EpiGraph creates a heterogeneous epilepsy knowledge graph that boosts LLM performance on clinical reasoning tasks by 30-41% in pharmacogenomics when used with Graph-RAG.
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arXiv preprint arXiv:2504.00993 (2025)
14 Pith papers cite this work. Polarity classification is still indexing.
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Set-to-set distances on sentence embeddings provide a permutation-invariant reward signal that improves GRPO training and enables efficient test-time scaling for vision-language models generating chest X-ray reports.
ClinSeekAgent automates active multimodal evidence seeking for clinical reasoning, improving LLM performance on raw EHR and CXR tasks while enabling distillation into smaller models.
RadThinking releases a large longitudinal CT VQA dataset stratified into foundation perception questions, single-rule reasoning questions, and compositional multi-step chains grounded in clinical reporting standards for cancer screening.
CLR-voyance reformulates inpatient reasoning as POMDP with clinician-validated outcome rubrics, yielding an 8B model that outperforms larger frontier models on the authors' new benchmark.
A new counterfactual multi-agent framework improves LLM diagnostic accuracy by quantifying confidence shifts from edited clinical findings and guiding specialist discussions.
SIE framework automatically constructs scalable, verifiable reasoning environments from structured data, improving in-domain performance and enabling generalization to out-of-domain math and logic tasks.
QCEA reformulates entity alignment as a query-conditioned ranking task with semantic encoding, graph learning, and direction-aware transformation to handle context-dependent, asymmetric correspondences in medical knowledge graphs.
ReMedi boosts LLM performance on EHR clinical predictions by up to 19.9% F1 through ground-truth-guided rationale regeneration and fine-tuning.
VeriLLMed is an interactive visual debugging tool that maps LLM diagnostic reasoning to knowledge graphs to identify and categorize relation, branch, and missing errors.
DeepER-Med introduces a three-module agentic AI workflow for evidence-based medical research that outperforms production platforms on a new expert-curated dataset of 100 questions and matches clinical recommendations in seven of eight real-world cases.
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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EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild
EpiGraph creates a heterogeneous epilepsy knowledge graph that boosts LLM performance on clinical reasoning tasks by 30-41% in pharmacogenomics when used with Graph-RAG.
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SDR: Set-Distance Rewards for Radiology Report Generation
Set-to-set distances on sentence embeddings provide a permutation-invariant reward signal that improves GRPO training and enables efficient test-time scaling for vision-language models generating chest X-ray reports.
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ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning
ClinSeekAgent automates active multimodal evidence seeking for clinical reasoning, improving LLM performance on raw EHR and CXR tasks while enabling distillation into smaller models.
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RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology
RadThinking releases a large longitudinal CT VQA dataset stratified into foundation perception questions, single-rule reasoning questions, and compositional multi-step chains grounded in clinical reporting standards for cancer screening.
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CLR-voyance: Reinforcing Open-Ended Reasoning for Inpatient Clinical Decision Support with Outcome-Aware Rubrics
CLR-voyance reformulates inpatient reasoning as POMDP with clinician-validated outcome rubrics, yielding an 8B model that outperforms larger frontier models on the authors' new benchmark.
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Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning
A new counterfactual multi-agent framework improves LLM diagnostic accuracy by quantifying confidence shifts from edited clinical findings and guiding specialist discussions.
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Structured In-context Environment Scaling for Large Language Model Reasoning
SIE framework automatically constructs scalable, verifiable reasoning environments from structured data, improving in-domain performance and enabling generalization to out-of-domain math and logic tasks.
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Query-Conditioned Knowledge Alignment for Reliable Cross-System Medical Reasoning
QCEA reformulates entity alignment as a query-conditioned ranking task with semantic encoding, graph learning, and direction-aware transformation to handle context-dependent, asymmetric correspondences in medical knowledge graphs.
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ReMedi: Reasoner for Medical Clinical Prediction
ReMedi boosts LLM performance on EHR clinical predictions by up to 19.9% F1 through ground-truth-guided rationale regeneration and fine-tuning.
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VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs
VeriLLMed is an interactive visual debugging tool that maps LLM diagnostic reasoning to knowledge graphs to identify and categorize relation, branch, and missing errors.
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DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI
DeepER-Med introduces a three-module agentic AI workflow for evidence-based medical research that outperforms production platforms on a new expert-curated dataset of 100 questions and matches clinical recommendations in seven of eight real-world cases.
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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.
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