arXiv:2610.33440v1 Announce Type: new Abstract: Selecting a compact training subset is an important step in adapting medical language models from existing question-answering banks. We investigate per...
arXiv:2609.09684v1 Announce Type: new Abstract: Medical question-answering datasets often contain answer labels, whereas high-quality rationales remain scarce, noisy, or costly to validate. This chan...
arXiv:2610.34871v1 Announce Type: new Abstract: Objective: The rapid advancement of large language models (LLMs) in healthcare has opened new possibilities for AI-assisted clinical decision-making; h...
The paper introduces TriageRA-CCF, a method for adaptively allocating low‑rank LoRA channels in medical large language models based on source‑side signals: answer confidence, clinical coverage, and a counterfactual close‑miss proxy. By supervising a budget router that selects among 2, 4, or 8 active ranks, the approach improves average accuracy over existing LoRA variants on Qwen3‑8B and Llama3.1‑8B, though gains vary across benchmarks. Ablation studies confirm that each signal contributes to better budget decisions, though their combined effect is not uniformly superior across all backbones.
arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation...
arXiv:2609. 31450v1 Announce Type: cross Abstract: Medical vision-language model (VLM) post-training is commonly evaluated through answer accuracy.
MIRA is a bilingual benchmark that evaluates whether large language models (LLMs) provide consistent medical information across different user phrasings, languages, and health literacy levels. It contains 4,320 prompts derived from 60 medically reviewed low‑risk health questions and reveals that models tend to omit key information and offer fewer concrete next steps when responding to low health‑literacy signals, a phenomenon termed Differential Information Dilution (DID). A knowledge‑guided mitigation prompt can reduce this dilution for most models, notably improving Claude and Qwen.
The paper presents a system for the MedReason 2026 challenge that tackles both multiple‑choice and open‑ended medical visual question answering using offline, containerized inference. Key findings include that comparing answer semantics rather than labels boosts retrieval‑only accuracy from 20.0 % to 57.5 % on a 200‑case holdout, and that varying the number of in‑prompt retrieved examples has minimal impact on final accuracy (93.5 %–94.0 %). The final system achieves 94.0 % MCQ accuracy on the development set and 93.20 % on the official pre‑evaluation, far surpassing the off‑the‑shelf baseline. "whyItMatters":"The results demonstrate that semantic‑aware retrieval and careful adapter tuning can dramatically improve medical VQA performance, offering a practical approach for high‑accuracy, offline inference in clinical settings."
arXiv:2607. 02175v1 Announce Type: new Abstract: Multiple-choice medical benchmarks are increasingly saturated, and recent rubric-based evaluations such as HealthBench have shown that open-ended clinical performance is far from solved - its "Hard" subset top score remains 32%.
The paper investigates using open‑weight large language models (LLMs) as both judges and simulated examinees to predict medical exam item difficulty from text alone. LLMs provided absolute difficulty ratings, pairwise comparisons, and response‑based signals such as correctness and option probabilities, which were combined with baseline features to improve predictions. The best models achieved Spearman correlations of .447 on USMLE and .494 on PANCE, surpassing prior shared‑task systems and matching proprietary models, demonstrating that LLM signals capture difficulty information beyond traditional text representations.
arXiv:2609.01361v1 Announce Type: cross Abstract: Linear classifiers trained on hidden states of a large language model (LLM), linear probes, can flag factual errors from a single forward pass. Geome...
arXiv:2606. 15735v1 Announce Type: cross Abstract: Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmission, ongoing care, and diagnostic decision-making.