Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents (2505.12065v1)

Abstract: LLM-based search agents have shown remarkable capabilities in solving complex tasks by dynamically decomposing problems and addressing them through interleaved reasoning and retrieval. However, this interleaved paradigm introduces substantial efficiency bottlenecks. First, we observe that both highly accurate and overly approximate retrieval methods degrade system efficiency: exact search incurs significant retrieval overhead, while coarse retrieval requires additional reasoning steps during generation. Second, we identify inefficiencies in system design, including improper scheduling and frequent retrieval stalls, which lead to cascading latency -- where even minor delays in retrieval amplify end-to-end inference time. To address these challenges, we introduce SearchAgent-X, a high-efficiency inference framework for LLM-based search agents. SearchAgent-X leverages high-recall approximate retrieval and incorporates two key techniques: priority-aware scheduling and non-stall retrieval. Extensive experiments demonstrate that SearchAgent-X consistently outperforms state-of-the-art systems such as vLLM and HNSW-based retrieval across diverse tasks, achieving up to 3.4$\times$ higher throughput and 5$\times$ lower latency, without compromising generation quality. SearchAgent-X is available at https://github.com/tiannuo-yang/SearchAgent-X.

Demystifying and Enhancing the Efficiency of LLM-Based Search Agents

LLM-based search agents are a promising advancement in the field of artificial intelligence, enhancing the computational capabilities of traditional Retrieval-Augmented Generation (RAG) systems. These agents employ dynamic and adaptive interleaving of reasoning and retrieval operations to solve complex tasks, aligning with the latest evolution in RAG systems known as Search Agents. This paper focuses on identifying and addressing the efficiency bottlenecks in such LLM-based systems, introducing an optimized framework named SearchAgent-X. Insights into efficiency factors such as retrieval accuracy and latency are explored, alongside innovative solutions such as priority scheduling and non-stall retrieval mechanisms.

Key Observations and Challenges

The paper delineates two key challenges faced by LLM-based search agents:

1. Retrieval Accuracy and Efficiency: A non-monotonic relationship exists between retrieval accuracy and system efficiency. Excessively high or low retrieval accuracy adversely impacts overall performance. High recall exact searches lead to substantial computational overhead whereas low recall necessitates additional reasoning iterations by the LLM. This results in inefficient resource utilization and increased latency.
2. Retrieval Latency and Scheduling: Search agents are notably sensitive to retrieval latency, with latency effects magnified due to improper scheduling and retrieval stalls. Unlike traditional RAG systems where retrieval latency is largely amortized, search agents face cascading latency issues due to misaligned scheduling policies. Improper scheduling and retrieval stalls lead to increased recomputation and prolonged inference times.

Proposed Solution: SearchAgent-X

SearchAgent-X addresses the aforementioned challenges through the following strategies:

• High-Recall Approximate Retrieval: Implementing a high-recall approximate retrieval method serves to balance the trade-off between recall and computational overhead, ensuring efficient retrieval processes that support multi-step reasoning without unnecessary delays.
• Priority Scheduling: This technique prioritizes requests based on metrics such as retrieval count, context length, and waiting time, enhancing Key-Value (KV) cache utilization to reduce recomputation and improve throughput.
• Non-Stall Retrieval: The non-stall retrieval mechanism aims to mitigate inefficiencies from retrieval latency by incorporating adaptive search termination strategies based on retrieval result maturity and LLM readiness, thus preventing unnecessary stalls during generation.

Experimental Validation and Implications

Extensive experimental evaluations reveal that SearchAgent-X consistently outperforms existing state-of-the-art systems in terms of throughput and latency across diverse tasks. By achieving up to 3.4 times higher throughput and 5 times lower latency, SearchAgent-X demonstrates substantial improvements in system performance while maintaining high generation quality akin to exact retrieval methods.

The implications of this research are profound both practically and theoretically. Practically, SearchAgent-X provides an efficient framework for deploying LLM-based search agents in real-world applications where high performance and low latency are critical. Theoretically, this research enhances the understanding of the intricacies of LLM-based search agent systems, opening avenues for further exploration in optimizing retrieval and reasoning interleaving techniques.

Future Directions

Moving forward, this research paves the way for integrating LLM-based search agents into scalable and robust AI systems capable of handling complex multi-turn interactions with external knowledge sources efficiently. Future developments may focus on enhancing retrieval strategies, exploring hybrid sparse-dense retrieval methods, and optimizing encoding techniques to complement the refined scheduling and retrieval mechanisms introduced by SearchAgent-X.

Overall, this paper contributes significant insights into the efficiency dynamics of search agents, proposing practical solutions for bridging the gap between reasoning quality and computational efficiency—an invaluable asset in the ongoing advancement of LLMs.