{"ID":2827428,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16262","arxiv_id":"2512.16262","title":"Learning to Wait: Synchronizing Agents with the Physical World","abstract":"Real-world agentic tasks, unlike synchronous Markov Decision Processes (MDPs), often involve non-blocking actions with variable latencies, creating a fundamental \\textit{Temporal Gap} between action initiation and completion. Existing environment-side solutions, such as blocking wrappers or frequent polling, either limit scalability or dilute the agent's context window with redundant observations. In this work, we propose an \\textbf{Agent-side Approach} that empowers Large Language Models (LLMs) to actively align their \\textit{Cognitive Timeline} with the physical world. By extending the Code-as-Action paradigm to the temporal domain, agents utilize semantic priors and In-Context Learning (ICL) to predict precise waiting durations (\\texttt{time.sleep(t)}), effectively synchronizing with asynchronous environment without exhaustive checking. Experiments in a simulated Kubernetes cluster demonstrate that agents can precisely calibrate their internal clocks to minimize both query overhead and execution latency, validating that temporal awareness is a learnable capability essential for autonomous evolution in open-ended environments.","short_abstract":"Real-world agentic tasks, unlike synchronous Markov Decision Processes (MDPs), often involve non-blocking actions with variable latencies, creating a fundamental \\textit{Temporal Gap} between action initiation and completion. Existing environment-side solutions, such as blocking wrappers or frequent polling, either lim...","url_abs":"https://arxiv.org/abs/2512.16262","url_pdf":"https://arxiv.org/pdf/2512.16262v1","authors":"[\"Yifei She\",\"Ping Zhang\",\"He Liu\",\"Yanmin Jia\",\"Yang Jing\",\"Zijun Liu\",\"Peng Sun\",\"Xiangbin Li\",\"Xiaohe Hu\"]","published":"2025-12-18T07:24:44Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}