TxBench-PP benchmark shows leading AI agents achieve at most 59% success on tasks requiring recovery of preclinical pharmacology conclusions from assay data.
Corsello, David D
3 Pith papers cite this work. Polarity classification is still indexing.
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Drug-blind cancer sensitivity prediction is limited by evaluation metric and training distribution rather than drug representation complexity.
CellScientist introduces a dual-space hierarchical orchestration system that enables closed-loop refinement of virtual cell models by routing execution discrepancies back to hypothesis or implementation updates, yielding improved benchmark performance with auditable traces.
citing papers explorer
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TxBench-PP: Analyzing AI Agent Performance on Small-Molecule Preclinical Pharmacology
TxBench-PP benchmark shows leading AI agents achieve at most 59% success on tasks requiring recovery of preclinical pharmacology conclusions from assay data.
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Training distribution determines the ceiling of drug-blind cancer sensitivity prediction
Drug-blind cancer sensitivity prediction is limited by evaluation metric and training distribution rather than drug representation complexity.
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CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models
CellScientist introduces a dual-space hierarchical orchestration system that enables closed-loop refinement of virtual cell models by routing execution discrepancies back to hypothesis or implementation updates, yielding improved benchmark performance with auditable traces.