Transition and Recovery Alignment via Evidence-Grounded Contracts for Mobile GUI Agents

Mobile GUI agents execute natural-language instructions through sequences of interface actions, but long-horizon execution remains vulnerable to silent transition failures, state drift, and ineffective recovery. A plausible action may fail to produce the intended state transition, causing subsequent decisions to be conditioned on a state that was never reached and allowing these errors to compound over time. Existing systems often address such failures through online reflection or auxiliary verification, introducing additional reasoning and model calls during execution. We introduce TRAVEC, a framework that moves transition prediction, verification, and recovery from the deployment-time execution loop into training. The key abstraction is an evidence-grounded transition contract, which specifies the task-relevant state change expected after an action, the observable evidence used to verify that change, and an optional recovery conditioned on transition failure. From heterogeneous mobile GUI trajectories, we construct 550K verifiable supervision items to support training a Contract Policy and a Verifier that audit self-generated trajectories and retain audited training steps. The resulting audited experience is then used to optimize the Action Policy through supervised learning and offline GRPO. These auxiliary components are used only during training, while deployment retains a single action-only policy with no additional verification or free-form reasoning. Across four offline and two online benchmarks, TRAVEC achieves strong performance while maintaining low inference latency, suggesting that transition-aware supervision can be incorporated into policy learning while preserving action-only deployment.