SocialBuddy: Tailoring Search Agent for Social Scenarios
In the era of digital social interaction, searching friends' posts from massive social streams has become a fundamental user need. However, while modern agentic search frameworks have achieved remarkable success in conventional retrieval tasks, they break down when confronted with heterogeneous user queries and multi-dimensional social feeds, resulting in severe performance degradation in complex social search. To bridge this gap, we introduce SocialBuddy, the first agentic search framework tailored for social scenarios. Specifically, we construct SocialEnv, the first large-scale simulated environment for social search. Powered by an automated data and trajectory synthesis pipeline, Socia...
| Authors | Mingxuan Li, Yirong Mao, FaZhan Zhang et al. |
|---|---|
| Primary category | cs.SI |
| Published | 28 August 2026 |
| Last updated at source | 4 September 2026 |
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