这篇论文实测了三个本地模型在 OSWorld 上的推理时扩展，发现多算不一定提升成功率，反而换着花样失败。做本地 CUA 部署的可以当避坑参考。
这项研究在 OSWorld 基准上测试了 Qwen3-VL-8B/30B-A3B、UI-TARS-1.5-7B 和 OpenCUA-7B 三类本地计算机使用智能体。结果表明，上下文扩展可提升轨迹稳定性但收益很快饱和，时间扩展减少 max-step 停滞却不提升任务成功率。结构分解会引入规划和格式化开销，并行扩展能部分缓解但计算成本显著增加。研究建议本地高效智能体需要选择性计算分配和失败感知控制机制。
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
Deploying autonomous computer-use agents (CUAs) locally is increasingly important for privacy, cost efficiency, and practical usability, yet improving their performance under strict hardware constraints remains challenging. While recent studies show that inference-time scaling can improve frontier computer-use agents through additional computation during execution, its effectiveness for resource-constrained local models remains poorly understood. We present a systematic empirical study of inference-time scaling in local CUAs across contextual, temporal, structural, and parallel dimensions. We evaluate Qwen3-VL-8B/30B-A3B, UI-TARS-1.5-7B, and OpenCUA-7B on the OSWorld benchmark. Our results show that additional computation often yields diminishing returns while changing failure modes. Contextual scaling provides historical grounding that improves trajectory stability and task accuracy, but its gains saturate as token cost increases and failures shift from repetitive or stalled trajectories toward premature false successes. Temporal scaling similarly reduces max-step stalls, yet does not substantially improve task success, indicating that longer horizons often extend erroneous trajectories rather than correct them. We further find that structural decomposition can introduce planning and formatting overhead in local two-stage agents, while parallel scaling partially mitigates these failures at a substantial computational cost. Overall, our findings suggest that efficient local CUAs require selective compute allocation, failure-aware control mechanisms, and agentic frameworks designed around the capabilities and limitations of local models.