@arcinstitute: Virtual cell models are only as good as the data they learn from. To predict how any cell type responds to a perturbati…
Summary
Arc Institute argues that virtual cell models can only predict how cell types respond to perturbations if they are trained on high-quality causal data across diverse contexts, so technical noise is not mistaken for real biology.
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Towards Autonomous Mechanistic Reasoning in Virtual Cells
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.
@arcinstitute: Nearly 300 teams have already submitted to the Virtual Cell Challenge leaderboard to see how their initial models rank …
Nearly 300 teams have submitted initial models to the Virtual Cell Challenge leaderboard, with current standings based on six metrics this year.
@arcinstitute: Start assembling your team because the 2026 Virtual Cell Challenge officially kicks off on Thursday, August 20. This is…
Arc Institute announces the 2026 Virtual Cell Challenge, a global competition to predict cell responses to stimuli, with a $100,000 grand prize, starting August 20.
PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction
PerturbCellRL introduces a reinforcement learning framework that post-trains a pretrained single-cell transcriptomic generator using cell-level verifiers as rewards, improving biological consistency of perturbation predictions beyond distributional matching.
CellRFT: Reinforcement Fine-Tuning for Single-Cell Perturbation Modeling
CellRFT introduces a reinforcement fine-tuning framework that uses biological evaluation as direct training feedback to improve single-cell perturbation models, addressing the mismatch between surrogate training losses and biological evaluation criteria.