VCAgent: A Mutation-Guided Self-Reflective Agent Framework for Virtual Cell Modeling
TL;DR
VCAgent is proposed, a self-evolving framework that optimizes how biological knowledge from external APIs is structured and integrated into LLM reasoning, and introduces a schema-based slot filling mechanism that transforms raw API responses into compact, typed representations, replacing unstructured text concatenation.
Abstract
Large Language Models (LLMs) are increasingly used for agent-based virtual cell modeling, yet existing frameworks rely on unstructured retrieval or static prompt engineering, injecting noisy evidence and wasting inference budget on redundant tool-use trajectories. We propose VCAgent, a self-evolving framework that optimizes how biological knowledge from external APIs is structured and integrated into LLM reasoning. VCAgent introduces a schema-based slot filling mechanism that transforms raw API responses into compact, typed representations, replacing unstructured text concatenation. To efficiently search the high-dimensional instruction space, we develop Mutation-Guided Monte Carlo Tree Search (MG-MCTS), which combines relevance-based priors derived from inter-API semantic similarity with an adaptive gating strategy to balance exploration and exploitation under a constrained evaluation budget. Experiments on single-cell perturbation prediction across five cell lines show that VCAgent improves both reasoning accuracy and search efficiency across multiple backbone LLMs, substantially outperforming existing prompt optimization methods. Codes are available at https://github.com/LZYBUPT/VCAgent.