The paper introduces ClinMPO, an evidence‑guided reinforcement‑learning framework that enhances psychiatric reasoning in small language models (SLMs). ClinMPO leverages a reward model (ClinRM) trained on 18,569 question–answer pairs from 4,474 psychiatry articles and is guided by the psychiatrist‑defined Clinical Psychiatry Thinking Strategy (CPTS). Evaluations on four Qwen3 model sizes show that ClinMPO outperforms baseline, supervised fine‑tuning, and standard policy optimization, with the 8B model surpassing a human baseline of senior pre‑licensure medical students and achieving the highest rank among 31 models. Blinded clinician assessment confirms improved rationale quality across CPTS criteria, demonstrating that clinical evidence and specialist knowledge can be effectively incorporated into medical AI development.
arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.
arXiv:2607. 02983v1 Announce Type: new Abstract: Recent reasoning-centric Large Language Models (LLMs) have made significant strides, yet they predominantly operate on a passive-inference pattern that assumes complete information.
arXiv:2608.22622v1 Announce Type: cross Abstract: Clinical decision-making relies on identifying relevant patient information to guide diagnosis and treatment, a challenge that is especially difficul...
arXiv:2606. 13176v1 Announce Type: new Abstract: Mental health problems such as anxiety, depression, and suicide remain urgent global challenges, where timely and accurate assessment is critical for effective intervention.
arXiv:2608. 16831v1 Announce Type: new Abstract: Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its behavior from instructions and demonstrations.
arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.
arXiv:2603.03677v2 Announce Type: replace-cross Abstract: Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when...
arXiv:2601. 03321v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have substantially advanced Radiology Report Generation (RRG), yet aligning them through reinforcement learning (RL) remains challenging due to heterogeneous medical supervision.
arXiv:2606. 28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy.
arXiv:2606. 05174v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong promise in healthcare applications.
arXiv:2606. 16149v3 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) rank the correct disease first in only 35.
CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models proposes a new framework to improve medical reasoning in LLMs. It introduces two key conditions—Causal Sufficiency and Proximal Learnability—to curate high-quality training trajectories, using agreement-based self-verification and dynamic entropy bounds. Experiments on medical multimodal and text-only benchmarks show that CARE outperforms competitors, reducing incorrect reasoning and enhancing training stability.