arXiv Machine Learning
Oct 22

Nominate-then-Adjudicate: LLM-Assisted One-Pass and Per-Instance MIP Solver Tuning

The paper introduces a "nominate-then-adjudicate" pipeline for tuning mixed‑integer programming (MIP) solvers, where a fixed portfolio of configurations is sampled once and reused across instances, eliminating per‑instance search at inference. A 20‑feature representation maps structurally different instances to the same vector, enabling a one‑pass, zero‑solve selection process that outperforms default settings and per‑instance baselines such as ISAC+. The approach demonstrates significant practical gains, with the adjudicator achieving 538 wins over the top nominee and improving win rates against defaults from 84.9% to 90.2%.

By Yifan Zhao, Qianyang Zhao, Xinyu Guan, Kai Wei, Yuming Deng
arXiv AI
Sep 30

More Programs or More Rolls? Separating Coverage from Specialization in LLM Harnesses

The paper introduces a controlled evaluation to disentangle answer coverage, repeatable task advantages, and gains from pre‑execution selection in large‑language‑model (LLM) harnesses. On 386 MATH‑500 tasks, eight generated harnesses and a baseline with nine identical copies were compared over three executions each, revealing that identical programs provide a 2.16‑point repeat‑averaged oracle headroom while generated programs show more repeatable score patterns but mainly expose persistent weaknesses. The study concludes that coverage and repeatability alone cannot justify claims of useful specialization and proposes an evaluation standard for harness diversity that requires task advantages to persist across executions and improve on additional fixed‑program executions under matched inference budgets.

By Ziyang Xu, Haitian Zhong, Hao Zhou, Hao Qin, Chenhan Jin, Te Qi, Shengze Xu, Tieyong Zeng
arXiv AI
Oct 2

ReSolve: Reusing Candidate Reasoning through Selective Generative Moderation

ReSolve is a training‑free inference method that reuses candidate reasoning by selectively moderating generative outputs. It examines existing derivations when candidates disagree or lack a parseable answer, then incorporates new solutions into a bounded loop. On 130 competition‑mathematics problems, ReSolve achieves 100 and 99 correct answers with significantly fewer tokens than eight‑sample self‑consistency, while a controlled ablation shows that visible derivations improve accuracy.

By Bangji Yang, Jiajun Fan, Hongba Ma, Xi Zhu, Weizhi Zhang, Minghao Guo, Ye Li, Hamid Palangi, Jiaxuan You
arXiv Machine Learning
Oct 5

Online Verification of Language Model Responses Under Cost Constraints

The paper introduces OMVV, an online multi-verifier algorithm that maintains a pool of weak verifiers with varying costs and performance. It adaptively selects a verifier each round using an online score combiner and exponential-weights routing, providing distribution-free guarantees on false-accept and false-reject rates. Experiments on reasoning benchmarks show OMVV achieves higher accuracy at lower verification cost than any single fixed verifier across different budgets.

By Erfan Hajihashemi, Yanning Shen
arXiv AI
Oct 23

ORSMT: Unleashing the Potential of Satisfiability Modulo Theories for Semantic Verification and Repair in LLM-Based Optimization

ORSMT is a tool that uses Satisfiability Modulo Theories to verify and repair artifacts produced by large language models when automating optimization modeling from natural language. It provides a typed semantic interface, exact SMT relation queries, and mechanisms to align decisions, project auxiliary variables, and reconstruct artifacts, thereby improving model-code match rates across thousands of problems. The system demonstrates significant gains in correctness and matching rates compared to existing methods such as ReLoop and Opt‑Verifier.

By Rui Zhang, Chenyu Zhou, Jianghao Lin, Yaoxin Wu, Dongdong Ge, Wen Song