arXiv:2606. 15419v1 Announce Type: cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA).
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arXiv:2607. 06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents.
The paper introduces an adaptive memory and reflection (AMR) multi‑agent system for medical question answering. Each agent has dedicated memory and uses reflection‑based feedback to retrieve relevant prior cases, improving reasoning. The system routes questions through solo, collaborative, or escalated workflows and includes consensus and ethical overseer modules, achieving strong performance on MedQA and MedMCQA datasets.
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:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.
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...
The paper introduces LLM‑PeerReview, an unsupervised ensemble method that selects the best response from multiple LLM-generated candidates by scoring each answer with several LLMs, aggregating those scores via averaging or a graphical model, and choosing the highest-scoring response. The approach is peer‑review inspired, transparent, and interpretable, and it outperforms the Smoothie‑Global model by 6.9%–7.3% across factual recall QA, math reasoning, and instruction‑following tasks. The authors also provide a curated benchmark suite of 12 ensemble methods evaluated on four datasets and three task families to aid reproducibility.