MA-WAM: Multi-Agent World-Action Model for Test-Time Planning

arXiv:2609.31281 · cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/ma-wam/MA-WAM

Project page: https://ma-wam.github.io

Terminology

• World Model — 38× in this paper · explained in 7 episodes
• Benchmark — 33× in this paper · explained in 2 episodes
• Accuracy — 25× in this paper · explained in 1 episode
• Regret — 23× in this paper · explained in 3 episodes
• Reinforcement Learning — 18× in this paper · explained in 4 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Transformer — 6× in this paper · explained in 2 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Routed World Model — 4× in this paper
• Actor — 3× in this paper · explained in 3 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Dec-POMDP — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Multi-Agent Reinforcement Learning (MARL) — 2× in this paper · explained in 4 episodes
• Q-learning — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Planning — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Coordinated Velocity Attention — 1× in this paper
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Model Predictive Control (MPC) — 1× in this paper · explained in 5 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Cosmos World Foundation Model Platform for Physical AI
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• Unifying Agent Interaction and World Information for Multi-agent Coordination
• Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
• Puzzle it Out: Local-to-Global World Model for Offline Multi-Agent Reinforcement Learning
• Value-Guidance MeanFlow for Offline Multi-Agent Reinforcement Learning

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