Socialized Continual Learning: Recursive Weight Space Editing for Scalable Multi-Expert Collaboration

Intelligent systems increasingly rely on collaboration among specialized agents to exchange complementary expertise. However, existing collaborative paradigms such as Federated Learning (FL) and Socialized Learning (SL) typically assume one-shot or fixed-stage interaction, limiting their ability to adapt to open-ended environments where new experts and capabilities emerge continuously. To address this gap, we introduce Socialized Continual Learning (SCL), a continual collaborative learning paradigm that enables previously socialized experts to interact with newly emerging experts while retaining their specialized capabilities. The central challenge is to reconcile newly acquired expertise with knowledge accumulated through previous interactions. To this end, we propose Recursive Weight Space Editing (RWSE), which formulates SCL as a constrained functional alignment problem and recursively integrates new expertise into an evolving shared model. RWSE derives recursive sufficient statistics and an exact closed-form residual editing rule, enabling efficient knowledge integration without revisiting previously incorporated experts. Furthermore, a nonlinear spectrally anisotropic regularization mechanism imposes stronger protection on historically sensitive directions while preserving flexibility for new expertise, thereby mitigating inter-expert interference and maintaining prior specialization. Extensive experiments across diverse continual collaborative learning settings demonstrate the effectiveness of RWSE for continual expert integration.