Latent World Action Model for GUI Agents

Key points

• However, this two-stage paradigm faces two key challenges in GUI environments: (L1) the high computational cost of pixel reconstruction and (L2) the need to reconstruct large and diverse appearance-level details beyond what is required for action selection.1
• In this work, we reformulate GUI world modeling by shifting the prediction target from full-screen reconstruction to action-induced future information in latent space.1
• Concretely, we leverage a large multimodal model as the latent world model, whose shared representations support two dedicated heads for both action selection and predictive transition modeling.1
• To mitigate the identity shortcut, we further introduce an action-aware contrastive objective that encourages the transition representations to capture discriminative action-induced changes.1

Sources (2)

• [1]Latent World Action Model for GUI Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 06:45 PM

  Pixel-space world models have recently emerged as a promising approach for GUI agents, predicting future screens prior to execution and subsequently re-perceiving them for action selection.

  However, this two-stage paradigm faces two key challenges in GUI environments: (L1) the high computational cost of pixel reconstruction and (L2) the need to reconstruct large and diverse appearance-level details beyond what is required for action selection.

• [2]Latent World Action Model for GUI Agents
  Hugging Face Daily Papers · Oct 9, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.