works on

From the 1 of 44 linked papers with an AI index.

activity
20242026
collaborators

44 papers

cs.SE2026

RepoReasoner: Evaluating Repository-Level Code Reasoning Ability of Long-Context Language Models

Yanlin Wang, Suiquan Wang, Yanli Wang +4

The paper presents RepoReasoner, a benchmark that evaluates how well large language models can reason about code across multiple files in a repository, testing both fine-grained ex…

cs.SE2026

VRExplorer: A Model-based Approach for Semi-Automated Testing of Virtual Reality Scenes

Zhengyang Zhu, Hong-Ning Dai, Hanyang Guo +2

With the proliferation of Virtual Reality (VR) markets, VR applications are rapidly expanding in scale and complexity, thereby driving an urgent need for assuring VR software quali…

cs.SE2026

AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion

Tianyue Jiang, Yanli Wang, Yanlin Wang +5

Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context an…

cs.LG2026

Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies

Jingze Song, Zihao Chen, Wenqing Chen +1

Data selection is a key component of efficient instruction tuning for large language models, as recent work has shown that data quality often matters more than data quantity. Accor…

cs.SE2026

ParityFuzz: Finding Inconsistencies across Solidity Compilers via Fine-Grained Mutation and Differential Analysis

Bowei Su, Mingxi Ye, Yuhong Na +2

The Solidity smart contract ecosystem has rapidly grown, leading to multiple compilers targeting different blockchain platforms or improving compilation efficiency. Although many c…

cs.CR2026

SkillScope: Toward Fine-Grained Least-Privilege Enforcement for Agent Skills

Jiangrong Wu, Yuhong Nan, Yixi Lin +4

Agent Skills have become a practical way to extend LLM agents by packaging metadata, natural-language instructions, and executable resources into reusable capability bundles. Howev…