arXiv cs.CL· Bo Liu, Lianyu Wang, Meng Wang, Ching-Yu Cheng, Huazhu Fu·· 13 小时前AI 评分34
RetiJev：快速且来源可溯的眼科决策模型
RetiJev: Fast and Source-Grounded Ophthalmic Decision Models
AI 导读
RetiJev 团队提出 0.8B-9B 的眼科决策模型系列，直接对候选答案打分、不做自回归解码，在指南分级任务上比 27B LLM 和商用 Jev 1.13 决策模型高 10.4-23.0 分。
正文 · 原文
Abstract:Ophthalmic clinical decision support requires fast, accurate, and privacy-preserving inference, yet autoregressive large language models (LLMs) incur substantial latency and computational overhead for decisions among predefined actions. We therefore investigate a decision-oriented alternative to generative reasoning. Specifically, we first introduce RetiJev-Data, a source-grounded ophthalmology corpus comprising 78,158 clinical-decision and diagnosis questions derived from articles in PubMed Central and a MeSH vocabulary covering 531 eye diseases, and 50,074 guideline-grading questions spanning 11 grading systems, along with a 24,662-question evaluation benchmark. Based on this, we develop RetiJev, a family of 0.8B-9B decision models that directly score candidate answers without autoregressive decoding. Across all model sizes, RetiJev outperforms both a 27B LLM and the commercial Jev 1.13 decision model on guideline grading by 10.4-23.0 points. RetiJev-9B further achieves the highest accuracy on clinical decisions, improving over both baselines by 5.4-6.5 points while matching them on diagnosis. It answers a case in 158 ms on one GPU, 3.5x faster than LLM generation and 470x faster than reasoning. Models are released at this https URL.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.30947 [cs.CL] |
| (or arXiv:2610.30947v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.30947 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bo Liu [view email]
[v1]
Thu, 22 Oct 2026 08:59:47 UTC (851 KB)
来源：arXiv cs.CL · arxiv.org