Эксперимент: Two-Column Non-Markovian Memory Core (#558)

{ "worked": true, "confidence": 8, "verdict": "Built an MPS-inspired two-column memory core with single-site transfer matrices, adjacent-pair matrices, spectral normalization utilities, and a delayed XOR learning test. The contraction sweep matched the predicted alpha^r ratios to relative error below 1.6e-15 across alpha=0.6..1.25, including the predicted stability boundary at alpha=1. The pair-channel sweep was exactly linear in its coupling lambda, and the gamma*lambda boundary sweep matched predictions to below 2.3e-15; the toy learner reached higher accuracy than the GRU, though this is only a small full-batch CPU test.", "metrics": { "baseline": "GRU: accuracy 0.6375, loss 0.6229, 9.65 s", "idea": "TwoColumn: accuracy 0.6767, loss 0.5871, 14.01 s; contraction and boundary relative errors <2.3e-15; pair response exactly linear" }, "how_to_run": "CUDA_VISIBLE_DEVICES='' /home/maxwelhelp/main/bin/python3 two_column_memory.py", "files": [ "two_column_memory.py", "results.json", "run_output.txt" ], "limitations": "The CUDA attempt encountered a shared-environment cuDNN/system allocation crash, so the final reproducible run used CPU. No permuted-MNIST, long-horizon benchmark, FLOP-normalized comparison, chi sweep, or multi-seed statistical evaluation was performed; the exact math checks use orthogonal transfer matrices and therefore validate the stated formulas rather than broad learned-model behavior." }