AI RESEARCH
IdleSpec: Exploiting Idle Time via Speculative Planning for LLM Agents
arXiv CS.AI
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ArXi:2605.22154v1 Announce Type: new Large language model (LLM)-based agents solve complex tasks by leveraging multi-step reasoning with iterative tool calls and environment interactions, which incur idle time while waiting for observations. Despite the prevalence of idle time in most agentic scenarios, existing works treat it as an unavoidable overhead or propose restricted solutions that overlook varying computational budgets across different tool calls and future observation uncertainty, thereby leading to suboptimal utilization of idle time. In this paper, we.