Med-OPD: Improving Medical Vision-Language Models via Evidence-Aware On-Policy Distillation

MedVCoT: Bridging the Modality Gap in Medical VQA Through Latent Visual Reasoning

This work proposes MedVCoT, which incorporates latent visual reasoning into the medical visual question answering (VQA) domain, and utilizes the specialized expertise of MedSAM to train a large vision-language model so that it can autonomously generate consistent and continuous latent visual tokens within Visual Chain-of-Thought.

MedUP: Awakening Unified Understanding and Perception in Medical Vision-Language Models

Medical Vision-Language Models (Med-VLMs) excel at verbalizing visual content, yet precise visual perception, segmentation, and grounding remain challenging. Existing approaches either verbalize regions as coordinate strings or rely on external modules that decouple perception from understanding, creating representation gaps for region-language alignment. We present MedUP, a Med-VLM that natively unifies perception and understanding within a shared token space. At its core lies UniMedTok, a region tokenizer that encodes masks as discrete tokens in the LLM vocabulary, enabling the model to seamlessly interleave mask tokens with text. We curate UniMed-Train, a 1.84M-instance corpus spanning text-guided segmentation, region-grounded understanding, medical VQA and CoT-based segmentation, and introduce UniMed-Bench for unified evaluation. Extensive experiments show that MedUP outperforms native, agentic, and dual-decoder Med-VLMs across all tasks while remaining competitive with specialist segmentors, demonstrating the strong potential of unified understanding and perception modeling.

Adaptive Diffusion Vision-Language Models for Reliable Medical Image Understanding

Biomedical vision–language models increasingly support image-grounded clinical dialogue, yet most deployable systems still depend on autoregressive language generation. Such systems tend to truncate answers, react poorly to length instructions, and offer no principled way to signal uncertainty when image evidence is weak. We present MedDiffVL, a biomedical vision-language model that pairs a masked language diffusion backbone with a SigLIP-2 visual encoder and a multimodal alignment pipeline that injects modality and question-type cues. Three inference-time mechanisms target the failure modes of diffusion-based generators in the clinical setting. An adaptive confidence-guided remasking rule uses a time-aware threshold and a short-window stability check to remove repetitive low-quality candidates. A clinically aware length controller selects a target length from question type, modality, and an internal uncertainty estimate. A reliability gate combines visual-evidence and answer-confidence scores to emit, hedge, or escalate a response. On VQA-RAD, SLAKE, and PathVQA, the model reaches 85.42, 92.78, and 94.91% closed-form accuracy and an overall conversation score of 53.42 against a fixed reference. Token repetition falls from 0.18 to 0.06. An ECE falls from 0.137 to 0.034, but this reflects an ECE-surrogate training loss and is not independently validated. These gains are not uniform. The closed-form gains over the prior diffusion model lie within run-to-run variance, and latency stays higher than autoregressive baselines. The main contribution is controllability and reliability-aware decoding, not higher closed-form accuracy. The results indicate that confidence-guided masked diffusion with reliability-aware decoding is a useful direction for controllable and reliability-aware clinical assistants.

Hierarchical Conditional Energy Modeling for Medical Vision–Language Pretraining

TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models

TCLA corrects inference logits based on a small set of support samples, boosting pretrained VLMs performance by improving inter-class deconfusion and reducing domain shift, and consistently improves OOD performance of Medical VLMs and outperforms existing training-based adaptation methods.

MedPixel: A Unified Pixel-Language Model for Medical Reasoning and Segmentation

Reliable medical image understanding requires models to connect clinical language and visual reasoning with pixel-level grounding. Yet medical vision-language models often lack precise localization, whereas medical segmenters typically rely on explicit target categories or precise spatial prompts. This divide is reinforced by a supervision mismatch: segmentation datasets provide precise masks but little language supervision, whereas medical vision-language data rarely pair language with dense spatial annotations. To address this gap, we present MedPixel, a unified medical pixel-language model built around a shared language--mask interface. To provide scalable supervision, we introduce MedPLG-440K, comprising approximately 440K pixel-language task samples constructed through a clinically motivated synthesis process without external LLM annotation. MedPixel is trained with joint multi-task supervised fine-tuning followed by Pixel-Level Preference Optimization, which uses ground-truth masks as offline verifiers to derive response preferences from mask quality. MedPixel supports a broad spectrum of tasks spanning explicit grounding, implicit reasoning, spatial interaction, grounded explanation, and medical VQA. Across this task spectrum, MedPixel achieves strong performance in both pixel-level prediction and response generation, together with effective zero-shot transfer to external grounding benchmarks and robustness to imperfect spatial prompts. Code and model checkpoints will be released at https://github.com/yhy-whu/Medpixel.