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CausalWAM: Learning the Causes of Actions in World Action Models

Paper recorded by Signals 4 on 2026-10-30 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-30 on arXiv · recorded by Signals 4 on 2026-11-02

Category: cs.AI · 人工智能 · first seen 2026-11-02

Abstract

World action models (WAMs) learn latent variables from observations and use these to both predict the evolution of the scene and to generate actions. The prediction has to be of the whole scene, whereas only a portion of the scene causes actions. WAMs fitted to demonstrations do not, in general, guarantee that they will generate actions solely from the latent causes of the demonstrated actions (th

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#46 most recent of 920 cs.AI papers we have recorded · ↑ newer: Subquadratic Attention with a Hierarchical Tree-Structured Indexer · ↓ older: The Knowledge Layer Model (KLM): Evidence-Visible AI Output, Routing D
Cite this page: CausalWAM: Learning the Causes of Actions in World Action Models: the #46 most recent of 920 cs.AI papers we have recorded (as of 2026-10-30). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/causalwam-learning-the-causes-of-actions-in-world-action-models.html
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