RL-LLMfuzzer, a reinforcement learning and LLM-based differential fuzzing framework that has successfully unearthed 28 officially confirmed vulnerabilities in GCC and LLVM/Clang, establishing a highly efficient and scalable paradigm for LLM-driven compiler validation.
Dong-Hui Li, Ying-Ying Li, Bo Zhao et al.· Journal of King Saud Univers...· 0 citations
CHORUS is presented, a post-training framework that pushes performance beyond what a conventional supervised fine-tuning (SFT)-to-reinforcement learning (RL) pipeline achieves, and consolidates the resulting specialists into a single 4B model.
He-Jia Zhang, Sheng Lu, Zhongming Yu et al.· 0 citations
A refined training strategy is presented, leveraging an encoder-based approach, to address two key challenges in FL: (1) the accurate classification of lines of code according to their suspiciousness scores, and (2) the class imbalance inherent in this type of problem.
Jesus-Angel del-Hoyo-Gabaldon, Antonio Garcia-Cabot, Eva García-López et al.· Applied Sciences· 0 citations
Experimental results demonstrate that the GNN-based structural analysis outperforms standalone stylistic and transformer-based base-lines across all subtasks, particularly in multi-class and adversarial settings.
This work proposes Entropy-based Code Adversarial Translation (ECAT), a multi-agent framework for automated Android-to-HarmonyOS repository migration and introduces A2H-RepoBench, the first real-world benchmark for Android-to-HarmonyOS repository migration.
Yushun Tang, Yi-Fan Cao, Zhicheng Chen et al.· 0 citations
CoGate is proposed, a confidence-gated co-decoding approach that controls the expert's influence on the co-decoding process based on its confidence, and outperforms existing co-decoding methods (CoSec+) across multiple benchmarks.
Minghao Hu, Lannan Luo, Allen G. Roush et al.· arXiv.org· 0 citations
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