PHILIA v11 Deliberative Homeostasis in a Goal-Adaptive Multi-Agent Recursive System: Stability–Individuality Trade-off under Physical Data

📋 Zenodo 설명란 (Description)

PHILIA v11 is a goal-adaptive multi-agent recursive system developed by Trinity AI Research to investigate the emergence of homeostasis, adaptability, and criticality in complex dynamical systems.

This paper presents three core experiments executed on real-world high-energy physics datasets — CERN Dielectron collision data and ATLAS Higgs decay data — and reports ground-truth results obtained from direct local execution by the lead researcher (GritMan_D.S).

Key findings:

• Strong homeostatic robustness: the system fully absorbs an instantaneous environmental shock (amplitude 2.0, 4× normal range) without SR trajectory disruption
• Pre-critical regime confirmed: no sharp SR transition observed across λ ∈ [0.000, 0.010]
• Fundamental stability–individuality trade-off identified: global mean-field goal alignment drives irreversible synchronization of all 50 agents within ~5,000 of 60,000 timesteps, eliminating agent-level diversity as a structural consequence of the architecture

PHILIA v11 is characterized as a pre-critical homeostatic system — adaptive and robust, but lacking the heterogeneity required for life-like behavior. This work establishes a reproducible quantitative baseline for future versions introducing diversity-preserving and criticality-inducing mechanisms.

Part of the ongoing PHILIA Engine research series (v8 → v13) by Trinity AI.