arXiv AI September 15, 2026 By Chance LaVoie, Eladio Andujar Lugo, Taylan G. Topcu, Levent Burak Kara

Natural-Language to SysMLv2 Translation via Conformance-Driven Iterative Refinement

The paper introduces a framework that translates natural‑language descriptions into SysMLv2 models using a generate‑check‑repair loop driven by a SysMLv2 conformance checker. By embedding the checker as an oracle, the system iteratively repairs generated models until they achieve zero conformance errors, ensuring they are deployable in industrial modeling environments. Evaluation on 151 prompts across four large language models shows the approach raises production‑conformance acceptance from 51.16% to 100%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Jul 7

LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2

arXiv:2508. 16181v2 Announce Type: replace-cross Abstract: Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models.

By Zirui Li, Stephan Husung, Haoze Wang

llms safety

arXiv AI

Jul 22

Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Fragments

arXiv:2607. 18357v1 Announce Type: cross Abstract: Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review.

By Shuoming Zhang, Ruiyuan Xu, Haofeng Li, Qiuchu Yu, Yangyu Zhang, Chunwei Xia, Xiaobing Feng, Chenxi Wang, Huimin Cui, Jiacheng Zhao

llms agents data-engineering

arXiv Machine Learning

Sep 10

Robustness of LLM-Generated SystemVerilog Assertions to Semantics-Preserving RTL Transformations

The paper evaluates how robust large language models are at generating SystemVerilog Assertions (SVA) when the underlying RTL code undergoes semantics‑preserving transformations such as operand reordering, identifier renaming, and redundant parenthesization. Using a curated dataset and two open‑source models (Qwen2.5‑Coder‑7B and DeepSeek‑Coder‑V2‑Lite), the authors find that 9.7%–27.0% of behaviors that were correct on the original RTL become incorrect after transformation, revealing significant instability that aggregate accuracy metrics can hide.

By FNU Aditi

llms

arXiv AI

Aug 18

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

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

llms fine-tuning benchmarks

arXiv AI

Aug 17

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

arXiv:2608. 13681v1 Announce Type: cross Abstract: Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vulnerabilities while preserving the functional behavior of legacy systems.

By Pu Zhao, Changdi Yang, Yixiao Chen, Yi Gao, Yifan Cao, Haochen Zeng, Yanzhi Wang

llms agents fine-tuning safety

Hugging Face Trending Papers

Jun 22

Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs

SysML v2's textual syntax enables compiler-based validation of model structure and language conformance. However, semantic mistakes that preserve syntactic validity but violate domain rules cannot be detected through compilers.

llms fine-tuning

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