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

This paper addresses the slowness of AI agents (LLMs that use external tools) by proposing PASTE, a method that predicts and executes likely future tool calls before the LLM finishes thinking. By recognizing that agents tend to follow recurring patterns of tool usage, PASTE can execute tools speculatively while the LLM is still processing, hiding wait times and achieving 48.5% faster task completion.