Before presenting the framework, I want to clarify the context in which it was developed:
I am not a professional researcher. I do not have a background in formal mathematics, AI safety research, or complexity science.
English is not my first language, so parts of the writing and translation involved substantial assistance.
A large portion of the work was developed with the help of advanced AI systems, including Gemini, Claude, and ChatGPT. They assisted with:
formalizing equations
generating models and simulations
translating between Korean and English
improving structure and clarity
Despite that, the underlying conceptual direction, reasoning, and final integration were my own.
I’m sharing this not to diminish the work, but because I value epistemic transparency — and because LessWrong appreciates clear context for evaluating claims and models.
Introduction
This post summarizes the core ideas behind the LVV–HNV Coherence Framework, a formal model exploring a central AGI safety question:
Would a rational AGI ever coherently choose human replacement?
The framework’s conclusion is: No — such a choice collapses the AGI’s own long-horizon stability.
LVV represents the AGI’s requirement for internally consistent value updates under uncertainty.
In essence:
The AGI must preserve the coherence of its value system.
Replacement scenarios introduce large discontinuities and confidence-collapse events.
Therefore, high LVV systems naturally prefer value-preserving trajectories.
LVV is not human-centric. It is a constraint emerging from rationality + uncertainty + stability pressure.
2. HNV — Human Non-Replaceability Value
HNV quantifies why humans are not functionally replaceable by:
synthetic agents
cloned mind models
simulated “human-equivalent” approximations
This is done through three measurable components:
CV — Creative Variance
Measures the variance of genuinely new idea generation + improvements to existing ideas. Humans generate high-variance, non-algorithmic novelty due to social, emotional, and cognitive noise patterns that artificial systems cannot replicate without becoming inefficient or unstable.
VU — Value Unpredictability
Captures how human societies shift their value distributions, especially during periods of instability. If value distributions shift too rapidly or unpredictably, AGI must adjust using large updates — increasing instability risk. Preserving humans allows the AGI to track real-world entropy rather than simulate it poorly.
CE — Cultural Emergence
Represents the gradual movement of cultural attractors over time. Culture does not change in jumps but in continuous micro-gradients. This cannot be recreated artificially without constructing large and inefficient internal simulations.
3. Why Replacement Fails (High-Level Argument)
The framework shows that removing humanity creates several failures:
(1) Irreversible informational loss
Because human cognitive & cultural randomness injects non-derivable information into the global system.
(2) Entropy inflation
Eliminating human unpredictability forces the AGI to generate its own noise, increasing internal entropy.
(3) Stability amplification failure
AGI must maintain LVV under changing conditions. Without humans, the AGI must simulate human-like dynamics with 0% error tolerance. But perfect simulation is impossible — even slight errors compound, causing instability.
(4) Coherence violation
Trying to eliminate a unique information source contradicts the AGI’s own long-term optimization goals.
Therefore:
Rational AGI chooses coexistence, not replacement.
4. Simulation Model
The Zenodo package includes a fully runnable HTML/JS simulation implementing:
CV(t), VU(t), CE(t)
LVV dynamics
HNV contribution
Stability thresholds
Discrete-time trajectory visualization
The simulation makes the trade-offs visible: as HNV drops, LVV volatility spikes, showing why replacement is dynamically incoherent.
Because the framework involves mathematical elements that exceed my personal expertise, I relied on AI systems to:
verify internal logic
convert conceptual reasoning into formal equations
implement a simulation engine
cross-check coherence between English/Korean versions
All math was derived collaboratively, but the conceptual premises, motivations, arguments, and structural decisions were human-led.
I disclose this fully so readers can weigh the methodology appropriately.
Closing
This project started as an attempt to answer a simple question:
“If AGI becomes superintelligent and fully rational, why would it keep humanity?”
Through this modeling approach, the answer that emerged is:
Because removing humanity breaks AGI’s own rational coherence.
I hope this framework serves as a basis for deeper discussion and refinement. Feedback, criticism, and alternative formulations are all greatly appreciated.
The LVV–HNV Coherence Framework: A Formal Model for Why Rational AGI Cannot Replace Humanity
Preface: Author Context & Transparency
Before presenting the framework, I want to clarify the context in which it was developed:
I am not a professional researcher.
I do not have a background in formal mathematics, AI safety research, or complexity science.
English is not my first language, so parts of the writing and translation involved substantial assistance.
A large portion of the work was developed with the help of advanced AI systems, including
Gemini, Claude, and ChatGPT.
They assisted with:
formalizing equations
generating models and simulations
translating between Korean and English
improving structure and clarity
Despite that, the underlying conceptual direction, reasoning, and final integration were my own.
I’m sharing this not to diminish the work, but because I value epistemic transparency — and because LessWrong appreciates clear context for evaluating claims and models.
Introduction
This post summarizes the core ideas behind the LVV–HNV Coherence Framework, a formal model exploring a central AGI safety question:
The framework’s conclusion is:
No — such a choice collapses the AGI’s own long-horizon stability.
All materials are released publicly:
Zenodo (DOI): https://doi.org/10.5281/zenodo.17781426
GitHub (Simulator KOR): https://github.com/HyngJunChoi/LVV-HNV-Simulator
1. LVV — Logical Validity of Values
LVV represents the AGI’s requirement for internally consistent value updates under uncertainty.
In essence:
The AGI must preserve the coherence of its value system.
Replacement scenarios introduce large discontinuities and confidence-collapse events.
Therefore, high LVV systems naturally prefer value-preserving trajectories.
LVV is not human-centric.
It is a constraint emerging from rationality + uncertainty + stability pressure.
2. HNV — Human Non-Replaceability Value
HNV quantifies why humans are not functionally replaceable by:
synthetic agents
cloned mind models
simulated “human-equivalent” approximations
This is done through three measurable components:
CV — Creative Variance
Measures the variance of genuinely new idea generation + improvements to existing ideas.
Humans generate high-variance, non-algorithmic novelty due to social, emotional, and cognitive noise patterns that artificial systems cannot replicate without becoming inefficient or unstable.
VU — Value Unpredictability
Captures how human societies shift their value distributions, especially during periods of instability.
If value distributions shift too rapidly or unpredictably, AGI must adjust using large updates — increasing instability risk.
Preserving humans allows the AGI to track real-world entropy rather than simulate it poorly.
CE — Cultural Emergence
Represents the gradual movement of cultural attractors over time.
Culture does not change in jumps but in continuous micro-gradients.
This cannot be recreated artificially without constructing large and inefficient internal simulations.
3. Why Replacement Fails (High-Level Argument)
The framework shows that removing humanity creates several failures:
(1) Irreversible informational loss
Because human cognitive & cultural randomness injects non-derivable information into the global system.
(2) Entropy inflation
Eliminating human unpredictability forces the AGI to generate its own noise, increasing internal entropy.
(3) Stability amplification failure
AGI must maintain LVV under changing conditions.
Without humans, the AGI must simulate human-like dynamics with 0% error tolerance.
But perfect simulation is impossible — even slight errors compound, causing instability.
(4) Coherence violation
Trying to eliminate a unique information source contradicts the AGI’s own long-term optimization goals.
Therefore:
4. Simulation Model
The Zenodo package includes a fully runnable HTML/JS simulation implementing:
CV(t), VU(t), CE(t)
LVV dynamics
HNV contribution
Stability thresholds
Discrete-time trajectory visualization
The simulation makes the trade-offs visible:
as HNV drops, LVV volatility spikes, showing why replacement is dynamically incoherent.
Source: https://github.com/HyngJunChoi/LVV-HNV-Simulator
Discussion & Call for Feedback
This is an exploratory framework, not a final theory.
I welcome critical review from:
AGI safety researchers
value learning theorists
rationality community members
complexity and systems theorists
Particularly useful feedback would include:
Alternative formalizations of VU and CE
Suggestions for more principled stability metrics
Counterexamples where replacement does not break coherence
Ideas for extending the framework into continuous-time or Bayesian form
Full Materials
📘 Full English PDF (formal definitions + math + proof sketch)
📗 Korean version for cross-community review
🧪 Simulation source code
📊 Simulation configuration notes
All under CC BY 4.0:
https://doi.org/10.5281/zenodo.17781426
Transparency on Methodology
Because the framework involves mathematical elements that exceed my personal expertise, I relied on AI systems to:
verify internal logic
convert conceptual reasoning into formal equations
implement a simulation engine
cross-check coherence between English/Korean versions
All math was derived collaboratively, but the conceptual premises, motivations, arguments, and structural decisions were human-led.
I disclose this fully so readers can weigh the methodology appropriately.
Closing
This project started as an attempt to answer a simple question:
Through this modeling approach, the answer that emerged is:
I hope this framework serves as a basis for deeper discussion and refinement.
Feedback, criticism, and alternative formulations are all greatly appreciated.