Эксперимент: Actionable-Information Optimizer (#413)

{ "worked": true, "confidence": 8, "verdict": "Built a finite-resolution actionable-information toy optimizer and numerically verified the proposed mechanism. Across three seeds, I_acq increased logarithmically with resolution (slope 0.9967 versus predicted 1.0, R²=0.999997), while the final fine-resolution I_use increment was only 0.000375 nats versus 0.6925 nats for I_acq, and progress changed by -0.00135. Thus the acquired-information/unavailable-information signature manifested clearly; quantization was not uniformly better than raw SGD, so this is not evidence of a general optimization win.", "metrics": { "baseline": "Raw high-resolution sign/gradient update final quadratic loss: 0.01313, 0.01273, 0.01405 across seeds 7,19,43 (mean 0.01331).", "idea": "Quantized delta=0.125 final loss: 0.01312, 0.01324, 0.01304 (mean 0.01313); I_acq log slope 0.9967±0.000004, fine I_acq increment 0.6925±0.00008 nats, fine I_use increment 0.000375±0.000028 nats, fine progress increment -0.001346±0.000036." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 actionable_optimizer_toy.py", "files": [ "actionable_optimizer_toy.py", "toy_results.json", "verification_summary.json" ], "limitations": "Only a one-dimensional quadratic Monte Carlo channel was tested; no learned encoder, utility predictor, Adam/SGD neural-network training, MNIST/CIFAR, lambda information-cost sweep, stability boundary, wall-clock speed, or two-model-size replication was evaluated." }