# Can Experts Adapt Without Training? On Test-Time Modality Generalization in MVLMs

arXiv: https://arxiv.org/abs/2607.16726 · HTML: https://mlsanity.com/paper/2607.16726/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 24 of its 29 references that are in the map.
- First posted on arXiv 18 Jul 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)
- Mixture-of-experts / [Medical and biomedical MoE](https://mlsanity.com/moe/medical-and-biomedical-moe/)

## Builds on (most cited first, 24 of 24)

- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs](https://mlsanity.com/paper/2303.00915/) (2023)
- [Efficient Test-Time Model Adaptation without Forgetting](https://mlsanity.com/paper/2204.02610/) (2022)
- [Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2209.07511/) (2022)
- [Efficient Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2403.18293/) (2024)
- [Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors](https://mlsanity.com/paper/2403.07366/) (2024)
- [Sparse Mixture-of-Experts are Domain Generalizable Learners](https://mlsanity.com/paper/2206.04046/) (2022)
- [ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset](https://mlsanity.com/paper/2405.10004/) (2024)
- [Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts](https://mlsanity.com/paper/2210.03885/) (2022)
- [WATT: Weight Average Test-Time Adaptation of CLIP](https://mlsanity.com/paper/2406.13875/) (2024)
- [BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models](https://mlsanity.com/paper/2411.15232/) (2024)
- [DOTA: Distributional Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2409.19375/) (2024)
- [Bayesian Test-Time Adaptation for Vision-Language Models](https://mlsanity.com/paper/2503.09248/) (2025)
- [BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping](https://mlsanity.com/paper/2410.15430/) (2024)
- [Test-Time Low Rank Adaptation via Confidence Maximization for Zero-Shot Generalization of Vision-Language Models](https://mlsanity.com/paper/2407.15913/) (2024)
- [BAPLe: Backdoor Attacks on Medical Foundational Models using Prompt Learning](https://mlsanity.com/paper/2408.07440/) (2024)
- [On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?](https://mlsanity.com/paper/2505.15425/) (2025)
- [MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction](https://mlsanity.com/paper/2508.21793/) (2025)
- [Noise is an Efficient Learner for Zero-Shot Vision-Language Models](https://mlsanity.com/paper/2502.06019/) (2025)
- [Delving into Out-of-Distribution Detection with Medical Vision-Language Models](https://mlsanity.com/paper/2503.01020/) (2025)
- [Exploring Expert Specialization through Unsupervised Training in Sparse Mixture of Experts](https://mlsanity.com/paper/2509.10025/) (2025)
- [A Large-scale Medical Visual Task Adaptation Benchmark](https://mlsanity.com/paper/2404.12876/) (2024)
- [Variational Inference, Entropy, and Orthogonality: A Unified Theory of Mixture-of-Experts](https://mlsanity.com/paper/2601.03577/) (2026)

## Built on by (most cited first, 1 of 1)

- [Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data](https://mlsanity.com/paper/2609.18842/) (2026)

## Abstract (arXiv)

Medical vision-language models (MVLMs) promise broad zero-shot generalization, yet their reliability collapses when confronted with unseen modalities and domains, precisely where clinical robustness matters most. To address this gap, we revisit test-time modality generalization from the perspective of Mixture-of-Experts (MoE) and ask: can experts route-and-adapt without any optimization during inference? We identify a fundamental specialization-generalization dilemma at test time, where blindly aggregating modality experts dilutes modality-specific knowledge, while selecting one highly confident expert risks mismatch under shift. To address this, we propose MoBE: a fully optimization-free framework that performs dynamic expert selection and adaptation at test time. MoBE combines entropy-guided dynamic routing in MoE settings with expert-wise Bayesian adaptation, enabling experts to update their confidence and adapt online without gradient updates. Without parametric updates, MoBE augments a static MVLM with test-time routing and online statistics, achieving average accuracy gains of +4.72, +7.17, and +4.3 over state-of-the-art TTA methods across seen, unseen, and heterogeneous medical benchmarks, highlighting the effectiveness of training-free expert adaptation for robust modality generalization.