vST for Large Language Models#

References#

1. High‑Dimensional Modeling and Representation Learning#

• Bengio, Y., Courville, A., & Vincent, P.
  Representation Learning: A Review and New Perspectives.
  IEEE TPAMI 35, 1798–1828 (2013).

• Coifman, R. R., & Lafon, S.
  Diffusion Maps.
  Applied and Computational Harmonic Analysis 21, 5–30 (2006).

• Tenenbaum, J. B., de Silva, V., & Langford, J. C.
  A Global Geometric Framework for Nonlinear Dimensionality Reduction.
  Science 290, 2319–2323 (2000).

2. Scaling Laws and Large Language Models#

• Kaplan, J., McCandlish, S., Henighan, T., et al.
  Scaling Laws for Neural Language Models.
  arXiv:2001.08361 (2020).

• Hoffmann, J., Borgeaud, S., Mensch, A., et al.
  Training Compute‑Optimal Large Language Models.
  arXiv:2203.15556 (2022).

• Bahri, Y., Kadmon, J., Pennington, J., et al.
  Statistical Mechanics of Deep Learning.
  Annual Review of Condensed Matter Physics 11, 501–528 (2020).

3. Transformer Architectures and Latent‑Space Behavior#

• Vaswani, A., Shazeer, N., Parmar, N., et al.
  Attention Is All You Need.
  NeurIPS (2017).

• Clark, K., Khandelwal, U., Levy, O., & Manning, C. D.
  What Does BERT Look At? An Analysis of Attention.
  ACL (2019).

• Rogers, A., Kovaleva, O., & Rumshisky, A.
  A Primer in BERTology: What We Know About How BERT Works.
  TACL 8, 842–866 (2020).

4. Regime Behavior, Stability, and Dynamics#

• Strogatz, S.
  Nonlinear Dynamics and Chaos.
  Westview Press (2014).

• Ott, E.
  Chaos in Dynamical Systems.
  Cambridge University Press (2002).

• Guckenheimer, J., & Holmes, P.
  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields.
  Springer (1983).

5. Validation, Drift Detection, and ML Systems#

• Breck, E., Cai, S., Nielsen, E., et al.
  The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction.
  Google Research (2017).

• Sculley, D., Holt, G., Golovin, D., et al.
  Hidden Technical Debt in Machine Learning Systems.
  NIPS (2015).

• Amershi, S., Begel, A., Bird, C., et al.
  Software Engineering for Machine Learning: A Case Study.
  ICSE‑SEIP (2019).

6. Substrate‑Level and Triadic‑Frameworks Canon#

• Loswin, N.
  Resonance Substrate Model (RSM): Structural Foundations for High‑Dimensional Inference.
  TriadicFrameworks (2025).

• Loswin, N.
  Triadic Dimensional Cores: A 3D–9D Substrate for Structural and Inference‑Level Alignment.
  TriadicFrameworks (2025).

• Loswin, N.
  Validation‑Space‑Time (vST): A Substrate‑Level Framework for Reproducibility and Drift Detection.
  TriadicFrameworks (2025).

• Loswin, N.
  Dimensional Substrate Structures: Scaling Laws and High‑Dimensional Regimes.
  TriadicFrameworks (2026).

• Loswin, N.
  vST for Large Language Models.
  TriadicFrameworks (2026).