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Harvard Medical School
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UniMod forces models to learn from both images and text independently, eliminating shortcut learning and achieving state-of-the-art diagnostic accuracy.
User-specific signals can dramatically enhance intent detection accuracy in e-commerce search, outperforming traditional population-level approaches.
Memory systems struggle to adapt as user profiles evolve, with over 93% of failures linked to memory retrieval rather than response generation.
Code agents struggle with evolving user requirements, revealing a 38-point gap in performance across leading LLMs when faced with iterative feedback.
LLMs that ace math and physics still struggle with general reasoning, achieving only 63% accuracy on a new K-12 level benchmark.
LLMs can sift through routine clinical notes to detect epilepsy with high accuracy, even boosting expert neurologists' diagnostic performance by over 10%.
