pith:2NBNCJJL
Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2
Polynomial-time algorithm samples the Sherrington-Kirkpatrick Gibbs measure with negligible TVD error for all β < 1/2.
arxiv:2605.03718 v2 · 2026-05-05 · math.PR · cs.DS · math-ph · math.MP
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Claims
We give a polynomial-time algorithm to sample from the Gibbs measure of the Sherrington--Kirkpatrick model with negligible total-variation distance (TVD) error up to inverse temperature β < 1/2.
The restricted log-Sobolev inequality holds on the time-T localized distribution and the free-probability argument controlling the diagonal sub-algebra of the Hessian yields an O(1) KL bound for the finite-time Hessian-ascent process at all β < 1/2.
A polynomial-time algorithm samples the SK model Gibbs measure with o(1) TVD error for β < 1/2 by combining potential Hessian ascent, stochastic localization, Jarzynski equality, and Glauber dynamics.
Receipt and verification
| First computed | 2026-05-20T02:05:44.253416Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
d342d1252bb49e79902dea1f0430126bb9edf14fb52bd7fdf915e7366c291764
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/2NBNCJJLWSPHTEBN5IPQIMASNO \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: d342d1252bb49e79902dea1f0430126bb9edf14fb52bd7fdf915e7366c291764
Canonical record JSON
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