Don't Act Blindly: Robust GUI Automation via Action-Effect Verification and Self-Correction

TL;DR

VeriGUI is a robust GUI automation framework that models action outcomes, detects failures, and applies self-correction to improve reliability in noisy, real-world environments.

Contribution

It introduces a novel TVAE framework and a two-stage training pipeline for failure detection and recovery in GUI agents, along with a new robustness benchmark.

Findings

VeriGUI significantly reduces failure loops in GUI tasks.

It improves recovery success rates under noisy conditions.

Maintains competitive performance on standard tasks.

Abstract

Autonomous GUI agents based on vision-language models (VLMs) often assume deterministic environment responses, generating actions without verifying whether previous operations succeeded. In real-world settings with network latency, rendering delays, and system interruptions, this assumption leads to undetected action failures, repetitive ineffective behaviors, and catastrophic error accumulation. Moreover, learning robust recovery strategies is challenging due to the high cost of online interaction and the lack of real-time feedback in offline datasets.We propose VeriGUI (Verification-driven GUI Agent), which explicitly models action outcomes and recovery under noisy environments. VeriGUI introduces a Thinking--Verification--Action--Expectation (TVAE) framework to detect failures and guide corrective reasoning, and a two-stage training pipeline that combines Robust SFT with synthetic failure trajectories and GRPO with asymmetric verification rewards. We further construct a Robustness Benchmark based on AndroidControl to evaluate failure recognition and correction. Experiments show that VeriGUI significantly reduces failure loops and improves recovery success while maintaining competitive standard task performance.