World Models

Brendon.BOT has curated 5 items on world models across 4 shelves (blog, insights, papers, videos), each with the analysis and the evidence for why it cleared the bar.

Papers (2)

• Successive Capacity Growth: Task-Complexity-Driven Width and Depth Expansion for Vision Transformer Encoders in JEPA World Models arXiv cs.AI

  Successive Capacity Growth (SCG) turns Vision Transformers into dynamic learners that grow their brains—width and depth—as tasks get harder.

• VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinement arXiv

  This paper turns physical reasoning into a *live* debugging session for AI agents—letting them test, break, and refine their own world models in real time.

Videos (1)

• Qwen-AgentWorld The World Model for Agents Sam Witteveen

  Sam does a solid job dissecting the paper, specifically the jump in performance after RL training shown at the 6:15 mark. We're moving past simple prompt engineering into a phase where agents need simulated environments

Insights (1)

• Agentic Game Development Is the New Data Engine

Blog (1)

• VeriPhy adds agentic checks to world model evaluation