| --- |
| pretty_name: DR-256 |
| language: |
| - en |
| license: other |
| task_categories: |
| - question-answering |
| tags: |
| - agentic-search |
| - deep-research |
| - local-search |
| - retrieval |
| - benchmark |
| - sqlite |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: frontier-v1 |
| data_files: |
| - split: test |
| path: benchmark/questions.jsonl |
| --- |
| |
| # DR-256: A Self-Contained Benchmark for Deep-Research Agents |
|
|
| DR-256 is an open, self-contained benchmark for reproducible evaluation of |
| deep-research agents. It pairs 256 curated multi-hop questions derived from |
| [DeepResearch-9K](https://huggingface.co/datasets/artillerywu/DeepResearch-9K) |
| with a frozen 3.5 GB web corpus and a local SQLite FTS5/BM25 search environment. |
| This design enables off-the-shelf evaluation against a common evidence base |
| without paid or external search APIs. |
|
|
| DR-256 is introduced in **OARS: On Post-Training for Cost-Efficient |
| Heterogeneous Multi-Agent Systems**. The evaluation harness and paper settings |
| are available in the |
| [OARS code release](https://github.com/mit-han-lab/oars). |
| In the OARS experiments, DR-256 is used only for evaluation: the worker model is |
| post-trained on BrowseComp-Plus and receives no further training on DR-256. |
|
|
| The canonical release is revision **`v1.1.0`**. |
|
|
| ## Benchmark overview |
|
|
| | Item | Value | |
| |---|---:| |
| | Evaluation questions | 256 | |
| | Hard questions | 128 | |
| | Challenge questions | 128 | |
| | Corpus URL records | 41,913 | |
| | Successfully fetched URLs | 32,127 | |
| | Unique indexed documents | 30,318 | |
| | FTS chunks | 243,615 | |
| | Documents blocked from retrieval | 28 | |
| | Retrieval | SQLite FTS5/BM25 | |
| | External search API | Not required | |
|
|
| For backward compatibility, the released metadata stores the paper's |
| **Hard** group under `calibration_tier: frontier`; `challenge` corresponds to |
| the paper's **Challenge** group. |
|
|
| ## Repository contents |
|
|
| The files required for evaluation are: |
|
|
| ```text |
| benchmark/ |
| questions.jsonl # questions, answers, prompts, qids and strata |
| blocked_docids.txt # documents excluded from search and open |
| summary.json |
| search/ |
| corpus.sqlite3 # frozen web snapshot and FTS5 index |
| coverage_summary.json |
| source_manifest_summary.json |
| README.md |
| TERMS.md |
| ``` |
|
|
| The repository also contains construction and audit artifacts: |
|
|
| ```text |
| selection/ # candidate filtering and calibration evidence |
| baselines/ # fresh reference runs used for protocol checks |
| audit/ # crawl, robots-policy and evaluation audits |
| tools/ # rebuild and release-validation utilities |
| manifest.json # machine-readable inventory and hashes |
| checksums.sha256 # checksums for release artifacts |
| LICENSE-CODE # Apache-2.0 license for packaged utilities |
| ``` |
|
|
| These additional artifacts document how the frozen benchmark was produced; |
| they are not required for a standard DR-256 evaluation. |
|
|
| ## Quick start with OARS |
|
|
| ### 1. Download DR-256 |
|
|
| ```bash |
| hf download Shangy/DR-256 \ |
| --repo-type dataset \ |
| --revision v1.1.0 \ |
| --local-dir dr256 |
| ``` |
|
|
| ### 2. Install the OARS evaluation code |
|
|
| ```bash |
| git clone https://github.com/mit-han-lab/oars.git |
| cd oars |
| pip install -e ".[search,data]" |
| ``` |
|
|
| ### 3. Prepare the tasks and start local search |
|
|
| ```bash |
| oars-data dr256 \ |
| --input ../dr256/benchmark/questions.jsonl \ |
| --output data/dr256.jsonl |
| |
| python -m oars.search.dr256_server \ |
| --database ../dr256/search/corpus.sqlite3 \ |
| --blocked-docids ../dr256/benchmark/blocked_docids.txt \ |
| --host 0.0.0.0 \ |
| --port 8000 \ |
| --workers 64 |
| ``` |
|
|
| The blocked-document list is part of the benchmark definition and must be |
| enabled during evaluation. The local service exposes: |
|
|
| - `GET /health` |
| - `GET /stats` |
| - `POST /search` with `{"query": str, "k": 1..100}` |
| - `POST /open` with `{"docid": str}` or `{"url": str}` |
|
|
| The search engine is lexical-only. Documents are divided into 800-word chunks |
| with 100-word overlap; search returns the highest-ranked chunk for each source |
| URL, and `/open` exposes the corresponding source text. |
|
|
| For complete model-serving, evaluation, grading and summarization commands, see |
| the [OARS README](https://github.com/mit-han-lab/oars#evaluation). |
|
|
| ## Benchmark construction |
|
|
| ### Source questions and retrieval provenance |
|
|
| DR-256 starts from DeepResearch-9K revision |
| `9eaf02da8608b91dc9440503ce10889160c14347`. The upstream trajectories provide |
| questions, reference answers, search queries and visited URLs, but not the |
| retrieved page contents. We therefore use their retrieval provenance to build a |
| new, executable local search environment rather than attempting to replay the |
| original observations. |
|
|
| Repeated questions are consolidated by normalized question identity. The |
| resulting construction pool yields 41,913 canonical URLs associated with 6,646 |
| questions. |
|
|
| ### Frozen local search environment |
|
|
| Source pages are fetched, converted to text and deduplicated. The benchmark |
| retains the full collected snapshot, including distractors, rather than |
| indexing only pages known to contain answers. This yields 30,318 unique indexed |
| documents and 243,615 searchable chunks. Freezing both the corpus and retrieval |
| index keeps the evidence environment stable across evaluation runs. |
|
|
| ### Validity and answerability checks |
|
|
| Twenty-eight dataset dumps, QA pages and other answer-bearing benchmark |
| artifacts are blocked from both `/search` and `/open`. Each retained question |
| must also have an independent source document containing its reference answer; |
| these supporting-document identifiers are used only for construction and audit |
| and are never supplied to the evaluated agent. |
|
|
| Questions solvable from answer-visible snippets, early page openings or a |
| single straightforward retrieval step are removed. The remaining candidates |
| require sustained evidence gathering, document inspection or query |
| reformulation. |
|
|
| ### Difficulty calibration |
|
|
| Each candidate is evaluated with three independent rollouts of an off-the-shelf |
| Qwen3-8B agent. Retained questions require complete, nonempty and successfully |
| graded rollouts with at least three interaction turns on average. |
|
|
| - **Hard:** solved in one or two of the three calibration rollouts. |
| - **Challenge:** not solved in any of the three calibration rollouts. |
|
|
| Questions solved in all three rollouts are excluded. We sample 128 questions |
| from each group to form the final 256-question benchmark. These labels describe |
| calibration behavior of the reference agent, not an intrinsic or permanent |
| difficulty ordering across models. |
|
|
| ## Evaluation protocol |
|
|
| The paper's DR-256 preset uses: |
|
|
| | Parameter | Value | |
| |---|---:| |
| | Temperature | 0.6 | |
| | Top-p | 0.95 | |
| | Top-k | 20 | |
| | Maximum agent turns | 20 | |
| | Per-question seed | `20260811 + benchmark_index` | |
| | Primary denominator | all 256 questions | |
|
|
| Empty answers, unfinished episodes, agent errors and missing judge scores count |
| as incorrect. Accuracy should not be reported conditional on successful |
| completion. |
|
|
| ## Data schema |
|
|
| Each row in `benchmark/questions.jsonl` contains: |
|
|
| ```text |
| label reference answer |
| prompt[] frozen system and user messages |
| metadata.question original question |
| metadata.qid stable normalized-question identifier |
| metadata.difficulty upstream difficulty (1/2/3) |
| metadata.teacher_correct outcome of the released upstream teacher trace |
| metadata.dataset_rows source row identifiers |
| metadata.row_fingerprints source-row fingerprints |
| metadata.benchmark.index 0..255 |
| metadata.benchmark.calibration_tier |
| metadata.benchmark.selection_bucket |
| metadata.benchmark.base_pilot calibration behavior of the reference agent |
| metadata.benchmark.clean_provenance_answer_docids |
| ``` |
|
|
| `clean_provenance_answer_docids` is construction metadata. It must not be |
| provided to an evaluated agent or used as an oracle retriever. |
|
|
| ## Reproducibility and versioning |
|
|
| Revision `v1.1.0` is the canonical OARS release. It repairs a path-specific |
| `robots.txt` caching issue in `v1.0.2`, re-audits the affected origins, derives |
| a new SQLite snapshot and rebuilds the FTS index. The older tag remains |
| available as a restore point but should not be used for new evaluations. |
|
|
| Key release identities: |
|
|
| | Artifact | Identity | |
| |---|---| |
| | Questions SHA-256 | `6c1bbdfad639b84224976d4a49d28e1eb060cb0a38f3f7284b9050b6963e7a85` | |
| | SQLite SHA-256 | `09172388ed6c0ae91cb43dff3a663bcfb219673ed991ff5c9922904eb61ee77e` | |
| | SQLite size | `3,499,802,624` bytes | |
|
|
| To validate the downloaded release: |
|
|
| ```bash |
| python ../dr256/tools/frontier_release.py validate --release-dir ../dr256 |
| python ../dr256/tools/frontier_release.py validate --release-dir ../dr256 --deep |
| ``` |
|
|
| The deep check additionally re-hashes the SQLite database and runs |
| `PRAGMA quick_check`. |
|
|
| ## Intended use and limitations |
|
|
| DR-256 is intended for: |
|
|
| - reproducible evaluation of deep-research and local-search agents; |
| - paired comparisons under a fixed evidence environment; |
| - orchestration, search-budget and termination ablations; |
| - agent-infrastructure and retrieval-interface validation. |
|
|
| Important limitations: |
|
|
| - The questions are derived from the public DeepResearch-9K training |
| provenance. DR-256 is not an independent held-out DeepResearch-9K test set. |
| - Question selection uses Qwen3-8B calibration rollouts and therefore reflects |
| that model's behavior. |
| - The benchmark measures performance against a frozen local corpus, not |
| generalization to the live web or robustness to changing retrieval systems. |
| - Corpus coverage is shaped by teacher-visited URLs, page availability and |
| crawl policy. Web text may contain errors, bias, personal information or |
| offensive content inherited from its sources. |
|
|
| ## Rights and takedown |
|
|
| The packaged Python utilities are distributed under Apache License 2.0; see |
| `LICENSE-CODE`. The pinned upstream DeepResearch-9K repository did not declare |
| a dataset license when this release was built. Extracted webpage text remains |
| subject to the rights and terms of its original sources; this repository does |
| not grant a blanket license over third-party content. |
|
|
| Read [`TERMS.md`](TERMS.md) before use. For correction or removal requests, |
| open a discussion on this dataset repository and identify the canonical URL or |
| document id. |
|
|
| ## Acknowledgements |
|
|
| DR-256 is constructed from questions and retrieval provenance released with |
| [DeepResearch-9K](https://huggingface.co/datasets/artillerywu/DeepResearch-9K). |
| We thank its authors for making these resources available. |
|
|
| ## Citation |
|
|
| Please cite OARS when using DR-256. To make results reproducible, also record |
| the dataset revision, question hash and SQLite hash. |
|
|
| ```bibtex |
| @article{yang2026oars, |
| title = {{OARS}: On Post-Training for Cost-Efficient Heterogeneous Multi-Agent Systems}, |
| author = {Yang, Shang and Zeng, Zhichen and Dong, Shi and Wang, Yichuan and Chen, Siqi and |
| Guo, Junxian and Gonzalez, Joseph E. and Zhu, Banghua and Han, Song}, |
| year = {2026} |
| } |
| ``` |
|
|
