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%.
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.
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.
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.
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.
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.
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.
arXiv:2609.37233v1 Announce Type: cross Abstract: Datalog underpins reasoning tasks such as program analysis, but its programs are hard to write. Existing synthesizers automate this task but require...
arXiv:2607. 29389v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications.
The paper introduces Program Executability Prediction (PrEx), a task that asks large language models (LLMs) to determine whether a program is semantically valid or invalid and, if invalid, to identify the violated formal rule. To evaluate this, the authors create a dataset of systematically generated invalid programs derived from valid ones and test open‑source coding LLMs across different semantic formalisms, semantic shifts, and program splits (human‑written, LLM‑translated, fuzzer‑generated). Results show that LLMs rely more on pre‑training priors than on the provided semantics, performing poorly on modified semantics and with increasing program complexity.
arXiv:2607. 13921v1 Announce Type: cross Abstract: Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult.
arXiv:2608. 03983v1 Announce Type: cross Abstract: Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed program representation.
arXiv:2603. 25450v2 Announce Type: replace Abstract: Detecting when a language model is wrong without ground truth labels is a fundamental challenge for safe deployment.