Structured Intelligence · patent pending
Watch their table go blind.
Billionaire-backed embeddings buy a slot per token. Ask a magnitude the net never saw and the slot is chance. Place value already has the column. Identical network. Identical data. Identical random seeds. The sole variable is the input encoding.
Task A · unseen
Structured
100.0%
train 100.0% · h=48
Task A · unseen
One-hot
52.7%
train 99.2% · h=16
Task A · unseen
Embedding
50.0%
train 100.0% · h=16
01
Run it
Same net, same seed. Only the encoding changes. Unseen magnitudes expose the slot. You do not take our word.
02
Try it
Sign in, mint a key, POST integers. Public place-value rungs. No charge to try.
03
Buy it
License the API, purchase, or stake. The composed family stays under NDA. You are not buying a PDF.
Product factory
Apps, not source
Counsel holds the closed family. The factory ships products that sit on it: a saturation clock, success mirrors, a cockpit. Clients pay for the run. They never see the codes.
Open the factoryLive miniature · seed 7
Task A — is this reading odd?
Taught on 0–127. Asked on 512–1,023 — never seen. Same logistic readout. The only change is the input encoding.
Structured
Train 0–127
Unseen 512–1,023
One-hot
Train 0–127
Unseen 512–1,023
Embedding
Train 0–127
Unseen 512–1,023
A slot that was never trained holds whatever it was born with.
Full protocol · measured
Held-out comparison
Both structured and one-hot reach 100% on the training set. Training-set performance is not the claim. Generalization to completely unseen inputs is. That is why you pick this over a table with a billion-dollar budget.
| Encoding | Seen | Unseen | Parameters |
|---|---|---|---|
| Structured (fixed place value) | 100.0% | 98.6% | 1,673 |
| Learned embedding | 37.1% → 100% | 1.7% → 0.9% | 3,721 → 51,721 |
| One-hot | 100.0% | 0.65% | 33,417 |
Embedding seen accuracy rises only after capacity is grown; unseen accuracy falls as parameters increase.
Task A · measured
Odd reading, unseen magnitude
- Structuredunseen 100.0%
- One-hotunseen 52.7%
- Embeddingunseen 50.0%
Task B · measured
Add two readings, 32× jump
Taught 0–15. Asked 128–511. Composed rungs trained nothing new — same primitives, one column wider.
- COMPOSED (place-value rungs)100.0%
- Structured, monolithic0.0%
- One-hot, monolithic0.0%
Live compose check on this page: 200 of 200 (place-value add, no retraining).
For a training run
Train it to know. Not to guess.
Today you train a model to guess. Show it enough examples and hope the next one looks like the last. When the next one is a size it rarely saw, it guesses — that is the 50% on this page. Chance in a nice jacket.
This is training to know. Unseen is still a thing. Same net, same seeds: 100% on a number it never trained. They guess. This knows.
What the rivals need
Atoms in the observable universe: ~10⁸⁰. A place-value code needs 12, 16, 32, 64, and 256 inputs.
| Range | Values | One-hot slots | Place-value inputs |
|---|---|---|---|
| 12-bit | 4,096 | 2¹² | 12 |
| 16-bit | 65,536 | 2¹⁶ | 16 |
| 32-bit | 4.29 billion | 2³² | 32 |
| 64-bit | 1.8×10¹⁹ | 2⁶⁴ | 64 |
| 256-bit | 1.16×10⁷⁷ | 2²⁵⁶ | 256 |
Try. Then buy.
The result is public. The family is not. Run the bench, mint a research key, POST integers. If you want the API in production, a license, purchase, or a stake — that is a named conversation, not a download.
Buy · named evaluator
License, purchase, or stake
Commercial use and the full encoder. NDA. You are buying a project that runs on the codes, not the codes.
Email Dr. Jarrod S. SeguraDr. Jarrod S. Segura