4 papers
OPERA: Operator-residual feedback for reliable autonomous optical experiments with language-model agents
Ning Xu, Xiang Zheng, Fuqiang Zhong +4
Autonomous agents choose actions using scores that may not reflect experimental success. We developed OPERA, an operator-residual framework for optical experiments. It represents e…
AgentCPM-Explore: Realizing Long-Horizon Deep Exploration for Edge-Scale Agents
Haotian Chen, Xin Cong, Shengda Fan +16
While Large Language Model (LLM)-based agents have shown remarkable potential for solving complex tasks, existing systems remain heavily reliant on large-scale models, leaving the…
Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs
Runchu Tian, Yanghao Li, Yuepeng Fu +10
Positional bias in large language models (LLMs) hinders their ability to effectively process long inputs. A prominent example is the "lost in the middle" phenomenon, where LLMs str…
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Yaxi Lu, Shenzhi Yang, Cheng Qian +12
Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scena…