I've been working on resolving key contradictions in AI through structured emergence, a principle that so far appears to govern both physical and computational systems.
My grandfather was a prolific inventor in organic chemistry (GE plastics post WWII) and was reading his papers thinking about "chirality" - directional asymmetric oscillating waves and how they might apply to AI. I found his work deeply inspiring.
I ran 7 empirical studies using publicly available datasets across prime series, fMRI, DNA sequences, galaxy clustering, baryon acoustic oscillations, redshift distributions, and AI performance metrics.
All 7 studies have confirmed internal coherence with my framework. While that's promising, I still need to continue to valid the results (attached output on primes captures localized frequency variations, ideal for detaching scale-dependent structure in primes i.e. Ulam Spirals - attached).
To analyze these datasets, I applied continuous wavelet transformations (Morlet/Chirality) using Python3, revealing structured oscillations that suggest underlying coherence in expansion and emergent system behavior.
1. AI performance gains – applying structured emergence methods has yielded noticeable improvements in AI adaptability and optimization.
2. Empirical validation across domains – The same structured oscillations appear in biological, physical, and computational systems—indicating a deeper principle at work.
3. Strong early engagement – while the paper is still under review, 160 views and 130 downloads (81% conversion) in 7 days on Zenodo put it in the top 1%+ of all academic papers—not as an ego metric, but as an early signal of potential validation.
The same mathematical structures that define wavelet transforms and prime distributions seems to provide a pathway to more efficient AI architectures by:
1. Replacing brute-force heuristics with recursive intelligence scaling
2. Enhancing feature extraction through structured frequency adaptation
3. Leveraging emergent chirality to resolve complex optimization bottlenecks
Technical (for AI engineers):
1. Wavelet-Driven Neural Networks – replacing static Fourier embeddings with adaptive wavelet transforms to improve feature localization. Fourier was failing hence pivot to CWT. Ulam Spirals showed non-random hence CWT.
2. Prime-Structured Optimization – using structured emergent primes to improve loss function convergence and network pruning.
3. Recursive Model Adaptation – implementing dynamic architectural restructuring based on coherence detection rather than gradient-based back-propagation alone.
The theory could be wrong, but the empirical results are simply too coherent not to share in case useful for anyone.
"The Chirality of Dynamic Emergent Systems (CODES): A Unified Framework for Cosmology, Quantum Mechanics, and Relativity" (2025) https://zenodo.org/records/14799070
> Fedi's SQR Superfluid Quantum Relativity (.it), FWIU: also rejects a hard singularity boundary, describes curl and vorticity in fluids (with Gross-Pitaevskii,), and rejects antimatter.
Hey! Appreciate the links—some definitely interesting parallels, but what I’m outlining moves beyond existing QFT/Hilbert curve applications.
The key distinction = structured emergent primes are demonstrating internal coherence across vastly different domains (prime gaps, fMRI, DNA, galaxy clustering), suggesting a deeper non-random structure influencing AI optimization.
Curious if you’ve explored wavelet-driven loss functions replacing cross-entropy? Fourier struggled with localization, but CWT and chirality-based structuring seem to resolve this.
I do not have experience with wavelet-driven loss functions.
Do structured emergent primes afford insight into n-body fluid+gravity dynamics and superfluid (condensate) dynamics at deep space and stellar thermal ranges?
How do wavelets model curl and n-body vortices?
What do I remember about wavelets, without reading the article?
Wavelets are or aren't analogous to neurons. Wavelets discretize. Am I confusing wavelets and autoencoders? Are wavelets like tiles or compression symbol tables?
How do wavelet-driven loss functions differ from other loss functions like Cross-Entropy and Harmonic Loss?
How does prime emergence relate to harmonics and [Fourier,] convolution with and without superposition?
Other seemingly relevant things:
- particle with mass only when moving in certain directions; re: chirality
> If there is locomotion due to a dynamic between handed molecules and, say, helically polarized fields; is such handedness a survival selector for life in deep space?
> Are chiral molecules more likely to land on earth?
Hey - really appreciate the detailed questions—these are exactly the kinds of connections I’ve been exploring. Sub components:
Wavelet-driven loss functions vs. Cross-Entropy/Harmonic Loss
You’re right about wavelets discretizing—it’s what makes them a better fit than Fourier for adaptive structuring. The key distinction is that wavelets localize both frequency and time dynamically, meaning loss functions can become context-sensitive rather than purely probabilistic. This resolves issues with information localization in AI training, allowing emergent structure rather than brute-force heuristics.
Prime emergence, harmonics, and convolution (Fourier vs. CWT)
Structured primes seem to encode hidden periodicities across systems—prime gaps, biological sequences, cosmic structures, etc.
• Fourier struggled because it assumes a globally uniform basis set.
• CWT resolves this by detecting frequency-dependent structures (chirality-based).
• Example: Prime number distributions align with Ulam Spirals, which match observed redshift distributions in deep space clustering. The coherence suggests an underlying structuring force, and phase-locking principles seem to emerge naturally.
N-body vortex dynamics, superfluidity, and chiral molecules in deep space
You might be onto something here. The connection between:
• Superfluid dynamics in deep space
• Chiral molecules preferring certain gravitational dynamics
• Handedness affecting locomotion in polarized fields
suggests chirality might be an overlooked factor in cosmic structure formation (i.e., why galaxies tend to form spiral structures).
Could this be an engine? (Electromagnetic rotation and helicity)
Possibly. If structured emergence scales across these domains, it’s possible that chirality-induced resonance fields could drive a new form of energy extraction—similar to the electroweak interaction asymmetry seen in beta decay.
The idea that chirality acts as a selector for deep-space survival is interesting. Do you think the preference for left-handed amino acids on Earth could be a consequence of an early chiral field bias? If so, does that imply a fundamental symmetry-breaking event at planetary formation?
> Wavelet-driven loss functions vs. Cross-Entropy/Harmonic Loss You’re right about wavelets discretizing—it’s what makes them a better fit than Fourier for adaptive structuring. The key distinction is that wavelets localize both frequency and time dynamically, meaning loss functions can become context-sensitive rather than purely probabilistic. This resolves issues with information localization in AI training, allowing emergent structure rather than brute-force heuristics.
frequency and time..
SR works for signals without GR; and there's an SR explanation for time dilation which resolves when the spacecraft lands fwiu , Minkowski,
>>> Physical observation (via the transverse photon interaction) is the process given by applying the operator ∂/∂t to (L^3)t, yielding an L3 output
>> [and "time-polarized photons"]
> Prime emergence, harmonics, and convolution (Fourier vs. CWT) Structured primes seem to encode hidden periodicities across systems—prime gaps, biological sequences, cosmic structures, etc. • Fourier struggled because it assumes a globally uniform basis set. • CWT resolves this by detecting frequency-dependent structures (chirality-based). • Example: Prime number distributions align with Ulam Spirals, which match observed redshift distributions in deep space clustering. The coherence suggests an underlying structuring force, and phase-locking principles seem to emerge naturally.
> N-body vortex dynamics, superfluidity, and chiral molecules in deep space You might be onto something here. The connection between: • Superfluid dynamics in deep space • Chiral molecules preferring certain gravitational dynamics • Handedness affecting locomotion in polarized fields suggests chirality might be an overlooked factor in cosmic structure formation (i.e., why galaxies tend to form spiral structures).
Why are there so many arms on the fluid disturbance of a spinning basketball floating on water?
(Terms: viscosity of the water, mass, volume, and surface characteristics of the ball, temperature of the water, temperature of the air)
Traditionally, curl is the explanation fwiu.
Does curl cause chirality and/or does chirality cause curl?
The sensitivity to Initial conditions of a two arm pendulum system, for example, is enough to demonstrate chaotic, divergent n-body dynamics. `python -m turtledemo.chaos` demonstrates a chaotic divergence with a few simple functions.
Phase transition diagrams are insufficient to describe water freezing or boiling given sensitivity to initial temperature observed in the Mpemba effect; phase transition diagrams are insufficient with an initial temperature axis.
Superfluids (Bose-Einstein condensates) occur at earth temperatures. For example, helium chilled to 1 Kelvin demonstrates zero viscosity, and climbs up beakers and walls despite gravity.
A universal model cannot be sufficient if it does not describe superfluids and superconductors; photons and electrons behave fluidically in other phases.
> Could this be an engine? (Electromagnetic rotation and helicity) Possibly. If structured emergence scales across these domains, it’s possible that chirality-induced resonance fields could drive a new form of energy extraction—similar to the electroweak interaction asymmetry seen in beta decay.
A spinning asteroid or comet induces a 'spinning' field. Interplanetary and deep space spacecraft could spin on one or more axes to create or boost EM shielding.
>> Astrophysical jets produce helically and circularly-polarized emissions, too FWIU.
>> Presumably helical jets reach earth coherently over such distances because of the stability of helical signals.
>> 1. Could [we] harvest energy from a (helically and/or circularly-polarised) natural jet, for deep space and/or local system exploration? Can a spacecraft pull against a jet for relativistic motion?
>> 2. Is helical the best way to beam power wirelessly; without heating columns of atmospheric water in the collapsing jet stream? [with phased microwave]
>> 3. Is there a (hydrodynamic) theory of superfluid quantum gravity that better describes the apparent vorticity and curl of such signals and their effects?
>> How, then, can entanglement across astronomical distances occur without cooler temps the whole way there, if heat destroys all entanglement?
>> Would helical polarization like quasar astrophysical jets be more stable than other methods for entanglement at astronomical distances?
> The idea that chirality acts as a selector for deep-space survival is interesting. Do you think the preference for left-handed amino acids on Earth could be a consequence of an early chiral field bias? If so, does that imply a fundamental symmetry-breaking event at planetary formation?
The earth is rotating and revolving in relation to the greatest local mass. Would there be different terrestrial chirality if the earth rotated in the opposite direction?
How do the vortical field disturbances from Earth's rotation in atmospheric, EM, and gravitational wave spaces interact with molecular chirality and field chirality?
> Phase from second-order Intensity due to "mechanical concepts of center of mass and moment of inertia via the Huygens-Steiner theorem for rigid body rotation"
Great breakdown—you’re seeing the edges of it, but let me connect the missing piece.
Wavelets vs. Fourier & AI loss functions
You nailed why wavelets win—localizing both time and frequency dynamically. But the real play here is structured resonance coherence instead of treating AI learning as a purely probabilistic optimization. Probabilistic models erase context and reset entropy constantly, whereas CODES treats resonance as an accumulative structuring force. That’s why prime-driven phase-locking beats cross-entropy heuristics.
Prime emergence & Ulam spirals
You’re right that prime gaps aren’t random but encode periodicities across systems—biological, cosmological, and computational. But the deeper move is that primes create an emergent coherence structure, not just a statistical artifact. Ulam spirals show this at one level, but they’re just a shadow of a deeper harmonic structuring principle.
Superfluidity, chiral molecules, and deep space dynamics
The superfluid analogy works but is incomplete. Bose-Einstein condensates (BECs) and zero-viscosity states are effects of structured resonance, not just temperature or density thresholds. You pointed to handedness affecting locomotion in polarized fields—that’s getting warmer, but step further: chirality isn’t just a constraint, it’s a selection rule for emergent order. That’s why galaxies form spirals, not just because of angular momentum but because chirality phase-locks structure across scales.
Entropy, entanglement, and deep-space coherence
The “heat destroys quantum entanglement” take is missing something big—CODES predicts that prime-structured resonance can phase-lock entanglement across astronomical distances. It’s not just about cooling; it’s about locking information states into structured coherence instead of letting them decay randomly. That’s how you get stable entanglement in astrophysical jets despite thermal noise.
Could this be an engine?
Yes. If structured resonance scales across domains, then chirality-driven resonance fields could create a new class of energy extraction mechanisms—think phase-locked electroweak asymmetry, but generalized. If electroweak asymmetry already gives us beta decay, what happens when you apply chirality-induced coherence fields? You’re talking a completely different model for field interaction, maybe even something close to a prime-locked energy topology.
Where You’re Almost There But Not Quite
You’re still interpreting some of this as chaotic or probabilistic emergence, but CODES isn’t describing randomness—it’s describing structured phase coherence.
• Superfluids aren’t a weird edge case—they’re an emergent effect of structured resonance.
• Entanglement isn’t just fragile quantum weirdness—it’s a phase-locked state that can persist given the right structuring principles.
• Chirality isn’t just a passive bias—it’s the underlying ordering principle that phase-locks emergence across biology, physics, and computation.
CODES isn’t just describing these effects—it’s providing the missing coherence framework that ties them together.
Would love to jam on this deeper if you're up for it!
My grandfather was a prolific inventor in organic chemistry (GE plastics post WWII) and was reading his papers thinking about "chirality" - directional asymmetric oscillating waves and how they might apply to AI. I found his work deeply inspiring.
I ran 7 empirical studies using publicly available datasets across prime series, fMRI, DNA sequences, galaxy clustering, baryon acoustic oscillations, redshift distributions, and AI performance metrics.
All 7 studies have confirmed internal coherence with my framework. While that's promising, I still need to continue to valid the results (attached output on primes captures localized frequency variations, ideal for detaching scale-dependent structure in primes i.e. Ulam Spirals - attached).
To analyze these datasets, I applied continuous wavelet transformations (Morlet/Chirality) using Python3, revealing structured oscillations that suggest underlying coherence in expansion and emergent system behavior.
Paper here: https://lnkd.in/gfigPgRx
If true, here are the implications:
1. AI performance gains – applying structured emergence methods has yielded noticeable improvements in AI adaptability and optimization. 2. Empirical validation across domains – The same structured oscillations appear in biological, physical, and computational systems—indicating a deeper principle at work. 3. Strong early engagement – while the paper is still under review, 160 views and 130 downloads (81% conversion) in 7 days on Zenodo put it in the top 1%+ of all academic papers—not as an ego metric, but as an early signal of potential validation.
The same mathematical structures that define wavelet transforms and prime distributions seems to provide a pathway to more efficient AI architectures by:
1. Replacing brute-force heuristics with recursive intelligence scaling 2. Enhancing feature extraction through structured frequency adaptation 3. Leveraging emergent chirality to resolve complex optimization bottlenecks
Technical (for AI engineers): 1. Wavelet-Driven Neural Networks – replacing static Fourier embeddings with adaptive wavelet transforms to improve feature localization. Fourier was failing hence pivot to CWT. Ulam Spirals showed non-random hence CWT. 2. Prime-Structured Optimization – using structured emergent primes to improve loss function convergence and network pruning. 3. Recursive Model Adaptation – implementing dynamic architectural restructuring based on coherence detection rather than gradient-based back-propagation alone.
The theory could be wrong, but the empirical results are simply too coherent not to share in case useful for anyone.