Paper Detail
Yunhui Jang, Lu Zhu, Jake Fawkes, Alisandra Kaye Denton, Dominique Beaini, Emmanuel Noutahi
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.
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@misc{jang2026autonomous,
title = {Towards Autonomous Mechanistic Reasoning in Virtual Cells},
author = {Yunhui Jang and Lu Zhu and Jake Fawkes and Alisandra Kaye Denton and Dominique Beaini and Emmanuel Noutahi},
year = {2026},
abstract = {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 falsificati},
url = {https://huggingface.co/papers/2604.11661},
keywords = {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, code available, huggingface daily},
eprint = {2604.11661},
archiveprefix = {arXiv},
}
{}