arXiv AI October 23, 2026 By Boqi Chen, Aren A. Babikian, Jos\'e Antonio Hern\'andez L\'opez

Metamodel-Guided LLM Decoding for Consistent DSL Generation

The paper introduces MeMoDecode, a decoding framework that enforces both concrete and abstract syntax as well as well‑formedness constraints for domain‑specific languages (DSLs) during large language model generation. By mapping generated text to partial metamodel instances and retrying from the last consistent snapshot, MeMoDecode guarantees consistency without sacrificing alignment metrics. Experiments on four DSLs and six open‑weight LLMs show a 23% average improvement over unconstrained decoding and an 8% improvement over grammar‑constrained decoding, with modest runtime overhead.

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arXiv AI

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arXiv AI

Jun 16

LLM4RTL: Tool-Assisted LLM for RTL Generation

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By Jing Jin, Robert Chu, Ning Yan, Masood S. Mortazavi

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arXiv AI

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By Serdar Kadioglu, Karthik Uppuluri

llms fine-tuning

arXiv AI

Jun 9

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By Jing Wang, Shang Liu, Wenji Fang, Yuchao Wu, Yugao Zhu, Zhiyao Xie

llms benchmarks

arXiv AI

Aug 18

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llms fine-tuning benchmarks

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

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The Hidden Cost of Structured Generation in LLMs: Draft-Conditioned Constrained Decoding

LLM4RTL: Tool-Assisted LLM for RTL Generation

Learn2Zinc: Fine-tuning Small Language Models for Text-to-Model Translation in MiniZinc

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