5 papers
To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation
Yitong Zhang, Chengze Li, Ruize Chen +4
Large Language Models (LLMs) have shown strong potential for code generation, yet they remain limited in private-library-oriented code generation, where the goal is to generate cod…
A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding
Yue Zhang, Liqiang Jing, Jia Li +4
Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approa…
Beyond Autoregression: An Empirical Study of Diffusion Large Language Models for Code Generation
Chengze Li, Yitong Zhang, Jia Li +2
LLMs have become the mainstream approaches to code generation. Existing LLMs mainly employ autoregressive generation, i.e. generating code token-by-token from left to right. Howeve…
AI-Driven Self-Evolving Software: A Promising Path Toward Software Automation
Liyi Cai, Yijie Ren, Yitong Zhang +1
Software automation has long been a central goal of software engineering, striving for software development that proceeds without human intervention. Recent efforts have leveraged…
DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Pattern
Lekang Yang, Yuetong Liu, Yitong Zhang +1
Diffusion large language models (dLLMs) enable parallel generation and are promising for unit test generation (UTG), where efficient and large-scale automated testing is essential…