arXiv Machine Learning July 9, 2026 By Ya Wang, Hanwei Fan, Zhenguo Liu, Xiaofeng Zhou, Yangdi Lyu, Jiang Xu, Wei Zhang

HiFuzz: Hierarchical Reinforcement Learning for Semantic-Aware and Adaptive CPU Fuzzing

Read the original on arXiv Machine Learning →

arXiv:2607. 06619v1 Announce Type: cross Abstract: Modern processor verification struggles to reach deep architectural states due to the inefficiencies of traditional mutation-based fuzzing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 30

Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis

arXiv:2606. 28403v1 Announce Type: cross Abstract: Vulnerability detection in C/C++ software remains a major security challenge due to code complexity, manual memory management, and the limitations of traditional static analysis.

By Bruno Caro-V\'asquez, Carola Figueroa-Flores, Gast\'on Marquez

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arXiv AI

Sep 10

SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction

arXiv:2609.06229v1 Announce Type: cross Abstract: Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical...

By Yuanxiang Shi, Jiayi Lin, Xuanyong Lin, Liangcai Su, Yeheng Duan, Wei Wang, Qi Han, Bing Zhao, Wei Hu, Xander Xu, Chenxiong Qian

llms agents benchmarks

arXiv AI

Jul 29

KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Models

arXiv:2607. 25647v1 Announce Type: cross Abstract: As quantum computing continually improves, ensuring the reliability and correctness of quantum libraries has become increasingly critical.

By Fuyuan Xia, Qixin Zhang, Chenhao Ying, Haojin Zhu, Shuai Wang, Yuan Luo, Pingchuan Ma, Yuxuan Du

llms benchmarks

arXiv AI

Oct 1

When Correct Memory Goes Wrong: Fuzzing Persistent Memory Use in LLM Agents

arXiv:2609.38275v1 Announce Type: cross Abstract: Persistent memory helps LLM agents carry information across long interactions, but correct memory can still be used incorrectly when queries change o...

By Yuqiao Meng, Luoxi Tang, Yingxue Zhang, Yuchen Yang, Zhaohan Xi

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arXiv Machine Learning

Aug 4

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation

arXiv:2608. 01804v1 Announce Type: new Abstract: Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities.

By Tankun Li, Zhi Chen, Yaohua Tang

llms reinforcement-learning efficiency benchmarks safety

arXiv AI

Aug 19

LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents

LEGO-RL is a framework that connects native coding-agent harnesses with scalable policy‑gradient training without altering the harnesses’ internal flow. It achieves faithful optimization through in‑process LLM proxying, reliable execution via sandbox orchestration, and observable training with automated validation and a Live UI. Experiments show LEGO‑RL improves the Qwen3.5‑35B‑A3B model’s performance on three native harnesses while preserving high rollout‑training probability correlation.

By Yiming Du, Yuxin Jiang, Tao Yuan, Jianbo Dai, Shaowei Wang, Jierun Chen, Chaofan Tao, Xianzhi Yu, Lifeng Shang, Kam-Fai Wong, Xiaohui Li, Haoli Bai

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Reinforcement Learning for Software Vulnerability Analysis: A Systematic Review with Emphasis on C/C++ Source Code and Static Analysis

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