vST for Generative Models#

References#

1. Diffusion Models & Denoising Processes#

• Ho, J., Jain, A., & Abbeel, P.
  Denoising Diffusion Probabilistic Models.
  NeurIPS (2020).

• Song, J., Sohl‑Dickstein, J., Kingma, D. P., et al.
  Score‑Based Generative Modeling Through Stochastic Differential Equations.
  ICLR (2021).

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

2. Autoregressive & Transformer‑Based Generators#

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

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

3. Flow Models & VAEs#

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

• Rezende, D. J., & Mohamed, S.
  Variational Inference with Normalizing Flows.
  ICML (2015).

• Kobyzev, I., Prince, S. J., & Brubaker, M. A.
  Normalizing Flows: An Introduction and Review.
  IEEE PAMI (2020).

4. GANs & Hybrid Generative Systems#

• Goodfellow, I., Pouget‑Abadie, J., Mirza, M., et al.
  Generative Adversarial Nets.
  NeurIPS (2014).

• Brock, A., Donahue, J., & Simonyan, K.
  Large Scale GAN Training for High Fidelity Natural Image Synthesis.
  ICLR (2019).

5. Scaling Laws & Latent‑Space Behavior#

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

• Ho, J., & Salimans, T.
  Classifier‑Free Diffusion Guidance.
  arXiv:2207.12598 (2022).

• Dhariwal, P., & Nichol, A.
  Diffusion Models Beat GANs on Image Synthesis.
  NeurIPS (2021).

6. 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).

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 Generative Models.
  TriadicFrameworks (2026).