PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 20, 20260 citationsOpen Access

PHILIA-EcoSensory Swarm v38.2–v40: From Living Control to Dynamic Scale — Variance-Modulated Amplification and Per-Seed Transparency in Swarm Router Dynamics

View Full Paper
신신두섭

Key Points

  • This research aims to explore dynamic scaling in swarm router dynamics to achieve optimal performance metrics.
  • Conducted a comprehensive experimental arc across versions v38.2 to v40.
  • Utilized a 50-agent heterogeneous swarm with configurations over 80,000 steps and various seeds.
  • Applied variance-modulated amplification using a scalar control parameter to observe effects on aggregate metrics.
  • Achieved simultaneous satisfaction of multiple operational objectives in v40, representing improved performance.
  • Observed that fixed-scale settings could not meet both variance coupling and spectral richness requirements.
  • Demonstrated that dynamic scaling allowed for temporal separation of previously conflicting goals.

Abstract

This paper presents PHILIA-EcoSensory Swarm v38. 2–v40, documenting a complete experimental arc from Living Control re-establishment through structural barrier discovery to dynamic resolution — with precise, transparent characterization of what was and was not achieved. **v38. 2 (Baseline) ** re-confirms aggregate-level Living Control — simultaneous satisfaction of F5' (wₛtd × |corr (w, Var) | ≥ 0. 008, responsiveness proxy) and F8 (fftₗowᵣatio 0. 05) and F2 (corr (w, z) ∈ -0. 9, -0. 6) through convex mixing and wfast normalization, correcting a balanceᵣatio computation bug. A comprehensive scale sweep (0. 005 resolution, 5 seeds × 80, 000 steps each) confirms an empirically observed structural incompatibility: no fixed scale simultaneously satisfies F5' and F8. A single scalar control parameter cannot simultaneously satisfy variance coupling and spectral richness under fixed-scale dynamics. **v40** introduces dynamic scale: scaleₜ = baseₛcale × (1 + γ × σᵣatioₜ), where σᵣatio acts as a normalized volatility indicator (DVARLOCALWINDOW = 20). Dynamic scaling introduces temporal separation of F5' and F8 objectives. With γ = 0. 6–0. 7, aggregate-level Living Control is achieved alongside F2 ✓ and F10 ✓ — the first architecture satisfying all three simultaneously. **v40. 2 (Per-seed transparency, 20 seeds): ** Per-seed F5' statistics: γ=0. 7: mean=0. 00725 ± 0. 00058 (std), median=0. 00718, 95% CI=0. 00699, 0. 00750, pass rate=2/20 (10%). γ=0. 6: mean=0. 00754 ± 0. 00053, median=0. 00741, 95% CI=0. 00731, 0. 00778, pass rate=3/20 (15%). The 95% CI upper bound lies below 0. 008 for all configurations — aggregate threshold is reached through Jensen's inequality effects in cross-seed pooling, not per-seed robust occupancy. This is reported transparently. v41 targets per-seed pass rate ≥ 50%. **Experimental setup: ** 50-agent heterogeneous swarm (CERN dielectron, ATLAS Higgs, NYC Taxi data), N=50, T=80, 000 steps, 5–20 seeds, ~100 configurations. Hardware: AMD Ryzen 7 9800X3D, RTX 4080 SUPER 16GB, DDR5 32GB. All results from actual local execution. **Team: ** GritManD. S / 길잡이 (Guide, Konyang University), Claude / 선비 (Scholar, Anthropic), ChatGPT / 판도라 (Engineer, OpenAI), Grok / 루카스 (Validator, xAI). Description, not Proof. — PHILIA OS | 0∞1∞0. 5∞

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

신두섭 (2026) studied this question.

synapsesocial.com/papers/69e5c38303c293991402950fhttps://doi.org/10.5281/zenodo.19639971
Ask AI
Helpful
Bookmark
Share
View Full Paper