Act While Thinking: Accelerating LLM Agents via Pattern-Aware Speculative Tool Execution
#LLM agents #speculative execution #tool prediction #latency reduction #pattern recognition #AI efficiency #agent acceleration
📌 Key Takeaways
- Researchers propose a method to speed up LLM agents by predicting and executing tools before full reasoning is complete.
- The approach uses pattern recognition to speculate on likely tool calls, reducing idle time during agent processing.
- This speculative execution can significantly improve efficiency without compromising accuracy in agent tasks.
- The technique addresses latency issues in complex workflows where LLMs interact with multiple external tools.
- Experimental results show reduced response times and maintained or improved task completion rates.
📖 Full Retelling
arXiv:2603.18897v1 Announce Type: cross
Abstract: LLM-powered agents are emerging as a dominant paradigm for autonomous task solving. Unlike standard inference workloads, agents operate in a strictly serial "LLM-tool" loop, where the LLM must wait for external tool execution at every step. This execution model introduces severe latency bottlenecks. To address this problem, we propose PASTE, a Pattern-Aware Speculative Tool Execution method designed to hide tool latency through speculation. PAST
🏷️ Themes
AI Acceleration, LLM Optimization
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Original Source
--> Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2603.18897 [Submitted on 19 Mar 2026] Title: Act While Thinking: Accelerating LLM Agents via Pattern-Aware Speculative Tool Execution Authors: Yifan Sui , Han Zhao , Rui Ma , Zhiyuan He , Hao Wang , Jianxun Li , Yuqing Yang View a PDF of the paper titled Act While Thinking: Accelerating LLM Agents via Pattern-Aware Speculative Tool Execution, by Yifan Sui and 6 other authors View PDF HTML Abstract: LLM-powered agents are emerging as a dominant paradigm for autonomous task solving. Unlike standard inference workloads, agents operate in a strictly serial "LLM-tool" loop, where the LLM must wait for external tool execution at every step. This execution model introduces severe latency bottlenecks. To address this problem, we propose PASTE, a Pattern-Aware Speculative Tool Execution method designed to hide tool latency through speculation. PASTE is based on the insight that although agent requests are semantically diverse, they exhibit stable application level control flows (recurring tool-call sequences) and predictable data dependencies (parameter passing between tools). By exploiting these properties, PASTE improves agent serving performance through speculative tool execution. Experimental results against state of the art baselines show that PASTE reduces average task completion time by 48.5% and improves tool execution throughput by 1.8x. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.18897 [cs.DC] (or arXiv:2603.18897v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2603.18897 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yifan Sui [ view email ] [v1] Thu, 19 Mar 2026 13:36:50 UTC (555 KB) Full-text links: Access Paper: View a PDF of the paper titled Act While Thinking: Accelerating LLM Agents via Pattern-Aware Speculative Tool Execution, by Yifan Sui and 6 other ...
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