Perspectra: Choosing Your Experts Enhances Critical Thinking in Multi-Agent Research Ideation
Yiren Liu, Viraj Nischal Shah, Sangho Suh, Pao Siangliulue, Tal August, Yun Huang
摘要
Early-stage interdisciplinary research ideation is often challenged by limited expert access, uncertainty about what to ask, and the cognitive burden of synthesizing unfamiliar domain perspectives. This paper presents Perspectra, a forum-style multi-agent system that structures and visualizes deliberation among LLM-simulated domain experts to support exploration and refinement of emerging research ideas, while encouraging critical thinking and reflections. The interface design combines 1) a threaded canvas for parallel topic exploration with visualization of agent discourse dynamics informed by argumentation theory to aid sensemaking; and 2) feature that enables users to invite multiple self-chosen agents into an ongoing discussion. We conducted a user study with 18 participants, comparing Perspectra against a vanilla chat baseline given a task for the user to develop a short research proposal. Our findings show that Perspectra’s design elicits significantly more higher-order critical thinking behaviors during interactions with agents when compared to a traditional chat interface. We also observed more interdisciplinary user replies via forum-styled design, and more frequent and structured proposal revisions (rather than unstructured note-taking). Based on our findings, we further contribute interaction design implications of using multi-agent deliberation for complex ideation and knowledge search, combining flexibility with structured exploration to support user sensemaking and critical thinking.
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