想测试你的桌面Agent到底能不能分清操作后的界面变化？这个新基准有2000多个真实案例，能看出模型在哪类失败上翻车。
Desktop-Delta Bench (DDB) 是一个离线步骤级基准，包含2,013个人工验证的实例，覆盖约15个应用和50个任务域。DDB通过463个三帧时序排序实例（含105个跨轨迹干扰）和1,550个前后对比对来评估模型。在8个闭源和开源模型家族、32个排序和16个单动作设置中，最佳非干扰和干扰精确匹配率分别为65.1%和65.7%。单动作结果显示，推断动作类型比定位更难：点击F1为0.96，拖拽为0.76。DDB填补了GUI定位与最终任务成功之间的诊断空白。
Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?
Computer-use agents (CUAs) increasingly act through desktop GUIs to complete long-horizon tasks. Current benchmarks primarily measure end-task success or single-frame grounding. Neither isolates whether a model can reconstruct the causal, task-relevant transition produced by an action- crucial for rejecting stale observations, verifying progress, and recovering from failure. This is difficult because inference, remote input, app rendering, and screenshot capture are asynchronous: the next observation may be delayed, occluded, transient, or unrelated, then misread as progress and carried into subsequent planning. We introduce Desktop-Delta Bench (DDB), an offline step-level benchmark with 2,013 human-verified instances from novel, multi-app Linux trajectories across ~15 applications and 50 task domains. DDB trajectories targets 3 failure dimensions- state verification, source tracking, and context-aware control- through 2 complementary tasks: 463 3-frame temporal-ordering instances, including 105 with a cross-trajectory decoy, and 1,550 before-after pairs labeled from 5 actions + its payload. We evaluate 8 closed and open-source model families across 32 ordering and 16 single-action settings, observing consistent gaps. Ordering remains unsaturated: best non-decoy and decoy exact-match rates are 65.1% and 65.7%. Task context improves decoy identification by 6.9 percentage points but reduces non-decoy exact match by 2.2 points; error analysis reveals systematic copying of the presented A-B-C order. Single-action results show that inferring the action family is harder than locating it: click F1 is 0.96 vs, 0.76 for drag, while recognized drags are generally localized well. DDB, thus, complements end-to-end benchmarks by filling the missing diagnostic layer between GUI grounding and final task success, enabling targeted improvements to desktop CUA verification, reliability, and recovery.