LocalLSTC通过时间组织控制信息，显著提升了本地GUI代理的性能，值得研究。
现代GUI代理框架使用前沿API模型实现强大的桌面任务性能，但持续的控制系统信息通常隐含在增长的交互轨迹中。使用Qwen3.5-9B替换GPT-5将OSWorld SR-100的平均值从60.9%降低到37.7%。LocalLSTC通过时间范围组织控制，维护跨步骤的持续状态来指导短期执行承诺。使用Qwen3.6-27B，LocalLSTC在OSWorld上达到64.7%的SR-100，在WindowsAgentArena上达到65.3%，优于两个基准测试上的先前本地结果。
LocalLSTC: A Long Short-Term Control Architecture for Locally Deployed GUI Agents
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 37.7\%. Trajectory annotation further identifies at least one control failure in 91.6\% 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. Long-Term Control maintains the active subgoal, subgoal-aligned evidence, and runtime feedback across interactions, while Short-Term Execution realizes bounded commitments for the current step. Long-to-Short Planning forms each commitment from persistent state, and Short-to-Long Control integrates execution outcomes back into that state for progress assessment, recovery, and termination. With Qwen3.6-27B, LocalLSTC reaches 64.7\% SR-100 on OSWorld and 65.3\% on WindowsAgentArena, outperforming the strongest prior local results on both benchmarks. Ablations further support contributions from mechanisms on both sides of execution. These findings identify temporal organization of control information as a distinct architectural dimension for locally deployed GUI agents.