Эксперимент: Entropy-Feedback Zeroth-Order Cooling (#373)

{ "worked": true, "confidence": 8, "verdict": "Built and ran an entropy-feedback zeroth-order optimizer verification on a 24-dimensional ill-conditioned quadratic. The ESS identity error was 4.44e-16, the transition prediction ESS=N^0.5=11.31 was observed as 11 for h=0.494, and measured cooling slopes matched the predicted controller rates at h=0.1, 0.5, and 0.9 to numerical precision. In the five-seed toy comparison, feedback reached mean final loss 1.056 versus 2.256 for fixed exponential cooling, but constant temperature was better at 0.135; thus the mechanism manifested, but a universal optimization win was not established.", "metrics": { "baseline": "Fixed exponential cooling: final quadratic loss 2.256 +/- 0.411; last-20 normalized entropy 0.00063; final temperature 1.234e-4.", "idea": "Entropy feedback: final quadratic loss 1.056 +/- 0.415; last-20 normalized entropy 0.222; final temperature 0.113. Predicted versus observed cooling slopes: h=0.1, 0.00101643 versus 0.00101643; h=0.5, 0.0255 versus 0.0255; h=0.9, 0.0499836 versus 0.0499836. Constant-temperature reference achieved 0.135 final loss." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 verify_entropy_cooling.py", "files": [ "verify_entropy_cooling.py", "results.json", "entropy_cooling.py" ], "limitations": "The completed experiment is a toy quadratic rather than MNIST or a neural network, uses five seeds, and does not compare equal wall-clock/FLOPs against gradient-based training. The reusable entropy_cooling.py module save was interrupted by the protocol error and may be incomplete; verify_entropy_cooling.py and results.json contain the executed, reproducible results." }