Deep research agents exhibit widespread search-time contamination on six public benchmarks, with three defined leakage types inflating performance by up to 4%.
Disentangling reasoning and knowledge in medical large language models.arXiv preprint arXiv:2505.11462.2025
3 Pith papers cite this work. Polarity classification is still indexing.
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Evo-MedAgent adds three evolving memory stores to LLM agents for chest X-ray diagnosis, raising MCQ accuracy from 0.68 to 0.79 on GPT-5-mini and 0.76 to 0.87 on Gemini-3 Flash without any training.
LLMs show strong exam performance on medical tasks but exhibit a clear gap in accuracy on authentic clinical decision-making as measured by the new MR-Bench benchmark and unified evaluations.
citing papers explorer
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Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation
Deep research agents exhibit widespread search-time contamination on six public benchmarks, with three defined leakage types inflating performance by up to 4%.
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Evo-MedAgent: Beyond One-Shot Diagnosis with Agents That Remember, Reflect, and Improve
Evo-MedAgent adds three evolving memory stores to LLM agents for chest X-ray diagnosis, raising MCQ accuracy from 0.68 to 0.79 on GPT-5-mini and 0.76 to 0.87 on Gemini-3 Flash without any training.
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Medical Reasoning with Large Language Models: A Survey and MR-Bench
LLMs show strong exam performance on medical tasks but exhibit a clear gap in accuracy on authentic clinical decision-making as measured by the new MR-Bench benchmark and unified evaluations.