README.md

---
{"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) | 293,800 | 205,180 | 259,363 | 259,363 | 69.84% | 79.11% | 88.28% |
| `snapshot` | 412,100 | 256,333 | 344,048 | 344,048 | 62.20% | 74.51% | 83.49% |
| `history` | 552,500 | 345,280 | 460,591 | 460,591 | 62.49% | 74.96% | 83.36% |

- `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- `history` contains every released row, including superseded generations, in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.

The builder never modifies source JSONLs. Each accepted source revision is
pinned by its exact hash, so intentional provenance corrections require an
explicit pin update. 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`