Recursively Self-Improving Universal AI Theory of Aging

A Living Computational Theory That Evolves

⟵ Input Layer: Existing Theories of Aging
Classical Theories
  • Damage / Wear-and-Tear
  • Evolutionary (AP, DS, MA)
  • Free Radical / Oxidative Stress
  • Telomere Attrition
  • Epigenetic Clock
  • Programmed Aging
Hallmarks & Mechanisms
  • Inflammaging
  • Proteostasis Collapse
  • Mitochondrial Dysfunction
  • Cellular Senescence
  • Stem Cell Exhaustion
  • Intercellular Communication
  • Genomic Instability
  • Nutrient Sensing Deregulation
Modern Frameworks
  • Information Theory (Sinclair)
  • Hyperfunction (Blagosklonny)
  • Developmental Drift
  • Network Theory
  • Cross-linkage
  • Immunosenescence
  • Neuroendocrine
Computational / Integrative
  • Control-Theoretic (CTAL)
  • Tri-Domain Dynamical (Paper 1)
  • ★ Recursive AI Theory (Meta-Theory)
◉ AI Orchestration Engine
⚡ Orchestration Core
Memory System
📝 Episodic Memory
🧠 Semantic Memory
⚙️ Procedural Memory
Knowledge Base
📚 World Literature
🧬 Omics Databases
💊 Clinical Trials
📊 Biomarker Data
Model Orchestra
🟢GPT-5.5
🟣Claude Opus 4
🔵Gemini
⚪Grok
🔷DeepSeek V4
🌙Kimi K2
☁️Qwen 3
🐉Baichuan
🔮Yi (01.AI)
💎GLM-5
Output Layer: Hypothesis & Experimentation ⟶
Virtual Experiments
🖥️ In-Silico Modeling
⚛️ Molecular Dynamics
🔀 Pathway Simulation
🧪 Virtual Organism Models
Real-World Experiments
🔬 Wet Lab Validation
👥 Clinical Studies
📈 Longitudinal Studies
Validation Engine
⏱️ Aging Clocks
🩸 Biomarker Panels
🏃 Phenotypic Assessment
🎯 Reverse Aging in Humans
🔬 Understand Aging Completely
🗺️ Personalized Longevity Roadmap
Input (Theories)
Processing (AI Engine)
Output (Experiments)
Feedback Loop
Meta-Theory (Self-Reference)