Title: PIVOT: Bridging Planning and Execution in LLM Agents via Trajectory Refinement
Source: http://arxiv.org/abs/2605.11225v1
Summary:
PIVOT introduces a novel self-supervised framework that treats agent trajectories as optimizable objects refined through iterative environment feedback, bridging the gap between high-level planning and execution. This methodology establishes a principled approach to trajectory optimization that enhances both constraint satisfaction and computational efficiency in autonomous systems.
