PaSa is an advanced academic paper search agent developed by ByteDance, based on large language model (LLM) technology. It can autonomously invoke search tools, read papers, and filter relevant references to obtain comprehensive and accurate results for complex academic queries. This technology is optimized through reinforcement learning, trained using the synthetic dataset AutoScholarQuery, and has shown outstanding performance on the real-world query dataset RealScholarQuery, significantly outperforming traditional search engines and GPT-based methods. The main advantages of PaSa lie in its high recall and precision rates, providing researchers with a more efficient academic search experience.