PackAgent: Evolving Three Coordinated Skills for State-Adaptive 3D Bin Packing

The three-dimensional bin packing problem aims to pack heterogeneous items into as few bins as possible while satisfying geometric and physical constraints, including containment, non-overlap, and support. Item selection and placement decisions must adapt to changes in the remaining items and the current spatial configuration, whereas fixed heuristics struggle to accommodate the decision requirements of different packing phases. To address this challenge, we propose PackAgent, a three-skill evolutionary framework for constrained multi-bin three-dimensional packing. PackAgent decomposes the core decisions into three skills: state reflection $\epsilon$, item selection $\varphi$, and placement scoring $\psi$. A large language model jointly evolves these executable skills offline. At each placement step, $\epsilon$ generates phase cues and factor weights from the current packing state, dynamically modulating the decision preferences of $\varphi$ and $\psi$. Candidate generation, rotation enumeration, and constraint verification are handled by a fixed solver to ensure geometric feasibility. Unlike automated heuristic design methods that search for a single fixed rule or rely on post-hoc selection among multiple strategies, PackAgent enables state-adaptive decisions through step-level factor modulation within a single packing trajectory. We evaluate PackAgent on the SSSCSP benchmark under offline, online, and semi-online settings, as well as across instance scales on the IVC dataset. PackAgent achieves the lowest average bin count among the compared single-trajectory methods in all three information-visibility settings. Deployment ablations further demonstrate the importance of dynamic modulation through state reflection for coordinating item selection and placement scoring.