Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.16262 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912773218238464 |
|---|---|
| author | She, Yifei Zhang, Ping Liu, He Jia, Yanmin Jing, Yang Liu, Zijun Sun, Peng Li, Xiangbin Hu, Xiaohe |
| author_facet | She, Yifei Zhang, Ping Liu, He Jia, Yanmin Jing, Yang Liu, Zijun Sun, Peng Li, Xiangbin Hu, Xiaohe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16262 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Wait: Synchronizing Agents with the Physical World She, Yifei Zhang, Ping Liu, He Jia, Yanmin Jing, Yang Liu, Zijun Sun, Peng Li, Xiangbin Hu, Xiaohe Artificial Intelligence 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. |
| title | Learning to Wait: Synchronizing Agents with the Physical World |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.16262 |