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A good synthesis approach:

1. Pick 3 foundational papers and write a one-page “shared definition” doc.
2. Collect evaluation methods and decide which metrics matter for your agent (accuracy, planning depth, generalization).
3. Translate the reading into experiments: minimal baselines first, then add complexity.

README excerpt (verbatim)

Awesome Agentic World Modeling

This repository accompanies the Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond, providing a taxonomy-aligned bibliography of 400+ cited works and 100+ representative systems. Papers are grouped by taxonomy section and listed in reverse chronological order within each subsection to support literature navigation, comparison, and ongoing updates. Released under the MIT License. Check out our poster here.

[!TIP] 👋 Welcome to join the discussion on or , share your work in progress, and help us grow the agentic world modeling community together.

We also welcome collaborations on distilled surveys, follow-up research, and related projects across the physical, digital, social, and scientific world modeling.

[!NOTE] 📚 If you find this resource useful, please cite and the repo:

@article{chu2026agenticworldmodelingfoundations,
  title         = {Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond},

FAQ

Q: Is world modeling only for robotics? A: No—directories like this span robotics, games, and general agent planning settings.

Q: How do I choose what to read first? A: Start with definitions and surveys, then move to evaluation and benchmarks.

Q: How do I keep notes useful? A: Record assumptions, metrics, and a short list of papers you actually validated via experiments.