bytedance/pasa
A 7B-parameter LLM agent that autonomously searches, reads, and selects academic papers for complex scholarly queries.
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PaSa is a paper search agent built by ByteDance that uses large language models to autonomously make decisions such as invoking search tools, reading papers, and selecting relevant references. The agent is optimized using reinforcement learning on a synthetic dataset of 35k academic queries and evaluated on a real-world benchmark. The project provides two 7B models (crawler and selector) on HuggingFace and significantly outperforms Google-based baselines and GPT-4o-powered approaches in recall and precision for scholarly search tasks.
Frequently asked
- What is bytedance/pasa?
- A 7B-parameter LLM agent that autonomously searches, reads, and selects academic papers for complex scholarly queries.
- Is pasa open source?
- Yes — bytedance/pasa is open source, released under the Apache-2.0 license.
- What language is pasa written in?
- bytedance/pasa is primarily written in Python.
- How popular is pasa?
- bytedance/pasa has 1.6k stars on GitHub.
- Where can I find pasa?
- bytedance/pasa is on GitHub at https://github.com/bytedance/pasa.