OneDayAgent：迈向自主智能体的长时程框架

摘要

LLM智能体越来越多地被应用于涵盖工作、学习和生活的开放式日常请求。这些任务具有长时程、跨环境和多模态的特点，迫使智能体在导航异构工具和附件的同时，在众多步骤中保持目标和约束。虽然已有工作解决了目标漂移、状态丢失和上下文溢出等个别失效模式，但对于单一管控框架能否联合管理这些问题并在不同后端上保持有效性，相关研究仍然较少。我们提出了OneDayAgent，一个用于自主智能体的长时程管控框架。OneDayAgent将开放式请求转化为受管理的执行过程，将任务分解为有界的子任务，在上下文压力下维护执行记忆，并验证和修复最终交付物。我们在AgentIF-OneDay的104个任务上评估了OneDayAgent。使用GLM-5.2后端，OneDayAgent取得了0.821的总体得分，创下新的最优性能。同一框架可运行于来自三个模型族的五个后端LLM，表明该框架无需调优即可跨后端泛化，即使不同模型在同一工作流程下会展现出不同的执行风格。

English

LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life. These tasks are long-horizon, cross-environment, and multimodal, forcing the agent to preserve goals and constraints across many steps while navigating heterogeneous tools and attachments. While prior work has addressed individual failure modes such as goals drift, states loss, and context overflow, whether a single harness can manage them jointly and remain effective across backends has received less study. We present OneDayAgent, a long-horizon harness for autonomous agents. OneDayAgent turns an open-ended request into a managed execution process that decomposes tasks into bounded subtasks, maintains execution memory under context pressure, and verifies and repairs the final deliverable. We evaluate OneDayAgent on AgentIF-OneDay across 104 tasks. With the GLM-5.2 backend, OneDayAgent sets a new state of the art with an overall score of 0.821. The same harness runs across five backend LLMs from three model families, indicating the harness generalizes across backends without tuning, even as different models induce distinct execution styles under the same workflow.