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The paper diagnoses failures in test-time reinforcement learning for medical QA due to answer-space structure and introduces PROSE, which rewards reasoning quality to improve model performance without labeled data.
The paper proposes RMS-RSP, a perturbation-sensitive method for selecting medical questions to receive rationale supervision, improving robust accuracy and semantic consistency in QA systems.
This study evaluates how AI chatbots like ChatGPT, Claude, and Gemini retrieve clinical studies for medical questions, finding significant performance differences by model and user role, with a bias toward larger sample sizes.
This paper investigates why LLMs underperform in Arabic medical tasks, showing via mechanistic analysis that knowledge exists internally but fails to surface, then proposes TLoRA, a targeted low-rank adaptation method that outperforms full-network LoRA on medical QA and introduces a new Arabic clinical dialogue benchmark.
This paper investigates authority bias in LLMs using a controlled medical QA setting, revealing that models override correct answers in a graded manner proportional to perceived authority. The effect is localized to a critical late layer where correct answer representations are actively erased.
The paper proposes BiRG-LoRA, a rank-gated LoRA method for medical question answering that uses clinically structured priors to select sparse rank subsets, achieving 69.31% macro-average accuracy across four benchmarks while using fewer parameters than mixture-of-experts approaches.
This paper introduces a multi-agent peer-reviewed reasoning method where multiple LLMs independently generate chain-of-thought reasoning and then evaluate each other's outputs to select the best answer. The method outperforms single-model reasoning and majority voting on medical QA benchmarks.
This paper proposes a Variance-Aware Reward Framework using GRPO to improve LLM performance on heart-focused medical question answering, achieving significant accuracy and F1 gains on a HealthBench subset.
Researchers introduce DoseBench, a benchmark of 81 OTC dosing scenarios to evaluate LLM decision-making under temporal uncertainty for acetaminophen and ibuprofen use. Results show LLMs frequently struggle with rolling-window reasoning and can produce confident but medically unsupported responses.
A tutorial and project demonstrating LoRA fine-tuning of Qwen3-1.7B on AMD MI300X using ROCm for clinical question answering, providing a CUDA-free alternative for medical AI development.