Establishes a sharp computational phase transition for learning-to-sample from constantly bounded-width Ising models exactly at the spectral threshold λ_max(J)−λ_min(J)=1, with hardness above and tractability below under crypto assumptions.
ISBN 978-3-540-24676-3
2 Pith papers cite this work. Polarity classification is still indexing.
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Syntactic separation of Skolem functions in local systems implies computational indistinguishability with Omega(n) or Omega(2^n) derivation lower bounds, presented as an abstract obstruction governing Natural Proofs, Type Omitting Theorem, and AC^0 barriers.
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A computational phase transition for learning-to-sample from Ising models
Establishes a sharp computational phase transition for learning-to-sample from constantly bounded-width Ising models exactly at the spectral threshold λ_max(J)−λ_min(J)=1, with hardness above and tractability below under crypto assumptions.
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Syntactic Separation Implies Computational Indistinguishability: An Abstract Obstruction Theorem
Syntactic separation of Skolem functions in local systems implies computational indistinguishability with Omega(n) or Omega(2^n) derivation lower bounds, presented as an abstract obstruction governing Natural Proofs, Type Omitting Theorem, and AC^0 barriers.