HealthAgentBench is a new benchmark of 54 healthcare agent tasks where even the strongest frontier AI agent reaches only about 42% success rate on end-to-end clinical workflows.
Ai hospital: Benchmarking large language models in a multi-agent medical interaction simulator
8 Pith papers cite this work. Polarity classification is still indexing.
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RE-MCDF introduces a generation-verification-revision closed-loop multi-expert LLM architecture guided by a medical knowledge graph to enforce inter-disease logical constraints and outperform baselines on neurology EMR diagnosis tasks.
DyReMe generates fresh, confounded medical consultation cases to expose weaknesses in state-of-the-art LLMs across accuracy, veracity, helpfulness, and consistency beyond static benchmarks.
The paper introduces TinyTroupe, an open-source LLM-powered multiagent persona simulation library supporting detailed persona definitions, population sampling, experimentation, and validation, with preliminary evidence that it can approximate some aspects of real consumer behavior.
DoctorAgent-RL trains a Qwen2.5-7B doctor agent via multi-agent RL on the new MTMedDialog dataset to conduct dynamic, question-driven consultations, reaching 70% exact diagnostic match in real-patient trials.
R2MED is the first benchmark for reasoning-driven medical retrieval, where even top models reach only 41.4 nDCG@10 on queries requiring inference beyond lexical or semantic overlap.
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.
citing papers explorer
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HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents
HealthAgentBench is a new benchmark of 54 healthcare agent tasks where even the strongest frontier AI agent reaches only about 42% success rate on end-to-end clinical workflows.
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RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis
RE-MCDF introduces a generation-verification-revision closed-loop multi-expert LLM architecture guided by a medical knowledge graph to enforce inter-disease logical constraints and outperform baselines on neurology EMR diagnosis tasks.
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Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
DyReMe generates fresh, confounded medical consultation cases to expose weaknesses in state-of-the-art LLMs across accuracy, veracity, helpfulness, and consistency beyond static benchmarks.
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TinyTroupe: An LLM-powered Multiagent Persona Simulation Toolkit
The paper introduces TinyTroupe, an open-source LLM-powered multiagent persona simulation library supporting detailed persona definitions, population sampling, experimentation, and validation, with preliminary evidence that it can approximate some aspects of real consumer behavior.
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Real-World Doctor Agent with Proactive Consultation through Multi-Agent Reinforcement Learning
DoctorAgent-RL trains a Qwen2.5-7B doctor agent via multi-agent RL on the new MTMedDialog dataset to conduct dynamic, question-driven consultations, reaching 70% exact diagnostic match in real-patient trials.
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R2MED: A Benchmark for Reasoning-Driven Medical Retrieval
R2MED is the first benchmark for reasoning-driven medical retrieval, where even top models reach only 41.4 nDCG@10 on queries requiring inference beyond lexical or semantic overlap.
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A Survey of Scaling in Large Language Model Reasoning
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
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Large Language Model Agent: A Survey on Methodology, Applications and Challenges
A survey that deconstructs LLM agent systems via a methodology-centered taxonomy linking design principles to emergent behaviors, applications, and challenges.