We propose a training-free test-time adaptation method that significantly improves zero-shot skeleton action recognition by using a training-free cache model during inference time.

We propose a training-free hybrid framework where LLMs generate high-level goals via prompt-based harmony search and optimizers enforce constraints in dynamic ride-hailing, outperforming RL, manual decomposition, and LLM-only baselines.