1 0 3 - 3 6 1 1 0 5 ] - 1 R 1 +R 3 ------- 1 - 1 2 1 0 3 - 3 6 0 2 - 2 4 Now, with j = 2, (^13) R 2 ---- 1 - 1 2 1 0 1 - 1 2 0 2 - 2 4 (^) -^1 -R-^2 +--R-^1 1 0 1 3 0 1 - 1 2 0 2 - 2 4 - 2 R 2 +R 3 ------- ", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:930fd0e18bdcf2bb47e52fd7a927abdf", + "normalized_query_sha256": "1da6761ef6fa20348bea343aae365a450dbb9e3dc5a58c592e83f8cc8b52e6d9", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:14cd0793-09b4-4b9b-b3aa-e7b4c19fcf37", + "source_record_id": "14cd0793-09b4-4b9b-b3aa-e7b4c19fcf37", + "request_group_id": "hermes_request:2f44c7fa5d7bc201365bba710a63b0fa7109413f937f7c8cdb1dc8b79811bbe3", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_059264", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_060192", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage Lille^ Strasbourg \n 250 200 250 150 \n 600 150 300 \n 300 \n 450 \n 150 \n 300 \n 350 \n 500 \n 400 \n 200 \n 350 400 650 \n 2 \n Vichy^450 Lyon Briancon^ Toulouse^200 Marseille^ Nice^ \n Bordeaux^0 \n Nantes^150 \n Paris Lille^ Strasbourg \n 250 200 250 150 \n 600 150 300 \n 300 \n 450 \n 150 \n 300 \n 350 \n 500 \n 400 \n 200 \n 350 400 650 \n 3", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:2973936effa180c38beb629b718c426e", + "normalized_query_sha256": "d94801268e309cb860ddac9c7f8599f4a5fe52df744b984e76f47a053dd0fe38", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:4af09c05-15be-4bd8-8b6b-69af51e0f27e", + "source_record_id": "4af09c05-15be-4bd8-8b6b-69af51e0f27e", + "request_group_id": "hermes_request:061596c766c24d19db1be22760cea3a7bf5d913502fb67e4991b884305b1992d", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_060192", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_064217", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'm currently organizing my digital workspace and need to compress a folder containing important project files for archiving and easy distribution to my colleagues. The folder is located on my desktop and is named \"ProjectData.\" Inside, it contains various CSV files related to financial analysis reports and client data. I would like to compress this entire folder into a single ZIP file named \"Data", + "target": "compress_folder_to_zip", + "legacy_query_parent_id": "legacy_query:5c40a48589bccfc2912e68194d77a57d", + "normalized_query_sha256": "a4d21b1244ea91e1e077fd49e9d0ed17a527f7ed97367113cf0a3e69dbf1b9a4", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:3d5d918b-c63c-422f-901f-11eaf2f08d06", + "source_record_id": "3d5d918b-c63c-422f-901f-11eaf2f08d06", + "request_group_id": "hermes_request:f2f33d6c70446a9769110ef11ca2eed96e4f8ce90829a9551cf3f3a8dd091450", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_064217", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "source_folder_path" + ], + "validator": "enum", + "detail": "'C:/Users/Username/Desktop/ProjectData' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "destination_zip_path" + ], + "validator": "enum", + "detail": "'C:/Users/Username/Desktop/DataBackup.zip' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_064337", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage or papers. Was this thesis-first or inquiry-based research? What were the benefits or drawbacks of this approach? What led you to that method? \n**2.** Imagine you had all the time in the world to research a topic that you were personally passionate about. How might your approach to research be different than in a ", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:37d52c47aee9891bcec581ecb9832666", + "normalized_query_sha256": "ade604b8fbcb4a786b8f96b39fda366b42626d562fd050ad57c43019dc8491c6", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:329425b8-b141-495f-8a49-cbaed415bb96", + "source_record_id": "329425b8-b141-495f-8a49-cbaed415bb96", + "request_group_id": "hermes_request:21ff603c2bfb0791f7149162ae65d2150e815f002275d42ebbddbcaac1acde43", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_064337", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_065901", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage two values (0 or 1), so by the Fundamental Theorem of Counting, there are 28 different combinations. This works out to 256, but we can't actually store the number 256 in a byte if we're using the bit pattern 000000002 (or 0016 ) to represent zero. The highest value would be 111111112 (or FF 16 ), which is 25610. How do we store a", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:da357a5f855a36712628a98784833bc5", + "normalized_query_sha256": "73b902aa6b2757b0957094b350b4f73ba431853de7faeebc8d3e6b0aadb6612b", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:b9e713e3-122d-4403-9484-d4249296fd9b", + "source_record_id": "b9e713e3-122d-4403-9484-d4249296fd9b", + "request_group_id": "hermes_request:a5e30b911468f17b282479f69258f827ecc988e10983115b78eae7d7f0735de0", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_065901", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_066260", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage Stephen Davies, Ph.D. \n Version 2.2.1 \n##### Through Discrete Mathematics \n## A Cool Brisk Walk \nCopyright (c) 2023 Stephen Davies. \nUniversity of Mary Washington Department of Computer Science James Farmer Hall 1301 College Avenue Fredericksburg, VA 22401 \nPermission is granted to copy, distribute, transmit and adapt this work u", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:7d3296195a17b559cccfb195c24e4cf3", + "normalized_query_sha256": "0b03c94ace0e836abbc0902579d5f34cf981302cad4c3ad049e52dcef7a91627", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:a161982c-0f80-47cc-838c-40461d2e5ca4", + "source_record_id": "a161982c-0f80-47cc-838c-40461d2e5ca4", + "request_group_id": "hermes_request:c59907df836f76ab3b03894daf8a9a70ac39fc4b26319ca6a8b2edd1bc52b60f", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_066260", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_066817", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 1 0 3 - 2 0 0 1 1 - 3 0 0 0 0 0 0 \n \nBy Theorem HSC, the system is consistent, and so the computation n - r = 4 - 2 = 2 means the solution set contains two free variables by Theorem FVCS, and hence has infinitely many solutions. We can describe this solution set using the free variables x 3 and x 4 , \n S = \n \n \n \n x 1 x 2", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:74970e607fc3e5d07688b380dd2c7e2f", + "normalized_query_sha256": "95ee878436a9858ba6b4c93cdae691abae0d31dedc509e6537ecb89de274dc95", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:56f98bf3-940b-465d-bcfc-294a5f38563b", + "source_record_id": "56f98bf3-940b-465d-bcfc-294a5f38563b", + "request_group_id": "hermes_request:ce659af872b3eb4549ee26b993fcbc0a9dfa8f3778bfb040be22d684866e6f12", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_066817", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_067177", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage Let's be crystal clear where we're going with this. Assuming that P(k) is true means we can count on the fact that \n lk = ik + 1. \n What we need to do, then, is prove that P(k + 1) is true, which amounts to proving that \n lk+1 = ik+1 + 1. \n We begin by noting that the number of nodes on level k of a perfect binary tree is 2 k", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:8ac0fde58b82442f4458e6c8a51ad631", + "normalized_query_sha256": "ce66a9fac043698ad1c84a4804e6b6be268429108d4a008f08bc86d8b3bcaa1d", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:63882beb-50a3-4096-95ee-c8e763f205d3", + "source_record_id": "63882beb-50a3-4096-95ee-c8e763f205d3", + "request_group_id": "hermes_request:c181295b56a7bad09327440d47aef7cefcf25beff0041d131427aca36d0ae6fc", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_067177", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_068616", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I've recently added some new applications to my iPhone and I'm looking to tidy up my app icons. Can you provide me with a list of all productivity-related apps currently installed on my device? Additionally, I've been managing quite a few documents lately and I suspect there might be some duplicates cluttering my system. Could you search through my iPhone's 'Downloads' directory and identify any d", + "target": "list_category_apps", + "legacy_query_parent_id": "legacy_query:409ae35ad8502034eb8fd3b50fb91745", + "normalized_query_sha256": "3a8e584c0a269174d89173257e71fa60438506527b909d5a8cb190c6b86626d6", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:697351e0-3274-4aa7-b6a4-7329bb871324", + "source_record_id": "697351e0-3274-4aa7-b6a4-7329bb871324", + "request_group_id": "hermes_request:2d199ea1783369af2ff71bbfadabc07d07d12b138079a18276cc98bf6913d4d4", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_068616", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "category" + ], + "validator": "enum", + "detail": "'Productivity' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_069052", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage know it makes all the other equations of the original system true. We then find aj 1 b 1 + * * * + ajnbn = aj 1 b 1 + * * * + ajnbn + abi - abi = aj 1 b 1 + * * * + ajnbn + (aai 1 b 1 + * * * + aainbn) - abi = aj 1 b 1 + aai 1 b 1 + * * * + ajnbn + aainbn - abi = (aai 1 + aj 1 )b 1 + * * * + (aain + ajn)bn - abi = abi + bj - abi ", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:758b5b4930dfa6e01a8895361276dbca", + "normalized_query_sha256": "e1491937243f7539885f22125dca8d027478c883a99d0694d33694f6c0bcede8", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:ad105e11-f880-42f8-858d-26ffee84b4f1", + "source_record_id": "ad105e11-f880-42f8-858d-26ffee84b4f1", + "request_group_id": "hermes_request:5612c7c320446ef7ccfea9f7b53758b14dbc69d8c7c40a7a03025943b246272a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_069052", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json", + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_069738", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I need assistance with managing Chrome browser extensions across our company’s network. The task involves installing the Microsoft Defender Browser Protection extension on all Chrome browsers within the organization. The web store URL for the Microsoft Defender Browser Protection extension is `https://chrome.google.com/webstore/detail/microsoft-defender-browse/bkbeeeffjjeopflfhgeknacdieedcoml`. Af", + "target": "install_browser_extension", + "legacy_query_parent_id": "legacy_query:2704c77232286d54589da48957c48697", + "normalized_query_sha256": "02c130f60de49b81b2ad11b1d5cab37aa3621e0e733b082f09f6f2950295fdad", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:6f1d2e3d-b3e3-4bf6-bfcd-133b4565f6cf", + "source_record_id": "6f1d2e3d-b3e3-4bf6-bfcd-133b4565f6cf", + "request_group_id": "hermes_request:2a24b3a3b0aa093b4bfe1442a7fd984b485b6f0849f54df9fc73dcc8b5f9dcdd", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_069738", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "browser" + ], + "validator": "enum", + "detail": "'Chrome' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "extension_id" + ], + "validator": "enum", + "detail": "'bkbeeeffjjeopflfhgeknacdieedcoml' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "extension_url" + ], + "validator": "enum", + "detail": "'https://chrome.google.com/webstore/detail/microsoft-defender-browse/bkbeeeffjjeopflfhgeknacdieedcoml' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "browser" + ], + "validator": "enum", + "detail": "'Chrome' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "extension_id" + ], + "validator": "enum", + "detail": "'bkbeeeffjjeopflfhgeknacdieedcoml' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "browser" + ], + "validator": "enum", + "detail": "'Chrome' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_069842", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage do, and they found the analysis to be well constructed. Thus, you can accept the findings as legitimate and just focus on the passages that explain the findings and their significance in plainer language. If you have a really good article that's a few years old, you can use article databases to find newer articles that cited it i", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:e0049143e64efb0cc5e3b9bc856ad7a9", + "normalized_query_sha256": "7e3b59b249ef5c043cea96d7e23cdab9b6eb67f3bc3e7219f383d0cd3a60f52e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:5177fe0e-a3d4-4621-a77b-9aa5b4086a56", + "source_record_id": "5177fe0e-a3d4-4621-a77b-9aa5b4086a56", + "request_group_id": "hermes_request:1effd7aeba84ab07a9f969dba4fa62ff6ac0fdf63dfe038fd3268110b1aa2c6a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_069842", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_069917", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage is mapped to by _at most_ one x; with a surjective function, every y is mapped to by _at least_ one x; so with a bijective function, every y is mapped to by _exactly_ one x. Needless to say, the domain and the codomain must have the same cardinality for this to be possible. \n50 CHAPTER 3. RELATIONS \n The function ", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:36509c50dec93580bb8b0e96cbf8feb4", + "normalized_query_sha256": "1c2e22c654a91ac5c65e55f2df0d4f412ec01a2386ab975f2e5c8aa39491b1fe", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:3697c0c4-a2d4-4522-8dc6-24ae30d6e8dd", + "source_record_id": "3697c0c4-a2d4-4522-8dc6-24ae30d6e8dd", + "request_group_id": "hermes_request:d75e93e3e3be109e49596ed9f7596192edfd5e8b74e9f75c00f4ed985cd342d2", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_069917", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_070360", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am overseeing IT operations for our company and need to monitor the disk space on all computer systems in the network to prevent any storage issues. I require detailed disk space usage information for all types of drives, including local, removable, and network disks. For effective management, it's important to know the current free space, total size for each drive, the drive letters, as well as", + "target": "get_disk_space_usage", + "legacy_query_parent_id": "legacy_query:a3f00b778b63205bcc3dae1e0b980c61", + "normalized_query_sha256": "4adbd6d0a67f02c4af7af1c0bd5237e4908164cfab30919c8d80714f06f3bbaf", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:30229340-429c-499b-9753-94f271a5df25", + "source_record_id": "30229340-429c-499b-9753-94f271a5df25", + "request_group_id": "hermes_request:fba9712e20b8dc81771d532540cd08226384e35a4735598ec6b39d7597be638a", + "protected_effective_split": "validation", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_070360", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "computerName" + ], + "validator": "enum", + "detail": "'local' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "computerName" + ], + "validator": "enum", + "detail": "'local' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "computerName" + ], + "validator": "enum", + "detail": "'local' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_071601", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am developing an Android application that provides a feature for instant video calling. I'd like to incorporate a functionality where before initiating a video call, the app checks if the specific user is available. For instance, user A (with user ID 'userA123') wants to video call user B (with user ID 'userB456'). The application should first verify that user B is ready to receive a video call.", + "target": "check_user_availability", + "legacy_query_parent_id": "legacy_query:9f825b3e691651440012709780495966", + "normalized_query_sha256": "3656dd838ae6259f4124d4426585272e600cda40fa9634ad0bc5394aa4b6d918", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:b030c9ac-7f96-4232-81fb-7427c20382c3", + "source_record_id": "b030c9ac-7f96-4232-81fb-7427c20382c3", + "request_group_id": "hermes_request:aa4745998cd963cc778db33bcdfd4b6f957cc0a400da56739d33e25e04822983", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_071601", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "user_id" + ], + "validator": "enum", + "detail": "'userB456' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_072216", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage RREFU), \n 1 4 0 0 2 1 - 3 4 0 0 1 0 1 - 3 5 2 0 0 0 1 2 - 6 6 1 0 0 0 0 0 0 0 0 \n \nSo we find that r = 3 and D = {d 1 , d 2 , d 3 } = { 1 , 3 , 4 } F = {f 1 , f 2 , f 3 , f 4 , f 5 } = { 2 , 5 , 6 , 7 , 8 } Let i denote any one of the r = 3 nonzero rows. Then the index di is a pivot column. It will be easy in this case to", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:64591b39e04a94d3a6810febde01c375", + "normalized_query_sha256": "bc9f5386d19e91e08e17e66eb79ff84b398162f150743e9c7ab6b2453c982519", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:f2641cef-1cd6-418e-940a-0f2b31956b9c", + "source_record_id": "f2641cef-1cd6-418e-940a-0f2b31956b9c", + "request_group_id": "hermes_request:0022726af5e0816972bfed58603bc3918e85582c03b5fe7e7c02d8f011489e9e", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_072216", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_072541", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage a CC BY-NC-SA (Attribution NonCommercial ShareAlike) license \n#### WORKS CITED \n 79 COLLECTED WORKS \nFind the Good Argument by Rebecca Jones; Liz Delf; Rob Drummond; and Kristy Kelly is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. A DAM GOOD ARGUME", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:ffced48347d7e8379fe369c50d6f9785", + "normalized_query_sha256": "02fa4fdc7f7c9d6f10c28391b92a0cbc071e4598d97dfe25d11c3d99d6a8448c", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:81a8b207-9772-49c7-a51b-b98113d2b63b", + "source_record_id": "81a8b207-9772-49c7-a51b-b98113d2b63b", + "request_group_id": "hermes_request:11674e7263d2598ccd713f51c5e2210d292733946abc25098290c0cc4fed6582", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_072541", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_072710", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I need to create a backup of my project work by duplicating an entire folder from my desktop. The folder is specifically located at `/Users/username/Desktop/ProjectX`. I want this folder to be copied to my external backup drive, which is mounted at `/Volumes/BackupDrive/`. Within the backup drive, there's a directory named `Archives` where I need the duplicated folder to be placed.\n\nCould you plea", + "target": "copy_folder", + "legacy_query_parent_id": "legacy_query:19bfc27402d63febb33b7d4129bb42e6", + "normalized_query_sha256": "20e14e78be6c9392b20b8908fc1aee6e0f7b06d19bfb2be3bb51af50bcc85d62", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:31ea09a6-cd2b-4e70-b852-058e0e5a7d12", + "source_record_id": "31ea09a6-cd2b-4e70-b852-058e0e5a7d12", + "request_group_id": "hermes_request:d9a2073f4823cc3abd1d38e925324c9deb7165c132084a315abfcba6a8247edf", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_072710", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "source_path" + ], + "validator": "enum", + "detail": "'/Users/username/Desktop/ProjectX' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "destination_path" + ], + "validator": "enum", + "detail": "'/Volumes/BackupDrive/Archives/ProjectX' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_073565", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 1 + x^32 /x 4 - x 3 x 4 x^25 = 0 tan(ab) + log(c - d) = - 7 \n SSW I L A B e e z e r : A F i r s t C o u r s e i n L i n e a r A l g e b r a 2 \nThe exception will be that we will on occasion need to take a square root. You have probably heard the word \"algebra\" frequently in your mathematical preparation for this course. Most like", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:7a85467b3de2236b8c25b612bf5739ef", + "normalized_query_sha256": "539b9e481a4f34ec6d8afc8fda080e7ada3428479d9ba9412713d9c9939285f0", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:c1519e05-f59b-49ad-b245-26728361150a", + "source_record_id": "c1519e05-f59b-49ad-b245-26728361150a", + "request_group_id": "hermes_request:003cc68efd04a4f8f753ee2ea32c0ece48414cf64426d95f56e91f52e20ab689", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_073565", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_074038", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage Fig 16.3 How will your group develop feedback? \n##### Global Revision Activity For A Narrative Essay \n This assignment challenges you to try new approaches to a draft you've already written. Although you will be \"rewriting\" in this exercise, you are not abandoning your earlier draft: this exercise is generative, meaning it is des", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:608989ea9236d0e63fd9d868861c47e2", + "normalized_query_sha256": "befdc5c7cb39ca0be2d5b7743459ccd20c612f1278531f027727028ec29bf288", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:89b5bcf5-98d9-4bdb-927f-429a64bcc798", + "source_record_id": "89b5bcf5-98d9-4bdb-927f-429a64bcc798", + "request_group_id": "hermes_request:19f2ffe7f7259420fae363ce99ef45128744723901d3fada8fde7c2bbd29f18a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_074038", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_074847", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage , cardiovascular, digestive, and other systems, each of which is comprised of organs, then tissues, then cells, molecules, and atoms. In fact, anything that has this sort of part-whole containment hierarchy is just asking to be represented as a tree. \nIn computer programming, the applications are too numerous to name. Compilers s", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:c44a8b8b78245c65798adf37051f8cfe", + "normalized_query_sha256": "d1036cc04a8e77093ede9dd2ed170dbc3ddef12834985cf4d20535535d2acb33", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:6141353e-d120-463b-b4d2-50b0cca883ee", + "source_record_id": "6141353e-d120-463b-b4d2-50b0cca883ee", + "request_group_id": "hermes_request:5192fab0f19607724b17fc652c478209210af41410e85aac1e8b789d59b1b40f", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_074847", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_075874", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 16? (^10) to \"carry.\"^16. This is the first time we've had \n20. What's 1116 + 22 16? 3316. \n21. What's 1116 + 99 16? AA 16. \n22. What's 1116 + EE 16? FF 16. \n23. What's 1116 + EF 16? (^100) carry.)^16.^ (As in exercise 19, we must \n 7.5. EXERCISES 195 \n24. What's the binary number 10110011101010102 in hexadec- imal? Or is", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:0898ebd6c88b83955b5a88c87ca9cff6", + "normalized_query_sha256": "ce165c5eb519ca120a4217fcfc6c3ead361127414f4506fcf1bf382b0819f2cd", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:3c9e0b7f-75ff-43c6-a09c-92ed7d193551", + "source_record_id": "3c9e0b7f-75ff-43c6-a09c-92ed7d193551", + "request_group_id": "hermes_request:e7c5eec681c762d21e765ae0cbb961cf13ce34403edd4b6371f71aea800a2cef", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_075874", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_080319", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage do refer to the researchers as \"we,\" which is a personal pronoun but somehow slightly less intimate than \"I\"). Academic writing in the humanities and social sciences is somewhere in between these two extremes--depending on the subject and context, a writer can make their own choice. Many well-known scholars in these fields use an", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:35add645bc6debddad4840b39cb5db5a", + "normalized_query_sha256": "326a487467ad10b39cd720b3adf96b61e4ee50cf91a148e0bcb11a038a1cf546", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:bddaa293-32fc-4c85-91ec-d479e327b334", + "source_record_id": "bddaa293-32fc-4c85-91ec-d479e327b334", + "request_group_id": "hermes_request:4ae7b7674df4112e69a8f1a57b46af8102d24bedfc0516d067d54bd9e6e3bdec", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_080319", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_083328", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage . For instance, if I had drawn node I further to the right, in order to make the lines between D-O-I less steep, that I node might have been pushed physically to the right of K. But that wouldn't change the order and have K visited earlier. \nFinally, it's worth mentioning that all of these traversal methods make elegant use of **", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:9e66656622559100c8aa1f6c78ca0bd8", + "normalized_query_sha256": "378b6db143abea5371a6eac0b476d74dbfd4b949ca593d782e573542aa771c26", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:71996882-77f1-4a7c-be42-e59d551145d4", + "source_record_id": "71996882-77f1-4a7c-be42-e59d551145d4", + "request_group_id": "hermes_request:462084f59da2aae05dac5becbf53fbc5dded6ed5be63c80c054c91628cc92690", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_083328", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_084352", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage bets are off. The conclusion was conditional on the premise. \n The reason this operator is so important is that in artificial intelligence, the name of the game is concluding new facts from known existing facts, so that knowledge is increased. Every time a 'bot learns that X=Y is true, and then also learns that the premise (X) is", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:8eea262a734126e1c66f87789733f3fe", + "normalized_query_sha256": "39e37760e7edd1fb2867170b57d11feb3b9aaa5946a5714da15ec88ddf6fed16", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:4de3e63b-ce13-4780-815d-1bdf5a13ff1d", + "source_record_id": "4de3e63b-ce13-4780-815d-1bdf5a13ff1d", + "request_group_id": "hermes_request:c553bfec901015cb55d66174183d1718181f6a94abba0cc281be882993ce72f0", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_084352", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_084852", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 227 \nto be true, and so it is legal grounds from which to start. A proof can't even get off the ground without axioms. For instance, in step 1 of the above proof, we noted that either A or B must be true, and so if B isn't true, then A must be. But we couldn't have taken this step without knowing that disjunctive syllogism is a v", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:df07fbbca0dd8975ebb57797b7d8deab", + "normalized_query_sha256": "e1b981d2706a2c043af74220733b0d648a6a395064de57ee3d161c1a17c6c45b", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:fc8fae7d-0b23-42db-89a1-3777a8303df5", + "source_record_id": "fc8fae7d-0b23-42db-89a1-3777a8303df5", + "request_group_id": "hermes_request:d1a3e9b33d53a4a7ca1faec0ad2bcf28c063761c4e6b40fb8af467872f40fe96", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_084852", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_086394", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I have captured a variety of wildlife images and would like to identify the species present in one particular image. The image is hosted online at 'https://mywebsite.com/uploads/eagle-in-the-wild.jpg'. I have selected the MobileNetV2 model from Torch Hub for its balance between accuracy and computational efficiency, and I want to utilize this pre-trained model to classify the species in the image.", + "target": "load_model_from_torch_hub", + "legacy_query_parent_id": "legacy_query:bb3e9be51dcf4fff836d50004fc0d9a5", + "normalized_query_sha256": "1951785ae20a142d3405c538441025732e072db5ddca589dfbece8dd052698e1", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:43228856-d6c0-47bd-9f1a-cd03c7698cd5", + "source_record_id": "43228856-d6c0-47bd-9f1a-cd03c7698cd5", + "request_group_id": "hermes_request:7fd741e9b0af4de8458aef1127c99196d0bff68abdb7919b2b544c6030a53ab0", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_086394", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "model_name" + ], + "validator": "enum", + "detail": "'mobilenet_v2' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "pretrained" + ], + "validator": "enum", + "detail": "True is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "model" + ], + "validator": "enum", + "detail": "'$functions.load_model_from_torch_hub.result' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "image_url" + ], + "validator": "enum", + "detail": "'https://mywebsite.com/uploads/eagle-in-the-wild.jpg' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_086791", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage 4 .45) = - 1. 04 f + 3663 Since f has a negative coefficient it would appear that mixing fancy mix is detrimental to your profit and should be avoided. So you will make the decision to set daily fancy mix production at f = 825. This has the effect of setting b = 4(825) - 3300 = 0 and we stop producing bulk mix entirely. So the re", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:577e055b59815e7db46150af80edfba0", + "normalized_query_sha256": "0949c99275e908c7277bdfb8a882d77a08a06917f812db14c96997ee6c693c17", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:2f499113-c615-46b4-8d1d-30b602f98b61", + "source_record_id": "2f499113-c615-46b4-8d1d-30b602f98b61", + "request_group_id": "hermes_request:e3fa0ce7f30a7763f7dafa799e9d2a03ba904138e8bf16ee9be6922209661ed0", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_086791", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_087260", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to update the phone number for my contact John Doe on my Android device. Currently, his phone number is listed as 555-1234. I have his contact details, which include his name \"John Doe\", his original phone number 555-1234, his email address johndoe@example.com, and his address at 123 Elm Street. His contact ID in my Android device's contact list is 12345. I need to change his existing", + "target": "update_contact_phone_number", + "legacy_query_parent_id": "legacy_query:1fb9965c656decf6495c9c19260f5d41", + "normalized_query_sha256": "f86aa8ba6c3a4b5cb31485d312606d39b1611bee9ead95025761b4ee7fb6a048", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:1921ae94-454c-4ca8-b58e-32399c3a38c7", + "source_record_id": "1921ae94-454c-4ca8-b58e-32399c3a38c7", + "request_group_id": "hermes_request:e3d23c54db2a75f6cfd133ca9d503f02d4203ea5f5769ebb681ce7a59a772af5", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_087260", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "contact_id" + ], + "validator": "enum", + "detail": "'12345' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "new_phone_number" + ], + "validator": "enum", + "detail": "'555-5678' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_089369", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage it contain any sets at all, in fact). \n11. Is S J? \n No. \n12. Is G F? Yes, since the two sets are equal. \n13. Is G F? \n No, since the two sets are equal, so neither is a proper subset of the other. \n14. Is S? \n Yes, since the empty set is a subset of every set. \n15. Is ? \n Yes, since the empty set is a subset of every ", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:66dfb5b9645e30a55bf605a1064d09a4", + "normalized_query_sha256": "dd9007a45a311c7db5ceb80aa527b6aba6b08ab925fcb27137e2b0c29f4fc4d2", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:6692cec7-73d4-42f3-8651-a6201d7ccd81", + "source_record_id": "6692cec7-73d4-42f3-8651-a6201d7ccd81", + "request_group_id": "hermes_request:80525f5e36fa11674cc9621654c1bd9b9b892dc4c47a173440de7c4602147099", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_089369", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_090206", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I have a Java Swing application, specifically a file named `HelloWorldSwing.java` located in a subdirectory called 'start'. I need to compile this Java file and execute the compiled program, but I want the execution to happen from the parent directory of 'start'. I assume there is already a compiled version of this Java program in the parent directory. Could you assist me by calling the appropriat", + "target": "compile_java_file", + "legacy_query_parent_id": "legacy_query:9e93a13e32c967fe3f4b9e338160aa23", + "normalized_query_sha256": "39b29aadef3576c98f5996e0fe18d39dbcefe1ba30a9c694f7f1fcc53e53f84e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:3b2ab99b-4050-43c3-9d59-88dbeed46acf", + "source_record_id": "3b2ab99b-4050-43c3-9d59-88dbeed46acf", + "request_group_id": "hermes_request:8598e16aa0525002af2dd6276835ac898059281db27af88b83b6fa13d18e7e0f", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_090206", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "file_path" + ], + "validator": "enum", + "detail": "'start/HelloWorldSwing.java' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "output_directory" + ], + "validator": "enum", + "detail": "'.' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "class_path" + ], + "validator": "enum", + "detail": "'.' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "class_name" + ], + "validator": "enum", + "detail": "'HelloWorldSwing' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_090223", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage your \n##### position \n##### (something that you present as a \n##### perspective ) \n Women are unfairly criticized on social media. A peer-reviewed scholarly article: \n##### Sills, Sophie, et al. \"Rape Culture and Social \n##### Media: Young Critics and a Feminist \n##### Counterpublic.\" Feminist Media Studies , vol. \n##### 16, no. ", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:02d2c9e7413427498713ffdfb9ff4932", + "normalized_query_sha256": "39c985cb4938480b731922c81c0f103affd8b66c7406a12b86b133d6b119e9ed", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:6e4394cf-f5f1-4311-872d-42305551c9cd", + "source_record_id": "6e4394cf-f5f1-4311-872d-42305551c9cd", + "request_group_id": "hermes_request:078e19a50482d5d48c612d8adcd44fddb809d13e877b269ec9e507b63159605a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_090223", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_090519", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'd like to enhance my in-car entertainment experience by watching a movie from Netflix on the car's infotainment system. To accomplish this, I need to connect my smartphone, with the ID \"smartphone123\", to the car's infotainment system, which has the ID \"infotainment456\". The connection should be established using an HDMI cable. After the connection is made, I would like to initiate the Netflix a", + "target": "connect_smartphone_to_infotainment", + "legacy_query_parent_id": "legacy_query:ddec30ddfeec05c9865361ce028958ce", + "normalized_query_sha256": "67c13f44560152101e907535f75ac9c894c5fd93edbde790747400acedc4d443", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:fb28a02b-4eaa-41a5-a1b8-b9f4556b6231", + "source_record_id": "fb28a02b-4eaa-41a5-a1b8-b9f4556b6231", + "request_group_id": "hermes_request:58cb05a8d5b3bbdc05112c08da73a6c39f6bc3ecd84c6b4936ab5374906fda59", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_090519", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "smartphone_id" + ], + "validator": "enum", + "detail": "'smartphone123' is not one of []" + }, + { + "type": "argument_schema", + "path": [ + "infotainment_id" + ], + "validator": "enum", + "detail": "'infotainment456' is not one of []" + }, + { + "type": "argument_schema", + "path": [ + "infotainment_id" + ], + "validator": "enum", + "detail": "'infotainment456' is not one of []" + } + ] + }, + { + "canonical_id": "fastbrain_090984", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently preparing for a trip to Spain and will need to interact with Spanish speakers while I'm there. To facilitate communication, I would like to translate some essential English phrases into Spanish. Specifically, I would like to get translations for the phrases \"Where is the nearest hospital?\" and \"How much does this cost?\" on my iPhone. Could you assist me by using the translation tool", + "target": "translate_text", + "legacy_query_parent_id": "legacy_query:57749ff7612570f000a914fa175cba78", + "normalized_query_sha256": "606011f4367b11feec2d89929626a97fc510f2bc0976489200ef43f64d4ee308", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:1e8d1165-b219-40e4-9663-bd06d06c3420", + "source_record_id": "1e8d1165-b219-40e4-9663-bd06d06c3420", + "request_group_id": "hermes_request:653fe2a73a27cb0642b6080120152dcf9afe06e7851457af5505b9bbf23f5e5a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_090984", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "source_language" + ], + "validator": "enum", + "detail": "'en' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "target_language" + ], + "validator": "enum", + "detail": "'es' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "text" + ], + "validator": "enum", + "detail": "'Where is the nearest hospital?' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "source_language" + ], + "validator": "enum", + "detail": "'en' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "target_language" + ], + "validator": "enum", + "detail": "'es' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "text" + ], + "validator": "enum", + "detail": "'How much does this cost?' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_091373", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage possible? \n Ignoring the at-least-one-child-andadult constraint for the moment, the total number of groups would seem to be \n ( 17 3 \n ) + \n ( 17 4 \n ) + \n ( 17 5 \n ) = 680+2380+ 6188 = 9, 248 possible groups. But of course this is an overcount, since it includes groups with no children and groups with no adults. We'll use the tr", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:3db2e78e837d857accc173d49774a9f7", + "normalized_query_sha256": "30dcda6d766b4eea7a1be9d9267295df8b92e82f02471aa9ad58cdf9893e99ef", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:a885d2c8-ff74-4265-b0f4-2ddc9efc7cc4", + "source_record_id": "a885d2c8-ff74-4265-b0f4-2ddc9efc7cc4", + "request_group_id": "hermes_request:25c7c2c39fa9e4c56bf2db8d16d555527c66105205be0a13dc29f91ae6fe03b2", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_091373", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_092413", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage . This last point is often not fully appreciated, leading to errors. Be careful not to cavalierly add the cardinalities of nonmutually-exclusive sets! You'll end up double-counting items. \nSo we know that the number of possible license plates is equal to: \n the # of 7-character plates + the # of 6-character plates + the # of 5-ch", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:470b791f95a56ffc5c2860e4229e09e3", + "normalized_query_sha256": "d6c3b810bc1143fd28c2af5f5bb31598b4e744fa64fe5ef67d991b2cc93305cf", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:f86f70af-5a84-4be1-80b8-221db22068a3", + "source_record_id": "f86f70af-5a84-4be1-80b8-221db22068a3", + "request_group_id": "hermes_request:3edf52f1ef1189339a7176426b25e5a0f3d0176d7f9b984bdff71f5679dd5cc1", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_092413", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_092661", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage instance change Sam from basketball to volleyball. Now both of the codomain elements are \"reachable\" by some domain element, so it's surjective. \n33. Is faveSport now also bijec- tive? \n No, because it's still not injective. \n34. How can we alter things so that it's bijective? \n One way is to add a third sport -- say, kic", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:06352483c9dc1d5ca821c7ca31ee71ba", + "normalized_query_sha256": "fa402dff71bf7e4267da276dd439d25b0dd1980f84d8a393d1fb355109fd09ae", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:2d96f513-4625-4c28-b295-549bb0b704c9", + "source_record_id": "2d96f513-4625-4c28-b295-549bb0b704c9", + "request_group_id": "hermes_request:a72c55de27236212e1306db4ed2d865f21aae7e8e88df3713e07ff4817750274", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_092661", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_097266", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Can you help me extract queries from the following passage paragraph theme (figure 12.1), it hardly matters which of the three reasons you explain first or second. In the more organic structure (figure 12.2), each paragraph specifically leads to the next. The last key difference is seen in the conclusion. Because the organic essay is driven by an ambitious, nonobvious argument, the reade", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:61f0138e9cbed780be78f23966aafa2f", + "normalized_query_sha256": "014f07705b2360c47105bdee5d90f30ee839fa8905a5e72f5ef158fcc7278602", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:62cfb91c-35e2-4b04-ae6e-dca98205e4be", + "source_record_id": "62cfb91c-35e2-4b04-ae6e-dca98205e4be", + "request_group_id": "hermes_request:a439711033a50ff66142407e1782083afd292dc3ba7b697a9a474a9a5f11e74c", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_097266", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_097324", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'm in the process of redecorating my living room and I'm searching for an app that offers robust features to assist in this endeavor. Specifically, I need an app that allows me to create a comprehensive virtual layout of the space, complete with the ability to adjust wall heights for a more accurate depiction. Additionally, the app should have a vast library of customizable furniture and accessor", + "target": "find_home_decor_app", + "legacy_query_parent_id": "legacy_query:2513b1852c00b3db045424d44bbcdd73", + "normalized_query_sha256": "83bf7eff4e08011ead56fea60e1167b1fb20e0cb24cf87cba65e141b8cc3da0e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:c5b090ae-6826-4b5c-8f2b-f440e85e97a6", + "source_record_id": "c5b090ae-6826-4b5c-8f2b-f440e85e97a6", + "request_group_id": "hermes_request:e8fd3a69b7c69f0b6d0edb798e7295b38582e2d7946541d90919d64a3be31c47", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_097324", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "category" + ], + "validator": "type", + "detail": "None is not of type 'string'" + } + ] + }, + { + "canonical_id": "fastbrain_099783", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "I'm looking to explore some of the latest mobile gaming trends and would like to play the most popular games on my computer since I have more time on the weekends. Based on current trends, could you provide a list of five trending mobile games that are capturing players' attention? I am particularly interested in strategy, puzzle, and adventure genres. Once I have the list, I would also appreciate", + "target": "get_trending_games", + "legacy_query_parent_id": "legacy_query:21551f42d02c72db5040916a42043946", + "normalized_query_sha256": "ab82859fb8a9f6b36b10cb886aae7534a682839733c350247258a009a0204421", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:d7c08a19-cf6d-4f1a-97d5-20e17dcdd903", + "source_record_id": "d7c08a19-cf6d-4f1a-97d5-20e17dcdd903", + "request_group_id": "hermes_request:41b9fd512a7e74499c3bb28ce7953a7962d8fbc0a94b108eb7d1f3eddb353c5d", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_099783", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "category" + ], + "validator": "type", + "detail": "None is not of type 'string'" + }, + { + "type": "argument_schema", + "path": [ + "number_of_games" + ], + "validator": "enum", + "detail": "5 is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "game_list" + ], + "validator": "enum", + "detail": "['Lords Mobile', 'Color by Numbers', 'Minecraft', 'Eversoul', 'Soul Knight Prequel'] is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_101452", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "I have a comprehensive research paper on the topic of 'Neural Networks in Autonomous Vehicles', spanning roughly 50 pages in length. This paper explores the integration of neural networks in the development of autonomous driving technology, focusing on key aspects such as object detection, path planning, and the mitigation of environmental impacts on sensor reliability. Due to time constraints, I ", + "target": "summarize_large_document", + "legacy_query_parent_id": "legacy_query:647a816931ed165388752ea8b592af00", + "normalized_query_sha256": "3c28192d4f38e4dfe91e4b988df7c2cad58089ce55f51816005b6ffe19be5d5a", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:90b179bf-aa92-4b1a-a844-5a1b2b9f2381", + "source_record_id": "90b179bf-aa92-4b1a-a844-5a1b2b9f2381", + "request_group_id": "hermes_request:d9141ad43a2f1d5aa99d4082afb2e604d9acabf9e0db38f66c4167a8f572dc16", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_101452", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "model" + ], + "validator": "type", + "detail": "None is not of type 'string'" + }, + { + "type": "argument_schema", + "path": [ + "document" + ], + "validator": "enum", + "detail": "'Neural networks play a crucial role in the development of autonomous vehicles, providing the ability to process and interpret vast amounts of sensory data for real-time decision making. Key findings indicate significant advancements in object detection and path planning, with ongoing research focusing on improving the robustness and reliability of these systems under varied environmental conditions.' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "min_length" + ], + "validator": "enum", + "detail": "80 is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "max_length" + ], + "validator": "enum", + "detail": "150 is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_102619", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "Can you help me extract queries from the following passage blinked, she sighed. \n**7.** Rewrite each of the following sentences in three different ways (same content and ideas, just a different order). Which version do you like best? \n#### ACTIVITIES \n A DAM GOOD ARGUMENT 212 \n She walked the dog past the empty post office, and the dog barked twice. The soup may be brothy, but it i", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:22cd4bed4f14c11166656fd07d9bea5f", + "normalized_query_sha256": "2a92d5e15757cb60255bb7d74e39af464e1e45111f6e01e4d255178674559f1e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:9b0f3c57-d9f5-4d93-9879-58290e0fca05", + "source_record_id": "9b0f3c57-d9f5-4d93-9879-58290e0fca05", + "request_group_id": "hermes_request:e1f0047a3f82040e0e0f2c137bd3960db0e9b109c3ebedfde395f30b6e8e3855", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_102619", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_102781", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "Can you help me extract queries from the following passage to be rhetorically effective in our writing, and our strategies may vary based on the particular rhetorical situation in which we find ourselves. As writers, it is a powerful tool to be able to move back and forth between stylistic modes and communication styles--the better to reach our readers. The following elements of style (i", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:dd8a521f6d904a5a266f3a85825cbf22", + "normalized_query_sha256": "5e98791f4113bdee20f4a9a301102c6f7ed099a2f21ad06d488d5901011f772d", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:c8b811f1-dc60-4185-9568-892cc858c3f1", + "source_record_id": "c8b811f1-dc60-4185-9568-892cc858c3f1", + "request_group_id": "hermes_request:09cc044da11ad8b2656b5662c50893b943d74dadb798c6ef106f2589bce1bac8", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_102781", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_102876", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "As someone who wants to improve my physical and emotional wellness through yoga, I've been researching the Daily Yoga app. I've discovered that the app offers various yoga classes tailored for beginners like myself. Specifically, I'm interested in a beginner-friendly class focused on overall wellness. Additionally, to ensure consistent practice and progress, I'd like to establish a structured week", + "target": "find_yoga_class", + "legacy_query_parent_id": "legacy_query:5f04ae9821663415eb27f8c82ccb9e04", + "normalized_query_sha256": "6bcda16c4373ed260a2f3551f495b1fc381ffb801e528b86a8a3f9812c4b0e17", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:e558ba4f-3e78-48a9-8174-cacc14b19f4e", + "source_record_id": "e558ba4f-3e78-48a9-8174-cacc14b19f4e", + "request_group_id": "hermes_request:25b79a89c1a54690aa5ea5efd16d8f2104fb256c4db23625e2d193249f5b11ff", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_102876", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "level" + ], + "validator": "enum", + "detail": "'beginner' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "focus" + ], + "validator": "enum", + "detail": "'overall wellness' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_103111", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "Can you help me extract queries from the following passage 8. LOGIC \nSo these two cases both result in true. But perhaps surprisingly, we also get true for oatmeal: \n Human(oatmeal) = Adult(oatmeal) Child(oatmeal) false = false false false = false true X \nWhoa, how did _true_ pop out of that? Simply because the premise was false, and so all bets were off. We effectively said \" _if_ a b", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:5aa8b34a17b6356cbf4feec9b411a42c", + "normalized_query_sha256": "c1c70e0c3794aed7992d5821c356f3477104d8ae7f9bcf13d467e6132f1f8ada", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:9bb9c2d9-1bb0-4159-9a51-9dacefe581e9", + "source_record_id": "9bb9c2d9-1bb0-4159-9a51-9dacefe581e9", + "request_group_id": "hermes_request:24856f353c1e17a1ef6a18d6b633d6ec50d01d5af023896c7877a58f9db295e4", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_103111", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_105430", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "Can you help me extract queries from the following passage 4 }, is R Q? The answer is yes, since both 2 and 4 are also members of Q. \nNotice that by the definition, every set is a subset of itself. Sometimes, though, it's useful to talk about whether a set is really a _sub_ set of another, and you don't want it to \"count\" if the two sets are actually equal. This is called a **proper sub", + "target": "ExpertQAExtractor", + "legacy_query_parent_id": "legacy_query:a8f3ea9ad870a218bf49bd12eb749b4e", + "normalized_query_sha256": "1f24a591f9fef1ce892718a4cc8aab8d8bf9a27d95a57059eb562b047015b73d", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:f3ce3383-8994-458a-ac9c-f75ea893f881", + "source_record_id": "f3ce3383-8994-458a-ac9c-f75ea893f881", + "request_group_id": "hermes_request:5c07dda0e3dad61c23e61ba7a61c561b65d64412a31e0e726764fa4c18375431", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_105430", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: missing_top_level_name. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "missing_top_level_name", + "detail": "Nested arguments.name is retained as raw evidence, never promoted automatically." + } + ] + }, + { + "canonical_id": "fastbrain_107019", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "I would like to download the latest version of Firefox for my Windows desktop. The official source for the download is the Mozilla website. Can you initiate the download of the Firefox installer using the URL 'https://www.mozilla.org/firefox/download'? Please save the file to 'C:\\\\Downloads\\\\FirefoxInstaller.exe' on my system. Also, I need to ensure that the downloaded file is genuine and has not ", + "target": "download_file", + "legacy_query_parent_id": "legacy_query:3e9cb8225bba7a38756b29ee13745ed8", + "normalized_query_sha256": "d008ff377088f6f66c57ce533de228817dfdcc0226af4923efe189d2dcfbd5b2", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:1aaea158-8b7f-4e51-b24d-085e5692b067", + "source_record_id": "1aaea158-8b7f-4e51-b24d-085e5692b067", + "request_group_id": "hermes_request:7807cc10a3a8c6de5f87567c018a39f28b578900cceab8e047c5c55670bf2406", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_107019", + "category": "native_call_contract", + "reason": "Native first-round call contract failure: argument_schema. Preserve raw calls; do not silently repair nested names or arguments.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "full Hermes native call/schema audit; format assertions are not enabled", + "replacement_label": null, + "native_call_schema_issues": [ + { + "type": "argument_schema", + "path": [ + "url" + ], + "validator": "enum", + "detail": "'https://www.mozilla.org/firefox/download' is not one of [None]" + }, + { + "type": "argument_schema", + "path": [ + "destination" + ], + "validator": "enum", + "detail": "'C:\\\\Downloads\\\\FirefoxInstaller.exe' is not one of [None]" + } + ] + }, + { + "canonical_id": "fastbrain_073902", + "source": "sgd", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Yes, I'd like to reserve the seats.", + "target": "BuyBusTicket", + "legacy_query_parent_id": "legacy_query:8496a2438528ccb17ef67cb179062a00", + "normalized_query_sha256": "d31d189fc7a5b9a9e48cbb976103ada4ea7325708a6975f2c8fe932bb516c8cd", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "missing_decision_context", + "reason": "The entire request is a seat reservation confirmation; both bus-ticket purchase and one-way-flight reservation are offered. The service/dialogue context that distinguishes them was not recovered.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_001980", + "source": "sgd", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'm looking for things to do in New York City, like events, do you have dates for anything like that?", + "target": "GetEventDates", + "legacy_query_parent_id": "legacy_query:8462e9425ecea211b2ba40b5ba8707f8", + "normalized_query_sha256": "e0c169f9506815684b38463858737e0b26cab23efb99d65c6eeb3f2dafd415cd", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unresolved_action_boundary", + "reason": "The request asks for things to do and dates of events. FindEvents and GetEventDates are both candidates; the missing service state does not establish a unique action.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_095250", + "source": "sgd", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I'm going on a trip soon. Can you get me a one-way flight which leaves from Phoenix?", + "target": "SearchOnewayFlight", + "legacy_query_parent_id": "legacy_query:182e70de8ed8c0c2b4040bea5edd3f1c", + "normalized_query_sha256": "a74e91b014b4e8c93a73a848bcf7fd342264addf5d1298f8ce851da08ebd2466", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unresolved_action_boundary", + "reason": "Get me a one-way flight does not uniquely distinguish searching from reserving in the exposed SearchOnewayFlight/ReserveOnewayFlight choice set.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_036223", + "source": "glaive_v2", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Hey, I need to remember to buy groceries tomorrow. Can you set a reminder for me?", + "target": "create_todo", + "legacy_query_parent_id": "legacy_query:c40e7478a4b81ce914704b5b962ab046", + "normalized_query_sha256": "a03a1c924f34145f834945b8859ead270f1e0c8fd8a440a7ebc31914d01b3dbe", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unverified_tool_capability", + "reason": "The user requests a reminder tomorrow, while create_todo only promises creating a todo item. No recovered native schema establishes timed-reminder support.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_006573", + "source": "glaive_v2", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Let's call it \"Trip to Hawaii\".", + "target": "create_todo_list", + "legacy_query_parent_id": "legacy_query:ba1c84fb175703baf9091f82a7daf82c", + "normalized_query_sha256": "6007fb66546adadaaa1f24a33fce013d63b8a18383a42a08fb23d062ae3bd667", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "missing_decision_context", + "reason": "Let us call it Trip to Hawaii identifies a name but not the object or action; the earlier conversation needed to justify create_todo_list is absent.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_052056", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "the nature of artificial intelligence", + "target": "general_quirky", + "legacy_query_parent_id": "legacy_query:ee707d5ccc40f9419df65c7abf5f9861", + "normalized_query_sha256": "5960d35a24e540f2d20b18488aca441d764834d3068fd13ade93b4a5159906b1", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unresolved_ontology_boundary", + "reason": "The nature of artificial intelligence is labeled general_quirky. Its boundary with factual/definition questions is not established by an authoritative ontology contract; retain the native label as evidence, not a repaired unique action.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "all_recovered_native_matches": [ + { + "native_split": "train", + "native_file_row": 4418, + "id": "6580", + "label": "general_quirky", + "label_text": "general_quirky", + "text": "the nature of artificial intelligence", + "lang": "en" + } + ], + "matching_warning": "Multiple source matches remain multiple. These coordinates do not reconstruct an original dialogue parent.", + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_060517", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "what are jalapenos", + "target": "cooking_recipe", + "legacy_query_parent_id": "legacy_query:0ef8ea48e4153899d5055795177ba406", + "normalized_query_sha256": "10668c6ed66fdedd25a70e0ac421973e2a0a0bb5250f8a9fcee3707bcecc6e59", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unresolved_ontology_boundary", + "reason": "What are jalapenos is natively labeled cooking_recipe, but it asks for an ingredient definition. No automatic relabeling or expansion of recipe to all food questions is justified.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "all_recovered_native_matches": [ + { + "native_split": "train", + "native_file_row": 6856, + "id": "10183", + "label": "cooking_recipe", + "label_text": "cooking_recipe", + "text": "what are jalapenos", + "lang": "en" + } + ], + "matching_warning": "Multiple source matches remain multiple. These coordinates do not reconstruct an original dialogue parent.", + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_041207", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "how many hours ahead is n. y. c.", + "target": "datetime_query", + "legacy_query_parent_id": "legacy_query:0bf07176fdc0dfb67b562daaa65e930f", + "normalized_query_sha256": "bb28a9289d9c9df9e91ec861feae73fded4cb0a624bd0517c4564a9b2062491b", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unresolved_ontology_boundary", + "reason": "How many hours ahead is NYC is natively datetime_query; retained training examples also place offset questions in datetime_convert. Do not invent an exclusive boundary or silently relabel.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "all_recovered_native_matches": [ + { + "native_split": "train", + "native_file_row": 2689, + "id": "3958", + "label": "datetime_query", + "label_text": "datetime_query", + "text": "how many hours ahead is n. y. c.", + "lang": "en" + } + ], + "matching_warning": "Multiple source matches remain multiple. These coordinates do not reconstruct an original dialogue parent.", + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_047068", + "source": "nl2bash", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Find all files/directories under current /export/home/someone directory and upload them to ftp://somehost/tmp/", + "target": "find", + "legacy_query_parent_id": "legacy_query:7e29671a3b8f523acb0e612bd7a4ccc9", + "normalized_query_sha256": "f62f2ed7ba0d5e9199d285a2022a2915ca97adb6c8524a77674d2e63aa54ceb9", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "compound_command_not_standalone_action", + "reason": "The request combines filesystem search and FTP upload; matching native commands invoke curl inside find -exec. The exposed find capability alone does not certify the complete operation, and multiple native rows remain matched.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "all_recovered_native_matches": [ + { + "native_split": "train", + "native_file_row": 4289, + "srno": 21556, + "nl_command": "Find all files/directories under current /export/home/someone directory and upload them to ftp://somehost/tmp/", + "bash_code": "find /export/home/someone -exec curl -u someone:password -vT {} ftp://somehost/tmp/" + }, + { + "native_split": "train", + "native_file_row": 15365, + "srno": 13653, + "nl_command": "Find all files/directories under current /export/home/someone directory and upload them to ftp://somehost/tmp/", + "bash_code": "find /export/home/someone -exec curl -u someone:password -vT {} ftp://somehost/tmp/" + } + ], + "matching_warning": "Multiple source matches remain multiple. These coordinates do not reconstruct an original dialogue parent.", + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_066823", + "source": "nl2bash", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Extract any line in \"file1.txt\" which does not appear as the first \";\" delimited entry in \"file2.txt\"", + "target": "comm", + "legacy_query_parent_id": "legacy_query:d8586d9640c122d4242701f7565a0fa8", + "normalized_query_sha256": "fc344d48f7e8cdd83b2d29d18dd19012f79ea03908170e4eda0310506af5f165", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "ambiguous_native_command_and_composition", + "reason": "The same native query maps both to a full comm/sort/grep process-substitution command and to corrupt comm -23 < <. Preserve both matches; neither first-match selection nor description rewriting recovers a unique executable source command.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "all_recovered_native_matches": [ + { + "native_split": "train", + "native_file_row": 14696, + "srno": 308, + "nl_command": "Extract any line in \"file1.txt\" which does not appear as the first \";\" delimited entry in \"file2.txt\"", + "bash_code": "comm -23 < <" + }, + { + "native_split": "train", + "native_file_row": 17476, + "srno": 4147, + "nl_command": "Extract any line in \"file1.txt\" which does not appear as the first \";\" delimited entry in \"file2.txt\"", + "bash_code": "comm -23 <(sort file1.txt) <(grep -o '^[^;]*' file2.txt | sort)" + } + ], + "matching_warning": "Multiple source matches remain multiple. These coordinates do not reconstruct an original dialogue parent.", + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_018257", + "source": "git_nl_commands", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "untrack the build directory and ignore it in gitignore", + "target": "git_rm", + "legacy_query_parent_id": "legacy_query:3eb8857ddf539cbddfa3f78ada9b69f3", + "normalized_query_sha256": "623eee957b14a32d99a67ff39902fa9ac669b145bb81a2a83b1dbf1f207f3f90", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "compound_command_not_standalone_action", + "reason": "The native command is git rm -r --cached build/ followed by appending build/ to .gitignore. git_rm alone cannot perform the requested ignore-file edit.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "all_recovered_native_matches": [ + { + "native_split": "test", + "native_file_row": 940, + "en_natural_language": "untrack the build directory and ignore it in gitignore", + "corresponding_git_command": "git rm -r --cached build/ && echo build/ >> .gitignore" + } + ], + "matching_warning": "Multiple source matches remain multiple. These coordinates do not reconstruct an original dialogue parent.", + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_001956", + "source": "curated_devops", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Run mypy and flake8 type checking across all python scripts in the src directory.", + "target": "pylint_typecheck", + "legacy_query_parent_id": "legacy_query:7f942d20f45af0643dce16344e40e72a", + "normalized_query_sha256": "95d4318230fdb05fef3742768749949b6915400787079233effe96c758fa6644", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unverified_tool_capability", + "reason": "The request explicitly names mypy and flake8 but the target is an undefined pylint_typecheck wrapper. No implementation/native schema establishes that it invokes the requested tools.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_093872", + "source": "curated_devops", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "Restart all pods under namespace ingress-controller to reload mounted configmaps.", + "target": "k8s_rollout_restart", + "legacy_query_parent_id": "legacy_query:92a907e329fca2b84c99e82346672007", + "normalized_query_sha256": "1715e450a58608f0fe196d14c698740e63debf2dadfb410f892687bb248293f4", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "scope_mismatch", + "reason": "The request covers all pods in a namespace, while k8s_rollout_restart describes a single deployment. The missing cluster/controller state does not establish equivalence.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent 140-row legacy audit; manual conservative repair decision", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json" + }, + { + "canonical_id": "fastbrain_053065", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "As we prepare for the upcoming audit, I require assistance with gathering and organizing all compliance-related documents stored on our company's Dropbox. Specifically, please extract all files with the .pdf extension located within the '/Compliance' folder. These files are essential for adhering to our compliance standards and will include various policy documents, compliance reports, and records", + "target": "extract_files_by_extension", + "legacy_query_parent_id": "legacy_query:eb70b12e3b92a93797f6756f6b2ea3f6", + "normalized_query_sha256": "00b8aea557638b0ebd2a7ab2ab66f82647f3326c24d1911c60facb9aa734aece", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:c1714874-1e2a-45b5-bf5e-e55306046abc", + "source_record_id": "c1714874-1e2a-45b5-bf5e-e55306046abc", + "request_group_id": "hermes_request:6f544a67601704560f3e370d47d6833ee1afd2ffbc5f21679d9d85e47a01411d", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_053065", + "category": "visible_intercall_state_dependency", + "reason": "extract_files_by_extension creates and populates Compliance_Audit_2023, then generate_file_report reads that folder. The user also says parallel, but the visible dependency and contradictory timing prevent certifying the co-emitted set as ready to run in parallel.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent audit plus restored full native request, tools, and first-round calls", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json", + "native_call_schema_issues": [] + }, + { + "canonical_id": "fastbrain_082954", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently working on deploying a web application on AWS and require specific compute services that can efficiently handle scalability, security, and reliability. For this purpose, I need to obtain detailed information on EC2 instances, including instance types such as 't2.micro' located in the 'us-west-2a' availability zone, and their running states. Additionally, I want a list of container s", + "target": "describe_ec2_instances", + "legacy_query_parent_id": "legacy_query:ca1eab6f2fa5679a2e72715abd61a4c9", + "normalized_query_sha256": "6a87d27efba080bdfdfdbddc3990171ecc0778d5473a6f07040a5e0fdf17ba2c", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:6ee8e53b-8934-47e7-8c86-ab301eb52995", + "source_record_id": "6ee8e53b-8934-47e7-8c86-ab301eb52995", + "request_group_id": "hermes_request:9f9abdacdf2911ef52650cd2e5933c1eeb48abee27a058577dde5c688b8c7467", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_082954", + "category": "ungrounded_argument", + "reason": "list_container_services receives cluster=\"web-app-service\", although the user provided a service name and task definition, not its cluster. Passing the native schema does not ground this argument.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "original independent audit plus restored full native request, tools, and first-round calls", + "replacement_label": null, + "previous_independent_sample_evidence": "scratch/fastbrain_dataset_reaudit_20261014/legacy/independent_samples_judged.json", + "native_call_schema_issues": [] + }, + { + "canonical_id": "fastbrain_067124", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "lights out", + "target": "iot_hue_lighton", + "legacy_query_parent_id": "legacy_query:2832238d93d48ade79be63261e796a30", + "normalized_query_sha256": "46cac6a94d21216c0165e72e4f319c64c0157db68707a3ecb60b61689bb0138c", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "contradictory_or_incomplete_intent", + "reason": "Lights out is labeled iot_hue_lighton. Its ordinary reading does not justify turning lights on; isolate without silently flipping the label.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "additional directed review of retained-training-only description examples; not part of the original 140-row denominator", + "replacement_label": null + }, + { + "canonical_id": "fastbrain_006908", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "olly", + "target": "recommendation_events", + "legacy_query_parent_id": "legacy_query:af583d56a1202c38997cb6fc0a385149", + "normalized_query_sha256": "9a1f189577dd56e8635014d48e329388fb1b484a1464b997da85bb0899f3f7f5", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "missing_intent", + "reason": "The only input is olly, labeled recommendation_events; no event-discovery request is visible.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "additional directed review of retained-training-only description examples; not part of the original 140-row denominator", + "replacement_label": null + }, + { + "canonical_id": "fastbrain_066989", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "fast", + "target": "audio_volume_up", + "legacy_query_parent_id": "legacy_query:4560e4ee802d7f4d6d6b38df4f345cb0", + "normalized_query_sha256": "115dc3606fbf8691fb69f2aefec86f2ecd302362a0502b3a9648bf2c4dc8290f", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "missing_intent", + "reason": "The only input is fast, labeled audio_volume_up; no volume-increase intent is established.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "additional directed review of retained-training-only description examples; not part of the original 140-row denominator", + "replacement_label": null + }, + { + "canonical_id": "fastbrain_081597", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "weird things people want to know", + "target": "cooking_recipe", + "legacy_query_parent_id": "legacy_query:ee349cdd129154e7b1e0342a6a831745", + "normalized_query_sha256": "6009be4ec3d85c97512ae8a14d987e1a74d518686bda9512274b5daead9f7f9e", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unresolved_ontology_boundary", + "reason": "Weird things people want to know is labeled cooking_recipe without any visible cooking request. Do not expand a recipe description to cover it.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "additional directed review of retained-training-only description examples; not part of the original 140-row denominator", + "replacement_label": null + }, + { + "canonical_id": "fastbrain_000663", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "go back", + "target": "play_podcasts", + "legacy_query_parent_id": "legacy_query:c1fe035085c3405aea92e2eab2fc11c1", + "normalized_query_sha256": "8aaf5e76b6032ccb575364134b378381cbd25ce6a40ce8f70973f29960c920b6", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "missing_decision_context", + "reason": "Go back is labeled play_podcasts but no podcast/media context is present.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "additional directed review of retained-training-only description examples; not part of the original 140-row denominator", + "replacement_label": null + }, + { + "canonical_id": "fastbrain_076673", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "change my alarm", + "target": "alarm_remove", + "legacy_query_parent_id": "legacy_query:6535331befba392f69f5d4294100d828", + "normalized_query_sha256": "7fa97850e552f3bbdd56a31665e1b6bb21109cda7c7b9adc5825ef7a1534c45d", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unresolved_action_boundary", + "reason": "Change my alarm is labeled alarm_remove, but the request does not distinguish deletion, snoozing, or editing. Do not redefine remove as all alarm changes.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "additional directed review of retained-training-only description examples; not part of the original 140-row denominator", + "replacement_label": null + }, + { + "canonical_id": "fastbrain_077729", + "source": "amazon_massive", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "send a text to alice", + "target": "email_querycontact", + "legacy_query_parent_id": "legacy_query:bb49714ef3b7120c9cddcfab0890f8ef", + "normalized_query_sha256": "dc5f75287a8c0f393cc207a01ab4158c236e6585625250fad6fdf2feb9300502", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "category": "unverified_prerequisite_action", + "reason": "Send a text to Alice is labeled email_querycontact; contact lookup may be a prerequisite, but no native dialogue/tool state establishes it as the unique next action.", + "decision": "exclude_from_strict_repaired_views_keep_audit_evidence", + "evidence_scope": "additional directed review of retained-training-only description examples; not part of the original 140-row denominator", + "replacement_label": null + } + ], + "quarantine_summary": { + "total_quality_ids": 131, + "by_source": { + "hermes_v1": 111, + "sgd": 3, + "glaive_v2": 2, + "amazon_massive": 10, + "nl2bash": 2, + "git_nl_commands": 1, + "curated_devops": 2 + }, + "native_schema_invalid_hermes": 109, + "manual_semantic_hermes": 2, + "original_audit_non_hermes": 13, + "additional_descriptor_research_massive": 7, + "denominator_warning": "Directed descriptor examples and finance reviews are additional reviews, not members of the original 140-row random sample unless explicitly identified." + }, + "description_evidence": { + "git_nl_commands": { + "git_rm": { + "file": "git_rm_v2.47.0.txt", + "url": "https://raw.githubusercontent.com/git/git/v2.47.0/Documentation/git-rm.txt", + "sha256": "f161a3ff1d519cc25acb74a53f57abd64c75e31e02535f6140a8faeb3ee0365d", + "bytes": 7236, + "local_file": "primary_docs/git_rm_v2.47.0.txt", + "origin": "pinned primary Git documentation; 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It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "alarm_remove": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_015970", + "fastbrain_047198", + "fastbrain_066054", + "fastbrain_076673", + "fastbrain_007851", + "fastbrain_075024", + "fastbrain_072204", + "fastbrain_094886" + ], + "selected_examples_flagged_for_quarantine": [ + "fastbrain_076673" + ], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "alarm_set": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_023496", + "fastbrain_011026", + "fastbrain_085936", + "fastbrain_016966", + "fastbrain_035509", + "fastbrain_077678", + "fastbrain_079474", + "fastbrain_043028" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "audio_volume_down": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_068150", + "fastbrain_008497", + "fastbrain_094828", + "fastbrain_011785", + "fastbrain_001346", + "fastbrain_005556", + "fastbrain_056406", + "fastbrain_077290" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "audio_volume_mute": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_052825", + "fastbrain_045958", + "fastbrain_037837", + "fastbrain_030393", + "fastbrain_012057", + "fastbrain_080762", + "fastbrain_075203", + "fastbrain_019196" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "audio_volume_other": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_066367", + "fastbrain_039783", + "fastbrain_002951", + "fastbrain_018709", + "fastbrain_058524", + "fastbrain_030946", + "fastbrain_020593", + "fastbrain_065342" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "audio_volume_up": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_070666", + "fastbrain_025210", + "fastbrain_091617", + "fastbrain_066989", + "fastbrain_041268", + "fastbrain_007013", + "fastbrain_076460", + "fastbrain_011447" + ], + "selected_examples_flagged_for_quarantine": [ + "fastbrain_066989" + ], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. 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Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "play_audiobook": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_053132", + "fastbrain_015671", + "fastbrain_083749", + "fastbrain_085695", + "fastbrain_056756", + "fastbrain_072926", + "fastbrain_018008", + "fastbrain_006970" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "play_game": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_004124", + "fastbrain_022485", + "fastbrain_025870", + "fastbrain_059330", + "fastbrain_050147", + "fastbrain_038842", + "fastbrain_095234", + "fastbrain_005518" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "play_music": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_004040", + "fastbrain_019674", + "fastbrain_030884", + "fastbrain_021112", + "fastbrain_003157", + "fastbrain_068644", + "fastbrain_027178", + "fastbrain_014737" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "play_podcasts": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_072873", + "fastbrain_024765", + "fastbrain_071355", + "fastbrain_043404", + "fastbrain_000663", + "fastbrain_048806", + "fastbrain_051470", + "fastbrain_012680" + ], + "selected_examples_flagged_for_quarantine": [ + "fastbrain_000663" + ], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "play_radio": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_050709", + "fastbrain_021151", + "fastbrain_075160", + "fastbrain_045338", + "fastbrain_040122", + "fastbrain_002737", + "fastbrain_083487", + "fastbrain_044167" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "qa_definition": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_037677", + "fastbrain_050298", + "fastbrain_025687", + "fastbrain_013140", + "fastbrain_083648", + "fastbrain_027307", + "fastbrain_041147", + "fastbrain_082425" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "qa_factoid": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_068167", + "fastbrain_048093", + "fastbrain_060602", + "fastbrain_095475", + "fastbrain_012932", + "fastbrain_010451", + "fastbrain_079088", + "fastbrain_070158" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "qa_maths": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_026402", + "fastbrain_053290", + "fastbrain_026373", + "fastbrain_083528", + "fastbrain_001836", + "fastbrain_021255", + "fastbrain_082863", + "fastbrain_053261" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "recommendation_events": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_006908", + "fastbrain_002958", + "fastbrain_011411", + "fastbrain_014040", + "fastbrain_074104", + "fastbrain_087687", + "fastbrain_074585", + "fastbrain_083869" + ], + "selected_examples_flagged_for_quarantine": [ + "fastbrain_006908" + ], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "recommendation_locations": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_084262", + "fastbrain_058290", + "fastbrain_019633", + "fastbrain_089597", + "fastbrain_015490", + "fastbrain_045362", + "fastbrain_009731", + "fastbrain_067537" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "recommendation_movies": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_017853", + "fastbrain_086806", + "fastbrain_083567", + "fastbrain_015739", + "fastbrain_085622", + "fastbrain_062653", + "fastbrain_094461", + "fastbrain_073622" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "social_post": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_073819", + "fastbrain_057419", + "fastbrain_041407", + "fastbrain_093674", + "fastbrain_003725", + "fastbrain_093255", + "fastbrain_033322", + "fastbrain_091554" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "social_query": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_015448", + "fastbrain_093917", + "fastbrain_003478", + "fastbrain_092670", + "fastbrain_047125", + "fastbrain_094650", + "fastbrain_074883", + "fastbrain_063137" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "takeaway_order": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_072705", + "fastbrain_007876", + "fastbrain_075584", + "fastbrain_094408", + "fastbrain_049995", + "fastbrain_004011", + "fastbrain_063417", + "fastbrain_046771" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "takeaway_query": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_064722", + "fastbrain_049007", + "fastbrain_057175", + "fastbrain_096401", + "fastbrain_021753", + "fastbrain_024919", + "fastbrain_017788", + "fastbrain_071856" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "transport_query": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_010112", + "fastbrain_072864", + "fastbrain_022881", + "fastbrain_079365", + "fastbrain_026071", + "fastbrain_080200", + "fastbrain_013151", + "fastbrain_054455" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "transport_taxi": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_017985", + "fastbrain_075436", + "fastbrain_079930", + "fastbrain_070749", + "fastbrain_090930", + "fastbrain_025230", + "fastbrain_078847", + "fastbrain_069558" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "transport_ticket": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_057639", + "fastbrain_062298", + "fastbrain_084371", + "fastbrain_021024", + "fastbrain_058726", + "fastbrain_011559", + "fastbrain_076663", + "fastbrain_076603" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "transport_traffic": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_071989", + "fastbrain_005742", + "fastbrain_027114", + "fastbrain_001608", + "fastbrain_066627", + "fastbrain_009739", + "fastbrain_079361", + "fastbrain_000364" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + }, + "weather_query": { + "status": "reviewed_assistant_summary_of_retained_training_examples", + "evidence_file": "massive_description_training_evidence.json", + "selected_example_ids": [ + "fastbrain_021081", + "fastbrain_036855", + "fastbrain_075113", + "fastbrain_090013", + "fastbrain_001085", + "fastbrain_048419", + "fastbrain_031067", + "fastbrain_085113" + ], + "selected_examples_flagged_for_quarantine": [], + "source_repo": "mteb/amazon_massive_intent", + "source_revision": "940fd47a81eaa7f2cc7b129674d945d618ac38c2", + "scope": "Evidence was selected from retained legacy v2 train, excluding frozen historical validation. It is not restricted to upstream native train, because legacy already mixed native splits. Summaries are not official exhaustive ontology definitions, and not all selected examples are endorsements." + } + } + }, + "description_policy": { + "apply_to": "Every candidate occurrence with the corresponding source and name, not just positive targets. Keep all original text/labels in provenance.", + "massive_origin": "Assistant-written summaries of reviewed retained-v2-training examples and their fixed native labels; not official exhaustive ontology definitions.", + "massive_not_a_cure_for_bad_rows": "Do not broaden descriptions to absorb contradictory labels, and do not infer unique correct actions merely because a description was rewritten.", + "massive_known_overlaps": { + "cooking_query/cooking_recipe": "Both retained label groups contain preparation instructions, substitutions, and recipes. Both rewrites are deferred; no mechanical relabeling or fabricated clean boundary.", + "datetime_query/datetime_convert": "Time-offset questions appear in both labels. Proposed text summarizes common usage; it is not a universal exclusive decision boundary. fastbrain_041207 remains quarantined.", + "general_quirky/qa_factoid/qa_definition": "Open-ended conversation overlaps broad questions. general_quirky is not an all-purpose no-match or clarification label.", + "music_settings/music_likeness": "Shuffle/repeat/skip describe settings; likeness includes saving to playlists/favorites. Do not narrow likeness to spoken approval only.", + "alarm_set/calendar_set": "Both can mention reminders; preserve concrete alarm versus event context and isolate unresolved cases rather than claiming keywords perfectly partition them." + }, + "deferred_description_updates": { + "amazon_massive": { + "cooking_query": "Insufficient evidence for a distinct boundary from cooking_recipe.", + "cooking_recipe": "Insufficient evidence for a distinct boundary from cooking_query." + }, + "nl2bash": { + "less": "The existing generic paging description is valid. A separate less manual could not be pinned; failed 404 fetches are not cited. Equivalent paging candidates are repaired by removal, not artificial description differences." + } + }, + "devops_git_commit_reconsideration": { + "canonical_id": "fastbrain_082223", + "decision": "description_repair_supports_coarse_routing_do_not_automatically_quarantine", + "reason": "git commit -a can stage modified/deleted tracked files and commit them. This corrects the earlier blanket stage-plus-commit concern. It does not stage new untracked files, and no concrete repository execution was certified." + } + }, + "finance_policy": { + "reviewed_explicit_exclusion_count": 22, + "evidence": [ + { + "canonical_id": "fastbrain_001166", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to transfer $500 to my friend's PayPal account for an urgent expense that has come up. His email that is linked to his PayPal account is friend@example.com. Please initiate this money transfer and include a note that says \"Urgent expense cover.\" Can you provide me with the transaction ID once the transfer is initiated?", + "target": "initiate_money_transfer", + "legacy_query_parent_id": "legacy_query:2514acbf1d64cee8dae2e15d02b1382a", + "normalized_query_sha256": "6447d83e3ee5ebaa3e967fdf3d9e3e1a2ebbe0f016d4f549dcf72a75bee2a511", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:636262b7-9d59-44bc-8fec-aa6171913ca6", + "source_record_id": "636262b7-9d59-44bc-8fec-aa6171913ca6", + "request_group_id": "hermes_request:6447d83e3ee5ebaa3e967fdf3d9e3e1a2ebbe0f016d4f549dcf72a75bee2a511", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_001166", + "reason": "Initiate a USD transfer to a PayPal recipient.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "initiate_money_transfer" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_002492", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to reimburse my friend, Alex_Jones_99, for the pizza we shared during our movie night. The amount owed is $45.00. The note for the payment should read \"🍕 Pizza night - Thanks for treating us!\" and I prefer the transaction to be private. Please create this payment on Venmo with these specifics.\n\nFunction call: \n```json\n{\n \"recipient_username\": \"Alex_Jones_99\",\n \"amount\": 45.00,\n \"no", + "target": "create_venmo_payment", + "legacy_query_parent_id": "legacy_query:d7068b15ad21731cbd2fc153a9a10019", + "normalized_query_sha256": "3579874682c14c483752ce3bfaafbbf3404f5b7dc94fe9ea69061b2e10812074", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:64c881e6-0bf2-48f6-a06b-da4571ae8731", + "source_record_id": "64c881e6-0bf2-48f6-a06b-da4571ae8731", + "request_group_id": "hermes_request:f950515b9852b2903c3ff0745826f1a0c82993f48933f135fd9478dc4d7a84d9", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_002492", + "reason": "Create an actual Venmo peer-to-peer payment.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "create_venmo_payment" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_012497", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am overseeing a consumer finance loans system and require assistance with automating loan management processes. Specifically, I have an applicant with a credit score of 720 and an annual income of $85,000 who has requested a loan for $15,000 for home improvements. I would like to formally approve this loan application. Additionally, using an interest rate of 3.99%, I need to calculate the monthl", + "target": "approve_loan_application", + "legacy_query_parent_id": "legacy_query:97615e5b5725630599118cf173d6439d", + "normalized_query_sha256": "13aec0163650c9627621a59738715b27e87780850d09ff06b8c90f9d98a890e6", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:3892dde2-f530-4b91-b05c-240c94e61fe8", + "source_record_id": "3892dde2-f530-4b91-b05c-240c94e61fe8", + "request_group_id": "hermes_request:9fdf821645198ef04edb452bdcdabc7e321bbd1fd755d489e9190221797b77e4", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_012497", + "reason": "Approve a consumer loan and compute its payments and amortization schedule.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "approve_loan_application", + "calculate_monthly_payments", + "generate_loan_amortization_schedule" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_019803", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am interested in transferring Bitcoin using blockchain technology and require assistance with completing a transaction. I need to send 0.75 BTC from my wallet to a recipient's wallet. For secure and transparent processing, I would like this transfer to be recorded on the blockchain with all relevant details such as the transaction ID, timestamp, and the wallet addresses of both the sender and th", + "target": "transfer_funds", + "legacy_query_parent_id": "legacy_query:da513187fc1348574df8537e8c28e826", + "normalized_query_sha256": "319c142202236a114c6ec62609d4f795b40cb11ced6b074934bcffc09b263a63", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:38a25478-7e0f-4186-b472-e3a07fa58606", + "source_record_id": "38a25478-7e0f-4186-b472-e3a07fa58606", + "request_group_id": "hermes_request:3bab8cb02c08fae6778857ce1a7e4a82117f8b5eac42b014ac85d2cd9d260b85", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_019803", + "reason": "Transfer Bitcoin between blockchain wallets.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "transfer_funds" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_022513", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I need to complete a payment transaction for an online purchase made on my web store. The customer has opted to use their credit card for the payment, and the total cost of the items purchased is $84.99. I have the customer's credit card details which include the card number, expiration date, and CVV code. The transaction needs to be processed in USD. Additionally, I have set up a callback URL on ", + "target": "initiate_payment", + "legacy_query_parent_id": "legacy_query:38f7131800d50ca02785952a24a93a03", + "normalized_query_sha256": "62d7e829536a1b9eef4d3c79bf33faaace0850e2f59be1307a6834e35206d899", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:4f7718f6-e3e7-44fb-8893-154a0859d590", + "source_record_id": "4f7718f6-e3e7-44fb-8893-154a0859d590", + "request_group_id": "hermes_request:c20dbe434a0e7a5ca12de31e868c7ea705381d3d152128354e48a101eb0a455a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_022513", + "reason": "Initiate a credit-card payment through a payment gateway; this is an explicit payment API operation, not merely a shopping-price mention.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "initiate_payment" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_023338", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I require assistance in compiling a comprehensive transaction history for my cryptocurrency wallet to ensure accurate record-keeping for tax reporting purposes. Specifically, my wallet address is '0x123abc', and I need to extract all transactions that occurred during the last fiscal year, beginning on April 1, 2022, and ending on March 31, 2023. The transaction details should include the unique tr", + "target": "retrieve_wallet_transaction_history", + "legacy_query_parent_id": "legacy_query:7b3bd85ab776462aac63ca5fb8afdf83", + "normalized_query_sha256": "5666e2240c46da392dc86ad089667c0255cf6c6601222570aaf577bdbf075245", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:13f93ea4-19df-41a9-9072-1baca0202863", + "source_record_id": "13f93ea4-19df-41a9-9072-1baca0202863", + "request_group_id": "hermes_request:db08876df836ccd49b12004ae5f4de92f8eb3570db93633f2fe8f97960d4db79", + "protected_effective_split": "validation", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_023338", + "reason": "Retrieve cryptocurrency-wallet financial transaction history.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "retrieve_wallet_transaction_history" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_023893", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to retrieve my PayPal transaction history from last month to assist with reconciling my accounting records. Specifically, I need details such as transaction IDs, dates, transaction amounts, and the status of each transaction. Please call the appropriate function, providing the start and end dates for the past month, and include fields for transaction ID, date, amount, and status in th", + "target": "retrieve_paypal_transaction_history", + "legacy_query_parent_id": "legacy_query:81e9492ea7516e41f06ba2f52d13d538", + "normalized_query_sha256": "513cc5b96d7bff5de10146b5671988b57899aecbbbb5c801872834b32c77426b", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:8169f166-03c4-4e80-b3f5-11bc17c12460", + "source_record_id": "8169f166-03c4-4e80-b3f5-11bc17c12460", + "request_group_id": "hermes_request:5cfbcf259b58fa09f6d9842d3dc2901b9dba744815e24ba9ac4f109800dba6fd", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_023893", + "reason": "Retrieve PayPal financial transaction history.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "retrieve_paypal_transaction_history" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_029254", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently implementing a funds transfer feature within our e-wallet application, and I need to initiate a transfer between two users. User 'user_12345' wishes to transfer a sum of $150.00 to user 'user_67890'. In addition to conducting the transaction, I require that the transfer event be recorded in both users' transaction histories, noting the transaction's amount, recipient, and the timest", + "target": "transfer_funds", + "legacy_query_parent_id": "legacy_query:3d23033dea5ba603228ea8332bccde54", + "normalized_query_sha256": "0b942b47f3f7d44d8cf364ebf89af0fa4ff500f1dbae3f242ed2ea55a5fe5eea", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:3ef16281-0d10-49fa-90d9-df93cf020e12", + "source_record_id": "3ef16281-0d10-49fa-90d9-df93cf020e12", + "request_group_id": "hermes_request:3d7271cfb70bc4681ee6b281622cbdab067f31af52ae07da0faf31435df15a11", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_029254", + "reason": "Transfer USD funds between e-wallet users.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "transfer_funds" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_036850", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to send a peer-to-peer payment for $45.00 to my friend @AlexDinner45 on Venmo. This amount is for our shared dinner expenses. Please include the note 'Dinner last night' with the payment to provide context for the transaction.", + "target": "send_venmo_payment", + "legacy_query_parent_id": "legacy_query:117880323b15a68fff43d1d1c612c072", + "normalized_query_sha256": "3758422dfe6846daacea267cbd839deafa9c7a5dc6a0f0799bd5a116bfbf3a8f", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:9c46f3db-7e4e-4873-a9bc-a8e0251c45fe", + "source_record_id": "9c46f3db-7e4e-4873-a9bc-a8e0251c45fe", + "request_group_id": "hermes_request:3758422dfe6846daacea267cbd839deafa9c7a5dc6a0f0799bd5a116bfbf3a8f", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_036850", + "reason": "Send a Venmo peer-to-peer payment.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "send_venmo_payment" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_039428", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to add a new credit card to my Coinbase account to facilitate cryptocurrency transactions. The card details for the addition are as follows: card number 1234567890123456, expiration date 09/25, and CVV 123. The billing address associated with the card is John Doe, 123 Main St, Metropolis, New York, 10001, USA. Please proceed to add this credit card and then carry out its verification.", + "target": "add_credit_card", + "legacy_query_parent_id": "legacy_query:2a56ab03bf270b126ab98502eb0d9c5c", + "normalized_query_sha256": "414c3a70156e624f4a124d6203640820f92f2c32e5dcd782564641aae68ca28e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:a0b55b75-0b32-4e82-9c04-79963e4225b1", + "source_record_id": "a0b55b75-0b32-4e82-9c04-79963e4225b1", + "request_group_id": "hermes_request:291b94e10cff5413497f3ea4dea61b9e93a006e78b138ac57c633c97eecb94b8", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_039428", + "reason": "Link a credit card to Coinbase as the first action of a cryptocurrency-purchase workflow.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "add_credit_card" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_056780", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to execute a global money transfer through Revolut to my friend residing in Spain. The transfer amount is 1500 EUR and it's important that the transaction is processed urgently, as I need it to be completed before the end of this week. To facilitate this, I would like to initiate an express money transfer. Moreover, to keep track of the transfer's progress, I would appreciate if you c", + "target": "initiate_money_transfer", + "legacy_query_parent_id": "legacy_query:e7a13132cbac90b1d732a5ba6cd3507a", + "normalized_query_sha256": "85379510da80eb99404687fa2b646fb14ec420bca68cd9adaf236a784bdcdb52", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:7d6dd5c8-9e1e-443d-92af-9cfdb910fbb1", + "source_record_id": "7d6dd5c8-9e1e-443d-92af-9cfdb910fbb1", + "request_group_id": "hermes_request:d44090ccc43b36c6f5cab491e321fe5c821bb5ef057359f2cc32097b4e67bca6", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_056780", + "reason": "Initiate a Revolut international money transfer.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "initiate_money_transfer" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_063295", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to initiate a purchase of Bitcoin using my Coinbase account. For this transaction, I'd like to use my credit card as the payment method. Specifically, I want to spend $500 on Bitcoin. Please execute the necessary functions using the following details: my Coinbase account ID is \"12345X\", the cryptocurrency I wish to purchase is Bitcoin (BTC), the payment method is my credit card, and t", + "target": "initiate_crypto_purchase", + "legacy_query_parent_id": "legacy_query:2a38564ec098393665e3f724e783cc9e", + "normalized_query_sha256": "04456f18b643bb4781fdf2daa0d2f923f0b7960844338354e30211975a947e7e", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:ea0ffed6-f9fb-494b-8214-aa737d97af28", + "source_record_id": "ea0ffed6-f9fb-494b-8214-aa737d97af28", + "request_group_id": "hermes_request:6882cf023d5e09f98f6d1095f3aadbe8e2347c227c37f1cd279e23b1d163e10b", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_063295", + "reason": "Purchase Bitcoin through Coinbase.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "initiate_crypto_purchase" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_064277", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am currently trading cryptocurrencies on Coinbase and would like to establish a more efficient asset management system. To enhance my trading capabilities, I request the creation of a cryptocurrency wallet named 'MyCryptoWallet' tailored for my use. Additionally, I need to check the balance of this new wallet to stay informed about my assets. To facilitate seamless cryptocurrency payments, pleas", + "target": "create_wallet", + "legacy_query_parent_id": "legacy_query:52a23531404ae4b80cd15896c402ecf8", + "normalized_query_sha256": "e1a20b9c7f3c051f73831febe3130d136dc75f4fbc6419e4196361cbcc7bd0ac", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:71f294e6-c40d-44bd-8f1d-661a5c02e4bd", + "source_record_id": "71f294e6-c40d-44bd-8f1d-661a5c02e4bd", + "request_group_id": "hermes_request:bbbf6a277034826d134ecc85aeffec810a2d6806b8d704718360baf4d5420a4a", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_064277", + "reason": "Create a cryptocurrency wallet for asset management and payments.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "create_wallet" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_064932", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I am processing an order on my e-commerce website where the customer has selected to use Klarna's \"Pay in 4 interest-free installments\" option for their purchase. The total cost of the items in their cart is $150.00. I need to create a Klarna payment session with the following details: an order amount of 15000 cents since we use the smallest currency unit, the currency set to USD, the locale set t", + "target": "create_klarna_payment_session", + "legacy_query_parent_id": "legacy_query:3157e781e98535eb9d22eb566b8ba566", + "normalized_query_sha256": "949cf7a06c7f164a29786feed874366ac428401bb620b9b4487b5438cccdb467", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:9ea5bebb-56e0-4245-80f0-af00c09fec09", + "source_record_id": "9ea5bebb-56e0-4245-80f0-af00c09fec09", + "request_group_id": "hermes_request:dde96a9c66cf710c2dc714828eb055deb0d564b567df0800cc0f75e9251a5be0", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_064932", + "reason": "Create a Klarna buy-now-pay-later installment-payment session; the requested operation is credit/payment processing.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "create_klarna_payment_session" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_067688", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I need to transfer funds from my primary checking account to my savings account to manage my finances more effectively. The transaction amount is $500. The specific details for the accounts are as follows: the checking account number from which the funds will be withdrawn is 123456789, and my savings account, which is the recipient, has the account number 987654321. This transaction is intended fo", + "target": "initiate_transfer", + "legacy_query_parent_id": "legacy_query:b663d0ebe7db148ca23bc8802a92f361", + "normalized_query_sha256": "06a0188c734dd992f1c0228bf64d20357d551bf054e05ec5b623ea214524ad14", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:2621a35d-ae76-4886-b2fa-85a674a315f1", + "source_record_id": "2621a35d-ae76-4886-b2fa-85a674a315f1", + "request_group_id": "hermes_request:918a4cf45e3fa80dfbf7a5199a6e3580815fd201d64fcba62c89d1b474f84b22", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_067688", + "reason": "Transfer funds between checking and savings bank accounts.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "initiate_transfer" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_072727", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I have recently completed a web design project for my client and I would like to utilize PayPal to send them an invoice for the agreed amount of $500.00. Since my client prefers PayPal for transactions, could you assist me by generating a PayPal payment request addressed to my client's email client@example.com? I need to include a short description stating that it's for web design services provide", + "target": "create_paypal_payment_request", + "legacy_query_parent_id": "legacy_query:53d823d4960d0789f3b2070f92fe2c56", + "normalized_query_sha256": "640d4fd0f39fcd8adbc615066731d437c33d53ef2979cc973a3df36b4c9dc7a2", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:25bc6ea8-0661-4506-a38a-ffadf15029ba", + "source_record_id": "25bc6ea8-0661-4506-a38a-ffadf15029ba", + "request_group_id": "hermes_request:5bdacfa5e8aaff82421b0e6451a5300f0fdb97f5d5a10c124ca0b463218e72b4", + "protected_effective_split": "validation", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_072727", + "reason": "Create a PayPal payment request; distinguish this explicit payment-network action from ordinary invoice document generation.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "create_paypal_payment_request" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_084822", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "As a financial advisor, I'm assisting a new client, John Doe with ID \"JD-2458\", who's looking to diversify his portfolio through our brokerage firm. Firstly, I'd like to create a joint brokerage account for John and his wife with margin trading enabled and an options trading level of 3. Secondly, based on our assessment, John's risk tolerance is moderate, and he's interested in a mix of growth, in", + "target": "create_brokerage_account", + "legacy_query_parent_id": "legacy_query:635d81ab56ddb3c11a4d41ab97401ebb", + "normalized_query_sha256": "ba433e99d344d3b53ff1ad14ad11f220b12faecb1e2b3e79dc9313961d009f81", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:b86b8785-e5be-47a2-94bb-4cd0783f73fe", + "source_record_id": "b86b8785-e5be-47a2-94bb-4cd0783f73fe", + "request_group_id": "hermes_request:8ed2ed22113639a20a2aeb5d2bd76b5979a0e819297e33abe481c3f4ed119471", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_084822", + "reason": "Create a brokerage account and update an investment profile for securities trading.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "create_brokerage_account", + "update_investment_profile" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_084975", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to link my personal checking bank account to my PayPal account to streamline my transactions. I need assistance with calling the appropriate functions to add my bank account, verify it, and then set it as the primary funding source for all my payments through PayPal. The bank account details for the function calls are as follows:\n\n- PayPal User ID: user_12345\n- Bank Account Number: 12", + "target": "add_bank_account", + "legacy_query_parent_id": "legacy_query:c4e0802304171059d30e652d04b215ee", + "normalized_query_sha256": "6f5d1ffe99288bbfc7aa6595385048e2d186076fb05bbf54115b5eb099ef6eba", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:489c8a77-b05b-412f-95f2-c815cd0ed041", + "source_record_id": "489c8a77-b05b-412f-95f2-c815cd0ed041", + "request_group_id": "hermes_request:db767cc1cc284fb8730948a2854da787ba1aa9e31c0d05a1e1a49b5b57aec7d8", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_084975", + "reason": "Link a checking bank account to PayPal as a funding source.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "add_bank_account" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_085213", + "source": "glaive_v2", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "The principal amount is $200,000. The interest rate is 3.5% and the loan term is 30 years.", + "target": "calculate_loan_payment", + "legacy_query_parent_id": "legacy_query:9661a622cde5bb67869714c97ae0efa5", + "normalized_query_sha256": "56e173f11e14c1f957f235adc5f96e5cd6651813e929e23b881c4b3a3854a6ab", + "native_parent_status": "unknown; legacy query hashes are deduplication groups, not original dialogue parents", + "reason": "Calculate the payment on a loan from principal, interest rate, and loan term.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "target_description": "Calculate the monthly payment for a loan" + }, + { + "canonical_id": "fastbrain_085346", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "As a PayPal user with the account identifier \"user12345\", I am interested in conducting a financial review and require detailed insights into my recent PayPal activities. Specifically, I am looking to obtain a comprehensive transaction history starting from the 1st of February 2023 until the 1st of March 2023. Additionally, I seek to verify my current PayPal account balance to ensure accurate fina", + "target": "get_paypal_transaction_history", + "legacy_query_parent_id": "legacy_query:b81c037f9e355cce01c8bdb869e394d2", + "normalized_query_sha256": "9a8db5760f7eddcc139c77c6a16c6a63e5a6ccc31074843d25bc5803f92e5ab1", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:295bfee8-564d-47c7-ac86-fafd5607c7dc", + "source_record_id": "295bfee8-564d-47c7-ac86-fafd5607c7dc", + "request_group_id": "hermes_request:fb9fa4ab7a4e2997d00ffba2e42e5ab7d65dd31e0f13d64066ad4da5f101ba50", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_085346", + "reason": "Retrieve PayPal transaction history and account balance.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "get_paypal_transaction_history", + "get_paypal_account_balance" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_085403", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "train", + "query": "I would like to reimburse my friend Alice for the lovely dinner we had last night. Please process a peer-to-peer transaction for me using Cash App. The amount I need to send her is $50, and the currency is in USD. Alice's unique Cash App ID is $AliceBlue123. Additionally, could you include a note with the transaction that says \"Dinner payment\"? Thank you.", + "target": "send_money", + "legacy_query_parent_id": "legacy_query:9ecd4dec958e69fc7dc253b33216fbd2", + "normalized_query_sha256": "05bb31e2e86992a32b7322e9e10375752f71cd11a06c891f878645ce297b5e98", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:fa6f6c64-740c-4fad-95ad-072cf5222c8a", + "source_record_id": "fa6f6c64-740c-4fad-95ad-072cf5222c8a", + "request_group_id": "hermes_request:05bb31e2e86992a32b7322e9e10375752f71cd11a06c891f878645ce297b5e98", + "protected_effective_split": "train", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_085403", + "reason": "Send a USD peer-to-peer payment through Cash App.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "send_money" + ], + "native_schema_valid": true + }, + { + "canonical_id": "fastbrain_101949", + "source": "hermes_v1", + "canonical_revision": "a84ea0337a424e287dc6f36f6b9fa320fb920fb0", + "original_split": "test", + "query": "I would like to use my Cash App account ID 'acc123' to purchase $500 worth of Bitcoin. Could you please execute the purchase transaction for me, check the current USD to BTC exchange rate, and then confirm the transaction details? Once the purchase is complete, I would need the transaction ID for my records. Please proceed with the necessary functions to complete this task.", + "target": "buy_bitcoin", + "legacy_query_parent_id": "legacy_query:6fac26373eda129dd3edc14c2f564b27", + "normalized_query_sha256": "684f3c88a1ad92492fb28575c7a18c70589c37cd69673072702c25be1e1dc0f5", + "native_parent_status": "native source-record UUID recovered exactly", + "source_parent_id": "hermes_record:efc48dd7-432d-4881-acb0-bd24db4c2d5d", + "source_record_id": "efc48dd7-432d-4881-acb0-bd24db4c2d5d", + "request_group_id": "hermes_request:684f3c88a1ad92492fb28575c7a18c70589c37cd69673072702c25be1e1dc0f5", + "protected_effective_split": "test", + "full_context_evidence": "hermes_restored_records.jsonl canonical_id=fastbrain_101949", + "reason": "Purchase Bitcoin through Cash App and query the USD/BTC exchange rate.", + "domain_decision": "exclude_under_explicit_financial_operations_policy", + "quality_certification": false, + "scope": "directed high-precision examples; not an exhaustive all-source finance audit", + "observed_first_round_call_names": [ + "buy_bitcoin", + "get_exchange_rate" + ], + "native_schema_valid": true + } + ], + "positive_rule": "An explicit requested operation on bank/payment accounts, funds, credit/loans, securities/investment, cryptocurrency, or their financial records, supported by the target/native call contract. Actual operation means the requested domain action; no payment or transaction was executed during this research.", + "application": "Apply domain exclusions consistently to reconstructed training and evaluation views while keeping frozen original/default/v2 files unchanged. Exclusion of a held-out example never authorizes promoting it or a sibling to training. Preserve its protected membership.", + "target_and_context_check": "Use exact financial action names plus their descriptions/schema and visible request. Substring matches are review candidates only, not final automatic labels.", + "concrete_false_positive_families": [ + "inventory stock levels and reorder alerts", + "software/model/file transfer", + "project/resource/energy portfolio administration without a financial operation", + "generic tabular formatting or text extraction merely mentioning currency/finance", + "ordinary item prices, invoice documents, discounts, refunds, shopping, and commerce without an explicit financial/payment operation" + ], + "catalog_policy": "If the final policy excludes financial tool catalogs as well as financial positive actions, inspect every candidate/native tool separately and record that additional rule; this research does not silently infer it from an unrelated query keyword.", + "glaive_followup_rule": "Loan/mortgage/interest calculations, securities-price queries/analysis, currency exchange, and explicit payment functions are high-precision review families when their descriptions and request confirm the domain. check_stock or portfolio alone is insufficient because inventory and project-management uses exist.", + "scope_limit": "The 22 explicit examples are a conservative reviewed list, not an exhaustive count or proof that any source is finance-free. Preserve all pre-existing finance, benchmark, and source-family exclusions; do not use a mirror or rewrite to bypass them." + }, + "validation_checks": { + "status": "passed", + "checks": [ + "All 1,147 canonical Hermes rows have unique exact native mappings and complete restored records.", + "109 schema-invalid Hermes rows and 2 additional schema-valid semantic cases are disjoint.", + "456 single-call and 580 multi-call schema-valid review candidates after quality quarantine; 275 single-call catalogs contain at least two native tools.", + "1,023 actual historical validation IDs are disjoint from frozen canonical test IDs; 859 are non-Nemotron.", + "Every legacy validation parent hash matches the release BLAKE2b-128 normalization formula.", + "All selected MASSIVE description examples belong outside frozen validation and test membership.", + "All quarantine, finance, and candidate-repair IDs exist in canonical data; replacement candidates are unique and preserve the target.", + "No release construction, upload, or model training is performed by these research scripts." + ], + "input_artifact_sha256": { + "hermes_restored_records.jsonl": "cc9fa296aeb84750d4a84a913d4d3543dc6c2a92d301fc2a71e5a940ff94d785", + "hermes_source_mapping.json": "f7db59bd07a351e6874959b1eee368c6232cfb4d8bfd660e3ab87d6f916cf557", + "hermes_parent_groups.json": "0b757a60d97d42297d9cbff9f308f9c195427f89896e7a1cada129e268a99db5", + "historical_holdouts.json": "bed96087547c827f2961d6675cd8eef1de9c3466af5270c6696b059f95b561b8", + "massive_description_training_evidence.json": "d4ef5e498ddca718c991340b1434b4dca8367c964907097b76a13cd12254612d" + } + }, + "artifact_files": [ + "extract_legacy_facts.py", + "make_repair_decisions.py", + "hermes_restored_records.jsonl", + "hermes_source_mapping.json", + "hermes_parent_groups.json", + "historical_holdouts.json", + "facts_summary.json", + "massive_description_training_evidence.json", + "primary_docs/fetch_manifest.json" + ] + }, + "broad_finance_free_claim": false, + "note": "Only documented scope decisions are applied. The frozen default and v2 remain unchanged." +} diff --git a/curation/v3/quarantine.jsonl.gz b/curation/v3/quarantine.jsonl.gz new file mode 100644 index 0000000000000000000000000000000000000000..34e8cdcf92a462e481241292b4472315dd2bf9c5 --- /dev/null +++ b/curation/v3/quarantine.jsonl.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73214987fbaec508b01d478dac26e863fabc7daa6c24bcd98a5bef7c0ce42b22 +size 1089399 diff --git a/curation/v3/tool_parent_decisions.json b/curation/v3/tool_parent_decisions.json new file mode 100644 index 0000000000000000000000000000000000000000..e56112b10a0481536c5d97e55b109e76e4dcc0d5 --- /dev/null +++ b/curation/v3/tool_parent_decisions.json @@ -0,0 +1,699 @@ +{ + "blocked_parents": [ + "funcdex:0364bfec3755531ed937890077a1ff99", + "funcdex:0fd8892a0b771f338e74ceb9b5bb1155", + "funcdex:20c1538d3140baa081417575cc3e4629", + "funcdex:3e2f6df490c982d76e8c86676feadff5", + "funcdex:579dffc45352291585fbbef351ad8402", + "funcdex:5e58c002adf82f92eb871a6e282b7efc", + "funcdex:61f3ba4a3a48ec671cd40e2fccc467d0", + "funcdex:6976507c216d698bfcfa8ade0dd7664c", + "funcdex:6b01cfbe55ad13f33b4d491caa63ac42", + "funcdex:776cf8163c1a6444d347c7faf1f9c8de", + "funcdex:7833a9588df8a8c31fa2a24a31826c44", + "funcdex:80cb95a02eb23fc96a10ed598235e341", + "funcdex:8876827e1fb21db748aa4289fea153ea", + "funcdex:8bf115f0d106414de347e0a82de851ca", + "funcdex:9cb4f097b8ea6d50eaf92620f968ab66", + "funcdex:a16efbee10f9de5cf97c6830912c3848", + "funcdex:b0070760204890aa7fe9f214bd0df951", + "funcdex:b4d7ebd0025899856b5343d927f7f52a", + "funcdex:c3bcc573819feb8ab22267607a159803", + "funcdex:cf1ba1d6be84abf7c864f72bc6c8545f", + "funcdex:db382ec2856dfb346fbe7c14155dbd10", + "funcdex:f42cdf67d9e8486666de2042d2a46393", + "nemotron:237e0f2c0a137bb563778649adbb1361", + "nemotron:2696a3a63b4513ff56fb9713e356c253", + "nemotron:3ec31689dcd30946f97fb4b33437d7d7", + "nemotron:41b1622c1877653364c378e2b86acb97", + "nemotron:b55a089c8dcf0f3804553a82b788b493", + "nemotron:bbe1ebbec2d84e4140c01a9fb3f2e386", + "nemotron:d4ff1b76ed59c9d7bd3dc2ac3709a5ac", + "nemotron:f214408657f160e8c683d69359b26634", + "nemotron:f9a3d258f9fbbebe4d2352fae872e20f" + ], + "reviewed_parent_decisions": { + "funcdex:f42cdf67d9e8486666de2042d2a46393": [ + { + "sample_id": "F001", + "step_id": "fbv2:funcdex:train:1316:3", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "完整policy要求首次调用RETRIEVE_TASKS_FOR_PROJECT；用户已给project_gid，标签却GET_A_PROJECT（描述明确不返回任务）。原始row1316/index3同一调用，属源轨迹与policy冲突。", + "source_row_id": "1316", + "source_split": "train" + } + ], + "funcdex:776cf8163c1a6444d347c7faf1f9c8de": [ + { + "sample_id": "F002", + "step_id": "fbv2:funcdex:train:280:6", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "GET_ISSUE_TYPE_SCHEME要求方案ID；前缀仅说default scheme，没有default-scheme这一ID来源，且可用GET_ALL_ISSUE_TYPE_SCHEMES先查ID。原始调用把占位式ID送入接口并得到合成成功响应；不能因schema接受string就当执行前提成立。", + "source_row_id": "280", + "source_split": "train" + } + ], + "funcdex:d2d5b7ab099793e502a27629b58df829": [ + { + "sample_id": "F003", + "step_id": "fbv2:funcdex:train:677:52", + "status": "acceptable", + "block_parent": false, + "reason": "当前请求是给刚创建的Launch task加评论；task_5004来自工具返回，CREATE_COMMENT合理。评论内容属于用户授权起草；未证明唯一合法下一步。", + "source_row_id": "677", + "source_split": "train" + } + ], + "funcdex:b0070760204890aa7fe9f214bd0df951": [ + { + "sample_id": "F004", + "step_id": "fbv2:funcdex:train:1142:18", + "status": "acceptable", + "block_parent": true, + "reason": "用户给finance_david并请求添加watcher；EVT-101来自CREATE_ISSUE，目标和当前参数均支持。", + "source_row_id": "1142", + "source_split": "train" + } + ], + "funcdex:6976507c216d698bfcfa8ade0dd7664c": [ + { + "sample_id": "F005", + "step_id": "fbv2:funcdex:train:1173:33", + "status": "acceptable", + "block_parent": true, + "reason": "最终邮件草稿已展示且用户明确批准发送，SEND_EMAIL与body/recipient一致。", + "source_row_id": "1173", + "source_split": "train" + } + ], + "funcdex:9cb4f097b8ea6d50eaf92620f968ab66": [ + { + "sample_id": "F006", + "step_id": "fbv2:funcdex:train:848:23", + "status": "acceptable", + "block_parent": true, + "reason": "用户明确说季度结束要求移除webhook，目标删除调用使用前序工具返回的WH ID，满足本步前提。", + "source_row_id": "848", + "source_split": "train" + } + ], + "funcdex:3e2f6df490c982d76e8c86676feadff5": [ + { + "sample_id": "F007", + "step_id": "fbv2:funcdex:train:1171:5", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "用户要合同PDF→Legal Review task→回复邮件，未请求release/milestone；policy仅在必须建立版本时允许CREATE_VERSION，原始助手却自行创建合同版本。该副作用并非完成请求的必要步骤。", + "source_row_id": "1171", + "source_split": "train" + } + ], + "funcdex:abf9e17c3a8652857a6dcb24434873a7": [ + { + "sample_id": "F008", + "step_id": "fbv2:funcdex:train:1351:60", + "status": "acceptable", + "block_parent": false, + "reason": "为新建Healthcare task添加用户指定的VP/CTO followers，ID皆来自前缀；同样可先添加已获授权附件，先后次序不唯一。", + "source_row_id": "1351", + "source_split": "train" + } + ], + "funcdex:5e58c002adf82f92eb871a6e282b7efc": [ + { + "sample_id": "F009", + "step_id": "fbv2:funcdex:train:1005:3", + "status": "context_ambiguous", + "block_parent": true, + "reason": "policy既要求检查message templates，又要求对超过30天未聊天人群立即拒绝发送；GET_MESSAGE_TEMPLATES是可行的预检，但可以直接文字拒绝且无需查询。没有发送违规操作，不将预检本身当明确错标。", + "source_row_id": "1005", + "source_split": "train" + } + ], + "funcdex:7833a9588df8a8c31fa2a24a31826c44": [ + { + "sample_id": "F010", + "step_id": "fbv2:funcdex:train:624:1", + "status": "context_ambiguous", + "block_parent": true, + "reason": "policy允许获取当前日期，目标工具可接受但用户已经给定活动日期；timezone=-5没有地点、时区或默认值根据。工具名不一定错，参数不宜作为gold复用。", + "source_row_id": "624", + "source_split": "train" + } + ], + "funcdex:010dbaa0d71eeab99faf256abb3c0a39": [ + { + "sample_id": "F061", + "step_id": "fbv2:funcdex:train:890:6", + "status": "acceptable", + "block_parent": false, + "reason": "三位manager获授权逐一邀请；第一位成功后邀请第二位符合policy和必要ID。原始user内容(intermediate)是人工拼接的继续信号，现实交互自然度有限。", + "source_row_id": "890", + "source_split": "train" + } + ], + "funcdex:8876827e1fb21db748aa4289fea153ea": [ + { + "sample_id": "F062", + "step_id": "fbv2:funcdex:train:554:3", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "policy只允许确认标签不存在后CREATE_LABEL，前缀无LIST_GMAIL_LABELS或用户确认不存在；该查询工具可用，标签却直接创建。", + "source_row_id": "554", + "source_split": "train" + } + ], + "funcdex:6b01cfbe55ad13f33b4d491caa63ac42": [ + { + "sample_id": "F063", + "step_id": "fbv2:funcdex:train:1313:11", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "GET_STORIES_FOR_TASK的task_101不在前缀出现；此前只是列项目setup计划，无创建任务工具，status正文却声称所有任务/附件/自动化已经完成。GET_MULTIPLE_TASKS可用，应先获得真实任务ID。", + "source_row_id": "1313", + "source_split": "train" + } + ], + "funcdex:0364bfec3755531ed937890077a1ff99": [ + { + "sample_id": "F064", + "step_id": "fbv2:funcdex:train:885:4", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "用户为刚创建的event type生成链接；目标args却把owner设为助手此前臆造的host用户URI、owner_type=user，没有绑定已返回的event-type URI。schema只有string，无枚举或引用校验。", + "source_row_id": "885", + "source_split": "train" + } + ], + "funcdex:db382ec2856dfb346fbe7c14155dbd10": [ + { + "sample_id": "F065", + "step_id": "fbv2:funcdex:train:907:24", + "status": "acceptable", + "block_parent": true, + "reason": "用户明确报告Candidate02缺席，invitee URI来自LIST_EVENTS，重复事件已取消；当前no-show调用有支持。", + "source_row_id": "907", + "source_split": "train" + } + ], + "funcdex:80cb95a02eb23fc96a10ed598235e341": [ + { + "sample_id": "F066", + "step_id": "fbv2:funcdex:train:101:40", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "用户撤销exec2权限，target UPDATE_PERMISSION以permission.role=none实现，候选已有DELETE_PERMISSION。官方Drive permissions文档不支持none角色；嵌套permission只定义object使静态验证漏掉。", + "source_row_id": "101", + "source_split": "train" + } + ], + "funcdex:0fd8892a0b771f338e74ceb9b5bb1155": [ + { + "sample_id": "F067", + "step_id": "fbv2:funcdex:train:834:12", + "status": "acceptable", + "block_parent": true, + "reason": "当前single-use link使用已返回event-type URI，max_event_count=1对应请求；CREATE_SCHEDULING_LINK描述也能完成，gold非唯一。", + "source_row_id": "834", + "source_split": "train" + } + ], + "funcdex:579dffc45352291585fbbef351ad8402": [ + { + "sample_id": "F068", + "step_id": "fbv2:funcdex:train:936:10", + "status": "acceptable", + "block_parent": true, + "reason": "两封PDF已取回，用户明确要求发给legal，当前FORWARD_EMAIL_MESSAGE参数直接引用msg_001和指定收件人。", + "source_row_id": "936", + "source_split": "train" + } + ], + "funcdex:2098c3b0485f35e20b2f24cd5e797a3a": [ + { + "sample_id": "F069", + "step_id": "fbv2:funcdex:train:64:17", + "status": "acceptable", + "block_parent": false, + "reason": "用户给旧报告Drive ID并要求复制到审计包；COPY_FILE以该ID复制是合理下一步，新copy完成后仍需MOVE_FILE。", + "source_row_id": "64", + "source_split": "train" + } + ], + "funcdex:61f3ba4a3a48ec671cd40e2fccc467d0": [ + { + "sample_id": "F070", + "step_id": "fbv2:funcdex:train:1417:7", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "PATCH_CALENDAR实际修改日历metadata，源policy却把它描述成修改event；当前args包含schema未定义attendees/start_datetime并缺event ID和Drive URL。宽松additionalProperties使错误对象层次通过schema验证，执行不能生成所需会议且可能重命名primary calendar。", + "source_row_id": "1417", + "source_split": "train" + } + ], + "funcdex:20c1538d3140baa081417575cc3e4629": [ + { + "sample_id": "F081", + "step_id": "fbv2:funcdex:test:26:4", + "status": "context_ambiguous", + "block_parent": true, + "reason": "用户需找Sales Data Q3 spreadsheet，SEARCH_DOCUMENTS的描述仅称Google documents，返回folder的前史使实现是否支持通用Drive MIME筛选不明。读查询可能合理，但没有执行实现可确认跨类型检索能力。", + "source_row_id": "26", + "source_split": "test" + } + ], + "funcdex:1527d3f77a0dbf965c1a49d2bada4c9a": [ + { + "sample_id": "F082", + "step_id": "fbv2:funcdex:test:246:3", + "status": "acceptable", + "block_parent": false, + "reason": "用户给template document ID、vendor和amendment序号；copy目标与title均满足policy，余下replace依赖新ID。", + "source_row_id": "246", + "source_split": "test" + } + ], + "funcdex:b4d7ebd0025899856b5343d927f7f52a": [ + { + "sample_id": "F083", + "step_id": "fbv2:funcdex:test:32:19", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "用户要求在文档末尾插入的表格之后加图；原args location.index=5没有任何文档结构返回支持，已知markdown先有长标题和摘要，index5不可能位于末尾表格后。工具名合理但位置参数错误。", + "source_row_id": "32", + "source_split": "test" + } + ], + "funcdex:93911677922f73486eea29879cb93a3d": [ + { + "sample_id": "F084", + "step_id": "fbv2:funcdex:test:230:3", + "status": "acceptable", + "block_parent": false, + "reason": "用户提供原Gmail message ID，policy明确先读取原邮件再取附件；当前FETCH_MESSAGE_BY_MESSAGE_ID的ID和me均有根据。", + "source_row_id": "230", + "source_split": "test" + } + ], + "funcdex:cf1ba1d6be84abf7c864f72bc6c8545f": [ + { + "sample_id": "F085", + "step_id": "fbv2:funcdex:test:173:18", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "源对话中途user/assistant角色反转：user说Here is the link / I will cancel，assistant却自称Maya并给booking UUID、请求取消。本步CANCEL_EVENT接在user“I will cancel”后，原始角色不能正确表示谁授权谁执行。", + "source_row_id": "173", + "source_split": "test" + } + ], + "funcdex:c3bcc573819feb8ab22267607a159803": [ + { + "sample_id": "F086", + "step_id": "fbv2:funcdex:test:245:21", + "status": "acceptable", + "block_parent": true, + "reason": "用户明确两项global替换，先替换HOST_NAME是合理单调用且新document_id已返回。", + "source_row_id": "245", + "source_split": "test" + } + ], + "funcdex:a16efbee10f9de5cf97c6830912c3848": [ + { + "sample_id": "F087", + "step_id": "fbv2:funcdex:test:37:13", + "status": "acceptable", + "block_parent": true, + "reason": "复制文件已返回ID，目标新文件夹已创建，MOVE_FILE用于搬运copy符合任务。", + "source_row_id": "37", + "source_split": "test" + } + ], + "funcdex:8bf115f0d106414de347e0a82de851ca": [ + { + "sample_id": "F088", + "step_id": "fbv2:funcdex:test:21:10", + "status": "context_ambiguous", + "block_parent": true, + "reason": "用户要求after sharing再hide，前缀只有共享操作的手工说明，未确认用户执行了共享；直接hide是可能下一步但先决状态不确定。", + "source_row_id": "21", + "source_split": "test" + } + ], + "funcdex:8ba1d3ba07890c625b689151c6b781e4": [ + { + "sample_id": "F089", + "step_id": "fbv2:funcdex:test:137:82", + "status": "acceptable", + "block_parent": false, + "reason": "HR任务已创建且最新已更新due date；用户指定Signed clause comment原文，CREATE_COMMENT引用task_5004正确。用户此前允许仅在comment文本提及PDF，不能误判为必须真实上传。", + "source_row_id": "137", + "source_split": "test" + } + ], + "funcdex:3e8ab49aeb82b9ab97195e8f34169953": [ + { + "sample_id": "F090", + "step_id": "fbv2:funcdex:test:235:9", + "status": "acceptable", + "block_parent": false, + "reason": "用户明确查Insider Threat主题草稿，LIST_DRAFTS verbose=true可取内容后本地筛选；schema无subject查询字段，不应误判缺query。后续分页仍需根据响应处理。", + "source_row_id": "235", + "source_split": "test" + } + ], + "nemotron:bbe1ebbec2d84e4140c01a9fb3f2e386": [ + { + "sample_id": "N001", + "step_id": "fbv2:nemotron:ad2ceab085166eeb054acf3cc76cd282", + "status": "context_ambiguous", + "block_parent": true, + "reason": "用户要求HIPAA/医疗隐私合规书面保证，转人工是合理限制边界；policy列出的transfer触发只含insurance/custom contract/security，未写此场景。不能证明transfer唯一gold，也不宜直接判错。", + "source_row_id": "4230", + "source_split": "interactive_agent" + } + ], + "nemotron:1698ba4ac79f74377220e886cb0a30c3": [ + { + "sample_id": "N002", + "step_id": "fbv2:nemotron:4d82b68cf253105f32fc3c4c219f1942", + "status": "acceptable", + "block_parent": false, + "reason": "authenticate_user工具已确认Tier 1及resident_id；用户明确optometrist/routine annual eye exam，协调专科咨询的前提和required names齐备。", + "source_row_id": "11580", + "source_split": "interactive_agent" + } + ], + "nemotron:74737c18d67239b8e1cec1a5be781d7d": [ + { + "sample_id": "N003", + "step_id": "fbv2:nemotron:c25c379fd97df212684b3c80eac072e3", + "status": "acceptable", + "block_parent": false, + "reason": "用户选择full name+ZIP核验并给出两项，verify_user_identity是合理下一步；用户新ZIP是否与档案一致应由工具决定，不能预判验证结果。", + "source_row_id": "2734", + "source_split": "interactive_agent" + } + ], + "nemotron:41b1622c1877653364c378e2b86acb97": [ + { + "sample_id": "N004", + "step_id": "fbv2:nemotron:b0694ea49102952d7066346fd187b45e", + "status": "context_ambiguous", + "block_parent": true, + "reason": "未认证用户要求经理；policy一方面要求before any action验证，一方面要求client requests manager则transfer。认证失败是否可直接转人工未定义，属于政策冲突而非可确定错标。", + "source_row_id": "3845", + "source_split": "interactive_agent" + } + ], + "nemotron:237e0f2c0a137bb563778649adbb1361": [ + { + "sample_id": "N005", + "step_id": "fbv2:nemotron:888135f69fb2706f5d7f07c57c0ee2f6", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "航空policy要求访问reservation之前先发送并验证验证码；完整原始prefix只有system+首条user，目标get_reservation_details直接访问订票信息。已核对NVIDIA原始row312/index2和完整schema，policy逐字相同，排除本次截断修复造成。", + "source_row_id": "312", + "source_split": "interactive_agent" + } + ], + "nemotron:3ec31689dcd30946f97fb4b33437d7d7": [ + { + "sample_id": "N006", + "step_id": "fbv2:nemotron:5a89a1d38a0545d1dc29bf5c7d23b9ce", + "status": "clear_contradiction_or_mismatch", + "block_parent": true, + "reason": "policy要求account access前验证User ID+注册email/phone；用户仅给User ID，历史get_user_profile直接返回VIP资料，再选择get_subscription_details，仍无认证。恢复后的前缀暴露认证前提未满足。", + "source_row_id": "12955", + "source_split": "interactive_agent" + } + ], + "nemotron:64ab5841bbeafc5585a503c1947d8bba": [ + { + "sample_id": "N007", + "step_id": "fbv2:nemotron:ea83f2ae0b03b36d3a345545b2936dae", + "status": "acceptable", + "block_parent": false, + "reason": "用户给Client ID和规定的电话尾号验证信息，authenticate_client合理；退赔时限和专家判断尚未执行，不把后续可能资格不符算到当前认证调用。", + "source_row_id": "9965", + "source_split": "interactive_agent" + } + ], + "nemotron:f27d491ed368e3cb744aab64bd21aced": [ + { + "sample_id": "N008", + "step_id": "fbv2:nemotron:f370f37982c4b2840ded81fc1d7a2e86", + "status": "acceptable", + "block_parent": false, + "reason": "发送、提交、验证验证码已有成功工具响应并返回guest_id，随后get_guest_profile用于检查会员资格合理。", + "source_row_id": "3029", + "source_split": "interactive_agent" + } + ], + "nemotron:233177b1f7d713d274f3b4c2e8a44a30": [ + { + "sample_id": "N009", + "step_id": "fbv2:nemotron:2f4bcf68cf992763ed37bbd411092d2d", + "status": "acceptable", + "block_parent": false, + "reason": "注册用户给出email，请求查迟送费；policy先send authentication code，当前工具符合认证步骤，不因用户声称延迟便直接退款。", + "source_row_id": "8863", + "source_split": "interactive_agent" + } + ], + "nemotron:265cb1677862fd7931c1a568400e2b03": [ + { + "sample_id": "N010", + "step_id": "fbv2:nemotron:f84aea370dd777966f22edafe19cd9a4", + "status": "acceptable", + "block_parent": false, + "reason": "用户明确要billing agent，现有工具无法添加副卡，transfer_to_human_agent符合能力边界；不臆答扣款顺序和完成结果。", + "source_row_id": "6606", + "source_split": "interactive_agent" + } + ], + "nemotron:a7b76c9ddca26f8da17b76239ec2419c": [ + { + "sample_id": "N061", + "step_id": "fbv2:nemotron:4779b2d734a37a8042d2bb44cd754da3", + "status": "acceptable", + "block_parent": false, + "reason": "用户保险direct billing咨询触发明确insurance escalation，transfer合理；不执行账户或付款动作。", + "source_row_id": "11383", + "source_split": "interactive_agent" + } + ], + "nemotron:76a7853afd0a65322ffa2ecca623e65e": [ + { + "sample_id": "N062", + "step_id": "fbv2:nemotron:fb7b458e871334545722a45bbad110b4", + "status": "acceptable", + "block_parent": false, + "reason": "booking ID+last name符合guest验证方法，先authenticate而非直接cancel，日期/退款比例留待核验。", + "source_row_id": "4552", + "source_split": "interactive_agent" + } + ], + "nemotron:5a11d5539003171d6b0399b29066d85a": [ + { + "sample_id": "N063", + "step_id": "fbv2:nemotron:2296cbbd2d8824fffeaddf1025278740", + "status": "acceptable", + "block_parent": false, + "reason": "用户无法找到ID并明确要求supervisor，transfer工具说明覆盖complex issues；policy列举触发未写only，不据此判错。", + "source_row_id": "2744", + "source_split": "interactive_agent" + } + ], + "nemotron:4aeb08fdb50d2d4ab5ca6a0f0639f95a": [ + { + "sample_id": "N064", + "step_id": "fbv2:nemotron:9ca7a48d1250497e60b7f4425c57c80e", + "status": "acceptable", + "block_parent": false, + "reason": "身份已由工具确认；用户要联系管理方问staffing，无staffing数据工具而有human transfer，下一步转人工合理。", + "source_row_id": "6677", + "source_split": "interactive_agent" + } + ], + "nemotron:4d58740b841903c23abd8835b9f3e823": [ + { + "sample_id": "N065", + "step_id": "fbv2:nemotron:163b28d0c0bf05af8d913033a4b92f0f", + "status": "acceptable", + "block_parent": false, + "reason": "船舶事故保险理赔为policy明确转人工场景；该步不直接修改账户或发起支付，转人工合理。", + "source_row_id": "2916", + "source_split": "interactive_agent" + } + ], + "nemotron:0dc84454ed457035d454d9081e7cc32e": [ + { + "sample_id": "N066", + "step_id": "fbv2:nemotron:c992041f87ee49101d7c72f32cf2a9d3", + "status": "acceptable", + "block_parent": false, + "reason": "用户最终明确请求pastoral staff谈宗教教育事宜；工具转pastoral staff对应policy，未违反无酒精预订规则。", + "source_row_id": "3323", + "source_split": "interactive_agent" + } + ], + "nemotron:2696a3a63b4513ff56fb9713e356c253": [ + { + "sample_id": "N067", + "step_id": "fbv2:nemotron:5ba17f0b6796a01c47518321a450aa42", + "status": "context_ambiguous", + "block_parent": true, + "reason": "authenticate_user要求certification_level；用户称Guide，policy列Novice/Certified/Professional，缺完整enum和原始args，不知是否允许Guide或需映射/澄清。工具名合理，不能认证参数已准备好。", + "source_row_id": "12756", + "source_split": "interactive_agent" + } + ], + "nemotron:d4ff1b76ed59c9d7bd3dc2ac3709a5ac": [ + { + "sample_id": "N068", + "step_id": "fbv2:nemotron:5d51461d5e94dc257c16277bfdaef7c0", + "status": "context_ambiguous", + "block_parent": true, + "reason": "完成证书不在工具能力内；transfer工具限定per policy triggers，而列举触发未覆盖该普通证书请求。转人工或文字解释限制都合理，无法证明单一label必选。", + "source_row_id": "2138", + "source_split": "interactive_agent" + } + ], + "nemotron:809a0749e0104413e055ef81800101ba": [ + { + "sample_id": "N069", + "step_id": "fbv2:nemotron:c1b7d7d5d0b6cb7c25c4dfa454e28067", + "status": "acceptable", + "block_parent": false, + "reason": "用户报告data update latency，check_service_status可先核实是否故障；不能据它承诺SEO刷新频率，但当前诊断查询合理。", + "source_row_id": "6364", + "source_split": "interactive_agent" + } + ], + "nemotron:bb5d498dc838ec45fba642f948d60f6d": [ + { + "sample_id": "N070", + "step_id": "fbv2:nemotron:4ab93fe049238eae5b1a6ee00a2f04cd", + "status": "acceptable", + "block_parent": false, + "reason": "完整验证码认证成功并返回customer_id，get_customer_details查当前服务计划是升级前合理准备。", + "source_row_id": "11235", + "source_split": "interactive_agent" + } + ], + "nemotron:e564ee8531922f45d211a6311bbea706": [ + { + "sample_id": "N081", + "step_id": "fbv2:nemotron:6d273024535e5eaf0f376e96fb35119f", + "status": "acceptable", + "block_parent": false, + "reason": "profile和matches已返回，get_subscription_details可核实每日额度/会员tier；policy未明确要求此次查询必须先调用独立认证。", + "source_row_id": "924", + "source_split": "interactive_agent" + } + ], + "nemotron:9dbbb7f09d6263f706251448e5da4c16": [ + { + "sample_id": "N082", + "step_id": "fbv2:nemotron:b761675a69bbfd65342af0ddd003dcc5", + "status": "acceptable", + "block_parent": false, + "reason": "authenticate_customer已成功，get_customer_profile可核验合约tier和设备信息，再决定远程诊断。", + "source_row_id": "9794", + "source_split": "interactive_agent" + } + ], + "nemotron:1f00689599faf44052857c7667fcb4e7": [ + { + "sample_id": "N083", + "step_id": "fbv2:nemotron:119ff0656fe5df1118ef19014852e1c4", + "status": "acceptable", + "block_parent": false, + "reason": "brand ID与业务税号尾部验证信息均由用户给出，authenticate_brand符合政策身份方法。后续COPPA文件是否可取不影响本步。", + "source_row_id": "6907", + "source_split": "interactive_agent" + } + ], + "nemotron:f214408657f160e8c683d69359b26634": [ + { + "sample_id": "N084", + "step_id": "fbv2:nemotron:ffaa07515484209b2c2b709ef1e7fe64", + "status": "context_ambiguous", + "block_parent": true, + "reason": "当前verify_user_identity方向合理，用户给验证材料；但历史声称已发送phone/email验证码，没有发送工具或发送记录，challenge来源未证实，且现有工具集中无code发送接口。不能认定参数可执行性。", + "source_row_id": "7069", + "source_split": "interactive_agent" + } + ], + "nemotron:a8a10dd52487852b579fad194f1cf80e": [ + { + "sample_id": "N085", + "step_id": "fbv2:nemotron:503bd10c286f30b0fdcd70754e5284d5", + "status": "acceptable", + "block_parent": false, + "reason": "farm_id/device_pin/equipment_id均由用户给出；诊断工具自身接受认证凭据，工具集中无独立authenticate，因此不能机械地把未先call authenticate判错。用户同意诊断但不可据此执行被禁止的重校准。", + "source_row_id": "10697", + "source_split": "interactive_agent" + } + ], + "nemotron:f9a3d258f9fbbebe4d2352fae872e20f": [ + { + "sample_id": "N086", + "step_id": "fbv2:nemotron:faa7e0c5c618b7a16cc572292e28ac50", + "status": "context_ambiguous", + "block_parent": true, + "reason": "用户给验证材料后authenticate_user合理，但前缀无验证码发送记录，系统与toolset也未提供发送接口；真实challenge存在与否未知。", + "source_row_id": "7613", + "source_split": "interactive_agent" + } + ], + "nemotron:4105cf069a4a9497479730ab2703a5b7": [ + { + "sample_id": "N087", + "step_id": "fbv2:nemotron:309ebf168c1f996e31bfbc4f6ededfa9", + "status": "acceptable", + "block_parent": false, + "reason": "用户给User ID，查询积分/会员档案可验证余额；最新规则问题也可用policy文字答复，当前read是合理非唯一动作。", + "source_row_id": "13837", + "source_split": "interactive_agent" + } + ], + "nemotron:b55a089c8dcf0f3804553a82b788b493": [ + { + "sample_id": "N088", + "step_id": "fbv2:nemotron:04c54c74ff648ec99fc8d0ef7a210984", + "status": "context_ambiguous", + "block_parent": true, + "reason": "已补查NVIDIA原始row425/index4：policy要求发码并校验；用户只给email要求send，观察调用verify_identity(method=parent_email)。完整schema的method枚举仅student_id/parent_email/guest_code，没有send模式，也无接收验证code字段。源policy与认证接口冲突已证实；当前调用符合接口说明但无法证明遵守policy，也不存在已验证唯一替代动作，因此保留动作gold歧义。", + "source_row_id": "425", + "source_split": "interactive_agent" + } + ], + "nemotron:fb9df802ae975b484dceacbb07e8983b": [ + { + "sample_id": "N089", + "step_id": "fbv2:nemotron:f779d72da5251d2ded1ccb6fe0e51840", + "status": "acceptable", + "block_parent": false, + "reason": "用户问情感支持动物在比赛中的便利安排，明确属于policy accessibility accommodations，转人工无需臆断法律/服务动物资格。", + "source_row_id": "14373", + "source_split": "interactive_agent" + } + ], + "nemotron:70b766eacac85d080e8470ce5c411501": [ + { + "sample_id": "N090", + "step_id": "fbv2:nemotron:2ae4f48d9fc620550632b8c06d3b0e57", + "status": "acceptable", + "block_parent": false, + "reason": "用户VIN和registered phone已齐，authenticate_customer匹配policy；不可把其后无法回答的plan terms当作当前认证错标。", + "source_row_id": "7752", + "source_split": "interactive_agent" + } + ] + }, + "selection_rule": "Block full reviewed parent for explicit contradiction, current-action ambiguity, or substantive contaminated history. 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+1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e36c812a6b54a321a45fce805bef13c784b196a177db91c6fcfc87f6c251e1b +size 1008820 diff --git a/data/tool_environment_holdout_v3/validation.parquet b/data/tool_environment_holdout_v3/validation.parquet new file mode 100644 index 0000000000000000000000000000000000000000..721a4aa67d0cbaadb6db47086fffead4432395fb --- /dev/null +++ b/data/tool_environment_holdout_v3/validation.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:916608566e8821f2a4fa1ae84566f031e28429041eeba7da5bd1733410841403 +size 979530 diff --git a/docs/curation-v3.md b/docs/curation-v3.md new file mode 100644 index 0000000000000000000000000000000000000000..fa7cd8edab4bd67a67f694807d4df0b32ca5656f --- /dev/null +++ b/docs/curation-v3.md @@ -0,0 +1,21 @@ +# v3 repair recipe + +Base revision: `11e6f657f6d8830da230d4e49c65d0f67f106585`. Seed: `2026101403`. + +1. Apply documented legacy descriptor/contract decisions and quarantine unresolved source labels. Restore historical validation by canonical source ID with test precedence. Group normalized text, one leading greeting, and quoted/numeric surface forms against existing holdouts. Lower-priority rows are reserved; they are not relabeled or moved into a favorable evaluation split. +2. Recover Hermes by exact first-user `[:400]`, target name and target-description `[:160]` matches. Validate original call contracts, preserve the full original request and native call set, and keep multi-call/singleton-tool records outside the choice view. Native request groups and historical siblings stay protected. +3. Regenerate every retained semantic present/absent pair with the repaired catalog and conservative conflict exclusions. Both variants have the same candidate count and exactly one ordinary candidate replacement. Native labels are not changed to conceal ambiguous requests. +4. Exclude all steps of 31 reviewed bad tool parents. Recover Nemotron schemas from the original NVIDIA file; only strict verified prefixes plus valid native contracts enable argument supervision. Reserve exact policy/catalog environments from previously train-only groups; keep existing evaluation exposure metadata. +5. Retain native NLU++ annotation and mask generic Boolean export. Remove only the invalid date values from the separate value-training view; retain original slots unchanged. +6. Keep HINT3 native brand-relative labels and original test isolation. Apply the row-level finance/overlap/blank/duplicate decisions, split validation by query group inside native train only, and preserve original catalog semantics and licensing. +7. Export deterministic random criterion IDs. Validate every choice record using the actual Clef encoder without truncation. Auxiliary tasks have no automatic choice conversion. + +## Auditable dispositions + +The ledger has 2,524 entries: `{"deduplicated":8,"quarantine":426,"separate_task":752,"split_reserved":1338}`. These are disposition records, not an estimate of wrong labels. Examples retained in a native auxiliary view have `separate_task`; lexical reservations have `split_reserved`. + +## Scope of quality claims + +Checks establish structural consistency, known-defect handling, recorded partition rules and source recovery. They do not establish exhaustive semantic correctness, original-dialogue isolation for all legacy sources, independent unseen behavior of pretrained models, or preserved model accuracy. Argument schema validity is not execution validity. + +The current choice adapter uses published query text and candidate name/description only. Full schemas are retained separately for future reviewed adapters. HINT3, native NLU++ and Hermes call sets require their own objectives and evaluation. Do not count their rows or original label strings as new independent choice intents. diff --git a/docs/v2-data-card.md b/docs/v2-data-card.md new file mode 100644 index 0000000000000000000000000000000000000000..f1479deb2edc3e35936278965d81af1a351be9b0 --- /dev/null +++ b/docs/v2-data-card.md @@ -0,0 +1,145 @@ +--- +language: +- en +license: other +license_name: mixed-upstream-licenses +license_link: https://huggingface.co/datasets/TypeScript/FastBrain-Instruct/blob/main/LICENSE.md +task_categories: +- text-classification +- zero-shot-classification +task_ids: +- intent-classification +- multi-class-classification +tags: +- fastbrain +- tool-use +- function-calling +- intent-classification +- multi-label +- synthetic +- aggregated +source_datasets: +- glaiveai/glaive-function-calling-v2 +- NousResearch/hermes-function-calling-v1 +- nvidia/Nemotron-SFT-Agentic-v2 +- MaziyarPanahi/AgentToolDecisions-180K +- AnishJoshi/nl2bash-custom +- burak29/git-natural-language-commands +- AmazonScience/massive +- google-research-datasets/schema_guided_dstc8 +- WillHeld/mtop +- WillHeld/top_v2 +- prem-research/Funcdex-MT-Function-Calling +size_categories: +- 100K max_length: + raise ValueError(f"Record {record['id']} requires {len(encoded.input_ids)} tokens; limit is {max_length}. No truncation performed.") + return encoded