Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals

arXiv:2609.31868 · cs.AI, cs.RO · Submitted 2026-09-25 · Read on arXiv

cs.AI, cs.RO

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/lucasmaes/le-wm

Terminology

• Benchmark — 29× in this paper · explained in 2 episodes
• World Model — 14× in this paper · explained in 7 episodes
• Reinforcement Learning — 9× in this paper · explained in 4 episodes
• International Conference on Learning Representations — 5× in this paper
• In International Conference on Machine Learning — 4× in this paper
• Latent Space — 4× in this paper · explained in 7 episodes
• Latent World Models — 4× in this paper · explained in 2 episodes
• Latent Dynamics — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Hierarchical Framework — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning — 2× in this paper · explained in 8 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel
• LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
• FF-JEPA: Long-Horizon Planning in World Models with Latent Planners
• DeepMind Control Suite
• Hierarchical Planning with Latent World Models

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection