# ResearcherBench: Evaluating Deep AI Research Systems on the Frontiers of Scientific Inquiry

arXiv: https://arxiv.org/abs/2507.16280 · HTML: https://mlsanity.com/paper/2507.16280/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning](https://mlsanity.com/paper/2503.09516/) (2025)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [Search-o1: Agentic Search-Enhanced Large Reasoning Models](https://mlsanity.com/paper/2501.05366/) (2025)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [Deep Research Agents: A Systematic Examination And Roadmap](https://mlsanity.com/paper/2506.18096/) (2025)
- [SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?](https://mlsanity.com/paper/2502.12115/) (2025)
- [Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2503.08979/) (2025)
- [Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge](https://mlsanity.com/paper/2506.21506/) (2025)
- [OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs](https://mlsanity.com/paper/2411.14199/) (2024)
- [Limits to scalable evaluation at the frontier: LLM as Judge won't beat twice the data](https://mlsanity.com/paper/2410.13341/) (2024)
- [Open Deep Search: Democratizing Search with Open-source Reasoning Agents](https://mlsanity.com/paper/2503.20201/) (2025)
- [Towards Human-AI Mutual Learning: A New Research Paradigm](https://mlsanity.com/paper/2405.04687/) (2024)

## Built on by (most cited first, 30 of 32)

- [EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery](https://mlsanity.com/paper/2603.08127/) (2026)
- [DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis](https://mlsanity.com/paper/2508.20033/) (2025)
- [Characterizing Deep Research: A Benchmark and Formal Definition](https://mlsanity.com/paper/2508.04183/) (2025)
- [Accumulating Context Changes the Beliefs of Language Models](https://mlsanity.com/paper/2511.01805/) (2025)
- [AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts](https://mlsanity.com/paper/2601.11044/) (2026)
- [InnovatorBench: Evaluating Agents' Ability to Conduct Innovative LLM Research](https://mlsanity.com/paper/2510.27598/) (2025)
- [ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence](https://mlsanity.com/paper/2605.26340/) (2026)
- [FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights](https://mlsanity.com/paper/2602.02905/) (2026)
- [DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation](https://mlsanity.com/paper/2601.09688/) (2026)
- [DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity](https://mlsanity.com/paper/2602.11685/) (2026)
- [DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation](https://mlsanity.com/paper/2512.17776/) (2025)
- [AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery](https://mlsanity.com/paper/2605.23204/) (2026)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [Understanding DeepResearch via Reports](https://mlsanity.com/paper/2510.07861/) (2025)
- [Can LLM-as-a-Judge Reliably Verify Rubrics in Agentic Scenarios?](https://mlsanity.com/paper/2606.29920/) (2026)
- [MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome](https://mlsanity.com/paper/2603.28407/) (2026)
- [Why Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory](https://mlsanity.com/paper/2601.22984/) (2026)
- [Deep Research, Shallow Evaluation: A Case Study in Meta-Evaluation for Long-Form QA Benchmarks](https://mlsanity.com/paper/2603.06942/) (2026)
- [SafeSearch: Automated Red-Teaming of LLM-Based Search Agents](https://mlsanity.com/paper/2509.23694/) (2025)
- [Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision](https://mlsanity.com/paper/2601.13217/) (2026)
- [SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?](https://mlsanity.com/paper/2604.10718/) (2026)
- [JADE: Expert-Grounded Dynamic Evaluation for Open-Ended Professional Tasks](https://mlsanity.com/paper/2602.06486/) (2026)
- [ReportLogic: Evaluating Logical Quality in Deep Research Reports](https://mlsanity.com/paper/2602.18446/) (2026)
- [Wiki Live Challenge: Challenging Deep Research Agents with Expert-Level Wikipedia Articles](https://mlsanity.com/paper/2602.01590/) (2026)
- [Evaluating Stochasticity in Deep Research Agents](https://mlsanity.com/paper/2602.23271/) (2026)
- [Evaluating Deep Research Agents on Expert Consulting Work: A Benchmark with Verifiers, Rubrics, and Cognitive Traps](https://mlsanity.com/paper/2605.17554/) (2026)
- [Can AI Agents Synthesize Scientific Conclusions?](https://mlsanity.com/paper/2606.11337/) (2026)
- [Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback](https://mlsanity.com/paper/2606.09748/) (2026)
- [DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks](https://mlsanity.com/paper/2606.12871/) (2026)

## Abstract (arXiv)

The emergence of deep research systems presents significant capabilities in problem-solving, extending from basic queries to sophisticated research tasks. However, existing benchmarks primarily evaluate these systems as agents for web retrieval and report generation, overlooking their potential to discover novel insights on the frontiers of scientific research. To address this gap, we introduce ResearcherBench, the first benchmark focused on evaluating the capabilities of these advanced, agentic systems - which we refer to as Deep AI Research Systems (DARS) - on frontier AI scientific questions. We compiled a dataset of 65 research questions expertly selected from real-world scientific scenarios such as laboratory discussions and interviews, spanning 35 different AI subjects and categorized into three types: technical details, literature review, and open consulting. Our dual evaluation framework combines rubric assessment, which uses expert-designed criteria to evaluate insight quality, with factual assessment, which measures citation accuracy (faithfulness) and coverage (groundedness). We evaluated several leading commercial DARS and baseline systems. Results show that OpenAI Deep Research and Gemini Deep Research significantly outperform other systems, with particular strength in open-ended consulting questions. Such capabilities represent a meaningful step toward AI self-improvement, aligning with the vision of ASI for AI. We open-source ResearcherBench to provide a standardized platform for promoting the development of next-generation AI research assistants, hoping to foster a new perspective in AI research evaluation for a novel pattern of scientific collaboration: https://github.com/GAIR-NLP/ResearcherBench.