用多个AI角色模拟真实观赛互动，提升孤独观影的社交感。
CompanionCast: Toward Social Collaboration with Multi-Agent Systems in Shared Experiences
- 设计多智能体框架，分工协作模拟真实群体互动
- 实测显示用户社交存在感和情绪分享显著提升
- 适合研究人机社交、虚拟共处体验的开发者与学者
共享体验是社会联结的核心，但媒体消费正日益孤立。尽管AI伴侣可提供实时反应与情绪调节，现有系统或依赖单一智能体，或缺乏社会意识与多方交互能力，无法复现真实群组动态。我们提出CompanionCast，一个通用框架，通过多个专业化AI智能体在实时共享情境中协同作为社会合作者。该框架整合多模态事件检测、滚动上下文缓存以增强语境锚定，并利用空间音频强化共在感。我们在具有丰富互动性与强烈社交传统的体育观赛场景中验证了CompanionCast。针对足球粉丝的小规模试点研究显示，相比独自观看，CompanionCast显著提升了用户的社交存在感与情绪共享水平。最后，我们讨论了多智能体作为社会合作者在共享体验中的意义与开放挑战。
原文摘要 · Abstract (English)
Shared experiences are fundamental to social connection, yet media consumption is increasingly solitary. While AI companions offer real-time reactions and emotional regulation, existing systems either rely on single-agent designs or lack the social awareness and multi-party interaction required to replicate authentic group dynamics. We present CompanionCast, a general framework for orchestrating multiple specialized AI agents as social collaborators within a live shared context. CompanionCast integrates multimodal event detection, rolling context caching for improved grounding, and spatial audio to enhance co-presence. We validate CompanionCast through sports viewing, a domain with rich dynamics and strong social traditions. Pilot studies with soccer fans demonstrate that CompanionCast significantly improves perceived social presence and emotional sharing compared to solitary viewing. We conclude by discussing implications and open challenges for multi-agent systems as social collaborators in shared experiences.
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