OneDayAgent：邁向自主智能體的長時程框架

摘要

LLM 智能體日益被應用於涵蓋工作、學習與生活的開放式日常請求。此類任務具有長時程、跨環境與多模態的特性，迫使智能體在瀏覽異質工具與附件時，於眾多步驟之間維持目標與約束。雖然先前的研究已處理個別的失敗模式，例如目標漂移、狀態遺失與上下文溢位，但對於單一架構能否同時管理這些問題，並在各種後端上維持有效性，相關研究仍然較少。我們提出 OneDayAgent，一個用於自主智能體的長時程管理框架。OneDayAgent 將開放式請求轉化為受管理的執行流程，將任務分解為有界限的子任務，在上下文壓力下維持執行記憶，並驗證與修復最終交付成果。我們在 AgentIF-OneDay 上評估 OneDayAgent，涵蓋 104 項任務。在以 GLM-5.2 為後端時，OneDayAgent 以 0.821 的整體分數創下新的最佳成果。相同的框架可在來自三個模型家族的五個後端 LLM 上運行，顯示此框架無需調校即可跨後端泛化，即使不同模型在同一工作流程下會產生不同的執行風格。

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.