SocialBuddy: Tailoring Search Agent for Social Scenarios

SocialBuddy is an agentic search framework designed specifically for social media scenarios, addressing challenges posed by heterogeneous user queries and multi-dimensional social feeds. It introduces SocialEnv, a large-scale simulated environment with 200K user profiles, 10 million posts, and 50K reasoning trajectories, and SocialPO, a hybrid‑granularity optimization framework that combines macro‑level reinforcement with micro‑level fine‑grained corrections to handle sparse rewards. The authors also present the SocialSearch Benchmark for evaluating social search capabilities, and report that SocialBuddy‑35B outperforms larger frontier LLMs in extensive experiments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

• llms
• agents
• benchmarks