Agentic Game Development as a Verifiable Trajectory Data Engine for Scaling World Models

arXiv:2608.25518 · cs.AI · Submitted 2026-08-26 · Read on arXiv

cs.AI

Submitted: 2026-08-26

Updated: 2026-08-26

Code: https://github.com/LanceZPF/cardinal-preview

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 78× in this paper · explained in 7 episodes
• Trace — 49× in this paper · explained in 2 episodes
• Verifier — 41× in this paper · explained in 2 episodes
• Verification — 36× in this paper · explained in 2 episodes
• Generalization — 26× in this paper · explained in 13 episodes
• Benchmark — 17× in this paper · explained in 2 episodes
• Reinforcement Learning — 13× in this paper · explained in 4 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Adaptation — 7× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Benchmarking — 6× in this paper · explained in 4 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Distribution Shift — 4× in this paper · explained in 6 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Embodied AI — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Calibration — 2× in this paper · explained in 7 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Multimodal Inputs — 2× in this paper · explained in 2 episodes
• Reinforcement Learning with Human-Engine Verification — 2× in this paper
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Affordance — 1× in this paper · explained in 1 episode
• Agentic Coding — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• On-Policy Distillation — 1× in this paper · explained in 3 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Sim-to-Real Gap — 1× in this paper · explained in 6 episodes
• Structural Reasoning — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Unified World-Development Protocol — 1× in this paper
• Verifiability — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• Cosmos 3: Omnimodal World Models for Physical AI
• Chameleon: Mixed-Modal Early-Fusion Foundation Models
• Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
• Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
• LoL: Longer than Longer, Scaling Video Generation to Hour
• D4RL: Datasets for Deep Data-Driven Reinforcement Learning
• 3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code
• Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
• A Survey on Large Language Model-Based Game Agents
• GameGen-Verifier: Parallel Keypoint-Based Verification for LLM-Generated Games via Runtime State Injection
• Unity: A General Platform for Intelligent Agents
• WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis
• Point-E: A System for Generating 3D Point Clouds from Complex Prompts
• GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents
• Seedance 2.0: Advancing Video Generation for World Complexity
• Distilling Game Code World Model Generation into Lightweight Large Language Models
• Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
• Towards Accurate Generative Models of Video: A New Metric & Challenges
• Aletheia: What Makes RLVR For Code Verifiers Tick?
• Video models are zero-shot learners and reasoners

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