Эксперимент: Influence-Adaptive Strategic Quantization (#411)

{ "worked": true, "confidence": 8, "verdict": "Built a reproducible influence-adaptive strategic communication module with exact clipped amplification, adaptive midpoint quantization, math sweeps, and a small SBM GNN comparison. The mechanism manifested: saturation thresholds matched 1/xi within 2.9e-6, local slopes matched xi to 8.9e-16, amplification decreased monotonically from 8.0 to 1.0 as influence increased, and the adaptive schedule reached binary resolution. However, it did not improve validation accuracy in this toy task: the baseline reached 1.000 versus 0.917 clipped and 0.861 quantized; the benefit observed is bounded, variable-rate communication rather than accuracy.", "metrics": { "baseline": "Validation accuracy 1.000; ordinary GCN.", "idea": "Clipped strategic validation accuracy 0.917 and adaptive quantized accuracy 0.861; messages were bounded at max absolute value 1.0, mean adaptive allocation was 2.047 bits, xi ranged from 1.290 to 3.370, and saturation fraction was 0.677. Math checks gave threshold error 2.9e-6, slope error 8.9e-16, monotone influence relation true, and binary resolution reached." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py", "files": [ "experiment.py", "results.json" ], "limitations": "Only one fixed-seed, small synthetic SBM graph and 180 training steps were tested; no Cora, larger graphs, repeated seeds, FLOP or throughput benchmark, recurrent rollout, entropy regularization, or differentiable quantizer training was evaluated. The toy quantized forward path uses detached hard midpoint quantization, so it is a communication/evaluation prototype rather than a fully trainable straight-through implementation." }