arXiv Machine Learning

RTLCurator: Label-Efficient Data Curation for RTL Generation

arXiv:2607. 29283v1 Announce Type: cross Abstract: Training large language models (LLMs) to write register-transfer level (RTL) requires large corpora of paired specifications and code, and such data is scarce enough that most public corpora are now synthesized.

arXiv AI

Jun 8

SWE-IF: Aligning Code Evaluation with Human Preference

arXiv:2510. 07315v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check.

By Ming Zhong, Xiang Zhou, Ting-Yun Chang, Qingze Wang, Nan Xu, Xiance Si, Dan Garrette, Shyam Upadhyay, Jeremiah Liu, Jiawei Han, Benoit Schillings, Jiao Sun

llms

arXiv Machine Learning

Jun 5

CASS-RTL: Correctness-Aware Subspace Steering for RTL Generation with LLMs

arXiv:2606. 05680v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the automatic synthesis (generation) of register-transfer level (RTL) code from natural language instructions, offering a promising pathway to accelerate chip design.

By Mohammad Akyash, Nowfel Mashnoor, Kimia Azar, Hadi Kamali

llms rag agents fine-tuning safety

arXiv Machine Learning

Jun 5

Can LLMs Write Correct TLA+ Specifications? Evaluating Natural-Language-to-TLA+ Generation

arXiv:2606. 05792v1 Announce Type: cross Abstract: TLA+ has supported industrial verification at companies such as Amazon and Microsoft, yet writing correct TLA+ specifications from natural language still requires time and expertise, which limits adoption.

By Arslan Bisharat, Brian Ortiz, Eric Spencer, Khushboo Bhadauria, TaiNing Wang, George K. Thiruvathukal, Konstantin Laufer, Mohammed Abuhamad

llms safety

arXiv Machine Learning

Jun 10

CodeAlchemy: Synthetic Code Rewriting at Scale

arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.

By Ankit Gupta, Aditya Prasad, Rameswar Panda

llms benchmarks

arXiv Machine Learning

Aug 5

M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models

arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.

By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y

llms nlp benchmarks safety

arXiv AI

Jun 29

Speculative Refinement: A Hybrid Autoregressive Diffusion Decoding Strategy and Its Behavior Across Benchmarks

arXiv:2606. 27474v1 Announce Type: cross Abstract: How should we evaluate generation systems that combine autoregressive (AR) and diffusion decoding?

By Aditi Gupta, Neel Mishra, Kushagra Trivedi, Pawan Kumar

llms diffusion benchmarks

arXiv AI

1d ago

LLMs Get Smarter from Targeted Synthetic Multilingual Data

arXiv:2608. 15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt.

By Ishika Agarwal, Arkajyoti Charaborty, Tanner Sorensen, Neha Gupta, Andreas Stolcke

llms fine-tuning

arXiv Machine Learning

Jul 3

HaloGuard 1.0: An Open Weights Constitutional Classifier for Multilingual AI Safety

arXiv:2607. 02079v1 Announce Type: cross Abstract: We present HaloGuard 1.

By Navaneeth Sangameswaran, Preetham S, Ashmiya Lenin

agents benchmarks safety

arXiv AI

Jun 16

LLM4RTL: Tool-Assisted LLM for RTL Generation

arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.

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

llms fine-tuning benchmarks

arXiv AI

Jun 10

Durable Evaluation Framework: Adversarial Arbitration for Sycophancy Reduction in Large Language Models

arXiv:2606. 07532v2 Announce Type: replace-cross Abstract: RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process.

By Sam Ryan

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jul 31

How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data

arXiv:2604. 13977v2 Announce Type: replace-cross Abstract: Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and source data, remain absent.

By Joel Niklaus, Atsuki Yamaguchi, Michal \v{S}tef\'anik, Guilherme Penedo, Hynek Kydl\'i\v{c}ek, Elie Bakouch, Lewis Tunstall, Edward Emanuel Beeching, Thibaud Frere, Colin Raffel, Leandro von Werra, Thomas Wolf

llms

arXiv AI

Jun 10

EstRTL: Functional Estimation Guided RTL Code Generation

arXiv:2606. 09867v1 Announce Type: cross Abstract: Optimizing register transfer level (RTL) code is of vital importance in hardware design.

By Qi Xiong, Renzhi Chen, Bowei Wang, Yuqing Xiong, Libo Huang, Lei Wang

llms agents fine-tuning

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