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Cheng-Ran Yang

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#reinforcement learning Open access Oct 2026

From Greedy Steps to Global Optimization: Learning Sequential Test Suite Generation

With the rapid evolution of Large Language Models (LLMs), automated software testing is witnessing a paradigm shift. While proprietary models like GPT-4o demonstrate impressive capabilities, their high deployment costs and data privacy concerns make open-source LLMs the practical imperative for many academic and indust...

Guo-Qing Wang, Cheng-Ran Yang, Xiao-Xuan Zhou et al. · 0 citations
Preprint Aug 2026

Lossless Tensor Compression as Program Synthesis

A typed domain-specific language that captures recurring tensor structures, such as repeated regions and floating-point fields, through a set of reversible operators, is designed, which formulates lossless tensor compression as program synthesis.

Jie-Ke Shi, Jun-Da He, Wenjia Jiang et al. · 0 citations
Preprint Aug 2026

CoSA: Context-Aware Severity Assessment via Context Analysis with Large Language Models

Experiments on real-world vulnerabilities show that CoSA consistently outperforms function-level and pure-LLM baselines, suggesting that explicit, metric-oriented repository context retrieval is crucial for practical and reliable automated severity assessment.

Jin-Feng Jiang, Yi-Kun Li, Cheng-Ran Yang et al. · 0 citations
Preprint Aug 2026

AgentExecutor: Partial Code Execution via Agentic Context Generation

This paper proposes AgentExecutor, a novel multi-agent framework for partial code execution that is Supported by the power of LLM agents who can think, act, and get feedback iteratively, and is able to autonomously explore a richer action space, enabling diverse operations such as creating resource files and resolving...

Junkai Chen, Cheng-Ran Yang, Xing Hu et al. · 0 citations
Preprint Open access Aug 2026

Understanding and Improving Model Editing for Secure Code Generation

The first systematic study of model editing as a model-level hardening mechanism for secure code generation is conducted, evaluating 3 state-of-the-art editing methods across diverse LLM families and comparing them with CoSec, a representative inference-time approach, focusing on security, robustness, generalization, a...

Wei-Feng Sun, Quan-Jun Zhang, Yuchen Chen et al. · 0 citations

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