Towards Autonomous Mechanistic Reasoning in Virtual Cells
This work addresses the need for factually grounded and actionable explanations in biological LLMs, enabling more reliable mechanistic reasoning for virtual cell modeling.
The authors introduce a structured explanation formalism for virtual cells and a multi-agent framework (VCR-Agent) that generates and validates mechanistic reasoning autonomously. They release the VC-TRACES dataset and show that training with these explanations improves factual precision and gene expression prediction.
Large language models (LLMs) have recently gained significant attention as a promising approach to accelerate scientific discovery. However, their application in open-ended scientific domains such as biology remains limited, primarily due to the lack of factually grounded and actionable explanations. To address this, we introduce a structured explanation formalism for virtual cells that represents biological reasoning as mechanistic action graphs, enabling systematic verification and falsification. Building upon this, we propose VCR-Agent, a multi-agent framework that integrates biologically grounded knowledge retrieval with a verifier-based filtering approach to generate and validate mechanistic reasoning autonomously. Using this framework, we release VC-TRACES dataset, which consists of verified mechanistic explanations derived from the Tahoe-100M atlas. Empirically, we demonstrate that training with these explanations improves factual precision and provides a more effective supervision signal for downstream gene expression prediction. These results underscore the importance of reliable mechanistic reasoning for virtual cells, achieved through the synergy of multi-agent and rigorous verification.