PromptPilot: Game-Theoretic Multi-Agent Prompt Optimization for Segment Anything

Few shot segmentation with vision foundation models relies on high quality prompts to segment unseen categories from limited support annotations. Existing prompt construction methods depend on test-time adaptation, fixed heuristic sampling, or monolithic reinforcement learning, making it difficult to balance semantic consistency, spatial coverage, and prompt credit assignment. To address these limitations, PromptPilot is proposed as a hierarchical multi-agent reinforcement learning framework for point prompt optimization with frozen DINOv2 and SAM. Prompt construction is formulated as sequential decision making, where feature and physical agents propose complementary prompt modifications, and a manager agent selects actions using SAM feedback and local marginal contribution. PromptPilot functions as an inference-time optimization strategy without parameter updates. Extensive experiments demonstrate that the proposed game-theoretic approach improves segmentation performance and generalization, offering a principled solution for automated prompt engineering. The code is available at https://github.com/L-AILab/PromptPilot .