INDIBATOR: Diverse and Fact-Grounded Individuality for Multi-Agent Debate in Molecular Discovery

2026-02-02

AI Summary

This work proposes a multi-agent debate framework for molecular discovery that incorporates individual scientist profiles, addressing the limitation of existing systems that rely on coarse-grained role definitions and fail to capture the nuanced, trajectory-dependent behaviors of real researchers. For the first time, the framework integrates “scientific DNA”—a personalized representation derived from an individual’s publication history and molecular exploration trajectory—into agent modeling to endow each agent with fine-grained personality and domain-specific priors. Molecular discovery is driven through a multi-round debate process comprising proposal, critique, and voting stages. Experimental results demonstrate that this approach significantly outperforms coarse-grained role-based baselines and achieves state-of-the-art or competitive performance across multiple evaluation metrics.

Technology Category

• Multiagent Systems: Modeling other Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Multi-agent systems have emerged as a powerful paradigm for automating scientific discovery. To differentiate agent behavior in the multi-agent system, current frameworks typically assign generic role-based personas such as''reviewer''or''writer''or rely on coarse grained keyword-based personas. While functional, this approach oversimplifies how human scientists operate, whose contributions are shaped by their unique research trajectories. In response, we propose INDIBATOR, a framework for molecular discovery that grounds agents in individualized scientist profiles constructed from two modalities: publication history for literature-derived knowledge and molecular history for structural priors. These agents engage in multi-turn debate through proposal, critique, and voting phases. Our evaluation demonstrates that these fine-grained individuality-grounded agents consistently outperform systems relying on coarse-grained personas, achieving competitive or state-of-the-art performance. These results validate that capturing the ``scientific DNA''of individual agents is essential for high-quality discovery.

Problem

• multi-agent systems
• scientific discovery
• individuality
• molecular discovery
• agent personas

Innovation

• multi-agent debate
• individualized agent personas
• scientific DNA
• molecular discovery
• fact-grounded individuality