AI RESEARCH
AgenticCache: Cache-Driven Asynchronous Planning for Embodied AI Agents
arXiv CS.LG
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ArXi:2604.24039v1 Announce Type: new Embodied AI agents increasingly rely on large language models (LLMs) for planning, yet per-step LLM calls impose severe latency and cost. In this paper, we show that embodied tasks exhibit strong plan locality, where the next plan is largely predictable from the current one. Building on this, we