AI 中文总结
针对云边LLM协作中全局共识与本地个性化难以平衡的问题，提出个性化双层优化框架及高效求解器CIDERS，通过共识变量校正实现协同演进，在数学推理和代码生成上分别提升3.1倍和1.7倍。
AI 中文摘要
在物理世界智能快速发展的背景下，云边协作的大语言模型（LLMs）已成为实际部署LLM的一条有前景的路线。然而，现有的云边范式难以在全局共识与本地个性化之间取得平衡，这无法满足云端统一知识基础与边缘领域特定适配的需求。为解决这一问题，我们首次引入了一个个性化双层优化框架，将云边LLM协作形式化为一个双重结构：上层优化边缘侧个性化，而下层管理云端知识迁移，实现云边协同演进。随后，我们提出了CIDERS，一种高效的求解器，将模型分解为可学习的主干网络和一个信使。当云端对可学习主干执行知识迁移时，关键在于通过共识变量校正将全局轨迹嵌入到每个本地个性化步骤中，以协调个性化与共识。我们提供了全面的理论分析，包括本地轨迹的几何刻画和完整的收敛性保证，揭示了个性化与全局收敛之间的显式权衡结构。大量实验表明，CIDERS在压缩边缘路径上持续优于竞争性基线，在数学推理和代码生成上分别取得3.1倍和1.7倍的提升，并在指令指标上获得10%的相对增益。机制实验将这些增益归因于早期共识校正协调和任务感知蒸馏。总体而言，CIDERS为云边LLM系统中共识引导的持续个性化提供了一条可行路径。
英文摘要
Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap for practical LLM deployment. However, existing cloud-edge paradigms struggle to balance global consensus with local personalization, which fails to satisfy the need for a unified knowledge foundation on the cloud and domain-specific adaptation at the edge. To address this, we introduce, for the first time, a personalized bilevel optimization framework that formalizes cloud-edge LLM collaboration as a dual structure: the upper level optimizes edge-side personalization, while the lower level governs cloud-side knowledge transfer, reaching cloud-edge evolving in coordination. We then propose CIDERS, an efficient solver that decomposes the model into a learnable backbone and a messenger. While the cloud performs knowledge transfer to the learnable backbone, the key lies in embedding global trajectories into each local personalization step via consensus-variate correction to reconcile personalization with consensus. We provide a comprehensive theoretical analysis, including a geometric characterization of the local trajectory and a full convergence guarantee, revealing an explicit trade-off structure between personalization and global convergence. Extensive experiments demonstrate that CIDERS consistently outperforms competitive baselines on the compressed edge path, with 3.1x and 1.7x gains on mathematical reasoning and code generation, respectively, and a 10\% relative gain on instruction metrics. Mechanism experiments attribute these gains to early consensus-corrected coordination and task-aware distillation. Overall, CIDERS offers a viable path toward consensus-guided continuous personalization in cloud-edge LLM systems.
