Model Comparison on AIBussin — AI applications, systems and books

The Shape of an Embedding Space

Mon, 07 Sep 2026 10:30:00 +0000

Part II — Inside the Space

Same corpus, five geometries

Freeze the input. Embed exactly the same 1,173 RELATE items with five models and, before scoring any task, measure the geometry each one imposes on identical text.

The evaluation harness requests L2-normalized embeddings (normalize_embeddings=True), so every vector analyzed in this chapter lies on the unit sphere, and cosine is purely a statement about direction.

MEASURED on RELATE v0.1, Wave 2 row 2.8 — artifact experiments/embeddings-from-first-principles/wave2/artifacts/shape-comparison.json. All descriptors computed from one run on the frozen corpus.

Change the Model, Change the Universe

Mon, 07 Sep 2026 13:00:00 +0000

Part V — Embedding Spaces Are Not Universal

The same sentence, three universes

Embed one sentence with three models:

model A (384-d):  [ ... ]
model B (768-d):  [ ... ]
model C (768-d):  [ ... ]

A and B differ in length, so nobody expects to compare them coordinate-wise. But B and C are both 768-dimensional. It is tempting to line up their vectors and read something off the alignment directly — to compute cos(B_sentence, C_sentence) and interpret whatever number comes back.

The Space Between Models Has Holes: Mapping the AI Gap

Wed, 22 Oct 2025 20:30:36 +0100

🌌 Summary

What if the most valuable insights in AI evaluation aren’t in model agreements, but in systematic disagreements?

This post reveals that the “gap” between large and small reasoning models contains structured, measurable intelligence about how different architectures reason. We demonstrate how to transform model disagreements from a problem into a solution, using the space between models to make tiny networks behave more like their heavyweight counterparts.

We start by assembling a high-quality corpus (10k–50k conversation turns), score it with a local LLM to create targets, and train both HRM and Tiny models under identical conditions. Then we run fresh documents through both models, collecting not just final scores but rich auxiliary signals (uncertainty, consistency, OOD detection, etc.) and visualize what these signals reveal.