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This work presents CellScientist, a protocol-constrained workflow that converts execution and validation feedback into auditable model-revision trajectories, and produces both a selected predictor and an inspectable record of its development.
ASECT improves over its backbone on three external pathology benchmarks covering classification and question answering beyond cellular composition tasks, and improves over its backbone on three external pathology benchmarks covering classification and question answering beyond cellular composition tasks.
Learning What to Skip is introduced, which learns action-specific safety models from these interventions and combines held-out calibration with domain-native guards to select skips and reduces recorded token cost while matching or improving aggregate full-workflow accuracy in the evaluated settings.
It is shown that cellular perturbation atlases can instead become reinforcement-learning environments, where measured gene responses provide computable rewards for biological reasoning, and introduces PertMind, which combines trusted-trajectory supervised initialization with gene-, pathway-, and format-level reinforcem...
AI virtual cells aim to predict cellular responses to specified interventions, yet held-out predictive performance alone does not establish use of the supplied perturbation information. This prediction-claim gap matters in agentic model discovery, where language-model agents generate and revise predictors using score-b...
Pathology vision-language models (VLMs) have recently progressed rapidly and are commonly evaluated by answer accuracy on pathology VQA benchmarks. However, we dig into current evaluations and identify three overlooked issues: 1) Visual evidence is not always necessary. For instance, Gemini-3-Pro achieves 53.5% average...
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