vST for Protein Language Models#

References#

This appendix lists references relevant to protein language models, high‑dimensional embedding analysis, scaling laws, structural biology, and validation frameworks. Citations are grouped by category for clarity and presented in a substrate‑agnostic, model‑independent format consistent with the RSM and vST canon.

1. Protein Language Models and Sequence Embeddings#

• Rives, A., Meier, J., Sercu, T., et al.
  Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences.
  PNAS 118, e2016239118 (2021).

• Elnaggar, A., Heinzinger, M., Dallago, C., et al.
  ProtTrans: Towards Cracking the Language of Life’s Code Through Self‑Supervised Deep Learning and High Performance Computing.
  IEEE TPAMI (2021).

• Rao, R., Liu, J., Verkuil, R., et al.
  MSA Transformer.
  ICML (2021).

• Madani, A., McCann, B., Naik, N., et al.
  ProGen: Language Modeling for Protein Generation.
  arXiv:2004.03497 (2020).

2. Structural Biology and Protein Representation#

• Jumper, J., Evans, R., Pritzel, A., et al.
  Highly Accurate Protein Structure Prediction with AlphaFold.
  Nature 596, 583–589 (2021).

• Baek, M., DiMaio, F., Anishchenko, I., et al.
  Accurate Prediction of Protein Structures and Interactions Using a Three‑Track Neural Network.
  Science 373, 871–876 (2021).

• AlQuraishi, M.
  End‑to‑End Differentiable Learning of Protein Structure.
  Cell Systems 8, 292–301 (2019).

3. 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).

4. Scaling Laws and Model Dynamics#

• 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).

5. 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).

6. 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).

7. 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 Protein Language Models.
  TriadicFrameworks (2026).