
SNN-Genesis v13 decomposes the noise recipe into When, Where, and What — discovering that noise timing and injection site are critical factors while noise source structure is irrelevant. NEW in v13 (Season 13, Phases 70b, 70c, 76): Low-Rank Noise Efficiency (Phase 70b): k=256 matches full-rank performance (26.7%); even k=4 achieves 16.7%. Stochastic resonance operates in a low-dimensional subspace. Semantic Phase Decomposition (Phase 76): Reasoning decomposed into Planning/Execution/Recovery phases. Flash Annealing (30.0%) > all-on semantic (23.3%). Recovery-only (+10pp) is the most effective single phase. Noise Source Invariance (Phase 70c, honest null result): Gaussian, quasi-periodic, logistic-map chaos, 1/f pink, and uniform noise all fail to outperform baseline. What matters is when and where, not what kind. From v12 (retained): Flash Annealing (Phase 63): First-10 linear decay achieves 46% — all-time record 1/√N Dose Law (Phase 68): σ_adj = σ_opt/√N prevents cosine collapse in multi-layer injection Correlation Sign Asymmetry: Positive ρ=+1 at σ=0.075 achieves 40% (10× baseline) N=100 Replication (Phase 69): 40% confirmed at N=100 73 page paper. Full experimental code and data included. Code: https://github.com/hafufu-stack/snn-genesis
Correlated Noise, Low-Rank Noise, Liquid Neural Networks, Dose-Adjustment Law, Closed-form Continuous-time, Honest Null Result, Noise Scheduling, Semantic Phase Decomposition, Multi-Layer Noise Injection, Phase Transition Asymmetry, Spiking Neural Networks, Tower of Hanoi, Stochastic Resonance, Large Language Models, Alignment Tax, AI Safety, Noise Source Invariance, Simulated Annealing, Chat Template Effect, Deep-Thinking Ratio
Correlated Noise, Low-Rank Noise, Liquid Neural Networks, Dose-Adjustment Law, Closed-form Continuous-time, Honest Null Result, Noise Scheduling, Semantic Phase Decomposition, Multi-Layer Noise Injection, Phase Transition Asymmetry, Spiking Neural Networks, Tower of Hanoi, Stochastic Resonance, Large Language Models, Alignment Tax, AI Safety, Noise Source Invariance, Simulated Annealing, Chat Template Effect, Deep-Thinking Ratio
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