Optimal girth-dependent bounds for the Bethe approximation of the permanent

Dingding Dong, Vishesh Jain

Dong and Jain report that, for a nonnegative n-by-n matrix whose bipartite support graph has girth at least even g>=4, Theorem 1.3 proves Bethe(A)<=per(A)<=2^(2n/g)Bethe(A); disjoint unions of g-cycles attain the upper bound when g divides 2n. The model-developed intermediate proof had a weaker exp(n Theta(log g/g)) factor; the exact model-origin step retained in the final sharp proof is not identified.

Sources for this record

What was already known

Gurvits established the lower inequality, and Anari-Rezaei proved the sharp universal upper factor 2^(n/2), attained at 4-cycles. Anari conjectured the optimal girth-dependent factor 2^(2n/g); forest support was already exact.

AI involvement AI-centered

Dong and Jain devised the initial strategy, then ChatGPT 5.6 Sol Pro developed a girth-dependent intermediate bound. Later model discussions contributed to the sharp proof; the authors wrote and explain the final argument, but the precise model-origin sharp step is not identified.

The authors document substantial model development of the central girth-dependent strategy and model interaction leading to the final proof; they retain leadership and final authorship. The precise sharp step remains unresolved.

Importance Notable

Proves Anari's conjectured girth-dependent factor within the stated matrix setting.

Limitations

The upper factor assumes a nonnegative matrix with bipartite support girth at least even g≥4. Equality examples require g to divide 2n. A completed technical check of the final sharp proof is not documented, and attribution of its exact sharp step remains unresolved.

Technical verification Reported

A full proof is published, but the AI-use statement assigns responsibility rather than recording a completed check of the final argument. No machine proof or explicit independent review was found.

Human understanding Full

The human authors explain their original strategy and the final good-vertex, information-flow and averaging mechanisms; no central reasoning gap is identified.

Correction and publication history