arXiv:2607. 29431v1 Announce Type: new Abstract: Large language models increasingly generate optimization models from natural language, but existing evaluation often reduces a generated model and its ground truth to a single equivalent/not-equivalent verdict or an execution-success rate--labels that are neither independently checkable nor faithful to the multiple distinct senses in which two formulations can agree.
By Penglin Zhu, Jungang Xu
arXiv:2602. 15983v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems.
By Junbo Jacob Lian, Yujun Sun, Huiling Chen, Chaoyu Zhang, Hanzhang Qin, Chung-Piaw Teo
SemVerBench is a benchmark that evaluates how well large language models (LLMs) understand and apply version-constraint resolution semantics, such as determining whether a version satisfies constraints like ^1.2.3 or >=2.0. The study finds that many models struggle with certain corner cases, with GPT‑5.1 performing poorly while Claude and Opus perform much better. The authors suggest that the failures stem from an activation/application gap rather than a lack of knowledge, and recommend that coding agents delegate version resolution to a dedicated resolver tool.
By Qibai Chen, Zeming Liu
The paper introduces DRAGO, a framework that repairs formal specifications generated by large language models (LLMs) using deterministic, formalism‑specific diagnosis instead of ungrounded LLM critiques. DRAGO selectively rewrites specifications only when the diagnosed error weight decreases, reducing regressions from 11.8% to 1.3% and improving answer accuracy by 12.9 percentage points over zero‑shot formalization and 8.7 percentage points over LLM‑diagnostic repair across five benchmarks. Additionally, the authors identified and corrected rendering defects in Planetarium that caused half of its floor‑tile split instances to be inconsistent with reference formalizations.
By Giovanni Vaccarino, Emanuele La Malfa, Dehai Min, Hanrong Zhang, Pier Luca Lanzi, Lu Cheng, Michael J. Wooldridge, Philip S. Yu, Sara Bernardini
arXiv:2606. 06133v1 Announce Type: cross Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols.
By Eric Spencer, Arslan Bisharat, Brian Ortiz, Khushboo Bhadauria, TaiNing Wang, George K. Thiruvathukal, Konstantin Laufer, Mohammed Abuhamad
arXiv:2607. 18642v1 Announce Type: new Abstract: Mined code corpora are abundant but uncontrolled: a snippet's semantics, surface "messiness," and difficulty are whatever the wild contained; there is no known-optimal reference to grade against; and any public sample may already sit in a model's training set.
By Yuxiang Ji
LGMT is an open‑source tool that tests logical reasoning reliability in large language models by generating logically equivalent follow‑up test cases from a source premise‑conclusion pair and checking for consistency in the model’s entailment predictions. It includes 20 built‑in metamorphic relations, allows custom relations verified by the Z3 theorem prover, and can be run via CLI or a web studio, even offline without an API key. In experiments on six popular LLMs across 3,091 metamorphic groups, LGMT uncovered inconsistency rates between 20.84% and 29.44%.
By Zenghui Zhou, Xiaoke Fang, Zheng Zheng
arXiv:2610.18415v1 Announce Type: cross
Abstract: Large Language Models (LLMs) are aligned to human preferences, after pre-training and supervised fine-tuning steps. However, standard RL alignment st...
By Pratuat Amatya, Vinay Setty, V Venktesh, Avishek Anand
The paper introduces LinguaArray, a framework that uses large language models (LLMs) to suggest a small set of focal ground terms or constraints—called a Semantic Focus Set—in quantified array SMT solving. The LLM’s suggestions are then verified by a conventional SMT solver, which remains the sole source of proof. Experiments on 1,015 SMT-LIB instances show that LinguaArray solves up to 69.1% of cases and increases the number of solved instances by 105%–397% compared to the backends alone.
By Kunhang Lv, Rui Han, Fuqi Jia, Yuhang Dong, Feifei Ma, Jian Zhang
arXiv:2608. 03983v1 Announce Type: cross Abstract: Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed program representation.
By Hailong Jiang, Feng Yu, Emran Hossain, Jianfeng Zhu, Mengfei Ren, Qiang Guan, Chunwei Xia
arXiv:2610.32235v1 Announce Type: new
Abstract: Small language models deployed under cost, latency, or privacy constraints cannot simply switch to larger models when reasoning fails. Multi-candidate...
By Qianli Ma, Zhiqing Tang, Fanshuai Meng, Zhi Yao, Hanshuai Cui, Weijia Jia
arXiv:2603. 15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification.
By Ido Pinto, Yizhak Yisrael Elboher, Haoze Wu, Nina Narodytska, Guy Katz