Evolving Prompt Adaptation for Vision-Language Models

• URL: http://arxiv.org/abs/2603.09493v1
• Date: Tue, 10 Mar 2026 10:53:01 GMT
• Title: Evolving Prompt Adaptation for Vision-Language Models
• Abstract summary: EvoPrompt is a novel framework designed to steer the prompt trajectory for stable, knowledge-preserving fine-tuning.<n>We show that EvoPrompt achieves performance in few-shot learning while robustly preserving the original zero-shot capabilities of pre-trained VLMs.
• Score: 11.24804817787306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge. While parameter-efficient prompt learning methods offer a promising path, they often suffer from catastrophic forgetting of pre-trained knowledge. Toward addressing this limitation, our work is grounded in the insight that governing the evolutionary path of prompts is essential for forgetting-free adaptation. To this end, we propose EvoPrompt, a novel framework designed to explicitly steer the prompt trajectory for stable, knowledge-preserving fine-tuning. Specifically, our approach employs a Modality-Shared Prompt Projector (MPP) to generate hierarchical prompts from a unified embedding space. Critically, an evolutionary training strategy decouples low-rank updates into directional and magnitude components, preserving early-learned semantic directions while only adapting their magnitude, thus enabling prompts to evolve without discarding foundational knowledge. This process is further stabilized by Feature Geometric Regularization (FGR), which enforces feature decorrelation to prevent representation collapse. Extensive experiments demonstrate that EvoPrompt achieves state-of-the-art performance in few-shot learning while robustly preserving the original zero-shot capabilities of pre-trained VLMs.

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