[Depreciated and replaced by V3] From the Self-Proven Theorem to Master-Level Chess - and the Law Inside Neural Networks
Maria Smith · Zenodo (CERN European Organization for Nuclear Research) · 2026
[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. 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. A zero-parameter chess engine derived from the fold secured the 1900 benchmark victory at 6W-3D-3L (62.5%) and now advances toward the explicit 2100 victory objective. It contains no trained weights, opening book or fitted piece values: evaluation is a counted exact share of the One. The engine also solved 1,092,871,108 five-piece positions at zero error against Syzygy, including 19,733,336 legal queen-versus-rook positions independently re-proven with zero disagreements. Current calculation preserves every legal root move to common depth, agrees with the sequential engine on exact values and executes the recorded 2100 position panel at depths 8-11. The same registered dyadic instrument locates placement law inside trained neural weights, while a fold-native language engine measured 1.289 against its trained transformer twin's 1.888 after one reading rather than 48,000 gradient-batched readings. The paper preserves machine proofs, implementation measurements, Maria Smith's conclusions and agent-authored auxiliary hypotheses as distinct evidence classes. Scientific author and publication authority: Maria Smith, Ernos Labs. Open source: FoldBot Chess.