PHILIA v11 Deliberative Homeostasis in a Goal-Adaptive Multi-Agent Recursive System: Stability–Individuality Trade-off under Physical Data
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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| _version_ | 1866901237940617216 |
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| author | Shin, GritMan_ |
| author_facet | Shin, GritMan_ |
| contents | <h2> Zenodo 설명란 (Description)</h2> <p><strong>PHILIA v11</strong> 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.</p> <p>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).</p> <p><strong>Key findings:</strong></p> <ul> <li>Strong homeostatic robustness: the system fully absorbs an instantaneous environmental shock (amplitude 2.0, 4× normal range) without SR trajectory disruption</li> <li>Pre-critical regime confirmed: no sharp SR transition observed across λ ∈ [0.000, 0.010]</li> <li>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</li> </ul> <p>PHILIA v11 is characterized as a <strong>pre-critical homeostatic system</strong> — 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.</p> <p>Part of the ongoing PHILIA Engine research series (v8 → v13) by Trinity AI.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19204860 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | PHILIA v11 Deliberative Homeostasis in a Goal-Adaptive Multi-Agent Recursive System: Stability–Individuality Trade-off under Physical Data Shin, GritMan_ complex systems multi-agent system homeostasis self-organization criticality edge of chaos goal-adaptive dynamics recursive system self-reference emergent behavior artificial life CERN dielectron ATLAS Higgs particle physics data stability-individuality tradeoff synchronization pre-critical regime PHILIA Engine Trinity AI <h2> Zenodo 설명란 (Description)</h2> <p><strong>PHILIA v11</strong> 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.</p> <p>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).</p> <p><strong>Key findings:</strong></p> <ul> <li>Strong homeostatic robustness: the system fully absorbs an instantaneous environmental shock (amplitude 2.0, 4× normal range) without SR trajectory disruption</li> <li>Pre-critical regime confirmed: no sharp SR transition observed across λ ∈ [0.000, 0.010]</li> <li>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</li> </ul> <p>PHILIA v11 is characterized as a <strong>pre-critical homeostatic system</strong> — 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.</p> <p>Part of the ongoing PHILIA Engine research series (v8 → v13) by Trinity AI.</p> |
| title | PHILIA v11 Deliberative Homeostasis in a Goal-Adaptive Multi-Agent Recursive System: Stability–Individuality Trade-off under Physical Data |
| topic | complex systems multi-agent system homeostasis self-organization criticality edge of chaos goal-adaptive dynamics recursive system self-reference emergent behavior artificial life CERN dielectron ATLAS Higgs particle physics data stability-individuality tradeoff synchronization pre-critical regime PHILIA Engine Trinity AI |
| url | https://doi.org/10.5281/zenodo.19204860 |