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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.
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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