UI-TARS-1.5-7B

Empirical study finds inference-time scaling yields diminishing returns for local computer-use agents

Researchers present a systematic empirical study of inference-time scaling across four dimensions (contextual, temporal, structural, parallel) for locally-deployed computer-use agents under hardware constraints. Evaluating Qwen3-VL-8B/30B-A3B, UI-TARS-1.5-7B, and OpenCUA-7B on OSWorld, they find that additional compute often shifts rather than eliminates failure modes—contextual scaling saturates, temporal scaling extends erroneous trajectories, and structural decomposition adds overhead. The findings argue for selective compute allocation and failure-aware control mechanisms tailored to local model capabilities.