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MEA is presented, a multi-agent framework that removes the explanation knowledge barrier entirely, and suggests that AI agents themselves can serve as a scalable, adaptable interface to ML explainability, opening a path toward natural-language explainability that generalizes beyond the fixed, single-purpose tools that...
Large language models (LLMs) have demonstrated strong capabilities in biological reasoning; however, genomic disease inference remains largely dependent on memorized gene-disease associations rather than understanding biological pathways. This shortcut learning undermines robustness and generalization, and breaks down...
MMTClinic is presented, a benchmark designed to evaluate large language models (LLMs) on complex reasoning and question-answering tasks involving clinical time-series and reveals notable differences in model performance across tasks, languages, and modalities, highlighting current limitations in clinical reasoning capa...
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