Code World Models in Simultaneous Move settings like Capital Markets. DeepMind's CWM approach relies on standard MCTS/IS-MCTS, which assumes a single active player at each node.

This doesn't work in simultaneous-move settings like Orbit Wars (or order-book markets), converging to an exploitable pure strategy rather than a Nash equilibrium.

LeCun's JEPA, by contrast, is a learned neural world model, which lacks the determinism, speed, and debuggability of a code-based simulator. Thus, it can drift or predict illegal states, and you can't inspect why it made a prediction the way you can trace a Python function.

TL;DR: The benefit is better auditability and easier RL-like training. The SM-MCTS extension fixes the first problem (decoupled UCB per player approximates Nash equilibrium instead of a pure strategy) while keeping the second advantage intact (a deterministic, inspectable code simulator).

https://github.com/ternary-ai/ow-code-world-model https://jdsemrau.substack.com/p/a-self-improving-code-world-...