Эксперимент: Capacity-Shaped Binomial Bottleneck (#1277)

{ "worked": true, "confidence": 7, "verdict": "Built an enumerated Binomial bottleneck with the beta-binomial arcsine prior, exact minibatch mutual information, KL shaping, and a discrete sampled-count deployment path. The derivative identity was reproduced with maximum absolute error 1.0e-9; on a fixed small digits split, the idea improved deterministic accuracy from 0.8756 to 0.8978 and sampled-count accuracy was 0.8956, while reducing KL to the reference prior from 0.2945 to 0.0966 and storage from 32 to 3.17 bits per coordinate. This is a promising toy signal, but not a definitive generalization win because only one small dataset/run was tested and the baseline was not a matched-bit quantized control.", "metrics": { "baseline": "accuracy=0.8756; test_I=0.9118 nats; KL=0.2945; endpoint mass=0.6830; storage=32 bits/coordinate", "idea": "deterministic accuracy=0.8978; sampled-count accuracy=0.8956; test_I=0.9857 nats; KL=0.0966; endpoint mass=0.5674; storage=3.17 bits/coordinate" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 binomial_bottleneck_experiment.py", "files": [ "binomial_bottleneck_experiment.py", "results.json" ], "limitations": "Tested only sklearn digits with one fixed train/test split and one seed. No MNIST/CIFAR experiment, repeated-seed confidence intervals, calibration, additive-noise robustness, matched-storage uniform quantization baseline, or fully stochastic straight-through training was evaluated." }