---
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}
}
```