Long-WAM: Scaling the Context of World-Action Models

arXiv:2610.10528 · cs.RO, cs.AI, cs.CV · Submitted 2026-10-07 · Read on arXiv

cs.RO, cs.AI, cs.CV

Submitted: 2026-10-07

Updated: 2026-10-07

Terminology

• Adaptation — 17× in this paper · explained in 1 episode
• Quantization — 11× in this paper · explained in 12 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• KV cache — 4× in this paper · explained in 3 episodes
• Edge Deployment — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Joint Attention — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Action Chunking — 1× in this paper · explained in 1 episode
• Action Flow — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adaptive Context Allocation — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes

Sources

• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• Motus: A Unified Latent Action World Model
• Training-Time Action Conditioning for Efficient Real-Time Chunking
• AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
• ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
• LongLive-2.0: An NVFP4 Parallel Infrastructure for Long Video Generation
• cuDNN: Efficient Primitives for Deep Learning
• Rethinking Video Generation Model for the Embodied World
• WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time
• RoboTTT: Context Scaling for Robot Policies
• RLDX-1 Technical Report
• OpenVLA: An Open-Source Vision-Language-Action Model
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• Echo-Memory: A Controlled Study of Memory in Action World Models
• Causal World Modeling for Robot Control
• HoloBrain-0 Technical Report
• World Action Models in Real Time: An Empirical Study of Smooth Execution via Asynchronous Deployment

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving