VersionShift: Evaluating and Adapting Prior Experience Across Software Versions for GUI Agents

GUI agents interact with graphical user interfaces (GUIs) to perform tasks in real-world software environments, yet these interfaces continuously evolve through software updates, changing layouts, element locations, and task workflows. Such evolution makes **cross-version experience adaptation** essential: prior experience may remain useful after an update, but can also become outdated or misleading. However, selective adaptation of prior experience across software versions remains underexplored, and existing benchmarks provide limited support for controlled evaluation of such experience transfer. To systematically study this problem, we introduce **VersionShift**, a benchmark that holds task intent fixed while pairing executions across chronological software versions. Through semantic alignment of paired trajectories, VersionShift characterizes source-experience validity at each target step using **Reuse, Reground, and Replan** annotations, distinguishing directly executable source actions, applicable semantics that require new grounding, and target steps with no applicable source action. VersionShift contains 312 tasks and 1,248 trajectories across 53 applications and websites spanning Mobile, Desktop, and Web. Evaluation on VersionShift shows that historical trajectories can improve action prediction while also introducing substantial negative transfer, with beneficial and harmful effects coexisting within the same model. To retain useful experience while limiting negative transfer, we propose **VAEA**, a validity-aware experience adaptation approach that learns to select applicable source steps and estimate direct-reuse validity. VAEA uses the predicted applicable source steps and reuse-validity score to conditionally reuse source actions, reground selected semantics, or replan when no source step is applicable, without requiring cross-version annotations at inference. Experiments show that VAEA improves action prediction over controlled experience-use baselines and consistently reduces history-induced negative transfer, while remaining competitive with other memory methods. We will release the benchmark and code to facilitate future research.