collaborators

7 papers

cs.SE2026

JevCoder: When Does Decision Delegation Pay Off in Code Repair?

Jiahao Zhang, Yifan Zhang, Yu Huang

A coding agent repeatedly makes decisions as it repairs code. These decisions shape repair trajectories and affect the token cost of successive model requests. We ask which repair…

cs.SE2026

Coupling Planning with Episodic Memory in LLM Agents for Software Issue Resolution

Jiahao Zhang, Yifan Zhang, Yu Huang

Resolving a real software issue with a large language model (LLM) agent is a long repair episode, often tens to hundreds of steps spanning exploration, hypothesis, implementation,…

cs.SE2026

RefEvo: Agentic Design with Co-Evolutionary Verification for Agile Reference Model Generation

Yifan Zhang, Jianmin Ye, Jiahao Yang +1

As the complexity of System-on-Chip (SoC) designs grows, the shift-left paradigm necessitates the rapid development of high-fidelity reference models (typically written in SystemC)…

cs.SE2026

SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair

Yifan Zhang, Jieyu Li, Kexin Pei +2

Large Language Models (LLMs) can generate plausible code patches, but plausibility is not enough for automated repair: a patch must compile, pass tests, and remove the target vulne…

cs.LG2026

ContextRL: Enhancing MLLM's Knowledge Discovery Efficiency with Context-Augmented RL

Xingyu Lu, Jinpeng Wang, YiFan Zhang +12

We propose ContextRL, a novel framework that leverages context augmentation to overcome these bottlenecks. Specifically, to enhance Identifiability, we provide the reward model wit…

cs.SE2026

EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization

Jiahao Zhang, Yifan Zhang, Kevin Leach +1

Code summarization is the task of generating natural language descriptions of source code, which is critical for software comprehension and maintenance. While large language models…

cs.AI2025

ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming

Xinwei Yang, Zhaofeng Liu, Chen Huang +4

While recent research increasingly emphasizes the value of human-LLM collaboration in competitive programming and proposes numerous empirical methods, a comprehensive understanding…