arXiv AI
RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control
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Oct 13
RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control
RAN-JEPA is an action-conditioned world model designed for energy-efficient mobile network control. It first learns a predictive latent representation from abundant observation-only telemetry, then adapts this representation to a target deployment domain and learns action-conditioned latent dynamics from limited controlled sleep/wake trajectories while keeping the encoder fixed. Evaluations on public operator data and a real-equipment 5G testbed show that RAN-JEPA outperforms a GPT-2 baseline and a mechanistic rule in prediction accuracy, produces coherent future trajectories under different sleep plans, and achieves higher sleep ratios in closed-loop control with comparable congestion costs.
arXiv Machine Learning
Oct 3
JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts
JEPA‑TTT is a method that continuously adapts the latent dynamics predictor of a pretrained Joint‑Embedding Predictive Architecture (JEPA) world model during test time. It performs self‑supervised updates across episodes while keeping the visual encoder and reward head fixed, using dense replay to sample prediction windows from a growing buffer. In experiments on eight dynamics shifts across four continuous‑control environments, JEPA‑TTT reduces latent prediction error by 83% and improves planning performance by 153% compared to the frozen model.
arXiv Machine Learning
Jun 8
Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning
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arXiv AI
Jul 14
JEPA for AI-Native 6G: Predictive Representations and Open Challenges
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arXiv AI
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Exploring Temporal Context-Aware Physical Grounding in Latent World Models
arXiv:2610.28483v1 Announce Type: new Abstract: Learning latent dynamics is fundamental to building action-conditioned world models for planning. Among existing families, Joint Embedding Predictive A...
arXiv AI
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V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents
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