Towards Autonomous Mechanistic Reasoning in Virtual Cells
Summary
This paper introduces VCR-Agent, a multi-agent framework that enhances large language models for biological research by generating and validating mechanistic explanations using structured formalism and the VC-TRACES dataset. The approach improves factual precision in gene expression prediction through verified mechanistic reasoning in virtual cells.
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Paper page - Towards Autonomous Mechanistic Reasoning in Virtual Cells
Source: https://huggingface.co/papers/2604.11661
Abstract
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 (https://huggingface.co/papers?q=Large%20language%20models)(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 forvirtual cells (https://huggingface.co/papers?q=virtual%20cells)that represents biological reasoning asmechanistic action graphs (https://huggingface.co/papers?q=mechanistic%20action%20graphs), enabling systematic verification and falsification. Building upon this, we propose VCR-Agent, amulti-agent framework (https://huggingface.co/papers?q=multi-agent%20framework)that integratesbiologically grounded knowledge retrieval (https://huggingface.co/papers?q=biologically%20grounded%20knowledge%20retrieval)with averifier-based filtering (https://huggingface.co/papers?q=verifier-based%20filtering)approach to generate and validatemechanistic reasoning (https://huggingface.co/papers?q=mechanistic%20reasoning)autonomously. Using this framework, we releaseVC-TRACES dataset (https://huggingface.co/papers?q=VC-TRACES%20dataset), which consists of verified mechanistic explanations derived from theTahoe-100M atlas (https://huggingface.co/papers?q=Tahoe-100M%20atlas). Empirically, we demonstrate that training with these explanations improves factual precision and provides a more effective supervision signal for downstreamgene expression prediction (https://huggingface.co/papers?q=gene%20expression%20prediction). These results underscore the importance of reliablemechanistic reasoning (https://huggingface.co/papers?q=mechanistic%20reasoning)forvirtual cells (https://huggingface.co/papers?q=virtual%20cells), achieved through the synergy of multi-agent and rigorous verification.
View arXiv page (https://arxiv.org/abs/2604.11661)View PDF (https://arxiv.org/pdf/2604.11661)Project page (https://valencelabs.substack.com/p/towards-reasoning-in-virtual-cells)GitHub4 (https://github.com/valence-labs/VCR-Agent)Add to collection (https://huggingface.co/login?next=%2Fpapers%2F2604.11661)
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