MedFlowBench evaluates VLM agents on full radiology and pathology studies by requiring both task answers and verifiable evidence like key slices and regions of interest, revealing that answer-only scores overestimate performance.
Title” is the title of the source case report that the question-answer pair was derived from, “pmc id
5 Pith papers cite this work. Polarity classification is still indexing.
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GW-Eyes is a new LLM-powered agent framework that autonomously associates gravitational-wave events with electromagnetic counterparts by integrating specialized tools and supporting natural-language interaction.
OGCaReBench is a new retrieval-focused benchmark for evaluating LLMs on off-guideline clinical questions from real case reports, showing retrieval augmentation raises accuracy from 56% to 82%.
LaaB improves LLM hallucination detection by mapping self-judgment labels back into neural feature space and using mutual learning under logical consistency constraints between responses and meta-judgments.
TrajOnco uses a chain-of-agents LLM architecture with memory to perform temporal reasoning on longitudinal EHR, achieving 0.64-0.80 AUROC for 1-year multi-cancer risk prediction in zero-shot mode on matched cohorts while matching supervised ML on lung cancer and outperforming single-agent baselines.
citing papers explorer
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When Cases Get Rare: A Retrieval Benchmark for Off-Guideline Clinical Question Answering
OGCaReBench is a new retrieval-focused benchmark for evaluating LLMs on off-guideline clinical questions from real case reports, showing retrieval augmentation raises accuracy from 56% to 82%.
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Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments
LaaB improves LLM hallucination detection by mapping self-judgment labels back into neural feature space and using mutual learning under logical consistency constraints between responses and meta-judgments.