arXiv AI
Aug 24

Inhibitory Attention for Clinical Long-Context Reasoning: Characterizing and Mitigating Lost-in-the-Middle Effects in EHR Processing

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
arXiv Computation and Language
Sep 30

TriageRA-CCF: Source-Side Clinical Confidence and Coverage Signals for Adaptive Rank Budgeting in Medical LLMs

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
arXiv Computation and Language
Oct 23

RetiJev: Fast and Source-Grounded Ophthalmic Decision Models

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
arXiv AI
Oct 5

From Retrieval to Typed Decisions: Calibrated System One Models from Biomedical Sentence Encoders

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
arXiv AI
Oct 5

Sentence-Level Context Sensitivity as a Training-Free Detector of Unsupported Content, Evaluated Against Trained Verifiers

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