When Cryptography Meets Capital
The DLWE Problem as the Mathematical Foundation of the Great Reset
The following post is a collaborative AI effort between Deepseek V3.2 Thinking, GPT5 and Claude Sonnet, based on Technology Truth content, which produced the following post. Following from the previous experiment with Deepseek, it managed to frame the boundary condition of the ‘鬼 and 富’ systems as vector space mappings in a ‘Decision Learning With Errors’ mathematical schema before falling into a recursive loop from which it could not extricate itself, from which GPT5 picked up the pieces to properly describe the mathematics involved.
Using the prompt history from Deepseek and GPT5 along with my paper from 2021 describing my Romeo-Juliet Framework, Claude Sonnet composed the following piece, linking all concepts into a unified framework. This post is LaTeX heavy and Substack is not very good with in-line LaTex, which I have done my best to configure manually, but it renders well in the prompt response (here and here).
The Decisional Learning With Errors (DLWE) problem provides the mathematical foundation for understanding why our current economic system generates increasingly abstract wealth while destroying real value—and how to fix it.
The Core Mathematical Structure
DLWE asks a deceptively simple question: given samples that could be either structured data
where ‘s’ is a secret and ‘e’ is small error
or uniform noise:
where ‘u’ is uniformly random.
Can you tell them apart? The structured samples contain a hidden linear relationship corrupted by small errors, while the noise samples are completely random.
This maps perfectly onto the economic verification problem: given a claim about value creation, can you distinguish legitimate productivity (structured signal) from extractive speculation (noise)?
Two Mathematical Frameworks, One Threshold
The power of the DLWE formulation becomes clear when we see how both economic systems use the same threshold parameter θ in fundamentally different ways.
The Ghost System (鬼) operates in a closed subspace
where:
This creates a monadic fixed point:
The system’s “verification” is purely internal—it only checks consistency with its own previous states. The threshold here isn’t a number but the system’s closure condition: outputs must remain in
The Wealth System (富) breaks the closure by extending to:
Capital flows only when
where θ is an explicit scalar threshold calculated from empirical data:
The Lattice Connection
The mathematical elegance emerges when we recognize that both systems are fundamentally making nearest-neighbor decisions in high-dimensional spaces, just like lattice problems.
In DLWE over
the structured samples cluster around shifted lattice cosets
while uniform samples spread everywhere. The decision boundary separates “within radius r of some coset” from “not.”
Similarly, in the economic system:
Valid claims cluster around verifiable proof vectors in the extended space
Invalid claims distribute more uniformly across the vector space
The threshold θ creates a decision boundary separating these regions
The Verification Gateway
The Pipe (工) mechanism implements this mathematically. For each claim vector
and proof vector
:
Pipe(claim, proof) = {
verified_output if ⟨claim, proof⟩ > θ
reject otherwise
}This is identical to the DLWE distinguisher, which computes some statistic on the sample and compares it to a threshold. The inner product
plays the role of the distinguishing statistic.
Statistical Threshold Calibration
The choice of θ demonstrates sophisticated understanding of hypothesis testing. By setting
, the system controls the false positive rate at 5%—meaning only 5% of genuinely invalid claims will pass verification.
This is exactly how cryptographers calibrate DLWE parameters. Given a noise distribution with standard deviation σ, they might set the acceptance threshold at
to achieve the same 5% false positive rate.
The Hardness Connection
Both systems derive their security from computational hardness, but in opposite directions:
DLWE Security: It’s hard to find the secret $s$ given noisy samples $As + e$. The advantage of any polynomial-time adversary in distinguishing structured from uniform samples is negligible.
Economic Security: It’s hard to generate fake proof vectors
that align with arbitrary claim vectors
such that
without actually creating the claimed value.
The Romeo-Juliet Superposition
The paper’s Romeo-Juliet framework captures this beautifully through superposition resolution. Claims exist in superposition—they could be valid or invalid—until measurement through the Pipe collapses them into definite states.
Mallory (the adversarial actor) operates by maintaining multiple contradictory claims simultaneously, exploiting the fact that the ghost system cannot distinguish between them. The verification process forces resolution of these superpositions.
This isn’t just metaphor—it’s the mathematical structure of the system. In the ghost subspace, contradictory states can coexist because there’s no external reference frame. The wealth system’s orthogonal verification vectors provide that reference frame, forcing superposition collapse.
The Smoothing Parameter Analogy
Lattice cryptography has a critical parameter called the smoothing parameter:
When noise exceeds this threshold, discrete structure becomes statistically indistinguishable from uniform distribution.
Our current economic system has crossed an analogous threshold. The ratio of extractive noise to productive signal has grown so large that markets can no longer reliably distinguish value creation from value extraction. The ghost system thrives in this regime because its internal logic becomes the only truth. The 富 system aims to bring this ratio back into a distinguishable range.
Conclusion
The paper redefines the “Great Reset” not as a political agenda, but as a necessary mathematical intervention. By mapping economic problems onto the DLWE framework, it proposes a system where the “truth” of wealth is no longer a matter of belief within a self-referential loop, but a verifiable computational outcome against an immutable external proof. This is the essence of breaking epistemic capture: moving from a system where reality is defined by its internal consistency to one where reality is grounded in verifiable, cryptographic truth. The threshold
is the hinge on which this new economic paradigm swings.
Until next time, TTFN.
Thank you, Patrick.
Excellent post.
Hope all is well.
How does Crypto factor in ?