arXiv AI
3d ago

GUIFlow: Temporal Redundancy-Aware Incremental Inference for GUI Agents

GUIFlow is a framework that enables temporal redundancy‑aware incremental inference for GUI agents built on vision‑language models. By selectively updating computational states—such as position‑aligned KV states, vision encoder outputs, and language model drafts—across interaction steps, it allows cross‑step reuse of visual encoding, prefill, and decoding without extra training or model changes. Experiments show up to 3.39× speedup on offline benchmarks and 3.49× in online OSWorld interactions while preserving task performance.

By Ye Li, Haoran Ding, Huanan Liu, Yuan Meng, Yutian Shen, Yuansong Wang, Kangye Ji, Hanyun Cui, Hongbo Duan, Chen Tang, Jiawei Li, Shu-Tao Xia, Zhi Wang
arXiv Machine Learning
Sep 22

Contrastive World Models

Contrastive World Models propose a new method for learning latent dynamics without pixel reconstruction. By replacing observation reconstruction with a Deep InfoMax-like objective that maximizes mutual information between state-action sequences and local patch features of future observations, the approach encourages state representations to retain predictive information while ignoring visually irrelevant details. Experiments show that this method matches existing baselines in simple settings and significantly outperforms them when distractors or natural video backgrounds are present, while also training more efficiently by eliminating the pixel decoder.

By Bonnie Li