aiqbench analysis
This arXiv paper argues that GUI world models, which predict the screen after a candidate action, may add little action-selection value once a strong direct evaluator is trained on reference actions. On AndroidWorld, the direct ranker improved task success by 5.8 to 16.4 points without retraining and transferred zero-shot to unseen apps. For AI researchers, the useful takeaway is an evaluation caution: prediction fidelity and end-to-end lookahead gains may overstate what predictions contribute, so ablations against a matched direct baseline are needed.
Beyond Next-Screen Prediction: The Decision Value of GUI World Models
arXiv:2610.24372v1 Announce Type: new Abstract: GUI world models are increasingly used to improve action selection by predicting the consequences of candidate actions, but it is unclear how much of the resulting gain comes from prediction itself rather than from the evaluator that interprets those predictions. We study this attribution problem through controlled interventions on GUI action selection. With candidates and predictions held fixed, lookahead gains can change with how the prediction is presented to the evaluator, so end-to-end gains cannot be attributed to prediction alone. We then introduce Direct Action Ranking (DAR), which learns from reference actions to score candidates without generating future screens, and compare it with a prediction-augmented variant under matched supervision. Predicted successors add no detectable ranking signal once evaluation is learned directly. Without retraining, DAR improves AndroidWorld task success by 5.8--16.4 points across three proposers, outperforms world-model lookahead, and transfers zero-shot to unseen apps, where every prediction-conditioned configuration falls below proposer confidence. These results suggest that GUI world models should be evaluated by their decision value, the incremental action-selection value of their predictions beyond a strong direct evaluator, rather than by prediction fidelity or end-to-end lookahead gain alone.
Abstract:GUI world models are increasingly used to improve action selection by predicting the consequences of candidate actions, but it is unclear how much of the resulting gain comes from prediction itself rather than from the evaluator that interprets those predictions. We study this attribution problem through controlled interventions on GUI action selection. With candidates and predictions held fixed, lookahead gains can change with how the prediction is presented to the evaluator, so end-to-end gains cannot be attributed to prediction alone. We then introduce Direct Action Ranking (DAR), which learns from reference actions to score candidates without generating future screens, and compare it with a prediction-augmented variant under matched supervision. Predicted successors add no detectable ranking signal once evaluation is learned directly. Without retraining, DAR improves AndroidWorld task success by 5.8--16.4 points across three proposers, outperforms world-model lookahead, and transfers zero-shot to unseen apps, where every prediction-conditioned configuration falls below proposer confidence. These results suggest that GUI world models should be evaluated by their decision value, the incremental action-selection value of their predictions beyond a strong direct evaluator, rather than by prediction fidelity or end-to-end lookahead gain alone.
Artificial Intelligence (cs.AI)
arXiv:2610.24372 [cs.AI]
(or arXiv:2610.24372v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.24372 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 279 words