| --- |
| {"pretty_name":"Forge Agentic Workflow Evaluation Corpus","license":"mit","tags":["agent-evaluation","tool-use","function-calling","local-inference","benchmark","tabular"],"configs":[{"config_name":"latest","data_files":[{"split":"train","path":"data/latest/*.parquet"}],"default":true},{"config_name":"snapshot","data_files":[{"split":"train","path":"data/snapshot/*.parquet"}]},{"config_name":"history","data_files":[{"split":"train","path":"data/history/*.parquet"}]}]} |
| --- |
| |
| # Forge Agentic Workflow Evaluation Corpus |
|
|
| Forge evaluates multi-step agent workflows across models, quantizations, |
| inference backends, function-calling modes, scenarios, guardrail ablations, and |
| reasoning-replay policies. This dataset publishes the released run-level outcome |
| records behind Forge's reports and dashboard. One row represents one attempted |
| evaluation run. |
|
|
| This is an **outcome corpus**, not a collection of complete agent traces. It does |
| not contain full prompts, conversations, tool-result transcripts, or hidden model |
| reasoning and cannot reconstruct an end-to-end trajectory. |
|
|
| ## Configurations and counts |
|
|
| | Configuration | Attempted | Correct | Validated | Completed | Score | Validated accuracy | Completion rate | |
| |---|---:|---:|---:|---:|---:|---:|---:| |
| | `latest` (default) | 260,000 | 175,267 | 228,420 | 228,420 | 67.41% | 76.73% | 87.85% | |
| | `snapshot` | 378,300 | 226,420 | 313,105 | 313,105 | 59.85% | 72.31% | 82.77% | |
| | `history` | 518,700 | 315,367 | 429,648 | 429,648 | 60.80% | 73.40% | 82.83% | |
|
|
| - `latest` is the default and contains selected rows at the corpus-wide maximum |
| evaluation generation. |
| - `snapshot` retains the selected arm for each comparable configuration, |
| including older carried evidence when no newer matching arm exists. |
| - `history` contains every released row, including superseded generations, in |
| pinned source-file and source-line order. |
|
|
| Dataset repository: https://huggingface.co/datasets/antoinezambelli/forge-evals |
|
|
| ## Quickstart |
|
|
| ```python |
| from datasets import load_dataset |
| |
| latest = load_dataset("antoinezambelli/forge-evals", "latest", split="train") |
| snapshot = load_dataset("antoinezambelli/forge-evals", "snapshot", split="train") |
| history = load_dataset("antoinezambelli/forge-evals", "history", split="train") |
| ``` |
|
|
| ## Metric contract |
|
|
| The canonical headline metric is **Score**: |
|
|
| - `score = correct_count / attempted_count` |
| - `validated_accuracy = correct_count / validated_count` |
| - `completion_rate = completed_count / attempted_count` |
|
|
| `attempted_count` means rows present in the selected cohort, not a theoretical |
| schedule. `correct` is `true`, `false`, or null when no usable correctness |
| judgment exists. A null judgment remains in the Score denominator and is |
| excluded from the validated-accuracy denominator. `completed` records whether |
| the workflow returned normally and is independent of correctness. Exact integer |
| components are included in the publication plan so every displayed rate is |
| reproducible without reverse-engineering rounded percentages. |
|
|
| ## Schema overview |
|
|
| All 52 columns use one explicit v2 schema: |
|
|
| - **Identity and condition:** model, backend, mode, ablation, tool choice, |
| reasoning replay/level, scenario, run index, and evaluation generation. |
| - **Outcome:** `correct`, `completed`, `validation_error`, execution error type, |
| and execution error message. |
| - **Efficiency:** iterations, ideal iterations, wasted calls, elapsed seconds, |
| context budget, and stream retries. |
| - **Guardrail and reasoning telemetry:** nudges, tool errors, compaction events, |
| captured reasoning counts, and on-wire reasoning counts. |
| - **Hosted accounting:** input, output, cache-creation, cache-read tokens, and |
| recorded cost when the source backend supplied them. |
| - **Provenance and selection:** release, source file and line hashes, generation |
| metadata, canonical configuration/arm identifiers, selection status, and view |
| membership. |
|
|
| Released source JSONLs remain byte-for-byte immutable. Their legacy |
| `accuracy`/`completeness` spelling is normalized to published |
| `correct`/`completed`; the legacy aliases do not appear in the Parquet schema. |
| Sparse source fields become null. |
|
|
| ## Generations, replay, and carried evidence |
|
|
| An evaluation `generation` is a **comparability epoch**, not generated model |
| text and not necessarily a Forge release. A generation changes when collection |
| semantics materially change. Several releases may share one generation. |
|
|
| The raw `reasoning_replay` field preserves the source value. Rows predating the |
| knob have no raw value and resolve to effective `full`; this legacy-inferred arm |
| remains distinct from an explicitly recorded `full` arm. Missing raw |
| `reasoning_level` resolves to effective `default`. Carried evidence is an older |
| selected cohort retained only because no newer matching cohort exists. |
|
|
| ## Methodology and statistical use |
|
|
| Forge scenarios exercise tool selection, argument fidelity, multi-step |
| sequencing, error recovery, stateful interactions, and context-pressure paths. |
| Repeated runs are stored individually. Cohort comparisons should control for |
| model, quantization, backend, mode, scenario set, replay policy, reasoning |
| level, generation, and collection environment. For paired arms, Forge uses |
| paired McNemar tests on matching `(scenario, run)` observations and Wilson |
| intervals for Score. The run rows contain the paired observations and exact |
| outcome components needed to recompute those analyses independently. This |
| bundle does not include a packaged Parquet-to-report or dashboard command. |
|
|
| ## Intended uses |
|
|
| - Recompute aggregate metrics and perform independent analyses from run-level |
| outcomes. |
| - Compare controlled model/backend/mode or ablation cohorts. |
| - Study completion, validation, efficiency, replay, and guardrail behavior. |
| - Build new statistical views while retaining source-level provenance. |
|
|
| This dataset should not be used as a general model-quality leaderboard, as |
| training text, or as evidence that one backend/model is universally superior. |
| It also does not measure subjective answer quality beyond each scenario's |
| deterministic validator. |
|
|
| ## Limitations |
|
|
| Many cells are intentionally absent or inapplicable, and a missing scenario is |
| not scored as an attempted failure. Historical evidence spans different rigs, |
| backends, quantizations, serving versions, context budgets, and model-specific |
| reasoning behavior. Timing, token, and cost fields are operational measurements |
| and are only comparable under compatible collection conditions. Repeated runs |
| within one scenario may be more correlated than independent scenario-level |
| effects. |
|
|
| ## Provenance and integrity |
|
|
| `provenance/sources.json` pins every released source, generation, dialect, row |
| count, and SHA-256. `provenance/schema.json` contains the complete source-dialect |
| and normalized-schema contract. `manifest.json` records every configuration, |
| logical digest, shard row count, file hash, builder-source hash, Git revision, |
| Python version, and Parquet writer version. Verify a downloaded bundle with: |
|
|
| ```bash |
| python -m tests.eval.dataset_builder verify --source-root . --bundle <bundle> |
| ``` |
|
|
| Project and methodology: https://github.com/antoinezambelli/forge/blob/main/docs/EVAL_GUIDE.md |
| |
| Paper citation: Zambelli, A. *Forge: Closing the Agentic Reliability Gap Between |
| Self-Hosted and Frontier Language Models.* https://doi.org/10.1145/3786335.3813193 |
| |
| License: `mit` |
| |
