arXiv AI June 30, 2026 By Nakul Vyas, Iliya D. Stoev

Structural Certification for Reliable Physical Design with Language Models

arXiv:2606. 30107v1 Announce Type: new Abstract: An unreliable language model can be made to produce reliable physical designs if the authority to assert is moved out of the model: the model proposes, and a deterministic engine alone certifies, returning certified, impossible, or unknown.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 5

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges

arXiv:2605. 10807v4 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry.

By Johann Knechtel, Ozgur Sinanoglu, Ramesh Karri

llms agents benchmarks safety

arXiv AI

Jun 4

SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems

arXiv:2605. 10246v2 Announce Type: replace Abstract: AI scientist systems are increasingly deployed for autonomous research, yet their academic integrity has never been systematically evaluated.

By Zonglin Yang, Xingtong Liu, Xinyan Xu

llms agents benchmarks safety

arXiv Machine Learning

Jul 8

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates

arXiv:2604. 04738v2 Announce Type: replace-cross Abstract: Fine-tuning is the dominant paradigm for adapting large machine learning models, yet current deployment pipelines provide no way to verify how a released model was updated.

By Zhenhang Shang, Yingzhe Yu, Kani Chen

llms fine-tuning efficiency benchmarks

arXiv AI

Jun 19

Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement

arXiv:2606. 19387v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable success in software development.

By You Li, Samuel Mandell, David Z. Pan

llms agents safety

arXiv AI

Jun 19

Analyzing the Narration Gap in LLM-Solver Loops

arXiv:2606. 19588v1 Announce Type: new Abstract: Formal tools such as SAT and SMT solvers are increasingly embedded in language model reasoning pipelines when a safety or security critical question can be formulated in logic.

By Zunchen Huang, Songgaojun Deng

llms safety

arXiv AI

Jul 7

Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models

arXiv:2607. 05199v1 Announce Type: new Abstract: Physics reasoning fails structurally in small language models: an error at any step propagates forward, corrupting every inference that follows.

By Raj Jaiswal, Dhruv Jain, Rishabh Dhawan, Sree Krishna Uppalapati, Shin'ichi Satoh, Tanuja Ganu, Rajiv Ratn Shah

llms reinforcement-learning benchmarks safety

Related stories

LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges

SciIntegrity-Bench: A Benchmark for Evaluating Academic Integrity in AI Scientist Systems

Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates

Interpretable and Verifiable Hardware Generation with LLM-Driven Stepwise Refinement

Analyzing the Narration Gap in LLM-Solver Loops

Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models