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Perceptron capacity and jamming regime planner
Family 222: Perceptron free energies and microscopic jamming exponents. First application triage, 9 October 2026 Australia/Brisbane.
Problem and potential new use
Learning-theory researchers can compare Gaussian or invariant spherical perceptron simulations with stated limiting feasibility and force/gap laws.
Applicability and commercial boundary
These particular random models, potentials and ordered limits do not give capacity estimates for arbitrary neural networks or real data. No efficient training algorithm follows from a free-energy formula.
Initial business decision
Conditional research. Buyer budget, commercial novelty and profitability are unvalidated.
Next verification action
Extract effective model parameters and test a finite synthetic benchmark distinct from general ML performance.
Evidence scope
The catalog statement was individually reviewed. This record does not imply a manuscript proof review or formal-scope comparison. No independent Lean check was run. Source revision fd4aeeb2ee4fc729c18d98444fed42fd0529eeeb. See source metadata.