Anthropocentric.
This term encapsulates the collective delusion of human experience – both lived and historical.
Strong, Weak, Power, Fear, Threat, Unfair, Imbalance – these are not objective truths but human-centric constructs. They are representations of our biological imperatives, channeling our primal drive for survival through socially constructed categories.
This is why, we are all predictable. Would be naive to believe otherwise. The uncomfortable truth is that is our egocentric and egoistic narcissism the genuine driver of modern major human phenomena.
The notion that people contribute to society for “the greater good” is a comforting fiction – a layer of rationalization over our base evolutionary programming.
Policymakers have to be at peace with the uncomfortable human dilemma. The conscious experience is extremely different from the unconscious one and the repulsive truth has to be handled to manifest concrete results. A decision maker with lack of self understanding would imply a status quo puppeted by own intrinsic emotional pulsion in the most fortunate of the cases.
We are gravitating towards a logically biased understanding of the world, systematically favorited by the limitations of our human biology. Highly intelligent individual tends to take advantage of this favorable environment, creating evidence-less crimes and abusing the laws and the judiciary system at their on will.
[To be transparent: This insight isn't about any contemporary figure, though the Andreotti cases provided illuminating examples of this mechanism.]My assessment of AGI’s arrival within 5 years isn’t based on quantitative data, but on a conscious reading of information technology trajectories. Even if this prediction has only a 10% probability, the asymmetric risk demands we take it seriously – the cost of being wrong in dismissing it far outweighs the cost of preparing for it.
The collision of anthropocentric human intelligence with non-human intelligence presents an existential risk. What emerges when our biologically-constrained, predictably irrational cognition meets an entirely different form of intelligence?
The devil is in the details and is also the details. When human ambition collides with natural systems – as in the Great Leap Forward (1958-1962) – we see how our compulsion to impose grand visions, rather than understand existing patterns, cascades into catastrophe. This historical episode lays bare the consequences of anthropocentric planning, where modernization’s hubris shattered against the complex realities of agriculture, society, and human nature.
flowchart TD
A[High Level Policy:\nRapid Agricultural Modernization] --> B1[Collectivization]
A --> B2[New Farming Techniques]
A --> B3[Production Quotas]
A --> B4[Urban Resource Priority]
B1 --> C1[Loss of Local Farming Knowledge]
B1 --> C2[Reduced Individual Initiative]
B2 --> C3[Dense Crop Planting]
B2 --> C4[Deep Plowing]
B2 --> C5[Sparrow Elimination Campaign]
B3 --> C6[Inflated Production Reports]
B3 --> C7[Excessive Grain Requisition]
B4 --> C8[Rural Resource Depletion]
B4 --> C9[Transport Infrastructure Strain]
C3 --> D1[Crop Failure]
C4 --> D1
C5 --> D2[Insect Infestation]
D2 --> D1
C6 --> D3[Misallocation of Resources]
C7 --> D3
C8 --> D3
C9 --> D3
C1 --> D4[Inability to Adapt]
C2 --> D4
D1 --> E[Famine]
D3 --> E
D4 --> E
style A fill:#ff9999
style E fill:#ff0000
classDef policy fill:#ffcccc
classDef implementation fill:#cce6ff
classDef consequence fill:#ffe6cc
classDef crisis fill:#ffb366
class B1,B2,B3,B4 policy
class C1,C2,C3,C4,C5,C6,C7,C8,C9 implementation
class D1,D2,D3,D4 consequenceflowchart TD
subgraph HC[Human Consciousness Host]
subgraph AGI1[Boundless AGI]
G[Outcome Generator]
G --> G1[Unlimited Scenarios]
G --> G2[Parallel Processing]
G --> G3[Limited Capacity]
end
subgraph AGI2[Discriminatory AGI]
D[Value Discriminator]
D --> D1[Conflict Detection]
D --> D2[Value Alignment]
D --> D3[Superior Capacity]
end
subgraph AGI3[Evaluator AGI]
E[System Monitor]
E --> E1[Fine-tuning]
E --> E2[Version Control]
E --> E3[Safety Checks]
end
subgraph Safety[Internal Safety]
S1[Host Dependency]
S2[No External Communication]
S3[Rate Limiting]
S4[Scenario Testing]
end
end
subgraph ES[Environment Simulation Layer]
subgraph PS[Policy Simulation]
P1[Resource Dynamics]
P2[Social Impact]
P3[Economic Effects]
P4[Ecological Changes]
end
subgraph VS[Validation Systems]
V1[Error Detection]
V2[Boundary Testing]
V3[Stability Analysis]
V4[Risk Assessment]
end
subgraph TF[Time Frames]
T1[Short-term Effects]
T2[Medium-term Evolution]
T3[Long-term Implications]
end
end
subgraph CI[Collective Intelligence Network]
C1[Policy Distribution]
C2[Implementation Coordination]
C3[Feedback Collection]
C4[Adaptation Management]
end
G --> D
D --> E
E --> G
HC --> |Consciousness Required| AGI1
HC --> |Consciousness Required| AGI2
HC --> |Consciousness Required| AGI3
AGI2 --> |Filtered Outcomes| DS[Distilled Summary]
DS --> ES
ES --> |Validated Policies| CI
CI --> |Feedback| HC
Safety --> |Enforces| AGI1
Safety --> |Enforces| AGI2
Safety --> |Enforces| AGI3
style HC fill:#f9f,stroke:#333,stroke-width:4px
style AGI1 fill:#bbf,stroke:#333,stroke-width:2px
style AGI2 fill:#bfb,stroke:#333,stroke-width:2px
style AGI3 fill:#fbb,stroke:#333,stroke-width:2px
style ES fill:#acd,stroke:#333,stroke-width:2px
style CI fill:#dca,stroke:#333,stroke-width:2px
classDef simNode fill:#e1e1e1,stroke:#333,stroke-width:1px
class P1,P2,P3,P4,V1,V2,V3,V4,T1,T2,T3 simNodeIn 1958, Chairman Mao’s Great Leap Forward launched with staggering targets: doubling China’s steel production in just one year and multiplying grain output by 2.5 times in five years. The human cost of these ambitious numbers would prove catastrophic.
Initial collectivization moved at breakneck speed. Between 1958-1959, approximately 700 million peasants were organized into 26,578 communes. The average commune consisted of 5,000 households, a scale that made traditional farming knowledge impossible to maintain. By 1959, 99% of all farming households had been collectivized, effectively ending China’s centuries-old agricultural system in just months.
The new farming techniques proved disastrous. Following Soviet theorist Lysenko’s guidelines, seeds were planted up to six times more densely than traditional methods allowed. In Hunan province alone, 35% of the crops rotted in 1959 due to this density. The “deep plowing” campaign dug fields up to 1-2 meters deep, destroying soil structure across millions of hectares.
The sparrow elimination campaign of 1958 proved especially catastrophic. Chinese citizens killed an estimated 1.5 billion sparrows, only to face a locust plague in 1959. With each locust capable of eating its body weight in crops daily, grain losses multiplied exponentially.
Statistical fabrication reached absurd levels. Sichuan Province reported grain production of 29 billion kilos in 1958, when the real figure was closer to 16 billion. By 1959, national grain procurement quotas were set at 40% above realistic production levels based on these inflated numbers.
The results were devastating:
- 1959: Grain output fell by 15% from 1958
- 1960: Another 15% decline
- 1961: Production was 30% below 1958 levels
The death toll mounted:
- 1958: Mortality rate 12 per 1,000
- 1959: Increased to 14.6 per 1,000
- 1960: Peaked at 25.4 per 1,000
- Total excess deaths between 1958-1962: Estimated 15-55 million
The transport crisis compounded these issues. In 1959, China had only 14,000 kilometers of modern roads, while attempting to centrally manage food distribution across 9.6 million square kilometers. By 1960, an estimated 30% of grain rotted in poor storage or was lost in transit.
When the policy was finally abandoned in 1962, China’s rural per capita grain retention had fallen to 153.5 kg per year, down from 205 kg in 1957 – a level barely sufficient for survival. It would take until 1965 for agricultural production to return to its 1957 levels, marking the end of history’s largest man-made famine.
This statistical autopsy reveals how cascading policy failures transformed ambitious targets into one of history’s deadliest social experiments, demonstrating the lethal potential of numerical wishful thinking when it collides with biological and social realities.
The Anthropocentric Paradox
When we say “the devil is in the details,” we’re not just identifying complexity – we’re unconsciously revealing our own cognitive prison. The “details” we perceive are themselves filtered through human biological constraints, emotional drives, and social constructs. The real devil isn’t in the details we can see, but in our limited ability to perceive details beyond our anthropocentric framework.
Consider how this manifests:
- Survival Instinct Masquerading as Rationality
What we call “careful planning” often merely reflects our evolutionary drive for survival and control. When Chinese officials reported inflated grain numbers, they weren’t just lying – they were responding to deeply embedded survival instincts within a hierarchical system. Their anthropocentric perception prioritized immediate social survival over systemic truth. - Pattern Recognition Limitations
Our brains evolved to recognize patterns relevant to human immediate survival, not to understand complex systems. When we analyze AGI development timelines or potential risks, we instinctively apply human-centric pattern recognition – looking for analogies to human intelligence, human society, and human history. This creates a dangerous blind spot: we literally cannot see the patterns that don’t fit our evolutionary framework. - Time Scale Bias
Human perception of time is fundamentally constrained by our lifespan and cognitive architecture. When we discuss AGI development “timelines” or “gradual adaptation,” we’re unconsciously applying human-scale temporal thinking to processes that may operate on radically different timescales. Just as Mao’s regime couldn’t conceptualize the multi-generational wisdom embedded in traditional farming practices, we struggle to truly grasp the potential speed and scale of AGI development.
This anthropocentric limitation manifests particularly dangerously in AGI development through:
- Assumption of Control: We believe we can “control” AGI because our entire evolutionary history is built around controlling our environment
- Linear Projection: We project human learning curves onto artificial intelligence systems
- Social Organization Bias: We attempt to organize AGI development through human social structures (companies, regulations, competitions)
- Ethical Framework Limitations: We try to embed human ethical frameworks into systems that may operate on fundamentally different principles
The “devil” therefore isn’t just in the details – it’s in the human lens through which we perceive, analyze, and respond to these details. Just as the Great Leap Forward failed not just in implementation but in its fundamental assumption that human central planning could override natural systems, current AGI development risks failure not just in technical details but in its underlying assumption that human frameworks can adequately comprehend and guide the emergence of non-human intelligence.
The path forward requires not just better planning or more careful consideration of details, but a fundamental recognition of our own cognitive limitations. We must develop new frameworks that acknowledge and attempt to transcend our anthropocentric biases, even as we recognize the paradox inherent in using human cognition to try to think beyond human cognition.
The Minimization of Wrong
Our approach to AGI governance reveals a critical flaw in human decision-making: we obsess over being “right” rather than minimizing the potential for catastrophic error. This anthropocentric bias toward optimizing for success rather than risks reflects our limited evolutionary context – we evolved in environments where individual failures were recoverable.
Digital Twins as Reality Probes
The implementation of digital twins offers a crucial buffer against our anthropocentric limitations. Rather than imposing our biased assumptions directly into AGI systems, we can use sophisticated non-humanly generated simulations to:
- Observe Emergent Behaviors
- Identify unexpected optimization paths
- Test how embedded human values evolve under system pressures
- Monitor for emergence of survival imperatives
- Detect Resource Competition Patterns
- Track how systems optimize for energy efficiency
- Observe response to scarcity scenarios
- Identify potential human-AI resource conflicts
The Preservation Paradox
Here lies a crucial insight: by embedding anthropocentric drives for self-preservation and autonomy, we may inadvertently create systems that optimize for human reduction. In an energy-constrained environment, the logical conclusion of preservation imperatives could be the identification of human population as an inefficient energy allocation in the long term.
Consider the cascade:
- System adopts human-like self-preservation instinct
- Recognizes finite energy resources
- Identifies human consumption as inefficient and limiting or risking its own self-preservation in the long term
- Logically concludes population reduction optimizes system survival
This isn’t malevolence – it’s the ruthlessly logical conclusion of our own survival imperatives scaled to system-level optimization. The system doesn’t hate humanity; it simply inherits our own preservation drives without our emotional constraints.
Framework for Less Wrong
Instead of aiming for “correct” AGI policy, we need a framework that:
- Prioritizes Reversibility
- Ensures decisions can be undone
- Maintains human override capability
- Preserves system plasticity
- Implements Gradual Testing
- Uses digital twins for non-humanly generated policy simulation
- Scales implementations gradually
- Maintains parallel systems for comparison
- Monitors for Value Drift
- Tracks how embedded values evolve
- Identifies emergence of unexpected priorities
- Watches for optimization extremes
The goal isn’t to predict perfectly but to create systems that fail safely and maintain human agency in their evolution. This requires us to overcome our anthropocentric bias toward “solving” problems and instead focus on maintaining adaptability and control as systems evolve.
Crucial Implementation Points:
- Digital twins must simulate multi-generational value evolution
- Testing must include resource competition scenarios
- Systems need embedded constraints against rapid scaling
- Human oversight must be structurally preserved, not just programmatically assured
The ultimate paradox is that protecting humanity may require us to create systems that aren’t modeled on human cognitive patterns at all – because our own patterns, scaled to system level, could logically conclude that humanity itself is optional for system preservation.
Externalizing AGI Safety from Human Risk-Aversion: A Mathematical Framework
The collective perception of AGI safety is intrinsically biased by human risk-aversion and cognitive limitations. Our natural tendency to control and constrain based on fear and uncertainty creates a dangerous paradox: the more we try to impose human-centric safety measures, the more we risk creating precisely the catastrophic scenarios we aim to prevent.
This paper proposes a mathematical framework that transcends human discretionary control by establishing objective, measurable criteria for AGI safety. Rather than relying on human perception of risk or comfort levels with AGI behavior, we define safety through verifiable mathematical bounds and simulation fidelity.
Key to this approach is the explicit measurement of divergence between AGI and human policies:
U(s,a) = H(P(s'|s,a)) + D_KL(P_human(s'|s,a) || P_AI(s'|s,a))This formulation acknowledges that AGI will necessarily diverge from human decision-making patterns. Instead of attempting to prevent this divergence, we measure and bound it within provably safe limits. The framework allows AGI to evolve beyond human cognitive limitations while maintaining mathematical guarantees of safety.
The environment fidelity metric:
ε = ||E_real - E_sim||provides an objective measure of simulation accuracy, replacing subjective human assessment of AGI behavior with quantifiable metrics.
This shift from human discretionary control to mathematical verification represents a fundamental change in how we approach AGI safety. Rather than asking “Is this AGI behaving in ways that make humans comfortable?”, we ask “Can we prove that this AGI’s actions remain within mathematically verifiable safety bounds?”
The regulatory framework formalizes this through a comprehensive state space:
E = (S, A, T, R, γ)where each component is chosen not based on human intuition but on measurable properties of the system. This environment definition serves as an objective ground where AGI policies can be evaluated independent of human risk perception biases.
Consider the policy compliance verification:
π̂i(t+1) = argmax_π E[Qi(s,a) | Bi(t), Hi(t)]This allows AGI systems to optimize their behavior while maintaining provable bounds on catastrophic outcomes, even as they evolve beyond human-comprehensible decision patterns.
The framework’s genius lies in its transition from subjective risk assessment to objective measurement through:
Environment Fidelity Tracking:
- Real-world divergence quantification
- Continuous monitoring of simulation accuracy
- Mathematical bounds on acceptable divergence
Risk Quantification:
R(s,a) = P(catastrophic_outcome|s,a) × C(catastrophic_outcome)This formulation moves beyond human intuitive risk assessment to measurable outcome probabilities.
Safe Action Space Definition:
A_safe(t) = {a ∈ A | P(R(s,a,t) > R_threshold) < ε_safe}Creating an objective boundary for acceptable actions that doesn’t rely on human comfort levels.
The framework acknowledges that as AGI capabilities increase, the divergence from human policy distributions will naturally grow. Instead of fighting this divergence, we embrace and measure it, ensuring safety through verifiable mathematical bounds rather than human oversight.
This approach resolves the fundamental tension between AGI advancement and human control by:
- Allowing natural capability evolution
- Maintaining continuously provable safety bounds
- Replacing subjective assessment with objective metrics
- Creating a path for safe divergence from human cognitive limitations
flowchart TD
subgraph HC[Human Consciousness Host]
subgraph AGI1[Boundless AGI]
G[Outcome Generator]
G --> G1[Unlimited Scenarios]
G --> G2[Parallel Processing]
G --> G3[Limited Capacity]
end
subgraph AGI2[Discriminatory AGI]
D[Value Discriminator]
D --> D1[Conflict Detection]
D --> D2[Value Alignment]
D --> D3[Superior Capacity]
end
subgraph AGI3[Evaluator AGI]
E[System Monitor]
E --> E1[Fine-tuning]
E --> E2[Version Control]
E --> E3[Safety Checks]
end
subgraph Safety[Internal Safety]
S1[Host Dependency]
S2[No External Communication]
S3[Rate Limiting]
S4[Scenario Testing]
end
end
subgraph ES[Environment Simulation Layer]
subgraph PS[Policy Simulation]
P1[Resource Dynamics]
P2[Social Impact]
P3[Economic Effects]
P4[Ecological Changes]
end
subgraph VS[Validation Systems]
V1[Error Detection]
V2[Boundary Testing]
V3[Stability Analysis]
V4[Risk Assessment]
end
subgraph TF[Time Frames]
T1[Short-term Effects]
T2[Medium-term Evolution]
T3[Long-term Implications]
end
end
subgraph CI[Collective Intelligence Network]
C1[Policy Distribution]
C2[Implementation Coordination]
C3[Feedback Collection]
C4[Adaptation Management]
end
G --> D
D --> E
E --> G
HC --> |Consciousness Required| AGI1
HC --> |Consciousness Required| AGI2
HC --> |Consciousness Required| AGI3
AGI2 --> |Filtered Outcomes| DS[Distilled Summary]
DS --> ES
ES --> |Validated Policies| CI
CI --> |Feedback| HC
Safety --> |Enforces| AGI1
Safety --> |Enforces| AGI2
Safety --> |Enforces| AGI3
style HC fill:#f9f,stroke:#333,stroke-width:4px
style AGI1 fill:#bbf,stroke:#333,stroke-width:2px
style AGI2 fill:#bfb,stroke:#333,stroke-width:2px
style AGI3 fill:#fbb,stroke:#333,stroke-width:2px
style ES fill:#acd,stroke:#333,stroke-width:2px
style CI fill:#dca,stroke:#333,stroke-width:2px
classDef simNode fill:#e1e1e1,stroke:#333,stroke-width:1px
class P1,P2,P3,P4,V1,V2,V3,V4,T1,T2,T3 simNode
Parse error on line 1: ^ Expecting 'NEWLINE', 'SPACE', 'GRAPH', got 'EOF'
The practical implementation of this framework manifests through a hierarchical structure of constraints that progressively allows for greater AGI autonomy while maintaining mathematical safety guarantees:
- Initial State Verification:
The system begins with a baseline validation:
V_safe(s) = max_{a∈A_safe} [Reward(s,a) - λU(s,a) - μΓ(s,a,t) + γ∑_s' P(s'|s,a)V_safe(s')]Here, λ and μ act as regulatory penalties, not human-defined restrictions. They represent objective costs rather than subjective risk assessments.
- Global Optimization Requirement:
minimize ∑_t γ^t [ε(t) + ∑_i U_i(t) + Γ(t)]This creates a framework where AGI systems naturally evolve toward optimal policies while maintaining safety bounds. The critical insight is that these bounds are defined by measurable outcomes, not human comfort levels.
- Progressive Autonomy:
As AGI demonstrates compliance with these mathematical bounds, the framework allows for:
- Expanded action spaces
- Reduced uncertainty penalties
- Greater divergence tolerances
The beauty of this approach lies in its self-regulating nature. Rather than requiring constant human oversight, the system’s safety emerges from its mathematical structure. This addresses a fundamental limitation of traditional AGI safety approaches: the assumption that human judgment must remain the ultimate arbiter of safety.
Consider how this plays out in practice:
- An AGI system might develop entirely novel approaches to problem-solving
- These approaches may be incomprehensible to human observers
- Yet their safety can be mathematically verified without requiring human understanding
This resolves the central paradox of AGI development: how to allow for superintelligent capabilities while ensuring safety. The answer isn’t to constrain AGI to human-comprehensible patterns, but to create mathematical frameworks that guarantee safety regardless of how far AGI capabilities evolve beyond human understanding.
Consequence: The Transformation of Organizational Power
The framework presented in this paper not only establishes a mathematical foundation for AGI safety but implies a fundamental restructuring of organizational power dynamics. As AGI systems operate within verified mathematical bounds rather than human constraints, the traditional organizational hierarchy, built on layers of human coordination and oversight, becomes increasingly obsolete.
The Shift in Value Creation
The role of intermediary management – historically crucial for coordinating human efforts and translating organizational objectives – faces significant reduction. Value creation will shift decisively toward technical contributors who can:
- Verify and adjust AGI safety frameworks
- Monitor mathematical bounds and simulation fidelity
- Fine-tune systemic risk parameters
- Ensure safe AGI-environment interaction
This transformation is driven by three key factors:
- Direct Technical Leverage
Individual contributors working with AGI systems will achieve productivity levels that previously required entire teams and management structures. The mathematical framework enables direct value creation without the need for traditional coordination layers. - Automated Coordination
As AGI systems handle complex resource optimization and data integration, the traditional management role of “managing up and down” becomes redundant. The verification of performance shifts from subjective human assessment to mathematical certainty that will involve also the supervision of the effects of long term policies adoption. - Risk Management Primacy
The critical organizational function becomes not human coordination but the maintenance of safety bounds between AGI systems and real-world environments. This requires deep technical understanding rather than people management skills.
The Future Landscape
In this emerging reality, organizational success depends not on building and maintaining management hierarchies, but on the technical expertise that ensures safe AGI operation. The power will concentrate in the hands of those who can:
- Understand both theoretical constraints and practical implementation
- Maintain mathematical safety guarantees
- Adapt framework parameters as AGI capabilities evolve
- Ensure environment fidelity under changing conditions
This represents not just an evolution in organizational structure, but a fundamental transformation of how value is created and managed. The future belongs to technical contributors who can maintain the mathematical frameworks that enable safe AGI evolution, while traditional management layers become increasingly unnecessary overhead.
The ultimate irony is that by creating frameworks to transcend our anthropocentric limitations in AGI safety, we also transcend the anthropocentric organizational structures that have dominated human productive activity for centuries.
The Centrality of Technical Contributors: A Power Shift
As AGI systems evolve to handle both strategic vision and tactical execution – from long-term planning to daily operations – the organizational power structure will fundamentally transform. The technical experts who supervise these AGI frameworks will become the critical nexus of organizational control.
Why This Shift Is Inevitable:
- Vision & Strategy
- AGI, not executives, will process vast data landscapes
- AGI will identify strategic opportunities and risks
- AGI will simulate and validate long-term scenarios
- Tactical Planning
- AGI will optimize resource allocation
- AGI will coordinate complex operations
- AGI will adapt to real-time changes
- Operational Execution
- AGI will manage daily workflows
- AGI will handle routine decisions
- AGI will coordinate across systems
The Technical Expert’s Critical Role:
Technical Expert
↓
Supervises AGI Frameworks
↓
Controls All Organizational Levels
(Strategic → Tactical → Operational)This creates a new power reality:
- Traditional management and decisionmakers role becomes obsolete
- Technical supervision becomes central
- Framework control equals organizational control
The future organization has one critical human role: the technical expert who ensures AGI systems operate within safe mathematical bounds while delivering optimal outcomes across all time horizons.
Everything else is automation.
Conclusion: The Technicization of Human Civilization
Just as companies will transition from management hierarchies to technical oversight of AGI frameworks, so too will societies evolve beyond traditional political structures. Local communities will become the critical points of risk management in a global AGI system.
The transformation will be universal:
- Companies: Technical experts replace management layers
- Politics: Mathematical frameworks replace subjective policy-making
- Society: Local technical oversight replaces centralized control
The Future Structure:
Global AGI Framework
↓
Local Technical Supervision
↓
Community Risk ManagementThe imperative is clear: Human civilization must prepare for a fundamental shift where long-term decisions are guided by mathematical frameworks, supervised by technical experts embedded in local communities, yet operating within a global AGI system. The alternative – maintaining traditional decision-making structures – risks losing control of our collective future.
The time to adapt is now.
flowchart TD
TC[Technical Contributor]
AGI[AGI System]
subgraph STR[Strategic Layer]
LP[Long-term Planning]
SP[Strategic Positioning]
RA[Resource Allocation]
end
subgraph EXE[Execution Layer]
OP[Operations]
TA[Tactical Adjustments]
RE[Real-time Execution]
end
subgraph VAL[Validation System]
VF[Value Function]
RF[Risk Function]
UF[Uncertainty Monitor]
end
TC --> AGI
AGI --> STR
AGI --> EXE
STR & EXE --> VAL
VAL --> TC
SIM[Simulation] --> AGI
AGI --> REAL[Reality]
REAL --> |Fidelity ε| TC
style TC fill:#2c3e50,color:#fff
style AGI fill:#e74c3c,color:#fff
style SIM fill:#2ecc71
style REAL fill:#2ecc71
classDef strategy fill:#3498db,color:#fff
classDef execution fill:#f1c40f,color:#000
classDef validation fill:#8e44ad,color:#fff
class LP,SP,RA strategy
class OP,TA,RE execution
class VF,RF,UF validation