GUI-CC: Benchmarking Contextual Consistency of GUI World Models as Agent Environments

2026-08-30

AI Summary

为解决GUI世界模型在多步交互中的上下文一致性问题，本文提出了GUI-CC基准测试方法，通过离线和在线两种方式评估模型的一致性和任务进展。

Abstract

GUI world models are increasingly evaluated as one-step next-screen predictors, yet their intended use is often as multi-step environments for GUI agents. This mismatch leaves a key requirement under-tested: generated states must remain contextually consistent when they are repeatedly reused for future interaction. We introduce GUI-CC, a benchmark that evaluates contextual consistency of GUI world models as agent environments rather than isolated next-screen predictors. GUI-CC contains two complementary tracks: an offline reference-action track that rolls models along real mobile GUI trajectories, and an online agent-loop track that lets fixed probing agents interact with model-generated UIs. We construct 500 offline trajectory tasks from GUIOdyssey and 200 emulator-verified online tasks across 30 mobile apps. GUI-CC evaluates transition fidelity, transition plausibility, contextual consistency, and task progress. Experiments show that plausible single-step generation does not guarantee reliable environment simulation: current models often produce usable-looking screens while failing to preserve task-relevant context or support executable multi-step rollouts.

Problem

• contextual consistency
• GUI world models
• agent environments

Innovation

• Contextual Consistency
• GUI World Models
• Agent Environments
• Multi-step Interaction