| # ResearchBench |
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| <p align="center"> |
| <a href="https://arxiv.org/abs/2503.21248"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b?logo=arxiv" alt="Paper"></a> |
| <a href="https://ankitala.github.io/ResearchBench/"><img src="https://img.shields.io/badge/Project-Page-2ea44f?logo=githubpages" alt="Project Page"></a> |
| <a href="https://huggingface.co/datasets/ankilok/ResearchBench"><img src="https://img.shields.io/badge/Dataset-HuggingFace-ffd21e?logo=huggingface" alt="Dataset"></a> |
| <a href="https://github.com/ankitala/ResearchBench"><img src="https://img.shields.io/badge/GitHub-Repo-blue?logo=github" alt="GitHub"></a> |
| </p> |
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| [English](README.md) | [δΈζ](README_zh.md) |
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| ResearchBench is the official code release for the ACL Findings 2026 paper [ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition](https://arxiv.org/abs/2503.21248). |
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| This GitHub repository contains the evaluation toolkit, command-line interface, prompts, metrics, tests, and a 12-sample `tiny` smoke-test split. The full benchmark dataset is released on [Hugging Face](https://huggingface.co/datasets/ankilok/ResearchBench): [ankilok/ResearchBench](https://huggingface.co/datasets/ankilok/ResearchBench). |
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| ResearchBench evaluates large language models on scientific discovery through three inspiration-based subtasks: |
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| 1. **Inspiration retrieval**: select the papers that can inspire a target research hypothesis. |
| 2. **Hypothesis composition**: compose a research hypothesis from a research question and gold inspirations. |
| 3. **Hypothesis ranking**: choose the better hypothesis in pairwise comparisons between a gold hypothesis and negative hypotheses. |
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| The full release contains **1,369 paper records**, **1,367 retrieval tasks**, **1,367 generation tasks**, and **1,323 ranking tasks**. |
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| ## π Quick Start |
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| Clone the code repository and run all three subtasks on the bundled `data/tiny/*.jsonl` smoke-test split with an OpenAI-compatible provider: |
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| ```bash |
| git clone https://github.com/ankitala/ResearchBench.git |
| cd ResearchBench |
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| python -m pip install -e ".[openai]" |
| |
| export OPENAI_API_KEY="<your-api-key>" |
| export OPENAI_BASE_URL="<your-base-url>" |
| MODEL="<model-name>" |
| |
| researchbench run-retrieve --data data/tiny/retrieve.jsonl --model-name "$MODEL" --concurrency 15 --out outputs/retrieve_tiny.jsonl |
| researchbench score-retrieve --pred outputs/retrieve_tiny.jsonl --data data/tiny/retrieve.jsonl |
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| researchbench run-generate --data data/tiny/generation.jsonl --model-name "$MODEL" --num-mutations 2 --num-itr-self-refine 1 --concurrency 15 --out outputs/generation_tiny.jsonl |
| researchbench score-generate --pred outputs/generation_tiny.jsonl --data data/tiny/generation.jsonl --judge-model "$MODEL" --concurrency 15 |
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| researchbench run-rank --data data/tiny/ranking.jsonl --model-name "$MODEL" --order both --concurrency 15 --out outputs/ranking_tiny.jsonl |
| researchbench score-rank --pred outputs/ranking_tiny.jsonl |
| ``` |
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| ## π Benchmark Results |
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| The figures below summarize model performance on the three ResearchBench subtasks. Higher scores indicate better performance. |
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| ### π Inspiration Retrieval |
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|  |
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| ### π‘ Hypothesis Composition |
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|  |
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| ### π Hypothesis Ranking |
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| ## βοΈ Installation |
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| ResearchBench requires Python 3.10 or later. |
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| Core local tooling: |
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| ```bash |
| python -m pip install -e . |
| ``` |
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| Development and tests: |
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| ```bash |
| python -m pip install -e ".[dev]" |
| ``` |
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| OpenAI-compatible provider calls: |
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| ```bash |
| python -m pip install -e ".[openai]" |
| export OPENAI_API_KEY="<provider-api-key>" |
| export OPENAI_BASE_URL="<provider-base-url>" |
| ``` |
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| You can also pass provider settings per command: |
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| ```bash |
| researchbench run-retrieve \ |
| --data data/tiny/retrieve.jsonl \ |
| --model-name "<provider-model-name>" \ |
| --api-key "<provider-api-key>" \ |
| --base-url "<provider-base-url>" \ |
| --concurrency 15 \ |
| --out outputs/retrieve_provider.jsonl |
| ``` |
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| `pyproject.toml` is the authoritative package configuration. `requirements.txt` is a convenience file for installing the editable package with development and OpenAI-compatible provider extras. |
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| <a id="data-layout"></a> |
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| ## ποΈ Data Layout |
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| This GitHub repository is code-first. It tracks only the small `tiny` JSONL files needed for smoke tests: |
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| - `data/tiny/papers.jsonl`: 12-sample paper records. |
| - `data/tiny/retrieve.jsonl`: 12-sample inspiration retrieval tasks. |
| - `data/tiny/generation.jsonl`: 12-sample hypothesis composition tasks. |
| - `data/tiny/ranking.jsonl`: 12-sample hypothesis ranking tasks. |
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| The full benchmark files are hosted on [Hugging Face](https://huggingface.co/datasets/ankilok/ResearchBench) and are not uploaded to GitHub: |
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| - `papers/papers.jsonl`: standardized paper records. |
| - `retrieve/retrieve.jsonl`: inspiration retrieval tasks. |
| - `generation/generation.jsonl`: hypothesis composition tasks. |
| - `ranking/ranking.jsonl`: hypothesis ranking tasks. |
| - `validation/*.csv` and `validation/*.json`: build and validation reports. |
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| Download any subset as needed: |
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| ```bash |
| huggingface-cli download ankilok/ResearchBench \ |
| --repo-type dataset \ |
| --include "retrieve/*.jsonl" \ |
| --include "generation/*.jsonl" \ |
| --include "ranking/*.jsonl" \ |
| --local-dir data |
| ``` |
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| Validate a local data directory: |
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| ```bash |
| researchbench validate data |
| ``` |
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| Validation checks candidate counts, gold-label coverage, empty fields, local absolute paths, API key leakage, and task-specific shape constraints. |
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| ## π οΈ Basic Usage |
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| All `run-*` commands support `--concurrency`, defaulting to 15. `run-generate` also supports `--inner-concurrency` for independent within-sample branches. Use lower values if your provider has strict rate limits. |
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| **Before running the full benchmark, download the corresponding JSONL files from [Hugging Face](https://huggingface.co/datasets/ankilok/ResearchBench) into `data/` as shown in the [Data Layout](#data-layout) section.** |
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| ### π Inspiration Retrieval |
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| ```bash |
| researchbench run-retrieve \ |
| --data data/retrieve/retrieve.jsonl \ |
| --model-name "<model>" \ |
| --concurrency 15 \ |
| --out outputs/retrieve.jsonl |
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| researchbench score-retrieve \ |
| --pred outputs/retrieve.jsonl \ |
| --data data/retrieve/retrieve.jsonl |
| ``` |
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| Default settings match the original experiment: window size 15, keep size 3, two rounds, no background survey, no similarity-only selection. Round 1 keeps 15 of 75 candidates; round 2 keeps 3 of 75. |
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| ### π‘ Hypothesis Composition |
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| ```bash |
| researchbench run-generate \ |
| --data data/generation/generation.jsonl \ |
| --model-name "<model>" \ |
| --num-mutations 2 \ |
| --num-itr-self-refine 2 \ |
| --max-inspiration-search-steps 3 \ |
| --concurrency 15 \ |
| --inner-concurrency 1 \ |
| --out outputs/generation.jsonl |
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| researchbench score-generate \ |
| --pred outputs/generation.jsonl \ |
| --data data/generation/generation.jsonl \ |
| --judge-model "<judge-model>" \ |
| --concurrency 15 |
| ``` |
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| Generation uses `main_hypothesis` as the gold hypothesis. `fine_grained_hypothesis` is retained only as optional metadata. The default generation run uses gold inspirations, two mutation lines, two refinement iterations, same-inspiration recombination, cross-inspiration recombination up to three inspiration steps, inter-EA screening window size 12, keep size 3, and recombination beam size 15. |
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| ### π Hypothesis Ranking |
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| ```bash |
| researchbench run-rank \ |
| --data data/ranking/ranking.jsonl \ |
| --model-name "<model>" \ |
| --order both \ |
| --concurrency 15 \ |
| --out outputs/ranking.jsonl |
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| researchbench score-rank \ |
| --pred outputs/ranking.jsonl |
| ``` |
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| `--order both` evaluates both comparison directions. You may also run `--order gold-first` or `--order negative-first`γ |
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| ## π Example CLI Output |
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| Retrieve scoring prints a readable summary: |
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| ```text |
| Retrieve score summary |
| - Round 1: kept 15 of 75 candidates |
| - Gold inspiration hit ratio: 0.7419 (74.19%) |
| - Negative tier 1 selection ratio: 0.5463 (54.63%) |
| - Negative tier 2 selection ratio: 0.0833 (8.33%) |
| - Negative tier 3 selection ratio: 0.0189 (1.89%) |
| - Round 2: kept 3 of 75 candidates |
| - Gold inspiration hit ratio: 0.3548 (35.48%) |
| ``` |
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| Generation scoring: |
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| ```text |
| Hypothesis generation score summary |
| - Scored samples: 12 |
| - Average matched score (0-5): 4.1667 |
| - Generation accuracy (average score / 5): 0.8333 (83.33%) |
| ``` |
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| Ranking scoring: |
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| ```text |
| Hypothesis ranking score summary |
| - Overall directional accuracy: 0.3750 (37.50%) |
| - Gold-first order accuracy (gold is candidate 1): 0.3333 (33.33%) |
| - Negative-first order accuracy (gold is candidate 2): 0.4167 (41.67%) |
| - Sample-level two-order outcomes |
| - Gold wins in neither order: 0.5000 (50.00%); count=6 |
| - Gold wins in exactly one order: 0.2500 (25.00%); count=3 |
| - Gold wins in both orders: 0.2500 (25.00%); count=3 |
| ``` |
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| The full machine-readable metric objects are written next to prediction files as `*.score.json` unless `--out` is specified. |
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| ## π Metrics |
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| - **Retrieve gold hit ratio**: selected gold inspirations divided by total gold inspirations. Reported for round 1 and round 2. |
| - **Retrieve negative selection ratio**: selected negative candidates in a distance tier divided by total candidates in that tier. This supports the negative-distance analysis. |
| - **Generation matched score**: judge score from 0 to 5 measuring how well the generated hypothesis covers the gold hypothesis key points. |
| - **Generation accuracy**: average matched score divided by 5. |
| - **Ranking directional accuracy**: whether the gold hypothesis wins under a single candidate order. |
| - **Ranking sample two-order outcomes**: whether gold wins in neither, exactly one, or both of the two reversed orders. |
| - **Ranking pair-level order consistency**: whether both orders choose gold, both choose negative, or the two orders disagree. |
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| ## π Notes |
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| - Full benchmark JSONL files are released through the [Hugging Face dataset repository](https://huggingface.co/datasets/ankilok/ResearchBench), not through this GitHub repository. |
| - Retrieve candidates are read from `merge.json`; `d0.json`, `d1.json`, `d2.json`, and `d3.json` label candidates as gold, negative tier 1, negative tier 2, and negative tier 3. |
| - Keep API keys in environment variables or pass them through CLI arguments. |
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| ## βοΈ License |
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| Code is released under the MIT License. The dataset is released on [Hugging Face](https://huggingface.co/datasets/ankilok/ResearchBench) under CC BY-NC 4.0 for non-commercial research use; see [DATA_LICENSE.md](DATA_LICENSE.md). |
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| ## π Citation |
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| If you use ResearchBench, please cite: |
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| ```bibtex |
| @misc{liu2026researchbenchbenchmarkingllmsscientific, |
| title={ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition}, |
| author={Yujie Liu and Zonglin Yang and Tong Xie and Jinjie Ni and Ben Gao and Yuqiang Li and Shixiang Tang and Wanli Ouyang and Erik Cambria and Dongzhan Zhou}, |
| year={2026}, |
| eprint={2503.21248}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2503.21248}, |
| } |
| ``` |
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