Chat Modeling: Interaction-Enhanced Agent Framework for Visualizing Literature-Grounded Biological Structures
For bioscientists needing to visualize literature-grounded biological structures, this framework reduces the complexity of 3D modeling software by enabling interaction through chat and memory-enhanced agents.
This paper introduces Chat Modeling, an agent framework that converts natural language and literature content into 3D biological structure models, demonstrating improved modeling performance over time through a customized memory system. Quantitative evaluation on a collected dataset shows effectiveness, and user studies confirm its potential for scientific workflows.
Bioscientists frequently seek to visualize the biological systems they have empirically characterized and reported in the literature. Realizing such visualizations requires biological structure modeling, an inherently complex process that demands both biological and geometric understanding. This paper addresses the problem of constructing such 3D models for visualization. In this paper, we introduce a novel agent framework that mitigates the challenges of operating 3D modeling software by transforming user inputs, including natural language descriptions, research publication content, and textual descriptions of the existing objects and structures in the current scene, into modeling operations in a structured JSON format and final 3D results. The major technical contribution lies in the collaborative agent design that simultaneously supports model planning, execution, and novel user interaction design, such as interactive modeling execution and dynamic widget generation that fuse text and mouse interaction within the chat window. The framework further incorporates a customized modeling memory to enhance user interaction, featuring components such as personalized memory management, feedback collection, and skill library design. This modeling memory is leveraged to enable improved 3D modeling performance over time. The quantitative evaluation on our collected dataset showcases the effectiveness of our framework. We also develop a prototype tool, Chat Modeling, and demonstrate its usage through two modeling case studies. Our user study and expert interviews highlight the potential of our approach for use in scientific workflows.