# Logit-Level Uncertainty Quantification in Vision-Language Models for Histopathology Image Analysis

arXiv: https://arxiv.org/abs/2603.03527 · HTML: https://mlsanity.com/paper/2603.03527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 9 of 9)

- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [An Introduction to Vision-Language Modeling](https://mlsanity.com/paper/2405.17247/) (2024)
- [PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathology](https://mlsanity.com/paper/2405.10254/) (2024)
- [VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge](https://mlsanity.com/paper/2411.12915/) (2024)
- [Uncertainty-Aware Evaluation for Vision-Language Models](https://mlsanity.com/paper/2402.14418/) (2024)
- [Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models](https://mlsanity.com/paper/2505.05163/) (2025)
- [Lifelong Whole Slide Image Analysis: Online Vision-Language Adaptation and Past-to-Present Gradient Distillation](https://mlsanity.com/paper/2505.01984/) (2025)
- [T3: Test-Time Model Merging in VLMs for Zero-Shot Medical Imaging Analysis](https://mlsanity.com/paper/2510.27265/) (2025)
- [Value-Guided Iterative Refinement and the DIQ-H Benchmark for Evaluating VLM Robustness](https://mlsanity.com/paper/2512.03992/) (2025)

## Abstract (arXiv)

Vision-Language Models (VLMs) with their multimodal capabilities have demonstrated remarkable success in almost all domains, including education, transportation, healthcare, energy, finance, law, and retail. Nevertheless, the utilization of VLMs in healthcare applications raises crucial concerns due to the sensitivity of large-scale medical data and the trustworthiness of these models (reliability, transparency, and security). This study proposes a logit-level uncertainty quantification (UQ) framework for histopathology image analysis using VLMs to deal with these concerns. UQ is evaluated for three VLMs using metrics derived from temperature-controlled output logits. The proposed framework demonstrates a critical separation in uncertainty behavior. While VLMs show high stochastic sensitivity (cosine similarity (CS) <0.71 and <0.84, Jensen-Shannon divergence (JS) <0.57 and <0.38, and Kullback-Leibler divergence (KL) <0.55 and <0.35, respectively for mean values of VILA-M3-8B and LLaVA-Med v1.5), near-maximal temperature impacts (DeltaT ≈ 1.00), and displaying abrupt uncertainty transitions, particularly for complex diagnostic prompts. In contrast, the pathology-specific PRISM model maintains near-deterministic behavior (mean CS >0.90, JS <0.10, KL <0.09) and significantly minimal temperature effects across all prompt complexities. These findings emphasize the importance of logit-level uncertainty quantification to evaluate trustworthiness in histopathology applications utilizing VLMs.