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MechHypoBench is introduced, the first benchmark for evaluating whether AI agents and AI scientists can generate mechanistic hypotheses from empirical data, and an evaluation framework is developed that assesses open-form mechanistic hypotheses through their consequences under withheld conditions.
SciHorizon-eLab is introduced, an agentic protocol-to-task compiler that formulates scientific embodied task construction as a compilation problem and constructs \BenchName, a ready-to-use benchmark comprising 300 certified tasks across diverse laboratory operations.
This work proposes SciHorizon-DataEVA, a novel agentic system to scalable AI-readiness evaluation of heterogeneous scientific data, based on the Sci-TQA² principles, which organize AI-readiness into four complementary dimensions: Governance Trustworthiness, Data Quality, AI Compatibility, and Scientific Adaptability.
Results indicate that SciDSK improves how agents locate and understand scientific datasets, providing a stronger foundation for actionable scientific data use.
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