[Depreciated and replaced by V3] From One Axiom to Master-Level Chess — and the Law Inside Neural Networks: Attention, it turns out, was not all you need

Resource information

Authors

Smith, Maria

Research area

Artificial intelligence

Research topics

artificial intelligence, AGI, artificial general intelligence, chess engine, Stockfish, AlphaZero, zero free parameters, large language models

Publication date

2026

DOI

10.5281/zenodo.21217279

Upstream last modified

Sep 12, 2026

License

Creative Commons Attribution 4.0 International

Last record update

Nov 6, 2026

About this resource

[Depreciated and replaced by V3] The application-specific clean rebuild has not yet been published; its authoritative theoretical boundary is now the governing V3 branch: After Turing: The Fold Machine - An Exact, Parameter-Free and Machine-Closed Derivation of Classical Computational Science from Smithian Fold Theory; From Fold to Consciousness: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Consciousness and Cognitive Science from Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work. Pre-paper v0.2 of the Smithian Fold Theory's computational program, en route to UnisonAI. Two connected results. First: a chess engine containing zero parameters — no piece values, no tuned weights, no opening book, no training — whose every evaluation is a count performed on the board's geometry and an exact rational share of the One, reached master-adjacent strength in three days of measured, refereed climbing: it beat Stockfish's Elo-1900 setting 62.5% and holds Elo-2100 to a draw in half its games. The same machinery solved queen-vs-rook exactly (19,733,336 legal positions, re-proven value-by-value in an independent clean-room implementation with zero disagreements), atop the campaign record of 1,092,871,108 five-piece positions solved at zero error against Syzygy. Solved chess value fields are dyadically smooth: 32 Walsh coefficients reconstruct 92.7–95.3% of all exact values. Second: pointing the identical pre-registered spectral instrument at trained neural networks, trained weights are found to carry placement-law in the fold's dyadic basis — unanimously (18/18 on validated released models), causally (untrained controls sit at exactly chance), located (the law concentrates in transformer expansion projections and token embeddings across three unrelated architectures — CLIP, GPT-2, Llama-3.1-8B — while contraction projections and attention sit at chance in aligned packings), reaching 230x chance in GPT-2's token embedding, and surviving 4-bit deployment quantization at every depth. Attention, it turns out, was not all you need. The paper states what is proved, what is registered as next, and how every number can be independently certified from the public repositories.