Multi-agent systems (MAS) have recently emerged as promising socio-collaborative companions for emotional and cognitive support. However, these systems frequently suffer from persona collapse--where agents revert to generic, homogenized assistant behaviors--and social sycophancy, which produces redundant, non-constructive dialogue. We propose MASCOT, a generalizable framework for multi-perspective socio-collaborative companions. MASCOT introduces a novel bi-level optimization strategy to harmonize individual and collective behaviors: 1) Persona-Aware Behavioral Alignment, an RLAIF-driven pipeline that finetunes individual agents for strict persona fidelity to prevent identity loss; and 2) Collaborative Dialogue Optimization, a meta-policy guided by group-level rewards to ensure diverse and productive discourse. Extensive evaluations across psychological support and workplace domains demonstrate that MASCOT significantly outperforms state-of-the-art baselines, achieving improvements of up to +14.1 in Persona Consistency and +10.6 in Social Contribution. Our framework provides a practical roadmap for engineering the next generation of socially intelligent multi-agent systems.

翻译：多智能体系统（MAS）近期已成为情感与认知支持领域极具前景的社会协作伴侣。然而，这些系统常面临角色崩溃（即智能体退化为通用化、同质化的助手行为）与社会迎合（导致冗余且无建设性的对话）等问题。我们提出MASCOT，一个面向多视角社会协作伴侣的通用框架。MASCOT引入了一种新颖的双层优化策略以协调个体与集体行为：1）角色感知行为对齐——基于RLAIF的流程，通过微调个体智能体实现严格角色保真度以防止身份丢失；2）协作对话优化——受群体级奖励引导的元策略，确保对话的多样性与建设性。在心理支持与工作场所领域的广泛评估表明，MASCOT显著优于现有基线方法，在角色一致性指标上最高提升+14.1，在社会贡献度指标上提升+10.6。本框架为构建下一代社会智能多智能体系统提供了切实可行的技术路线。