PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature

• URL: http://arxiv.org/abs/2510.10909v3
• Date: Mon, 10 Nov 2025 03:21:21 GMT
• Title: PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature
• Authors: Daoyu Wang, Mingyue Cheng, Shuo Yu, Zirui Liu, Ze Guo, Qi Liu,
• Abstract summary: We propose PaperArena, an evaluation benchmark for large language model (LLM) based agents to address real-world research questions.<n>Given a research question, agents should integrate diverse formats across multiple papers through reasoning and interacting with appropriate tools.<n> Experimental results reveal that even the most advanced LLM powering a well-established agent system achieves merely 38.78% average accuracy.
• Score: 11.804526152911386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding and reasoning on the web-scale scientific literature is a crucial touchstone for large language model (LLM) based agents designed to support complex knowledge-intensive tasks. However, existing works are mainly restricted to tool-free tasks within isolated papers, largely due to the lack of a benchmark for cross-paper reasoning and multi-tool orchestration in real research scenarios. In this work, we propose PaperArena, an evaluation benchmark for agents to address real-world research questions that typically require integrating information across multiple papers with the assistance of external tools. Given a research question, agents should integrate diverse formats across multiple papers through reasoning and interacting with appropriate tools, thereby producing a well-grounded answer. To support standardized evaluation, we provide a modular and extensible platform for agent execution, offering tools such as multimodal parsing, context retrieval, and programmatic computation. Experimental results reveal that even the most advanced LLM powering a well-established agent system achieves merely 38.78% average accuracy. On the hard subset, accuracy drops to only 18.47%, highlighting great potential for improvement. We also present several empirical findings, including that all agents tested exhibit inefficient tool usage, often invoking more tools than necessary to solve a task. We invite the community to adopt PaperArena to develop and evaluate more capable agents for scientific discovery. Our code and data are available https://github.com/Melmaphother/PaperArena.

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