| --- |
| pretty_name: SWE-bench Science |
| language: |
| - en |
| tags: |
| - code |
| - software-engineering |
| - scientific-computing |
| - coding-agents |
| - benchmark |
| - long-horizon |
| - harbor |
| - pier |
| size_categories: |
| - n<1K |
| license: mit |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/tasks.csv |
| features: |
| - name: task_id |
| dtype: string |
| - name: science_knowledge_ablation |
| dtype: bool |
| - name: title |
| dtype: string |
| - name: domain |
| dtype: string |
| - name: language |
| dtype: string |
| - name: repository_url |
| dtype: string |
| - name: base_commit |
| dtype: string |
| - name: source_license |
| dtype: string |
| - name: gpl_family |
| dtype: bool |
| - name: restricted_license |
| dtype: bool |
| - name: license_gate |
| dtype: string |
| - name: material_license |
| dtype: string |
| - name: material_license_source |
| dtype: string |
| - name: material_restricted |
| dtype: string |
| - name: materials_gate |
| dtype: string |
| - name: materials_manifest_sha256 |
| dtype: string |
| - name: material_licenses |
| dtype: string |
| - name: materials_provenance |
| dtype: string |
| - name: restricted_reason |
| dtype: string |
| - name: environment_image |
| dtype: string |
| - name: verifier_image |
| dtype: string |
| - name: image_platform |
| dtype: string |
| - name: task_path |
| dtype: string |
| - name: status |
| dtype: string |
| --- |
| |
| # SWE-bench Science |
|
|
| SWE-bench Science evaluates coding agents on software-engineering tasks drawn from scientific-computing repositories. The release contains 119 tasks across 20 scientific domains, with isolated environments and separate programmatic verifiers. |
|
|
| - **GitHub release repository:** [OpenMOSS/SWE-bench-Science](https://github.com/OpenMOSS/SWE-bench-Science) |
| - **Runtime images:** [Docker Hub](https://hub.docker.com/u/kevinxulearning), pinned by immutable `linux/amd64` digests |
| - **Evaluation framework:** [Pier](https://github.com/datacurve-ai/pier), compatible with Harbor task format |
|
|
| ## Dataset Summary |
|
|
| | Metric | Value | |
| | --- | ---: | |
| | Tasks | 119 | |
| | Scientific domains | 20 | |
| | Default selection | 96 unrestricted-license tasks | |
| | Restricted selection | 23 tasks | |
| | GPL/LGPL/AGPL-family tasks | 18 | |
| | Environment images | 119 Docker Hub images | |
| | Verifier images | 119 Docker Hub images | |
| | Image platform | `linux/amd64` | |
|
|
| The `science_knowledge_ablation` column is `true` for the 91-task science-knowledge |
| split used by the ablation experiment. Its release IDs are `002-082`, `084`, `086`, |
| `090`, `097-101`, `111`, and `114`; all other rows are `false`. |
|
|
| ## Dataset Viewer And Files |
|
|
| The Dataset Viewer reads the canonical [`data/tasks.csv`](data/tasks.csv) table and generates its preview automatically. The release does not commit a duplicate Parquet export, so the CSV remains the single source of truth for the 119 task rows. |
|
|
| The repository also includes: |
|
|
| | Path | Purpose | |
| | --- | --- | |
| | `data/tasks.csv` | Human-readable task table | |
| | `data/statistics.md` | Generated release statistics | |
| | `manifests/tasks.jsonl` | Canonical machine-readable release manifest | |
| | `manifests/release_provenance.json` | Authoritative configuration and clean-verification provenance | |
| | `selections/` | Reproducible task selections | |
| | `tasks/task_NNN/` | Thin Harbor/Pier task bundles | |
| | `scripts/` | Materialization, provider, batch, and summary tools | |
| | `docs/run-batch.md` | Full provider and batch-runner reference | |
|
|
| The environment image contains the baseline source, public fixtures, dependencies, and compilers. The separate verifier image contains held-out tests and the grader. The dataset does not contain reference-answer patches, credentials, agent trajectories, or private verifier tests. |
|
|
| ## Quick Start |
|
|
| The pinned `datacurve-pier==0.3.0` release requires Python 3.12 or newer. |
|
|
| ~~~bash |
| python3.12 -m pip install "huggingface_hub[cli]" |
| hf auth login |
| hf download OpenMOSS-Team/SWE-bench-Science \ |
| --repo-type dataset --local-dir swe-bench-science |
| cd swe-bench-science |
| |
| uv tool install --python 3.12 "datacurve-pier==0.3.0" |
| docker login |
| ~~~ |
|
|
| Materialize the default selection: |
|
|
| ~~~bash |
| python3 scripts/materialize.py \ |
| --output tasks-selected --force |
| ~~~ |
|
|
| Materialize one task, a comma-separated list, or inclusive ranges: |
|
|
| ~~~bash |
| python3 scripts/materialize.py \ |
| --task-id 002,005-007 \ |
| --output tasks-selected-small --force |
| ~~~ |
|
|
| Materialize the complete 91-task science-knowledge ablation split. It contains |
| restricted-license tasks, so the explicit license opt-in is required: |
|
|
| ~~~bash |
| python3 scripts/materialize.py \ |
| --task-id 002-082,084,086,090,097-101,111,114 \ |
| --allow-restricted-licenses \ |
| --output tasks-science-knowledge-ablation --force |
| ~~~ |
|
|
| Every materialization writes `selection.json` with the exact task IDs used for the run. |
|
|
| ## Restricted Licenses |
|
|
| Twenty-three tasks are excluded from the default selection because they contain GPL/LGPL/AGPL-family code, academic non-commercial sources or materials, or restricted third-party data. Include them only after confirming that your use is permitted: |
|
|
| ~~~bash |
| python3 scripts/materialize.py \ |
| --allow-restricted-licenses \ |
| --output tasks-selected-all --force |
| ~~~ |
|
|
| The GPL/LGPL/AGPL-family task IDs are `003, 020, 021, 023, 032, 057, 066, 074, 075, 082, 083, 084, 085, 096, 097, 098, 100, 118`. Tasks `019`, `026`, `035`, `101`, and `102` are restricted for other reasons. There is no `--allow-GPL` option. The selection flag controls which task bundles are materialized; it does not replace the upstream license obligations. |
|
|
| ## Run An Evaluation |
|
|
| Docker Engine 28+ is required. `scripts/run_batch.py` applies a shared isolated |
| network policy to all harnesses: the agent can reach its model provider through |
| an allowlisted proxy, and the verifier has no network access. Clearing proxy |
| variables does not create direct internet access. See |
| [offline network isolation](docs/run-batch.md#offline-network-isolation). |
|
|
| Run an infrastructure smoke with no model: |
|
|
| ~~~bash |
| pier run -p tasks-selected-small \ |
| --agent nop --env docker \ |
| --n-concurrent 1 --n-attempts 1 \ |
| --no-force-build --no-delete --yes |
| ~~~ |
|
|
| Run a real agent by selecting a harness, model, and provider profile: |
|
|
| ~~~bash |
| # Claude Code |
| pier run -p tasks-selected-small \ |
| --agent claude-code --env docker \ |
| --env-file ~/.config/swe-bench-science/claude.env \ |
| --model anthropic/claude-opus-4-7 --n-concurrent 1 |
| |
| # mini-swe-agent |
| pier run -p tasks-selected-small \ |
| --agent mini-swe-agent --env docker \ |
| --env-file ~/.config/swe-bench-science/mini-swe-agent.env \ |
| --model openai/gpt-5 --n-concurrent 1 |
| ~~~ |
|
|
| For Codex gateway profiles, use the included wrapper: |
|
|
| ~~~bash |
| python3 scripts/run_batch.py \ |
| --path tasks-selected-small \ |
| --agent codex \ |
| --env-file ~/.config/swe-bench-science/codex.env \ |
| --n-concurrent 2 --n-attempts 1 \ |
| --jobs-dir jobs --job-name codex-small |
| ~~~ |
|
|
| For an approximately 120-second agent-stage smoke, add |
| `--agent-timeout-multiplier 0.0223`; for an approximately 30-second smoke, use |
| `--agent-timeout-multiplier 0.0055556`. Verifier and scientific-build timeouts |
| remain independent. The pinned `datacurve-pier==0.3.0` release requires Python |
| 3.12 or newer. |
|
|
| ## Provider Profiles |
|
|
| Create profiles outside the downloaded dataset: |
|
|
| ~~~bash |
| mkdir -p ~/.config/swe-bench-science |
| cp profiles/codex.env.example ~/.config/swe-bench-science/codex.env |
| cp profiles/claude.env.example ~/.config/swe-bench-science/claude.env |
| cp profiles/mini-swe-agent.env.example ~/.config/swe-bench-science/mini-swe-agent.env |
| chmod 600 ~/.config/swe-bench-science/*.env |
| ~~~ |
|
|
| Codex profiles use `MODEL`, `OPENAI_API_KEY`, `CODEX_BASE_URL`, `CODEX_WIRE_API`, `CODEX_VERSION`, and `CODEX_REASONING_EFFORT`. Set `CODEX_WIRE_API=responses` for the OpenAI Responses API or `chat` for Chat Completions. Claude Code uses `ANTHROPIC_AUTH_TOKEN`, `ANTHROPIC_BASE_URL`, and optional `ANTHROPIC_CUSTOM_HEADERS`. mini-swe-agent uses the provider variables expected by its selected model adapter. |
|
|
| Credentials are read at runtime. They are not stored in task metadata, Dockerfiles, image layers, or result summaries. |
|
|
| ## Results |
|
|
| Pier writes one aggregate result and one trial directory per task and attempt: |
|
|
| ~~~text |
| jobs/<job-name>/result.json |
| jobs/<job-name>/<task>__<trial>/verifier/reward.json |
| jobs/<job-name>/<task>__<trial>/verifier/ctrf.json |
| jobs/<job-name>/<task>__<trial>/verifier/test-stdout.txt |
| ~~~ |
|
|
| The wrapper additionally writes `jobs/summary.json` and `jobs/summary.csv`. For a direct Pier run, generate the same summaries with: |
|
|
| ~~~bash |
| python3 scripts/summarize_results.py --jobs-dir jobs |
| ~~~ |
|
|
| Use `pier view jobs` to inspect trajectories. |
|
|
| See [`docs/run-batch.md`](docs/run-batch.md) for the complete option reference, |
| gateway/profile configuration, dry-run mode, retry and timeout controls, and |
| result paths. |
|
|
| ## Licensing And Attribution |
|
|
| The dataset card, release metadata, and helper tools use the repository's MIT terms. Task source, papers, figures, fixtures, and other third-party materials retain their upstream licenses. The source-license field does not automatically license copied scientific materials; audited material notices and modification notes are retained in the relevant task bundles. |
|
|
| The dataset is independent of GitHub at runtime. After download, materialization and evaluation use the local task bundle and the Docker Hub image digests recorded in `task.toml` and `data/tasks.csv`. |
|
|
