arXiv AI

A Finite Certificate for the Positive $n=9$ Vasc Inequality

arXiv:2606. 06136v1 Announce Type: cross Abstract: We prove the positive-real $n=9$ case of the Vasc cyclic inequality.

arXiv AI
Jun 3

Optimizing Explicit Unit-Distance Lower-Bound Certificates

arXiv:2606. 03419v1 Announce Type: cross Abstract: The 2026 disproof of Erd\H{o}s's unit-distance conjecture and Sawin's subsequent explicit quantitative refinement show that the maximum number $u(n)$ of unit distances among $n$ planar points can exceed $n^{1+\varepsilon}$ for a fixed positive $\varepsilon$.

By Michael T. M. Emmerich
arXiv Machine Learning
Sep 21

The Refutation Gap: Certifying Both Halves of an Optimality Claim

The paper addresses the asymmetry in verifying optimality claims for synthesis pipelines, distinguishing between the upper bound (existence of a program) and the lower bound (non-existence of a smaller program). It introduces a pipeline that synthesizes minimal linear straight‑line programs over GF(2) and produces DRAT proofs for every UNSAT result, thereby closing the so‑called refutation gap for 121 previously uncertified optimality claims. The authors report that the median proof size is 1.1 MB, checking takes 1.9× the solving time, and that their verification process uncovered defects missed by code review, highlighted interface obstacles, and exposed a budget‑related audit failure.

By Rohan Pandey
arXiv AI
Sep 3

When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic

The paper investigates the reliability of machine‑parsed statutes by developing a passive survival certificate for the Duquenne‑Guigues implication basis of extracted legal contexts. It measures inter‑extractor disagreement, runs 1,000 Monte‑Carlo trials, and certifies an implication only when a one‑sided Wilson 95% lower bound on survival reaches 0.95, providing premise spans and minimal counterexamples. Applied to 29,365 Missouri sections and 502 Indian central‑Act sections, the method passes a held‑out gate for many statute families, yet a global error model shows that 93.2% of held‑out chapters fall below the informativeness floor, attributing this to calibration‑rate transfer rather than selection bias.

By Surya Saka
arXiv AI
Jun 12

MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling

arXiv:2606. 13473v1 Announce Type: cross Abstract: We present MaxProof, a population-level test-time scaling framework for competition-level mathematical proof in the MiniMax-M3 series.

By Jiacheng Chen, Xinyu Zhang, Shunkai Zhang, Yanmohan Wang, Lin Li, Tiancheng Qin, Qin Wang, Zhengmao Zhu, Tianle Li, Jingyang Li, Zehan Li, Binyang Jiang, Jin Zhu, Han Ding, Fei Yu, Chenyu Du, Zijian Song, Jiayuan Song, Zhi Zhang, Yunan Huang, Weiyu Cheng, Pengyu Zhao, Yu Cheng