DiffLogic CA fully converges to 16x16 checkerboard target pattern with both soft and hard losses reaching zero

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

• DiffLogic CA fully converges on Game of Life rules — both soft and hard losses converge to zero on all 512 configurationsfinding0.847
  Core result of Experiment 1 validating DiffLogic CA's ability to learn discrete CA rules
• DiffLogic CA with 128-bit cell state and 12 steps successfully learns 20x20 lizard pattern, generalizing to 40x40 gridfinding0.838
  Demonstration of DiffLogic CA on complex non-regular shapes with arbitrary memorization requirements
• DiffLogic CA has strong kinship with CAM-8 architecture where each cell uses DRAM for state variables and SRAM for processingclaim0.822
  Authors' architectural analogy between DiffLogic CA and Toffoli-Margolus CAM-8
• DiffLogic CA with 64 channels successfully learns 16x16 colored Google G pattern in 15 steps using 8-color binary palettefinding0.807
  Demonstration of multi-channel RGB color pattern generation with binary states
• Asynchronously trained DiffLogic CA shows greater robustness to 10x10 pixel damage than synchronously trained version, measured by sum of absolute differencesfinding0.793
  Quantitative comparison of synchronous vs asynchronous training for noise resilience
• DiffLogic CA mirrors how biological systems achieve reliability through networks of imperfect componentsclaim0.784
  Authors' analogy between emergent fault tolerance in DiffLogic CA and biological robustness
• Asynchronous DiffLogic CA requires 50 steps vs 20 steps for synchronous training to reconstruct checkerboard patternfinding0.776
  Cost of asynchronous training in terms of convergence time steps
• Synchronously trained DiffLogic CA circuit succeeds at asynchronous inference without retrainingfinding0.771
  Unexpected result demonstrating robustness of learned circuits beyond their training regime