vST for Embedding Stores & Vector Databases#

References#

This appendix lists references relevant to embedding models, vector databases, high‑dimensional retrieval systems, scaling laws, clustering, dynamical systems, 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. Embedding Models & Representation Learning#

• Mikolov, T., Chen, K., Corrado, G., & Dean, J.
  Efficient Estimation of Word Representations in Vector Space.
  arXiv:1301.3781 (2013).

• Devlin, J., Chang, M.‑W., Lee, K., & Toutanova, K.
  BERT: Pre‑training of Deep Bidirectional Transformers.
  NAACL (2019).

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

2. Vector Databases & Index Structures#

• Johnson, J., Douze, M., & Jégou, H.
  Billion‑Scale Similarity Search with GPUs.
  IEEE Transactions on Big Data (2019). (FAISS)

• Malkov, Y. A., & Yashunin, D. A.
  Efficient and Robust Approximate Nearest Neighbor Search Using HNSW.
  IEEE TPAMI (2020).

• Guo, R., Sun, Y., Lindgren, E., et al.
  Accelerating Large‑Scale Inference with Anisotropic Vector Quantization.
  ICML (2020). (PQ / IVF‑PQ)

3. Clustering & High‑Dimensional Geometry#

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

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

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

4. Scaling Laws & Embedding‑Space Behavior#

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

• Reimers, N., & Gurevych, I.
  Sentence‑BERT: Sentence Embeddings Using Siamese BERT‑Networks.
  EMNLP (2019).

• Mu, J., & Viswanath, P.
  All-but-the-Top: Simple and Effective Postprocessing for Word Representations.
  ICLR (2018).

5. Retrieval, Similarity Search & Evaluation#

• Wang, J., Zhang, T., Song, J., et al.
  Survey on Learning to Hash.
  IEEE TPAMI (2018).

• Jegou, H., Douze, M., & Schmid, C.
  Product Quantization for Nearest Neighbor Search.
  IEEE TPAMI (2011).

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

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 Embedding Stores & Vector Databases.
  TriadicFrameworks (2026).