AI 中文总结
该研究针对类增量学习中单模型范式在优化冲突下的灾难性遗忘问题，提出社会化分工与协作（SDC）框架，通过多专门模型分工协作及能量型相容性准则解决问题，提供持续学习新设计原则。
AI 中文摘要
类增量学习通常被实例化为单模型范式，即统一模型依次适应无限的会话流。在温和的分布偏移下该范式有效，但当后续会话引发不相容的优化方向时，会出现破坏性干扰与灾难性遗忘，该范式便难以适用。我们认为此类遗忘反映了在单一参数空间内强制异构学习动态的结构局限。受社会团结理论启发，我们提出社会化分工与协作（Socialized Division and Collaboration，SDC）作为持续学习的重构方案，其会针对优化冲突将会话学习分解至多个专门模型，同时实现协同协作。为以原则性分配机制支撑该方案，我们引入基于亥姆霍兹自由能的能量型会话-模型相容性准则，其可在冲突目标下指导自适应会话分配与模型演化。该框架将会话分配、模型演化与协同推理整合为统一流水线，为整体式持续学习方案提供替代选择，并为持续优化冲突下的学习凸显更广泛的设计原则。
英文摘要
Class-incremental learning is commonly instantiated as a single-model paradigm, where a unified model sequentially adapts to an unbounded stream of sessions. While effective under mild distributional shifts, this formulation becomes strained when successive sessions induce incompatible optimization directions, leading to destructive interference and catastrophic forgetting. We argue that such forgetting reflects a structural limitation of enforcing heterogeneous learning dynamics within a single parameter space. Motivated by social solidarity theory, we propose Socialized Division and Collaboration (SDC) as a reformulation of continual learning that decomposes session learning across specialized models in response to optimization conflicts, while enabling coordinated collaboration. To support this formulation with a principled allocation mechanism, we introduce an energy-based session-model compatibility criterion grounded in Helmholtz free energy, which guides adaptive session allocation and model evolution under conflicting objectives. This framework integrates session assignment, model evolution, and collaborative inference into a unified pipeline, offering an alternative to monolithic continual learning formulations and highlighting a broader design principle for learning under persistent optimization conflicts.
