Abstract: To address the collaborative dilemmas in smart logistics, such as information silos, lack of trust, and goal conflicts during deep collaboration, a novel solution is proposed from the perspective of large language models (LLMs). The bottlenecks of collaborative management in smart logistics operations are systematically analyzed. The mechanisms through which five key technologies of LLMs—natural language understanding, multimodal perception fusion, agent-based interactive reasoning, simulation and prediction, and resource scheduling optimization—empower collaborative management are elaborated. A four-layer collaborative management framework is constructed, comprising a data layer, a support layer, an application layer, and a guarantee layer. An information collaboration platform is proposed to achieve automated alignment and interoperability of logistics data. A data-driven collaborative negotiation mechanism is designed to provide transparent quantitative analysis of benefits and risks. Furthermore, a global resource optimization system is built to coordinate multiple constraints and reconcile conflicting goals. The framework introduces novel mechanisms for cross-entity semantic interoperability, trust establishment, and global resource optimization, providing systematic theoretical support and a feasible implementation pathway for the evolution of smart logistics from decentralized operations to integrated intelligent collaboration.