Same net, same seed – 100% vs. 50% on unseen magnitudes

Structured Intelligence, a company with a patent-pending encoding method, reports that its structured place-value input encoding achieves 100.0% accuracy on unseen magnitudes in Task A, compared to 52.7% for one-hot and 50.0% for learned embeddings, using identical networks, data, and random seeds. The company claims its method generalizes to completely unseen inputs without retraining, citing a held-out comparison where structured encoding reaches 98.6% unseen accuracy versus 0.65% for one-hot and 0.9% for learned embeddings. Structured Intelligence is offering a public benchmark, API access, and licensing under NDA, with Dr. Jarrod S. Segura as the contact.

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 factory /products Live 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. Segura /cdn-cgi/l/email-protection 5b313a2929343f283e2f331b33342f363a32377538343664282e39313e382f66092e353c7e696b7e1e697e636b7e626f7e696b3732383e35283e7e696b34297e696b282f3a303e7d3a362b6039343f2266127e696b293a357e696b2f333e7e696b37322d3e7e696b393e353833757e696b127e696b2c3a352f7e696b2f347e696b2f3a37307e696b3732383e35283e7e69187e696b2b2e2938333a283e7e69187e696b34297e696b282f3a303e757e6b1a7e6b1a153a363e7e681a7e6b1a1834362b3a35227e681a7e6b1a Dr. Jarrod S. Segura