Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?
Score
78该基准首次聚焦GUI转换理解这一被忽视的评估维度，提供了新的诊断工具和实证结果，信息增量大。
实验设计系统，涵盖多个任务和模型家族，数据人工验证，但未提供完整方法论细节，质量较高。
对开发更可靠的桌面CUA具有直接指导意义，可帮助定位模型失败原因，实用价值高。
论文近日发布，计算机使用模型是当前热点，时效性强。
来源为arXiv预印本，代码和数据集可能公开，但尚未正式发表，可信度中等偏上。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The benchmark innovatively focuses on GUI transition understanding, a previously under-evaluated capability, with rigorous experimental design revealing critical gaps, making a significant contribution to the AI agent field.
AI commentary
「This paper hits a blind spot in current computer-use model evaluation and is highly relevant for teams working on GUI agent reliability. The diagnostic layer it proposes will help developers pinpoint failure causes more precisely, especially for those building desktop automation systems.」
Quick overview
This paper introduces Desktop-Delta Bench (DDB), an offline step-level benchmark with 2,013 human-verified instances across ~15 applications and 50 task domains. DDB evaluates computer-use models' ability to understand GUI state transitions through two tasks: temporal ordering and before-after comparison, filling a diagnostic gap in existing benchmarks. Evaluating 8 model families reveals consistent gaps: best exact-match rates for ordering tasks are around 65%, and inferring action families is harder than localizing them. DDB enables targeted improvements for verification, reliability, and recovery of desktop CUAs.
- DDB contains 2,013 human-verified instances across 15 applications and 50 task domains, targeting three failure dimensions.
- Two tasks: 463 three-frame temporal-ordering instances (including 105 cross-trajectory decoys) and 1,550 before-after pairs.
- Evaluation of 8 model families shows ordering is unsaturated: non-decoy and decoy exact-match rates are 65.1% and 65.7%.
- In single-action tasks, click F1 is 0.96 vs. drag F1 0.76, with inferring action families harder than localizing them.
Full text
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