Embedding-Native Cognition

Note: A formal write-up is in progress. I’m exploring collaborations; message me if you’d like to co-author a theory paper. A brief AI-assisted mock-up is provided as a starting point: Embedding-Native Cognition: Geometry as a Substrate for Retrieval, Planning, and Safety

Embedding-Native Cognition: Geometry as a Substrate for Retrieval, Planning, and Safety

TL;DR. High-dimensional embeddings look a lot like how humans organize concepts: similar things are close, categories form clusters, and linear directions encode relations. This Space lets you see that geometry: paste categories and words, embed them with a Sentence-Transformer, project with UMAP, and inspect prototypes (category centroids). It’s a small demo of a bigger idea: embedding-native agents that plan and self-improve using geometry, not just tokens.

Demo: embeddings-as-cognition-umap

Open the Space, edit categories, press Run, and explore the plot. Prototype distances show which items are most “typical” within each category.

Sample:

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Why embeddings (again)?

High-dimensional embeddings give you a continuous geometry of meaning: nearby points are related, directions encode relations, and clusters map to human categories. This demo lets you see that geometry and work with it—without getting lost in prompt engineering.

What you’ll notice:

• Category “islands” (e.g., bear/wolf near forest/woods/nature).
• Prototype effects: some items sit closer to a category centroid (more “typical”).
• Model choice matters (small = fast; larger = crisper clusters).

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What this Space does

• Embeds your words/phrases (select a Sentence-Transformer).
• Projects them with UMAP for 2D visualization.
• Draws per-category centroids (⭐) and optional labels.
• Computes prototype distances (cosine to each category centroid).
• Lets you add/remove categories in the sidebar and download CSVs.

How to use: Pick a model → edit categories (one term per line) → Run → explore the plot + prototype table. Use downloads for offline analysis.

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Important note about 2D projections

UMAP (and t-SNE) compress hundreds of dimensions into 2D, which necessarily distorts some distances and neighborhoods. Treat the plots as intuition aids, not ground truth. For decisions, rely on original-space metrics (cosine similarity, centroid distance, k-NN overlap), not the 2D layout. Small changes to n_neighbors / min_dist can shift the picture without changing underlying semantics.

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Where I’m heading (high-level)

This demo is a thin slice of a broader effort toward embedding-native agents that:

• retrieve and prune context geometrically,
• emit compact semantic hints for downstream steps,
• route tasks through auditable procedures with watchers / doubt checks / topic locks, and
• learn from logs what actually helped.

Details are intentionally withheld for now; if this direction fits your roadmap, I’m open to discussing under NDA.

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Known limits & mitigations (brief)

• Polysemy / context mixing → context-conditioned reps; multi-view scoring
• Hubness / anisotropy → hubness-aware neighbors; local normalization
• Projection artifacts → use 2D only for intuition; score in original space
• Domain shift → lightweight adaptation; guarded fallbacks

I’ve explored practical remedies for these and related topics (e.g., geometry-aware retrieval pruning). Serious inquiries welcome.

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Collaboration

I published an AI-assisted mock-up of the theory as a public doc: 👉 Embedding-Native Cognition: Geometry as a Substrate for Retrieval, Planning, and Safety

If you’d like to co-author a formal paper (theory, proofs, experiments, benchmarks), DM me with your background and interest area. I’m also open to sharing implementation details under NDA and discussing exclusive/shared-rights collaborations depending on scope.

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Responsible use

Embeddings reflect their training data. Treat prototype distances and plots as diagnostics, not verdicts. Validate with task-level metrics, prefer high-quality sources, and keep guardrails on any system with side effects.

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Citation

If this demo or the ideas here are useful, please cite:

Jaired Hall (2025). Embedding-Native Cognition: Geometry as a Substrate for Retrieval, Planning, and Safety. Demo & preprint, Google Doc.

BibTeX:

@misc{hall2025embeddingnative,
  title         = {Embedding-Native Cognition: Geometry as a Substrate for Retrieval, Planning, and Safety},
  author        = {Jaired Hall},
  year          = {2025},
  howpublished  = {\url{https://docs.google.com/document/d/e/2PACX-1vR2yfHEJYRxcS1Y756s1KiDKer1DkCHZj95KpYi340tyA8nO5hNVwYRwLkg0TpH_Q/pub}},
  note          = {Demo and AI-assisted mock-up preprint}
}