SearchAgent-X - A high-efficiency inference framework developed by Nankai University and other institutions.
SearchAgent-X is an efficient reasoning framework developed by researchers at Nankai University and the University of Illinois at Urbana-Champaign (UIUC), which can improve the efficiency of search agents based on large language models (LLM).
What is SearchAgent-X?
SearchAgent-X is a high-efficiency inference framework developed by researchers at Nankai University and the University of Illinois at Urbana-Champaign (UIUC). It improves the efficiency of search agents based on large language models (LLMs). Based on high-recall approximate retrieval and two key technologies—priority-aware scheduling and uninterrupted retrieval—it significantly increases system throughput (1.3 to 3.4 times) and reduces latency (to 1/1.7 to 1/5 of the original), without sacrificing generation quality. The framework addresses the two major efficiency bottlenecks of retrieval accuracy and latency, optimizes resource utilization, and provides an important reference for the practical deployment of complex AI agents.
Main functions of SearchAgent-X
- Significantly improve throughputSearchAgent-X can achieve a 1.3 to 3.4x increase in throughput, significantly improving the system's processing capabilities.
- Significantly reduce latencyThis reduces latency to 1/1.7 to 1/5 of the original, ensuring a rapid response.
- Maintain generation qualityWhile improving efficiency, we do not sacrifice the quality of the generated answers, ensuring the system's usability and reliability.
- Dynamic interaction optimizationIt efficiently handles complex multi-step reasoning tasks and supports flexible retrieval and reasoning interactions.
The technical principles of SearchAgent-X
- Priority-Aware Scheduling: Dynamically sort concurrent requests based on their real-time status (such as the number of completed retrievals, the context length of the current sequence, and the request's wait time). By prioritizing high-value computational tasks, unnecessary waiting and redundant computations are reduced, significantly improving the utilization of the KV-cache.
- Non-Stall RetrievalMonitor the maturity of search results and the readiness of the LLM engine, and adaptively terminate search tasks early. Avoid unnecessary waiting for search tasks, ensure timely generation, and significantly reduce end-to-end latency.
- High recall approximate retrievalEmploying high-recall approximate retrieval methods avoids the negative impact of excessively high or low retrieval precision on system efficiency. Based on reasonable retrieval scope settings, it ensures that the retrieval process efficiently supports high-quality inference.
SearchAgent-X project address
- GitHub repository:https://github.com/tiannuo-yang/SearchAgent-X
- arXiv technical paper:https://arxiv.org/pdf/2505.12065
Application scenarios of SearchAgent-X
- Intelligent Customer ServiceTo answer customer questions quickly and accurately, improving response speed and customer satisfaction.
- Search enginesProvides accurate search results and dynamic content generation, optimizing the user experience.
- Enterprise knowledge managementEfficiently retrieve internal knowledge base and support multi-step reasoning for complex problems.
- Intelligent Question AnsweringIt handles complex multi-hop problems and enables real-time interaction with users.
- R&D supportIt enables rapid literature retrieval and optimized experimental design, accelerating the research process.