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.
Donghui Li, Yingying 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.
Hejia 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
Detecting machine-generated code is crucial for maintaining software security, quality and academic integrity. Traditional approaches often rely on stylistic or statistical features, which are increasingly circumvented by advanced code generation models. This paper introduces a novel approach leveraging Graph Neural Networks (GNNs) to capture the structural characteristics of code, specifically modeling source snippets as Abstract Syntax Trees (ASTs). To enhance semantic comprehension, we integrate pre-trained CodeBERT embeddings into the architecture, creating a hybrid model that incorporates structural and semantic information. We evaluate our approach on SemEval-2026 Task 13, covering binary detection, multi-class authorship attribution, and hybrid code classification. Experimental results demonstrate that our GNN-based structural analysis outperforms standalone stylistic and transformer-based base-lines across all subtasks, particularly in multi-class and adversarial settings. This work highlights the potential of GNNs for a more structural understanding of code authorship.
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.· 0 citations