Act While Thinking: Accelerating LLM Agents via Pattern-Aware Speculative Tool Execution

2026-03-19

AI Summary

Current LLM agents are constrained by a serial “LLM–tool” execution loop, resulting in significant latency. This work proposes PASTE, the first approach to enable speculative parallel execution of tool calls by exploiting stable control-flow patterns and predictable data dependencies inherent in tasks. PASTE employs a pattern-aware speculation mechanism, explicit control-flow modeling, and dynamic dependency analysis to schedule tool invocations in parallel, effectively masking execution latency. Experimental results demonstrate that PASTE reduces average task completion time by 48.5% compared to state-of-the-art methods and achieves a 1.8× improvement in tool throughput, substantially overcoming the bottleneck imposed by sequential execution.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• LLM agents
• tool execution latency
• serial execution
• autonomous task solving
• latency bottleneck

Innovation

• speculative execution
• LLM agents
• tool latency hiding
• control flow patterns
• data dependency prediction