Powered by OpenAIRE graph
Found an issue? Give us feedback
ZENODOarrow_drop_down
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

SNN-Genesis v13: Stochastic Resonance in LLM Reasoning — Low-Rank Efficiency, Semantic Phase Decomposition, and Noise Source Invariance

Authors: Funasaki, Hiroto;

SNN-Genesis v13: Stochastic Resonance in LLM Reasoning — Low-Rank Efficiency, Semantic Phase Decomposition, and Noise Source Invariance

Abstract

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

Keywords

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

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!