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Jaehong Oh

χPreprint · 2025

Advanced Topology-Preserving Neural Networks: An Extension of ONN/ORTSF Framework with Dynamic Structural Optimization

Jaehong Oh

201 words1 min read

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Abstract

This work presents the first comprehensive empirical investigation into the practical realization of performance bounds theoretically established by the Ontology Neural Network (ONN) and Ontological Real-Time Semantic Fabric (ORTSF) framework. While the original framework provided rigorous mathematical foundations for topology-preserving neural networks through projection-consensus systems, the empirical instantiation of these theoretical limits remained unexplored. We systematically identify parameter regimes that approach the theoretical optimality conditions — surgery decay rates δ = 0.0005, cycle thresholds θ = 8, and minimal connectivity k = 2 — and demonstrate that strategic configurations reach 99.75% of the predicted optimality (topology loss 0.0234 vs. baseline 9.23). The results provide empirical confirmation that ONN's projection-consensus operators and contextual-constraint mechanisms can be instantiated in practice, bridging the gap between mathematical formalism and computational implementation.

Update (2026-07-10). This 2025 preprint is retained as a manuscript of record. Its headline empirical figures — reaching 99.75% of "predicted optimality" (topology loss 0.0234 vs. baseline 9.23), and the transfer results (14.7% perplexity reduction, 2.3× faster convergence) — are not reproducible from the current authoritative research source (onn_ws/ONN) and are superseded by the programme's audit, which resolved the higher-order thesis to a scoped No-Go boundary. Read the numbers below as the original draft's claims, not established results; see the current ONN research status.

Overview

A direct empirical follow-up to the ONN + ORTSF framework paper. The original manuscript established mathematical performance bounds for topology-preserving neural networks; this one asks whether — and under what conditions — those bounds are actually reachable on a working machine.

What the paper shows

  • Enhanced regime (𝓛_topo = 0.0792) reaches 99.14% of the theoretically predicted optimum.
  • Advanced regime (𝓛_topo = 0.0234) reaches 99.75%.
  • Counter-intuitively, minimal connectivity (k = 2) and extreme precision (surgery decay δ = 0.0005) outperform denser, coarser configurations — inverting conventional neural-network design wisdom.
  • Results transfer to transformer architectures (14.7% perplexity reduction) and graph neural networks (2.3× faster convergence on WikiText-103).

Where it sits

Downstream of the accepted ONN + ORTSF paper, which provides the theory this one validates. Upstream of the production-grade ONN implementation documented in the research notes.

BibTeX· generated

@misc{oh2025advanced,
  title   = {Advanced Topology-Preserving Neural Networks: An Extension of ONN/ORTSF Framework with Dynamic Structural Optimization},
  author  = {Jaehong Oh},
  year    = {2025},
  url     = {https://jack0682.github.io/papers/advanced-onn-ortsf-extension/},
}