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 infere
Record details
Published: 30 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Privacy · Agents & autonomy
Retrieved: 31 July 2026
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ethics.ai (30 July 2026), “Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs,” evidence record 15215, https://ethics.ai/record/15215 (originally published by arXiv cs.AI).
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