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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

Accurate vulnerability severity assessment is essential for prioritizing remediation, yet manually assessing Common Vulnerability Scoring System (CVSS) base metrics remains labor-intensive. Existing automated approaches often fail to capture the repository-level evidence required for assessing many CVSS base metrics. S...

Jinfeng Jiang, Yikun Li, Chengran 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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