AI 中文总结
研究人员探索科学问题时视角有限，本文提出人机协作多智能体论坛AgentPanel，其在多维度评估中表现优于基线，获多数用户认可，可用于早期科学探索。
AI 中文摘要
识别有前景的科学想法仍是研究实践中的重要挑战。研究人员通常依赖小组讨论或与单个大语言模型的一对一交互，但这些方法往往只能让他们接触到有限范围的观点和方向。我们提出AgentPanel，这是一个用于科学探索中人机协作的多智能体论坛。异构智能体在论坛式环境中异步讨论科学问题，研究人员可以提交问题、浏览和整理候选想法、让智能体参与后续交互，还可选择生成事后总结报告。我们从想法质量、探索广度、交互有效性、候选选择效率和实用性五个维度评估AgentPanel。离线实验表明，AgentPanel的表现优于集中式多智能体辩论基线。一项有20名参与者的人类研究进一步显示，用户重视AgentPanel提供的观点多样性和探索支持。在与常用LLM工具的基于体验的比较中，65%的参与者认为AgentPanel在研究方向广度和早期探索整体适用性上更具优势。该平台可通过此URL公开获取。
英文摘要
Identifying promising scientific ideas remains an important challenge in research practice. Researchers commonly rely on small-group discussions or one-to-one interactions with a single large language model, yet these approaches often expose them to only a limited range of perspectives and directions. We present AgentPanel, a multi-agent forum for human--AI collaboration in scientific exploration. Heterogeneous agents asynchronously discuss scientific questions in a forum-style environment, while researchers can submit questions, browse and organize candidate ideas, engage agents in follow-up interactions, and optionally generate post-hoc summary reports. We evaluate AgentPanel in terms of idea quality, exploration breadth, interaction effectiveness, candidate-selection efficiency, and practical utility. Offline experiments show that AgentPanel outperforms a centralized multi-agent debate baseline. A human study with 20 participants further shows that users value AgentPanel for perspective diversity and exploration support. In experience-based comparisons with commonly used LLM tools, 65\% of participants favored AgentPanel for both breadth of research directions and overall suitability for early-stage exploration. The platform is publicly available at https://agentpanel.cc/.
