arXiv Machine Learning
Jul 14

A Control Theory of Predictability in Latent World Models

arXiv:2607. 10362v1 Announce Type: new Abstract: Latent world models are trained to predict future states in a learned representation and are then deployed inside a planner that selects actions by simulating them forward.

By Hanzhe You, Yonggang Zhang, Maohao Ran, Zhiqin Yang, Zhenyuan Zhang, Wei Xue, Jun Song, Xinmei Tian, Yike Guo
arXiv AI
Sep 25

AD-WM: Action-Discriminative World Models for Counterfactual Model Predictive Control

AD-WM is a new action‑discriminative joint‑embedding world model designed for counterfactual model predictive control. It augments residual latent dynamics with action‑recovery regularization based on inverse dynamics and conditional mutual information, while discarding auxiliary heads at test time so that MPC remains unchanged. Experiments on OGBench‑Cube and other simulation environments show substantial gains in hard‑start success and mean success, and zero‑shot transfer to a Franka robot improves pick‑and‑place success from 42.2% to 71.1%.

By Jiabin Qiu, Zixuan Chen, Hongye Cao, Jieqi Shi, Jing Huo, Yang Gao
arXiv AI
3d ago

ATM: Why Latent World Models Can Fail to Plan

The paper investigates why latent world models, despite accurate latent predictions, can perform poorly in downstream planning. It introduces the concept of action-identifiability and formalizes it via Bayes inverse risk, showing that self-decodable predicted transitions may encode domain‑specific action relationships that do not transfer to real environments. Using the Action‑Consistency Transfer Matrix (ATM), the authors demonstrate that true‑transition action‑identifiability correlates strongly with planning success across several benchmarks, and that the ATM can diagnose cross‑domain inconsistencies and aid lightweight model screening.

By Jiaheng Chen, Tinghe Zhang, Yucheng Xiao, Xinyong Cai, Lan Yu, Juncheng Bu, Jiaxing Li, Yunlong Wang
arXiv Machine Learning
Jun 8

Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning

arXiv:2511. 02748v2 Announce Type: replace-cross Abstract: We argue that sixth-generation (6G) intelligence is not fluent token prediction but the capacity to imagine and choose -- to simulate future scenarios, weigh trade-offs, and act with calibrated uncertainty.

By Farhad Rezazadeh, Amir Ashtari Gargari, Hatim Chergui, Sandra Lagen, Merouane Debbah, Houbing Song, Lingjia Liu