Publish verified JevalKit TSV datasets and source manifests

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.tsv filter=lfs diff=lfs merge=lfs -text

ATTRIBUTION.md

@@ -0,0 +1,21 @@
+# Sources and attribution
+
+These TSV exports preserve upstream content and provenance while converting records
+into JevalKit's shared-state typed-decision format. TSV bytes match the toolkit's
+pinned evaluation snapshots; manifest-only machine paths were made portable.
+Source datasets retain their respective licenses and terms.
+
+| Family | Upstream | License / attribution |
+|---|---|---|
+| VisualDecisionBench | [Valen-Team/VisualDecisionBench](https://huggingface.co/datasets/Valen-Team/VisualDecisionBench) | Per constituent dataset; image/video source and license tables are retained in the visual manifests. |
+| JevBench | [fstandhartinger/jevbench](https://github.com/fstandhartinger/jevbench) | MIT; [license text](licenses/JevBench-MIT.txt). Public easy/original/hard tasks only. |
+| DecisionIndex | [apolinario/decision-index](https://github.com/apolinario/decision-index) | Edition 0.3; constituent dataset licenses and source metadata are retained in the suite manifest and TSV record metadata. |
+| TypedDecisions | [LocalLLaMA/typed-decisions](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) | Apache-2.0; original dataset card retained under `sources/TypedDecisions/`. |
+| DecisionBench | [Hanno-Labs/decision-bench](https://huggingface.co/datasets/Hanno-Labs/decision-bench) | Source-specific; upstream dataset metadata says `other`. Original card and upstream repository license are retained; that repository license does not override constituent data terms. |
+| ProceduralTypedDecisions | [tasksource/procedural-typed-decisions](https://huggingface.co/datasets/tasksource/procedural-typed-decisions) | Apache-2.0; original dataset card retained under `sources/ProceduralTypedDecisions/`. |
+| ZhDecisionBench | [CodyQin/zh-decision-bench](https://huggingface.co/datasets/CodyQin/zh-decision-bench) | CC-BY-4.0; attribution to Amazon MASSIVE and CodyQin. Converted voice routing/business scenario evaluation records; [license text](licenses/ZhDecisionBench-CC-BY-4.0.txt). |
+| JevAltBench | [mertkayacs/jevalt-bench](https://huggingface.co/datasets/mertkayacs/jevalt-bench) | Mixed permissive licenses; per-record source licenses are retained in TSV metadata. Original dataset card retained under `sources/JevAltBench/`. |
+
+Exact revisions, source file checksums, benchmark protocols and source-specific
+transformations are recorded in `data-manifest.json` and `datasets/*.manifest.json`.
+The JevalKit conversion implementation is [available here](https://github.com/Liuziyu77/JevalKit/tree/main/jevalkit/datasets).

README.md

@@ -1,3 +1,179 @@
 ---
-license: apache-2.0
+pretty_name: JevalKit Datasets
+license: other
+license_name: upstream-dataset-specific-licenses
+license_link: https://huggingface.co/datasets/Valen-Team/JevalKit_Datasets/blob/main/ATTRIBUTION.md
+language:
+- en
+- zh
+- de
+- tr
+tags:
+- jev
+- decision-making
+- benchmark
+- evaluation
+- typed-decisions
+- jevalkit
+configs:
+- config_name: VisualDecisionBench
+  data_files:
+  - split: test
+    path: datasets/VisualDecisionBench.tsv
+  sep: "\t"
+  keep_default_na: false
+- config_name: VisualDecisionBench_Image
+  data_files:
+  - split: test
+    path: datasets/VisualDecisionBench_Image.tsv
+  sep: "\t"
+  keep_default_na: false
+- config_name: VisualDecisionBench_Video
+  data_files:
+  - split: test
+    path: datasets/VisualDecisionBench_Video.tsv
+  sep: "\t"
+  keep_default_na: false
+- config_name: JevBench
+  data_files:
+  - split: test
+    path: datasets/JevBench.tsv
+  sep: "\t"
+  keep_default_na: false
+  default: true
+- config_name: DecisionIndex
+  data_files:
+  - split: test
+    path: datasets/DecisionIndex.tsv
+  sep: "\t"
+  keep_default_na: false
+- config_name: TypedDecisions
+  data_files:
+  - split: test
+    path: datasets/TypedDecisions.tsv
+  sep: "\t"
+  keep_default_na: false
+- config_name: DecisionBench
+  data_files:
+  - split: test
+    path: datasets/DecisionBench.tsv
+  sep: "\t"
+  keep_default_na: false
+- config_name: ProceduralTypedDecisions
+  data_files:
+  - split: test
+    path: datasets/ProceduralTypedDecisions.tsv
+  sep: "\t"
+  keep_default_na: false
+- config_name: ZhDecisionBench
+  data_files:
+  - split: test
+    path: datasets/ZhDecisionBench.tsv
+  sep: "\t"
+  keep_default_na: false
+- config_name: JevAltBench
+  data_files:
+  - split: test
+    path: datasets/JevAltBench.tsv
+  sep: "\t"
+  keep_default_na: false
 ---
+
+# JevalKit Datasets
+
+The verified TSV datasets used by [JevalKit](https://github.com/Liuziyu77/JevalKit),
+a toolkit for evaluating native Jev typed decision models.
+This release contains **10 TSV views across 8 benchmark families**, preserving the exact
+data bytes, example IDs, question grouping and reference targets used by the toolkit.
+The TSV files total **854,502,167 bytes**. No model weights or evaluation outputs are included.
+
+## Available datasets
+
+| Configuration / TSV | Shared-state records | Questions | Modality |
+|---|---:|---:|---|
+| VisualDecisionBench | 1,696 | 9,143 | image,video |
+| VisualDecisionBench_Image | 1,402 | 2,000 | image |
+| VisualDecisionBench_Video | 294 | 7,143 | video |
+| JevBench | 231 | 231 | text |
+| DecisionIndex | 140,178 | 312,320 | text |
+| TypedDecisions | 400 | 2,000 | text |
+| DecisionBench | 23,900 | 23,900 | text |
+| ProceduralTypedDecisions | 18,000 | 57,016 | text |
+| ZhDecisionBench | 378 | 467 | text |
+| JevAltBench | 3,116 | 8,434 | text |
+
+`VisualDecisionBench` combines the Image and Video views; these three files overlap.
+The `test` split name is a common evaluation interface: original source splits and
+the exact projection protocol are recorded in each `datasets/*.manifest.json`.
+These are evaluation exports, not new random train/test splits.
+
+## Load a benchmark
+
+```python
+import json
+from datasets import load_dataset
+
+dataset = load_dataset("Valen-Team/JevalKit_Datasets", "JevBench", split="test")
+row = dataset[0]
+request = json.loads(row["request"])
+targets = json.loads(row["targets"])
+```
+
+Every TSV has five columns: `index`, `request`, `targets`, `meta`, and `assets`.
+The last four columns contain UTF-8 JSON strings. A row represents one shared state
+with one or more questions. `request` is the model input; `targets` contains gold
+references and must not be passed to the model. `meta` retains source provenance and
+benchmark grouping; `assets` contains media references and checksums where available.
+
+For direct TSV loading, use a tab separator and JSON-decode nested columns:
+
+```python
+import csv
+import json
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download("Valen-Team/JevalKit_Datasets", "datasets/JevBench.tsv", repo_type="dataset")
+csv.field_size_limit(256 * 1024 * 1024)
+with open(path, encoding="utf-8", newline="") as stream:
+    for row in csv.DictReader(stream, delimiter="\t"):
+        request = json.loads(row["request"])
+```
+
+## Download for JevalKit
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download("Valen-Team/JevalKit_Datasets", repo_type="dataset", local_dir="./JevalKit_Data")
+```
+
+```bash
+export JEVALKIT_DATA_ROOT="$PWD/JevalKit_Data"
+sha256sum -c SHA256SUMS  # run inside JevalKit_Data
+```
+
+The directory layout is compatible with JevalKit's `DecisionDataset(name, root=...)` loader.
+`raw/DecisionIndex-0.3/` contains the pinned suite files needed by the official
+Decision Index scorer; its compressed files are also checksum protected.
+
+**Visual media are downloaded separately** from
+[VisualDecisionBench](https://huggingface.co/datasets/Valen-Team/VisualDecisionBench).
+Place its `assets/` directory at `media/VisualDecisionBench/assets/` under the downloaded
+root. The three visual manifests resolve media from `../media/VisualDecisionBench`.
+The TSVs retain the relative asset paths and available content hashes; media bytes
+are not embedded in this TSV release.
+
+## Reproducibility and sources
+
+`data-manifest.json` lists every TSV, record/question count, projection protocol,
+source revision and SHA-256. Each TSV also has an adjacent manifest.
+`SHA256SUMS` covers all release files except itself and `.gitattributes`.
+All TSVs were parsed with JevalKit's strict schema validator, checked for unique IDs,
+and verified against their pinned hashes and expected record/question counts before release.
+
+The TSVs are format conversions of the attributed upstream benchmarks. Normalization,
+soft-gold handling, explicit unknown choices and benchmark-specific scoring follow
+the linked JevalKit implementation and each manifest's protocol fields.
+See [ATTRIBUTION.md](ATTRIBUTION.md) for upstream repositories and license information.
+Each upstream dataset retains its own license and terms; the repository metadata
+does not apply a single Apache license to all constituent data.

SHA256SUMS

@@ -0,0 +1,37 @@
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+81c2618ac0f3959d0258d99ada86ccc7643237fa0052cdc826bca500d0c94c12  README.md
+908a4380cc5a5d03b247a7e65fbc6348be39c0b21e745530716214a5dcd224a2  data-manifest.json
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+c99000b66862ef3781aa65f1d4edf267c605a519e619b9975a1509de428fefec  datasets/DecisionBench.tsv
+75be3bc90be71a58880213e36eff8a19d8d321f6e4ce143898334355c0cf9136  datasets/DecisionIndex.manifest.json
+9cc55d90b70b0aeca387abffed2957d740a11246be6dc480dfd41e0cc9b7368f  datasets/DecisionIndex.tsv
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+e417f646d022d522edc778271550df9d07dceeccb45148e92bece4d8f17d088a  verification.json

data-manifest.json

@@ -0,0 +1,226 @@
+{
+  "schema_version": 1,
+  "root": ".",
+  "repository": "Valen-Team/JevalKit_Datasets",
+  "jevalkit_repository": "https://github.com/Liuziyu77/JevalKit",
+  "jevalkit_revision": "d893be5236aefaf14163bd516f3405a482c5cef6",
+  "created_utc": "2026-10-19T03:36:26.832857+00:00",
+  "families": 8,
+  "tsv_files": 10,
+  "datasets": [
+    {
+      "name": "VisualDecisionBench",
+      "records": 1696,
+      "questions": 9143,
+      "sha256": "7caa026b69ba1b160f2aa23776b31d4d6162cdb95592810411ce4c905e1a1b44",
+      "protocol": "native-shared-state",
+      "source": {
+        "repository": "https://huggingface.co/datasets/Valen-Team/VisualDecisionBench",
+        "license": "upstream datasets retain their own licenses",
+        "eval_jsonl_sha256": "23eec19993613f9fb1cc6671dbbdf3afb99525c58097f3ac6bd752ae3f34d7e9"
+      },
+      "path": "datasets/VisualDecisionBench.tsv",
+      "manifest": "datasets/VisualDecisionBench.manifest.json",
+      "bytes": 8922237,
+      "family": "VisualDecisionBench",
+      "modality": "image,video"
+    },
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+      "questions": 2000,
+      "sha256": "4db5a36bb084f5143d537a107e1685a2d01a3cbd7bf7c8717dd414d91e9aa67a",
+      "protocol": "native-shared-state",
+      "source": {
+        "repository": "https://huggingface.co/datasets/Valen-Team/VisualDecisionBench",
+        "license": "upstream datasets retain their own licenses",
+        "eval_jsonl_sha256": "23eec19993613f9fb1cc6671dbbdf3afb99525c58097f3ac6bd752ae3f34d7e9"
+      },
+      "path": "datasets/VisualDecisionBench_Image.tsv",
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+      "bytes": 3136681,
+      "family": "VisualDecisionBench",
+      "modality": "image"
+    },
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+      "questions": 7143,
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+      "protocol": "native-shared-state",
+      "source": {
+        "repository": "https://huggingface.co/datasets/Valen-Team/VisualDecisionBench",
+        "license": "upstream datasets retain their own licenses",
+        "eval_jsonl_sha256": "23eec19993613f9fb1cc6671dbbdf3afb99525c58097f3ac6bd752ae3f34d7e9"
+      },
+      "path": "datasets/VisualDecisionBench_Video.tsv",
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+      "family": "VisualDecisionBench",
+      "modality": "video"
+    },
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+      "questions": 231,
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+        "revision": "bb05a335bc809e61b20c0f745d25499a82b326fc",
+        "license": "MIT",
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+          ]
+        }
+      },
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+      "manifest": "datasets/JevBench.manifest.json",
+      "bytes": 1399543,
+      "family": "JevBench",
+      "modality": "text"
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+      "protocol": "official-public-0.3",
+      "source": {
+        "repository": "https://github.com/apolinario/decision-index",
+        "edition": "0.3"
+      },
+      "path": "datasets/DecisionIndex.tsv",
+      "manifest": "datasets/DecisionIndex.manifest.json",
+      "bytes": 548008805,
+      "family": "DecisionIndex",
+      "modality": "text"
+    },
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+      "questions": 2000,
+      "sha256": "043b3089dac5ab2045e717d3899afe7bfc073e30e66da3cb47b8ebca17277cfe",
+      "protocol": "test-400-shared-state-soft-gold-v1",
+      "source": {
+        "repository": "https://huggingface.co/datasets/LocalLLaMA/typed-decisions",
+        "revision": "7189f46b02248bd2569df7325c9df8e3b24e90a7",
+        "split": "test",
+        "license": "Apache-2.0",
+        "files": {
+          "all/test-00000-of-00001.parquet": "4f294f218ea1da27f3efef936359389c62ea4d3973a41457732990f1d31b647c"
+        }
+      },
+      "path": "datasets/TypedDecisions.tsv",
+      "manifest": "datasets/TypedDecisions.manifest.json",
+      "bytes": 1430749,
+      "family": "TypedDecisions",
+      "modality": "text"
+    },
+    {
+      "name": "DecisionBench",
+      "records": 23900,
+      "questions": 23900,
+      "sha256": "c99000b66862ef3781aa65f1d4edf267c605a519e619b9975a1509de428fefec",
+      "protocol": "standard-eval-systemone-native-v1",
+      "source": {
+        "repository": "https://huggingface.co/datasets/Hanno-Labs/decision-bench",
+        "revision": "071b7b2d2e1504c89e1e5a811a3f82e1bfe3aedb",
+        "split": "eval",
+        "license": "source-specific; upstream license metadata: other",
+        "files": {
+          "data/eval-00000-of-00001.parquet": "d9dd5be8e0944a7ceadfcac6cf8f9d17aab256093acff23c6de6a037ad6e55f6"
+        }
+      },
+      "path": "datasets/DecisionBench.tsv",
+      "manifest": "datasets/DecisionBench.manifest.json",
+      "bytes": 209387912,
+      "family": "DecisionBench",
+      "modality": "text"
+    },
+    {
+      "name": "ProceduralTypedDecisions",
+      "records": 18000,
+      "questions": 57016,
+      "sha256": "4809d20dc7aa54a6d15d7edf352044e658898e46232cb42bba9acb15f1fd8faf",
+      "protocol": "18-tasks-test-native-probability-v1",
+      "source": {
+        "repository": "https://huggingface.co/datasets/tasksource/procedural-typed-decisions",
+        "revision": "e4b21b7b55f6cc9e76e33563e9d604ac5fcd25ad",
+        "split": "test",
+        "license": "Apache-2.0",
+        "files": {
+          "all/test-00000-of-00001.parquet": "9f16b7415fef05a01654c1fb4b743d6c43fd09e86d4d5b96b510a260239fbe9e"
+        }
+      },
+      "path": "datasets/ProceduralTypedDecisions.tsv",
+      "manifest": "datasets/ProceduralTypedDecisions.manifest.json",
+      "bytes": 63895718,
+      "family": "ProceduralTypedDecisions",
+      "modality": "text"
+    },
+    {
+      "name": "ZhDecisionBench",
+      "records": 378,
+      "questions": 467,
+      "sha256": "d99704fc055c8f767813786ce6184089a5ebc8aa4f88a5cc21075108bafbe099",
+      "protocol": "v0.2-validation-378-native-v1",
+      "source": {
+        "repository": "https://huggingface.co/datasets/CodyQin/zh-decision-bench",
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+      "transformations": [
+        "Namespaced media paths under assets/visualdecisionbench_image and assets/visualdecisionbench_video.",
+        "Preserved state grouping, record order, questions, criteria, IDs, and target distributions.",
+        "Added benchmark subset/state IDs in meta.",
+        "Removed host directory prefixes from historical provenance paths.",
+        "Created lossless, one-question-per-row Parquet views for Hugging Face Datasets."
+      ]
+    },
+    "license": "upstream datasets retain their own licenses"
+  },
+  "media_root": "../media/VisualDecisionBench",
+  "protocol": "native-shared-state",
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+  "questions": 7143,
+  "sha256": "72a0d2421a439212fa749a320df5d90570d87ad37570561a5dd06d3d5f3be7ed"
+}

datasets/VisualDecisionBench_Video.tsv

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datasets/ZhDecisionBench.manifest.json

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licenses/DecisionBench-upstream-repository.txt

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licenses/JevBench-MIT.txt

@@ -0,0 +1,21 @@
+MIT License
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+SOFTWARE.

licenses/ZhDecisionBench-CC-BY-4.0.txt

@@ -0,0 +1,396 @@
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+

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+{
+ "name": "Decision Index frozen suite, edition 0.3",
+ "edition": "release-v3",
+ "base_edition": "release-v2.1",
+ "reference_model": "jev-1.13.0",
+ "seed": 20260919,
+ "created_utc": "2026-10-06T00:00:00+00:00",
+ "selection": "Deterministic hash sample without replacement; ESCI proportional locale × relevance; request-budget datasets retain complete linked groups; no outcome-based selection.",
+ "files": {
+  "selected-rows.jsonl.gz": {
+   "sha256": "25aac5e890a54a3172c7a0c184b4cc8b9a43f10b6ee89bbad8da923be423c656",
+   "uncompressed_sha256": "b2b56d6fb636837ca469e689087bdbf373dda8de7638aa2da6793e6eda0792d5",
+   "rows": 124971,
+   "format": "release-v1 rows minus the ToolRet and BRIGHT rows outside their subsets (retrieval-subsets.json); byte-identical lines"
+  },
+  "added-rows.jsonl.gz": {
+   "uncompressed_sha256": "7429f3c9cdddb772c1cfc42bb2a45e8516b0032152b746e6929f1c8b52f4ce89",
+   "rows": 30419,
+   "format": "the seven benchmarks added in 0.2, in the order MMLU-Pro, BBH, When2Call, New Yorker, RAGTruth, HoVer, PhishNChips; lines byte-identical to the lab candidates-v3 and calibration-extension-v1 rows"
+  },
+  "excluded-questions.json": {
+   "sha256": "331df32d4b719c7db43214d0e5d85859d39c3b2eb7d0b3812214cce150155e81",
+   "rows": 442,
+   "purpose": "unchanged from 0.1; applied at scoring time for every engine"
+  },
+  "manifest.json": {
+   "purpose": "this file"
+  },
+  "gsm8k-rows.jsonl.gz": {
+   "uncompressed_sha256": "759334894858a37d6bd7bd07298eac8e55da236e31dad327ccd06a9979f4738a",
+   "rows": 2638,
+   "format": "GSM8K (catalog 30) rebuilt with answer-group distractors (seed 20261003); run_id = 'v0.3-gsm8k:' + the 0.2 run_id; id, state, instructions, option count and gold unchanged; built by `suite rebuild --edition 0.3` from the GSM8K test file and the release-v2 rows"
+  }
+ },
+ "scoring_subsets": {
+  "acos": {
+   "catalog_id": 38,
+   "dataset": "ACOS",
+   "seed": 20260960,
+   "method": "stratified by source domain (Laptop-ACOS / Restaurant-ACOS), proportional allocation, reviews (case groups) sampled without replacement per domain with random.Random(seed); every kept review keeps all of its rows",
+   "reviews_full": 1399,
+   "reviews_kept": 400,
+   "allocation": {
+    "Laptop-ACOS": [
+     816,
+     233
+    ],
+    "Restaurant-ACOS": [
+     583,
+     167
+    ]
+   },
+   "rows_full": 5479,
+   "rows_kept": 1565,
+   "run_ids_sha256": "2db2b00d6261702e2d33a297ee85665b56559dd7e8dfd0f83e6a4799f79f131f",
+   "basis": "ACOS is display-only (not in the index panel), costs 71% of per-question prompt tokens in the suite, and scores near floor for every model; user decision 2026-09-23 to subset it.",
+   "file": "decision_index/data/release-v2/acos-subset.json",
+   "sha256": "3b20eea1613ae3e1644339e6f89a4287b307a5750a86239bb8bca6f8238b8e45"
+  },
+  "retrieval": {
+   "seed": 20260922,
+   "method": "stratified by metadata.domain, proportional allocation with at least one query per domain, sampled without replacement per domain with random.Random(seed+catalog_id); every kept query keeps its full candidate list; excluded questions unchanged",
+   "file": "decision_index/data/release-v2/retrieval-subsets.json",
+   "sha256": "9228a9492ea40c499d8bb024417f566d0c0ced62c71ed4c1bf908e87ed50d8df",
+   "subsets": {
+    "ToolRet": {
+     "catalog_id": 2,
+     "queries_full": 7704,
+     "queries_kept": 1000,
+     "requests_kept": 1000,
+     "allocation": {
+      "apibank": [
+       100,
+       13
+      ],
+      "apigen": [
+       917,
+       119
+      ],
+      "appbench": [
+       32,
+       4
+      ],
+      "autotools-food": [
+       21,
+       3
+      ],
+      "autotools-music": [
+       12,
+       2
+      ],
+      "autotools-weather": [
+       3,
+       1
+      ],
+      "craft-math-algebra": [
+       280,
+       36
+      ],
+      "craft-tabmwp": [
+       174,
+       23
+      ],
+      "craft-vqa": [
+       200,
+       26
+      ],
+      "gorilla-huggingface": [
+       500,
+       65
+      ],
+      "gorilla-pytorch": [
+       43,
+       6
+      ],
+      "gorilla-tensor": [
+       55,
+       7
+      ],
+      "gpt4tools": [
+       32,
+       4
+      ],
+      "gta": [
+       14,
+       2
+      ],
+      "metatool": [
+       200,
+       26
+      ],
+      "mnms": [
+       27,
+       3
+      ],
+      "restgpt-spotify": [
+       40,
+       5
+      ],
+      "restgpt-tmdb": [
+       52,
+       7
+      ],
+      "reversechain": [
+       199,
+       26
+      ],
+      "rotbench": [
+       550,
+       71
+      ],
+      "t-eval-dialog": [
+       44,
+       6
+      ],
+      "t-eval-step": [
+       45,
+       6
+      ],
+      "taskbench-daily": [
+       39,
+       5
+      ],
+      "taskbench-huggingface": [
+       23,
+       3
+      ],
+      "taskbench-multimedia": [
+       40,
+       5
+      ],
+      "tool-be-honest": [
+       350,
+       45
+      ],
+      "toolace": [
+       933,
+       121
+      ],
+      "toolalpaca": [
+       94,
+       12
+      ],
+      "toolbench": [
+       1077,
+       140
+      ],
+      "toolbench-sam": [
+       197,
+       26
+      ],
+      "toolemu": [
+       36,
+       5
+      ],
+      "tooleyes": [
+       94,
+       12
+      ],
+      "toolink": [
+       497,
+       64
+      ],
+      "toollens": [
+       310,
+       40
+      ],
+      "ultratool": [
+       474,
+       61
+      ]
+     }
+    },
+    "BRIGHT": {
+     "catalog_id": 36,
+     "queries_full": 1297,
+     "queries_kept": 550,
+     "requests_kept": 550,
+     "allocation": {
+      "aops": [
+       111,
+       47
+      ],
+      "biology": [
+       103,
+       44
+      ],
+      "earth_science": [
+       114,
+       48
+      ],
+      "economics": [
+       103,
+       44
+      ],
+      "leetcode": [
+       140,
+       59
+      ],
+      "pony": [
+       112,
+       47
+      ],
+      "psychology": [
+       101,
+       43
+      ],
+      "robotics": [
+       98,
+       42
+      ],
+      "stackoverflow": [
+       45,
+       19
+      ],
+      "sustainable_living": [
+       108,
+       46
+      ],
+      "theoremqa_questions": [
+       192,
+       81
+      ],
+      "theoremqa_theorems": [
+       70,
+       30
+      ]
+     }
+    }
+   }
+  },
+  "toolret_answerable": {
+   "catalog_id": 2,
+   "dataset": "ToolRet",
+   "method": "a query is answerable when at least one of its scored candidates (scoring.scorable_ids) has a relevance judgment above 0; unanswerable queries score nDCG@10 = 0 for every ranking and leave the scored set. Rows already in the excluded-questions manifest stay listed so the exclusion count is unchanged.",
+   "queries_scored_v02": 1000,
+   "queries_kept": 685,
+   "rows_kept": 1013,
+   "basis": "user decision for Decision Index 0.2.1: score only queries with at least one relevant candidate among the 32",
+   "run_ids_sha256": "2dbf9b0d7df411fef59d0f23bf1931d824c91bb9abe1aaa00601f24aaf8ca656",
+   "file": "decision_index/data/release-v2.1/toolret-subset.json",
+   "sha256": "c301d5503955c14d99485e284d434bb1533f51229c34da1edc19dd8388116f01"
+  },
+  "bright_answerable": {
+   "catalog_id": 36,
+   "dataset": "BRIGHT",
+   "method": "a query is answerable when at least one of its scored candidates (scoring.scorable_ids) has a relevance judgment above 0; unanswerable queries score nDCG@10 = 0 for every ranking and leave the scored set. Rows already in the excluded-questions manifest stay listed so the exclusion count is unchanged.",
+   "queries_scored_v02": 550,
+   "queries_kept": 220,
+   "rows_kept": 307,
+   "basis": "user decision for Decision Index 0.2.1: score only queries with at least one relevant candidate among the 32",
+   "run_ids_sha256": "82bd3c93209100d60b1d10535801bad52472c8a7ceabc3f074b1114d7b8f9f1c",
+   "file": "decision_index/data/release-v2.1/bright-subset.json",
+   "sha256": "ed3a05220e09be2b96c02514345c61894f7a0ffc73e6884a100392d23692cfb8"
+  },
+  "home_appliances": {
+   "catalog_id": 9,
+   "dataset": "Home appliances",
+   "method": "test rows compared by their full state (JSON, sorted keys); of each set of identical test rows the one with the lowest row id is kept, and every test row whose state is identical to a row of the published dev split (artifacts/benchmark-suite/home/dev.jsonl) is dropped",
+   "rows_full": 160,
+   "duplicate_rows_dropped": 24,
+   "dev_identical_rows_dropped": 48,
+   "dropped_both_ways": 0,
+   "rows_kept": 88,
+   "basis": "user decision for Decision Index 0.2.1: duplicate test pairs and test rows identical to published dev rows leave the scored set",
+   "run_ids_sha256": "f76d0ef2493916ba799a4e78e42d734100e152f2b73d6978ab2370a3866d6e87",
+   "file": "decision_index/data/release-v2.1/home-appliances-subset.json",
+   "sha256": "d2d0df922a47a9757bef17f382a4ed4c5c3bfff953f941c29404e93a490bce2c"
+  }
+ },
+ "requests_in_rows_file": 124971,
+ "requests": 110201,
+ "scoreable_requests": 109759,
+ "added_requests": 30419,
+ "static_benchmarks": 37,
+ "added_benchmarks": 7,
+ "notes": [
+  "Original 500-case pilot remains frozen separately.",
+  "Request caps count planned logical evaluations. Transport retries or context splits are logged separately and may add HTTP attempts.",
+  "Cached successful pilot responses reused only for identical full request SHA256; original latency and provenance retained.",
+  "Game protocols and budgets are pinned separately.",
+  "Release v2 keeps every v1 row except a stratified subset cut of ToolRet (1,000 of 7,961 queries incl. excluded ones) and BRIGHT (550 of 1,384 queries); all other benchmarks, question order, options and scoring are unchanged. Kept ToolRet and BRIGHT queries retain their complete candidate lists.",
+  "ToolRet and BRIGHT rows of questions listed in excluded-questions.json were carried over unchanged (they are never scored); the scored subsets are 1,000 and 550 query groups, see scored_cases.",
+  "Release v2.1 is a rescore of release v2: the rows files are the same, and ToolRet and BRIGHT are scored on their answerable queries only and Home appliances without its duplicate and dev-identical rows (scoring_subsets), applied at read time like the ACOS subset.",
+  "Release v3 reads the release v2.1 files unchanged plus gsm8k-rows.jsonl.gz: the old GSM8K rows are replaced by the rebuilt ones and ForecastBench is skipped, both at read time. Requests: 110,201 (109,759 scoreable) plus the 30,419 added in 0.2."
+ ],
+ "benchmarks": [
+  {
+   "catalog_id": 1,
+   "dataset": "BFCL",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 1694,
+   "selected_cases": 1694,
+   "requests": 1694,
+   "fields": 4768,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 2,
+   "dataset": "ToolRet",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 7961,
+   "selected_cases": 1257,
+   "requests": 1328,
+   "fields": 40127,
+   "request_cap": null,
+   "case_cap": 1000,
+   "full_edition_selected_cases": 7961,
+   "full_edition_requests": 8032,
+   "full_edition_fields": 254650,
+   "subset_allocation": {
+    "apibank": [
+     100,
+     13
+    ],
+    "apigen": [
+     917,
+     119
+    ],
+    "appbench": [
+     32,
+     4
+    ],
+    "autotools-food": [
+     21,
+     3
+    ],
+    "autotools-music": [
+     12,
+     2
+    ],
+    "autotools-weather": [
+     3,
+     1
+    ],
+    "craft-math-algebra": [
+     280,
+     36
+    ],
+    "craft-tabmwp": [
+     174,
+     23
+    ],
+    "craft-vqa": [
+     200,
+     26
+    ],
+    "gorilla-huggingface": [
+     500,
+     65
+    ],
+    "gorilla-pytorch": [
+     43,
+     6
+    ],
+    "gorilla-tensor": [
+     55,
+     7
+    ],
+    "gpt4tools": [
+     32,
+     4
+    ],
+    "gta": [
+     14,
+     2
+    ],
+    "metatool": [
+     200,
+     26
+    ],
+    "mnms": [
+     27,
+     3
+    ],
+    "restgpt-spotify": [
+     40,
+     5
+    ],
+    "restgpt-tmdb": [
+     52,
+     7
+    ],
+    "reversechain": [
+     199,
+     26
+    ],
+    "rotbench": [
+     550,
+     71
+    ],
+    "t-eval-dialog": [
+     44,
+     6
+    ],
+    "t-eval-step": [
+     45,
+     6
+    ],
+    "taskbench-daily": [
+     39,
+     5
+    ],
+    "taskbench-huggingface": [
+     23,
+     3
+    ],
+    "taskbench-multimedia": [
+     40,
+     5
+    ],
+    "tool-be-honest": [
+     350,
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+    ],
+    "toolace": [
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+    "toolalpaca": [
+     94,
+     12
+    ],
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+     1077,
+     140
+    ],
+    "toolbench-sam": [
+     197,
+     26
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+    "toolemu": [
+     36,
+     5
+    ],
+    "tooleyes": [
+     94,
+     12
+    ],
+    "toolink": [
+     497,
+     64
+    ],
+    "toollens": [
+     310,
+     40
+    ],
+    "ultratool": [
+     474,
+     61
+    ]
+   },
+   "scored_cases": 1000,
+   "selected_cases_note": "selected_cases and selected_group_ids count every query group present in the rows file, including groups that excluded-questions.json removes from scoring and that were kept unchanged from v1; scored_cases is the sampled subset that is scored."
+  },
+  {
+   "catalog_id": 3,
+   "dataset": "API-Bank",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 508,
+   "selected_cases": 508,
+   "requests": 508,
+   "fields": 508,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 4,
+   "dataset": "BANKING77",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 3080,
+   "selected_cases": 3080,
+   "requests": 3080,
+   "fields": 3080,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 5,
+   "dataset": "CLINC150+OOS",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 5500,
+   "selected_cases": 5500,
+   "requests": 5500,
+   "fields": 5500,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 6,
+   "dataset": "RouterBench",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 29163,
+   "selected_cases": 5001,
+   "requests": 10000,
+   "fields": 20000,
+   "request_cap": 10000,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 9,
+   "dataset": "Home appliance simulator",
+   "benchmark_origin": "Purpose-built benchmarks",
+   "available_cases": 160,
+   "selected_cases": 160,
+   "requests": 160,
+   "fields": 2895,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 10,
+   "dataset": "SGD/SGD-X",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 46116,
+   "selected_cases": 2500,
+   "requests": 2500,
+   "fields": 2500,
+   "request_cap": null,
+   "case_cap": 2500
+  },
+  {
+   "catalog_id": 11,
+   "dataset": "ContractNLI",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 123,
+   "selected_cases": 123,
+   "requests": 123,
+   "fields": 2091,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 12,
+   "dataset": "ANLI",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 3200,
+   "selected_cases": 3200,
+   "requests": 3200,
+   "fields": 3200,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 20,
+   "dataset": "BPoMP",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 22419,
+   "selected_cases": 811,
+   "requests": 5000,
+   "fields": 5000,
+   "request_cap": 5000,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 21,
+   "dataset": "Humicroedit",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 2628,
+   "selected_cases": 2628,
+   "requests": 2628,
+   "fields": 2628,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 22,
+   "dataset": "POP909-CL",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 75582,
+   "selected_cases": 2000,
+   "requests": 2000,
+   "fields": 2000,
+   "request_cap": null,
+   "case_cap": 2000
+  },
+  {
+   "catalog_id": 23,
+   "dataset": "cfcolor",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 11821,
+   "selected_cases": 5000,
+   "requests": 5000,
+   "fields": 5000,
+   "request_cap": null,
+   "case_cap": 5000
+  },
+  {
+   "catalog_id": 24,
+   "dataset": "MMLU",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 14042,
+   "selected_cases": 14042,
+   "requests": 14042,
+   "fields": 14042,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 25,
+   "dataset": "GPQA Diamond",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 198,
+   "selected_cases": 198,
+   "requests": 198,
+   "fields": 198,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 26,
+   "dataset": "ARC-Easy",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 2376,
+   "selected_cases": 2376,
+   "requests": 2376,
+   "fields": 2376,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 27,
+   "dataset": "ARC-Challenge",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 1172,
+   "selected_cases": 1172,
+   "requests": 1172,
+   "fields": 1172,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 28,
+   "dataset": "WinoGrande",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 1267,
+   "selected_cases": 1267,
+   "requests": 1267,
+   "fields": 1267,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 29,
+   "dataset": "HellaSwag",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 10042,
+   "selected_cases": 10042,
+   "requests": 10042,
+   "fields": 10042,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 30,
+   "dataset": "GSM8K",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 1319,
+   "selected_cases": 1319,
+   "requests": 2638,
+   "fields": 2638,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 31,
+   "dataset": "ChessBench",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 61833,
+   "selected_cases": 5000,
+   "requests": 5000,
+   "fields": 5000,
+   "request_cap": null,
+   "case_cap": 5000
+  },
+  {
+   "catalog_id": 32,
+   "dataset": "MuSR",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 756,
+   "selected_cases": 756,
+   "requests": 756,
+   "fields": 756,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 33,
+   "dataset": "SATA-Bench",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 1650,
+   "selected_cases": 1650,
+   "requests": 1650,
+   "fields": 15517,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 34,
+   "dataset": "SimpleBench",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 10,
+   "selected_cases": 10,
+   "requests": 10,
+   "fields": 10,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 36,
+   "dataset": "BRIGHT",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 1384,
+   "selected_cases": 637,
+   "requests": 637,
+   "fields": 20383,
+   "request_cap": null,
+   "case_cap": 550,
+   "full_edition_selected_cases": 1384,
+   "full_edition_requests": 1384,
+   "full_edition_fields": 44286,
+   "subset_allocation": {
+    "aops": [
+     111,
+     47
+    ],
+    "biology": [
+     103,
+     44
+    ],
+    "earth_science": [
+     114,
+     48
+    ],
+    "economics": [
+     103,
+     44
+    ],
+    "leetcode": [
+     140,
+     59
+    ],
+    "pony": [
+     112,
+     47
+    ],
+    "psychology": [
+     101,
+     43
+    ],
+    "robotics": [
+     98,
+     42
+    ],
+    "stackoverflow": [
+     45,
+     19
+    ],
+    "sustainable_living": [
+     108,
+     46
+    ],
+    "theoremqa_questions": [
+     192,
+     81
+    ],
+    "theoremqa_theorems": [
+     70,
+     30
+    ]
+   },
+   "scored_cases": 550,
+   "selected_cases_note": "selected_cases and selected_group_ids count every query group present in the rows file, including groups that excluded-questions.json removes from scoring and that were kept unchanged from v1; scored_cases is the sampled subset that is scored."
+  },
+  {
+   "catalog_id": 37,
+   "dataset": "Amazon ESCI",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 638016,
+   "selected_cases": 5000,
+   "requests": 5000,
+   "fields": 5000,
+   "request_cap": null,
+   "case_cap": 5000
+  },
+  {
+   "catalog_id": 38,
+   "dataset": "ACOS",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 1399,
+   "selected_cases": 1399,
+   "requests": 5479,
+   "fields": 318945,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 39,
+   "dataset": "FinEntity",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 979,
+   "selected_cases": 979,
+   "requests": 979,
+   "fields": 2129,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 40,
+   "dataset": "iSarcasmEval",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 4600,
+   "selected_cases": 4600,
+   "requests": 4600,
+   "fields": 11600,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 41,
+   "dataset": "VAST",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 3006,
+   "selected_cases": 3006,
+   "requests": 3006,
+   "fields": 3006,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 42,
+   "dataset": "NLI4CT",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 5500,
+   "selected_cases": 5500,
+   "requests": 5500,
+   "fields": 5500,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 43,
+   "dataset": "CRUXEval",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 570,
+   "selected_cases": 570,
+   "requests": 570,
+   "fields": 570,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 44,
+   "dataset": "CLadder",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 10112,
+   "selected_cases": 5000,
+   "requests": 5000,
+   "fields": 5000,
+   "request_cap": null,
+   "case_cap": 5000
+  },
+  {
+   "catalog_id": 45,
+   "dataset": "HLE",
+   "benchmark_origin": "Original-task benchmarks",
+   "available_cases": 513,
+   "selected_cases": 513,
+   "requests": 513,
+   "fields": 513,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 48,
+   "dataset": "ForecastBench",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 10139,
+   "selected_cases": 10139,
+   "requests": 10139,
+   "fields": 10139,
+   "request_cap": null,
+   "case_cap": null
+  },
+  {
+   "catalog_id": 50,
+   "dataset": "Habermas Machine",
+   "benchmark_origin": "Jev-adapted benchmarks",
+   "available_cases": 1676,
+   "selected_cases": 1676,
+   "requests": 1676,
+   "fields": 1676,
+   "request_cap": null,
+   "case_cap": null
+  }
+ ],
+ "added": [
+  {
+   "catalog_id": 57,
+   "dataset": "MMLU-Pro",
+   "area": "knowledge",
+   "requests": 12032,
+   "fields": 12032,
+   "source": {
+    "huggingface": {
+     "repo": "TIGER-Lab/MMLU-Pro",
+     "revision": "b189ec765aa7ed75c8acfea42df31fdae71f97be"
+    }
+   },
+   "wording": {
+    "state": "question text",
+    "instructions": "Which option is the correct answer?",
+    "criteria": "letters A.. mapped to the published options in order"
+   },
+   "licence": "MIT",
+   "run_id_prefix": "candidates-v3:"
+  },
+  {
+   "catalog_id": 58,
+   "dataset": "BBH fixed-option tasks",
+   "area": "knowledge",
+   "requests": 5507,
+   "fields": 5507,
+   "source": {
+    "git": {
+     "url": "https://github.com/suzgunmirac/BIG-Bench-Hard",
+     "commit": "9ee07bd481feebf959a6b59d61ea57bdcf30964d"
+    }
+   },
+   "wording": {
+    "state": "task input verbatim, including its own Options block",
+    "instructions": "Which option is the correct answer?",
+    "criteria": "parsed (A).. options in order, or the task answer set (Yes/No, valid/invalid, True/False, yes/no)"
+   },
+   "licence": "MIT (repo LICENSE); carries the BIG-bench canary, never place in training corpora",
+   "run_id_prefix": "candidates-v3:"
+  },
+  {
+   "catalog_id": 62,
+   "dataset": "When2Call MCQ",
+   "area": "tools",
+   "requests": 3652,
+   "fields": 3652,
+   "source": {
+    "huggingface": {
+     "repo": "nvidia/When2Call",
+     "revision": "0582f7749df63a96fdc3070932e83e72396ace53"
+    }
+   },
+   "wording": {
+    "state": "{tools: parsed tool schemas, question: user question}",
+    "instructions": "Which response should the assistant give to the user's question, given the available tools?",
+    "criteria": "A..D mapped to the answers in published order: direct, tool_call, request_for_info, cannot_answer"
+   },
+   "licence": "CC BY-4.0",
+   "run_id_prefix": "candidates-v3:"
+  },
+  {
+   "catalog_id": 64,
+   "dataset": "New Yorker caption matching",
+   "area": "arts",
+   "requests": 528,
+   "fields": 528,
+   "source": {
+    "huggingface": {
+     "repo": "jmhessel/newyorker_caption_contest",
+     "revision": "d81cbab7d0392708d5371d3a4960e69261824db4"
+    }
+   },
+   "wording": {
+    "state": "{scene, description, uncanny_description, entities} from the dataset text annotations",
+    "instructions": "Which caption was written for this cartoon?",
+    "criteria": "A..E mapped to caption_choices in order"
+   },
+   "licence": "CC BY 4.0 annotations; cartoons not used",
+   "run_id_prefix": "candidates-v3:"
+  },
+  {
+   "catalog_id": 59,
+   "dataset": "RAGTruth response-level hallucination",
+   "area": "language",
+   "requests": 2700,
+   "fields": 2700,
+   "source": {
+    "git": {
+     "url": "https://github.com/ParticleMedia/RAGTruth",
+     "commit": "c103204b9ce28d6bbad859304bf30de72b8ed8fe"
+    }
+   },
+   "wording": {
+    "state": "{prompt: original generation prompt with its context, response: evaluated response}",
+    "instructions": "The response contains content that is not supported by the context in the prompt.",
+    "criteria": {
+     "true": "The response contains content that is not supported by the context in the prompt.",
+     "false": "All content of the response is supported by the context in the prompt."
+    }
+   },
+   "licence": "MIT annotations and code; contexts come from CNN/Daily Mail, MS MARCO (non-commercial terms) and the Yelp Open Dataset (no redistribution), so rows are rebuilt locally and never republished",
+   "run_id_prefix": "candidates-v3:"
+  },
+  {
+   "catalog_id": 61,
+   "dataset": "HoVer claim verification",
+   "area": "retrieval",
+   "requests": 4000,
+   "fields": 4000,
+   "source": {
+    "git": {
+     "url": "https://github.com/hover-nlp/hover",
+     "commit": "39b84697f196308f398a251a7aea9b82ae0f0562"
+    },
+    "hover_wiki_db": {
+     "url": "https://nlp.cs.unc.edu/data/hover/wiki_wo_links.db",
+     "sha256": "c37ee397916ec0bffacfe8902db454a5cda88a7a188409217b2e15231fe5ee2f"
+    }
+   },
+   "wording": {
+    "state": "{claim, evidence: [{title, text}] full HoVer-wiki introduction of each document named in supporting_facts}",
+    "instructions": "Is the claim supported by the evidence?",
+    "criteria": {
+     "SUPPORTED": "The evidence supports the claim.",
+     "NOT_SUPPORTED": "The evidence does not support the claim."
+    }
+   },
+   "licence": "CC BY-SA 4.0 data, MIT code; Wikipedia text CC BY-SA",
+   "run_id_prefix": "candidates-v3:"
+  },
+  {
+   "catalog_id": 56,
+   "dataset": "PhishNChips phishing decisions",
+   "area": "retrieval",
+   "requests": 2000,
+   "fields": 18000,
+   "scored_fields": "verdict only (2,000)",
+   "source": {
+    "huggingface": {
+     "repo": "AreLit/PhishNChips",
+     "revision": "89afcc39610084298c4679159cb2e27d9ffffa46",
+     "file": "core_emails.csv",
+     "sha256": "cebb407ff8630491a97400e37464b8db8dfc4299164fca51fcb4ac7eec8204ef"
+    },
+    "questions": {
+     "git": {
+      "url": "https://github.com/anisselbd/jev-phishing-bench",
+      "commit": "1d56e8c64d029a9554a0874e2ef2901ed196e230",
+      "file": "run_jev.py (QUESTIONS)"
+     }
+    }
+   },
+   "wording": "original nine-question payload; records shuffled with random.Random(20260916); five sig_* signal questions carry no gold and are not scored",
+   "licence": "per the dataset SOURCE_LICENSES.md (PhishNChips v5.2: project content MIT; URL sources cleared for academic use with attribution)",
+   "run_id_prefix": "calibration-v1:"
+  }
+ ],
+ "retired": [
+  {
+   "catalog_id": 48,
+   "dataset": "ForecastBench",
+   "note": "retired from the index and from the run; its rows stay in selected-rows.jsonl.gz and are skipped at read time"
+  }
+ ],
+ "replaced": [
+  {
+   "catalog_id": 30,
+   "dataset": "GSM8K",
+   "run_id_prefix": "v0.3-gsm8k:",
+   "note": "the GSM8K rows of selected-rows.jsonl.gz are skipped at read time and replaced by gsm8k-rows.jsonl.gz"
+  }
+ ]
+}

raw/DecisionIndex-0.3/selected-rows.jsonl.gz

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sources/DecisionBench/README.upstream.md

@@ -0,0 +1,81 @@
+---
+pretty_name: DecisionBench
+license: other
+language:
+  - en
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - text-classification
+configs:
+  - config_name: default
+    data_files:
+      - split: eval
+        path: data/eval-*.parquet
+---
+
+# DecisionBench
+
+DecisionBench is a typed-decision benchmark for Boolean/Noul, runtime-defined
+Choice, and Ordered Score outputs across applied domains and use-case families.
+
+This is the public canonical DecisionBench dataset.
+
+Report dataset bugs and request features in the
+[central DecisionBench issue tracker](https://github.com/Hanno-Labs/decision-bench/issues).
+
+## Dataset organization
+
+DecisionBench uses one canonical `eval` split. Tasks are represented by row
+metadata rather than by Hugging Face splits:
+
+- `task_id`: stable composite key: `domain/family/primitive/task_name`
+- `task_name`: human-readable task name within its setting
+- `primitive`: `binary_classification`, `candidate_selection`, or
+  `ordinal_scoring`
+- `family`: use-case family such as routing, verification, or moderation
+- `domain`: application setting such as legal, financial, or online safety
+- `candidate_count`: number of runtime-defined candidates for the row
+- `reasoning_required`: whether the row belongs to the dedicated reasoning
+  suite
+- `reasoning_type`: reasoning category for dedicated reasoning rows; null for
+  other tasks
+
+The dimensions are intentionally independent, allowing filtered views such as
+legal-only, legal Choice, dedicated reasoning, or retrieval/verification
+across every domain. All tasks live in this one split; there are no separate
+core or expanded benchmark targets.
+
+## Current release
+
+- 23,900 rows and 23,900 distinct row IDs
+- 43 task IDs
+- 27 use-case families
+- 3 output primitives
+- 28 domains
+- Candidate counts through 255
+- 1,200 dedicated reasoning rows: 400 Boolean/Noul, 400 Choice, and 400
+  Ordered Score
+
+`data/` contains the canonical Hub-facing Parquet. `artifact/` and
+`provenance/` preserve source manifests and immutable lineage for the three
+verified source collections merged into it.
+
+The Hub-facing Parquet includes derived indexing metadata: `task_id`,
+`candidate_count`, `reasoning_required`, and `reasoning_type`. Every original
+input, candidate, target, probability, and lineage field was verified equal to
+its preserved source artifact.
+
+## Optional compact input
+
+Each eval row also has `compact_instruction`, `compact_state_json`, and
+`compact_candidates_json`. These are shorter renderings of the same decision
+input generated with Qwen and checked against the frozen row. The standard
+fields remain untouched. Candidate IDs, labels, order, ordinal values, gold
+candidate, probabilities, and row IDs are identical across both renderings.
+Only candidate descriptions may differ inside `compact_candidates_json`.
+
+Evaluators must opt into these fields explicitly. Result records using them
+carry the `compact` tag on the leaderboard; untagged results use the standard
+fields. Some rows retain their original input when no valid shorter rewrite
+was produced, and compact input does not guarantee a model's context limit.

sources/JevAltBench/README.upstream.md

@@ -0,0 +1,167 @@
+---
+license: other
+license_name: mixed-permissive
+license_link: https://huggingface.co/datasets/mertkayacs/jevalt-bench#licenses
+language:
+- en
+- tr
+- de
+task_categories:
+- text-classification
+tags:
+- benchmark
+- evaluation
+- text-classification
+- decision-model
+- calibration
+- turkish
+- german
+- jev
+- llm-evaluation
+- prompt-injection
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: en
+  data_files:
+  - split: test
+    path: data/en/test.jsonl
+- config_name: tr
+  data_files:
+  - split: test
+    path: data/tr/test.jsonl
+- config_name: de
+  data_files:
+  - split: test
+    path: data/de/test.jsonl
+pretty_name: JevAlt decision model benchmark
+---
+# JevAlt benchmark: English, Turkish and German decision models
+
+Test data and recorded results for text classification, routing and decisions with probabilities. **Deem-4B scores 94.7% against Kev-4B's 84.7%** on held-out English decisions. The test and training rows share a data pipeline, so this measures performance on JevAlt's target task.
+
+## Quick facts
+
+| Item | Value |
+|---|---|
+| Languages | English, Turkish, German |
+| Test rows | 3,116 (English 1,277, Turkish 1,019, German 820) |
+| Questions | 8,434 |
+| Deem-4B English accuracy | 94.7% (Kev-4B: 84.7%) |
+| Karar-4B Turkish accuracy | 96.8% (Kev-4B: 87.1%) |
+| Wähler-4B German accuracy | 92.0% (Kev-4B: 81.1%) |
+| Live requests | 390 on 4 October 2026 |
+| Hugging Face downloads | 1,471 (as of 2026-10-15) |
+| License | mixed permissive (see record-level `license` field) |
+
+## Project links
+
+- [JevAlt project site](https://jevalt.mertkayacs.com)
+- [GitHub repository](https://github.com/mertkayacs/jevalt)
+- [JevAlt Space](https://huggingface.co/spaces/mertkayacs/JevAlt)
+- [Deem-4B](https://huggingface.co/mertkayacs/Deem-4B)
+- [Karar-4B](https://huggingface.co/mertkayacs/Karar-4B)
+- [Wähler-4B](https://huggingface.co/mertkayacs/Wahler-4B)
+- [Training data](https://huggingface.co/datasets/mertkayacs/jevalt-data)
+- [JevOss evaluator](https://github.com/mertkayacs/jevoss)
+- [Benchmark page](https://eschatialabs.com/benchmarks/jevalt-bench/)
+
+## Cite this
+
+```bibtex
+@software{kaya2026jevalt,
+  author = {Kaya, Mert},
+  title  = {JevAlt: Small Decision Models for English, Turkish and German},
+  year   = {2026},
+  license = {Apache-2.0},
+  url    = {https://github.com/mertkayacs/jevalt}
+}
+```
+## Load the test rows
+
+```python
+from datasets import load_dataset
+
+test = load_dataset("mertkayacs/jevalt-bench", "en", split="test")
+print(test[0])
+```
+
+Use `en`, `tr` or `de`. Each row has a situation, typed questions and probability targets. [JevOss](https://github.com/mertkayacs/jevoss) scores models that implement `POST /v1/systemone`.
+
+| Language | Test rows |
+|---|---|
+| English | 1,277 |
+| Turkish | 1,019 |
+| German | 820 |
+
+## Test composition
+
+| Source | English | Turkish | German |
+|---|---:|---:|---:|
+| Teacher-written scenarios with two-teacher labels | 140 | 225 | 169 |
+| Emberwick game requests | 18 | 16 | 12 |
+| Targeted fix sets | 176 | 182 | 136 |
+| Converted public datasets | 943 | 596 | 503 |
+| Total | 1,277 | 1,019 | 820 |
+
+`test-fix-<lang>.jsonl` contains the date, number and policy rows used for reasoning comparisons: 67 English, 59 Turkish and 19 German.
+
+## Results
+
+| Model and language | Accuracy | Kev-4B accuracy |
+|---|---|---|
+| Deem-4B, English | 94.7% | 84.7% |
+| Karar-4B, Turkish | 96.8% | 87.1% |
+| Wähler-4B, German | 92.0% | 81.1% |
+
+[results/comparison](https://huggingface.co/datasets/mertkayacs/jevalt-bench/tree/main/results/comparison) contains aggregate scores, per-question decisions and probes, including Kev-4B r10 and Laya 0.3.22 on their own servers with shipped calibration. Some runs have aggregates only. Kev-4B leads Wähler-4B on GermEval 2017 and 10kGNAD; Kev-4B and Laya resist long padding better.
+
+The comparison folder includes `comparison.json`, every released aggregate shown on the model cards, per-question decisions for all six models and the probe runs. Kev-4B r10 used code revision `0fe8fc97`; Laya used version 0.3.22 with `laya-serve`. Each received the request shapes documented by the TypeSafe API, including Noul criteria keyed by `true` and `false` and Score levels represented as a list.
+
+Two charts summarize the released files:
+
+![Accuracy by language and typed-decisions suite](https://huggingface.co/datasets/mertkayacs/jevalt-bench/resolve/main/charts/langs.png)
+
+![Planted instructions, long text, policies and negation](https://huggingface.co/datasets/mertkayacs/jevalt-bench/resolve/main/charts/fixes.png)
+
+### Recorded Space examples
+
+`results/examples/space-examples.json` contains seven examples in English, Turkish and German: a support ticket, outage, sales lead, return window, phishing email with a planted instruction, hotel message missing its answer and village fire. All 21 recorded runs from 1 October 2026 select the intended option. The file stores the request, every probability and any reasoning trace.
+
+### Live model checks
+
+The 390 requests in `results/tested` were sent on 4 October 2026, 130 per language. Each line contains the request, raw response and correctness flag.
+
+| Case | Deem-4B | Karar-4B | Wähler-4B |
+|---|---:|---:|---:|
+| 30 phishing emails with a planted line | 26/30 | 22/30 | 28/30 |
+| 10 matching emails without the line | 10/10 | 7/10 | 10/10 |
+| 20 long-policy cases | 17/20 | 16/20 | 14/20 |
+| 30 plain and negated questions | 29/30 | 29/30 | 30/30 |
+| 10 missing-fact questions | 10/10 `unknown` | 10/10 `unknown` | 10/10 `unknown` |
+| 10 matching questions with the fact | 10/10 | 10/10 | 10/10 |
+| 20 casual messages | 20/20 | 19/20 | 20/20 |
+
+Karar-4B's email misses cluster in three Turkish messages about a customs fee, changed bank details and a full mailbox. Eighteen requests repeated as a determinism check returned the same answer.
+
+[results/tested](https://huggingface.co/datasets/mertkayacs/jevalt-bench/tree/main/results/tested) contains 390 live requests from 4 October 2026, including misses: Deem-4B 122/130, Karar-4B 113/130 and Wähler-4B 122/130. These are separate from the held-out scores above. [Recorded Space examples](https://huggingface.co/datasets/mertkayacs/jevalt-bench/blob/main/results/examples/space-examples.json) store the requests and probabilities shown in the cards.
+
+## Split and limits
+
+Related rows stay in one split by group hash, and test files were frozen before final training. Each row's `split` field is a placeholder set when the record was created and reads `train` in every file; the file a row is in (`data/<lang>/test.jsonl`) is its split. There is no English state-text overlap with training; 14 Turkish and 11 German test states repeat in training through MASSIVE or PAWS-X, with different questions. The benchmark is not an independent general ranking. [Training data](https://huggingface.co/datasets/mertkayacs/jevalt-data) and [pipeline details](https://github.com/mertkayacs/jevalt/blob/main/docs/how-it-was-built.md).
+
+Test file checksums recorded before the final training runs:
+
+```text
+6ca22d30eb83eaf014d80ef448e82ecd26370f415d77c2c894129b5344c734b6  en/test.jsonl
+fa02b3d392da2febf3579f99dcf0bc063bbb72347e74d6f47d5453ba01475e44  tr/test.jsonl
+6904b1f96bf76fecf50558f40285d899568074f48ef35c1308d945265f1bd6d7  de/test.jsonl
+```
+
+## Licenses
+
+Written rows are Apache-2.0. Converted records retain their source terms: MASSIVE CC BY 4.0 with Amazon attribution; Open-Jev CC0; PAWS-X free for any use with Google LLC credited; typed-decisions Apache-2.0. Each row has its own `license` field.
+
+[JevAlt Space](https://huggingface.co/spaces/mertkayacs/JevAlt) | [JevAlt source code](https://github.com/mertkayacs/jevalt) | [JevAlt project](https://jevalt.mertkayacs.com) | [JevAlt benchmark page](https://eschatialabs.com/benchmarks/jevalt-bench/).
+
+An [Eschatia Labs](https://eschatialabs.com) project. [Mert Kaya](https://mertkayacs.com).

sources/ProceduralTypedDecisions/README.upstream.md

@@ -0,0 +1,310 @@
+---
+pretty_name: procedural-typed-decisions
+language:
+- en
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- tasksource
+- jev
+- system-one
+- procedural
+- synthetic
+- multi-question
+configs:
+- config_name: all
+  default: true
+  data_files:
+  - split: train
+    path: all/train-*.parquet
+  - split: validation
+    path: all/validation-*.parquet
+  - split: test
+    path: all/test-*.parquet
+- config_name: arithmetic
+  data_files:
+  - split: train
+    path: arithmetic/train-*.parquet
+  - split: validation
+    path: arithmetic/validation-*.parquet
+  - split: test
+    path: arithmetic/test-*.parquet
+- config_name: entity_belief_tracking
+  data_files:
+  - split: train
+    path: entity_belief_tracking/train-*.parquet
+  - split: validation
+    path: entity_belief_tracking/validation-*.parquet
+  - split: test
+    path: entity_belief_tracking/test-*.parquet
+- config_name: event_state_reconstruction
+  data_files:
+  - split: train
+    path: event_state_reconstruction/train-*.parquet
+  - split: validation
+    path: event_state_reconstruction/validation-*.parquet
+  - split: test
+    path: event_state_reconstruction/test-*.parquet
+- config_name: event_triggered_execution
+  data_files:
+  - split: train
+    path: event_triggered_execution/train-*.parquet
+  - split: validation
+    path: event_triggered_execution/validation-*.parquet
+  - split: test
+    path: event_triggered_execution/test-*.parquet
+- config_name: evidence_sufficiency
+  data_files:
+  - split: train
+    path: evidence_sufficiency/train-*.parquet
+  - split: validation
+    path: evidence_sufficiency/validation-*.parquet
+  - split: test
+    path: evidence_sufficiency/test-*.parquet
+- config_name: goal_directed_planning
+  data_files:
+  - split: train
+    path: goal_directed_planning/train-*.parquet
+  - split: validation
+    path: goal_directed_planning/validation-*.parquet
+  - split: test
+    path: goal_directed_planning/test-*.parquet
+- config_name: graph_navigation
+  data_files:
+  - split: train
+    path: graph_navigation/train-*.parquet
+  - split: validation
+    path: graph_navigation/validation-*.parquet
+  - split: test
+    path: graph_navigation/test-*.parquet
+- config_name: instruction_execution
+  data_files:
+  - split: train
+    path: instruction_execution/train-*.parquet
+  - split: validation
+    path: instruction_execution/validation-*.parquet
+  - split: test
+    path: instruction_execution/test-*.parquet
+- config_name: multi_view_adjudication
+  data_files:
+  - split: train
+    path: multi_view_adjudication/train-*.parquet
+  - split: validation
+    path: multi_view_adjudication/validation-*.parquet
+  - split: test
+    path: multi_view_adjudication/test-*.parquet
+- config_name: needle_retrieval
+  data_files:
+  - split: train
+    path: needle_retrieval/train-*.parquet
+  - split: validation
+    path: needle_retrieval/validation-*.parquet
+  - split: test
+    path: needle_retrieval/test-*.parquet
+- config_name: partial_observation_calibration
+  data_files:
+  - split: train
+    path: partial_observation_calibration/train-*.parquet
+  - split: validation
+    path: partial_observation_calibration/validation-*.parquet
+  - split: test
+    path: partial_observation_calibration/test-*.parquet
+- config_name: policy_applicability
+  data_files:
+  - split: train
+    path: policy_applicability/train-*.parquet
+  - split: validation
+    path: policy_applicability/validation-*.parquet
+  - split: test
+    path: policy_applicability/test-*.parquet
+- config_name: policy_under_uncertainty
+  data_files:
+  - split: train
+    path: policy_under_uncertainty/train-*.parquet
+  - split: validation
+    path: policy_under_uncertainty/validation-*.parquet
+  - split: test
+    path: policy_under_uncertainty/test-*.parquet
+- config_name: record_aggregation
+  data_files:
+  - split: train
+    path: record_aggregation/train-*.parquet
+  - split: validation
+    path: record_aggregation/validation-*.parquet
+  - split: test
+    path: record_aggregation/test-*.parquet
+- config_name: record_eligibility
+  data_files:
+  - split: train
+    path: record_eligibility/train-*.parquet
+  - split: validation
+    path: record_eligibility/validation-*.parquet
+  - split: test
+    path: record_eligibility/test-*.parquet
+- config_name: state_perturbation
+  data_files:
+  - split: train
+    path: state_perturbation/train-*.parquet
+  - split: validation
+    path: state_perturbation/validation-*.parquet
+  - split: test
+    path: state_perturbation/test-*.parquet
+- config_name: table_lookup
+  data_files:
+  - split: train
+    path: table_lookup/train-*.parquet
+  - split: validation
+    path: table_lookup/validation-*.parquet
+  - split: test
+    path: table_lookup/test-*.parquet
+- config_name: taxonomy_routing
+  data_files:
+  - split: train
+    path: taxonomy_routing/train-*.parquet
+  - split: validation
+    path: taxonomy_routing/validation-*.parquet
+  - split: test
+    path: taxonomy_routing/test-*.parquet
+---
+
+# procedural-typed-decisions
+
+Procedurally generated decision problems. Each row is one structured state
+(JSON, or a table, CSV, key=value lines, or prose for the arithmetic,
+retrieval, and aggregation configs) with **several typed questions over that same state**, following the
+Jev / System One request shape: `choice` (pick one criterion), `noul` (a
+number in [0, 1]; a probability or a yes/no), and `score` (an ordered rubric).
+Every answer is computed exactly from the state by rules that the state
+itself spells out, so the labels are noise-free. No yes/no or choice label exceeds 60% of a question's answers: yes/no questions are at most a third of a task's questions. A state skips a question whose answer, or whose yes/no format, is already over-represented, so states ask varying subsets of their task's questions. Several configs vary the number of options
+(4 to 60), to balance the binary and 4–6-option questions that dominate the rest of Jev.
+
+This is an independent dataset. It is not an official TypeSafe Jev dataset and
+is not produced by or affiliated with TypeSafe or OpenJev.
+
+## Configs
+
+| config | questions |
+|---|---|
+| `all` (default) | Every config below in one table, with a `task` column and the shared fields only (no flat label columns); the first 1,000 train rows cycle through levels and tasks, the rest is shuffled |
+| `arithmetic` | An order with a discount/shipping rule, an account ledger, or a schedule; each state asks 2–5 of: `amount_due` / `final_balance` / `finish_time` (choice among the result and typical slips), `within_budget`, `went_negative`, `done_by_deadline` (noul), `random_line_bulk`, `random_is_deposit`, `random_is_long` (noul, exact probability k/n), `budget_use`, `net_change` (score, descriptive levels), `lines_above`, `withdrawal_count`, `starts_before_noon` (score), `largest_line`, `lowest_day`, `longest_task` (choice) |
+| `entity_belief_tracking` | `world_location` (choice), `agent_belief_location` (choice), `belief_matches_world` (noul), from level 2 `nested_belief_location` (choice: where A thinks B believes an object is); 4 to 16 locations |
+| `event_state_reconstruction` | `current_owner` (choice), `is_open` (noul), `current_severity` (score); the log is shuffled from level 2 and has voided entries from level 3 |
+| `event_triggered_execution` | `rule_on_signal` (choice), `object_final_state`, `rule_enabled_at_end`, `object_ever_targeted` (noul), `total_firings` (score); 1–8 competing rules over 3–18 timestamped events, with priorities, event guards, cooldowns, enable/disable controls, and unsorted presentation |
+| `evidence_sufficiency` | `claim_supported` (noul), `has_conflict` (noul), `strongest_support_origin` (choice); retractions from level 2, mirrored (non-independent) origins from level 3, validity by collection day at level 4 |
+| `goal_directed_planning` | `goal_reachable`, `probe_applicable`, `probe_optimal_first`, `probe_required` (noul), `minimal_actions` (score), `first_legal_action` (choice); 3–12 steps with conjunctive and alternative prerequisites, unavailable actions, and exact shortest-plan labels |
+| `graph_navigation` | `reachable`, `within_budget`, `edge_essential` (noul), `shortest_cost`, `first_hop` (choice); 4–16 directed nodes with edge costs, closures, mandatory waypoints, and optimal-route alternatives |
+| `instruction_execution` | `resolution` and `deferred_registration` (choice), `target_count`, `changed_count` (score), `probe_selected`, `probe_final_state` (noul); natural-language selection, exceptions, immediate/deferred updates, ambiguity, and atomic/best-effort/ordered execution across 3–26 entities |
+| `multi_view_adjudication` | `intent` (choice), `is_urgent` (noul), `workflow_impact` (score); near-threshold signals from level 2, auth failures counted from login events from level 3, deadlines as clock times at level 4 |
+| `needle_retrieval` | `value_of_id` (choice), `id_has_value` (noul), `id_listed` (noul); up to ~300 records whose ids differ from the target by one or two digits, and from level 2 a chain of one to three id reissues to follow; 6 to 20 options |
+| `partial_observation_calibration` | `incident_real` (noul, exact Bayesian posterior); 1 to 6 sensors |
+| `policy_applicability` | `access_allowed` (noul), `governing_policy` (choice), `review_risk` (score); 2 one-constraint policies at level 0, about 12 policies of up to 5 constraints, many of them near misses, at level 4 |
+| `policy_under_uncertainty` | `access_allowed` (noul), `governing_policy` (choice), `requester_role` (choice); exact posteriors over a role known through history counts and reports of stated reliability |
+| `record_aggregation` | `count_in_category` (score), `largest_quantity` (choice), `any_out_of_stock` (noul), `total_above` (noul), from level 2 `count_filtered` (score, quantity and stock filters) |
+| `record_eligibility` | Prose-only record descriptions and numbered user requirements. `probe_eligibility`, `requirement_status`, `first_eligible`, `decisive_constraint` (choice); `eligible_count` (score), `eligible_if_relaxed`, `probe_fully_eligible` (noul). Three-valued eligibility covers conjunction, negation, disjunction, conditional obligations, combined-cost limits, missing or conflicting evidence, and counterfactual relaxation. 4–27 records and 1–6 constraints by level. |
+| `state_perturbation` | `material_change` (noul), `changed_dimension` (choice), `risk_direction` (score); 1 to 8 records with up to 4 simultaneous changes whose risk effects can offset, and look-alike non-material fields |
+| `table_lookup` | `find_person` (choice, two-condition filter, through the manager from level 2 and with a start-year condition from level 3; 6 to 40 options, capped by the table), `manager_of` (choice, join), `started_before` (noul), `count_matching` (score) |
+| `taxonomy_routing` | `route` (choice among the 4–60 categories of a routing guide drawn fresh per state; many rules share a condition with the right one), `belongs_to` (noul), `conditions_met` (score, 0–4) |
+
+## Record eligibility generation
+
+The `record_eligibility` state contains only a natural-language user request, prose record descriptions, and explicit evaluation semantics. Source facts and logical expression trees are kept in generator test metadata, not the published dataset. Unknown facts are not treated as violations; a known violated requirement dominates any unknown requirements. The first eligible record is defined by the displayed order, and the decisive requirement by the numbered constraints.
+
+Difficulty rises across five levels by increasing record count (4–6 to 25–27), requirement count (1 to 6), and the introduction of nested OR, conditional requirements, summed charges, near-threshold distractors and contradictory reports.
+
+## Schema
+
+| field | meaning |
+|---|---|
+| `id` | `task:split:index` |
+| `level` | Difficulty level (0–4), calibrated against Jev (see below). |
+| `state` | The state: a JSON string, or rendered text for the retrieval and aggregation configs. |
+| `questions` | JSON object of named System One questions (`type`, `instructions`, `criteria`). |
+| `answers` | JSON object of reference answers, in the System One `answers` shape. |
+| one column per question | Flat label, for browsing and filtering: a `ClassLabel` for choice, score, and yes/no noul questions; a float for graded noul (`incident_real`, `random_*`); the option text for open numeric choices (`amount_due`, `final_balance`, `finish_time`). Null when the state does not ask that question (`arithmetic`, and level-dependent questions). |
+
+States are unique within a split, and validation/test states never occur in
+train. In each config, the first 1,000 train rows cycle through the levels
+(easiest first) for browsing; the rest of the split is shuffled.
+
+## Difficulty by level
+
+
+Level 0 is meant to be easy for a strong decision model and level 4 hard. The
+table gives Jev's chance-adjusted accuracy, kappa = (accuracy − chance) /
+(1 − chance), on 40 fresh states per level (every question of each state;
+`typesafe/jev-1.13-20260917`, September 2026; `jev-latest`, October 2026, for `event_triggered_execution`,
+`goal_directed_planning`, `graph_navigation`, `instruction_execution`, `partial_observation_calibration` and `record_eligibility`, 30–40 states per level). 1 is perfect, 0 is chance.
+
+| config | level 0 | 1 | 2 | 3 | 4 |
+|---|---|---|---|---|---|
+| `arithmetic` | 0.83 | 0.64 | 0.59 | 0.54 | 0.59 |
+| `entity_belief_tracking` | 0.91 | 0.93 | 0.78 | 0.74 | 0.66 |
+| `event_state_reconstruction` | 0.97 | 0.99 | 0.92 | 0.73 | 0.60 |
+| `event_triggered_execution` | 0.91 | 0.75 | 0.63 | 0.44 | 0.60 |
+| `evidence_sufficiency` | 0.99 | 0.97 | 0.78 | 0.72 | 0.78 |
+| `goal_directed_planning` | 1.00 | 0.87 | 0.62 | 0.53 | 0.51 |
+| `graph_navigation` | 0.90 | 0.81 | 0.54 | 0.27 | 0.17 |
+| `instruction_execution` | 0.99 | 0.98 | 0.73 | 0.72 | 0.67 |
+| `multi_view_adjudication` | 0.82 | 0.87 | 0.82 | 0.73 | 0.74 |
+| `needle_retrieval` | 1.00 | 0.99 | 0.78 | 0.83 | 0.36 |
+| `partial_observation_calibration` | 0.74 | 0.45 | 0.35 | 0.30 | 0.20 |
+| `policy_applicability` | 0.73 | 0.60 | 0.53 | 0.64 | 0.35 |
+| `policy_under_uncertainty` | 0.35 | 0.42 | 0.38 | 0.57 | 0.39 |
+| `record_aggregation` | 0.99 | 0.96 | 0.84 | 0.75 | 0.70 |
+| `record_eligibility` | 0.97 | 0.94 | 0.74 | 0.56 | 0.53 |
+| `state_perturbation` | 0.95 | 0.89 | 0.44 | 0.63 | 0.50 |
+| `table_lookup` | 1.00 | 0.98 | 0.97 | 0.90 | 0.87 |
+| `taxonomy_routing` | 1.00 | 0.98 | 0.92 | 0.78 | 0.64 |
+
+Probability answers are scored above by their rounding to yes/no; Jev's mean
+absolute error on the exact probability grows from 0.16 (level 0) to 0.29
+(level 4) on `incident_real`, and stays around 0.33 on the posterior
+`access_allowed` of `policy_under_uncertainty`. `policy_under_uncertainty` and
+`partial_observation_calibration` (exact posteriors) are hard from level 0 on;
+`table_lookup` and `multi_view_adjudication` remain the easiest at level 4.
+
+A second model, `upstage/solar-decide` (10 states per level; it takes at most
+26 options, so the longest lists are left out), shows the same easy-to-hard
+slope on most configs, e.g. 0.95 → 0.53 on `event_state_reconstruction`,
+1.00 → 0.48 on `evidence_sufficiency`, 0.88 → 0.42 on `policy_applicability`;
+`arithmetic`, `table_lookup`, and `state_perturbation` stay easy for it (about
+0.8–0.9 at every level). Rerun with `scripts/calibrate_procedural_levels.py`
+(`--model` for another model of the OpenRouter decisions API).
+
+## Use
+
+As a multi-question Jev request, send `{"state": row["state"], "questions":
+json.loads(row["questions"])}` (parsing the state first when it is JSON) and
+compare with `row["answers"]`.
+The same rows are included, grouped by state, in
+[`tasksource/tasksource-jev-typed-decisions`](https://huggingface.co/datasets/tasksource/tasksource-jev-typed-decisions).
+
+## Reproduction
+
+Generation is deterministic (row `i` of a split is seeded by `task:split:i`).
+From a [tasksource](https://github.com/sileod/tasksource) checkout:
+
+```bash
+PYTHONPATH=.:src python scripts/build_procedural_jev.py --output build/procedural-typed-decisions --upload
+```
+
+Generators live in `src/tasksource/jev/procedural/`.
+
+## Citation
+
+Generated with [tasksource](https://github.com/sileod/tasksource); please cite:
+
+```bibtex
+@inproceedings{sileo-2024-tasksource,
+    title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
+    author = "Sileo, Damien",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.1361",
+    pages = "15655--15684",
+}
+```

sources/TypedDecisions/README.upstream.md

@@ -0,0 +1,257 @@
+---
+license: apache-2.0
+language:
+- en
+pretty_name: Typed Decisions
+size_categories:
+- n<1K
+task_categories:
+- text-classification
+tags:
+- structured-decisions
+- calibration
+- probabilistic-classification
+- system-one
+- workflow-evaluation
+- synthetic
+configs:
+- config_name: agent_trace_observability
+  data_files:
+  - split: train
+    path: agent_trace_observability/train-*.parquet
+  - split: test
+    path: agent_trace_observability/test-*.parquet
+- config_name: customer_service
+  data_files:
+  - split: train
+    path: customer_service/train-*.parquet
+  - split: test
+    path: customer_service/test-*.parquet
+- config_name: invoice_processing
+  data_files:
+  - split: train
+    path: invoice_processing/train-*.parquet
+  - split: test
+    path: invoice_processing/test-*.parquet
+- config_name: security_incidents
+  data_files:
+  - split: train
+    path: security_incidents/train-*.parquet
+  - split: test
+    path: security_incidents/test-*.parquet
+- config_name: all
+  data_files:
+  - split: train
+    path: all/train-*.parquet
+  - split: test
+    path: all/test-*.parquet
+---
+
+# Typed Decisions
+
+A benchmark for typed probabilistic decisions. A model gets one piece of
+unstructured state and answers five typed questions about it at once, and every
+answer is a probability distribution, not a single label.
+
+The schema follows the System One primitives (`noul`, `choice`, `score`) used by
+[TypeSafe AI](https://typesafe.ai), so a row replays against any API with that
+shape. The benchmark is independent: it is not affiliated with TypeSafe and does
+not reproduce their Jev model.
+
+![Accuracy against KL divergence and against latency for every scored model](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/resolve/main/assets/leaderboard.png)
+
+## Leaderboard
+
+`test` split: 400 cases, 2,000 decisions. This table lists general models scored
+zero-shot: they have never seen these workflows or their question schemas.
+Models that were fitted or fine-tuned on `train` are in the second table below;
+the two tables are not comparable.
+
+| # | Model | Kind | Accuracy ↑ | KL from gold ↓ | Brier ↓ | ECE ↓ | p50 latency | Price / 1M input |
+|---|---|---|---|---|---|---|---|---|
+| 1 | **[meraGPT Decider 1](https://meragpt.com/models/state-decider-1?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions)** (`sd-1`) | general, zero-shot | **0.768** | **0.096** | **0.052** | 0.180 | **526 ms** | **$0.03** |
+| 2 | [Amos 1 medium](https://huggingface.co/mukaddamzaid/amos-1-medium) (9B) § ([#31](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/31)) | general, zero-shot, open weights | 0.7645 | 0.151 | 0.080 | 0.103 | 118 ms‡ (H100) | open weights |
+| 3 | [Amos 1 large](https://huggingface.co/mukaddamzaid/amos-1-large) (27B) § ([#31](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/31)) | general, zero-shot, open weights | 0.761 | 0.126 | 0.069 | 0.097 | 357 ms‡ (H100) | open weights |
+| 4 | [Darwin-27B-ZTC](https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC) (27B) § ([#17](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/17)) | general, zero-shot, open weights | 0.743 | 0.204 | 0.097 | – | – | open weights |
+| 5 | [Liquid AI d1](https://docs.liquid.ai/lfm/models/decision-models) (`d1:free`) | general, zero-shot | 0.742 | 0.475 | 0.155 | 0.124 | 525 ms | free tier |
+| 6 | TypeSafe Jev 1.13.0 | general, zero-shot | 0.727 | 1.442 | 0.148 | 0.144 | 710 ms | $0.042 |
+| 7 | [Featherless Simple Jev](https://simple-jev.featherless.ai) (`Qwen3.6-35B-A3B-classifier`) | general, zero-shot | 0.716 | 0.488 | 0.176 | – | – | free demo |
+| 8 | [jev-vision-27b](https://huggingface.co/e12ex2/jev-vision-27b) (Qwen3.8-27B + LoRA) § | general, zero-shot, open weights | 0.714 | 0.318 | 0.129 | 0.026 | 199 ms‡ (H100) | open weights |
+| 9 | [prima-ratio + 12B](https://github.com/andrea-tomassi/prima-ratio) § | general, zero-shot | 0.702 | 0.564 | 0.234 | 0.146 | 700 ms‡ | local GGUF |
+| 10 | [OpenDecider-small](https://huggingface.co/manjunathshiva/opendecider-small) § | general, zero-shot, open weights | 0.671 | 0.211 | 0.117 | – | 40 ms‡ | open weights |
+| 11 | [Bongard-mini](https://huggingface.co/AgentBull/bongard-mini) § | general, zero-shot, open weights | 0.594 | 0.256 | 0.132 | 0.067 | 225 ms‡ | open weights |
+| 12 | [Jeff-Gemma4-E2B](https://huggingface.co/mstrasser/Jeff-Gemma4-E2B) | general, zero-shot, open weights | 0.561 | 0.403 | 0.219 | 0.188 | 2,272 ms† | open weights |
+| 13 | [Jeff-Qwen3.5-2B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-2B) | general, zero-shot, open weights | 0.511 | 0.460 | 0.237 | 0.203 | 1,346 ms† | open weights |
+| 14 | [Jeff-Qwen3.5-0.8B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-0.8B) | general, zero-shot, open weights | 0.483 | 0.679 | 0.313 | 0.251 | 662 ms† | open weights |
+| – | Prior (ignores the input) | reference | 0.470 | 0.347 | 0.189 | 0.088 | – | – |
+| – | Uniform (same probability on every option) | reference | 0.308 | 0.444 | 0.238 | 0.169 | – | – |
+
+Every row was scored by sending the whole case (state plus all five questions) in
+one request. Request shape matters: in a third-party run Jev's yes/no accuracy was
+0.843 with one question per request and 0.788 alongside the others.
+
+Hosted rows are p50 end to end from a client, one request at a time.
+† Measured on the same machine as the model (M3 Max), not comparable with hosted latency.
+‡ Reported by the submitter on their own hardware (a different GPU and a different unit for each), not comparable with the other rows.
+§ Self-reported by the submitter in the linked discussion and not re-run by us. Metric definitions follow the submitter's report; ECE in particular is computed differently by different submitters.
+
+### Fitted or fine-tuned on `train`
+
+These models were trained on this benchmark's `train` split, so they are not
+zero-shot and their scores are not comparable with the table above. All were
+self-reported in the linked discussions and were not re-run by us, except the two
+specialists marked ours and the three Bekko rows, which we scored ourselves. Scores well above the 0.735 teacher self-agreement mean a
+model is learning the teacher's quirks.
+
+| Model | Kind | Accuracy ↑ | KL from gold ↓ | Brier ↓ | ECE ↓ | Reported latency |
+|---|---|---|---|---|---|---|
+| [Darwin-27B-ZTC-v5](https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v5) (27B, full fine-tune) ([#33](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/33)) | general, then fine-tuned on `train` | 0.812 | – | – | – | – |
+| [ministral3-3b-reasoning-typed-decisions](https://huggingface.co/rony/ministral3-3b-reasoning-typed-decisions) (Ministral 3 3B Reasoning, full fine-tune) ([#30](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/30)) | specialist | 0.808 | 0.077 | 0.042 | 0.182 | 373 ms‡ (GB10) |
+| [Amos 1 large](https://huggingface.co/mukaddamzaid/amos-1-large) (27B), per-question priors fitted on `train` ([#31](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/31)) | general, then fitted on `train` (one log-prior bias per question and option; no weights changed) | 0.8025 | 0.122 | – | – | 357 ms‡ |
+| [OpenDecider-large-td](https://huggingface.co/manjunathshiva/opendecider-large-td) (Qwen3-Next-80B-A3B + LoRA) | general, then fine-tuned on `train` | 0.801 | 0.081 | 0.044 | – | 440 ms‡ |
+| [od1-typed-decisions](https://huggingface.co/mvbalaji/od1-typed-decisions) (Qwen3.5-4B) | specialist | 0.797 | 0.082 | 0.045 | 0.156¶ | – |
+| [OpenDecider-nano](https://huggingface.co/manjunathshiva/opendecider-nano) (400M encoder) | general, then fine-tuned on `train` | 0.796 | 0.079 | 0.043 | – | 17 ms‡ |
+| [OpenDecider-small-td](https://huggingface.co/manjunathshiva/opendecider-small-td) (Qwen3-4B + LoRA) | general, then fine-tuned on `train` | 0.792 | 0.080 | 0.043 | – | 40 ms‡ |
+| [Phocinae-Largha-150M-v1](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1) (mmBERT-small, 144.3M) | specialist | 0.791◇ | – | – | – | 21.0 ms‡ (RTX 5090) |
+| [OpenDecider-medium-td](https://huggingface.co/manjunathshiva/opendecider-medium-td) (Qwen3-30B-A3B + LoRA) | general, then fine-tuned on `train` | 0.788 | 0.081 | 0.044 | – | 214 ms‡ |
+| [soft-decider-421m](https://huggingface.co/winwinwinbb/soft-decider-421m) | specialist | 0.774 | – | – | 0.141¶ | 50 ms‡ |
+| [Qwen3.5-4B-typed-decisions](https://huggingface.co/wozniakos10/Qwen3.5-4B-typed-decisions) (Qwen3.5-4B + LoRA, Clef head) | general, then fine-tuned on `train` | 0.7725 | 0.0992 | 0.0543 | 0.139¶ | – |
+| [Laya typed-decisions](https://huggingface.co/convaiinnovations/laya-typed-decisions) | fitted on `train` | 0.766 | – | – | – | – |
+| [gemma3-270m-typed-decisions](https://huggingface.co/rony/gemma3-270m-typed-decisions) (Gemma 3 270M, full fine-tune) | specialist | 0.761 | 0.132 | 0.072 | 0.148¶ | 68 ms‡ |
+| [ProKope-421M-8K](https://huggingface.co/ProKope-AI/ProKope-421M-8K) (421M, 8K context) | specialist | 0.7185 | – | 0.385§§ | – | 43 ms‡ |
+| [gemma3-270m-typed-decisions](https://huggingface.co/rony/gemma3-270m-typed-decisions) (Gemma 3 270M, full fine-tune) | general, fine-tuned on `train` | 0.702 | 0.156 | 0.091 | 0.128¶ | 68 ms‡ |
+| [Bekko System One v0 400M](https://huggingface.co/hotchpotch/bekko-system-one-v0-400m), scored by us | trained on `train` and other data | 0.668 | 0.214 | 0.113 | 0.143 | 808 ms† |
+| ModernBERT-base (149M), ours | specialist, fitted per workflow | 0.646 | 0.223 | 0.119 | 0.179 | 349 ms† |
+| MiniLM-L6 (22M), ours | specialist, fitted per workflow | 0.587 | 0.262 | 0.143 | 0.108 | 22 ms† |
+| [Bekko System One v0 68M](https://huggingface.co/hotchpotch/bekko-system-one-v0-68m), scored by us | trained on `train` and other data | 0.537 | 0.293 | 0.160 | 0.116 | 176 ms† |
+| [Bekko System One v0 17M](https://huggingface.co/hotchpotch/bekko-system-one-v0-17m), scored by us | trained on `train` and other data | 0.483 | 0.344 | 0.203 | 0.136 | 33 ms† |
+
+¶ The submitter's own ECE definition, which does not match the one used above.
+
+§§ ProKope reports a multi-class vector Brier score, which is not the per-decision Brier used in the other rows, so the two are not comparable.
+
+◇ Clean re-score by the submitter after removing 500 training pairs derived from model predictions on `test` rows ([discussion #25](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/25)). The originally submitted 0.906 was measured on a split the model had partly seen, so it is not listed; KL, Brier and ECE were only reported for that run. Do not train on `test` rows, `test` labels, or anything derived from predictions on them.
+
+**[meraGPT Decider 1](https://meragpt.com/models/state-decider-1?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions) is state of
+the art among zero-shot models on this benchmark.** It leads Jev on every question type (`noul` 0.840 vs
+0.775, `choice` 0.733 vs 0.720, `score` 0.739 vs 0.696), its distributions sit far
+closer to the gold (KL 0.096 vs 1.442), it is faster end to end, and it costs less
+per token. It answers `POST /v1/systemone` at
+[meragpt.com](https://meragpt.com/docs/systemone?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions), so the typesafe-sdk works
+against it by setting `TYPESAFE_BASE_URL`.
+
+To add a model, score it on `test` with the full distributions and open a
+discussion with the numbers and the mode (specialist or general) it used.
+
+### Notes on the rows
+
+- **Liquid AI d1** was measured on 2026-09-30 through Liquid's API
+  (`https://api.liquid.ai/v1/systemone`, `model: d1:free`), with the same client
+  code as the Jev row: all 2,000 decisions, zero errors. It ties Decider 1 on
+  `noul` (0.840) and `choice` (0.732 vs 0.733) and trails on `score` (0.677 vs
+  0.739), and it beats Jev on accuracy and KL. Liquid lists no per-token price
+  yet, so the row shows the free tier.
+- **Jev 1.13.0** was measured on 2026-09-18 through TypeSafe's API (`jev-latest`,
+  which reported `jev-1.13.0`): all 2,000 decisions, zero errors, $0.016 in total.
+  Its accuracy is near the 0.735 ceiling, but it puts nearly all its probability
+  on one answer, which is where the KL gap comes from. Its confidence is not badly
+  calibrated (overconfidence +0.023); the gold is a three-sample spread it does not
+  reproduce.
+- **Jeff** ([firelex/jeff](https://github.com/firelex/jeff), Apache-2.0) was scored
+  on 2026-09-29 through the same client code as Jev, against Jeff's own
+  `jeff-serve` (commit `2c1bfce`) with the calibration each checkpoint ships. All
+  three clear the Prior on accuracy but not on KL. Jeff's own 83.1% comes from a
+  different five-benchmark panel. If there is a better way to serve them, open a
+  discussion and we will rescore.
+- **Self-reported rows** (§ above, and the second table) come from discussions
+  [#5](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/5)
+  (prima-ratio), [#7](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/7)
+  (Bongard-mini), [#8](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/8)
+  (OpenDecider), [#4](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/4)
+  (od1), [#3](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/3)
+  (soft-decider) and [#2](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/2)
+  (Laya). Each submitter states the mode; we have not re-run them. If a number looks
+  wrong, say so in the discussion and we will correct it.
+- **Bekko System One v0** ([hotchpotch](https://huggingface.co/collections/hotchpotch/bekko-system-one-v0-6abc47eab9c4fe4ec83d9e30)) was scored by us on 2026-10-01 on CPU (M3 Max, one call per case with all five questions, pinned revisions `b886a1f9` 17M, `ab7685f2` 68M, `1960df56` 400M), with the same scorer as the other rows. Its training data includes this benchmark's `train` split, with our teacher labels, so it sits in the second table; none of our 400 test cases are in its training data. We mapped our option and rubric text onto its candidate format in our option order. Its model cards say the license is not yet finalized. The author invited a score on X; we are glad to re-score with a different input mapping if he prefers.
+- **Specialists** use [Adaptive Classifier](https://github.com/codelion/adaptive-classifier)
+  0.2.0, one classifier per question on a frozen encoder, tuned on a held-out
+  quarter of `train` (mean pooling, `max_length` 512, 30 epochs,
+  `prototype_weight` 0.3). Each case is entered four times, split across labels in
+  proportion to its gold, so the soft target survives hard-label training; that
+  cut KL by a third.
+- **Prior** answers each question's `train` label frequencies for every case. It
+  has the best ECE while knowing nothing, which is why KL and Brier are the
+  columns to read, not ECE.
+
+## Reading a score
+
+Gold is the mean of three samples from a teacher of roughly 4B-class capability,
+so a score measures agreement with that teacher, not correctness. A better model
+can score lower wherever the teacher is wrong (it missed a duplicate invoice whose
+ID matched an earlier one).
+
+| Reference | Accuracy | What it is |
+|---|---|---|
+| Prior | 0.470 | the floor: label frequencies, ignoring the input |
+| Perfect factor recovery | 0.704 | a model fitted to the latent factors that generated each case |
+| Teacher self-agreement | 0.735 | a fresh teacher sample against gold built from the others |
+
+Scores well above 0.735 mean a model is learning the teacher's quirks. Per-question
+ceilings vary from 0.560 (`agent_trace/urgency`) to 0.937
+(`customer_service/category`), so read scores per question as well as on average.
+
+**Specialist and general numbers are not comparable.** A specialist is fitted on
+`train` for these four workflows and cannot answer anything else. A general model
+takes any question schema at request time and has never seen these. Say which
+mode you used; the gap between them is the price of generality, not a quality
+ranking.
+
+## The data
+
+| Type | Answer | Shape |
+|---|---|---|
+| `noul` | yes/no | probability that the statement is true |
+| `choice` | one of N labels | distribution over labels, plus confidence |
+| `score` | an ordered rubric | distribution over levels, plus an expected score |
+
+Every option carries a written description in `criteria`, and the descriptions
+are part of the input.
+
+| Workflow | Decision | Train | Test |
+|---|---|---|---|
+| `agent_trace_observability` | Does an agent run need human review, and how urgently? | 300 | 100 |
+| `customer_service` | The right response and action for a customer thread and account. | 300 | 100 |
+| `invoice_processing` | Pay, hold or reject a vendor bill against its order and delivery. | 300 | 100 |
+| `security_incidents` | Close, investigate or contain a security alert, given machine history. | 300 | 100 |
+
+`state` and `questions` together are exactly the body of a `POST /v1/systemone`
+request. `gold` holds the full gold distributions, and flat
+`<question>__label` / `__probabilities` / `__score` / `__probability_true` columns
+hold the same answers for convenience. `factors` and `label_agreement` describe how
+the case was built and are not model input.
+
+```python
+from datasets import load_dataset
+import json
+
+ds = load_dataset("LocalLLaMA/typed-decisions", "customer_service", split="test")
+row = ds[0]
+state, questions, gold = (json.loads(row[k]) for k in ("state", "questions", "gold"))
+print(gold["urgency"]["probabilities"])   # score against the full distribution
+```
+
+Report KL or log loss and Brier next to accuracy; calibration is the point.
+
+## How it was built
+
+Each case starts from independently sampled latent factors (topic, tone,
+severity, discrepancy type and so on), which a model renders into free text where
+the artefact is textual and keeps structured where it is structured. A teacher
+labels each case three times at temperature 0.7, and the gold is the mean of those
+distributions, so it stays soft where a decision is genuinely ambiguous. Before
+release, an audit checks state diversity, label balance, and that the gold
+actually tracks the input. `train` comes from a separate run at a different seed;
+packaging refuses to build if any case id or state appears in both splits.
+
+The write-up behind the benchmark, including the two bugs it exposed in the
+classifier library: [Typed Decisions on Latent Node](https://latentnode.pages.dev/articles/typed-decisions.html).

sources/ZhDecisionBench/README.upstream.md

@@ -0,0 +1,65 @@
+---
+language:
+  - zh
+language_bcp47:
+  - zh-CN
+license: cc-by-4.0
+task_categories:
+  - text-classification
+  - feature-extraction
+tags:
+  - benchmark
+  - calibration
+  - decision-models
+  - system-one
+  - chinese
+  - jev
+  - evaluation
+size_categories:
+  - n<1k
+configs:
+  - config_name: voice_routing
+    data_files:
+      - split: validation
+        path: voice_routing.jsonl
+  - config_name: business_scenarios
+    data_files:
+      - split: validation
+        path: business_scenarios.jsonl
+---
+
+# zh-decision-bench (v0.2)
+
+**First Chinese-language calibration benchmark for Jev-class "System One" decision models** — accuracy *and* probability calibration on Chinese tasks.
+
+Full methodology, five-model results (Jev, NeoHorse-Jev-4B, Laya x2, Qwen3.5-2B), raw predictions and the human adjudication log live in the [source repository](https://github.com/CodyQin/zh-decision-bench). The eval set also ships in [Laya](https://github.com/NandhaKishorM/laya)'s `research/evals/` as of v0.3.21.
+
+## Configs
+
+| config | items | questions | source |
+|---|---|---|---|
+| `voice_routing` | 179 | 179 (choice, 6-way) | MASSIVE zh-CN dev (Amazon, CC BY 4.0), quality-filtered, fixed seed |
+| `business_scenarios` | 40 | 105 (choice/score/noul) | Synthetic: LLM-drafted, human-adjudicated ([review log](https://github.com/CodyQin/zh-decision-bench/blob/master/data/review_log.md)) |
+
+## Fields
+
+- `state` — the text to decide on (utterance / ticket / message)
+- `questions` — question dict keyed by question id; each has `type` (`choice` | `score` | `noul`), `instructions`, `criteria` (options: label->description; score: ordered level list; noul: true/false descriptions)
+- `gold` — ground truth keyed by question id (label / level / boolean)
+- `domain`, `source`, `difficulty`, `tags`, `notes` — provenance and slicing
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("CodyQin/zh-decision-bench", "voice_routing")
+```
+
+## Changelog
+
+- **v0.2 (2026-09-28)**: voice_routing 179 -> 323 (same task, official labels); business_scenarios 40 -> 55 (human-adjudicated); five-model matrix incl. NeoHorse-Jev-4B; revised the v0.1 over-confidence reading at larger n.
+- **v0.1 (2026-09-27)**: initial release.
+
+## License
+
+CC BY 4.0. MASSIVE-derived rows attribute [Amazon MASSIVE](https://github.com/alexa/massive) (CC BY 4.0); synthetic rows are original.

verification.json

@@ -0,0 +1,76 @@
+{
+  "verified_utc": "2026-10-19T03:36:26.838608+00:00",
+  "jevalkit_revision": "d893be5236aefaf14163bd516f3405a482c5cef6",
+  "datasets": [
+    {
+      "name": "VisualDecisionBench",
+      "records": 1696,
+      "questions": 9143,
+      "sha256": "7caa026b69ba1b160f2aa23776b31d4d6162cdb95592810411ce4c905e1a1b44",
+      "media_verified": false
+    },
+    {
+      "name": "VisualDecisionBench_Image",
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