Learned Google G circuit uses 927 active gates (excluding pass-through gates A and B)

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Learned Game of Life circuit uses 336 active gates (excluding pass-through gates A and B), predominantly OR and ANDfinding0.853
  Circuit analysis result for Game of Life experiment
• Total active gates in checkerboard circuit is 22 (excluding pass-through gates A and B)finding0.827
  Circuit size finding for checkerboard experiment before pruning
• Learned checkerboard generation circuit reduces to just 5 active logic gates after pruning (6 with one redundant AND)finding0.822
  Remarkably minimal circuit discovered for checkerboard pattern generation
• Learned lizard pattern circuit uses 577 active gates (excluding pass-through gates A and B)finding0.822
  Circuit complexity metric for lizard experiment
• DiffLogic CA with 64 channels successfully learns 16x16 colored Google G pattern in 15 steps using 8-color binary palettefinding0.739
  Demonstration of multi-channel RGB color pattern generation with binary states
• Checkerboard circuit trained on 16x16 grid successfully generalizes to 64x64 grid with 4x more time stepsfinding0.717
  Grid scaling generalization result demonstrating boundary-size invariance
• Token usage varies roughly 20× across models, from ~14,800 (G3.1-FL) to ~275,000 (G3-F) per gamefinding0.701
  Reasoning verbosity does not predict strategic strength: both top and weak models span a wide range of token usage.
• Pass-Through Gate Biasconcept0.691
  Initial gate distribution biased toward pass-through gates A and B to facilitate training stability