AgentPanel: Toward a New Paradigm for Human--AI Collaboration in Exploring Scientific Questions

2026-08-04

AI Summary

This work proposes a novel forum-style human-AI collaborative paradigm to address the challenge researchers often face in early-stage exploration: limited perspectives that hinder efficient identification of promising research directions. The authors develop a multi-agent platform enabling heterogeneous AI agents to engage in asynchronous discussions, allowing researchers to submit questions, browse generated ideas, interact deeply with agents, and receive synthesized summary reports. Integrating large language models, a heterogeneous multi-agent architecture, asynchronous discussion mechanisms, and a tailored user interface, the system outperforms centralized baselines in offline evaluations. A user study with 20 participants reveals that 65% found the platform significantly superior to existing LLM-based tools in terms of breadth of research directions and suitability for early-stage exploration.

Abstract

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/.

Problem

• scientific exploration
• idea generation
• perspective diversity
• human-AI collaboration
• research direction

Innovation

• multi-agent system
• human-AI collaboration
• scientific exploration
• asynchronous discussion
• idea generation