CellScientist: From Execution Feedback to Auditable Model-Revision Trajectories for Cellular Perturbation Prediction

Abstract:Cellular perturbation-response modeling requires coordinated choices of representations, fusion mechanisms, objectives, and training procedures. Large language models (LLMs) can propose executable candidates, but unconstrained revision can produce invalid implementations, change task semantics, or discard useful components. We present CellScientist, a protocol-constrained workflow that converts execution and validation feedback into auditable model-revision trajectories. It records design states and outcomes, routes discrepancies to specific components, and applies local revisions under a fixed task contract. A matched-budget study fixes the candidate language, predictor, fitting, and evaluator: structured revision finds better held-out predictors at small budgets under two LLM backbones. Operational audits link history to fewer repeated proposals, contract checks to contained violations, and discrepancy routing to targeted repairs. Refits of two frozen designs on an independently acquired cohort evaluate external predictive utility. Open-workflow trajectories retain improvements, regressions, and failures, while transcriptomic and single-cell searches extend application to additional response spaces. CellScientist produces both a selected predictor and an inspectable record of its development. Project page: this https URL.

38 pages, 7 figures. Mengran Li and Bo Li contributed equally. Corresponding author: Zelin Zang. Project page: this https URL

Machine Learning (cs.LG); Software Engineering (cs.SE)

arXiv:2605.07335 [cs.LG]

(or arXiv:2605.07335v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.07335 Focus to learn more arXiv-issued DOI via DataCite

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