vST for Large Language Models#

References#

This appendix lists references relevant to high‑dimensional modeling, latent‑space analysis, scaling laws, regime behavior, and validation frameworks for Large Language Models (LLMs). Citations are grouped by category for clarity and presented in a substrate‑agnostic, model‑independent format consistent with the RSM and vST canon.

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).