The paper identifies a new problem in clinical natural language processing called the clinical lost‑in‑the‑middle (CLitM) effect, where large language models perform poorly on information located near the center of long electronic health record (EHR) documents. Using the MedAlign dataset, the authors quantify a 21.9‑percentage‑point accuracy gap across 2,196 instruction‑response pairs and six models, showing that most critical facts lie in the CLitM trough. They propose Query‑Conditioned Clinical Suppression (QCCS), a lightweight context‑selection gate that outperforms traditional retrieval methods (BM25, dense retrieval, cross‑encoder reranking) on a held‑out set of 83 instructions, achieving up to 25.3% accuracy for middle‑position queries.
whyItMatters":"The study demonstrates that standard retrieval strategies fail to reliably surface central clinical information, and that a query‑aligned selection mechanism can substantially improve model performance on critical EHR data."
By Sanjay Basu
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.
By Shucan Ji, Yining Huang, Hongliang Guo
RetiJev is a family of 0.8B‑9B decision models designed for ophthalmic clinical decision support, offering fast, accurate, and privacy‑preserving inference without autoregressive decoding. The authors created RetiJev‑Data, a source‑grounded corpus of 78,158 clinical‑decision and diagnosis questions, 50,074 guideline‑grading questions, and a 24,662‑question evaluation benchmark. RetiJev outperforms a 27B LLM and the commercial Jev 1.13 model on guideline grading and clinical decisions, achieving the highest accuracy while answering a case in 158 ms on a single GPU—3.5× faster than LLM generation and 470× faster than reasoning.
By Bo Liu, Lianyu Wang, Meng Wang, Ching-Yu Cheng, Huazhu Fu
The paper introduces SBERT2S1, a method that transforms Sentence-Transformers encoders into typed decision models for biomedical text, and presents BIODECIDE, a suite for evaluating such models, along with MEDLINE‑S1, a large training set of 243k decisions derived from NLM indexing. Experiments show that retrieval‑trained encoders improve zero‑shot matching of content‑bearing options, and that a prior‑fused residual (PFR) head benefits most from retrieval pre‑training, while a cross‑head (C) head generally outperforms PFR across all objectives. The authors also release code, data, and a model, and discuss calibration and reward‑normalisation effects in their RLCD recipe.
By Pritam Deka
The paper proposes a training‑free detector that uses sentence‑level context sensitivity to identify unsupported content in retrieval‑augmented generation (RAG) answers. By re‑scoring each sentence with full context, no context, and each chunk removed, the method flags the chunk whose removal most reduces a sentence’s likelihood as the likely source. Evaluated on RAGTruth, TofuEval, and RAGBench, the detector outperforms answer‑level faithfulness scores, achieving AUCs up to 0.745 and matching per‑chunk fact‑checkers while using only a fraction of the compute required by large‑language‑model judges.
By Mohamed Aly Bouke
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...
By Nishant Mishra, Ameen Abu-Hanna, Iacer Calixto
The paper introduces Clinical System One (CS1), a 4‑billion‑parameter decision model that rapidly produces typed judgments from clinical records. Trained on 12.4 million Q&A pairs from 3.6 million patients, CS1 can answer multiple questions in a single forward pass, providing probability estimates for uncertain cases. In evaluations on 963,408 held‑out patients and 21 external datasets, CS1 outperformed larger open models and, when paired with a language‑model agent, efficiently identified trial‑eligible patients with far fewer tokens.
By Andrew Zhang, Trinh T. L. Vuong, Sophia J. Wagner, Dongmin Bang, Tong Ding, Yuchang Su, Mary Cipperman, Christina S. Chen, Judith Lefkes, Ramin Kahidi, Ming Y. Lu, Caiwei Tian, Tianhao Luo, Jingnan Ma, Rowland Pettit, Long Phi Le, Faisal Mahmood
arXiv:2607. 04223v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) reduces but does not eliminate hallucination, and existing detectors return a single answer-level score that does not indicate which sentence is unsupported, or why.
By Mohamed Aly Bouke
The paper introduces HeadEntropy, a training‑free method that predicts the correctness of large language model (LLM) answers by measuring how stable each attention head’s pattern is to further gradient updates. By linking the trace of the softmax Jacobian to 2‑Renyi entropy, the authors show that attention spread correlates with gradient stability, enabling accurate hallucination detection without reference annotations. Across five instruction‑tuned LLMs and five diverse datasets—including medicine, multi‑hop reasoning, and mathematics—HeadEntropy achieves a 0.736 AUROC, outperforming other training‑free baselines and matching hidden‑state probes while incurring less than 1% of inference cost.
By Sophie Ostmeier, Brian Axelrod, Maya Varma, Asad Aali, Yabin Zhang, Magdalini Paschali, Sanmi Koyejo, Curtis Langlotz, Akshay Chaudhari
PROOF is a benchmark that profiles the reliability of object-level facts in instruction-tuned language models by converting a frozen Wikidata snapshot into 18,486 English multiple-choice questions grounded in 11,779 semantic facts across 101 classes, 392 properties, and 14 domains. Each question includes an explicit "I don't know" option, a "No correct option" control, and nine controlled formulations, with 1,849 questions designed as no-correct-option traps. The study evaluates 18 open-weight model deployments on 166,374 prompts, revealing wide variability in factual accuracy, sensitivity to wording changes, and the impact of decoder perturbations.
By Andrei Chetvergov, Mikhail Solovev, Timofei Sivoraksha, Stepan Ukolov, Valeriia Kuschenko, Alexander Evseev, Sergey Bolovtsov
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...
By Yuexin Wu, Dayou Yu, Vasile Rus
arXiv:2608. 11922v1 Announce Type: cross Abstract: Predictive-distribution entropy makes a strong selection rule in retrieval-augmented question answering: across five QA benchmarks, keeping the candidate answer that a frozen respondent LLM produces with the lowest answer-token entropy lifts mean answer $F_1$ from 0.
By Po-Jen Ko, Che-Cheng Wu, Hung-Chun Hsu, Li-Yang Chang, Chuan-Ju Wang