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Jax / numpy simulation of a stochastic turing tumble like game

Hopefully can be made trainable then it will be first (to our knowledge) physically realizable autoregressive transformer-decoder-ish-ly-kinda-maybe model.

Screen.Recording.2025-05-12.at.5.01.24.PM.mov

Image

TODO

ML

  • perplexity? / zip compression for randomness? run_sim but dont print choices, print perplexity etc instead
  • grad and train
  • force prefix

ML / physical? problems:

  • low perplexity, BOO state is 50% L/R, and looks like ball will almost never fall far from init pos

Bugs

  • some weird step bug: python pboard.py --force-np-random -r -n2 -i 0.0
  • -vvv breaks render

Refactor

  • logical sim, disentangle from debug/render
  • -rr render should show "sampling mask" and "physically achievale states"

Physical board

  • boundaries
  • multilayer board
    • switch layers from off boundaries
    • or just "drop down on next layer" essentially "holes". How to make them trainable?
    • some "jump forward" holes too?

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