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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.
Compared to contemporary or one-feature-one-agent RL-based approaches, HRLFS improves the downstream ML performance with iterative feature subspace exploration while accelerating total runtime by reducing the number of agents involved.
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.
A significant gap exists between static QA and dynamic execution tasks, with top LLMs perform well on static QA but falter in real-world execution scenario; specialized agents outperform general models in real-world execution through environmental interaction and iterative refinement; and domain knowledge remains the p...
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.
The rapid advancement of high-throughput technologies has led to an explosion of biological data and a subsequent surge in bioinformatics analysis tools, thereby creating an urgent demand for automated bioinformatics workflows. Recently Large Language Models (LLMs) and LLM-based agents show great potential in this area...
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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