arXiv Machine Learning July 16, 2026 By Xiaofeng Zhou, Guangyu Hu, Hongce Zhang, Wei Zhang

A-IC3: Learning-Guided Adaptive Inductive Generalization for Hardware Model Checking

Read the original on arXiv Machine Learning →

arXiv:2604. 21688v2 Announce Type: replace-cross Abstract: The IC3 algorithm represents the state-of-the-art (SOTA) hardware model checking technique, owing to its robust performance and scalability.

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

arXiv AI

Jun 11

GPO: Learning from Critical Steps to Improve LLM Reasoning

arXiv:2509. 16456v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in various domains, showing impressive potential on different tasks.

By Jiahao Yu, Zelei Cheng, Xian Wu, Xinyu Xing

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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.

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arXiv Machine Learning

Jun 25

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

arXiv:2606. 25207v1 Announce Type: new Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget.

By Taicheng Guo, Haomin Zhuang, Kehan Guo, Yujun Zhou, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

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arXiv AI

Jun 4

StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis

arXiv:2606. 04246v1 Announce Type: new Abstract: Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Verilog and VHDL.

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arXiv Machine Learning

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From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors

arXiv:2603. 02792v2 Announce Type: replace Abstract: Large Language Models (LLMs) have already been widely adopted for automated algorithm design, demonstrating strong abilities in generating and evolving algorithms across various fields.

By Qi Huang, Furong Ye, Ananta Shahane, Thomas B\"ack, Niki van Stein

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arXiv AI

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SMART: When is it Actually Worth Expanding a Speculative Tree?

arXiv:2604. 09731v2 Announce Type: replace-cross Abstract: Tree-based speculative decoding accelerates autoregressive generation by verifying a branching tree of draft tokens in a single target-model forward pass.

By Lifu Wang, Pan Zhou

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GPO: Learning from Critical Steps to Improve LLM Reasoning

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

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

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