AndroidFlux：评测移动多模态智能体的故障恢复与奖励建模能力
AndroidFlux: Evaluating and Reward Modeling Failure Recovery Capabilities in Mobile Use Multimodal Agents
AI 导读
AndroidFlux 是一个基于 AndroidWorld 构建的故障恢复基准，通过将记录轨迹回放到首个可恢复错误之前、之时、之后的受控检查点，让不同智能体在相同设备状态和历史下继续任务。
正文 · 原文
Abstract:Mobile-use multimodal agents are typically evaluated by task success from a clean initial state, which does not isolate their ability to recover from intermediate errors. Because mobile environments change dynamically, a general mobile-use agent must complete tasks robustly even from unexpected states and in unseen environments. This requires knowing when and where an agent makes errors, and whether it can recover from errors and subsequent states. To evaluate this capability, we introduce AndroidFlux, a failure-recovery benchmark built on AndroidWorld. AndroidFlux restores recorded trajectories to controlled checkpoints before, at, and after the first recoverable error, so that different agents continue from the same device state and history. On these checkpoints, a complementary reward-modeling benchmark evaluates recovery-action preferences using downstream rollout outcomes. We further release an offline reinforcement learning (RL) dataset that improves both error recovery and overall task success. Our evaluations show that recovery performance only partially correlates with clean-start success, and that explicit reasoning helps only models trained with GUI-domain specific RL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.34384 [cs.LG] |
| (or arXiv:2610.34384v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34384 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gyubeum Lim [view email]
[v1]
Sun, 25 Oct 2026 15:39:24 UTC (3,672 KB)
来源：arXiv cs.AI · arxiv.org