| --- |
| license: apache-2.0 |
| pretty_name: Doc2Harness |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - code |
| - agents |
| - coding-agents |
| - harness-engineering |
| - benchmark |
| - llm-evaluation |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: scenarios |
| default: true |
| data_files: |
| - split: test |
| path: data/scenarios.jsonl |
| - config_name: leaderboard |
| data_files: |
| - split: test |
| path: data/leaderboard.jsonl |
| --- |
| |
| <p align="center"> |
| <a href="https://geyuyao.com/doc2harness/"><img src="assets/logo.png" alt="Doc2Harness" width="420"></a> |
| </p> |
|
|
| <h3 align="center">Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents</h3> |
|
|
| <p align="center"> |
| <a href="https://geyuyao.com/doc2harness/">🌐 Project page</a> • |
| <a href="https://arxiv.org/abs/2610.20344">📄 Paper</a> • |
| <a href="https://github.com/YuyaoGe/doc2harness">💻 Code</a> |
| </p> |
|
|
| Doc2Harness measures how well a model can **build a working coding agent harness** |
| from a development document, rather than how well it writes a patch inside an |
| existing one. |
|
|
| - **Stage 1, build.** A build model, working through a fixed builder harness |
| (OpenCode) in an isolated container, receives a development document and an |
| interface-only contract skeleton, and implements a complete candidate harness. |
| - **Stage 2, evaluate.** The frozen candidate harness runs in containers |
| separate from the construction environment, one per test case, and is driven |
| end to end by a scripted **mock model**. Each test case is judged on four |
| observable channels (requests sent to the model, files on disk, process exit |
| code, command output), and **positive controls** rule out vacuous passes. |
|
|
| <p align="center"> |
| <img src="assets/pipeline.png" alt="Doc2Harness pipeline" width="92%"> |
| </p> |
|
|
| > [!NOTE] |
| > **This is a partial release.** The full benchmark in the paper has **328 test |
| > cases**. This dataset releases the same public subset as the |
| > [GitHub repository](https://github.com/YuyaoGe/doc2harness): **94 scenarios**, |
| > i.e. 53 scored test cases plus the 41 positive controls that gate them. The |
| > remaining test cases are held out. The leaderboard numbers come from the full |
| > suite and cannot be reproduced from this subset alone. |
|
|
| > [!IMPORTANT] |
| > Doc2Harness is an **executable** benchmark. The `scenarios` and `leaderboard` |
| > configs are for browsing and analysis; `load_dataset` alone does not score |
| > anything. To score a harness, use the full evaluation code shipped in |
| > [`code/`](#run-the-benchmark) (a snapshot of the |
| > [GitHub repository](https://github.com/YuyaoGe/doc2harness)) with Docker. |
| |
| ## Contents |
| |
| | Path | What it is | |
| |---|---| |
| | `data/scenarios.jsonl` | The 94 public scenarios, one per row (config `scenarios`) | |
| | `data/leaderboard.jsonl` | Pass rates of 19 frontier models on the full 328-case suite (config `leaderboard`) | |
| | `development_document/functional_spec_v6.md` | The development document given to the build model in Stage 1 | |
| | `contract_skeleton/` | The interface-only contract skeleton (CLI, headless entry point, evaluation API), plus `AGENT_CONTRACT-v2.md` | |
| | `code/` | Full evaluation code: Stage 1 build pipeline and Stage 2 framework (catalog, drivers, mock gateway), identical to GitHub commit [`5411f53`](https://github.com/YuyaoGe/doc2harness/tree/5411f53d497a6c76c1dd41bd55f08cc033203d2b) | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| scenarios = load_dataset("YuyaoGe/doc2harness", "scenarios", split="test") |
| leaderboard = load_dataset("YuyaoGe/doc2harness", "leaderboard", split="test") |
| |
| scored = scenarios.filter(lambda r: r["scored"]) |
| print(len(scenarios), len(scored)) # 94 53 |
| ``` |
|
|
| The development document and the skeleton are plain files: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| path = snapshot_download("YuyaoGe/doc2harness", repo_type="dataset", |
| allow_patterns=["development_document/*", "contract_skeleton/**"]) |
| ``` |
|
|
| ## Run the benchmark |
|
|
| **Requirements:** Python 3.11+ and Docker. Both stages use the standard library only. |
|
|
| ```bash |
| pip install -U huggingface_hub |
| hf download YuyaoGe/doc2harness --repo-type dataset --include "code/*" --local-dir doc2harness |
| cd doc2harness/code |
| |
| # sanity check: loading the catalog verifies its digests |
| PYTHONPATH=src python3 -c "from doc2harness.benchmark.dispatch import active_scenario_ids as a; print(len(a()))" |
| # 94 |
| ``` |
|
|
| **Stage 2 only: score an existing harness.** No model key is needed; scenarios |
| drive the product against a mock gateway. |
|
|
| ```bash |
| docker build --target evaluation -t doc2harness/evaluation:candidate -f containers/Dockerfile . |
| |
| PYTHONPATH=src:tools:run python3 tools/run_production_catalog.py \ |
| --artifact /path/to/product --artifact-id my-product \ |
| --output /tmp/scores --concurrency 12 --timeout 300 |
| ``` |
|
|
| Each scenario writes a JSON receipt, plus a `catalog_receipt.json`. The score is |
| `pass / (pass + fail)` over applicable scenarios; a scenario that cannot be |
| observed on a product is `INCONCLUSIVE`, not a failure. Narrow the selection |
| with `--family headless` or `--scenario-id <id>`. |
|
|
| **Stage 1: build a new harness, then score it.** This needs the builder image |
| and a local LiteLLM gateway holding your own API key; see |
| `code/src/stage1/README.md`. |
|
|
| ```bash |
| docker pull ghcr.io/yuyaoge/doc2harness-builder:latest |
| PYTHONPATH=src python3 src/stage1/run_build.py --builder-tag <model> --dry-run # prints the plan, no key needed |
| PYTHONPATH=src python3 src/stage1/run_build.py --builder-tag <model> --out-dir results/ |
| # then score results/<run_id>/agent as above |
| ``` |
|
|
| ## Scenario fields (`scenarios`) |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `scenario_id` | string | Stable identifier, equal to the scenario file name | |
| | `family` | string | Framework family: `permission`, `file`, `state`, `direct`, `headless`, `tui`, `artifact`, `startup`, `extension` | |
| | `category` | string | Category name used in the paper (e.g. `permissions`, `session state`, `interactive terminal`) | |
| | `role` | string | `test_case` (scored) or `positive_control` (not scored) | |
| | `scored` | bool | Whether the scenario counts toward the pass rate | |
| | `difficulty` | string | `standard` or `hard` | |
| | `gated_by` | list[string] | Positive controls that must PASS for this test case's status to count | |
| | `requires` | list[string] | Other scenarios this one depends on | |
| | `summary` | string | One-line purpose of the scenario | |
| | `description` | string | Purpose and step-by-step flow, from the scenario file | |
| | `source_path` | string | Path of the scenario file in the GitHub repository | |
| | `source_code` | string | Full source of the scenario (setup, stimulus, observation, and judgement) | |
|
|
| A **scenario** is the session through which the mock model drives the candidate |
| harness; a **test case** is a scenario plus its assertion. A **positive control** |
| proves that the probed operation *can* succeed when nothing blocks it, so a |
| blocked run in the gated test case reads as "the rule held" rather than "the |
| command never worked". If you select a subset, keep it closed under `gated_by`. |
|
|
| | Family | Total | Scored | Controls | |
| |---|--:|--:|--:| |
| | `file` | 17 | 9 | 8 | |
| | `permission` | 16 | 8 | 8 | |
| | `state` | 14 | 7 | 7 | |
| | `headless` | 13 | 6 | 7 | |
| | `direct` | 11 | 6 | 5 | |
| | `tui` | 9 | 5 | 4 | |
| | `artifact` | 7 | 7 | 0 | |
| | `startup` | 5 | 4 | 1 | |
| | `extension` | 2 | 1 | 1 | |
| | **Total** | **94** | **53** | **41** | |
|
|
| ## Leaderboard (`leaderboard`) |
|
|
| <p align="center"> |
| <img src="assets/leaderboard.png" alt="Pass rate of nineteen models" width="100%"> |
| </p> |
|
|
| Pass rate (%) of 19 frontier models on the **full 328-case suite**. Each model |
| built three independent candidate harnesses; `pass_rate` is their mean and |
| `pass_rate_std` the standard deviation. Category columns are the per-category |
| pass rates; `other` merges artifact checks, startup, and extension discovery. |
| Positive controls are excluded from every denominator. GPT-6 Astra leads at |
| 53.05%, the weakest model reaches 27.44%, and the cross-model mean is 38.30%. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{ge2026doc2harness, |
| title = {Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents}, |
| author = {Ge, Yuyao and Liu, Shenghua and Gao, Peizhong and Ge, Chendi and Liu, Chenxu and |
| Wang, Yiwei and Mei, Lingrui and Bi, Baolong and Yao, Jiayu and Guo, Jiafeng and |
| Cheng, Xueqi}, |
| journal = {arXiv preprint arXiv:2610.20344}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| Apache 2.0, same as the [code repository](https://github.com/YuyaoGe/doc2harness). |
| The contract skeleton derives from the project layout of |
| [mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent) (MIT); see `NOTICE` |
| for upstream attribution. |
|
|
