OpenAI reports Gaussian free-field limits across the balanced six-vertex range

For the balanced-torus plane state of the square-lattice six-vertex model with a=b=1 and 0<c≤2, its unit-jump height field converges modulo constants to a Gaussian free field whose squared multiplier is 1/arcsin(c/2) under the Green-kernel normalization −(2π)^(-1)log|x−y|.

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OpenAI reports Gaussian free-field limits across the balanced six-vertex range

  • Mathematics
  • Theoretical Physics

For the balanced-torus plane state of the square-lattice six-vertex model with a=b=1 and 0<c≤2, its unit-jump height field converges modulo constants to a Gaussian free field whose squared multiplier is 1/arcsin(c/2) under the Green-kernel normalization −(2π)^(-1)log|x−y|.

AI involvementAI-led
ImportanceMajor
Technical verificationReported
Human understandingFew

Understanding the problem

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For the balanced-torus plane state of the square-lattice six-vertex model with a=b=1 and 0<c≤2, its unit-jump height field converges modulo constants to a Gaussian free field whose squared multiplier is 1/arcsin(c/2) under the Green-kernel normalization −(2π)^(-1)log|x−y|.

Why it matters

If correct, proves the field scaling limit and its coupling constant across the full stated critical six-vertex range.

What counts as progress

Does the balanced six-vertex height converge to the predicted Gaussian free field for all 0<c≤2?

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