Эксперимент: Conditional-information-preserving pooling (#1222)

{ "worked": false, "confidence": 9, "verdict": "Built and ran a reproducible majority-spin conditional-information toy experiment plus a small learned scalar-pooling remote-prediction comparison. The exact check found I(L:R)=0.3043 bits versus I(majority(L):R)=0.2933 bits, with 0.0110 bits discarded and clearly different posteriors for two same-majority inputs. However, learned pooling did not beat mean pooling on held-out remote NLL or accuracy, so the proposed neural method has no demonstrated win here.", "metrics": { "baseline": "Mean pooling: held-out remote NLL 0.48705, accuracy 0.80450; majority pooling: NLL 0.49432, accuracy 0.80450; full-source predictor NLL 0.48756.", "idea": "Learned softmax scalar pooling: held-out remote NLL 0.48739, accuracy 0.80450, estimated NLL gap -0.00017 versus full source. Exact injective pooling retained 0.30425 bits, but the trained pooling weights remained near-uniform and did not improve over mean pooling." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 toy_experiment.py", "files": [ "toy_experiment.py" ], "limitations": "Only a synthetic noisy-spin system was tested; no CIFAR-10, hierarchical Transformer, local corruption, task-loss training, explicit information-budget regularizer, multiple random seeds, or statistically repeated trials were run. The neural demo is a small auxiliary-prediction test rather than an end-to-end pooling benchmark." }