Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding
Paper recorded by Signals 4 on 2026-10-29 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30
Category: cs.CV · 计算机视觉 · first seen 2026-10-30
Abstract
Medical diagnostic reasoning requires verifiable links between visual evidence and clinical knowledge, yet current medical vision language models are aligned with objectives that reward the wrong behavior. Uniform supervised fine tuning lets easy cases dominate updates while clinically critical boundary cases saturate prematurely. Sparse rewards based only on final-answer correctness cannot assess
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Cite this page: Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding: the #81 most recent of 710 cs.CV papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/aligning-medical-vision-language-models-at-the-capability-boundary-with-entropy-.html
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