vST for Multi‑Model Alignment#

References#

This appendix lists references relevant to cross‑model alignment, multimodal representation learning, scaling laws, latent‑space geometry, and validation frameworks. Citations are grouped by category for clarity and presented in a substrate‑agnostic, architecture‑neutral format consistent with the RSM and vST canon.

1. Cross‑Model & Multimodal Alignment#

• Radford, A., Kim, J. W., Hallacy, C., et al.
  Learning Transferable Visual Models From Natural Language Supervision (CLIP).
  arXiv:2103.00020 (2021).

• Jia, C., Yang, Y., Xia, Y., et al.
  Scaling Up Visual and Vision‑Language Representation Learning With Noisy Text Supervision.
  ICML (2021).

• Alayrac, J.‑B., Donahue, J., Luc, P., et al.
  Flamingo: A Visual Language Model for Few‑Shot Learning.
  arXiv:2204.14198 (2022).

2. Latent‑Space Geometry & Representation Learning#

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

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

• von Luxburg, U.
  A Tutorial on Spectral Clustering.
  Statistics and Computing (2007).

3. Scaling Laws Across Architectures#

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

• Zhai, X., Puigcerver, J., Mustafa, B., et al.
  Scaling Vision Transformers.
  CVPR (2022).

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

4. Multimodal & Cross‑Architecture Systems#

• Ramesh, A., Dhariwal, P., Nichol, A., et al.
  Zero‑Shot Text‑to‑Image Generation.
  ICML (2021).

• Karras, T., Aittala, M., Laine, S., et al.
  Elucidating the Design Space of Diffusion‑Based Generative Models.
  NeurIPS (2022).

• Kingma, D. P., & Welling, M.
  Auto‑Encoding Variational Bayes.
  ICLR (2014).

5. Validation, Verification & Drift Detection#

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

• Amodei, D., Olah, C., Steinhardt, J., et al.
  Concrete Problems in AI Safety.
  arXiv:1606.06565 (2016).

• Oberkampf, W. L., & Roy, C. J.
  Verification and Validation in Scientific Computing.
  Cambridge University Press (2010).

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 Multi‑Model Alignment.
  TriadicFrameworks (2026).