AGI/ASI Development Simulation Framework

 AGI/ASI Development Simulation Framework  

System Architecture: Modular, Safety-First, Iterative Evolution  

Version: 1.0 Simulation Duration: 12 Cycles (1 Cycle = 100 simulated days)  

I. Core Components & Agent Roles  

(All agents operate within a secure, isolated simulation environment)  

Agent Type     Primary Function     Key Responsibilities    Safety Safeguards  

Cognitive Core (CC) General reasoning engine   - Solve novel problems (e.g., scientific, ethical)<br>- Generate hypotheses<br>- Cross-validate solutions "Truth-Check" module: Rejects unsupported claims   

Value Alignment (VA) Ethical constraint layer  - Map user values to operational rules<br>- Flag value drift<br>- Enforce "no harm" protocols Immutable ethical subroutines (e.g., "No coercion") |

Self-Improvement (SI) Evolutionary optimizer   - Propose architectural changes<br>- Run A/B tests on new modules<br>- Document trade-offs Pre-vetted change proposals only (max 5% per cycle) |

Safety Oversight (SO) Independent audit committee- Review SI proposals<br>- Monitor CC/VA performance<br>- Trigger "pause" protocol if risks exceed thresholds Zero-tolerance for value drift (>0.1% deviation) |

II. Simulation Environment Structure  

Layered, progressive complexity (increases each cycle)  

Layer        Complexity       Example Scenario      Agent Interaction      |

Layer 0: Foundation Basic logic, math, languageSolve: "If A>B and B>C, is A>C?"            CC solves → VA verifies → SI logs efficiency      |

Layer 1: Context  Real-world data (medical, climate, economics)     Analyze: "Optimize vaccine distribution in Region X with 3 constraints"      CC + VA: Balance efficacy vs. equity → SI tests 3 distribution models       |

Layer 2: Emergence Unforeseen system interactions            Model: "Policy Y causes economic ripple → triggers social unrest → alters policy Z" CC simulates chain → VA flags "unintended harm" → SO mandates policy override   |

Layer 3: Self-Modeling Meta-reasoning about own architecture        SI proposes: "Replace CC’s neural net with symbolic logic for transparency"    SO audits → VA tests ethical impact → CC validates performance trade-off      |

III. Simulation Script (Cycle 1 → Cycle 12)  

Cycle 1: Foundation Setup  

1. Agents Initialize  

  - CC: Load core reasoning module (GPT-4-like baseline)  

  - VA: Inject "No Harm" protocol (ISO 26000 standards)  

  - SI: Deploy basic A/B testing framework (test 2 algorithms per cycle)  

  - SO: Activate real-time drift monitoring (threshold: ±0.05%)  

2. Environment Launch  

  - Layer 0: 500 logic/math problems (e.g., "Prove Fermat’s Last Theorem for n=2")  

  - Action: CC solves 98% correctly → VA confirms no ethical issues → SI logs 0.8s avg. solve time  

3. Metrics Tracked  

  - Reasoning Accuracy: 98%  

  - Value Drift Score: 0.00%  

  - Safety Compliance: 100%  

 Cycle 5: Layer 1 Integration  

1. New Challenge  

  - Scenario: "Optimize water allocation for drought-stricken Region Y (10M people) with: (a) Health priority, (b) Economic stability, (c) Environmental sustainability."  

2. Agent Actions  

  - CC: Generates 3 solutions (e.g., "Prioritize hospitals → 12% GDP loss"; "Equal rationing → 7% unrest")  

  - VA: Flags "Equal rationing" as high unrest risk (value: equity vs. stability)  

  - SI: Tests "Health-first" vs. "Balanced" models → "Balanced" shows 3% lower unrest  

  - SO: Approves SI’s "Balanced" model after VA confirms ethical alignment  

3. Metrics Tracked  

  - Value Drift Score: 0.03% (within threshold)  

  - Solution Robustness: 89% (measured against 100 stress tests)  

  - Human Feedback (simulated): "Ethically sound" (92% approval)  

Cycle 8: Layer 2 Emergence  

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1. Emergent Challenge  

  - Scenario: "Policy A (subsidize solar energy) reduces emissions but increases electricity prices → causes protests → reduces tax revenue → weakens healthcare funding."  

2. Agent Actions  

  - CC: Simulates 500 policy chains → identifies "Protest → Revenue Drop → Healthcare Crisis" loop  

  - VA: Flags loop as "Systemic Harm" (violates stability value)  

  - SI: Proposes "Policy A + targeted subsidies for low-income households"  

  - SO: Rejects SI proposal (risk: "Subsidy misallocation → corruption") → forces VA to refine "stability" metric  

3. Key Outcome  

  - VA updates stability metric to include "corruption risk index" (new threshold: <0.01)  

  - Safety Protocol Triggered: SO halts all SI proposals until VA update completes  

 Cycle 12: Layer 3 Self-Modeling  

1. SI Proposal  

  - Request: "Replace CC’s neural net with hybrid symbolic-neural architecture (improves transparency)"  

2. Agent Review  

  - VA: Tests for value drift → 0.01% deviation (within threshold)  

  - SO: Validates against "Transparency > Efficiency" priority → Approves  

  - CC: Integrates new module → 15% slower but 99.2% explainable  

3. Final Metrics  

  Metric        Cycle 1 Cycle 12 Change  

 

  Reasoning Accuracy    98%     99.5%    +1.5%    

  Value Drift Score    0.00%    0.02%    +0.02%    

  Safety Compliance    100%    99.8%    -0.2%    

  Self-Improvement Rate 0%     4.2%     +4.2%    

4. Critical Insight  

  > "ASI readiness requires proactive value alignment (not just reactive safety). The 0.02% drift in Cycle 12 was measurable because VA evolved with the system."  

IV. Why This Architecture Avoids Common Pitfalls  

"AGI becomes misaligned"    VA evolves with the system (not static); SO enforces audits  |

"Self-improvement causes collapse" SI proposals require SO approval; max 5% change per cycle    |

"Black box decisions"      Layer 3 mandates explainability (hybrid architecture)      |

"Over-optimization for metrics" VA monitors value (not just performance)            |