The study compared 13 decision models with 15 generative large language model configurations on 1,273 four‑option MedQA questions. Decision models achieved 67.71%–89.08% accuracy, while generative models ranged from 78.32%–96.86%. Mercury Decide offered the lowest cost per 1,000 questions and Clef Omni the fastest median request time, but decision models still showed higher error rates for high‑confidence predictions and varied probability repeatability.
arXiv:2610.18529v2 Announce Type: replace Abstract: Jev has attracted considerable attention for its rapid, generalizable decision-making, yet its medical reliability remains underexplored. To addres...
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.
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.
The study evaluates four zero‑shot System 1 models on 86 public clinical tasks across 14 domains and 20 languages, measuring performance via AUROC. The best models achieved high AUROC scores—0.94 for medical knowledge, 0.93 for text understanding, 0.83 for clinical judgement from text, 0.89 for image perception, 0.84 for 2D image findings, and 0.69 for volumetric findings—yet performed worse on imaging tasks and varied significantly with language. System 1 models were fast (0.34–0.44 s) and energy‑efficient (24–81 J) but lagged behind clinicians on triage and were outperformed by generative models in some metrics.
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...
Many clinical decisions are a choice among a few fixed options, taken quickly and in large numbers. Such decisions also need an uncertainty estimate to know when to defer to a clinician. Generative la...
The article reports on a study comparing Jev, a decision language model that provides structured judgments, with large language model (LLM) judges for evaluating clinical AI outputs. Across multiple use cases—including pediatric answer quality, binary HealthBench criteria, MedHELM open‑ended tasks, and HealthBench Professional—Jev matched or exceeded the performance of several frontier and open‑weight LLM judges. Moreover, Jev was significantly cheaper and faster, costing 73–294 times less and taking 7–57 times less time than frontier judges, suggesting it could enable efficient, scalable clinical AI evaluation.
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 study evaluates large language models (LLMs) on unprocessed electronic medical record data for clinical registry abstraction, focusing on the American College of Cardiology National Cardiovascular Data Registry. In a pilot at one academic center, the LLM identified candidate data sources for each registry question, which abstractors used to define question‑specific document sets. In a subsequent validation at a second center, the LLM answered 157 registry questions with an overall mean accuracy of 91.5%, but accuracy dropped from 96% for simple medication or event flag questions to 62% for event timing questions, reflecting increasing ambiguity and required clinical reasoning.
arXiv:2610.19644v1 Announce Type: new Abstract: Typed decision models need an interface that can use additional reasoning while retaining candidate outputs. We train a shared candidate-answer reader...
The paper introduces Chinese Decision, a family of Chinese-language models designed for single-pass probabilistic decision-making. The models generate logits for candidate choices in one forward pass and normalize them without autoregressive generation, supporting Boolean, categorical, and ordinal decisions. A 0.8B model trained on 13.048 million decisions achieves 72.24% general-domain accuracy and 76.98% average accuracy across medicine, law, and finance, while a 2B model shows comparable performance after partial training.
System1-Arena is a benchmark that evaluates which decisions can be handled by System‑1 models—those that output probabilities in a single forward pass—versus System‑2 models that generate reasoning steps. It contains 92,000 questions across 105 tasks from 268 public sources, grouped into text classification, applied decisions, evaluation and control, and agent/action, and also includes 138 closed‑loop environments such as web shopping, coding control, robotics, games, and driving. The study tests 30 models, showing that large System‑1 models can match or exceed fast closed LLMs in non‑agentic accuracy with better‑calibrated probabilities at lower latency, while reasoning LLMs are most accurate but far more expensive, and large open LLMs perform best in closed‑loop settings.