# DynaPrompt: Dynamic Test-Time Prompt Tuning

arXiv: https://arxiv.org/abs/2501.16404 · HTML: https://mlsanity.com/paper/2501.16404/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- First posted on arXiv 27 Jan 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Built on by (most cited first, 30 of 31)

- [UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens](https://mlsanity.com/paper/2505.14671/) (2025)
- [FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts](https://mlsanity.com/paper/2510.08396/) (2025)
- [Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking](https://mlsanity.com/paper/2505.20199/) (2025)
- [Prompt-based Adaptation in Large-scale Vision Models: A Survey](https://mlsanity.com/paper/2510.13219/) (2025)
- [EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answering](https://mlsanity.com/paper/2604.05149/) (2026)
- [On-the-Fly VLA Adaptation via Test-Time Reinforcement Learning](https://mlsanity.com/paper/2601.06748/) (2026)
- [CrossLMM: Decoupling Long Video Sequences from LMMs via Dual Cross-Attention Mechanisms](https://mlsanity.com/paper/2505.17020/) (2025)
- [Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and Reduced Training Time in Pre-trained Model-based Continual Representation Learning](https://mlsanity.com/paper/2510.16877/) (2025)
- [Generalizing vision-language models to novel domains: A comprehensive survey](https://mlsanity.com/paper/2506.18504/) (2025)
- [Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment](https://mlsanity.com/paper/2508.15568/) (2025)
- [Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2603.05235/) (2026)
- [Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learning](https://mlsanity.com/paper/2603.13341/) (2026)
- [D-TPT: Dimensional Entropy Maximization for Calibrating Test-Time Prompt Tuning in Vision-Language Models](https://mlsanity.com/paper/2510.09473/) (2025)
- [Adaptive Debiasing Tsallis Entropy for Test-Time Adaptation](https://mlsanity.com/paper/2602.11743/) (2026)
- [Limited Reasoning Space: The cage of long-horizon reasoning in LLMs](https://mlsanity.com/paper/2602.19281/) (2026)
- [Dual-Modality Anchor-Guided Filtering for Test-time Prompt Tuning](https://mlsanity.com/paper/2604.12403/) (2026)
- [Adapting in the Dark: Efficient and Stable Test-Time Adaptation for Black-Box Models](https://mlsanity.com/paper/2604.15609/) (2026)
- [Improving Calibration in Test-Time Prompt Tuning for Vision-Language Models via Data-Free Flatness-Aware Prompt Pretraining](https://mlsanity.com/paper/2604.27715/) (2026)
- [Progressive Scaling Visual Object Tracking](https://mlsanity.com/paper/2505.19990/) (2025)
- [ComMem: Complementary Memory Systems for Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2606.28719/) (2026)
- [Doubly Debiased Test-Time Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2511.11690/) (2025)
- [Measurement Plasticity: Sensor-Level Adaptation for Vision-Language Models](https://mlsanity.com/paper/2512.12571/) (2025)
- [LiteEmbed: Adapting CLIP to Rare Classes](https://mlsanity.com/paper/2601.09661/) (2026)
- [SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models](https://mlsanity.com/paper/2606.06943/) (2026)
- [Concept Alignment Contrast and Long-Short Prompt Memory for Test-Time Adaptation of SAM3 in Medical Image Segmentation](https://mlsanity.com/paper/2606.22963/) (2026)
- [Cross-device Collaborative Test-time Adaptation with Zeroth-order Optimization and Model Merging](https://mlsanity.com/paper/2607.02988/) (2026)
- [From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model](https://mlsanity.com/paper/2608.18339/) (2026)
- [Fine-Tuning of Transformer models with Frames](https://mlsanity.com/paper/2608.26430/) (2026)
- [Rethinking the Test-Time Prompt Tuning Objective from the Perspective of Calibration](https://mlsanity.com/paper/2608.30230/) (2026)
- [Bridging the Confidence Gap: Temperature Scaling for Calibrating Test-Time Prompt Tuning](https://mlsanity.com/paper/2609.17386/) (2026)

## Abstract (arXiv)

Test-time prompt tuning enhances zero-shot generalization of vision-language models but tends to ignore the relatedness among test samples during inference. Online test-time prompt tuning provides a simple way to leverage the information in previous test samples, albeit with the risk of prompt collapse due to error accumulation. To enhance test-time prompt tuning, we propose DynaPrompt, short for dynamic test-time prompt tuning, exploiting relevant data distribution information while reducing error accumulation. Built on an online prompt buffer, DynaPrompt adaptively selects and optimizes the relevant prompts for each test sample during tuning. Specifically, we introduce a dynamic prompt selection strategy based on two metrics: prediction entropy and probability difference. For unseen test data information, we develop dynamic prompt appending, which allows the buffer to append new prompts and delete the inactive ones. By doing so, the prompts are optimized to exploit beneficial information on specific test data, while alleviating error accumulation. Experiments on fourteen datasets demonstrate the effectiveness of dynamic test-time prompt tuning.