WorldGuide: Goal-Directed Video World Model for Procedural Task Execution

arXiv:2610.12459 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Accuracy — 19× in this paper · explained in 1 episode
• World Model — 13× in this paper · explained in 7 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Embeddings — 10× in this paper · explained in 2 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Ablation — 7× in this paper · explained in 1 episode
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Motion Smoothness — 3× in this paper
• Verification — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• Optical Flow — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• World Simulation with Video Foundation Models for Physical AI
• Qwen2.5-VL Technical Report
• Autoregressive Video Generation without Vector Quantization
• NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• LTX-2: Efficient Joint Audio-Visual Foundation Model
• CollabVR: Collaborative Video Reasoning with Vision-Language and Video Generation Models
• OpenVLA: An Open-Source Vision-Language-Action Model
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• SkyReels-V3 Technique Report
• PhysAgent: Reflective Agentic Physics Control for Physically Plausible Video Generation
• Yume-1.5: A Text-Controlled Interactive World Generation Model
• Yume: An Interactive World Generation Model
• VideoWorld 2: Learning Transferable Knowledge from Real-world Videos
• EgoForge: Goal-Directed Egocentric World Simulator
• WorldPlay: Towards Long-Term Geometric Consistency for Real-Time Interactive World Modeling
• Bernini: Latent Semantic Planning for Video Diffusion
• MAGI-1: Autoregressive Video Generation at Scale
• ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas

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