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20232026
most citedAre We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

9 citations · 14 across the 14 of their papers we have counts for

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cs.CL2026

HyperTool: Beyond Step-Wise Tool Calls for Tool-Augmented Agents

Yaxin Du, Yifan Zhou, Yujie Ge +7

Tool-augmented LLM agents commonly rely on step-wise atomic tool calls, where each invocation, observation, and value transfer is exposed in the main reasoning trace. This creates…

cs.CL2025

InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents

Yaxin Du, Yuanshuo Zhang, Xiyuan Yang +10

Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content…

cs.CL2025

MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems

Rui Ye, Keduan Huang, Qimin Wu +17

LLM-based multi-agent systems (MAS) have demonstrated significant potential in enhancing single LLMs to address complex and diverse tasks in practical applications. Despite conside…

cs.CL20249 cited

Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

Rui Ye, Xianghe Pang, Jingyi Chai +6

Scholarly peer review is a cornerstone of scientific advancement, but the system is under strain due to increasing manuscript submissions and the labor-intensive nature of the proc…

cs.CL2024

Self-Alignment of Large Language Models via Monopolylogue-based Social Scene Simulation

Xianghe Pang, Shuo Tang, Rui Ye +4

Aligning large language models (LLMs) with human values is imperative to mitigate potential adverse effects resulting from their misuse. Drawing from the sociological insight that…