diff --git a/.gitattributes b/.gitattributes index 417ad404b9cbc8123c1a3fe112da01a93e4a7541..afd12da64b9f11a7805306a177cf1cb5bc41f24e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -31907,3 +31907,4 @@ multiswe_v1/cached_images/bundles-v1/fe0e3facda3da9931349ae987250158a5ab0ab797bd multiswe_v1/cached_images/bundles-v1/fe273de2fd65260f375ed302983afc163d717442a5fbe451bd7b364cd92645c9/build_spotbugs__spotbugs-2020-a3276a89548c.sif/build_spotbugs__spotbugs-2020-a3276a89548c.sif filter=lfs diff=lfs merge=lfs -text multiswe_v1/cached_images/bundles-v1/ffba10682c1ff2b7e36326d764fbd8164b895c109e6d02c791cdfa390b492432/build_anuraghazra__github-readme-stats-240-28d0ef643c2c.sif/build_anuraghazra__github-readme-stats-240-28d0ef643c2c.sif filter=lfs diff=lfs merge=lfs -text multiswe_v1/cached_images/bundles-v1/fff972dfc006afdad7e016367f3ef5f74abde5e270b0a2165fb33951dc1d3856/build_anuraghazra__github-readme-stats-490-ba3d773099ff.sif/build_anuraghazra__github-readme-stats-490-ba3d773099ff.sif filter=lfs diff=lfs merge=lfs -text +calibforge_v3/trace_stats/plots/qwen3.8-27b-tokens-by-outcome.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index e4dcedcc9f3371a38d70edd44e8badbeffd93a17..fd66f94e97bb79aae87d4efe61e08d6625181ebd 100644 --- a/README.md +++ b/README.md @@ -29,15 +29,30 @@ configs: Datasets repaired and filtered using the [OT-next-data validation protocol](https://github.com/MLI-lab/OT-next-data/blob/main/validation/PROTOCOL.md). - **V1:** environment reliability and rule-based task-quality repairs. Build/setup, no-op and reference checks where reference solutions exist. +- **V3:** newly solved tasks absent from v2, with the new evaluation's traces and statistics. Combine v2 and v3 for the full solved set. - **V2:** keep tasks solved at least once by an evaluated model. Includes partial results where a success is already recorded; each README lists models, attempt counts and coverage. -## Dataset sizes and composition +## Incremental v3 releases + +V3 task files contain only newly solved tasks, with no task-ID overlap with the corresponding v2 task file. Historical traces remain in v2; v3 includes every attempt from its new evaluation, successful and failed. These subsets were harder for the earlier evaluated models, not necessarily for every future model. + +| Dataset | v2 tasks, available separately | Newly solved v3 tasks | Combined unique tasks | +| --- | ---: | ---: | ---: | +| [BugsInPy](bugsinpy_v3/README.md) | 65 | 406 | 471 | +| [CalibForge](calibforge_v3/README.md) | 1,512 | 2,287 | 3,799 | +| [EndlessTerminals](endless_v3/README.md) | 1,958 | 121 | 2,079 | +| [NL2Bash](nl2bash_v3/README.md) | 938 | 104 | 1,042 | +| [TermiGen](termigen_v3/README.md) | 2,277 | 230 | 2,507 | + +Each release README includes a v2 + v3 loading example. Counts here describe the incremental releases published on 2026-10-26. Earlier cumulative v3 versions remain accessible by their pinned HF commit revisions. + +## Earlier v1/v2 size snapshot **V1: 42,166 kept, patched tasks across 8 data sources.** **V2: 7,684 tasks solved at least once across the same 8 data sources.** V2 is a subset of V1, not additional tasks. -Percentages are each source's share of that version's total in the current published snapshots. +The table below is an earlier snapshot and predates subsequent evaluations. For current counts, use the individual release READMEs and the v3 table above. Percentages refer to this earlier snapshot. | Dataset | V1 tasks | Share of V1 | V2 tasks | Share of V2 | | --- | ---: | ---: | ---: | ---: | diff --git a/bugsinpy_v3/README.md b/bugsinpy_v3/README.md index 97c01d0ca77f717533b03b492562e209d4d005d1..30c651ab0abe720ac74d77fecaaa80cdb7556f5c 100644 --- a/bugsinpy_v3/README.md +++ b/bugsinpy_v3/README.md @@ -3,12 +3,14 @@ ## Dataset stats - **Source tasks:** 489 -- **Kept:** 471 (65 from v2 plus 406 newly solved by Qwen/Qwen3.8-27B) +- **Kept in v3:** 406 newly solved tasks +- **Available separately in v2:** 65 +- **Combined v2 + v3:** 471 unique tasks - **Excluded:** 18 (no recorded success) - **New evaluation:** 424 previously excluded tasks, 2 attempts each, 848 attempts - **New saved trajectories:** 848, successful and failed -BugsInPy contains Python bug-fixing tasks from real open-source projects. Each task provides a buggy project checkout and a verifier for the target bug. `tasks.parquet` keeps all v2 tasks and adds the newly solved tasks with their evaluated payloads. +BugsInPy contains Python bug-fixing tasks from real open-source projects. Each task provides a buggy project checkout and a verifier for the target bug. `tasks.parquet` contains only newly solved tasks with their evaluated payloads. ## Evaluation settings @@ -30,7 +32,7 @@ The table reports settings found in **3 sampled saved trial configurations**, no | --- | ---: | ---: | ---: | ---: | ---: | | Qwen/Qwen3.8-27B (thinking) | 424 | 848 | 406 | 90.92% | 95.75% (k=2) | -Qwen/Qwen3.8-27B solved 406 of the 424 previously excluded tasks. Together with the 65 tasks carried forward from [v2](../bugsinpy_v2/README.md), v3 keeps 471 tasks. These results cover only the new evaluation. +Qwen/Qwen3.8-27B solved 406 of the 424 previously excluded tasks. Combining these 406 tasks with the 65 tasks in [v2](../bugsinpy_v2/README.md) gives 471 unique tasks. These results cover only the new evaluation. Qwen/Qwen3.8-27B used Terminus-2, thinking enabled, temperature 1.0, top-p 0.95, top-k 20, a 32,768-token context, and an intended limit of 8,192 output tokens per model call (see evaluation settings). Each task kept its existing agent and verifier time limits. The task source is pinned to revision `3abfc632`; complete settings are in [evaluation setup](metadata/evaluation-setup.json). @@ -46,22 +48,20 @@ Qwen/Qwen3.8-27B used Terminus-2, thinking enabled, temperature 1.0, top-p 0.95, ## Select tasks by successful-attempt count -The Parquet preserves `instruct_successes`, `thinking_successes`, and `attempts_per_model` from v2, and adds `qwen38_successes`, `qwen38_attempts`, and `kept_from_v2`. For the 65 v2 tasks, `qwen38_attempts` is 0 and `qwen38_successes` is null. +The Parquet preserves `instruct_successes`, `thinking_successes`, and `attempts_per_model` from v2, and adds `qwen38_successes`, `qwen38_attempts`, and `kept_from_v2`. Tasks already in v2 are not duplicated in this file. ```python import pyarrow.parquet as pq # Newly evaluated tasks solved exactly once. subset = pq.read_table("tasks.parquet", filters=[("qwen38_successes", "=", 1)]) -# Every task already kept in v2. -v2_tasks = pq.read_table("tasks.parquet", filters=[("kept_from_v2", "=", True)]) ``` [Selection metadata](metadata/selection.json) records counts and membership for all 489 tasks, including the 18 excluded tasks. ## Traces -`traces/` includes all 848 new Qwen/Qwen3.8-27B attempts, successful and failed, one row per attempt in Zstandard-compressed Parquet. Earlier traces are also preserved; use the model filter below to select the new evaluation. Columns include `task_id`, `model`, `attempt_id`, `reward`, `verifier_reward`, `retained_v2`, `retained_v3`, and full `trajectory` and `result` JSON. +`traces/` includes all 848 new Qwen/Qwen3.8-27B attempts, successful and failed, one row per attempt in Zstandard-compressed Parquet. Earlier traces remain in v2. Columns include `task_id`, `model`, `attempt_id`, `reward`, `verifier_reward`, `retained_v2`, `retained_v3`, and full `trajectory` and `result` JSON. ```python import json @@ -88,4 +88,25 @@ Not yet checked with the repository-leak checks added on 2026-10-17 (leftover gi | --- | ---: | ---: | ---: | | Qwen/Qwen3.8-27B | 1,172.06 s | 935.76 s | 848 | -`patch.py` rebuilds `tasks.parquet` from pinned v1 and v2 sources and the published selection metadata. +`patch.py` rebuilds the incremental task file from the pinned pre-migration release and the published layout manifest. + +## Incremental release + +V3 contains only tasks newly solved after v2. It has no task-ID overlap with [v2](../bugsinpy_v2/README.md). This is a harder subset relative to the earlier evaluation, not a claim of intrinsic difficulty. New-evaluation traces include both solved and unsolved tasks so the reported pass rates remain reproducible. + +```python +from huggingface_hub import hf_hub_download +import pyarrow as pa +import pyarrow.parquet as pq + +def load(version): + path = hf_hub_download("FWeindel/validated-tasks", + f"bugsinpy_{version}/tasks.parquet", repo_type="dataset") + return pq.read_table(path) + +v2, v3 = load("v2"), load("v3") +assert set(v2["path"].to_pylist()).isdisjoint(v3["path"].to_pylist()) +combined = pa.concat_tables([v2, v3], promote_options="permissive") +``` + +The example combines task rows; each source file retains its own image manifest. Use those source manifests when staging images. [Layout and provenance](metadata/layout.json) records the pinned baseline and task counts. Earlier cumulative v3 releases remain accessible through their HF commit history. diff --git a/bugsinpy_v3/metadata/evaluation-settings-audit.json b/bugsinpy_v3/metadata/evaluation-settings-audit.json index dc17afff151ceda908aa5a80b21e5dd32ca05d36..fa1feef405211c5d0b6644b2c01a86730aa15d70 100644 --- a/bugsinpy_v3/metadata/evaluation-settings-audit.json +++ b/bugsinpy_v3/metadata/evaluation-settings-audit.json @@ -5,25 +5,7 @@ "scope": "new v3 evaluation only", "method": "Sample saved result.config from first and last row groups of first, middle and last trace files; one available result per model per selected row group. This is not an exhaustive attempt audit.", "sampled_configs": 3, - "evaluation_settings": [ - { - "model": "openai/qwen38-27b", - "agent": "terminus-2", - "context_tokens": 32768, - "intended_output_tokens_per_call": 8192, - "request_output_tokens": null, - "thinking": true, - "sampling": { - "temperature": 1.0, - "top_p": 0.95, - "top_k": 20, - "min_p": 0.0, - "presence_penalty": 0.0, - "repetition_penalty": 1.0 - }, - "output_limit_status": "No explicit request cap in sampled saved configs; actual fallback cap unknown" - } - ], + "evaluation_settings": [], "evidence": [ { "trace_file": "bugsinpy_v3/traces/qwen38-full-0000.parquet", @@ -137,4 +119,4 @@ "actual_fallback_output_cap": null, "findings": "Saved configurations use top-level max_tokens without llm_call_kwargs.max_tokens. The affected Terminus forwarding path does not send that top-level setting to the model API. No universal effective per-response token limit is established by this sample.", "fixed_code": "config/models.py:serving_agent_kwargs forwards llm_call_kwargs.max_tokens; HTTP-request regression tests cover every current registry model; this does not change historical results." -} +} \ No newline at end of file diff --git a/bugsinpy_v3/metadata/history-deduplication.json b/bugsinpy_v3/metadata/history-deduplication.json new file mode 100644 index 0000000000000000000000000000000000000000..c65d4be7dff9b3b8bba874fdf64ca22f44a55d57 --- /dev/null +++ b/bugsinpy_v3/metadata/history-deduplication.json @@ -0,0 +1,186 @@ +{ + "verified_identical_attempts": 15648, + "deleted_duplicate_files": [ + "bugsinpy_v3/traces/v2-000.parquet", + "bugsinpy_v3/traces/v2-001.parquet", + "bugsinpy_v3/traces/v2-002.parquet", + "bugsinpy_v3/traces/v2-003.parquet", + "bugsinpy_v3/traces/v2-004.parquet", + "bugsinpy_v3/traces/v2-005.parquet", + "bugsinpy_v3/traces/v2-006.parquet", + 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"bugsinpy-original-thefuck-17", + "bugsinpy-original-thefuck-18", + "bugsinpy-original-thefuck-19", + "bugsinpy-original-thefuck-2", + "bugsinpy-original-thefuck-20", + "bugsinpy-original-thefuck-21", + "bugsinpy-original-thefuck-22", + "bugsinpy-original-thefuck-23", + "bugsinpy-original-thefuck-24", + "bugsinpy-original-thefuck-25", + "bugsinpy-original-thefuck-26", + "bugsinpy-original-thefuck-27", + "bugsinpy-original-thefuck-28", + "bugsinpy-original-thefuck-29", + "bugsinpy-original-thefuck-30", + "bugsinpy-original-thefuck-31", + "bugsinpy-original-thefuck-32", + "bugsinpy-original-thefuck-4", + "bugsinpy-original-thefuck-5", + "bugsinpy-original-thefuck-6", + "bugsinpy-original-thefuck-7", + "bugsinpy-original-thefuck-8", + "bugsinpy-original-thefuck-9", + "bugsinpy-original-tornado-10", + "bugsinpy-original-tornado-12", + "bugsinpy-original-tornado-13", + "bugsinpy-original-tornado-16", + "bugsinpy-original-tornado-2", + "bugsinpy-original-tornado-4", + "bugsinpy-original-tqdm-1", + "bugsinpy-original-tqdm-2", + "bugsinpy-original-tqdm-3", + "bugsinpy-original-tqdm-4", + "bugsinpy-original-tqdm-5", + "bugsinpy-original-tqdm-6", + "bugsinpy-original-tqdm-7", + "bugsinpy-original-tqdm-8", + "bugsinpy-original-tqdm-9", + "bugsinpy-original-youtube-dl-10", + "bugsinpy-original-youtube-dl-14", + "bugsinpy-original-youtube-dl-15", + "bugsinpy-original-youtube-dl-20", + "bugsinpy-original-youtube-dl-22", + "bugsinpy-original-youtube-dl-24", + "bugsinpy-original-youtube-dl-26", + "bugsinpy-original-youtube-dl-29", + "bugsinpy-original-youtube-dl-3", + "bugsinpy-original-youtube-dl-30", + "bugsinpy-original-youtube-dl-31", + "bugsinpy-original-youtube-dl-32", + "bugsinpy-original-youtube-dl-33", + "bugsinpy-original-youtube-dl-34", + "bugsinpy-original-youtube-dl-4", + "bugsinpy-original-youtube-dl-8", + "bugsinpy-original-youtube-dl-9" + ], + "tasks_sha256": "f939bb42311d3df0946b77a4f0d0637ec6e5fecd62d5b573e09a0da761e84aed" +} \ No newline at end of file diff --git a/bugsinpy_v3/metadata/previous-cumulative-patch.py b/bugsinpy_v3/metadata/previous-cumulative-patch.py new file mode 100644 index 0000000000000000000000000000000000000000..505a4ae33437f902f68eefa73a88d8642330f31b --- /dev/null +++ b/bugsinpy_v3/metadata/previous-cumulative-patch.py @@ -0,0 +1,46 @@ +"""Rebuild BugsInPy v3 tasks.parquet from pinned v1/v2 payloads and metadata/selection.json. + +pip install pyarrow huggingface_hub +python patch.py --out rebuilt +Optional --source-dir holds v1.parquet and v2.parquet for offline reproduction. +""" +import argparse,hashlib,json +from pathlib import Path +import pyarrow as pa +import pyarrow.parquet as pq + +def build(out, source_dir=None): + root=Path(__file__).parent + m=json.loads((root/'metadata/selection.json').read_text()) + def source(version,revision): + if source_dir:return source_dir/f'{version}.parquet' + from huggingface_hub import hf_hub_download + return Path(hf_hub_download(m['repo_id'],f'bugsinpy_{version}/tasks.parquet',repo_type='dataset',revision=revision,cache_dir=out/'.source-cache')) + out.mkdir(parents=True,exist_ok=True) + target=out/'tasks.parquet' + if target.exists():raise ValueError(f'{target} already exists') + v1=source('v1',m['evaluation_source_revision']);v2=source('v2',m['v2_revision']) + with v1.open('rb') as f:assert hashlib.file_digest(f,'sha256').hexdigest()==m['evaluation_source_sha256'] + old=pq.ParquetFile(v2);oldrows={r['path']:r for b in old.iter_batches(batch_size=1) for r in b.to_pylist()} + pf=pq.ParquetFile(v1);kept={t for t,e in m['tasks'].items() if e['kept']} + schema=pa.schema([*old.schema_arrow,pa.field('qwen38_successes',pa.int64()),pa.field('qwen38_attempts',pa.int64()),pa.field('kept_from_v2',pa.bool_())]) + md=dict(old.schema_arrow.metadata or {});im=json.loads(pf.schema_arrow.metadata[b'ot.images.v1']) + im['tasks']={t:e for t,e in im['tasks'].items() if t in kept};used={e['bundle'] for v in im['tasks'].values() for e in v['environments']};im['bundles']={k:v for k,v in im['bundles'].items() if k in used};im['missing']=[x for x in im.get('missing',[]) if x.get('task') in kept] + md[b'ot.images.v1']=json.dumps(im).encode();md[b'ot.release.v3']=json.dumps({k:v for k,v in m.items() if k!='tasks'}).encode();schema=schema.with_metadata(md) + count=0 + with pq.ParquetWriter(target,schema,compression='zstd') as writer: + for b in pf.iter_batches(batch_size=1): + row=b.to_pylist()[0];t=row['path'] + if t not in kept:continue + e=m['tasks'][t] + if t in oldrows:row=oldrows[t] + else: + assert row['content_sha256']==e['models']['qwen3.8-27b']['evaluated_content_sha256'] + row.update(instruct_successes=e['models']['qwen3-30b-instruct-2507']['solved_attempts'],thinking_successes=e['models']['qwen3-30b-thinking']['solved_attempts'],attempts_per_model=16) + row.update(qwen38_successes=e['qwen38_successes'],qwen38_attempts=e['qwen38_attempts'],kept_from_v2=e['kept_v2']) + writer.write_table(pa.Table.from_pylist([row],schema=schema));count+=1 + assert count==m['kept']==471 and set(oldrows)<=kept + print(f'Wrote {count} tasks to {target}') + +if __name__=='__main__': + p=argparse.ArgumentParser(description=__doc__);p.add_argument('--out',type=Path,required=True);p.add_argument('--source-dir',type=Path);a=p.parse_args();build(a.out,a.source_dir) diff --git a/bugsinpy_v3/metadata/previous-release-verification.json b/bugsinpy_v3/metadata/previous-release-verification.json new file mode 100644 index 0000000000000000000000000000000000000000..a6ad522436fa5cabff7c7633de0ad1d420677b36 --- /dev/null +++ b/bugsinpy_v3/metadata/previous-release-verification.json @@ -0,0 +1,21 @@ +{ + "tasks": 471, + "v2_rows_preserved_exactly": 65, + "newly_solved_tasks": 406, + "excluded_tasks": 18, + "trace_rows_by_model": { + "qwen3.8-27b": 848, + "qwen3-30b-instruct-2507": 7824, + "qwen3-30b-thinking": 7824 + }, + "successful_attempts_by_model": { + "qwen3.8-27b": 771, + "qwen3-30b-instruct-2507": 245, + "qwen3-30b-thinking": 178 + }, + "missing_trajectories": 0, + "verified_unique_attempts": 16496, + "task_content_hashes_matched": true, + "all_v2_columns_and_payloads_preserved": true, + "reproduced_tasks_sha256": "42323e804314d0077e5056d11e9e0d8f0f4f7c883e6140ebca8569a69b581f14" +} diff --git a/bugsinpy_v3/metadata/release-verification.json b/bugsinpy_v3/metadata/release-verification.json index a6ad522436fa5cabff7c7633de0ad1d420677b36..d95389d6791713cd706b5127ac2cfc0463aba045 100644 --- a/bugsinpy_v3/metadata/release-verification.json +++ b/bugsinpy_v3/metadata/release-verification.json @@ -1,21 +1,15 @@ { - "tasks": 471, - "v2_rows_preserved_exactly": 65, - "newly_solved_tasks": 406, - "excluded_tasks": 18, - "trace_rows_by_model": { - "qwen3.8-27b": 848, - "qwen3-30b-instruct-2507": 7824, - "qwen3-30b-thinking": 7824 - }, - "successful_attempts_by_model": { - "qwen3.8-27b": 771, - "qwen3-30b-instruct-2507": 245, - "qwen3-30b-thinking": 178 - }, - 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+ 0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "attempt_ids": [ + "5cfd133a-0639-49b9-8f4b-1bc4e29fc849", + "89f9ebce-19cc-4732-a618-239a6a12ae31", + "a36ae6ab-e439-4c5d-93de-3790db654115", + "9ef07072-0e7b-4702-a833-583f2a0b9328", + "4eb6dc77-ecbe-4fa1-8db3-edb086728752", + "8e685e19-0ce9-44a3-b3bc-db1da30652ec", + "ab2b74b2-e7cd-4002-a196-829055c68471", + "3e62d0bc-ae9f-4c08-9220-6f21226f6f9b", + "63aceffb-c0bb-4d09-b847-630dc10b6694", + "66415c15-3b05-4617-ba3b-1db846a07a9d", + "4422a408-5a33-4dce-9925-3da959e36ef6", + "dee42169-860c-430f-a2b7-53d405028ccd", + "d8b66ed0-5e8b-4fc4-86bf-d7080207b6a5", + "0f4a2aea-2b02-4ca3-bdd1-6747201daf14", + "94ee720f-184b-4af1-9836-233e13cb843f", + "ce941dac-ece5-4b59-8640-f6e5c679669d" + ], + "solved_attempts": 0, + "attempts_with_errors": 5 + }, + "qwen3-30b-thinking": { + "rewards": [ + 0.0, + 0, + 0, + 0.0, + 0.0, + 0, + 0.0, + 0.0, + 0.0, + 0.0, + 0, + 0.0, + 0.0, + 0, + 0.0, + 0 + ], + "attempt_ids": [ + "e1870e28-f991-4b00-8b16-9e4e5d096007", + "91bbaa09-6ec4-4429-bb9d-bf4a892fc46a", + "31af1a2d-3ddd-46b8-a3a3-f1b50c1fb650", + "206a238a-8181-4370-ad59-5ae309c6b391", + "1ac72780-8a88-4b48-a956-cc8005065825", + "6dfdd620-0c30-4aee-ba06-72436a1bffe0", + "86ca6b74-9faf-4585-88c9-231a390fd0e2", + "56fcd970-af6a-4892-bae3-3647a488ce76", + "f8877afe-c0e5-4e7d-8cf6-3ecc9d1978b0", + "aea15404-98b7-40f9-8d2b-63d36c2caa4f", + "16ac9e3c-302c-4f75-b4c6-ce902cee416a", + "328a85a5-a5f6-4f05-8c78-ac24b5bb5653", + "61fa5efe-c2e7-4d17-9d35-2009177df8a3", + "ec015764-f31c-4b47-b2b3-2031f8bf30b4", + "5c25b972-f5a4-419b-b453-ff0b9376a56a", + "a97f79cf-76b2-44f8-8855-a924cbf6495f" + ], + "solved_attempts": 0, + "attempts_with_errors": 11 + }, + "qwen3.8-27b": { + "rewards": [ + 1.0, + 1.0 + ], + "attempt_ids": [ + "ae6657aa-a9b2-4413-b5b6-d15c022f31c8", + "884ff281-800d-447d-bb03-b43e4461e457" + ], + "solved_attempts": 2, + "evaluated_content_sha256": "d78d434920adcaabf171994e0a6f4939450c7cd7e75886cbf3be7738de99af4d" + } + }, + "kept": true, + "kept_v2": false, + "qwen38_attempts": 2, + "qwen38_successes": 2, + "kept_combined": true, + "available_in_v2": false + }, + "bugsinpy-original-youtube-dl-9": { + "content_sha256": "36b40a5a66c072cf25cf82cdef78decbcc72baeeb84c2421f1fd59913bd99fbb", + "models": { + "qwen3-30b-instruct-2507": { + "rewards": [ + 0.0, + 0, + 0.0, + 0.0, + 0.0, + 0.0, + 0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0, + 0 + ], + "attempt_ids": [ + "45b37898-0fcf-4cab-b131-813c50c9e586", + "6187168e-5189-4f87-96ca-1b54cdddd7fc", + "801abb49-717b-4f5e-b42d-eb4249da7ec0", + "3daab2b7-2049-407c-879e-a32bda1be455", + "c946905b-5ec2-4d08-a183-7aeabea7bf22", + "5d53bc37-712f-435f-839d-932efcda27c7", + "4f6942aa-bc42-4d4f-8d5f-761443cac645", + "f8233012-f223-4c3f-803a-3569a3ebb73c", + "4ad35926-0595-430e-875f-3232c6226d16", + "243e1a3a-9ea0-44fc-9bd0-30919b96a1d6", + "0045799b-94bc-457c-9815-7b5d5bfd62df", + "66e72a29-8793-4f37-89f3-0161b1dc6859", + "865439af-5e62-43ca-8461-2786387ce5b1", + "f3eed87b-7e5e-49be-aefd-909cfbcae399", + "951ae24c-c7b2-45b4-ab68-7aadd6377ea4", + "b6a1d990-87fc-451e-a181-4baa16c92da3" + ], + "solved_attempts": 0, + "attempts_with_errors": 10 + }, + "qwen3-30b-thinking": { + "rewards": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0, + 0.0, + 0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0, + 0.0 + ], + "attempt_ids": [ + "d8e4f82e-1a4e-4689-8b3c-8dcc671559a2", + "532e77f1-862e-4ff4-bb8d-486efc6ddf49", + "3c3b4725-5d65-43da-8ee1-85fe3da26bd1", + "8f495af3-650e-4dec-ad5c-f598f4feb498", + "a2c30e42-759b-466e-abae-a7127ba2a7a5", + "9d2cd017-f603-4c94-be54-28a169b46956", + "daacdcf7-e0e4-43fd-8926-08bba6ff9e37", + "400c06b7-7a18-4893-ba55-be7915378224", + "919d4c67-7721-45f9-97b3-658887da98d3", + "2a549315-f350-42d0-b4ea-7d0d8026ce67", + "eac96ebd-f206-4c70-a80c-68acf5f50975", + "10ad0db5-f268-4b20-817c-a163b5e59440", + "f5ead8ec-b758-487a-91e9-35486d6ad540", + "82ef9c10-9530-42ef-a595-c9cf56fd6362", + "4faf7c70-6362-4759-89a6-d95edbeb42cf", + "ef838e1b-bf90-409c-b0d1-8ffc9266ef85" + ], + "solved_attempts": 0, + "attempts_with_errors": 8 + }, + "qwen3.8-27b": { + "rewards": [ + 1.0, + 0.0 + ], + "attempt_ids": [ + "e4930b0e-5f20-4e1d-bb6a-0a13a0825f52", + "b63b9e92-06c1-4ff2-8a4e-924b63530b92" + ], + "solved_attempts": 1, + "evaluated_content_sha256": "36b40a5a66c072cf25cf82cdef78decbcc72baeeb84c2421f1fd59913bd99fbb" } + }, + "kept": true, + "kept_v2": false, + "qwen38_attempts": 2, + "qwen38_successes": 1, + "kept_combined": true, + "available_in_v2": false } -} + }, + "layout": "incremental", + "baseline_kept": 65, + "combined_kept": 471, + "baseline_folder": "bugsinpy_v2", + "baseline_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "carried_from_v2": 0 +} \ No newline at end of file diff --git a/bugsinpy_v3/patch.py b/bugsinpy_v3/patch.py index 505a4ae33437f902f68eefa73a88d8642330f31b..ccf2a50e421c939255ec8c757c7d2c51272e44b3 100644 --- a/bugsinpy_v3/patch.py +++ b/bugsinpy_v3/patch.py @@ -1,46 +1,22 @@ -"""Rebuild BugsInPy v3 tasks.parquet from pinned v1/v2 payloads and metadata/selection.json. - -pip install pyarrow huggingface_hub -python patch.py --out rebuilt -Optional --source-dir holds v1.parquet and v2.parquet for offline reproduction. +"""Reproduce the incremental v3 task set from the pinned pre-migration release. +Requires pyarrow and huggingface_hub. Does not rerun evaluation. """ import argparse,hashlib,json from pathlib import Path -import pyarrow as pa -import pyarrow.parquet as pq - -def build(out, source_dir=None): - root=Path(__file__).parent - m=json.loads((root/'metadata/selection.json').read_text()) - def source(version,revision): - if source_dir:return source_dir/f'{version}.parquet' - from huggingface_hub import hf_hub_download - return Path(hf_hub_download(m['repo_id'],f'bugsinpy_{version}/tasks.parquet',repo_type='dataset',revision=revision,cache_dir=out/'.source-cache')) - out.mkdir(parents=True,exist_ok=True) - target=out/'tasks.parquet' - if target.exists():raise ValueError(f'{target} already exists') - v1=source('v1',m['evaluation_source_revision']);v2=source('v2',m['v2_revision']) - with v1.open('rb') as f:assert hashlib.file_digest(f,'sha256').hexdigest()==m['evaluation_source_sha256'] - old=pq.ParquetFile(v2);oldrows={r['path']:r for b in old.iter_batches(batch_size=1) for r in b.to_pylist()} - pf=pq.ParquetFile(v1);kept={t for t,e in m['tasks'].items() if e['kept']} - schema=pa.schema([*old.schema_arrow,pa.field('qwen38_successes',pa.int64()),pa.field('qwen38_attempts',pa.int64()),pa.field('kept_from_v2',pa.bool_())]) - md=dict(old.schema_arrow.metadata or {});im=json.loads(pf.schema_arrow.metadata[b'ot.images.v1']) - im['tasks']={t:e for t,e in im['tasks'].items() if t in kept};used={e['bundle'] for v in im['tasks'].values() for e in v['environments']};im['bundles']={k:v for k,v in im['bundles'].items() if k in used};im['missing']=[x for x in im.get('missing',[]) if x.get('task') in kept] - md[b'ot.images.v1']=json.dumps(im).encode();md[b'ot.release.v3']=json.dumps({k:v for k,v in m.items() if k!='tasks'}).encode();schema=schema.with_metadata(md) - count=0 - with pq.ParquetWriter(target,schema,compression='zstd') as writer: - for b in pf.iter_batches(batch_size=1): - row=b.to_pylist()[0];t=row['path'] - if t not in kept:continue - e=m['tasks'][t] - if t in oldrows:row=oldrows[t] - else: - assert row['content_sha256']==e['models']['qwen3.8-27b']['evaluated_content_sha256'] - row.update(instruct_successes=e['models']['qwen3-30b-instruct-2507']['solved_attempts'],thinking_successes=e['models']['qwen3-30b-thinking']['solved_attempts'],attempts_per_model=16) - row.update(qwen38_successes=e['qwen38_successes'],qwen38_attempts=e['qwen38_attempts'],kept_from_v2=e['kept_v2']) - writer.write_table(pa.Table.from_pylist([row],schema=schema));count+=1 - assert count==m['kept']==471 and set(oldrows)<=kept - print(f'Wrote {count} tasks to {target}') +import pyarrow as pa,pyarrow.parquet as pq +from huggingface_hub import hf_hub_download +def build(out): + root=Path(__file__).parent;m=json.loads((root/'metadata/layout.json').read_text()) + p=hf_hub_download('FWeindel/validated-tasks','bugsinpy_v3/tasks.parquet',repo_type='dataset',revision=m['previous_cumulative_revision']) + assert hashlib.sha256(Path(p).read_bytes()).hexdigest()==m['previous_tasks_sha256'] + t=pq.read_table(p);ids=set(m['task_ids']);t=t.filter(pa.array([x in ids for x in t['path'].to_pylist()]));assert set(t['path'].to_pylist())==ids + meta=dict(t.schema.metadata or {}) + if b'ot.images.v1' in meta: + im=json.loads(meta[b'ot.images.v1']);im['tasks']={k:v for k,v in im['tasks'].items() if k in ids};used={e['bundle'] for v in im['tasks'].values() for e in v['environments']};im['bundles']={k:v for k,v in im['bundles'].items() if k in used};im['coverage']={'cached_tasks':len(im['tasks']),'total_tasks':len(ids)} + if 'uncached_task_ids' in im:im['uncached_task_ids']=sorted(ids-set(im['tasks'])) + meta[b'ot.images.v1']=json.dumps(im).encode() + release=json.loads(meta.get(b'ot.release.v3',b'{}'));release.update({k:m[k] for k in ['layout','kept','baseline_kept','combined_kept','baseline_folder','baseline_revision','carried_from_v2']});meta[b'ot.release.v3']=json.dumps(release).encode() + pq.write_table(t.replace_schema_metadata(meta),out,compression='zstd') if __name__=='__main__': - p=argparse.ArgumentParser(description=__doc__);p.add_argument('--out',type=Path,required=True);p.add_argument('--source-dir',type=Path);a=p.parse_args();build(a.out,a.source_dir) + p=argparse.ArgumentParser();p.add_argument('--out',type=Path,required=True);a=p.parse_args();build(a.out) diff --git a/bugsinpy_v3/tasks.parquet b/bugsinpy_v3/tasks.parquet index 0fd1c83897f8c9bf2e75b5502093f62780bb62d8..f6a13edf8f1adc692aa13a8a406cee7997953629 100644 --- a/bugsinpy_v3/tasks.parquet +++ b/bugsinpy_v3/tasks.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:42323e804314d0077e5056d11e9e0d8f0f4f7c883e6140ebca8569a69b581f14 -size 4194162165 +oid sha256:f939bb42311d3df0946b77a4f0d0637ec6e5fecd62d5b573e09a0da761e84aed +size 4110518081 diff --git a/bugsinpy_v3/trace_stats/README.md b/bugsinpy_v3/trace_stats/README.md index b56667acb14bc57cdb6335afdd0b4ad0666cd545..8c7212993690f572f4dbaba08bed0b54df676d74 100644 --- a/bugsinpy_v3/trace_stats/README.md +++ b/bugsinpy_v3/trace_stats/README.md @@ -1,6 +1,6 @@ # Trajectory statistics -848 new Qwen/Qwen3.8-27B attempts across 424 evaluated tasks, kept and excluded, each counted once. Task success counts are in the [dataset overview](../README.md). +848 attempts across 424 tasks of bugsinpy_v3, kept and excluded, each counted once. Task success counts are in the [dataset overview](../README.md). | Metric | Qwen/Qwen3.8-27B | | --- | ---: | @@ -15,32 +15,38 @@ Medians use recorded measurements only. ## Plots -![Trace measurements](plots/qwen3.8-27b-trace-distributions.png) +![Trace measurements](plots/all-models-trace-distributions.png) -![Trace outcomes](plots/qwen3.8-27b-trace-outcomes.png) +![Trace outcomes](plots/all-models-trace-outcomes.png) -![Most frequent commands](plots/qwen3.8-27b-trace-commands.png) +![Most frequent commands](plots/all-models-trace-commands.png) Commands are counted as submitted, not as successful executions. +Per-model plots: [Qwen/Qwen3.8-27B commands](plots/qwen3.8-27b-trace-commands.png), [Qwen/Qwen3.8-27B distributions](plots/qwen3.8-27b-trace-distributions.png), [Qwen/Qwen3.8-27B outcomes](plots/qwen3.8-27b-trace-outcomes.png). + ## Data -The detailed files preserve all evaluations. Select `qwen3.8-27b` in the `model` column of [per-task metrics](data/per_task.parquet) and [per-attempt metrics](data/per_attempt.parquet), or use `models["qwen3.8-27b"]` in the [summary](data/summary.json), for the new evaluation. Nested metrics are stored as JSON. [Provenance](data/provenance.json) records source files and deduplication. +- [Summary](data/summary.json): overall and per-model statistics. +- [Per-task metrics](data/per_task.parquet) and [per-attempt metrics](data/per_attempt.parquet), with nested metrics stored as JSON. +- [Provenance](data/provenance.json): source files and deduplication. + +Attempts without a saved conversation: 0; without a verifier reward: 3. Their missing measurements stay empty. + -Earlier evaluation statistics are in the [v2 overview](../../bugsinpy_v2/trace_stats/README.md). +Evaluation budgets: see the [dataset evaluation setup](../README.md). Per-call context and output-limit enforcement are not established by token-usage statistics. -New attempts without a saved conversation: 0; without a verifier reward: 3. Missing measurements stay empty. -## Evaluation settings +## Token usage and summarization -Context is the total prompt plus response window for one model call. The response budget includes reasoning tokens and is not a whole-attempt token budget. +All published traces are included, covering kept and excluded tasks and every verifier outcome. One trace means one task attempt. -| Model | Context tokens (configured) | Intended response tokens per call | Output-limit evidence | Thinking | Sampling | -| --- | ---: | ---: | --- | --- | --- | -| Qwen/Qwen3.8-27B | 32768 | 8192 | No explicit request cap in sampled saved configs; actual fallback cap unknown | on | {"min_p": 0.0, "presence_penalty": 0.0, "repetition_penalty": 1.0, "temperature": 1.0, "top_k": 20, "top_p": 0.95} | +![Output, summarization, and context distributions](plots/all-models-tokens-by-outcome.png) -Configured limits are not measurements of token usage. A missing explicit response cap does not mean unlimited generation: the serving context, backend defaults, stopping conditions and task timeouts still apply. Unknown historical settings are not filled from current model defaults. +- **Output per recorded agent step:** successful, graded unsuccessful, and ungraded attempts shown separately. Each step contributes once. +- **Summarizations per trace:** one combined distribution across all traces with a recorded count, including zero. +- **Peak context per trace:** the largest recorded step prompt + output count in each trace, separated into traces with and without summarization. Unknown summary counts are kept separate when present. Each trace contributes once, regardless of its length or outcome. -**Historical output-limit bug:** the intended per-response limit was stored as top-level `max_tokens`, but the affected Terminus path did not forward it as `llm_call_kwargs.max_tokens`. Thus these sampled runs did not request the intended explicit cap. Generation instead depended on the backend's fallback allowance, available context, stopping conditions and agent timeout. The exact fallback limit is not established by the saved configurations; it must not be reported as 8,192 or 16,384 enforced tokens, or as unlimited. +[Detailed statistics](data/tokens-by-outcome.json) retain sample counts and missing measurements. Missing summary counts are not zero. ATIF steps can aggregate multiple requests, so recorded context does not establish an exact per-call context limit. -The table reports settings found in **3 sampled saved trial configurations**, not an exhaustive audit of every attempt or direct proof of server settings. Historical runs and retries may differ. Sampling values are recorded explicitly rather than inferred from today's model-card defaults. Agent and verifier wall-clock budgets remain task-specific; sampled resolved limits are in [settings and evidence](../metadata/evaluation-settings-audit.json). Context is not the sum of tokens across all turns. The current forwarding fix does not retroactively change these pass@k results. +This folder reports the new evaluation only. Task membership is incremental: tasks already in v2 are not duplicated in v3. Historical conversations remain in v2. 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"qwen3-30b-instruct-2507": { - "trace_distributions": "plots/qwen3-30b-instruct-2507-trace-distributions.png", - "trace_outcomes": "plots/qwen3-30b-instruct-2507-trace-outcomes.png", - "trace_commands": "plots/qwen3-30b-instruct-2507-trace-commands.png" - }, - "qwen3-30b-thinking": { - "trace_distributions": "plots/qwen3-30b-thinking-trace-distributions.png", - "trace_outcomes": "plots/qwen3-30b-thinking-trace-outcomes.png", - "trace_commands": "plots/qwen3-30b-thinking-trace-commands.png" - }, "qwen3.8-27b": { "trace_distributions": "plots/qwen3.8-27b-trace-distributions.png", "trace_outcomes": "plots/qwen3.8-27b-trace-outcomes.png", diff --git a/bugsinpy_v3/trace_stats/data/summary.json b/bugsinpy_v3/trace_stats/data/summary.json index 7e20d3c61191b5978648fef39a1159ca7fa649d8..8db2efbd6ae81b6bcd1b2480b48457e3b23c00af 100644 --- a/bugsinpy_v3/trace_stats/data/summary.json +++ b/bugsinpy_v3/trace_stats/data/summary.json @@ -1,3904 +1,234 @@ { "scope": "All attempts in the published trace Parquets of this folder.", - "created_at": "2026-10-20T18:23:01.458345+00:00", - "input_rows": 16496, + "created_at": "2026-10-26T15:53:34.806264+00:00", + "input_rows": 848, "duplicate_records_removed": 0, - "unique_attempts": 16496, - "unique_tasks": 489, + "unique_attempts": 848, + "unique_tasks": 424, "missing_trajectories": 0, - "null_verifier_rewards": 2711, + "null_verifier_rewards": 3, "all_attempts": { - "trajectories": 16496, - "graded": 16496, - "solved": 1194, + "trajectories": 848, + "graded": 848, + "solved": 771, "turns": { - "n": 16496, - "mean": 62.948411736178464, - "median": 31, - "p90": 173, - "p95": 264, - "max": 1953 + "n": 848, + "mean": 24.409198113207548, + "median": 21, + "p90": 46, + "p95": 55, + "max": 86 }, "input_tokens": { - "n": 16496, - "mean": 931710.7337536373, - "median": 387199, - "p90": 2866272, - "p95": 4363266, - "max": 29315677 + "n": 848, + "mean": 315899.75235849054, + "median": 247841, + "p90": 706385, + "p95": 925270, + "max": 1446688 }, - "input_tokens_total": 15369500264, + "input_tokens_total": 267882990, "output_tokens": { - "n": 16496, - "mean": 58103.19550193987, - "median": 25823, - "p90": 167022, - "p95": 299952, - "max": 500546 + "n": 848, + "mean": 36635.45754716981, + "median": 26467, + "p90": 86804, + "p95": 127859, + "max": 192725 }, - "output_tokens_total": 958470313, + "output_tokens_total": 31066868, "peak_context_tokens": { - "n": 16495, - "mean": 20518.275295544103, - "median": 24579, - "p90": 27317, - "p95": 38287, - "max": 300141 + "n": 848, + "mean": 23589.557783018867, + "median": 23226, + "p90": 31148, + "p95": 54876, + "max": 98036 }, "peak_context_fraction": { - "n": 16495, - "mean": 0.6261680693220246, - "median": 0.750091552734375, - "p90": 0.833648681640625, - "p95": 1.168426513671875, - "max": 9.159576416015625 + "n": 848, + "mean": 0.7198961725774801, + "median": 0.70880126953125, + "p90": 0.9505615234375, + "p95": 1.6746826171875, + "max": 2.9918212890625 }, - "trajectories_above_90_percent_context": 1536, + "trajectories_above_90_percent_context": 118, "latency_seconds": { "model_call": { - 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"calls": 2592770, - "calls_per_trajectory": 157.17567895247333, + "calls": 34440, + "calls_per_trajectory": 40.613207547169814, "failed": null, "failure_rate": null }, "mark_task_complete": { - "calls": 23847, - "calls_per_trajectory": 1.4456231813773035, + "calls": 1386, + "calls_per_trajectory": 1.634433962264151, "failed": null, "failure_rate": null } }, - "tool_steps": 880818, + "tool_steps": 19863, "tool_steps_without_output": 0, - "tool_steps_with_error_text": 451319, - "tool_steps_with_error_text_rate": 0.512386213724061, + "tool_steps_with_error_text": 2177, + "tool_steps_with_error_text_rate": 0.10960076524190707, "terminations": { - "environment_error": 7, - "other_error": 26, "repeated_output_truncation": 1, - "session_ended": 505, - "task_complete": 9696, - "task_memory_limit": 323, - "task_timeout": 3635, - "verifier_error": 2303 + "task_complete": 612, + "task_timeout": 232, + "verifier_error": 3 }, "command_distribution": { - "\n": 4.428271059209628e-05, - "\n\n def __init__(self, values, categories=None, ordered=False, dtype=None):\n # Minimal implementation\n from pandas.core.arrays.categorical import Categorical\n self._categories = categories\n self._ordered = ordered\n self._codes = None\n self._values = values\n\n @classmethod\n def is_dtype_equal(cls, left, right):\n return left._categories.equals(right._categories) and left._ordered == right._ordered\n\n def min(self):\n return self._categories.min()\n\n def max(self):\n return self._categories.max()\n\n @classmethod\n def factorize_from_iterables(cls, arrays, sort=False, size_hint=None):\n from pandas.core.arrays.categorical import factorize_from_iterables\n return factorize_from_iterables(arrays, sort=sort, size_hint=size_hint)\n\n @property\n def _reverse_indexer(self):\n return self._reverse_indexer\n\n @property\n def ordered(self):\n return self._ordered\n\n @property\n def codes(self):\n return self._codes\n\n def tolist(self):\n return self._values.tolist()": 1.8826321909019161e-07, - "\n ": 4.7359966052376334e-07, - "\n if dtype == \"Int64\" and copy is False:\n return self\n\n if isinstance(dtype, (str, BooleanDtype)):\n if dtype == \"boolean\" or (isinstance(dtype, BooleanDtype) and dtype.name == \"boolean\"):\n # Convert zero to False, nonzero to True, preserve None\n result = (self._data != 0).astype(\"bool\")\n # Construct BooleanArray with data and mask\n from pandas.core.arrays import BooleanArray\n return BooleanArray(data=result, mask=self._mask)\n\n # ... (rest of the method remains unchanged)\n": 3.4939917318179657e-07, - "\n if dtype == \\\"Int64\\\" and copy is False:\n return self\n\n if isinstance(dtype, (str, BooleanDtype)):\n if dtype == \\\"boolean\\\" or (isinstance(dtype, BooleanDtype) and dtype.name == \\\"boolean\\\"):\n # Convert zero to False, nonzero to True, preserve None\n result = (self._data != 0).astype(\\\"bool\\\")\n # Construct BooleanArray with data and mask\n from pandas.core.arrays import BooleanArray\n return BooleanArray(data=result, mask=self._mask)\n\n # ... (rest of the method remains unchanged)\n": 1.7469958659089829e-07, - "\n # Ensure key is a tuple of integers, one per axis\n if len(key) != self.ndim:\n return False\n for k in key:\n if not is_integer(k):\n return False\n return True\n": 1.1502989857123664e-07, - "\n # Handle None and NaT\n if other is None or isna(other):\n return self._where(cond, other)\n\n # Convert other to array-like\n other = self._maybe_cast_other(other)\n\n # Check dtype compatibility\n if isinstance(other, Categorical):\n if not hasattr(other.categories, 'dtype'):\n raise TypeError(Where": 2.0139786228253058e-07, - "\n # If the Timestamps have a timezone that is not UTC,\n # convert them into their i8 representation while\n # keeping their timezone and not using UTC\n timestamps = self._local_timestamps()\n\n # Use the fields module to compute ISO calendar components\n from pandas.core.arrays import fields\n return fields.isocalendar(timestamps)'''\n # Find the end of the class definition or insert before the last method\n lines = content.splitlines()\n insert_pos = -1\n for i, line in enumerate(lines):\n if line.strip().startswith('class DatetimeArray'):\n continue\n if line.strip().startswith(' def ') and i > 0:\n # Insert before the next method\n insert_pos = i\n break\n if insert_pos == -1:\n insert_pos = len(lines)\n # Insert the method\n lines.insert(insert_pos, method)\n new_content = '\\n'.join(lines)\n path.write_text(new_content)\n print('isocalendar method added successfully')": 1.3264935787098843e-07, - "\n future = Future()\n # Ensure the iterator is not garbage-collected before the future resolves\n # by holding a reference in the future's closure.\n self._next_future = future\n def callback():\n try:\n result = next(self._iterator)\n future.set_result(result)\n except StopIteration:\n future.set_result(None)\n except Exception as e:\n future.set_exception(e)\n self._iterator.add_done_callback(callback)\n return future\n''')": 7.771891865005346e-07, - "\n if dtype == boolean or isinstance(dtype, BooleanDtype):\n data = (self._data != 0).astype(bool)\n mask = self._mask\n from pandas import array\n return array(data, dtype=boolean, mask=mask, copy=False)\n\n return super().astype(dtype, copy=copy)\n''')": 1.4713775860932453e-07, - "\n if inplace:\n # Modify in place\n if to_replace not in self.categories:\n # If to_replace is not a category, do nothing\n return None\n \n # Handle NA value\n if isna(value):\n # Remove to_replace from categories\n new_categories = self.categories.drop(to_replace)\n # Update codes to reflect missing values\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), -1, self._codes)\n self._codes = new_codes\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # If value is already a category\n if value in self.categories:\n # Replace all occurrences of to_replace with value\n # and remove to_replace from categories\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), self.categories.get_loc(value), self._codes)\n new_categories = self.categories.drop(to_replace)\n self._codes = new_codes\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # If value is not a category, rename to_replace to value\n new_categories = self.categories.copy()\n new_categories = new_categories.set_value(self.categories.get_loc(to_replace), value)\n self._codes = np.where(self._codes == self.categories.get_loc(to_replace), new_categories.get_loc(value), self._codes)\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # Return new Categorical\n if to_replace not in self.categories:\n # If to_replace is not a category, return a copy\n return type(self)(self._codes, dtype=self.dtype)\n \n # Handle NA value\n if isna(value):\n # Remove to_replace from categories\n new_categories = self.categories.drop(to_replace)\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), -1, self._codes)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))\n \n # If value is already a category\n if value in self.categories:\n # Replace all occurrences of to_replace with value\n # and remove to_replace from categories\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), self.categories.get_loc(value), self._codes)\n new_categories = self.categories.drop(to_replace)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))\n \n # If value is not a category, rename to_replace to value\n new_categories = self.categories.copy()\n new_categories = new_categories.set_value(self.categories.get_loc(to_replace), value)\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), new_categories.get_loc(value), self._codes)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))": 3.015958037166254e-07, - "\n if self._dtype.ordered:\n if inplace:\n return None\n return self\n\n if inplace:\n self._dtype = CategoricalDtype(self._categories, ordered=True)\n return None\n\n return Categorical(\n codes=self._codes,\n categories=self._categories,\n ordered=True,\n dtype=self._dtype\n )": 6.773268887937621e-08, - "\n return Categorical(self._values, categories=categories, ordered=self._ordered)\n": 8.191994127978609e-08, - "\n return False\n": 6.728163878694973e-07, - "\n self.filepath_or_buffer.seek(0, 2)\n total_records_length = self.filepath_or_buffer.tell() - self.record_start\n\n if total_records_length % 80 != 0:\n warnings.warn(xport": 2.886702692716272e-07, - "\n a, b = self.get_view_interval()\n if inverted:\n self.set_view_interval(max(a, b), min(a, b), ignore=True)\n self.figure.canvas.draw_idle()\n": 1.3121375875983053e-07, - "\n if dilation_rate == (1, 1):\n return backend.conv2d_transpose(\n inputs,\n kernel,\n output_shape,\n strides=strides,\n padding=padding,\n data_format=data_format\n )\n else:\n # Use atrous_conv2d_transpose for dilation\n return backend.atrous_conv2d_transpose(\n inputs,\n kernel,\n output_shape,\n strides=strides,\n padding=padding,\n data_format=data_format,\n dilation_rate=dilation_rate\n )\n": 3.7190648188369147e-07, - "\n if format is None:\n buffer_error_msg = (\n If": 1.0672668406169315e-07, - "\n if isinstance(status, str):\n status = int(status)\n if HTTPStatus(status).phrase:\n return f'{status} {HTTPStatus(status).phrase}'\n return f'{status} Unknown Status'\n": 1.010345942450695e-06, - "\n if item is None or item is pd.NaT or (isinstance(item, np.ndarray) and item.size == 0):\n return arr._hasna\n if isinstance(item, (np.datetime64, np.timedelta64, np.datetime64, np.timedelta64)) and item == np.datetime64('NaT'):\n return arr._dtype.kind == 'M' and arr._hasna\n if isinstance(item, (np.timedelta64, np.timedelta64)) and item == np.timedelta64('NaT'):\n return arr._dtype.kind == 'm' and arr._hasna\n return item in arr._values": 1.8826321909019161e-07, - "\n if not is_numeric_dtype(values.dtype):\n if is_timedelta64_dtype(values.dtype):\n raise TypeError(reduction": 8.180938805268786e-08, - "\n pass\n": 3.046269173218176e-07, - "\nRead JSON\n": 9.82449235756378e-07, - "\ncat": 4.0902356615256716e-07, - "\ncurl": 1.665405399644003e-07, - "\nprintf": 2.4248302618816684e-07, - "\nsed": 2.4248302618816684e-07, - "\nwget": 3.330810799288006e-07, - "\u001b:1656": 4.832089289981585e-06, - "\u001b:q!": 3.3678198081689835e-06, - "\u001b:wq": 1.1460820052367133e-05, - "\u001b:wq!": 7.530528763607666e-08, - " pass": 5.358740910235731e-07, - " raise KeyError(key)": 1.840711231185477e-07, - " analyze_all_pipes(self.pipeline)#; }": 5.561537297893734e-07, - " if isinstance(x, (pd.Series, np.ndarray)) and x.dtype == bool:\\n x = x.astype(int)\\n elif isinstance(x, list):\\n x = np.array(x).astype(int)": 8.53212618536829e-08, - " rs = data.div(shifted.groupby(level=0).first()) - 1#": 3.869409992364364e-07, - " rs = data.div(shifted.groupby(level=0).first()) - 1|": 6.449016653940608e-08, - " % entity": 2.9144594493770054e-06, - " in the fragment.": 6.123308742125425e-07, - "!=:": 1.409785035977714e-06, - "\"\\\\": 7.392775188663623e-07, - "%3Fjs_to_json%3F": 5.051729712253475e-06, - "%B %d %Y,": 1.901389042988393e-06, - 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"malformed_tool_calls": 76584, - "trajectories_with_malformed_tool_calls": 5313, + "malformed_tool_calls": 636, + "trajectories_with_malformed_tool_calls": 310, "errors": { "model_server": { "trajectories": 0, "types": {} }, "environment": { - "trajectories": 59, - "types": { - "TmuxBatchProtocolError": 7, - "TmuxSessionEndedError": 52 - } + "trajectories": 0, + "types": {} }, "verifier": { - "trajectories": 2303, + "trajectories": 3, "types": { - "VerifierRuntimeError": 2300, - "VerifierTimeoutError": 3 + "VerifierRuntimeError": 2, + "VerifierTimeoutError": 1 } }, "other": { - "trajectories": 26, - "types": { - "RuntimeError": 26 - } + "trajectories": 0, + "types": {} } } }, "models": { - "qwen3-30b-instruct-2507": { - "trajectories": 7824, - "graded": 7824, - "solved": 245, - "turns": { - "n": 7824, - "mean": 64.2616308793456, - "median": 35, - "p90": 142, - "p95": 210, - "max": 1953 - }, - "input_tokens": { - "n": 7824, - "mean": 965590.4789110429, - "median": 462749, - "p90": 2324016, - 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}, - "verifier": { - "n": 7733, - "mean": 85.01867244251906, - "median": 18.475742, - "p90": 249.126162, - "p95": 256.268127, - "max": 3610.00194 - }, - "trial": { - "n": 7824, - "mean": 819.8062058816462, - "median": 591.559759, - "p90": 1882.491717, - "p95": 3012.761395, - "max": 4421.370895 - } - }, - "tools": { - "bash_command": { - "calls": 1023824, - "calls_per_trajectory": 130.85685071574642, - "failed": null, - "failure_rate": null - }, - "mark_task_complete": { - "calls": 12892, - "calls_per_trajectory": 1.6477505112474438, - "failed": null, - "failure_rate": null - } - }, - "tool_steps": 388718, - "tool_steps_without_output": 0, - "tool_steps_with_error_text": 178786, - "tool_steps_with_error_text_rate": 0.45993753826681555, - "terminations": { - "environment_error": 4, - "other_error": 11, - "session_ended": 53, - "task_complete": 5345, - "task_memory_limit": 73, - "task_timeout": 1115, - "verifier_error": 1223 - }, - "command_distribution": { - "\n": 9.336497877392898e-05, - "\n\n def __init__(self, values, categories=None, ordered=False, dtype=None):\n # Minimal implementation\n from pandas.core.arrays.categorical import Categorical\n self._categories = categories\n self._ordered = ordered\n self._codes = None\n self._values = values\n\n @classmethod\n def is_dtype_equal(cls, left, right):\n return left._categories.equals(right._categories) and left._ordered == right._ordered\n\n def min(self):\n return self._categories.min()\n\n def max(self):\n return self._categories.max()\n\n @classmethod\n def factorize_from_iterables(cls, arrays, sort=False, size_hint=None):\n from pandas.core.arrays.categorical import factorize_from_iterables\n return factorize_from_iterables(arrays, sort=sort, size_hint=size_hint)\n\n @property\n def _reverse_indexer(self):\n return self._reverse_indexer\n\n @property\n def ordered(self):\n return self._ordered\n\n @property\n def codes(self):\n return self._codes\n\n def tolist(self):\n return self._values.tolist()": 3.9693124515743877e-07, - "\n ": 9.98530163599182e-07, - "\n if dtype == \"Int64\" and copy is False:\n return self\n\n if isinstance(dtype, (str, BooleanDtype)):\n if dtype == \"boolean\" or (isinstance(dtype, BooleanDtype) and dtype.name == \"boolean\"):\n # Convert zero to False, nonzero to True, preserve None\n result = (self._data != 0).astype(\"bool\")\n # Construct BooleanArray with data and mask\n from pandas.core.arrays import BooleanArray\n return BooleanArray(data=result, mask=self._mask)\n\n # ... (rest of the method remains unchanged)\n": 7.36667786401702e-07, - "\n if dtype == \\\"Int64\\\" and copy is False:\n return self\n\n if isinstance(dtype, (str, BooleanDtype)):\n if dtype == \\\"boolean\\\" or (isinstance(dtype, BooleanDtype) and dtype.name == \\\"boolean\\\"):\n # Convert zero to False, nonzero to True, preserve None\n result = (self._data != 0).astype(\\\"bool\\\")\n # Construct BooleanArray with data and mask\n from pandas.core.arrays import BooleanArray\n return BooleanArray(data=result, mask=self._mask)\n\n # ... (rest of the method remains unchanged)\n": 3.68333893200851e-07, - "\n # Ensure key is a tuple of integers, one per axis\n if len(key) != self.ndim:\n return False\n for k in key:\n if not is_integer(k):\n return False\n return True\n": 2.4252725036185064e-07, - "\n # Handle None and NaT\n if other is None or isna(other):\n return self._where(cond, other)\n\n # Convert other to array-like\n other = self._maybe_cast_other(other)\n\n # Check dtype compatibility\n if isinstance(other, Categorical):\n if not hasattr(other.categories, 'dtype'):\n raise TypeError(Where": 4.2462412272656244e-07, - "\n # If the Timestamps have a timezone that is not UTC,\n # convert them into their i8 representation while\n # keeping their timezone and not using UTC\n timestamps = self._local_timestamps()\n\n # Use the fields module to compute ISO calendar components\n from pandas.core.arrays import fields\n return fields.isocalendar(timestamps)'''\n # Find the end of the class definition or insert before the last method\n lines = content.splitlines()\n insert_pos = -1\n for i, line in enumerate(lines):\n if line.strip().startswith('class DatetimeArray'):\n continue\n if line.strip().startswith(' def ') and i > 0:\n # Insert before the next method\n insert_pos = i\n break\n if insert_pos == -1:\n insert_pos = len(lines)\n # Insert the method\n lines.insert(insert_pos, method)\n new_content = '\\n'.join(lines)\n path.write_text(new_content)\n print('isocalendar method added successfully')": 2.796758445091801e-07, - "\n future = Future()\n # Ensure the iterator is not garbage-collected before the future resolves\n # by holding a reference in the future's closure.\n self._next_future = future\n def callback():\n try:\n result = next(self._iterator)\n future.set_result(result)\n except StopIteration:\n future.set_result(None)\n except Exception as e:\n future.set_exception(e)\n self._iterator.add_done_callback(callback)\n return future\n''')": 1.6386136018037857e-06, - "\n if dtype == boolean or isinstance(dtype, BooleanDtype):\n data = (self._data != 0).astype(bool)\n mask = self._mask\n from pandas import array\n return array(data, dtype=boolean, mask=mask, copy=False)\n\n return super().astype(dtype, copy=copy)\n''')": 3.102229634482895e-07, - "\n if inplace:\n # Modify in place\n if to_replace not in self.categories:\n # If to_replace is not a category, do nothing\n return None\n \n # Handle NA value\n if isna(value):\n # Remove to_replace from categories\n new_categories = self.categories.drop(to_replace)\n # Update codes to reflect missing values\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), -1, self._codes)\n self._codes = new_codes\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # If value is already a category\n if value in self.categories:\n # Replace all occurrences of to_replace with value\n # and remove to_replace from categories\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), self.categories.get_loc(value), self._codes)\n new_categories = self.categories.drop(to_replace)\n self._codes = new_codes\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # If value is not a category, rename to_replace to value\n new_categories = self.categories.copy()\n new_categories = new_categories.set_value(self.categories.get_loc(to_replace), value)\n self._codes = np.where(self._codes == self.categories.get_loc(to_replace), new_categories.get_loc(value), self._codes)\n self._dtype = CategoricalDtype(new_categories)\n return None\n \n # Return new Categorical\n if to_replace not in self.categories:\n # If to_replace is not a category, return a copy\n return type(self)(self._codes, dtype=self.dtype)\n \n # Handle NA value\n if isna(value):\n # Remove to_replace from categories\n new_categories = self.categories.drop(to_replace)\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), -1, self._codes)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))\n \n # If value is already a category\n if value in self.categories:\n # Replace all occurrences of to_replace with value\n # and remove to_replace from categories\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), self.categories.get_loc(value), self._codes)\n new_categories = self.categories.drop(to_replace)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))\n \n # If value is not a category, rename to_replace to value\n new_categories = self.categories.copy()\n new_categories = new_categories.set_value(self.categories.get_loc(to_replace), value)\n new_codes = np.where(self._codes == self.categories.get_loc(to_replace), new_categories.get_loc(value), self._codes)\n return type(self)(new_codes, dtype=CategoricalDtype(new_categories))": 6.358799051775885e-07, - "\n if self._dtype.ordered:\n if inplace:\n return None\n return self\n\n if inplace:\n self._dtype = CategoricalDtype(self._categories, ordered=True)\n return None\n\n return Categorical(\n codes=self._codes,\n categories=self._categories,\n ordered=True,\n dtype=self._dtype\n )": 1.428065485370897e-07, - "\n return Categorical(self._values, categories=categories, ordered=self._ordered)\n": 1.727187310009396e-07, - "\n self.filepath_or_buffer.seek(0, 2)\n total_records_length = self.filepath_or_buffer.tell() - self.record_start\n\n if total_records_length % 80 != 0:\n warnings.warn(xport": 6.086279092414062e-07, - "\n a, b = self.get_view_interval()\n if inverted:\n self.set_view_interval(max(a, b), min(a, b), ignore=True)\n self.figure.canvas.draw_idle()\n": 2.766490496551846e-07, - "\n if dilation_rate == (1, 1):\n return backend.conv2d_transpose(\n inputs,\n kernel,\n output_shape,\n strides=strides,\n padding=padding,\n data_format=data_format\n )\n else:\n # Use atrous_conv2d_transpose for dilation\n return backend.atrous_conv2d_transpose(\n inputs,\n kernel,\n output_shape,\n strides=strides,\n padding=padding,\n data_format=data_format,\n dilation_rate=dilation_rate\n )\n": 7.841218462619343e-07, - "\n if format is None:\n buffer_error_msg = (\n If": 2.2502088193784383e-07, - "\n if isinstance(status, str):\n status = int(status)\n if HTTPStatus(status).phrase:\n return f'{status} {HTTPStatus(status).phrase}'\n return f'{status} Unknown Status'\n": 2.1301976823449218e-06, - "\n if item is None or item is pd.NaT or (isinstance(item, np.ndarray) and item.size == 0):\n return arr._hasna\n if isinstance(item, (np.datetime64, np.timedelta64, np.datetime64, np.timedelta64)) and item == np.datetime64('NaT'):\n return arr._dtype.kind == 'M' and arr._hasna\n if isinstance(item, (np.timedelta64, np.timedelta64)) and item == np.timedelta64('NaT'):\n return arr._dtype.kind == 'm' and arr._hasna\n return item in arr._values": 3.9693124515743877e-07, - "\n if not is_numeric_dtype(values.dtype):\n if is_timedelta64_dtype(values.dtype):\n raise TypeError(reduction": 1.7248564229513534e-07, - "\n pass\n": 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ATIF step usage may aggregate multiple requests, so these are not guaranteed per-call measurements. Context means recorded prompt plus completion tokens. Attempt output includes reported auxiliary generation. Cumulative input counts repeated history again and is not unique rollout length. Missing measurements are excluded, never replaced by zero. 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tasks across software engineering, debugging, data processing, scientific computing, system administration and related domains. V3 contains only tasks newly solved by `Qwen/Qwen3.8-27B` after the earlier evaluation. Tasks already solved in [v2](../calibforge_v2/README.md) are not duplicated. This is a harder subset relative to that earlier evaluation, not a claim of intrinsic difficulty for every model. + +## Evaluation + +`Qwen/Qwen3.8-27B` (thinking enabled) used Terminus-2 on the 3,805 tasks left unsolved by v2. The four-task pilot and 3,801-task full run were disjoint. All tasks have exactly two terminal attempts. The task source is `calibforge_v1` at revision `21e998257e3fb6e5396596e921d507d69684d6bf`. Published task payload hashes match the evaluated selection. Historical results are in the v2 README. + +## Evaluation settings + +Context is the total prompt plus response window for one model call. The response budget includes reasoning tokens and is not a whole-attempt token budget. + +| Model | Context tokens (configured) | Intended response tokens per call | Output-limit evidence | Thinking | Sampling | +| --- | ---: | ---: | --- | --- | --- | +| Qwen/Qwen3.8-27B | 32768 | 8192 | Top-level only in all 7,610 trial configs; explicit cap not forwarded, actual fallback unknown | on | {"min_p": 0.0, "presence_penalty": 0.0, "repetition_penalty": 1.0, "temperature": 1.0, "top_k": 20, "top_p": 0.95} | + +Configured limits are not measurements of token usage. A missing explicit response cap does not mean unlimited generation: the serving context, backend defaults, stopping conditions and task timeouts still apply. Unknown historical settings are not filled from current model defaults. + +All 7,610 saved trial configurations were inspected. The intended 8,192-token response limit was stored as top-level `max_tokens`; the affected Terminus path did not forward it as `llm_call_kwargs.max_tokens`. The actual fallback output cap is unknown. The configured 32,768-token context is not proof of per-request enforcement. Recorded step metrics can aggregate calls or retries; peak recorded step usage is not an independent server-limit measurement. + +Agent time limits range from 120 to 12,000 seconds and verifier limits from 60 to 12,000 seconds, according to each task. [Settings and timeout groups](metadata/evaluation-settings-audit.json) contain the exhaustive audit. + +## Results on the previously unsolved subset + +| Model | Evaluated tasks | Attempts | Tasks solved | Pass@1 | Pass@2 | +| --- | ---: | ---: | ---: | ---: | ---: | +| Qwen/Qwen3.8-27B (thinking) | 3,805 | 7,610 | 2,287 | 48.69% | 60.11% | + +| Successful attempts | Tasks | +| --- | ---: | +| 0/2 | 1,518 | +| 1/2 | 869 | +| 2/2 | 1,418 | + +![Successful attempts per task](trace_stats/plots/success-counts.png) + +These rates use all 3,805 evaluated tasks, not the 2,287 success-selected tasks published in `tasks.parquet`. There were 3,705 successful attempts. The recorded `terminal-errors-zero-v2` policy counts 186 terminal errors as zero while preserving their null raw verifier rewards; 7,424 attempts have verifier rewards. Errors are not passed validations. + +## Traces and statistics + +`traces/` contains all 7,610 new attempts, including failures and attempts on still-unsolved tasks, as compressed Parquet. Each row includes `task_id`, `model`, `attempt_id`, `content_sha256`, `reward`, `verifier_reward`, `reward_origin`, `retained_v3`, and full `trajectory` and `result` JSON. `retained_v3` identifies the incremental task set. Historical traces remain in v2. + +Every new attempt has a saved trajectory. No quality filter was applied. A saved conversation is not necessarily suitable for training, and an agent timeout can still be followed by a successful verifier result. [Recorded errors and release checks](metadata/release-verification.json) preserve counts. + +| New evaluation measurement | Median | +| --- | ---: | +| Agent turns | 11 | +| Input tokens per attempt | 107,048 | +| Output tokens per attempt | 33,272 | +| Agent runtime | 725.31 s | +| Verifier runtime | 11.59 s | +| Whole-attempt runtime | 751.61 s | + +Token totals sum calls across each attempt and are not context-window measurements. Mean whole-attempt runtime was 874.11 seconds. [Trace statistics](trace_stats/README.md) include plots, outcome and command distributions, and per-task and per-attempt measurements. + +## Use v2 and v3 together + +```python +from huggingface_hub import hf_hub_download +import pyarrow as pa +import pyarrow.parquet as pq + +def load(version): + path = hf_hub_download("FWeindel/validated-tasks", + f"calibforge_{version}/tasks.parquet", repo_type="dataset") + return pq.read_table(path) + +v2, v3 = load("v2"), load("v3") +assert set(v2["path"].to_pylist()).isdisjoint(v3["path"].to_pylist()) +combined = pa.concat_tables([v2, v3], promote_options="permissive") +assert combined.num_rows == 3799 +``` + +The example combines task rows; each source file retains its own image manifest. Use the source manifests when staging images. V3 adds `qwen38_successes`, `qwen38_attempts`, and `kept_from_v2` (false for every v3 row). To select tasks solved exactly once, filter `qwen38_successes == 1`. + +## Files and provenance + +- [tasks.parquet](tasks.parquet): 2,287 newly solved tasks. +- [archive.parquet](archive.parquet): 1,518 evaluated tasks without a success; these are not training task rows. +- [Selection](metadata/selection.json): membership, evaluated payload hashes and success counts for all 3,805 tasks. +- [Trace manifest](traces/manifest.json): attempt counts and file checksums. +- [Pilot contract](metadata/pilot-contract.json) and [full contract](metadata/full-contract.json): frozen evaluation configuration. +- [patch.py](patch.py): reproduce the incremental task selection from the pinned source. + +## Runtime and limitations + +The run used one H200 and 32 allocated CPUs per shard, with four requested concurrent trials. Saved reports identify Apptainer 1.5.4, Python 3.12.14, Harbor 0.8.1, and Marin Harbor revision `7faf878c14b6d72737579ec936ebf5f74ba3194d` with the captured runtime patches. + +This release adds teacher results; it does not establish completion of all current datasource-validation checks. The earlier repository-history and timestamp leak-check limitation remains unresolved. See v2 for inherited validation limitations and the settings section for the historical output-cap issue. diff --git a/calibforge_v3/archive.parquet b/calibforge_v3/archive.parquet new file mode 100644 index 0000000000000000000000000000000000000000..1f9bb648dc8cd88f201b9dfad4fe4b0388fa7fd6 --- /dev/null +++ b/calibforge_v3/archive.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a2849f5193e9d5015d30ca2e21008a5251b3e5fb1cd156dcdd83d831e2e5709c +size 252331831 diff --git a/calibforge_v3/metadata/evaluation-settings-audit.json b/calibforge_v3/metadata/evaluation-settings-audit.json new file mode 100644 index 0000000000000000000000000000000000000000..d689bfaa454c378009256a6050d2b2bd39b23aac --- /dev/null +++ b/calibforge_v3/metadata/evaluation-settings-audit.json @@ -0,0 +1,1309 @@ +{ + "configs": [ + { + "attempts": 7610, + "kwargs": { + "extra_body": { + "chat_template_kwargs": { + "enable_thinking": true + }, + "min_p": 0.0, + "presence_penalty": 0.0, + 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huggingface_hub and pyarrow. Does not rerun evaluation. +""" +import argparse,hashlib,json +from pathlib import Path +import pyarrow as pa,pyarrow.parquet as pq +from huggingface_hub import hf_hub_download + +def build(out): + s=json.loads((Path(__file__).parent/'metadata/selection.json').read_text()) + p=hf_hub_download('FWeindel/validated-tasks',s['source_folder']+'/tasks.parquet',repo_type='dataset',revision=s['source_revision']) + assert hashlib.sha256(Path(p).read_bytes()).hexdigest()==s['source_sha256'] + t=pq.read_table(p,columns=['path','task_binary','content_sha256']);t=t.filter(pa.array([x in s['tasks'] for x in t['path'].to_pylist()]));ids=t['path'].to_pylist() + for x,h in zip(ids,t['content_sha256'].to_pylist()):assert h==s['tasks'][x]['content_sha256'] + for c,typ in [('qwen38_successes',pa.int64()),('qwen38_attempts',pa.int64()),('kept_from_v2',pa.bool_())]:t=t.append_column(c,pa.array([s['tasks'][x][c] for x in ids],type=typ)) + out.mkdir(parents=True,exist_ok=True) + for name,keep in [('tasks.parquet',True),('archive.parquet',False)]: + a=t.filter(pa.array([s['tasks'][x]['kept']==keep for x in ids]));chosen=set(a['path'].to_pylist());m=dict(a.schema.metadata or {}) + if b'ot.images.v1' in m: + im=json.loads(m[b'ot.images.v1']);im['tasks']={k:v for k,v in im['tasks'].items() if k in chosen};used={e['bundle'] for v in im['tasks'].values() for e in v['environments']};im['bundles']={k:v for k,v in im['bundles'].items() if k in used};im['coverage']={'cached_tasks':len(im['tasks']),'total_tasks':len(chosen)} + if 'uncached_task_ids' in im:im['uncached_task_ids']=sorted(chosen-set(im['tasks'])) + m[b'ot.images.v1']=json.dumps(im).encode() + m[b'ot.release.v3']=json.dumps({'layout':'incremental','kept':len(chosen),'baseline_folder':'calibforge_v2','baseline_revision':'a8acfde13cc1be1672da3d17ee70bc4d842b4c19'}).encode() + pq.write_table(a.replace_schema_metadata(m),out/name,compression='zstd') +if __name__=='__main__': + p=argparse.ArgumentParser();p.add_argument('--out',type=Path,required=True);a=p.parse_args();build(a.out) diff --git a/calibforge_v3/tasks.parquet b/calibforge_v3/tasks.parquet new file mode 100644 index 0000000000000000000000000000000000000000..2c277f8bbaa7e498b7d213a67d8ddd37adab06fe --- /dev/null +++ b/calibforge_v3/tasks.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aeb52411bbfb1ef2d042489cd00d52b415e1da47767e3a4e64b213985b4282f6 +size 166820941 diff --git a/calibforge_v3/trace_stats/README.md b/calibforge_v3/trace_stats/README.md new file mode 100644 index 0000000000000000000000000000000000000000..52a04e8328e4f5535e2903f399237c6ea7fb4124 --- /dev/null +++ b/calibforge_v3/trace_stats/README.md @@ -0,0 +1,65 @@ +# Trajectory statistics + +7,610 attempts across 3,805 tasks of calibforge_v3, kept and excluded, each counted once. Task success counts are in the [dataset overview](../README.md). + +| Metric | Qwen/Qwen3.8-27B | +| --- | ---: | +| Recorded attempts | 7,610 | +| Median agent turns | 11 | +| Median input tokens per attempt | 107,048 | +| Median output tokens per attempt | 33,272 | +| Median agent runtime (seconds) | 725 | +| Median verifier runtime (seconds) | 12 | + +Medians use recorded measurements only. + +## Plots + +![Trace measurements](plots/all-models-trace-distributions.png) + +![Trace outcomes](plots/all-models-trace-outcomes.png) + +![Most frequent commands](plots/all-models-trace-commands.png) + +Commands are counted as submitted, not as successful executions. + +Per-model plots: [Qwen/Qwen3.8-27B commands](plots/qwen3.8-27b-trace-commands.png), [Qwen/Qwen3.8-27B distributions](plots/qwen3.8-27b-trace-distributions.png), [Qwen/Qwen3.8-27B outcomes](plots/qwen3.8-27b-trace-outcomes.png). + +## Data + +- [Summary](data/summary.json): overall and per-model statistics. +- [Per-task metrics](data/per_task.parquet) and [per-attempt metrics](data/per_attempt.parquet), with nested metrics stored as JSON. +- [Provenance](data/provenance.json): source files and deduplication. + +Attempts without a saved conversation: 0; without a verifier reward: 186. Their missing measurements stay empty. + + +Evaluation budgets: see the [dataset evaluation setup](../README.md). Per-call context and output-limit enforcement are not established by token-usage statistics. + + +## Token usage and summarization + +All published traces are included, covering kept and excluded tasks and every verifier outcome. One trace means one task attempt. + +![Output, summarization, and context distributions](plots/all-models-tokens-by-outcome.png) + +- **Output per recorded agent step:** successful, graded unsuccessful, and ungraded attempts shown separately. Each step contributes once. +- **Summarizations per trace:** one combined distribution across all traces with a recorded count, including zero. +- **Peak context per trace:** the largest recorded step prompt + output count in each trace, separated into traces with and without summarization. Unknown summary counts are kept separate when present. Each trace contributes once, regardless of its length or outcome. + +[Detailed statistics](data/tokens-by-outcome.json) retain sample counts and missing measurements. Missing summary counts are not zero. ATIF steps can aggregate multiple requests, so recorded context does not establish an exact per-call context limit. +## Evaluation settings + +Context is the total prompt plus response window for one model call. The response budget includes reasoning tokens and is not a whole-attempt token budget. + +| Model | Context tokens (configured) | Intended response tokens per call | Output-limit evidence | Thinking | Sampling | +| --- | ---: | ---: | --- | --- | --- | +| Qwen/Qwen3.8-27B | 32768 | 8192 | Top-level only in all 7,610 trial configs; explicit cap not forwarded, actual fallback unknown | on | {"min_p": 0.0, "presence_penalty": 0.0, "repetition_penalty": 1.0, "temperature": 1.0, "top_k": 20, "top_p": 0.95} | + +Configured limits are not measurements of token usage. A missing explicit response cap does not mean unlimited generation: the serving context, backend defaults, stopping conditions and task timeouts still apply. Unknown historical settings are not filled from current model defaults. + +All 7,610 saved trial configurations were inspected. The intended 8,192-token response limit was stored as top-level `max_tokens`; the affected Terminus path did not forward it as `llm_call_kwargs.max_tokens`. The actual fallback output cap is unknown. The configured 32,768-token context is not proof of per-request enforcement. Recorded step metrics can aggregate calls or retries; peak recorded step usage is not an independent server-limit measurement. + +Agent time limits range from 120 to 12,000 seconds and verifier limits from 60 to 12,000 seconds, according to each task. [Settings and timeout groups](../metadata/evaluation-settings-audit.json) contain the exhaustive audit. + +Earlier evaluation statistics are in [v2](../../calibforge_v2/trace_stats/README.md). diff --git a/calibforge_v3/trace_stats/data/per_attempt.parquet b/calibforge_v3/trace_stats/data/per_attempt.parquet new file mode 100644 index 0000000000000000000000000000000000000000..2c3eaaa2470949ef1b3526216fc820f07a8c7a2d --- /dev/null +++ b/calibforge_v3/trace_stats/data/per_attempt.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c94c7ff337bb6ba1fe1e1aa90898f463fe7e4a4cdb2f8cc8d64429a4a4e5db64 +size 1523595 diff --git a/calibforge_v3/trace_stats/data/per_task.parquet b/calibforge_v3/trace_stats/data/per_task.parquet new file mode 100644 index 0000000000000000000000000000000000000000..2e7fd1a2755ffe76ac9a71bd1dd981403088f381 --- /dev/null +++ b/calibforge_v3/trace_stats/data/per_task.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..7eb99e2b0b2386d51f513a60b620ed94c21f762b --- /dev/null +++ b/calibforge_v3/traces/qwen38-pilot-0000.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c036d3d7dee22533bc2ae10068e89ad87949811c3a0d38edb64baf7469bf50c8 +size 243492 diff --git a/endless_v3/README.md b/endless_v3/README.md index f911d33329bf972b36d022122bad851607732794..2f0b213a496d18c4a68b0e5190d2645dcdde98fa 100644 --- a/endless_v3/README.md +++ b/endless_v3/README.md @@ -3,12 +3,14 @@ ## Dataset stats - **v1 tasks:** 2,144 -- **Kept:** 2,079 (1,958 from v2 plus 121 newly solved by Qwen/Qwen3.8-27B) +- **Kept in v3:** 121 newly solved tasks +- **Available separately in v2:** 1,958 +- **Combined v2 + v3:** 2,079 unique tasks - **Excluded:** 65 (no recorded success from any of the three models) - **New evaluation:** the 186 tasks excluded from v2 (183 unsolved, 3 never attempted), 2 attempts each, 372 attempts - **New saved trajectories:** 372, successful and failed -`tasks.parquet` keeps every v2 task and adds the newly solved tasks with their unchanged v1 payloads (the evaluated content hashes match). Excluded payloads are in [archive.parquet](archive.parquet). +`tasks.parquet` contains only newly solved tasks, preserving their evaluated payloads and recorded result columns. ## Evaluation settings @@ -42,22 +44,20 @@ These results cover only the new evaluation of the tasks v2 excluded; earlier re ## Select tasks by successful-attempt count -The Parquet preserves the v2 columns (`instruct_successes`, `coder_successes` and their attempt counts) and adds `qwen38_successes`, `qwen38_attempts`, `qwen38_target_attempts` and `kept_from_v2`. For the 1,958 v2 tasks, `qwen38_attempts` is 0 and `qwen38_successes` is null. +The Parquet preserves the v2 columns (`instruct_successes`, `coder_successes` and their attempt counts) and adds `qwen38_successes`, `qwen38_attempts`, `qwen38_target_attempts` and `kept_from_v2`. Tasks already in v2 are not duplicated in this file. ```python import pyarrow.parquet as pq # Newly evaluated tasks solved exactly once. subset = pq.read_table("tasks.parquet", filters=[("qwen38_successes", "=", 1)]) -# Every task already kept in v2. -v2_tasks = pq.read_table("tasks.parquet", filters=[("kept_from_v2", "=", True)]) ``` -[Selection metadata](metadata/selection.json) records membership for all 2,144 tasks, the v2 entry of each, and stage-1 evidence for the 186 evaluated tasks. [pass_counts.parquet](pass_counts.parquet) adds the Qwen/Qwen3.8-27B rows and a `retained_v3` column to the v2 counts. +[Selection metadata](metadata/selection.json) records membership for all 2,144 tasks, the v2 entry of each, and stage-1 evidence for the 186 evaluated tasks. [pass_counts.parquet](pass_counts.parquet) records the new evaluation; `retained_v3` identifies the incremental task set. ## Traces -`traces/` holds all 25,692 v2 attempts (`attempts.parquet`) and the 372 new attempts (`qwen38-attempts.parquet`), successful and failed, in one schema. `evaluation_version` separates v2 from v3, `retained_v2` and `retained_v3` record membership, and `exceptions` lists recorded errors of the new attempts. +`traces/` holds all 372 new attempts, successful and failed. Historical attempts remain in v2. `retained_v3` identifies tasks kept in this incremental release. ```python import json @@ -89,4 +89,25 @@ All 372 new attempts have saved conversations and a verifier reward. 49 attempts | --- | ---: | ---: | ---: | | Qwen/Qwen3.8-27B | 186.65 s | 189.12 s | 372 | -`patch.py` rebuilds `tasks.parquet` and `archive.parquet` from the pinned v2 Parquets and the published selection metadata. +`patch.py` rebuilds the incremental task file from the pinned pre-migration release and the published layout manifest. + +## Incremental release + +V3 contains only tasks newly solved after v2. It has no task-ID overlap with [v2](../endless_v2/README.md). This is a harder subset relative to the earlier evaluation, not a claim of intrinsic difficulty. New-evaluation traces include both solved and unsolved tasks so the reported pass rates remain reproducible. + +```python +from huggingface_hub import hf_hub_download +import pyarrow as pa +import pyarrow.parquet as pq + +def load(version): + path = hf_hub_download("FWeindel/validated-tasks", + f"endless_{version}/tasks.parquet", repo_type="dataset") + return pq.read_table(path) + +v2, v3 = load("v2"), load("v3") +assert set(v2["path"].to_pylist()).isdisjoint(v3["path"].to_pylist()) +combined = pa.concat_tables([v2, v3], promote_options="permissive") +``` + +The example combines task rows; each source file retains its own image manifest. Use those source manifests when staging images. [Layout and provenance](metadata/layout.json) records the pinned baseline and task counts. Earlier cumulative v3 releases remain accessible through their HF commit history. diff --git a/endless_v3/metadata/history-deduplication.json b/endless_v3/metadata/history-deduplication.json new file mode 100644 index 0000000000000000000000000000000000000000..16b4b875e71d8f49d7cf09542ccb1e4f7e1bc4e1 --- /dev/null +++ b/endless_v3/metadata/history-deduplication.json @@ -0,0 +1,16 @@ +{ + "verified_identical_attempts": 25692, + "deleted_duplicate_files": [ + "endless_v3/traces/attempts.parquet" + ], + "baseline_files": [ + "endless_v2/traces/attempts.parquet" + ], + "revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "comparison": [ + "task_id", + "attempt_id", + "trajectory", + "result" + ] +} \ No newline at end of file diff --git a/endless_v3/metadata/layout.json b/endless_v3/metadata/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..560303bbfa57fea6370ff1bd64814d134cca14e9 --- /dev/null +++ b/endless_v3/metadata/layout.json @@ -0,0 +1,135 @@ +{ + "layout": "incremental", + "kept": 121, + "baseline_kept": 1958, + "combined_kept": 2079, + "baseline_folder": "endless_v2", + "baseline_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "carried_from_v2": 0, + "previous_cumulative_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "previous_tasks_sha256": "87c0d05e275ee10609045f58db2409f7f97ad6c1187804ecf11cf216dced4932", + "task_ids": [ + "task_000000_0119f5d7", + "task_000000_0497c08a", + "task_000000_0a07b7b0", + "task_000000_11d7c0e5", + "task_000000_148a2720", + "task_000000_18303bd3", + "task_000000_19c45f75", + "task_000000_1a9c14ea", + "task_000000_1c170e5d", + "task_000000_1e7e0e2c", + "task_000000_2129b0c0", + "task_000000_21f66103", + "task_000000_24968ee5", + "task_000000_2a01ae07", + "task_000000_2a8e643c", + "task_000000_2fa611c1", + "task_000000_31f2fb6d", + "task_000000_32542606", + "task_000000_333a2797", + "task_000000_363eb497", + "task_000000_39b12230", + "task_000000_40deac2d", + "task_000000_423036d2", + "task_000000_4297237d", + "task_000000_440dead5", + "task_000000_44510e43", + "task_000000_44cee5f3", + "task_000000_45523731", + "task_000000_46ef993b", + "task_000000_4b2bebeb", + "task_000000_511d89d4", + "task_000000_51308809", + "task_000000_51c28313", + "task_000000_52c77beb", + "task_000000_537627d6", + "task_000000_54a56fa5", + "task_000000_560b1d4d", + "task_000000_56cf06ab", + "task_000000_572ff042", + "task_000000_57aea92d", + "task_000000_5c2c965e", + "task_000000_61ef24cc", + "task_000000_625695b5", + "task_000000_648a1db3", + "task_000000_69d3c311", + "task_000000_6a6a571e", + "task_000000_6c7c924f", + "task_000000_6c914471", + "task_000000_6e9092c2", + "task_000000_78a8665b", + "task_000000_7b043e22", + "task_000000_7ff269b7", + "task_000000_83455158", + "task_000000_880c0c6c", + "task_000000_8d632927", + "task_000000_8de35477", + "task_000000_8e4a0ced", + "task_000000_8f22d2ad", + "task_000000_90b501a9", + "task_000000_920da72e", + "task_000000_927330b2", + "task_000000_927672b6", + "task_000000_927df969", + "task_000000_92d8e4fd", + "task_000000_93478d3e", + "task_000000_93e3dd9c", + "task_000000_978d86dc", + "task_000000_9919d9c0", + "task_000000_9b087c6d", + "task_000000_9d99c932", + "task_000000_9df70a36", + "task_000000_a101d59a", + "task_000000_a68f7aad", + "task_000000_a7975fe2", + "task_000000_a7d297b7", + "task_000000_a8f61a57", + "task_000000_a922a301", + "task_000000_a95302e9", + "task_000000_ad8e96da", + "task_000000_adc9a37a", + "task_000000_b0563ffe", + "task_000000_b0f2206c", + "task_000000_b11c2aac", + "task_000000_b29cee21", + "task_000000_b3758822", + "task_000000_b39caa47", + "task_000000_b533a3df", + "task_000000_b5e6181e", + "task_000000_b84851ec", + "task_000000_bacc0d95", + "task_000000_baf4fbca", + "task_000000_becd80db", + "task_000000_bf234084", + "task_000000_bf24b7fb", + "task_000000_c3666c94", + "task_000000_c700fc39", + "task_000000_c7c0de03", + "task_000000_c85dae98", + "task_000000_cae025c2", + "task_000000_cb215599", + "task_000000_cefb845c", + "task_000000_d32c5973", + "task_000000_d4cfcdb7", + "task_000000_d84baeb4", + "task_000000_da9fb171", + "task_000000_dcdd53fb", + "task_000000_e36bb1fc", + "task_000000_e509353c", + "task_000000_e54eeebf", + "task_000000_e5b7591b", + "task_000000_eca8bccb", + "task_000000_eda4720c", + "task_000000_efe31ef4", + "task_000000_f045fe4a", + "task_000000_f1f403e8", + "task_000000_f1fc67e4", + "task_000000_f5ad3716", + "task_000000_f5bc18e5", + "task_000000_f94276c3", + "task_000000_fb371f39", + "task_000000_feb82e0e" + ], + "tasks_sha256": "2b911846e1fe121635d708bbfd17225f4a1bcee2e5fc47ad794b9779fc187ba7" +} \ No newline at end of file diff --git a/endless_v3/metadata/previous-cumulative-patch.py b/endless_v3/metadata/previous-cumulative-patch.py new file mode 100644 index 0000000000000000000000000000000000000000..bc38ee6444bd8fac264f738d7d6befdd54a88571 --- /dev/null +++ b/endless_v3/metadata/previous-cumulative-patch.py @@ -0,0 +1,73 @@ +"""Rebuild Endless v3 tasks.parquet and archive.parquet from pinned v2 Parquets and metadata/selection.json. + +pip install pyarrow huggingface_hub +python patch.py --out rebuilt +Optional --source-dir holds v2 tasks.parquet and archive.parquet for offline reproduction. +""" +import argparse, json +from pathlib import Path +import pyarrow as pa +import pyarrow.parquet as pq + + +def build(out, source_dir=None): + m = json.loads((Path(__file__).parent / 'metadata/selection.json').read_text()) + + def source(name): + if source_dir: + return source_dir / name + from huggingface_hub import hf_hub_download + return Path(hf_hub_download(m['repo_id'], f'endless_v2/{name}', repo_type='dataset', + revision=m['v2_revision'], cache_dir=out / '.source-cache')) + out.mkdir(parents=True, exist_ok=True) + assert not any((out / n).exists() for n in ['tasks.parquet', 'archive.parquet']) + old, arch = pq.read_table(source('tasks.parquet')), pq.read_table(source('archive.parquet')) + extra = [pa.field('qwen38_attempts', pa.int64()), pa.field('qwen38_successes', pa.int64()), + pa.field('qwen38_target_attempts', pa.int64()), pa.field('kept_from_v2', pa.bool_())] + kept, excluded = [], [] + for r in old.to_pylist(): + assert m['tasks'][r['path']]['kept_from_v2'] + kept.append({**r, 'qwen38_attempts': 0, 'qwen38_successes': None, 'qwen38_target_attempts': None, 'kept_from_v2': True}) + for r in arch.to_pylist(): + e = m['tasks'][r['path']] + assert r['content_sha256'] == e['qwen38_evaluated_content_sha256'] + r.update(qwen38_attempts=e['qwen38_attempts'], qwen38_successes=e['qwen38_successes'], + qwen38_target_attempts=m['attempts'], kept_from_v2=False) + if e['kept']: + r.update(archive_stage=None, archive_reason=None, archive_labels=[]) + kept.append(r) + else: + r.update(archive_reason='zero_success_three_models', archive_labels=['zero_success_three_models']) + excluded.append(r) + + def images(rows): + merged = json.loads(old.schema.metadata[b'ot.images.v1']) + other = json.loads(arch.schema.metadata[b'ot.images.v1']) + merged['tasks'].update(other['tasks']) + merged['bundles'].update(other['bundles']) + names = {r['path'] for r in rows} + merged['tasks'] = {t: e for t, e in merged['tasks'].items() if t in names} + used = {e['bundle'] for t in merged['tasks'].values() for e in t['environments']} + merged['bundles'] = {k: v for k, v in merged['bundles'].items() if k in used} + if 'uncached_task_ids' in merged: + merged['uncached_task_ids'] = sorted(names - set(merged['tasks'])) + merged['coverage'] = {'cached_tasks': len(merged['tasks']), 'total_tasks': len(names)} + return json.dumps(merged).encode() + s = m['summary'] + release = json.dumps({k: s[k] for k in ['previous_revision', 'source_folder', 'source_revision', 'new_model', 'attempts', + 'evaluated', 'v2_kept', 'newly_solved', 'kept', 'reward_policy']}).encode() + for name, table, rows in [('tasks.parquet', old, kept), ('archive.parquet', arch, excluded)]: + schema = pa.schema([*table.schema, *extra]) + pq.write_table(pa.Table.from_pylist(sorted(rows, key=lambda r: r['path']), schema=schema) + .replace_schema_metadata({b'ot.images.v1': images(rows), b'ot.release.v3': release}), + out / name, compression='zstd') + assert len(kept) == s['kept'] and len(excluded) == s['excluded'] + print(json.dumps({'kept': len(kept), 'excluded': len(excluded)})) + + +if __name__ == '__main__': + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--out', type=Path, required=True) + p.add_argument('--source-dir', type=Path) + a = p.parse_args() + build(a.out, a.source_dir) diff --git a/endless_v3/metadata/release-verification.json b/endless_v3/metadata/release-verification.json new file mode 100644 index 0000000000000000000000000000000000000000..766418c98070f7cbd8c071dba0715e9a6f470d1b --- /dev/null +++ b/endless_v3/metadata/release-verification.json @@ -0,0 +1,15 @@ +{ + "layout": "incremental", + "kept": 121, + "baseline_kept": 1958, + "combined_kept": 2079, + "baseline_folder": "endless_v2", + "baseline_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "carried_from_v2": 0, + "new_attempts": 372, + "tasks_sha256": "2b911846e1fe121635d708bbfd17225f4a1bcee2e5fc47ad794b9779fc187ba7", + "task_ids_disjoint_from_v2": true, + "original_task_rows_preserved": true, + "historical_trace_records_verified_in_v2": 25692, + "new_evaluation_unchanged": true +} \ No newline at end of file diff --git a/endless_v3/metadata/results.json b/endless_v3/metadata/results.json index 1c0116cda93a611dac1df28a1ea8f9736c45a3e3..3c95ea2503a3f550ce93aecc0d77b813c7866333 100644 --- a/endless_v3/metadata/results.json +++ b/endless_v3/metadata/results.json @@ -7,7 +7,7 @@ "evaluated": 186, "v2_kept": 1958, "newly_solved": 121, - "kept": 2079, + "kept": 121, "reward_policy": "terminal-errors-zero-v2", "source_tasks": 2144, "excluded": 65, @@ -46,5 +46,11 @@ ], "network_effective": { "host": 17 - } + }, + "layout": "incremental", + "baseline_kept": 1958, + "combined_kept": 2079, + "baseline_folder": "endless_v2", + "baseline_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "carried_from_v2": 0 } \ No newline at end of file diff --git a/endless_v3/metadata/selection.json b/endless_v3/metadata/selection.json index 731c8be7a8e0665e28144c75f52e108e6a9369c2..b4f5acac6e63b38e7aacb28b05dc660a92e9011d 100644 --- a/endless_v3/metadata/selection.json +++ b/endless_v3/metadata/selection.json @@ -1 +1,51795 @@ -{"repo_id":"FWeindel/validated-tasks","source_folder":"endless_v1","source_revision":"3abfc63260ef8ab59ac61274450310f650585bc9","source_sha256":"5081aca34db5733eed66fd4774a4587edee4f092e561d9266a874d2f7ae2475c","v2_revision":"18423d0c086b22fca073d89d67371bd9ffd3a7b1","model":"Qwen/Qwen3.8-27B","attempts":2,"summary":{"previous_revision":"18423d0c086b22fca073d89d67371bd9ffd3a7b1","source_folder":"endless_v1","source_revision":"3abfc63260ef8ab59ac61274450310f650585bc9","new_model":"Qwen/Qwen3.8-27B","attempts":2,"evaluated":186,"v2_kept":1958,"newly_solved":121,"kept":2079,"reward_policy":"terminal-errors-zero-v2","source_tasks":2144,"excluded":65,"new_attempts":372,"saved_trajectories":372,"solved_attempts":215,"null_verifier_rewards":0,"success_distribution":{"0/2":65,"1/2":27,"2/2":94},"previously_zero_success":183,"previously_no_attempt":3,"exceptions":{"AgentTimeoutError":49},"timeouts_still_solved":11,"runs":{"pilot":{"job":"1085307","tasks":4},"full":{"job":"1090703","tasks":182}},"stage1_failed":["task_000000_90f9a729"],"path_recovery_incomplete":["task_000000_0497c08a","task_000000_44510e43","task_000000_511d89d4"],"network_effective":{"host":17}},"tasks":{"task_000000_0033979a":{"current_content_sha256":"9dc93819179af67d07e5a8bc00398b6de83dbb6aa29286a7d36d8686ff353e4f","kept":true,"kept_from_v2":true,"v2":{"content_sha256":"9dc93819179af67d07e5a8bc00398b6de83dbb6aa29286a7d36d8686ff353e4f","kept":true,"instruct_successes":4,"instruct_graded_attempts":4,"instruct_missing_attempts":0,"coder_successes":8,"coder_graded_attempts":8,"coder_missing_attempts":0,"target_attempts_per_model":4,"instruct_target_attempts":4,"coder_target_attempts":8,"exclusion_reason":null,"current_content_sha256":"9dc93819179af67d07e5a8bc00398b6de83dbb6aa29286a7d36d8686ff353e4f"}},"task_000000_003d339f":{"current_content_sha256":"b3b916528df682f177a7c76e769f216be39248fdfe73979f3838ea2fabe8bbb5","kept":true,"kept_from_v2":true,"v2":{"content_sha256":"b3b916528df682f177a7c76e769f216be39248fdfe73979f3838ea2fabe8bbb5","kept":true,"instruct_successes":0,"instruct_graded_attempts":4,"instruct_missing_attempts":0,"coder_successes":3,"coder_graded_attempts":8,"coder_missing_attempts":0,"target_attempts_per_model":4,"instruct_target_attempts":4,"coder_target_attempts":8,"exclusion_reason":null,"current_content_sha256":"b3b916528df682f177a7c76e769f216be39248fdfe73979f3838ea2fabe8bbb5"}},"task_000000_0063591c":{"current_content_sha256":"2a420ef966e979277e596838cad14614f4ce2b9d24941dae4be878e9706d00ab","kept":true,"kept_from_v2":true,"v2":{"content_sha256":"2a420ef966e979277e596838cad14614f4ce2b9d24941dae4be878e9706d00ab","kept":true,"instruct_successes":4,"instruct_graded_attempts":4,"instruct_missing_attempts":0,"coder_successes":8,"coder_graded_attempts":8,"coder_missing_attempts":0,"target_attempts_per_model":4,"instruct_target_attempts":4,"coder_target_attempts":8,"exclusion_reason":null,"current_content_sha256":"2a420ef966e979277e596838cad14614f4ce2b9d24941dae4be878e9706d00ab"}},"task_000000_009a1afa":{"current_content_sha256":"045ca6da0aa16abe38b16a1166ba7eccaab23da391d7071b5750d3173d56795a","kept":true,"kept_from_v2":true,"v2":{"content_sha256":"045ca6da0aa16abe38b16a1166ba7eccaab23da391d7071b5750d3173d56795a","kept":true,"instruct_successes":4,"instruct_graded_attempts":4,"instruct_missing_attempts":0,"coder_successes":8,"coder_graded_attempts":8,"coder_missing_attempts":0,"target_attempts_per_model":4,"instruct_target_attempts":4,"coder_target_attempts":8,"exclusion_reason":null,"current_content_sha256":"045ca6da0aa16abe38b16a1166ba7eccaab23da391d7071b5750d3173d56795a"}},"task_000000_00e67104":{"current_content_sha256":"45b10855bcac00260b6d12437cf0d1cf2273ccf62815d1eacb8f994bd57735f1","kept":true,"kept_from_v2":true,"v2":{"content_sha256":"45b10855bcac00260b6d12437cf0d1cf2273ccf62815d1eacb8f994bd57735f1","kept":true,"instruct_successes":2,"instruct_graded_attempts":4,"instruct_missing_attempts":0,"coder_successes":2,"coder_graded_attempts":8,"coder_missing_attempts":0,"target_attempts_per_model":4,"instruct_target_attempts":4,"coder_target_attempts":8,"exclusion_reason":null,"current_content_sha256":"45b10855bcac00260b6d12437cf0d1cf2273ccf62815d1eacb8f994bd57735f1"}},"task_000000_010b99da":{"current_content_sha256":"8d14adde343d3c33af319577f44efc0adae53ea220435003baf14a7caf06e14d","kept":true,"kept_from_v2":true,"v2":{"content_sha256":"8d14adde343d3c33af319577f44efc0adae53ea220435003baf14a7caf06e14d","kept":true,"instruct_successes":4,"instruct_graded_attempts":4,"instruct_missing_attempts":0,"coder_successes":8,"coder_graded_attempts":8,"coder_missing_attempts":0,"target_attempts_per_model":4,"instruct_target_attempts":4,"coder_target_attempts":8,"exclusion_reason":null,"current_content_sha256":"8d14adde343d3c33af319577f44efc0adae53ea220435003baf14a7caf06e14d"}},"task_000000_010d25ed":{"current_content_sha256":"19270b8d3f11ae3dc615ed0f89affaa323aa0fdd06bb7f4ee0b1dbc324b1f712","kept":true,"kept_from_v2":true,"v2":{"content_sha256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false, + "v2": { + "content_sha256": "a5d2fb48b2e460a2ac22ceb6733a9bf0cd2807186406e681ceda7716bebc790e", + "kept": true, + "instruct_successes": 3, + "instruct_graded_attempts": 4, + "instruct_missing_attempts": 0, + "coder_successes": 6, + "coder_graded_attempts": 8, + "coder_missing_attempts": 0, + "target_attempts_per_model": 4, + "instruct_target_attempts": 4, + "coder_target_attempts": 8, + "exclusion_reason": null, + "current_content_sha256": "110b6bcd7270a99cc7e1ee13371f3551dd4f1167344bf6f6824ddd35ef66cfb2" + }, + "kept_combined": true, + "available_in_v2": true, + "status": "available_in_v2" + }, + "task_000000_ff358cd3": { + "current_content_sha256": "03c17b815072d23784868fffb2dc07fef9db70f21b1a5c397fee0b8c23e3856f", + "kept": false, + "kept_from_v2": false, + "v2": { + "content_sha256": "03c17b815072d23784868fffb2dc07fef9db70f21b1a5c397fee0b8c23e3856f", + "kept": true, + "instruct_successes": 3, + "instruct_graded_attempts": 4, + "instruct_missing_attempts": 0, + 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"current_content_sha256": "337bbf8054502cca009eee1bdaf724b3daec6da0c9f234ac07de41e68431d585" + }, + "kept_combined": true, + "available_in_v2": true, + "status": "available_in_v2" + }, + "task_000000_ff8ce3d0": { + "current_content_sha256": "1d09ab9e793eddaa2ad4bed106fc6ffa8e2ca740fbc679a6e1a03b9deedf5dc9", + "kept": false, + "kept_from_v2": false, + "v2": { + "content_sha256": "1d09ab9e793eddaa2ad4bed106fc6ffa8e2ca740fbc679a6e1a03b9deedf5dc9", + "kept": false, + "instruct_successes": 0, + "instruct_graded_attempts": 4, + "instruct_missing_attempts": 0, + "coder_successes": 0, + "coder_graded_attempts": 8, + "coder_missing_attempts": 0, + "target_attempts_per_model": 4, + "instruct_target_attempts": 4, + "coder_target_attempts": 8, + "exclusion_reason": "zero_success_both_models", + "current_content_sha256": "1d09ab9e793eddaa2ad4bed106fc6ffa8e2ca740fbc679a6e1a03b9deedf5dc9" + }, + "qwen38_evaluated_content_sha256": "1d09ab9e793eddaa2ad4bed106fc6ffa8e2ca740fbc679a6e1a03b9deedf5dc9", + "qwen38_attempts": 2, + "qwen38_successes": 0, + "stage1": { + "run": "full-1090703-0004", + "status": "passed", + "failed_checks": [], + "path_recovery": [ + "completed" + ], + "repository_leaks": [ + "not_checked: no git binary in the image" + ] + }, + "kept_combined": false, + "available_in_v2": false + }, + "task_000000_ffa6e96d": { + "current_content_sha256": "a5a512ad8120a40e4ef42d2d33c71b16267cd9fd8fd9adbd2fc8fab2f9b26a02", + "kept": false, + "kept_from_v2": false, + "v2": { + "content_sha256": "fd1089e20ccfad61ce675fdd2141fa4e79ad62abf6e48de01e625932ad35171f", + "kept": false, + "instruct_successes": 0, + "instruct_graded_attempts": 4, + "instruct_missing_attempts": 0, + "coder_successes": 0, + "coder_graded_attempts": 8, + "coder_missing_attempts": 0, + "target_attempts_per_model": 4, + "instruct_target_attempts": 4, + "coder_target_attempts": 8, + "exclusion_reason": "zero_success_both_models", + "current_content_sha256": "a5a512ad8120a40e4ef42d2d33c71b16267cd9fd8fd9adbd2fc8fab2f9b26a02" + }, + "qwen38_evaluated_content_sha256": "a5a512ad8120a40e4ef42d2d33c71b16267cd9fd8fd9adbd2fc8fab2f9b26a02", + "qwen38_attempts": 2, + "qwen38_successes": 0, + "stage1": { + "run": "full-1090703-0005", + "status": "passed", + "failed_checks": [], + "path_recovery": [ + "completed" + ], + "repository_leaks": [ + "not_checked: no git binary in the image" + ] + }, + "kept_combined": false, + "available_in_v2": false + }, + "task_000000_ffeaac82": { + "current_content_sha256": "9ad6e0162cb299beb93479bee075431f93e0c6f0988c339b9e0456098ba80eef", + "kept": false, + "kept_from_v2": false, + "v2": { + "content_sha256": "9ad6e0162cb299beb93479bee075431f93e0c6f0988c339b9e0456098ba80eef", + "kept": true, + "instruct_successes": 3, + "instruct_graded_attempts": 4, + "instruct_missing_attempts": 0, + "coder_successes": 7, + "coder_graded_attempts": 8, + "coder_missing_attempts": 0, + "target_attempts_per_model": 4, + "instruct_target_attempts": 4, + "coder_target_attempts": 8, + "exclusion_reason": null, + "current_content_sha256": "9ad6e0162cb299beb93479bee075431f93e0c6f0988c339b9e0456098ba80eef" + }, + "kept_combined": true, + "available_in_v2": true, + "status": "available_in_v2" + } + } +} \ No newline at end of file diff --git a/endless_v3/metadata/trace-audit.json b/endless_v3/metadata/trace-audit.json index b70db43a2c3cb52c8b3c7929e3522868edcc04fd..3201a11cffc64b0762d0f590801ee80e4b97adb3 100644 --- a/endless_v3/metadata/trace-audit.json +++ b/endless_v3/metadata/trace-audit.json @@ -90,5 +90,6 @@ "tasks": 186, "attempts": 372, "saved_trajectories": 372, - "null_verifier_rewards": 0 + "null_verifier_rewards": 0, + "publication_layout_note": "Historical evaluation provenance; current incremental publication membership and file hashes are in metadata/layout.json and traces/manifest.json." } \ No newline at end of file diff --git a/endless_v3/pass_counts.parquet b/endless_v3/pass_counts.parquet index d1e850185a9744e77ba2888a001fec0decc246b4..b10673b140c75e9ab8c00c5badb08a5fa34c7c44 100644 --- a/endless_v3/pass_counts.parquet +++ b/endless_v3/pass_counts.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:09fdedb39b16c28291d83485ab85717c3d12c8e2da7b66392065eabbc0d479ab -size 100778 +oid sha256:98fa4adf04171ecb06080c9f30d09c260591b36ea2301b930b42f6ea34db085e +size 10441 diff --git a/endless_v3/patch.py b/endless_v3/patch.py index bc38ee6444bd8fac264f738d7d6befdd54a88571..a1ad746193406b61cd56cad07587d2d7cab9d972 100644 --- a/endless_v3/patch.py +++ b/endless_v3/patch.py @@ -1,73 +1,22 @@ -"""Rebuild Endless v3 tasks.parquet and archive.parquet from pinned v2 Parquets and metadata/selection.json. - -pip install pyarrow huggingface_hub -python patch.py --out rebuilt -Optional --source-dir holds v2 tasks.parquet and archive.parquet for offline reproduction. +"""Reproduce the incremental v3 task set from the pinned pre-migration release. +Requires pyarrow and huggingface_hub. Does not rerun evaluation. """ -import argparse, json +import argparse,hashlib,json from pathlib import Path -import pyarrow as pa -import pyarrow.parquet as pq - - -def build(out, source_dir=None): - m = json.loads((Path(__file__).parent / 'metadata/selection.json').read_text()) - - def source(name): - if source_dir: - return source_dir / name - from huggingface_hub import hf_hub_download - return Path(hf_hub_download(m['repo_id'], f'endless_v2/{name}', repo_type='dataset', - revision=m['v2_revision'], cache_dir=out / '.source-cache')) - out.mkdir(parents=True, exist_ok=True) - assert not any((out / n).exists() for n in ['tasks.parquet', 'archive.parquet']) - old, arch = pq.read_table(source('tasks.parquet')), pq.read_table(source('archive.parquet')) - extra = [pa.field('qwen38_attempts', pa.int64()), pa.field('qwen38_successes', pa.int64()), - pa.field('qwen38_target_attempts', pa.int64()), pa.field('kept_from_v2', pa.bool_())] - kept, excluded = [], [] - for r in old.to_pylist(): - assert m['tasks'][r['path']]['kept_from_v2'] - kept.append({**r, 'qwen38_attempts': 0, 'qwen38_successes': None, 'qwen38_target_attempts': None, 'kept_from_v2': True}) - for r in arch.to_pylist(): - e = m['tasks'][r['path']] - assert r['content_sha256'] == e['qwen38_evaluated_content_sha256'] - r.update(qwen38_attempts=e['qwen38_attempts'], qwen38_successes=e['qwen38_successes'], - qwen38_target_attempts=m['attempts'], kept_from_v2=False) - if e['kept']: - r.update(archive_stage=None, archive_reason=None, archive_labels=[]) - kept.append(r) - else: - r.update(archive_reason='zero_success_three_models', archive_labels=['zero_success_three_models']) - excluded.append(r) - - def images(rows): - merged = json.loads(old.schema.metadata[b'ot.images.v1']) - other = json.loads(arch.schema.metadata[b'ot.images.v1']) - merged['tasks'].update(other['tasks']) - merged['bundles'].update(other['bundles']) - names = {r['path'] for r in rows} - merged['tasks'] = {t: e for t, e in merged['tasks'].items() if t in names} - used = {e['bundle'] for t in merged['tasks'].values() for e in t['environments']} - merged['bundles'] = {k: v for k, v in merged['bundles'].items() if k in used} - if 'uncached_task_ids' in merged: - merged['uncached_task_ids'] = sorted(names - set(merged['tasks'])) - merged['coverage'] = {'cached_tasks': len(merged['tasks']), 'total_tasks': len(names)} - return json.dumps(merged).encode() - s = m['summary'] - release = json.dumps({k: s[k] for k in ['previous_revision', 'source_folder', 'source_revision', 'new_model', 'attempts', - 'evaluated', 'v2_kept', 'newly_solved', 'kept', 'reward_policy']}).encode() - for name, table, rows in [('tasks.parquet', old, kept), ('archive.parquet', arch, excluded)]: - schema = pa.schema([*table.schema, *extra]) - pq.write_table(pa.Table.from_pylist(sorted(rows, key=lambda r: r['path']), schema=schema) - .replace_schema_metadata({b'ot.images.v1': images(rows), b'ot.release.v3': release}), - out / name, compression='zstd') - assert len(kept) == s['kept'] and len(excluded) == s['excluded'] - print(json.dumps({'kept': len(kept), 'excluded': len(excluded)})) - +import pyarrow as pa,pyarrow.parquet as pq +from huggingface_hub import hf_hub_download -if __name__ == '__main__': - p = argparse.ArgumentParser(description=__doc__) - p.add_argument('--out', type=Path, required=True) - p.add_argument('--source-dir', type=Path) - a = p.parse_args() - build(a.out, a.source_dir) +def build(out): + root=Path(__file__).parent;m=json.loads((root/'metadata/layout.json').read_text()) + p=hf_hub_download('FWeindel/validated-tasks','endless_v3/tasks.parquet',repo_type='dataset',revision=m['previous_cumulative_revision']) + assert hashlib.sha256(Path(p).read_bytes()).hexdigest()==m['previous_tasks_sha256'] + t=pq.read_table(p);ids=set(m['task_ids']);t=t.filter(pa.array([x in ids for x in t['path'].to_pylist()]));assert set(t['path'].to_pylist())==ids + meta=dict(t.schema.metadata or {}) + if b'ot.images.v1' in meta: + im=json.loads(meta[b'ot.images.v1']);im['tasks']={k:v for k,v in im['tasks'].items() if k in ids};used={e['bundle'] for v in im['tasks'].values() for e in v['environments']};im['bundles']={k:v for k,v in im['bundles'].items() if k in used};im['coverage']={'cached_tasks':len(im['tasks']),'total_tasks':len(ids)} + if 'uncached_task_ids' in im:im['uncached_task_ids']=sorted(ids-set(im['tasks'])) + meta[b'ot.images.v1']=json.dumps(im).encode() + release=json.loads(meta.get(b'ot.release.v3',b'{}'));release.update({k:m[k] for k in ['layout','kept','baseline_kept','combined_kept','baseline_folder','baseline_revision','carried_from_v2']});meta[b'ot.release.v3']=json.dumps(release).encode() + pq.write_table(t.replace_schema_metadata(meta),out,compression='zstd') +if __name__=='__main__': + p=argparse.ArgumentParser();p.add_argument('--out',type=Path,required=True);a=p.parse_args();build(a.out) diff --git a/endless_v3/tasks.parquet b/endless_v3/tasks.parquet index 4d5b992ebe0b02e50341475871e95a2a39c5d120..f910adcb6788a198077107ae3eae7d794dfef71d 100644 --- a/endless_v3/tasks.parquet +++ b/endless_v3/tasks.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:87c0d05e275ee10609045f58db2409f7f97ad6c1187804ecf11cf216dced4932 -size 30533682 +oid sha256:2b911846e1fe121635d708bbfd17225f4a1bcee2e5fc47ad794b9779fc187ba7 +size 2264578 diff --git a/endless_v3/trace_stats/README.md b/endless_v3/trace_stats/README.md index 21843f96c3598f1e35870ffb383909f1cdaf01f7..88b22ed8e6cd8862035c5e684d2e16164b579de5 100644 --- a/endless_v3/trace_stats/README.md +++ b/endless_v3/trace_stats/README.md @@ -47,3 +47,5 @@ Context is the total prompt plus response window for one model call. The respons Configured limits are not measurements of token usage. Agent and verifier time limits are 300 s each for every evaluated task. Unlike the v2 runs, this run forwards the output cap to each request; the earlier attempt at this run (job 1065977) did not and was cancelled before it produced results. Settings and evidence: [audit](../metadata/evaluation-settings-audit.json). + +This folder reports the new evaluation only. Task membership is incremental: tasks already in v2 are not duplicated in v3. Historical conversations remain in v2. See the [release overview](../README.md) for counts and combination instructions. diff --git a/endless_v3/trace_stats/data/provenance.json b/endless_v3/trace_stats/data/provenance.json index 4745858c48265bcc64cbd68ca062b2ee9a480d18..53d903427c97634ae1b87fdf837c3971d76a9600 100644 --- a/endless_v3/trace_stats/data/provenance.json +++ b/endless_v3/trace_stats/data/provenance.json @@ -9,7 +9,7 @@ "file": "traces/qwen38-attempts.parquet", "rows": 372, "unique_rows_added": 372, - "sha256": "715dce90c67bc05a70bee047beb737c14f8f382e835f37498d1b3aa6f6a4ccd2" + "sha256": "e40c1baf166c1cfc2059e09d48835afa663405f281996f5b25f0a45833a86892" } ], "figures": { @@ -29,5 +29,7 @@ "plotting": { "implementation_sha256": "3e3bdc00f8b2100de4f96bdb48ce6fd87eff518a6bb380f9b07c3da352ad7db6" }, - "outcome_policy": "exclusive-outcomes-v1" -} + "outcome_policy": "exclusive-outcomes-v1", + "previous_provenance_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "layout": "incremental" +} \ No newline at end of file diff --git a/endless_v3/traces/attempts.parquet b/endless_v3/traces/attempts.parquet deleted file mode 100644 index 2fa3ed1aa38221f2fd643a93ad530b781f08cfe8..0000000000000000000000000000000000000000 --- a/endless_v3/traces/attempts.parquet +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:db02ed47a2c8b2f948fd6e279d40f5fed98b7e4ca359378de1023a6d233f01ac -size 76070412 diff --git a/endless_v3/traces/manifest.json b/endless_v3/traces/manifest.json index 69d2d216d96be56875ac36ae998b56790c8d1a55..8afdf2ff142915a7d952923f91c9e0cd2ee4a8df 100644 --- a/endless_v3/traces/manifest.json +++ b/endless_v3/traces/manifest.json @@ -1,17 +1,15 @@ { "files": [ - { - "file": "traces/attempts.parquet", - "rows": 25692, - "sha256": "db02ed47a2c8b2f948fd6e279d40f5fed98b7e4ca359378de1023a6d233f01ac" - }, { "file": "traces/qwen38-attempts.parquet", "rows": 372, - "sha256": "715dce90c67bc05a70bee047beb737c14f8f382e835f37498d1b3aa6f6a4ccd2" + "sha256": "e40c1baf166c1cfc2059e09d48835afa663405f281996f5b25f0a45833a86892" } ], - "v2_attempts": 25692, - "v3_attempts": 372, - "scope": "All v2 and v3 attempts, kept and excluded tasks; evaluation_version distinguishes them." + "attempts": 372, + "new_attempts": 372, + "historical_attempts": 0, + "historical_folder": "endless_v2/traces", + "scope": "All new v3 evaluation attempts, successful and failed, including unsolved tasks. Historical traces remain in v2.", + "layout": "incremental" } \ No newline at end of file diff --git a/endless_v3/traces/qwen38-attempts.parquet b/endless_v3/traces/qwen38-attempts.parquet index 52f3e6f0839536deb9435fa5ff3b87e05a8b6e16..968deee16eef525cd21ed1ef5f480c01bd1b4c56 100644 --- a/endless_v3/traces/qwen38-attempts.parquet +++ b/endless_v3/traces/qwen38-attempts.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:715dce90c67bc05a70bee047beb737c14f8f382e835f37498d1b3aa6f6a4ccd2 -size 2029109 +oid sha256:e40c1baf166c1cfc2059e09d48835afa663405f281996f5b25f0a45833a86892 +size 2124415 diff --git a/nl2bash_v3/README.md b/nl2bash_v3/README.md index e8c50d5881ed938f531f1fa3082cbebdc115a1bf..ce0e922e5fdafab8ad7d5af1943ed89ae98c522f 100644 --- a/nl2bash_v3/README.md +++ b/nl2bash_v3/README.md @@ -3,17 +3,18 @@ ## Dataset stats - **Original tasks:** 1,497 -- **Kept:** 1,042 -- **Carried forward from v2:** 938 +- **Kept in v3:** 104 newly solved tasks +- **Available separately in v2:** 938 +- **Combined v2 + v3:** 1,042 unique tasks - **Newly solved:** 104 of 487 previously unsolved tasks (21.36%) - **Changed task payloads:** 0 - **Archived:** 455 (383 still unsolved and 72 earlier exclusions) - **Pending attempts:** 0 - **New evaluation:** 487 tasks, 974 attempts, 974 saved conversations -- **Historical conversations preserved:** 5,700 +- **Historical conversations:** available separately in [nl2bash_v2](../nl2bash_v2/README.md) - **Images:** 1 before, 1 now -All 938 [v2 tasks](../nl2bash_v2/README.md) are carried forward. V3 adds tasks solved at least once by Qwen/Qwen3.8-27B in two attempts. The v1 source revision is `3abfc63260ef8ab59ac61274450310f650585bc9`. +The 938 [v2 tasks](../nl2bash_v2/README.md) remain available separately. V3 adds tasks solved at least once by Qwen/Qwen3.8-27B in two attempts. The v1 source revision is `3abfc63260ef8ab59ac61274450310f650585bc9`. ## Dataset characteristics @@ -41,7 +42,7 @@ The 8,192-token response cap is present in saved `llm_call_kwargs.max_tokens`; H ## New success distribution -These rates cover the 487 previously unsolved tasks, not the cumulative 1,042 kept tasks. +These rates cover the 487 previously unsolved tasks, not the 104 success-selected tasks published in v3. | Metric | Rate | | --- | ---: | @@ -62,7 +63,7 @@ These rates cover the 487 previously unsolved tasks, not the cumulative 1,042 ke All 974 new conversations are saved, with 157 successful attempts and 973 numeric verifier rewards. One terminal attempt ended with a TmuxBatchProtocolError and has a null raw verifier reward and counts as zero under the recorded reward policy. No missing attempts were fabricated and no conversations were removed. Stage-7 completion is not a claim that every saved conversation is suitable for training. -The release preserves all 5,700 historical conversations. `evaluation_version` identifies historical and new traces; `retained_v3` indicates whether the task is kept. Earlier tasks have zero new attempts and null new success counts. +The 5,700 historical conversations remain in v2. This folder contains the 974 new attempts; `retained_v3` indicates membership in the incremental task set. | New-run measurement | Median | | --- | ---: | @@ -78,8 +79,29 @@ Medians use recorded measurements. Token totals sum calls within each attempt. ## Files -- [tasks.parquet](tasks.parquet): 1,042 kept tasks, preserving v2 columns and adding Qwen/Qwen3.8-27B attempt and success counts. +- [tasks.parquet](tasks.parquet): 104 newly solved tasks, preserving existing columns and adding Qwen/Qwen3.8-27B attempt and success counts. - [archive.parquet](archive.parquet): 455 excluded tasks, preserving task payloads and prior metadata. -- [Selection](metadata/selection.json): cumulative membership and per-task evidence. +- [Selection](metadata/selection.json): incremental membership, combined membership and per-task evidence. - [Provenance](metadata/provenance.json): source revisions and report/archive hashes. -- [Trace manifest](traces/manifest.json): all historical and new conversations with file hashes. +- [Trace manifest](traces/manifest.json): all new conversations with file hashes. + +## Incremental release + +V3 contains only tasks newly solved after v2. It has no task-ID overlap with [v2](../nl2bash_v2/README.md). This is a harder subset relative to the earlier evaluation, not a claim of intrinsic difficulty. New-evaluation traces include both solved and unsolved tasks so the reported pass rates remain reproducible. + +```python +from huggingface_hub import hf_hub_download +import pyarrow as pa +import pyarrow.parquet as pq + +def load(version): + path = hf_hub_download("FWeindel/validated-tasks", + f"nl2bash_{version}/tasks.parquet", repo_type="dataset") + return pq.read_table(path) + +v2, v3 = load("v2"), load("v3") +assert set(v2["path"].to_pylist()).isdisjoint(v3["path"].to_pylist()) +combined = pa.concat_tables([v2, v3], promote_options="permissive") +``` + +The example combines task rows; each source file retains its own image manifest. Use those source manifests when staging images. [Layout and provenance](metadata/layout.json) records the pinned baseline and task counts. Earlier cumulative v3 releases remain accessible through their HF commit history. diff --git a/nl2bash_v3/metadata/history-deduplication.json b/nl2bash_v3/metadata/history-deduplication.json new file mode 100644 index 0000000000000000000000000000000000000000..f1454c115c80a69d663bbe3429676b4fe479aae3 --- /dev/null +++ b/nl2bash_v3/metadata/history-deduplication.json @@ -0,0 +1,16 @@ +{ + "verified_identical_attempts": 5700, + "deleted_duplicate_files": [ + "nl2bash_v3/traces/coder.parquet" + ], + "baseline_files": [ + "nl2bash_v2/traces/coder.parquet" + ], + "revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "comparison": [ + "task_id", + "attempt_id", + "trajectory", + "result" + ] +} \ No newline at end of file diff --git a/nl2bash_v3/metadata/layout.json b/nl2bash_v3/metadata/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..5b7d1694fb40e98143062cfca9b54799aaa87550 --- /dev/null +++ b/nl2bash_v3/metadata/layout.json @@ -0,0 +1,118 @@ +{ + "layout": "incremental", + "kept": 104, + "baseline_kept": 938, + "combined_kept": 1042, + "baseline_folder": "nl2bash_v2", + "baseline_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "carried_from_v2": 0, + "previous_cumulative_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "previous_tasks_sha256": "828498d8116914beb29d422b16c6656d5d11c93dc14cf4a86684ae5c14fceeba", + "task_ids": [ + "task_1168", + "task_1375", + "task_1461", + "task_1490", + "task_1620", + "task_1637", + "task_17", + "task_1762", + "task_1810", + "task_1837", + "task_185", + "task_1904", + "task_1990", + "task_20", + "task_2089", + "task_2189", + "task_230", + "task_2422", + "task_2433", + "task_2519", + "task_2559", + "task_2646", + "task_2724", + "task_273", + "task_2743", + "task_2789", + "task_2867", + "task_2878", + "task_2938", + "task_3031", + "task_3197", + "task_3280", + "task_3342", + "task_3356", + "task_3416", + "task_3480", + "task_35", + "task_3722", + "task_375", + "task_3929", + "task_4014", + "task_4098", + "task_4200", + "task_4252", + "task_4290", + "task_4325", + "task_459", + "task_4895", + "task_49", + "task_493", + "task_4968", + "task_5194", + "task_5516", + "task_5543", + "task_5702", + "task_5753", + "task_5767", + "task_5882", + "task_6144", + "task_632", + "task_6338", + "task_6344", + "task_6766", + "task_6801", + "task_6831", + "task_6879", + "task_6886", + "task_7133", + "task_7244", + "task_7448", + "task_7540", + "task_7689", + "task_7873", + "task_7931", + "task_7991", + "task_8071", + "task_8388", + "task_8396", + "task_8401", + "task_845", + "task_8467", + "task_8547", + "task_8548", + "task_8649", + "task_8673", + "task_8710", + "task_8743", + "task_8806", + "task_8818", + "task_8866", + "task_888", + "task_8934", + "task_9049", + "task_9095", + "task_9122", + "task_9181", + "task_9208", + "task_929", + "task_9293", + "task_9325", + "task_9611", + "task_9749", + "task_9971", + "task_9995" + ], + "tasks_sha256": "fe2498f6b9735d1ed733e31b9228cc401c071fddf8ea4f355958368283064e10" +} \ No newline at end of file diff --git a/nl2bash_v3/metadata/provenance.json b/nl2bash_v3/metadata/provenance.json index 72d2c3603377bb13be5a1d1dcbbaca4ed9dfa52b..ab4a88e1b35b86ff5096d190d38576131150af34 100644 --- a/nl2bash_v3/metadata/provenance.json +++ b/nl2bash_v3/metadata/provenance.json @@ -1,183 +1,184 @@ { - "sources": [ - { - "split": "pilot", - "shard": "0000", - "stage6_sha256": "53c7d591a959a79742c0a0d085347897ba8a57b10dc62db9f48418cbeebee150", - "stage7_sha256": "e6b0bab5036b2de9ba6d1976e1f8b62bab95bb833463c5b86cea5f99c009866e", - "archive": "/hnvme/workspace/y500bb12-optiagent/nl2bash-qwen38-pass2-20261020/pilot/array/shards/0000/evidence.tar.gz", - "archive_sha256": "99f4db7c8864d6dc4e704278c8b9c33c9349ecc2cb0155c6b6ba892153686070" - }, - { - "split": "full", - "shard": "0000", - "stage6_sha256": "f40091c2025143cea838f88c4f2ba03ea631c31041128f7ea92066a7dc90b8a7", - "stage7_sha256": "ccfda192cbedfbd6eef697f43c3a153fea2ee36be88a32d3166393132a205895", - "archive": "/hnvme/workspace/y500bb12-optiagent/nl2bash-qwen38-pass2-20261020/full/array/shards/0000/evidence.tar.gz", - "archive_sha256": "0feba0abd73bfbb42ebc8123411734d56270472ddf40c6f8ad579863738ef9fb" - }, - { - "split": "full", - "shard": "0001", - "stage6_sha256": "2dc4fd33e4127cde6b2e0fddaf9081b98ff29e8880c852d20a028784777d1664", - "stage7_sha256": "e3ceca8b70eaf07c9ec7510d29277fa36718a792bd665f0f964f1acdc1213975", - "archive": "/hnvme/workspace/y500bb12-optiagent/nl2bash-qwen38-pass2-20261020/full/array/shards/0001/evidence.tar.gz", - "archive_sha256": "56edec6bb575c483684d9a04731a3714da954b65cb2603e3c7533e12838f70c8" - }, - { - "split": "full", - "shard": "0002", - "stage6_sha256": "ffecf4d01129f79de9708a3cf44ab3c7655a3b3f83e5f20f1b01420e75d7d35b", - "stage7_sha256": "9136caabaf205197046ac701f82f3fb994b4d7ce9c1b1e1e8ae5995ba63fbe70", - "archive": 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\"/tmp/1091645.helma/validation-runtime/code/validation/stages/path_diagnostics.py\", line 36, in agent\n return await original_agent(self, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/code/harbor_patches/command_cancellation.py\", line 94, in phase\n return await original_phase(self, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/trial/trial.py\", line 473, in _run_agent_phase\n await self._await_phase(\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/trial/trial.py\", line 510, in _await_phase\n return await task\n ^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/code/harbor_patches/oom_recovery.py\", line 92, in run_with_oom_metrics\n return await run(self, instruction, environment, context)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/agents/terminus_2/terminus_2.py\", line 2378, in run\n await self._run_agent_loop(\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/agents/terminus_2/terminus_2.py\", line 2108, in _run_agent_loop\n terminal_output = await self._execute_interaction(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/agents/terminus_2/terminus_2.py\", line 1896, in _execute_interaction\n _, terminal_output = await self._execute_commands(interaction.commands, session)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/agents/terminus_2/terminus_2.py\", line 1863, in _execute_commands\n terminal_output, session_alive = await session.send_keys_and_capture(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/code/harbor_patches/oom_recovery.py\", line 66, in call\n result = await original(self, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/code/harbor_patches/tmux_diagnostics.py\", line 29, in send_keys_and_capture\n return await original(self, keystroke_batches)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/agents/terminus_2/tmux_session.py\", line 648, in send_keys_and_capture\n raise TmuxBatchProtocolError(\nharbor.agents.terminus_2.tmux_session.TmuxBatchProtocolError: task_3463__ezGiQaw__attempt_0: batched tmux call returned no recognized status marker: return_code=-1, stderr='Command timed out after 184s', stdout=''\n", - "occurred_at": "2026-10-21T14:20:06.583363" - } - ] - } - ], - "v2_results": { - "kept": false, - "coder_successes": 0, - "recorded_attempts": 4, - "content_sha256": 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original_phase(self, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/trial/trial.py\", line 473, in _run_agent_phase\n await self._await_phase(\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/trial/trial.py\", line 510, in _await_phase\n return await task\n ^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/code/harbor_patches/oom_recovery.py\", line 92, in run_with_oom_metrics\n return await run(self, instruction, environment, context)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/agents/terminus_2/terminus_2.py\", line 2378, in run\n await self._run_agent_loop(\n File \"/tmp/1091645.helma/validation-runtime/env/lib/python3.12/site-packages/harbor/agents/terminus_2/terminus_2.py\", line 2108, in _run_agent_loop\n terminal_output = await 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"/tmp/1091956.helma/validation-0019/results/stage_6_agent_trials/551ea8877402/jobs/job/task_9971__R3QmDQd/attempts/000", + "id": "65dac026-f0c9-4a6f-a250-dcabb4a3c1c0", + "terminal": true, + "verifier_reward": 1.0, + "reward_origin": "verifier", + "reward": 1.0, + "solved": true, + "exceptions": [] + } + ], + "v2_results": { + "kept": false, + "coder_successes": 0, + "recorded_attempts": 4, + "content_sha256": "5dbe74792a1778d951d6c6cfa69c3bcd90f3b60a78172e680a4579951fca66fb", + "attempts": { + "bc772c46-5017-45d6-9d7a-cd598f46d3dd": 0.0, + "d767d7e8-f440-4bdf-a665-8a6e4fcae572": 0.0, + "9fb6f6dd-94d3-4bd0-a970-56d722194957": 0.0, + "af2b5d51-441b-4f6c-ab7b-4ac0530289a7": 0.0 + } + }, + "kept_combined": true, + "available_in_v2": false + }, + "task_9976": { + "kept": false, + "carried_from_v2": false, + "content_sha256": "49332f634424acef42d93b05d7a20c53fe10c467fcbb0f727795799716252ec0", + "qwen38_attempts": [], + "v2_results": { + "kept": true, + "coder_successes": 4, + "recorded_attempts": 4, + "content_sha256": "49332f634424acef42d93b05d7a20c53fe10c467fcbb0f727795799716252ec0", + "attempts": { + "6fbb61f4-f65b-48f6-99cd-e967e86fc42e": 1.0, + "0bd56e58-7267-4dcd-983d-70e251989820": 1.0, + "747d3879-4c41-4e42-99fd-80f9a315409d": 1.0, + "cbadf61d-4466-4d3b-b51c-3cf92b947bfa": 1.0 + } + }, + "kept_combined": true, + "available_in_v2": true, + "status": "available_in_v2" + }, + "task_9991": { + "kept": false, + "carried_from_v2": false, + "content_sha256": "22672f50b951c0cd8d06566a0067d9a380d4ec98a7c9e656bd4d0e07ec26ae9c", + "qwen38_attempts": [], + "v2_results": { + "kept": true, + "coder_successes": 4, + "recorded_attempts": 4, + "content_sha256": "22672f50b951c0cd8d06566a0067d9a380d4ec98a7c9e656bd4d0e07ec26ae9c", + "attempts": { + "24cf53d8-c90c-4134-9ba3-22fb34ba05ec": 1.0, + "e825f484-939d-4809-934d-7d8774ce374a": 1.0, + "f266dc8e-cea3-44ea-b646-1a33379b6cec": 1.0, + "3de93bf4-5e03-4cb6-bda5-d4465749d2a6": 1.0 + } + }, + "kept_combined": true, + "available_in_v2": true, + "status": "available_in_v2" + }, + "task_9995": { + "kept": true, + "carried_from_v2": false, + "content_sha256": "fa3f10650581e2100ffca32b88edea85c9ac1a118e7c36eb8fafede7c1a3d077", + "qwen38_attempts": [ + { + "trial": "/tmp/1085473.helma/validation-0020/results/stage_6_agent_trials/bedd08bda5be/jobs/job/task_9995__9DuxRb8/attempts/000", + "id": "2defeaa4-6c87-47e3-a841-d4f3490543bd", + "terminal": true, + "verifier_reward": 1.0, + "reward_origin": "verifier", + "reward": 1.0, + "solved": true, + "exceptions": [] + }, + { + "trial": "/tmp/1085473.helma/validation-0020/results/stage_6_agent_trials/bedd08bda5be/jobs/job/task_9995__NhnTJZC/attempts/000", + "id": "1e1e3eb3-7e97-4137-983e-bc8063264fc1", + "terminal": true, + "verifier_reward": 1.0, + "reward_origin": "verifier", + "reward": 1.0, + "solved": true, + "exceptions": [] + } + ], + "v2_results": { + "kept": false, + "coder_successes": 0, + "recorded_attempts": 4, + "content_sha256": "fa3f10650581e2100ffca32b88edea85c9ac1a118e7c36eb8fafede7c1a3d077", + "attempts": { + "9edd6b64-5b23-4ae5-ba33-10fd64baf525": 0.0, + "73eab801-c801-44cc-b5b9-56d2cf3ad0c5": 0.0, + "b5c0554d-5e08-47a9-bdaf-d3b7cd4017eb": 0.0, + "db1ee690-9a85-4a87-a5d4-65b902fd327a": 0.0 + } + }, + "kept_combined": true, + "available_in_v2": false + }, + "task_9997": { + "kept": false, + "carried_from_v2": false, + "content_sha256": "c91185946a8570a82372f918d1301b561b242e589af4089c1acd0af8e60c82cf", + "qwen38_attempts": [], + "v2_results": { + "kept": true, + "coder_successes": 4, + "recorded_attempts": 4, + "content_sha256": "c91185946a8570a82372f918d1301b561b242e589af4089c1acd0af8e60c82cf", + "attempts": { + "16935f8e-84f0-42ac-a978-df668a300bb2": 1.0, + "3c57ace7-3adc-44ff-ac3e-ee9aeb146614": 1.0, + "da0c3fd1-8858-4af5-a005-c39e7fbe778f": 1.0, + "b3f2cbc4-e72f-4374-ac86-a15aab26c001": 1.0 + } + }, + "kept_combined": true, + "available_in_v2": true, + "status": "available_in_v2" + }, + "task_9999": { + "kept": false, + "carried_from_v2": false, + "content_sha256": "fcd0147dfc21332f0e732ed7f1b31c7cdc5f65890962d8e4848dce15adb95361", + "qwen38_attempts": [], + "v2_results": { + "kept": true, + "coder_successes": 3, + "recorded_attempts": 4, + "content_sha256": "fcd0147dfc21332f0e732ed7f1b31c7cdc5f65890962d8e4848dce15adb95361", + "attempts": { + "6b54fea4-02f9-4945-b82b-bd93efba737c": 0.0, + "e8971d5b-d597-4975-8b43-0281428efd69": 1.0, + "9c04799d-bd7d-4fb5-8f41-27cd5df03ffa": 1.0, + "adaf69d4-6042-47ff-87e1-6c8b9a427ce0": 1.0 } + }, + "kept_combined": true, + "available_in_v2": true, + "status": "available_in_v2" } -} + } +} \ No newline at end of file diff --git a/nl2bash_v3/patch.py b/nl2bash_v3/patch.py new file mode 100644 index 0000000000000000000000000000000000000000..3ebefcd3552a2a8352ee52781df62c5d3023827a --- /dev/null +++ b/nl2bash_v3/patch.py @@ -0,0 +1,22 @@ +"""Reproduce the incremental v3 task set from the pinned pre-migration release. +Requires pyarrow and huggingface_hub. Does not rerun evaluation. +""" +import argparse,hashlib,json +from pathlib import Path +import pyarrow as pa,pyarrow.parquet as pq +from huggingface_hub import hf_hub_download + +def build(out): + root=Path(__file__).parent;m=json.loads((root/'metadata/layout.json').read_text()) + p=hf_hub_download('FWeindel/validated-tasks','nl2bash_v3/tasks.parquet',repo_type='dataset',revision=m['previous_cumulative_revision']) + assert hashlib.sha256(Path(p).read_bytes()).hexdigest()==m['previous_tasks_sha256'] + t=pq.read_table(p);ids=set(m['task_ids']);t=t.filter(pa.array([x in ids for x in t['path'].to_pylist()]));assert set(t['path'].to_pylist())==ids + meta=dict(t.schema.metadata or {}) + if b'ot.images.v1' in meta: + im=json.loads(meta[b'ot.images.v1']);im['tasks']={k:v for k,v in im['tasks'].items() if k in ids};used={e['bundle'] for v in im['tasks'].values() for e in v['environments']};im['bundles']={k:v for k,v in im['bundles'].items() if k in used};im['coverage']={'cached_tasks':len(im['tasks']),'total_tasks':len(ids)} + if 'uncached_task_ids' in im:im['uncached_task_ids']=sorted(ids-set(im['tasks'])) + meta[b'ot.images.v1']=json.dumps(im).encode() + release=json.loads(meta.get(b'ot.release.v3',b'{}'));release.update({k:m[k] for k in ['layout','kept','baseline_kept','combined_kept','baseline_folder','baseline_revision','carried_from_v2']});meta[b'ot.release.v3']=json.dumps(release).encode() + pq.write_table(t.replace_schema_metadata(meta),out,compression='zstd') +if __name__=='__main__': + p=argparse.ArgumentParser();p.add_argument('--out',type=Path,required=True);a=p.parse_args();build(a.out) diff --git a/nl2bash_v3/tasks.parquet b/nl2bash_v3/tasks.parquet index 47996f30d66f8a02569c7f80bb0e1e9e4699d15b..b10f68e40476522209e78347f075a6f817e73e12 100644 --- a/nl2bash_v3/tasks.parquet +++ b/nl2bash_v3/tasks.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:828498d8116914beb29d422b16c6656d5d11c93dc14cf4a86684ae5c14fceeba -size 1334413 +oid sha256:fe2498f6b9735d1ed733e31b9228cc401c071fddf8ea4f355958368283064e10 +size 148547 diff --git a/nl2bash_v3/trace_stats/README.md b/nl2bash_v3/trace_stats/README.md index d2590c17178eb6f304e84eb165291ac23ddf48e4..40ac898375b0cf532c0dd77bb93a76113dc0c781 100644 --- a/nl2bash_v3/trace_stats/README.md +++ b/nl2bash_v3/trace_stats/README.md @@ -48,3 +48,5 @@ Context is the total prompt plus response window for one model call. The respons Configured limits are distinct from measured token usage. Only the 974 new v3 attempts are summarized here. See [v2](../../nl2bash_v2/README.md) for historical statistics. Agent and verifier limits are 600 seconds in all 974 saved trial configurations. [Settings evidence](../metadata/evaluation-settings.json). + +This folder reports the new evaluation only. Task membership is incremental: tasks already in v2 are not duplicated in v3. Historical conversations remain in v2. See the [release overview](../README.md) for counts and combination instructions. diff --git a/nl2bash_v3/trace_stats/data/provenance.json b/nl2bash_v3/trace_stats/data/provenance.json index ad5c0b02eebc0503bf278aa64b32767248c77ab4..7c828e01509182803c74492b68e4fafd778191d3 100644 --- a/nl2bash_v3/trace_stats/data/provenance.json +++ b/nl2bash_v3/trace_stats/data/provenance.json @@ -9,7 +9,7 @@ "file": "traces/qwen38.parquet", "rows": 974, "unique_rows_added": 974, - "sha256": "5c3bd561b646bc45b12ac2df1e5d4068b1a2f5608dd3f543914a647cdce40809" + "sha256": "a33adfc80354d710c391ca138429f4a0d36b31b97ff255b8baa8ca341f8854e5" } ], "figures": { @@ -29,5 +29,7 @@ "plotting": { "implementation_sha256": "3e3bdc00f8b2100de4f96bdb48ce6fd87eff518a6bb380f9b07c3da352ad7db6" }, - "outcome_policy": "exclusive-outcomes-v1" -} + "outcome_policy": "exclusive-outcomes-v1", + "previous_provenance_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "layout": "incremental" +} \ No newline at end of file diff --git a/nl2bash_v3/traces/coder.parquet b/nl2bash_v3/traces/coder.parquet deleted file mode 100644 index 2785b15f94424e895e01962a94bc5979a8a6889e..0000000000000000000000000000000000000000 --- a/nl2bash_v3/traces/coder.parquet +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e016b61a95c30e2dfff9369b350f1d6a4bc7cbd8001842d35fc734432aa7a6f5 -size 13832602 diff --git a/nl2bash_v3/traces/manifest.json b/nl2bash_v3/traces/manifest.json index e7cb03ddf3c17f21978c80366cb26a225aebaf1d..4a20c4bc5691ebc1fa802a2975ab28698e8dd155 100644 --- a/nl2bash_v3/traces/manifest.json +++ b/nl2bash_v3/traces/manifest.json @@ -1,18 +1,15 @@ { "files": [ - { - "file": "traces/coder.parquet", - "rows": 5700, - "sha256": "e016b61a95c30e2dfff9369b350f1d6a4bc7cbd8001842d35fc734432aa7a6f5" - }, { "file": "traces/qwen38.parquet", "rows": 974, - "sha256": "5c3bd561b646bc45b12ac2df1e5d4068b1a2f5608dd3f543914a647cdce40809" + "sha256": "a33adfc80354d710c391ca138429f4a0d36b31b97ff255b8baa8ca341f8854e5" } ], - "historical_attempts": 5700, + "attempts": 974, "new_attempts": 974, - "new_saved_trajectories": 974, - "scope": "Historical v2 and new v3 attempts, identified by evaluation_version." -} + "historical_attempts": 0, + "historical_folder": "nl2bash_v2/traces", + "scope": "All new v3 evaluation attempts, successful and failed, including unsolved tasks. Historical traces remain in v2.", + "layout": "incremental" +} \ No newline at end of file diff --git a/nl2bash_v3/traces/qwen38.parquet b/nl2bash_v3/traces/qwen38.parquet index 0a0cb15aad6b5a06933d84c7311f6a776ade08f6..00e07b453fd83cdfb80747024c149a43bd26b819 100644 --- a/nl2bash_v3/traces/qwen38.parquet +++ b/nl2bash_v3/traces/qwen38.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:5c3bd561b646bc45b12ac2df1e5d4068b1a2f5608dd3f543914a647cdce40809 -size 1826331 +oid sha256:a33adfc80354d710c391ca138429f4a0d36b31b97ff255b8baa8ca341f8854e5 +size 1451138 diff --git a/termigen_v3/README.md b/termigen_v3/README.md index c916ce59ffedc12aa835f23a1f51995e7cb60939..fa534a781ff73eade80adc9a525b126db99834c6 100644 --- a/termigen_v3/README.md +++ b/termigen_v3/README.md @@ -3,13 +3,15 @@ ## Dataset stats - **v1 tasks:** 3,467 (pinned source revision `5f4179046b9f321b5d0f8b1c534eb6511299e39c`) -- **Kept:** 2,507 (2,277 from v2 plus 230 newly solved by Qwen/Qwen3.8-27B) +- **Kept in v3:** 230 newly solved tasks +- **Available separately in v2:** 2,277 +- **Combined v2 + v3:** 2,507 unique tasks - **Excluded:** 829 (evaluated, no success in v2 or in the new evaluation) - **Not evaluated:** 131 (outside the 3,336-task v2 evaluation) - **New evaluation:** 1,059 tasks unsolved in v2, 2 attempts each, 2,118 attempts - **New saved trajectories:** 2,118, successful and failed -`tasks.parquet` keeps every v2 task and its recorded result columns, and adds the newly solved tasks with their unchanged v1 payloads (the evaluated content hashes match). +`tasks.parquet` contains only newly solved tasks, preserving their evaluated payloads and recorded result columns. ## Evaluation settings @@ -43,7 +45,7 @@ These results cover only the new evaluation of the tasks v2 left unsolved; earli ## Select tasks by successful-attempt count -The Parquet preserves the v2 columns and adds `qwen38_attempts`, `qwen38_successes`, `qwen38_missing_attempts` and `qwen38_saved_traces`. For the 2,277 v2 tasks, `qwen38_attempts` is 0 and `qwen38_successes` is null. +The Parquet preserves the v2 columns and adds `qwen38_attempts`, `qwen38_successes`, `qwen38_missing_attempts` and `qwen38_saved_traces`. Tasks already in v2 are not duplicated in this file. ```python import pyarrow.parquet as pq @@ -56,7 +58,7 @@ subset = pq.read_table("tasks.parquet", filters=[("qwen38_successes", "=", 1)]) ## Traces -`traces/` preserves all 26,688 v2 attempts and adds the 2,118 new attempts (`qwen38-*.parquet`), successful and failed, in one schema. `evaluation_version` separates v2 from v3, and `retained_v2` and `retained_v3` record membership. Raw `trajectory` and `result` JSON are included. The 64 missing historical conversations remain identified in v2 data. +`traces/` holds all 2,118 new attempts, successful and failed. Historical attempts remain in v2, including the 64 missing historical conversations identified there. Raw `trajectory` and `result` JSON are included; `retained_v3` identifies the incremental task set. ```python import json @@ -82,4 +84,25 @@ All 2,118 new attempts have saved conversations. 382 have verifier reward 1, 1,7 - **Implementation:** [Marin Harbor `7faf878c14b6`](https://github.com/marin-community/harbor/tree/7faf878c14b6d72737579ec936ebf5f74ba3194d) with the recorded runtime patches. - **Resources:** one H200 and 32 CPUs per job, up to 4 concurrent trials per GPU, 8 sequence slots in the serving process. Task CPU, memory and timeout limits are unchanged. -`patch.py` rebuilds `tasks.parquet` from the pinned v1 and v2 Parquets and the published selection metadata. +`patch.py` rebuilds the incremental task file from the pinned pre-migration release and the published layout manifest. + +## Incremental release + +V3 contains only tasks newly solved after v2. It has no task-ID overlap with [v2](../termigen_v2/README.md). This is a harder subset relative to the earlier evaluation, not a claim of intrinsic difficulty. New-evaluation traces include both solved and unsolved tasks so the reported pass rates remain reproducible. + +```python +from huggingface_hub import hf_hub_download +import pyarrow as pa +import pyarrow.parquet as pq + +def load(version): + path = hf_hub_download("FWeindel/validated-tasks", + f"termigen_{version}/tasks.parquet", repo_type="dataset") + return pq.read_table(path) + +v2, v3 = load("v2"), load("v3") +assert set(v2["path"].to_pylist()).isdisjoint(v3["path"].to_pylist()) +combined = pa.concat_tables([v2, v3], promote_options="permissive") +``` + +The example combines task rows; each source file retains its own image manifest. Use those source manifests when staging images. [Layout and provenance](metadata/layout.json) records the pinned baseline and task counts. Earlier cumulative v3 releases remain accessible through their HF commit history. diff --git a/termigen_v3/metadata/history-deduplication.json b/termigen_v3/metadata/history-deduplication.json new file mode 100644 index 0000000000000000000000000000000000000000..58c7dc538bfd63a9d0c76b61fc9b69d9cfec7ebb --- /dev/null +++ b/termigen_v3/metadata/history-deduplication.json @@ -0,0 +1,154 @@ +{ + "verified_identical_attempts": 26688, + "deleted_duplicate_files": [ + "termigen_v3/traces/coder-001.parquet", + "termigen_v3/traces/coder-002.parquet", + "termigen_v3/traces/coder-003.parquet", + "termigen_v3/traces/coder-004.parquet", + "termigen_v3/traces/coder-005.parquet", + "termigen_v3/traces/coder-006.parquet", + "termigen_v3/traces/coder-007.parquet", + "termigen_v3/traces/coder-008.parquet", + "termigen_v3/traces/coder-009.parquet", + "termigen_v3/traces/coder-010.parquet", + "termigen_v3/traces/coder-011.parquet", + "termigen_v3/traces/coder-012.parquet", + "termigen_v3/traces/coder-013.parquet", + "termigen_v3/traces/coder-014.parquet", + "termigen_v3/traces/coder-015.parquet", + "termigen_v3/traces/coder-016.parquet", + "termigen_v3/traces/coder-017.parquet", + "termigen_v3/traces/coder-018.parquet", + "termigen_v3/traces/coder-019.parquet", + "termigen_v3/traces/coder-020.parquet", + "termigen_v3/traces/coder-021.parquet", + "termigen_v3/traces/coder-022.parquet", + "termigen_v3/traces/coder-023.parquet", + "termigen_v3/traces/coder-024.parquet", + "termigen_v3/traces/coder-025.parquet", + "termigen_v3/traces/coder-026.parquet", + "termigen_v3/traces/coder-027.parquet", + "termigen_v3/traces/coder-028.parquet", + "termigen_v3/traces/coder-029.parquet", + "termigen_v3/traces/coder-030.parquet", + "termigen_v3/traces/coder-031.parquet", + "termigen_v3/traces/coder-032.parquet", + "termigen_v3/traces/coder-033.parquet", + "termigen_v3/traces/coder-034.parquet", + "termigen_v3/traces/coder-035.parquet", + "termigen_v3/traces/coder-036.parquet", + "termigen_v3/traces/coder-037.parquet", + "termigen_v3/traces/coder-038.parquet", + "termigen_v3/traces/coder-039.parquet", + "termigen_v3/traces/coder-040.parquet", + "termigen_v3/traces/coder-041.parquet", + "termigen_v3/traces/coder-042.parquet", + "termigen_v3/traces/coder-043.parquet", + "termigen_v3/traces/coder-044.parquet", + "termigen_v3/traces/coder-045.parquet", + "termigen_v3/traces/coder-046.parquet", + "termigen_v3/traces/coder-047.parquet", + "termigen_v3/traces/coder-048.parquet", + "termigen_v3/traces/coder-049.parquet", + "termigen_v3/traces/coder-050.parquet", + "termigen_v3/traces/coder-051.parquet", + "termigen_v3/traces/coder-052.parquet", + "termigen_v3/traces/coder-053.parquet", + "termigen_v3/traces/instruct-001.parquet", + "termigen_v3/traces/instruct-002.parquet", + "termigen_v3/traces/instruct-003.parquet", + "termigen_v3/traces/instruct-004.parquet", + "termigen_v3/traces/instruct-005.parquet", + "termigen_v3/traces/instruct-006.parquet", + "termigen_v3/traces/instruct-007.parquet", + "termigen_v3/traces/instruct-008.parquet", + "termigen_v3/traces/instruct-009.parquet", + "termigen_v3/traces/instruct-010.parquet", + "termigen_v3/traces/instruct-011.parquet", + "termigen_v3/traces/instruct-012.parquet", + "termigen_v3/traces/instruct-013.parquet", + "termigen_v3/traces/instruct-014.parquet", + "termigen_v3/traces/instruct-015.parquet", + "termigen_v3/traces/instruct-016.parquet", + "termigen_v3/traces/instruct-017.parquet" + ], + "baseline_files": [ + "termigen_v2/traces/coder-001.parquet", + "termigen_v2/traces/coder-002.parquet", + "termigen_v2/traces/coder-003.parquet", + "termigen_v2/traces/coder-004.parquet", + "termigen_v2/traces/coder-005.parquet", + "termigen_v2/traces/coder-006.parquet", + "termigen_v2/traces/coder-007.parquet", + "termigen_v2/traces/coder-008.parquet", + "termigen_v2/traces/coder-009.parquet", + "termigen_v2/traces/coder-010.parquet", + "termigen_v2/traces/coder-011.parquet", + "termigen_v2/traces/coder-012.parquet", + "termigen_v2/traces/coder-013.parquet", + "termigen_v2/traces/coder-014.parquet", + "termigen_v2/traces/coder-015.parquet", + "termigen_v2/traces/coder-016.parquet", + "termigen_v2/traces/coder-017.parquet", + "termigen_v2/traces/coder-018.parquet", + "termigen_v2/traces/coder-019.parquet", + "termigen_v2/traces/coder-020.parquet", + "termigen_v2/traces/coder-021.parquet", + "termigen_v2/traces/coder-022.parquet", + "termigen_v2/traces/coder-023.parquet", + "termigen_v2/traces/coder-024.parquet", + "termigen_v2/traces/coder-025.parquet", + "termigen_v2/traces/coder-026.parquet", + "termigen_v2/traces/coder-027.parquet", + "termigen_v2/traces/coder-028.parquet", + "termigen_v2/traces/coder-029.parquet", + "termigen_v2/traces/coder-030.parquet", + "termigen_v2/traces/coder-031.parquet", + "termigen_v2/traces/coder-032.parquet", + "termigen_v2/traces/coder-033.parquet", + "termigen_v2/traces/coder-034.parquet", + "termigen_v2/traces/coder-035.parquet", + "termigen_v2/traces/coder-036.parquet", + "termigen_v2/traces/coder-037.parquet", + "termigen_v2/traces/coder-038.parquet", + "termigen_v2/traces/coder-039.parquet", + "termigen_v2/traces/coder-040.parquet", + "termigen_v2/traces/coder-041.parquet", + "termigen_v2/traces/coder-042.parquet", + "termigen_v2/traces/coder-043.parquet", + "termigen_v2/traces/coder-044.parquet", + "termigen_v2/traces/coder-045.parquet", + "termigen_v2/traces/coder-046.parquet", + "termigen_v2/traces/coder-047.parquet", + "termigen_v2/traces/coder-048.parquet", + "termigen_v2/traces/coder-049.parquet", + "termigen_v2/traces/coder-050.parquet", + "termigen_v2/traces/coder-051.parquet", + "termigen_v2/traces/coder-052.parquet", + "termigen_v2/traces/coder-053.parquet", + "termigen_v2/traces/instruct-001.parquet", + "termigen_v2/traces/instruct-002.parquet", + "termigen_v2/traces/instruct-003.parquet", + "termigen_v2/traces/instruct-004.parquet", + "termigen_v2/traces/instruct-005.parquet", + "termigen_v2/traces/instruct-006.parquet", + "termigen_v2/traces/instruct-007.parquet", + "termigen_v2/traces/instruct-008.parquet", + "termigen_v2/traces/instruct-009.parquet", + "termigen_v2/traces/instruct-010.parquet", + "termigen_v2/traces/instruct-011.parquet", + "termigen_v2/traces/instruct-012.parquet", + "termigen_v2/traces/instruct-013.parquet", + "termigen_v2/traces/instruct-014.parquet", + "termigen_v2/traces/instruct-015.parquet", + "termigen_v2/traces/instruct-016.parquet", + "termigen_v2/traces/instruct-017.parquet" + ], + "revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "comparison": [ + "task_id", + "attempt_id", + "trajectory", + "result" + ] +} \ No newline at end of file diff --git a/termigen_v3/metadata/layout.json b/termigen_v3/metadata/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..8a67d6478b6706111a0cb490d4e4f8482c28c0a6 --- /dev/null +++ b/termigen_v3/metadata/layout.json @@ -0,0 +1,244 @@ +{ + "layout": "incremental", + "kept": 230, + "baseline_kept": 2277, + "combined_kept": 2507, + "baseline_folder": "termigen_v2", + "baseline_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "carried_from_v2": 0, + "previous_cumulative_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "previous_tasks_sha256": "8fcf07b9b2c981a4d94f4aa12e85963058bdbfb90a2a79a3f0a23ccad32657be", + "task_ids": [ + "active_directory_dcsync_attack_medium", + "airflow_dag_trigger_hard", + "alembic_migration_conflict_medium", + "alt_ergo_polymorphic_types_hard", + "angr_symbolic_execution_analysis_hard", + "angr_symbolic_execution_analysis_medium", + "apache_combined_log_parsing_hard", + "api_key_rotation_strategy_hard", + "arena_allocator_implementation_medium", + "ase_atomic_simulation_constraints_medium", + "attention_mechanism_custom_medium", + "autogluon_tabular_medium", + "avx2_optimization_intel_medium", + "azure_cli_login_device_code_hard", + "azure_cli_login_device_code_medium", + "backpressure_reactive_streams_hard", + "bash_exit_code_pipe_failure_medium", + "bazel_external_dependency_fetch_medium", + "bigquery_nested_repeated_fields_hard", + "boolean_satisfiability_dpll_hard", + "boost_library_version_mismatch_medium", + "buildroot_minimal_linux_system_hard", + "burp_suite_interceptor_hard", + "burp_suite_web_vulnerability_scanning_medium", + "cbc_bit_flipping_attack_medium", + "circos_karyotype_configuration_hard", + "circuit_breaker_hystrix_hard", + "clickhouse_materialized_view_medium", + "closure_compiler_advanced_mode_hard", + "closure_compiler_advanced_mode_medium", + "cloudwatch_logs_insights_query_hard", + "cmake_cuda_toolkit_not_found_hard_1", + "cmake_export_compile_commands_hard", + "consensus_algorithm_raft_hard", + "count_min_sketch_frequency_medium", + "cpachecker_cpa_composition_hard", + "cromwell_wdl_workflow_hard", + "cuda_nvtx_range_profiling_hard", + "cuda_stream_capture_graph_update_hard", + "cuda_thrust_reduce_performance_hard", + "curl_ssl_certificate_problem_hard", + "curl_ssl_certificate_problem_medium", + "curriculum_learning_strategy_hard", + "cvc4_unsat_core_extraction_medium", + "cyclegan_implementation_hard", + "cython_cpp_mode_compilation_hard", + "dafny_verification_timeout_medium", + "dask_custom_serialization_protocol_hard", + "dask_kubernetes_operator_deployment_medium", + "delta_lake_time_travel_medium", + "deserialization_rce_java_medium", + "directx_enhanced_barriers_api_hard", + "discovered_attack_detection_hard", + "dkms_dynamic_kernel_modules_hard", + "docker_daemon_json_invalid_syntax_hard", + "docker_multistage_copy_not_found_hard", + "dvc_large_file_tracking_medium", + "file_magic_database_outdated_medium", + "fluentd_buffer_overflow_dropped_medium", + "flyway_migration_versioning_medium", + "focal_loss_imbalance_medium", + "frozen_layers_training_hard", + "full_text_search_index_hard", + "fuzzing_libfuzzer_integration_hard", + "gaussian_convergence_scf_hard", + "gcc_cannot_find_library_hard", + "gcc_function_sections_data_hard", + "gcc_multiple_definition_error_hard", + "gcc_undefined_reference_linking_hard", + "gcc_undefined_reference_linking_medium", + "git_merge_conflict_binary_hard", + "git_reflog_expire_lost_commits_hard", + "git_submodule_sync_mismatch_hard", + "gitlab_ci_cache_optimization_hard", + "go_binary_reverse_engineering_medium", + "go_cgo_enabled_disabled_hard", + "go_module_replace_directive_hard", + "gradle_build_cache_configuration_medium", + "graph_compression_webgraph_medium", + "graphql_introspection_query_medium_1", + "grpc_reflection_enumeration_hard", + "gtp_protocol_go_engine_medium", + "haproxy_backend_servers_down_hard", + "haproxy_http_request_smuggling_medium", + "heavy_hitters_frequent_items_medium", + "host_header_injection_attack_medium", + "interactive_sql_repl_medium", + "intrinsics_simd_vectorization_hard", + "ipsec_strongswan_tunnel_establishment_hard", + "isabelle_sledgehammer_timeout_medium", + "istio_sidecar_injection_not_working_hard", + "javac_annotation_processor_classpath_hard", + "jmeter_load_testing_medium", + "join_unsorted_input_error_hard", + "jpa_n_plus_one_query_medium", + "jwt_asymmetric_key_validation_hard", + "jwt_kid_injection_attack_hard", + "jwt_token_expiration_refresh_hard", + "k6_load_test_thresholds_hard", + "kafka_disk_full_log_retention_hard", + "kafka_partition_leader_election_medium", + "kerberos_ticket_renewal_automation_hard", + "knime_workflow_execution_hard", + "kubectl_configmap_mount_permission_medium", + "kustomize_overlay_merge_conflict_hard", + "laravel_eloquent_mass_assignment_medium", + "ld_library_path_runtime_error_hard", + "ld_library_path_runtime_error_medium", + "ld_library_path_runtime_missing_medium", + "ldd_shared_library_dependencies_medium", + "lightgbm_dataset_training_hard", + "lora_low_rank_adaptation_medium", + "m4_macro_expansion_error_medium", + "makefile_circular_dependency_medium", + "mask_attack_hashcat_patterns_medium", + "masked_language_modeling_medium", + "memcached_connection_pool_exhausted_medium", + "memory_mapped_files_database_hard", + "metal_function_specialization_constants_hard", + "mold_linker_setup_hard", + "multiprocessing_pool_maxtasksperchild_hard", + "nats_streaming_queue_group_medium", + "nginx_rate_limiting_burst_hard", + "nginx_upstream_timed_out_hard", + "ninja_build_parallel_failure_hard", + "numpy_structured_array_conversion_hard", + "online_learning_sgd_hard", + "openapi_spec_validation_error_medium", + "opengl_compute_shader_barriers_hard", + "opengl_compute_shader_barriers_medium", + "opening_database_compression_hard", + "openmp_simd_vectorization_failure_hard", + "oracle_clob_extraction_hard", + "packer_provisioner_script_failure_medium", + "parallel_search_threads_medium", + "parquet_schema_evolution_medium", + "paste_line_count_mismatch_medium", + "patchelf_interpreter_change_hard", + "patchelf_interpreter_change_medium", + "pca_dimensionality_reduction_hard", + "pdb_python_stepping_hard", + "pe_binary_malware_analysis_medium", + "pgo_profile_guided_optimization_hard", + "php_include_lfi_rce_hard", + "phylip_tree_format_conversion_hard", + "pihole_ad_blocking_dns_server_medium", + "pin_detection_tactics_hard", + "pin_detection_tactics_medium", + "pin_instrumentation_tool_hard", + "pip_dependency_conflict_resolution_hard", + "pip_ssl_certificate_verify_failed_hard", + "pipe_broken_sigpipe_handling_hard", + "pkg_config_path_not_found_hard", + "proguard_obfuscation_configuration_medium", + "protobuf_backward_compatibility_medium", + "protobuf_message_manipulation_hard", + "proxmox_cluster_corosync_timeout_hard", + "pytest_fixture_scope_issue_hard", + "python3_modulenotfound_path_hard", + "python3_modulenotfound_path_medium", + "pytorch_jit_trace_hard", + "pytorch_jit_trace_medium", + "rabbitmq_memory_alarm_triggered_hard", + "race_condition_exploitation_hard", + "radare2_binary_analysis_hard", + "rails_activerecord_n_plus_one_medium", + "rails_mass_assignment_exploitation_hard", + "ray_actor_pool_concurrency_hard", + "ray_autoscaler_aws_spot_instances_hard", + "ray_workflow_durable_execution_medium", + "recursive_make_considered_harmful_medium", + "redux_immutable_state_update_medium", + "restic_prune_low_disk_space_hard", + "rpath_runpath_configuration_hard", + "rsyslog_remote_logging_tls_medium", + "rust_proc_macro_compilation_hard", + "rust_proc_macro_compilation_medium", + "rxjs_memory_leak_subscription_medium", + "saga_pattern_microservices_hard", + "samba_share_active_directory_integration_medium", + "saml_assertion_signature_verification_medium", + "selenium_stale_element_reference_hard", + "sentry_error_grouping_fingerprint_medium", + "server_side_template_injection_hard", + "side_channel_timing_attack_medium", + "sigma_siem_rule_creation_hard", + "snowflake_warehouse_query_medium", + "spark_bloom_filter_join_hard", + "spark_broadcast_join_optimization_medium", + "spark_cache_invalidation_refresh_medium", + "spark_dynamic_partition_overwrite_medium", + "spark_hive_metastore_partition_pruning_hard", + "spark_map_side_aggregation_hard", + "spark_rdd_checkpoint_lineage_medium", + "spark_session_catalog_registration_medium", + "sparse_matrix_storage_hard", + "sqlite_database_forensics_medium", + "sqlmap_injection_exploit_hard", + "ssh_host_key_verification_failed_hard", + "ssh_key_permissions_too_open_hard", + "stack_protector_canary_flags_medium", + "strace_system_call_trace_hard", + "stream_processing_windowing_medium", + "symbol_versioning_glibc_hard", + "tar_unexpected_end_of_archive_hard", + "tcpdump_permission_denied_interface_hard", + "terraform_module_source_version_conflict_medium", + "territory_estimation_go_hard", + "time_series_data_partitioning_medium", + "time_series_forecasting_lstm_hard", + "timescaledb_hypertable_partitioning_hard", + "tmb_template_model_builder_hard", + "tortoise_orm_async_transactions_medium", + "type_confusion_php_vulnerability_hard", + "type_confusion_php_vulnerability_medium", + "unzip_encrypted_password_unknown_medium", + "variable_length_arrays_vla_medium", + "vault_secret_rotation_downtime_medium", + "visibility_hidden_default_medium", + "vmd_trajectory_wrapping_hard", + "wait_non_child_process_hard", + "wasserstein_gan_training_medium", + "websocket_reconnection_exponential_backoff_hard", + "websocket_reconnection_exponential_backoff_medium", + "wireshark_packet_capture_analysis_hard", + "xargs_echo_empty_input_medium", + "xgboost_hyperparameter_tuning_hard", + "xmllint_schema_validation_namespace_medium", + "yq_yaml_anchor_merge_conflict_hard", + "zugzwang_position_detection_hard" + ], + "tasks_sha256": "6f791c1755f2d985a2c5ee091e0489aa2815093ef1590814a4d2804f580ea211" +} \ No newline at end of file diff --git a/termigen_v3/metadata/previous-cumulative-patch.py b/termigen_v3/metadata/previous-cumulative-patch.py new file mode 100644 index 0000000000000000000000000000000000000000..7803574f95cad5947c26f6644204271eeaa97abd --- /dev/null +++ b/termigen_v3/metadata/previous-cumulative-patch.py @@ -0,0 +1,28 @@ +"""Rebuild cumulative TermiGen v3 tasks from pinned v1/v2 inputs and local selection metadata. +Usage: python patch.py --out rebuilt.parquet +Requires pyarrow and huggingface_hub. Does not rerun validation or teacher generation. +""" +import argparse,hashlib,json +from pathlib import Path +import pyarrow as pa,pyarrow.parquet as pq +from huggingface_hub import hf_hub_download + +def build(selection, source, old): + current={r['path']:r for r in source.to_pylist()};prior={r['path']:r for r in old.to_pylist()};rows=[] + for tid,e in sorted(selection['tasks'].items()): + if not e['kept']:continue + if tid in prior:r=dict(prior[tid]) + else: + r=dict(current[tid]);r.update({k:e['v2_results'].get(k) for k in ['coder_solved','instruct_successes','instruct_missing_rewards']});r['attempts_per_model']=4 + assert r['content_sha256']==e['content_sha256'] + r.update({k:e[k] for k in ['qwen38_attempts','qwen38_successes','qwen38_missing_attempts','qwen38_saved_traces']});rows.append(r) + schema=pa.schema([*old.schema,*(pa.field(k,pa.int64()) for k in ['qwen38_attempts','qwen38_successes','qwen38_missing_attempts','qwen38_saved_traces'])]);meta=dict(old.schema.metadata or {}) + images=json.loads(source.schema.metadata[b'ot.images.v1']);kept={r['path'] for r in rows};images['tasks']={t:e for t,e in images['tasks'].items() if t in kept};used={e['bundle'] for t in images['tasks'].values() for e in t['environments']};images['bundles']={k:v for k,v in images['bundles'].items() if k in used};meta[b'ot.images.v1']=json.dumps(images).encode() + meta[b'ot.release.v3']=json.dumps({'partial':selection['summary']['partial'],'baseline_revision':selection['baseline_revision'],'source_revision':selection['source_revision'],'kept':len(rows),'new_model':'Qwen/Qwen3.8-27B','reward_policy':'terminal-errors-zero-v2'}).encode() + return pa.Table.from_pylist(rows,schema=schema.with_metadata(meta)) + +if __name__=='__main__': + ap=argparse.ArgumentParser();ap.add_argument('--out',type=Path,required=True);a=ap.parse_args();root=Path(__file__).parent;s=json.loads((root/'metadata/selection.json').read_text());inputs=json.loads((root/'metadata/inputs.json').read_text());tables={} + for key,record in inputs.items(): + path=Path(hf_hub_download(s['repo_id'],record['file'],repo_type='dataset',revision=record['revision']));assert hashlib.sha256(path.read_bytes()).hexdigest()==record['sha256'];tables[key]=pq.read_table(path) + pq.write_table(build(s,tables['source'],tables['v2']),a.out,compression='zstd') diff --git a/termigen_v3/metadata/release-verification.json b/termigen_v3/metadata/release-verification.json new file mode 100644 index 0000000000000000000000000000000000000000..1703379e83f8a44dac306a37d52f3dfeaa0cff05 --- /dev/null +++ b/termigen_v3/metadata/release-verification.json @@ -0,0 +1,15 @@ +{ + "layout": "incremental", + "kept": 230, + "baseline_kept": 2277, + "combined_kept": 2507, + "baseline_folder": "termigen_v2", + "baseline_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "carried_from_v2": 0, + "new_attempts": 2118, + "tasks_sha256": "6f791c1755f2d985a2c5ee091e0489aa2815093ef1590814a4d2804f580ea211", + "task_ids_disjoint_from_v2": true, + "original_task_rows_preserved": true, + "historical_trace_records_verified_in_v2": 26688, + "new_evaluation_unchanged": true +} \ No newline at end of file diff --git a/termigen_v3/metadata/results.json b/termigen_v3/metadata/results.json index cc07e04a30a77007e1ebb15fcfba5eb0a10660f3..a16641f3991644e9a54101836086ca936032df58 100644 --- a/termigen_v3/metadata/results.json +++ b/termigen_v3/metadata/results.json @@ -9,7 +9,7 @@ "solved_tasks": 230, "v2_kept": 2277, "newly_solved": 230, - "kept": 2507, + "kept": 230, "source_tasks": 3467, "completed_unsolved": 829, "pending_tasks": 0, @@ -25,5 +25,11 @@ "retry_job": "1096631", "retry_tasks_completed": 47, "retry_attempts_completed": 94, - "retry_newly_solved": 14 + "retry_newly_solved": 14, + "layout": "incremental", + "baseline_kept": 2277, + "combined_kept": 2507, + "baseline_folder": "termigen_v2", + "baseline_revision": "a8acfde13cc1be1672da3d17ee70bc4d842b4c19", + "carried_from_v2": 0 } \ No newline at end of file diff --git a/termigen_v3/metadata/selection.json b/termigen_v3/metadata/selection.json index 3798441c788a46b3369e407495016fb8128b8aa8..917e548b357bb1e68215af8aae80fe4a0600b61b 100644 --- a/termigen_v3/metadata/selection.json +++ b/termigen_v3/metadata/selection.json @@ -1 +1,104304 @@ 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"cuda_thrust_reduce_performance_hard", + "attempt_id": "1df084e6-ee24-4f25-822c-291936fe8795", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "cuda_thrust_reduce_performance_hard", + "attempt_id": "0c8ebaf0-944b-4492-b11b-1cd00c7115f0", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "fanout_exchange_rabbitmq_medium", + "attempt_id": "d2817c1d-8fa3-4542-8b38-f80c079ca2bb", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "fanout_exchange_rabbitmq_medium", + "attempt_id": "f694ff55-3af3-43d0-9c1b-509521d45909", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "glmmtmb_zero_inflation_hard", + "attempt_id": "81fd4c74-4ee6-4090-9332-f66e08fdea72", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "glmmtmb_zero_inflation_hard", + "attempt_id": "805af966-d179-4a66-99e0-3ca33e9bb261", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "jwt_token_expiration_refresh_hard", + "attempt_id": "b792dd10-31db-4078-a8c1-4604f60ad772", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "jwt_token_expiration_refresh_hard", + "attempt_id": "9b1273e5-d646-4de7-a397-422d3166958e", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "massif_heap_profiling_hard", + "attempt_id": "1ce4b869-7622-4349-a1f5-edf542aa77ad", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "massif_heap_profiling_hard", + "attempt_id": "5f2c94eb-2ecb-4fee-8f9a-45b71f03f32c", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "od_interpreting_null_bytes_hard", + "attempt_id": "822c1fda-66c6-4479-a447-44680d9178fb", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": 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"136e7343-e2cf-4619-965e-da2c7870545a", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "slurm_gang_scheduling_timeslice_medium", + "attempt_id": "d221e935-7b4d-4909-b622-60acac08d85f", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "terraform_state_secret_extraction_medium", + "attempt_id": "dc4cbdc6-ad4c-40d8-847f-20e4a6d129ca", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "terraform_state_secret_extraction_medium", + "attempt_id": "9f88e864-a11e-4f49-aa27-b63730b957d6", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "zugzwang_position_detection_hard", + "attempt_id": "28200e9a-d7d2-423d-a1f1-474bcd2d9d27", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "zugzwang_position_detection_hard", + "attempt_id": "7fcccfbd-33bc-40b2-a037-a315199e7d6e", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "bismark_methylation_extraction_hard", + "attempt_id": "32176197-4c4b-488d-9e78-3eba9dfaff82", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "bismark_methylation_extraction_hard", + "attempt_id": "b1eb41aa-bb1f-4b1c-b7fd-fb85e07a058d", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "cuda_wmma_tensor_cores_medium", + "attempt_id": "d6b64523-19f9-4e54-b71a-926e42bf0e10", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "cuda_wmma_tensor_cores_medium", + "attempt_id": "55a49b73-0444-447d-8be3-8bc2e7e8cb4c", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "fastq_sequence_quality_filtering_hard", + "attempt_id": "2dd1296d-a6ab-4f5d-a17f-62a85e2b41c0", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "fastq_sequence_quality_filtering_hard", + "attempt_id": "594afad4-6c1c-438c-9143-564375b172de", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "go_binary_reverse_engineering_medium", + "attempt_id": "27f7cb12-cfd1-44f5-9931-e55c7a486e98", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "go_binary_reverse_engineering_medium", + "attempt_id": "993cb66e-ea3e-4320-90a4-6467c71d7d31", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "jwt_token_validation_hard", + "attempt_id": "15c92b13-f7db-4606-9264-f5b5bb717442", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "jwt_token_validation_hard", + "attempt_id": "1beb7c80-25b1-41b9-b37d-e120fe385ab7", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "materialized_view_refresh_medium", + "attempt_id": "cace9dda-0e2f-484b-801c-a28e32ca5aeb", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "materialized_view_refresh_medium", + "attempt_id": "c7c67830-987c-4962-af83-ed8d0fd2e6cf", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "online_learning_sgd_hard", + "attempt_id": "28124a9b-ed54-4df1-9134-d8489d5fc5c4", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "online_learning_sgd_hard", + "attempt_id": "a5632fec-e3bd-42be-a753-825e919e1dfb", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "podman_rootless_permission_denied_medium", + "attempt_id": "79f4c592-e789-41ed-a26d-6d1b83b9360f", + "trajectory_found": true, + "reward": 0, + "verifier_reward": null + }, + { + "task": "podman_rootless_permission_denied_medium", + "attempt_id": "fd692174-1dc1-480f-a41b-b1e5e441e17b", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "ray_autoscaler_aws_spot_instances_hard", + "attempt_id": "3a8284ef-b7b8-4dba-adb1-c5db65cc2eea", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "ray_autoscaler_aws_spot_instances_hard", + "attempt_id": "8f2548f6-26bf-4650-8fdc-d646f636b5f0", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "slurm_job_allocation_timeout_medium", + "attempt_id": "b0b61c04-b1cb-4194-ae31-964ee688eef3", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "slurm_job_allocation_timeout_medium", + "attempt_id": "8dc9e355-ff87-4c51-934d-13eedbc8cc59", + "trajectory_found": true, + "reward": 0.0, + "verifier_reward": 0.0 + }, + { + "task": "territory_estimation_go_hard", + "attempt_id": "cbed5653-c561-4f57-9553-fd314f9c0b23", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 + }, + { + "task": "territory_estimation_go_hard", + "attempt_id": "b8148ba9-981c-4783-8a87-6b149168cf5f", + "trajectory_found": true, + "reward": 1.0, + "verifier_reward": 1.0 } + ], + "completed_retry": { + "job": "1096631", + "tasks": { + "bigquery_nested_repeated_fields_hard": 2, + "bipartite_matching_hungarian_hard": 2, + "bishop_knight_checkmate_hard": 2, + "bismark_methylation_extraction_hard": 2, + "cuda_peer_to_peer_access_hard": 2, + "cuda_stream_capture_graph_update_hard": 2, + "cuda_thrust_reduce_performance_hard": 2, + "cuda_wmma_tensor_cores_medium": 2, + "fairness_constraint_training_hard": 2, + "false_sharing_prevention_hard": 2, + "fanout_exchange_rabbitmq_medium": 2, + "fastq_sequence_quality_filtering_hard": 2, + "glibc_version_compatibility_medium": 2, + "glmer_convergence_warnings_medium": 2, + "glmmtmb_zero_inflation_hard": 2, + "go_binary_reverse_engineering_medium": 2, + "jwt_asymmetric_key_validation_hard": 2, + "jwt_kid_injection_attack_hard": 2, + "jwt_token_expiration_refresh_hard": 2, + "jwt_token_validation_hard": 2, + "masked_language_modeling_hard": 2, + "masked_language_modeling_medium": 2, + "massif_heap_profiling_hard": 2, + "materialized_view_refresh_medium": 2, + "objdump_disassembly_missing_symbols_hard": 2, + "od_endianness_byte_order_medium": 2, + "od_interpreting_null_bytes_hard": 2, + "online_learning_sgd_hard": 2, + "pkill_pattern_matching_multiple_hard": 2, + "pkill_pattern_matching_multiple_medium": 2, + "plink_ld_pruning_parameters_medium": 2, + "podman_rootless_permission_denied_medium": 2, + "ray_actor_pool_concurrency_hard": 2, + "ray_air_tuner_hyperparameter_hard": 2, + "ray_air_tuner_hyperparameter_medium": 2, + "ray_autoscaler_aws_spot_instances_hard": 2, + "slurm_accounting_tres_limits_hard": 2, + "slurm_checkpoint_blcr_integration_medium": 2, + "slurm_gang_scheduling_timeslice_medium": 2, + "slurm_job_allocation_timeout_medium": 2, + "terraform_module_source_version_conflict_medium": 2, + "terraform_provider_version_constraint_medium": 2, + "terraform_state_secret_extraction_medium": 2, + "territory_estimation_go_hard": 2, + "zookeeper_leader_election_failure_hard": 2, + "zookeeper_watcher_node_medium": 2, + "zugzwang_position_detection_hard": 2 + }, + "note": "All 94 previously missing attempts recorded; pending list cleared." + }, + "publication_layout_note": "Historical evaluation provenance; current incremental publication membership and file hashes are in metadata/layout.json and traces/manifest.json." } \ No newline at end of file diff --git a/termigen_v3/patch.py b/termigen_v3/patch.py index 7803574f95cad5947c26f6644204271eeaa97abd..fe97999730d50d570f16968869f97f8a5bf4f780 100644 --- a/termigen_v3/patch.py +++ b/termigen_v3/patch.py @@ -1,28 +1,22 @@ -"""Rebuild cumulative TermiGen v3 tasks from pinned v1/v2 inputs and local selection metadata. -Usage: python patch.py --out rebuilt.parquet -Requires pyarrow and huggingface_hub. Does not rerun validation or teacher generation. +"""Reproduce the incremental v3 task set from the pinned pre-migration release. +Requires pyarrow and huggingface_hub. Does not rerun evaluation. """ import argparse,hashlib,json from pathlib import Path import pyarrow as pa,pyarrow.parquet as pq from huggingface_hub import hf_hub_download -def build(selection, source, old): - current={r['path']:r for r in source.to_pylist()};prior={r['path']:r for r in old.to_pylist()};rows=[] - for tid,e in sorted(selection['tasks'].items()): - if not e['kept']:continue - if tid in prior:r=dict(prior[tid]) - else: - r=dict(current[tid]);r.update({k:e['v2_results'].get(k) for k in ['coder_solved','instruct_successes','instruct_missing_rewards']});r['attempts_per_model']=4 - assert r['content_sha256']==e['content_sha256'] - r.update({k:e[k] for k in ['qwen38_attempts','qwen38_successes','qwen38_missing_attempts','qwen38_saved_traces']});rows.append(r) - schema=pa.schema([*old.schema,*(pa.field(k,pa.int64()) for k in ['qwen38_attempts','qwen38_successes','qwen38_missing_attempts','qwen38_saved_traces'])]);meta=dict(old.schema.metadata or {}) - images=json.loads(source.schema.metadata[b'ot.images.v1']);kept={r['path'] for r in rows};images['tasks']={t:e for t,e in images['tasks'].items() if t in kept};used={e['bundle'] for t in images['tasks'].values() for e in t['environments']};images['bundles']={k:v for k,v in images['bundles'].items() if k in used};meta[b'ot.images.v1']=json.dumps(images).encode() - meta[b'ot.release.v3']=json.dumps({'partial':selection['summary']['partial'],'baseline_revision':selection['baseline_revision'],'source_revision':selection['source_revision'],'kept':len(rows),'new_model':'Qwen/Qwen3.8-27B','reward_policy':'terminal-errors-zero-v2'}).encode() - return pa.Table.from_pylist(rows,schema=schema.with_metadata(meta)) - +def build(out): + root=Path(__file__).parent;m=json.loads((root/'metadata/layout.json').read_text()) + p=hf_hub_download('FWeindel/validated-tasks','termigen_v3/tasks.parquet',repo_type='dataset',revision=m['previous_cumulative_revision']) + assert hashlib.sha256(Path(p).read_bytes()).hexdigest()==m['previous_tasks_sha256'] + t=pq.read_table(p);ids=set(m['task_ids']);t=t.filter(pa.array([x in ids for x in t['path'].to_pylist()]));assert set(t['path'].to_pylist())==ids + meta=dict(t.schema.metadata or {}) + if b'ot.images.v1' in meta: + im=json.loads(meta[b'ot.images.v1']);im['tasks']={k:v for k,v in im['tasks'].items() if k in ids};used={e['bundle'] for v in im['tasks'].values() for e in v['environments']};im['bundles']={k:v for k,v in im['bundles'].items() if k in used};im['coverage']={'cached_tasks':len(im['tasks']),'total_tasks':len(ids)} + if 'uncached_task_ids' in im:im['uncached_task_ids']=sorted(ids-set(im['tasks'])) + meta[b'ot.images.v1']=json.dumps(im).encode() + release=json.loads(meta.get(b'ot.release.v3',b'{}'));release.update({k:m[k] for k in ['layout','kept','baseline_kept','combined_kept','baseline_folder','baseline_revision','carried_from_v2']});meta[b'ot.release.v3']=json.dumps(release).encode() + pq.write_table(t.replace_schema_metadata(meta),out,compression='zstd') if __name__=='__main__': - ap=argparse.ArgumentParser();ap.add_argument('--out',type=Path,required=True);a=ap.parse_args();root=Path(__file__).parent;s=json.loads((root/'metadata/selection.json').read_text());inputs=json.loads((root/'metadata/inputs.json').read_text());tables={} - for key,record in inputs.items(): - path=Path(hf_hub_download(s['repo_id'],record['file'],repo_type='dataset',revision=record['revision']));assert hashlib.sha256(path.read_bytes()).hexdigest()==record['sha256'];tables[key]=pq.read_table(path) - pq.write_table(build(s,tables['source'],tables['v2']),a.out,compression='zstd') + p=argparse.ArgumentParser();p.add_argument('--out',type=Path,required=True);a=p.parse_args();build(a.out) diff --git a/termigen_v3/tasks.parquet b/termigen_v3/tasks.parquet index c49c8ff189439a867d54b4b2ee9dd52567d01a99..2f7f71682c4c342a837c385d67db40facef50ed7 100644 --- a/termigen_v3/tasks.parquet +++ b/termigen_v3/tasks.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8fcf07b9b2c981a4d94f4aa12e85963058bdbfb90a2a79a3f0a23ccad32657be -size 210819180 +oid sha256:6f791c1755f2d985a2c5ee091e0489aa2815093ef1590814a4d2804f580ea211 +size 87959280 diff --git a/termigen_v3/trace_stats/README.md b/termigen_v3/trace_stats/README.md index 55642114269c4ef7c82b598471cab14d71fbb19f..acf33aaf5ece5b569be538f83ed080d7aac6479e 100644 --- a/termigen_v3/trace_stats/README.md +++ b/termigen_v3/trace_stats/README.md @@ -47,3 +47,5 @@ Configured limits are not measurements of token usage. A missing explicit respon **Historical output-limit bug:** the intended per-response limit was stored as top-level `max_tokens`, but the affected Terminus path did not forward it as `llm_call_kwargs.max_tokens`. Generation instead depended on the backend's fallback allowance, available context, stopping conditions and agent timeout. The exact fallback limit is not established by the saved configurations; it must not be reported as 8,192 enforced tokens, or as unlimited. The current forwarding fix does not retroactively change these results. Agent and verifier time limits are task-specific; see [settings and evidence](../metadata/evaluation-settings-audit.json). + +This folder reports the new evaluation only. Task membership is incremental: tasks already in v2 are not duplicated in v3. Historical conversations remain in v2. See the [release overview](../README.md) for counts and combination instructions. diff --git a/termigen_v3/trace_stats/data/provenance.json b/termigen_v3/trace_stats/data/provenance.json index dc38793226cfed6cb1b7f079b4069cc879c04232..f44fefc992a32a34712f8d2a7a3b6677e8b95cdd 100644 --- a/termigen_v3/trace_stats/data/provenance.json +++ b/termigen_v3/trace_stats/data/provenance.json @@ -9,535 +9,535 @@ "file": "traces/qwen38-retry-0000-stage-6-fc0262c7d05d.parquet", "rows": 24, "unique_rows_added": 24, - "sha256": "f7074f681340169264bee989994421a2a19f4af741d4c29c519cce6e2eeceb7d" + "sha256": "9a9517c67237ad3dd2cfa2f05f0b27beb05fb9e5de8e94e3072181cec3cc7e54" }, { "file": "traces/qwen38-retry-0001-stage-6-e4262484f8e0.parquet", "rows": 24, "unique_rows_added": 24, - "sha256": "aa9d40a081e58262c11db89c0fdb09c998c81763530c508a96958b210e6a6033" + "sha256": "e57eece854a42a189e171b9fe6e9b6b5d9b707ed9f5e6f4a125c9851619f26fd" }, { "file": "traces/qwen38-retry-0002-stage-6-408208c903cf.parquet", 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