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R12 Sparse-Law Constraint-Intersection Result

The architecture demonstrates exact bounded sparse-law identification, but misses one preregistered promotion criterion because that criterion was mathematically impossible on the frozen board.

R12_SPARSE_LAW_CONSTRAINT_INTERSECTION_RESULT.mdOpen original Markdown ↗

R12 Sparse-Law Constraint-Intersection Result

Decision

The architecture demonstrates exact bounded sparse-law identification, but misses one preregistered promotion criterion because that criterion was mathematically impossible on the frozen board.

Newton job 704761 completed 2,000 updates on one H100 in 3 minutes 7 seconds. Treatment reached:

  • 2,400/2,400 = 100% development transition states;
  • 60/60 complete hash-disjoint unseen maps;
  • 60/60 exact hidden queries;
  • 60/60 source-free deployed packets; and
  • zero training/development action-map overlap.

Same-Weight Controls

ArmTransition accuracyComplete mapsExact queries
Constraint intersection100.0000%60/6060/60
Direction negated36.0833%6/6020/60
Observation targets shifted8.1667%0/601/60
Observations zeroed8.1667%0/600/60

The treatment beats the strongest control by 40 exact queries. The frozen preregistration required 45. Independent exact analysis shows that reversing every observed permutation and executing the original query has exactly 20/60 answer collisions on this board. Therefore no perfect treatment could exceed the direction-negated control by more than 40. The result must not be reported as passing every frozen gate, but the control does not contradict the capability result.

Mechanism

The learned byte encoder predicts source-versus-target direction. Every observation then remains a separate compatibility factor over a fixed finite-domain program bank:

V(D) = intersection_i {p : p(source_i) = target_i}.

A dense tensor energy evaluates all factors in parallel. The unique surviving map is sealed, source bytes are discarded, and a fixed categorical executor answers the late query. Candidate inference calls no host parser, callback, solver, search routine, oracle, or verifier.

This fixes the specific failure of direct attention, generic generators, and supervised microcode: those candidates averaged the individually necessary records before predicting a program.

Receipts

  • learned parameters: 232,065
  • conceptual complete system: 125,313,729
  • report SHA-256: 22d22d8ac9079ee1722ce971c50d2307c5ad1d9132d49c329ef779f01adb2ede
  • model SHA-256: b54fc0d2c113fff07a5a70629b5499ecc8412b6ecc2732ef3728359a2dc3de89
  • optimizer updates: 2,000
  • training rows: 3,300
  • development rows: 60
  • candidate-time oracle/search/verifier calls: 0/0/0

Boundary And Successor

Constraint intersection and the complete operation library are fixed algorithmic priors. Dense parallel evaluation is semantically equivalent to exhaustive version-space elimination. This is architecture-native execution, but it is not learned ontology discovery or general reasoning.

The only justified successor removes the global program bank. A fresh episode must provide unfamiliar complete support generators; the same architecture must construct an episode-local closure, infer sparse target programs, seal them, delete source, and execute. Development must include unseen generator families and longer target programs with matched support-shuffle, witness- deletion, contradiction, direction, and record-order controls.