arXiv AI
MedZERO: Self-Evolving Agents for Open-Ended Medical Reasoning Through Controlled Knowledge Accumulation
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arXiv AI
Jun 9
From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
arXiv:2603. 03292v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields.
arXiv AI
Sep 4
Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
The paper reviews how Large Language Models (LLMs) are being adapted for medical reasoning, moving beyond single-step answers to systems that can systematically, transparently, and verifiably reason. It introduces a taxonomy of enhancement techniques, split into training-time methods such as supervised fine‑tuning and reinforcement learning, and test-time methods like prompt engineering and multi‑agent systems. The review examines their application across text, image, and code modalities in key clinical areas—diagnosis, education, and treatment planning—and tracks the shift in evaluation benchmarks from simple accuracy to more nuanced assessments of reasoning quality and visual interpretability.
arXiv AI
Jun 2
Process Reward Agents for Steering Knowledge-Intensive Reasoning
arXiv:2604. 09482v2 Announce Type: replace Abstract: Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require synthesizing clues across large external knowledge sources.
arXiv Machine Learning
Sep 22
Fathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards
arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conv...
arXiv AI
Jun 9
Experience Makes Skillful: Enabling Generalizable Medical Agent Reasoning via Self-Evolving Skill Memory
arXiv:2606. 09365v1 Announce Type: new Abstract: Medical agent systems are increasingly expected to support interactive clinical decision making rather than only static question answering.
arXiv AI
Jun 2
CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation
arXiv:2606. 01094v1 Announce Type: new Abstract: Clinical order generation serves as a critical bridge between clinical decision-making and real-world practice, translating medical decisions into concrete and executable orders.