Multi-agent systems (MAS) are emerging as promising socio-collaborative companions for emotional and cognitive support. However, existing systems frequently suffer from persona collapse, where agents revert to generic, homogenized assistant behaviors, and social sycophancy, where agents produce redundant, non-constructive dialogue. We propose MASCOT, a multi-agent 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 fine-tunes individual agents for agent-specific identities; and 2) Collaborative Dialogue Optimization, a group-level adaptation process that promotes complementary, diverse, and productive discourse. We evaluate MASCOT using human-grounded contexts drawn across both in-domain and out-of-domain (OOD) settings against state-of-the-art baselines. MASCOT improves persona consistency by up to +14.1 and social contribution by up to +10.6. A broad evaluation suite, including human evaluation, multiple LLM judges, three-way comparisons, and automatic metrics, further shows that MASCOT produces more role-consistent and less redundant multi-agent dialogue.

翻译：多智能体系统（MAS）正成为提供情感与认知支持的新型社会协作伴侣。然而，现有系统常面临角色坍塌（智能体退化为通用化、同质化的助手行为）与社会谄媚（智能体产生冗余、非建设性对话）问题。我们提出MASCOT——一种面向多视角社会协作伴侣的多智能体框架。MASCOT引入创新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，一种基于RLAIF的流水线，用于微调个体智能体以保持其独特身份；2）协作对话优化，一种促进互补性、多样性和建设性话语的群体级自适应过程。我们采用涵盖领域内与领域外设置的人类语境评估MASCOT，并与最先进基线进行对比。MASCOT将角色一致性提升高达14.1分，社会贡献度提升高达10.6分。通过包含人工评估、多LLM评审、三方对比及自动指标的广泛评估套件，进一步证明MASCOT能生成更符合角色定位且冗余度更低的多智能体对话。