LocalLSTC：面向本地部署GUI智能体的长短期控制架构
LocalLSTC: A Long Short-Term Control Architecture for Locally Deployed GUI Agents
- School of Computer Science and Engineering（计算机科学与工程学院）
- The University of New South Wales（新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对本地部署GUI智能体的控制信息隐含问题，提出无需训练的LocalLSTC架构，在OSWorld和WindowsAgentArena基准上均取得优于现有本地方法的性能。
AI中文摘要：
现代GUI智能体框架借助前沿API模型在桌面任务上实现了出色性能，但不断增长的交互轨迹中常隐含着持续的控制信息。每一步规划器都会重构当前任务阶段、累积证据和运行时反馈，再决定下一步动作，这种依赖在本地推理主干较弱时更为明显。在四个代表性的最先进框架中，将GPT-5替换为Qwen3.5-9B后，OSWorld的平均SR-100从60.9%降至37.7%；轨迹标注显示，至少91.6%的失败轨迹存在至少一处控制失效。为解决该问题，本文提出LocalLSTC，一种无需训练的架构，通过时间范围组织控制，维护跨步骤的持久状态以指导短期执行承诺：长期控制在交互间维护活跃子目标、与子目标对齐的证据及运行时反馈，短期执行实现当前步骤的有界承诺；长短规划从持久状态形成每个承诺，短长控制将执行结果整合回该状态以进行进度评估、恢复和终止判断。使用Qwen3.6-27B时，LocalLSTC在OSWorld上达到64.7%的SR-100，在WindowsAgentArena上达到65.3%，均优于两个基准上最强的现有本地结果；消融实验进一步验证了执行两侧机制的贡献，这些发现表明控制信息的时间组织是本地部署GUI智能体的一个独特架构维度。
英文摘要：
Modern GUI-agent frameworks achieve strong desktop task performance with frontier API models, yet persistent control information often remains implicit in growing interaction trajectories. At each step, the planner reconstructs the active task stage, accumulated evidence, and runtime feedback before deciding the next action. This dependence becomes more pronounced under weaker local reasoning backbones. Across four representative state-of-the-art frameworks, replacing GPT-5 with Qwen3.5-9B reduces average OSWorld SR-100 from 60.9\% to 35.2\%. Trajectory annotation further identifies at least one control failure in 94.7\% of failed trajectories. To address this problem, we introduce LocalLSTC, a training-free architecture that organizes control by temporal scope, maintaining persistent cross-step state to guide short-term execution commitments. The framework comprises two complementary mechanisms. Long-to-Short Planning forms each commitment from persistent state, while Short-to-Long Control integrates execution outcomes back into that state for progress assessment, recovery, and termination. With Qwen3.8-27B, LocalLSTC reaches 73.7\% SR-100 on OSWorld and 68.5\% on WindowsAgentArena, achieving the strongest local OSWorld result and a new state of the art on WindowsAgentArena. Ablations further support contributions from both mechanisms. Together, these results show that temporal organization of control information can reduce cross-step control failures and complement backbone scaling in locally deployed GUI agents.
