activity
20242026
most citedAutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Toward Efficient Agents: Memory, Tool learning, and Planning

Xiaofang Yang, Lijun Li, Heng Zhou +12

Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has continued to improve, efficiency, whi…

cs.AI20261 cited

AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery

Lei Xiong, Kun Luo, Ziyi Xia +15

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…

cs.CR2025

SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents

Sheng Yin, Xianghe Pang, Yuanzhuo Ding +7

With the integration of large language models (LLMs), embodied agents have strong capabilities to understand and plan complicated natural language instructions. However, a foreseea…

cs.CL2025

MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems

Rui Ye, Shuo Tang, Rui Ge +4

LLM-based multi-agent systems (MAS) have shown significant potential in tackling diverse tasks. However, to design effective MAS, existing approaches heavily rely on manual configu…

cs.CL2024

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…