Towards Autonomous Mechanistic Reasoning in Virtual Cells
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.
69e1ce72860e47828575b592
Yunhui Jang
69e1ce72860e47828575b593
Lu Zhu
69e1ce72860e47828575b594
Jake Fawkes
69e1ce72860e47828575b595
Alisandra Kaye Denton
69e1ce72860e47828575b596
Dominique Beaini
69e1ce72860e47828575b597
Emmanuel Noutahi
Towards Autonomous Mechanistic Reasoning in Virtual Cells
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.
Large language models are enhanced for biological research through a multi-agent framework that generates and validates mechanistic explanations using structured formalism and verified datasets.
large language models multi-agent framework mechanistic action graphs virtual cells biologically grounded knowledge retrieval verifier-based filtering mechanistic reasoning VC-TRACES dataset Tahoe-100M atlas gene expression prediction