AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery

About this episode

This episode discusses AutoResearchBench, a benchmark for evaluating AI agents in the context of scientific literature discovery.

🤗 Upvotes: 26 | cs.AI Authors: Lei Xiong, Kun Luo, Ziyi Xia, Wenbo Zhang, Jin-Ge Yao, Zheng Liu, Jingying Shao, Jianlyu Chen, Hongjin Qian, Xi Yang, Qian Yu, Hao Li, Chen Yue, Xiaan Du, Yuyang Wang, Yesheng Liu, Haiyu Xu, Zhicheng Dou Title: AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery Arxiv: http://arxiv.org/abs/2604.25256v1 Abstract: Autonomous scientific research is significantly advanced thanks to the development of AI agents. One key step in this process is finding the right scientific literature, whether to explore existing knowledge for a research problem, or to acquire evidence for verifying assumptions and supporting claims. To assess AI agents' capability in driving this process, we present AutoResearchBench, a dedicated benchmark for autonomous scientific literature discovery. AutoResearchBench consists of two complementary task types: (1) Deep Research, which requires tracking down a specific target paper through a progressive, multi-step probing process, and (2) Wide Research, which requires comprehensively collecting a set of papers satisfying given conditions. Compared to previous benchmarks on agentic web browsing…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

• AI agents
• scientific literature
• benchmarking
• autonomous research
• literature discovery
• deep research
• wide research

Keywords

• AI agents
• scientific literature
• benchmarking
• research
• literature discovery
• deep research
• wide research

Mentioned in this episode

Books & works: AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery, AutoResearchBench, arxiv

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