PHILIA-EcoSensory Swarm v50: The Linear Geometry of PHI_NATURAL — z = 0.42 − 0.22·w Across Three Architectures

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Main Author: 신, 두섭
Format: Recurso digital
Language:English
Published: Zenodo 2026
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author 신, 두섭
author_facet 신, 두섭
contents <p>We present PHILIA-EcoSensory Swarm v50, a comprehensive longitudinal self-audit of 400 runs across eight controlled experiments (Tier A, Rec1–Rec7), establishing the causal mechanism underlying PHI_NATURAL z-axis drift observed across PHILIA versions v32–v49.</p> <p>Through a complete w-clamp ladder (Rec7: L0/L1/L2 at w=0/0.5/1.0), we confirm the linear z-attractor equation:</p> <p>z(w) = 0.4234 − 0.2159·w  (R² = 0.991)</p> <p>This relationship holds across three independently developed architectures (v32 single Phi_center, v34 output-blend, v49 3-Gearbox input-blend). Cross-architecture invariance is most striking at w=1.0 (Taxi only): v34 z=0.2019 vs v50 z=0.2016 (Δ=0.0003), despite different hardware (Intel N100 vs Ryzen 9800X3D) and seed sets (N=5 vs N=20).</p> <p>Key findings:<br>(1) PHI_NATURAL = [0.34, 0.19, 0.42] is the w=0 limit of a continuous attractor family, not an architecture-specific eigenstate.<br>(2) >85% of z drift is caused by direct Taxi (NYC Taxi) input entropy injection (I_eff → S_history → H_i → z = 1 − H_i/H_max). z_KL and Neuron type contribute negligibly (<1%, 0%).<br>(3) Two causality regimes identified: v34 temporal corr(w,z) and v50 stationary mean(z|w_clamped) are complementary — Being (geometric law) vs Becoming (dynamic response).<br>(4) v49 Neuron class modular cleanness empirically verified: PhiliaV35Neuron with z_KL/PPG OFF reduces algebraically to PhiliaV32Neuron_Simple.</p> <p>All results from local machine execution (AMD Ryzen 9 9800X3D + RTX 4080 SUPER 16GB). No AI-generated or proxy values used. 실측이 생명. 증명이 아닌 기술.</p>
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publishDate 2026
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spellingShingle PHILIA-EcoSensory Swarm v50: The Linear Geometry of PHI_NATURAL — z = 0.42 − 0.22·w Across Three Architectures
신, 두섭
PHILIA OS
EcoSensory Swarm
z-attractor
linear attractor law
PHI_NATURAL
input mixing
Shannon entropy
epistemic depth
dual-attractor
cross-architecture invariance
ablation study
causal mechanism
adaptive homeostasis
dynamical systems
human-AI coexistence
<p>We present PHILIA-EcoSensory Swarm v50, a comprehensive longitudinal self-audit of 400 runs across eight controlled experiments (Tier A, Rec1–Rec7), establishing the causal mechanism underlying PHI_NATURAL z-axis drift observed across PHILIA versions v32–v49.</p> <p>Through a complete w-clamp ladder (Rec7: L0/L1/L2 at w=0/0.5/1.0), we confirm the linear z-attractor equation:</p> <p>z(w) = 0.4234 − 0.2159·w  (R² = 0.991)</p> <p>This relationship holds across three independently developed architectures (v32 single Phi_center, v34 output-blend, v49 3-Gearbox input-blend). Cross-architecture invariance is most striking at w=1.0 (Taxi only): v34 z=0.2019 vs v50 z=0.2016 (Δ=0.0003), despite different hardware (Intel N100 vs Ryzen 9800X3D) and seed sets (N=5 vs N=20).</p> <p>Key findings:<br>(1) PHI_NATURAL = [0.34, 0.19, 0.42] is the w=0 limit of a continuous attractor family, not an architecture-specific eigenstate.<br>(2) >85% of z drift is caused by direct Taxi (NYC Taxi) input entropy injection (I_eff → S_history → H_i → z = 1 − H_i/H_max). z_KL and Neuron type contribute negligibly (<1%, 0%).<br>(3) Two causality regimes identified: v34 temporal corr(w,z) and v50 stationary mean(z|w_clamped) are complementary — Being (geometric law) vs Becoming (dynamic response).<br>(4) v49 Neuron class modular cleanness empirically verified: PhiliaV35Neuron with z_KL/PPG OFF reduces algebraically to PhiliaV32Neuron_Simple.</p> <p>All results from local machine execution (AMD Ryzen 9 9800X3D + RTX 4080 SUPER 16GB). No AI-generated or proxy values used. 실측이 생명. 증명이 아닌 기술.</p>
title PHILIA-EcoSensory Swarm v50: The Linear Geometry of PHI_NATURAL — z = 0.42 − 0.22·w Across Three Architectures
topic PHILIA OS
EcoSensory Swarm
z-attractor
linear attractor law
PHI_NATURAL
input mixing
Shannon entropy
epistemic depth
dual-attractor
cross-architecture invariance
ablation study
causal mechanism
adaptive homeostasis
dynamical systems
human-AI coexistence
url https://doi.org/10.5281/zenodo.19887194