arXiv AI June 4, 2026 By Tran Dinh Tien, Zhiqiang Shen

Geometry-Aware Distillation for Prompt Tuning Biomedical Vision-Language Models

arXiv:2606. 04922v1 Announce Type: cross Abstract: Current prompt-based and adapter-based tuning of vision-language models (VLMs) is attractive for medical imaging, where clinical data sensitivity favors frozen backbones and annotations are limited.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 21

LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering

LiteMedCoT-VL is a parameter‑efficient pipeline that transfers chain‑of‑thought reasoning from a 235B teacher model to a 2B student model using LoRA fine‑tuning on explanation‑enriched data. The approach enables a compact vision‑language model to perform medical visual question answering without relying on image captions, achieving 64.9% accuracy on the PMC‑VQA benchmark—an 11‑point improvement over the zero‑shot Qwen3‑VL‑4B baseline. Visual grounding analysis confirms that the model bases its predictions on image content rather than textual priors.

By Runze Ma, Shunbo Jia, Haonan Lyu, Guo Liu, Caizhi Liao

llms nlp fine-tuning efficiency multimodal benchmarks

arXiv AI

Jul 8

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology

arXiv:2605. 18419v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology.

By Franciskus Xaverius Erick, Johanna Paula M\"uller, Bernhard Kainz

llms rag fine-tuning efficiency multimodal safety

arXiv Computer Vision

3d ago

Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs

arXiv:2609.38362v1 Announce Type: new Abstract: Generative vision-language models (VLMs) such as Qwen-VL and LLaVA achieve strong zero-shot performance on tasks overlapping with their pretraining dis...

By Hung-Jen Chen, Yu-Heng Ho, Ting-Yao Huang, Po-Hsiang Hsu, Li-Yu Chen, Chun-Yi Lee, Min Sun

llms multimodal benchmarks

arXiv AI

Jun 3

PAND: Prompt-Aware Neighborhood Distillation for Lightweight Fine-Grained Visual Classification

arXiv:2602. 07768v3 Announce Type: replace-cross Abstract: Distilling knowledge from large Vision-Language Models (VLMs) into lightweight networks is crucial yet challenging in Fine-Grained Visual Classification (FGVC), due to the reliance on fixed prompts and global alignment.

By Qiuming Luo, Yuebing Li, Feng Li, Chang Kong

llms efficiency multimodal benchmarks safety

arXiv Computer Vision

Aug 24

When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning

arXiv:2608.21300v1 Announce Type: new Abstract: Foundation models for medical image segmentation, like prompt-based MedSAM, generalize well across domains and modalities, often in zero or few-shot se...

By Marko Haralovi\'c, Sounic Akkaraju, Carlo Baretta, Vasil Zapryanov, Alexia Briassouli

computer-vision fine-tuning benchmarks safety

arXiv AI

Jun 11

OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models

arXiv:2606. 12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers.

By Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci, Abeer Badawi, Adibvafa Fallahpour, Arash Afkanpour, Leonid Sigal, Ali Etemad, Elham Dolatabadi

llms fine-tuning multimodal benchmarks safety

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Related stories

LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology

Inductive Visual Logic for Few-Shot Out-Of-Distribution Adaptation in VLMs

PAND: Prompt-Aware Neighborhood Distillation for Lightweight Fine-Grained Visual Classification

When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuning

OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models