Temporal Anchoring in Deepening Embedding Spaces: Event-Indexed Projections, Drift, Convergence, and an Internal Computational Architecture
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Abstract (arXiv)
We develop an operator-theoretic framework for temporal anchoring in embedding spaces, modeled as drift maps interleaved with event-indexed blocks culminating in affine projections. We provide complete proofs for a variable-block contraction lemma (products of Lipschitz factors), a drift--projection convergence theorem with explicit uniform-gap envelopes, and ontological convergence under nested affine anchors with a robustness variant. We formalize an internal Manuscript Computer (MC) whose computations are defined purely by these operators and prove a rigorous finite-run equivalence theorem (with perturbation bounds). For attention layers, we give a self-contained proof that softmax is 1/2-Lipschitz in ℓ2 and derive sufficient layer-contraction conditions (orthogonal/non-orthogonal heads). All floats are placed exactly where written; the manuscript uses only in-paper pseudocode and appendix figures.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 11 references that are in the map. First posted on arXiv 13 Aug 2025.
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References · 2
- 2017Attention Is All You Needcited by 59,932
- 2015Automatic differentiation in machine learning: a surveycited by 846
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