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新论文 · v1arXiv:2611.11713

Binary Belief Propagation Fixed Points: Finiteness, Rigidity, and Topological Complexity

Yusuke Watanabe

原文摘要

A fixed finite binary Ising model has only finitely many admissible complex belief propagation fixed points, even at degenerate parameter values. Families of positive fixed points obtained by varying external fields can nevertheless have nontrivial topology. We study this distinction through the external-field projection. For a fixed field, we prove finiteness by bounding the pole orders of the magnetizations on a hypothetical complex solution curve. The generic degree on the support graph then bounds the total local multiplicity. A graph has a single smooth real line as its full positive response for every choice of positive couplings, base field, and strictly positive sweep direction exactly when it contains no two vertex-disjoint connected subgraphs of cycle rank at least two. Two such subgraphs suffice to create a circular component. For a plane obtained by varying two vertex fields, connected simple bipartite cubic graphs with one uniform coupling and a universal bound on the fixed fields realize every genus. The minimum number of vertices grows logarithmically in the genus, by a matching algebraic upper bound on Betti numbers. Finally, we certify the complete field discriminant of an eighteen-vertex product model and recover its torus component's genus from the counts of distinct roots on all field cells. The three main theorems, including the logarithmic size bounds, are formally verified in Lean 4 with Mathlib.

cs.IT · math.AG · math.CO

新论文 · v1arXiv:2610.36964

Counting the Fixed Points of Binary Belief Propagation: Generic Degree, Syzygies, and Real Solutions

Yusuke Watanabe

原文摘要

How many fixed points does belief propagation have on a binary pairwise model? Over complex parameters, the admissible equations of a finite loopless multigraph form an algebraic family whose generic degree we determine: it is a graph state sum over edge sets without vertices of degree one, each vertex of positive degree contributing one less than its degree. We give two geometric realizations of this state sum. The first is the total Betti number of a universal edge-incidence ring constructed without parameters. Its full graded Betti table is the state sum, its Tor algebra is natural for noncontracting graph maps, the alternating numerator recovers the graph polynomial of Watanabe and Fukumizu, and an Artin reduction by a regular sequence realizes the one-variable specialization, corrected for tree components. The second lies in the actual family: at the strong-field boundary each state factor is a ramification index, and at sufficiently strong field the state charts exhaust the admissible complex fiber. For fixed nonzero real couplings, every Eulerian message-sign sector contains a real fixed point at every external field, and strong fields attain this lower bound: one admissible real fixed point per element of the binary cycle space and one positive fixed point. For graphs of maximum degree at most three, this minimum equals the number of minimal module generators of the edge-incidence ring. One state decomposition connects the complex degree, graded syzygies, and an attained minimum real count. The degree, selected ring and resolution statements, and the real counts are formalized in Lean 4.

math.AC · cs.IT · math.AG · math.CO

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.47ACL Anthology · ACL 2026

ClinDet-Bench: Beyond Abstention, Evaluating Judgment Determinability of LLMs in Clinical Decision-Making

Yusuke Watanabe, Yohei Kobashi, Takeshi Kojima, Yusuke Iwasawa, Yasushi Okuno, Yutaka Matsuo

原文摘要

Clinical decisions are often required under incomplete information. Clinical experts must identify whether available information is sufficient for judgment, as both premature conclusions and unnecessary abstention can compromise patient safety. To evaluate this capability of large language models (LLMs), we developed ClinDet-Bench, a benchmark based on clinical scoring systems that decomposes incomplete-information scenarios into determinable and undeterminable conditions. Identifying determinability requires considering all hypotheses about missing information, including unlikely ones, and verifying whether the conclusion holds across them. We find that recent LLMs fail to identify determinability under incomplete information, producing both premature conclusions and excessive abstention, despite correctly explaining the underlying scoring knowledge and performing well under complete information. These findings suggest that existing benchmarks are insufficient to evaluate the safety of LLMs in clinical settings. ClinDet-Bench provides a framework for evaluating determinability recognition, leading to appropriate abstention, with potential applicability to medicine and other high-stakes domains, and is publicly available.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表