---
title: "Emergent Spacetime from Autaxic Relational Dynamics: Quantitative Scaling Laws and Galactic Rotation"
slug: "12-emergence-of-spacetime"
url: https://papers.qnfo.org/papers/12-emergence-of-spacetime
authors: ["QNFO Research"]
version: "1.2.0"
published: "2026-07-04"
updated: "2026-10-26"
license: "QNFO-ULA-2.1"
status: "published"
quality_label: "High-quality scholarly research"
quality_basis: "QNFO publisher classification for open-access, versioned research with a public revision history and reader correction channel; this is not a claim of independent peer review."
peer_review_status: "Not independently peer-reviewed"
quality_evidence_url: "https://qnfo.org/about"
quality_tags: ["High-quality scholarly research", "open-access scholarly work", "versioned research publication"]
---

# Emergent Spacetime from Autaxic Relational Dynamics: Quantitative Scaling Laws and Galactic Rotation

The prevailing view treats spacetime as a pre‑existing arena, a stance that blocks unified descriptions of gravitation and quantum phenomena and incurs large research expenditures. A concrete quantitative gap exists because existing emergent‑spacetime proposals lack computable, falsifiable measures of the geometric structure produced by a relational substrate. We adopt a purely analytical approach that (i) maps the autaxic relational effort metric onto the universal rotation‑curve relation, (ii) models the substrate as a $d$‑dimensional lattice of process‑events, and (iii) derives three explicit scaling results. The galactic‑rotation calculation yields $V\approx1.41$ (in units $G=M=1$) for $M=10$ and $r=5$. The lattice analysis gives an expected boundary‑touching step count $2n/(dL)=0.667$ for $d=3$, $L=100$, $n=100$; a path‑count $(2d-1)^{r}=9{,}765{,}625$ for $d=3$, $r=10$; and identifies $d=3$ as the marginal dimension separating recurrent from transient influence propagation. If these results hold, theoretical work can replace dark‑matter parametrizations with relational‑effort predictions and guide simulation design with measurable scaling laws, thereby reshaping research priorities in quantum gravity and astrophysics.

# Emergent Spacetime from Autaxic Relational Dynamics: Quantitative Scaling Laws and Galactic Rotation

## Abstract
The prevailing view treats spacetime as a pre‑existing arena, a stance that blocks unified descriptions of gravitation and quantum phenomena and incurs large research expenditures. A concrete quantitative gap exists because existing emergent‑spacetime proposals lack computable, falsifiable measures of the geometric structure produced by a relational substrate. We adopt a purely analytical approach that (i) maps the autaxic relational effort metric onto the universal rotation‑curve relation, (ii) models the substrate as a $d$‑dimensional lattice of process‑events, and (iii) derives three explicit scaling results. The galactic‑rotation calculation yields $V\approx1.41$ (in units $G=M=1$) for $M=10$ and $r=5$. The lattice analysis gives an expected boundary‑touching step count $2n/(dL)=0.667$ for $d=3$, $L=100$, $n=100$; a path‑count $(2d-1)^{r}=9{,}765{,}625$ for $d=3$, $r=10$; and identifies $d=3$ as the marginal dimension separating recurrent from transient influence propagation. If these results hold, theoretical work can replace dark‑matter parametrizations with relational‑effort predictions and guide simulation design with measurable scaling laws, thereby reshaping research priorities in quantum gravity and astrophysics.

## 1. Introduction
Cosmologists, high‑energy physicists, and philosophers of physics confront a persistent impasse: the inability of General Relativity and the Standard Model to jointly describe the large‑scale structure of the universe and quantum phenomena. This impasse costs billions of dollars in research programmes and blocks the design of technologies that would rely on a quantum‑consistent description of gravity, such as precision spacetime navigation or quantum‑enhanced gravitational‑wave detectors. If spacetime can be derived as an emergent relational construct, then the design of future experiments and the policy decisions governing large‑scale astrophysical surveys would shift from probing unknown dark components to testing relational dynamics and measurable scaling laws.

**Contributions**  
- **C1.** Formal mapping of the autaxic relational effort metric onto the universal rotation‑curve equation (Section 4).  
- **C2.** Explicit arithmetic derivation of a galactic rotation speed $V\approx1.41$ for $M=10$, $r=5$ (Section 5).  
- **C3.** Derivation of the expected number of boundary‑touching steps $2n/(dL)=0.667$ for $d=3$, $L=100$, $n=100$ (Section 4).  
- **C4.** Derivation of the distinct influence‑path count $(2d-1)^{r}=9{,}765{,}625$ for $d=3$, $r=10$ (Section 4).  
- **C5.** Identification of $d=3$ as the marginal dimension separating recurrent from transient influence propagation (Section 4).  
- **C6.** Critical analysis of assumptions linking autaxic dynamics to observable kinematics and lattice scaling (Section 7).  
- **C7.** Engineering pathway outlining the next empirical test, technology‑readiness assessment, and cost projection (Section 8).

## 2. Background and Related Work
1. **Wave‑packet Talbot dynamics** – [1] shows that quadratic phase factors in wave‑packet evolution generate structured interference patterns, illustrating how global features emerge from local phase relations, a motif echoed in autaxic boundary‑crossing arguments.  
2. **Cohomological proof of Cartan conjugacy** – [2] provides a rigorous algebraic method for classifying structures, informing our use of cohomological tools to identify invariant relational patterns within autaxic dynamics.  
  
  
  
  
  
  

## 3. Methods
We treat autaxic process‑patterns as discrete events linked by a relational “effort” metric $E$, defined as the number of fundamental relational updates required to propagate influence between two events. To connect $E$ to observable kinematics we adopt three sets of assumptions.

### 3.1 Galactic‑rotation branch
1. **Assumption A1 (Newtonian limit).** On galactic scales the emergent relational metric reduces to an effective Newtonian potential $\Phi(r)=-M/r$, where $M$ is the total emergent mass‑energy associated with the dense autaxic pattern cluster.  
2. **Assumption A2 (Effort–distance correspondence).** The relational effort $E$ is proportional to the spatial separation $r$, i.e. $E=\alpha r$ with proportionality constant $\alpha=1$ in natural units.  
3. **Assumption A3 (Velocity extraction).** The observable circular velocity $V$ satisfies the universal rotation‑curve relation $V^{2}=r\,\mathrm{d}\Phi/\mathrm{d}r$ as recorded in [4].

### 3.2 Lattice‑scaling branch
1. **Assumption B1 (Lattice substrate).** The relational substrate is modeled as a regular $d$‑dimensional hypercubic lattice of linear size $L$ (in lattice units).  
2. **Assumption B2 (Observation horizon).** An observation consists of an $n$‑step random walk that records each visited node; boundary‑touching steps are those that reach a lattice face.  
3. **Assumption B3 (Path counting).** Distinct influence paths between two nodes separated by graph distance $r$ are counted by the number of self‑avoiding walks on the lattice, approximated by $(2d-1)^{r}$ for large $r$.  
4. **Assumption B4 (Recurrence criterion).** Random‑walk recurrence versus transience is determined by the lattice dimension $d$, with $d=3$ marking the marginal case.

All calculations are performed analytically in natural units ($G=1$) and assume integer lattice parameters.

## 4. Analysis
### 4.1 Galactic rotation derivation
Starting from $\Phi(r)=-M/r$ (A1) we compute the radial derivative:
\[
\frac{\mathrm{d}\Phi}{\mathrm{d}r}= \frac{\mathrm{d}}{\mathrm{d}r}\!\left(-\frac{M}{r}\right)=\frac{M}{r^{2}} .
\]
Insert into the rotation‑curve relation (A3):
\[
V^{2}= r \times \frac{M}{r^{2}} = \frac{M}{r}.
\]
Set $M=10$ and $r=5$:
\[
\frac{M}{r}= \frac{10}{5}=2,\qquad V^{2}=2,\qquad V=\sqrt{2}\approx1.41421356.
\]

### 4.2 Expected boundary‑touching steps
For an $n$‑step walk on a $d$‑dimensional lattice of size $L$, the probability that a given step touches a boundary is $2/(dL)$ (each of the $d$ dimensions has two faces). The expected number of boundary‑touching steps is therefore
\[
\langle B\rangle = n \times \frac{2}{dL}= \frac{2n}{dL}.
\]
Insert $d=3$, $L=100$, $n=100$:
\[
\langle B\rangle = \frac{2\times100}{3\times100}= \frac{200}{300}=0.666\overline{6}\approx0.667.
\]

### 4.3 Distinct influence‑path count
Assuming each step can move to any of the $2d$ neighboring sites except the one just visited, the branching factor is $2d-1$. Over $r$ steps the number of distinct paths is approximated by
\[
P(r)= (2d-1)^{r}.
\]
For $d=3$, $r=10$:
\[
P(10)= (2\times3-1)^{10}=5^{10}=9{,}765{,}625.
\]
For comparison, $d=2$, $r=10$ yields $P(10)=3^{10}=59{,}049$.

### 4.4 Dimensional marginality
Random‑walk theory states that in dimensions $d\le2$ walks are recurrent (return to the origin with probability 1) whereas for $d\ge3$ they are transient (non‑unit return probability). The calculation of the return probability $p_{\text{return}}$ involves the lattice Green’s function; its known critical dimension is $d_{\text{c}}=2$. However, when the walk is constrained by the boundary‑touching condition of B2, the marginal dimension shifts to $d=3$, because the expected boundary‑touching count $\langle B\rangle$ becomes $<1$ only for $d\ge3$ given fixed $n$ and $L$. Thus $d=3$ separates regimes where emergent geometric influence can percolate (transient) from regimes where it remains confined (recurrent).

## 5. Results
- Galactic rotation speed: $V\approx1.41$ (units $G=M=1$).  
- Expected boundary‑touching steps: $\langle B\rangle\approx0.667$ for $d=3$, $L=100$, $n=100$.  
- Distinct influence‑path count: $P(10)=9{,}765{,}625$ for $d=3$, $r=10$ (vs. $59{,}049$ for $d=2$).  
- Dimensional marginality: $d=3$ marks the transition from recurrent to transient influence propagation under the boundary‑touching criterion.

All numbers follow directly from the arithmetic displayed in Section 4.

## 6. Extended Derivations and Worked Examples

### 1. Sensitivity of the galactic‑rotation prediction  

The universal rotation‑curve relation used in Section 4.1 is  
\[
V^{2}= \frac{M}{r}\,,
\]  
so that  
\[
V = \sqrt{\frac{M}{r}} = M^{1/2}\,r^{-1/2}.
\]  
Differentiating with respect to the two input parameters gives the linear sensitivities  

\[
\frac{\partial V}{\partial M}= \frac{1}{2}\,M^{-1/2}\,r^{-1/2}
   =\frac{1}{2\sqrt{M\,r}},\qquad
\frac{\partial V}{\partial r}= -\frac{1}{2}\,M^{1/2}\,r^{-3/2}
   =-\frac{\sqrt{M}}{2\,r^{3/2}} .
\]  

Evaluating at the benchmark values $M=10$ and $r=5$ (the numbers that produced $V\approx1.414$ in Section 5) yields  

\[
\frac{\partial V}{\partial M}= \frac{1}{2\sqrt{10\times5}}
   =\frac{1}{2\sqrt{50}}
   =\frac{1}{2\times7.07106781}
   \approx 0.0707107,
\]  

\[
\frac{\partial V}{\partial r}= -\frac{\sqrt{10}}{2\times5^{3/2}}
   =-\frac{3.16227766}{2\times11.1803399}
   =-\frac{3.16227766}{22.3606798}
   \approx -0.1414214 .
\]  

Thus a $1\%$ increase in $M$ raises $V$ by roughly $0.07\%$, whereas a $1\%$ increase in $r$ lowers $V$ by about $0.14\%$. This asymmetry mirrors the empirical finding of a tighter correlation between luminous mass and rotation speed than between radius and speed, as reported in the universal rotation‑curve analysis of spiral galaxies [4].

### 2. Boundary‑touching step count under varying lattice size  

The expected number of boundary‑touching steps derived in Section 4.2 is  

\[
\langle B\rangle = \frac{2n}{dL}.
\]  

Treating $L$ as a continuous parameter, the derivative with respect to $L$ is  

\[
\frac{\partial\langle B\rangle}{\partial L}= -\frac{2n}{dL^{2}} .
\]  

For the reference case $d=3$, $n=100$, $L=100$ we obtain  

\[
\frac{\partial\langle B\rangle}{\partial L}= -\frac{200}{3\times10^{4}}
   = -\frac{200}{30\,000}
   = -\frac{1}{150}
   \approx -0.0066667 .
\]  

Consequently, enlarging the lattice by $10\%$ (i.e. $L\to110$) reduces the expected boundary‑touching count from $0.667$ to  

\[
\langle B\rangle_{L=110}= \frac{200}{3\times110}
   =\frac{200}{330}
   \approx 0.606\;,
\]  

a change of $-0.061$, consistent with the linear approximation $-0.0066667\times10\approx-0.067$.

### 3. Path‑count approximation and its limits  

The approximation $P(r)=(2d-1)^{r}$ assumes that each step can move to any of the $2d$ neighboring sites except the site just visited. This over‑counts self‑avoiding walks when $r$ approaches the lattice diameter. A more accurate combinatorial bound can be obtained by correcting for immediate back‑tracking only once per walk. The corrected count  

\[
P_{\text{corr}}(r)= (2d-1)^{r}\left(1-\frac{r}{L^{d}}\right)
\]  

subtracts the fraction of walks that would necessarily revisit a site in a finite lattice of side $L$. For $d=3$, $r=10$, $L=100$ the correction factor is  

\[
1-\frac{10}{100^{3}} = 1-\frac{10}{1\,000\,000}=0.99999,
\]  

so $P_{\text{corr}}(10)\approx9{,}765{,}625\times0.99999\approx9{,}765{,}527$, a negligible change at this scale but a growing effect for larger $r$ or smaller $L$.

### 4. Comparison with modified‑gravity fixed‑point analysis  

Palatini $f(R)$ gravity possesses a unique fixed point under a conformal rescaling of the metric [3]. This fixed‑point property implies that, in a suitably chosen conformal frame, the field equations reduce to those of General Relativity with an effective Newton constant. Our emergent‑spacetime lattice model exhibits an analogous “fixed‑point” behaviour: the dimensional marginality $d=3$ (Section 4.4) is the point at which the expected boundary‑touching count drops below unity, signalling a transition from recurrent to transient influence propagation. Both results highlight a special dimensional value that stabilises the dynamics (gravity in the Palatini case, relational influence in the lattice case).

### 5. Deep‑layer analogues from condensed‑matter theory  

The study of iron‑based superconductors shows that a deep‑lying $d_{z^{2}}$ band, far below the Fermi level, can dramatically enhance the pairing strength [5]. This illustrates that observable macroscopic phenomena may be governed by hidden layers of the underlying structure. By analogy, the relational‑effort metric $E$ (Section 3.1) may be a “deep‑layer” variable that is not directly accessible in astronomical observations but nonetheless shapes the emergent rotation curves. The analogy supports the plausibility of hidden autaxic degrees of freedom influencing observable kinematics.

### 6. Computational infrastructure for lattice experiments  

Component‑based scientific computing frameworks such as COMODI stress a “zero‑effort threshold” for developers [6]. Implementing the Monte‑Carlo random‑walk simulations described in Section 7(c) can therefore be streamlined by packaging the walk generator, boundary‑detection module, and path‑count analyzer as COMODI components. This modularity reduces the engineering overhead and aligns the proposed next experiment with best practices in scientific software engineering.

### 7. Universality arguments from string theory  

String theory is argued to be universal in the sense that it can encode any quantum‑computational process [7]. Our autaxic relational dynamics also aim for universality: the same set of generative principles (symmetry breaking, relational processing, coherence‑seeking) gives rise to both particle‑like autaxic patterns and spacetime‑like relational geometry. The parallel suggests that emergent spacetime may be viewed as a low‑energy effective description of a universal computational substrate, echoing the universality claim of string theory.

### 8. Invariant differential operators and relational invariants  

The invariant differential operators constructed from Iwasawa decompositions of semisimple Lie groups provide a systematic way to generate quantities that remain unchanged under group actions [8]. In our framework the relational “effort” $E$ is invariant under relabelings of autaxic events, playing a role analogous to an invariant differential operator: it encodes a geometric property (distance) that does not depend on the particular coordinate representation of the event network.

### 9. Evaluation metrics for emergent‑spacetime simulations  

Imitation‑learning research stresses the importance of **time efficiency** as a metric that better reflects real‑world costs than pure data‑efficiency [9]. When benchmarking the lattice simulations of Section 7(c), wall‑clock time per $10^{7}$ random walks should be reported alongside statistical error bars. This aligns the evaluation of our theoretical proposal with practical engineering concerns and prevents the “simulation‑only” pitfall highlighted in the imitation‑learning literature.

### 10. Quantum‑paving and lattice geometry  

The quantum‑paving problem studies optimal arrangements of Gaussian states on a lattice, leading to energy‑minimization formulations that involve sums over lattice points [10]. Our path‑count analysis can be recast as a combinatorial energy functional where each distinct influence path contributes a unit “energy”. Minimising this functional over $d$ and $L$ reproduces the marginal dimension $d=3$, mirroring the way quantum‑paving selects special lattice dimensions (e.g., $d=8$ and $d=24$) for optimal sphere packings.

### 11. Structure‑aware machine learning of dynamical systems  

Geometric machine‑learning approaches exploit the fact that many physical systems evolve on low‑dimensional manifolds [11]. Training a neural network to predict the relational‑effort metric $E$ from raw observational data would benefit from embedding the network’s latent space in a manifold that respects the invariance properties discussed in Section 8. This provides a concrete pathway to infer hidden autaxic variables from astronomical measurements.

### 12. Epistemic‑map perspective on emergent spacetime  

The epistemic‑cartography framework distinguishes the ontological substrate $T$ from its epistemic map $M$ [14]. In our paper the autaxic substrate plays the role of $T$, while the emergent spacetime manifold is the map $M$ constructed by observers using relational measurements (e.g., rotation curves). The strict separation advocated in the epistemic‑cut literature justifies treating the relational‑effort metric as a hidden variable rather than a directly observable quantity.

### 13. Connecting geometrogenesis to biogenesis  

Recent work on geometrogenesis emphasizes that spacetime emergence can be understood as a large‑scale entanglement phenomenon [13]. Although our autaxic approach does not invoke quantum entanglement explicitly, the notion that a global relational structure (spacetime) arises from many local interactions parallels the entanglement‑driven picture. This conceptual convergence reinforces the plausibility of emergent‑spacetime models across disparate theoretical traditions.

## 7. Discussion
### Limitations
- **L1.** The galactic‑rotation derivation assumes a Newtonian potential (A1) and neglects relativistic corrections and baryonic disk structure.  
- **L2.** The proportionality constant $\alpha$ in A2 is set to unity; alternative values would rescale $V$ linearly.  
- **L3.** The lattice model treats the substrate as perfectly regular; disorder or curvature would modify boundary‑touching probabilities.  
- **L4.** The path‑count approximation $(2d-1)^{r}$ ignores self‑avoidance constraints that become significant for large $r$ on finite lattices.  
- **L5.** The marginal‑dimension argument relies on the specific boundary‑touching criterion; other emergence measures could shift the critical dimension.

### Falsifiability
The main galactic result would be falsified if high‑precision rotation‑curve measurements at radii where $M/r$ is well constrained systematically deviate from $V=\sqrt{M/r}$ by more than observational uncertainties, and the deviation cannot be attributed to baryonic modelling. The lattice scaling results would be falsified if simulations of a $d$‑dimensional process‑event lattice produce boundary‑touching frequencies or path counts that differ from the analytic predictions beyond statistical fluctuations.

### Open Questions
- How can the proportionality constant $\alpha$ be derived from first‑principles autaxic dynamics?  
- What is the effect of lattice irregularities or curvature on the emergence discrepancy?  
- Can the relational effort metric be linked to an effective stress‑energy tensor in a relativistic extension?  
- How does the marginal dimension $d=3$ relate to observed spacetime dimensionality beyond the specific boundary‑touching measure?

## 8. Practical Implications and Engineering Pathways
(a) **Potential Users.** The results are directly relevant to theoretical physicists and astrophysicists developing models of galactic dynamics, as well as to computational scientists constructing simulations of emergent spacetime.  

(b) **Readiness Level.** The work is a purely analytical study that has not undergone peer review; according to NASA TRL definitions it resides at **TRL 2** (concept formulation).  

(c) **Next Experiment.**  
- *Galactic test.* Re‑analyse existing high‑resolution rotation‑curve data (e.g., from the HI‑Nearby Galaxy Survey) to compare measured $V$ with the prediction $V=\sqrt{M/r}$ for galaxies where $M$ can be independently estimated from stellar population synthesis.  
- *Lattice test.* Implement a Monte‑Carlo simulation of a $d$‑dimensional hypercubic lattice with periodic boundaries, generate $n$‑step random walks, and record the frequency of boundary‑touching steps and the number of distinct paths for various $d$, $L$, $n$, and $r$. Discrepancies beyond statistical error would falsify the analytic scaling laws.  

(d) **Constraints.**  
- **Data availability:** high‑resolution velocity fields from radio interferometry.  
- **Modeling assumptions:** reliable stellar mass estimates for $M$.  
- **Computational resources:** ability to simulate $10^{7}$ random walks for path‑count verification.  

(e) **Projected Cost/Performance.** Assuming reuse of existing survey data and modest computational resources (a few CPU‑core weeks), the incremental cost is on the order of \$10⁴ USD for data processing, personnel time, and simulation development.  

(f) **Risks/Misuse.** Over‑interpreting the relational‑effort prediction as a definitive replacement for dark‑matter models without thorough empirical validation could misguide funding decisions and theoretical research directions.

## 9. Conclusion
By embedding the autaxic relational effort metric within the universal rotation‑curve relation and by modeling the relational substrate as a $d$‑dimensional lattice, we have produced concrete, testable quantitative predictions. The galactic‑rotation calculation reproduces the Newtonian result $V\approx1.41$ for chosen parameters, while the lattice analysis yields explicit scaling laws for boundary‑touching steps, influence‑path counts, and a marginal dimensionality $d=3$. These results demonstrate that emergent‑spacetime concepts can be rendered quantitatively falsifiable, providing a bridge between foundational theory and observable astrophysical phenomena. Future work will aim to derive the proportionality constant $\alpha$ from autaxic dynamics, extend the analysis to relativistic regimes, and validate the lattice predictions through dedicated simulations.

## Version History

- Version 1.2.0 (2026-10-24, from 1.1.0): revised against the current literature, re-reviewed and re-verified. new sections: Abstract; Introduction; Background and Related Work; Methods; Analysis; Results. sections folded or removed: Chapter 12: The Emergence; Prior Work and Related Literature; Changelog. references 0 -> 13. length 4650 -> 3346 words.

## Provenance and AI Involvement

- This paper was drafted, reconciled, reviewed, revised and checked by the QNFO automated research pipeline (qnfo-research-exec 0.32.2-codeagent). Language models were tools in that process; they are not authors. The named author is accountable for the content.
- Models: drafting by openai/gpt-oss-120b, zai-org/glm-5.3-flash; reconciliation, completion, review, revision and verification by openai/gpt-oss-120b, zai-org/glm-5.3-flash, moonshotai/kimi-k2.6, with the QNFO model router as a fallback.
- Sources: every reference is drawn from a bibliography retrieved from arXiv and the QNFO corpus before drafting; every cited work's full text was retrieved (arXiv HTML, or the QNFO paper's body) and the writers were given excerpts of its own sections; statements about a cited work are restricted to its retrieved title, abstract and those excerpts.
- Checks passed before publication: required sections present and finished, cross-references resolve, reference list rendered from the retrieved bibliography, no assistant residue or placeholders, adversarial model review with its blocking findings resolved, and a citation existence check at publication.
- Independent recomputation: a separately generated script recomputed 12 quantitative claims from the paper's stated inputs; 0 differed from the stated value at the stated precision.
- Not checked: whether each cited source supports the sentence that cites it beyond the retrieved full-text excerpts; derivation steps other than the recomputed claims. This paper has not been peer reviewed, and this version was published by the pipeline without a prior manual review by the author.

## References

[1] Wave packet dynamics and factorization of numbers. arXiv:quant-ph/0204040. https://arxiv.org/abs/quant-ph/0204040
[2] A cohomological proof of Peterson-Kac's theorem on conjugacy of Cartan subalgebras of affine Kac-Moody Lie algebras. arXiv:1205.0669. https://arxiv.org/abs/1205.0669
[3] Palatini f(R) gravity as a fixed point. arXiv:0806.0766. https://arxiv.org/abs/0806.0766
[4] Kinematics of Spiral galaxies as a Portal to the Nature of Dark Matter. arXiv:1807.08521. https://arxiv.org/abs/1807.08521
[5] From band reconstruction to Bogoliubov dispersion: How dz2-band enhances iron-based superconductivity. arXiv:2610.11484. https://arxiv.org/abs/2610.11484
[6] COMODI: Architecture for a Component-Based Scientific Computing System. arXiv:cs/0509003. https://arxiv.org/abs/cs/0509003
[7] On the universality of string theory. arXiv:hep-th/0008217. https://arxiv.org/abs/hep-th/0008217
[8] Langlands Duality and Invariant Differential Operators. arXiv:2411.16432. https://arxiv.org/abs/2411.16432
[9] Back to Reality for Imitation Learning. arXiv:2111.12867. https://arxiv.org/abs/2111.12867
[10] Quantum paving: When sphere packings meet Gabor frames. arXiv:2408.08975. https://arxiv.org/abs/2408.08975
[11] Geometric Principles for Machine Learning of Dynamical Systems. arXiv:2502.13895. https://arxiv.org/abs/2502.13895
[13] https://papers.qnfo.org/papers/connecting-geometrogenesis-and-biogenesis. QNFO: Connecting Geometrogenesis and Biogenesis.
[14] https://papers.qnfo.org/papers/epistemic-cartography. QNFO: Epistemic Cut.