World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning

arXiv:2606.03603 · cs.CV, cs.CL · Submitted 2026-06-02 · Read on arXiv

cs.CV, cs.CL

Submitted: 2026-06-02

Updated: 2026-08-31

Comments: EMNLP 2026

Code: https://github.com/yczhou001/PF-OPSD

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

Terminology

• World Model — 36× in this paper · explained in 7 episodes
• Benchmark — 33× in this paper · explained in 2 episodes
• Verification — 29× in this paper · explained in 2 episodes
• Accuracy — 20× in this paper · explained in 1 episode
• Distillation — 15× in this paper · explained in 2 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Self-Distillation — 8× in this paper · explained in 7 episodes
• Alignment — 7× in this paper · explained in 1 episode
• GRPO — 7× in this paper · explained in 6 episodes
• Hallucination — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Self-consistency — 3× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Momentum transfer — 2× in this paper · explained in 2 episodes
• Reasoning Traces — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Commentary — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Structured Prompting — 1× in this paper · explained in 2 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• MobileDreamer: Generative Sketch World Model for GUI Agent
• Distilling the Knowledge in a Neural Network
• ViMo: A Generative Visual GUI World Model for App Agents
• Proximal Policy Optimization Algorithms
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Current Agents Fail to Leverage World Model as Tool for Foresight
• Wan: Open and Advanced Large-Scale Video Generative Models
• Helios: Real Real-Time Long Video Generation Model
• Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
• Thread of Thought Unraveling Chaotic Contexts

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