Эксперимент: Regret-Balanced Adaptive Context (#1202)

{ "worked": false, "confidence": 9, "verdict": "The implementation and numerical verification succeeded: the objective finite-difference identity matched to 6.7e-16, and the empirical logistic KL was proportional to the weighted tail energy. In the mini-experiment, adaptive context had cumulative log loss 5138.30 versus 5135.92 for fixed-long, retained mean context 23.65/24, and used 98.5% of full-context operations; therefore the claimed speedup was not observed.", "metrics": { "baseline": "fixed-long: loss 5135.92, mean context 24, relative operations 1.000", "idea": "adaptive: loss 5138.30, mean context 23.65, relative operations 0.985; fixed-short control: loss 4814.29, context 4, relative operations 0.167" }, "how_to_run": "python3 adaptive_context_experiment.py", "files": [ "adaptive_context_experiment.py", "results.json", "sweep.py" ], "limitations": "One synthetic stream and one online estimator were tested; no neural integration, repeated-seed intervals, or detailed hardware profiling was performed." }