代码大模型 / AI 编程

When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions

Maya Larbi, Amal Akli, Mike Papadakis, Rihab Bouyousfi, Maxime Cordy, Federica Sarro, Yves Le Traon

机构 * University of Luxembourg（卢森堡大学） ； University College London（伦敦大学学院） 纠错

专题命中 代码评测 ：code model(title)；code generation(abstract)；分类 cs.SE、cs.AI

MOCHA: Are Code Language Models Robust Against Multi-Turn Malicious Coding Prompts?

Muntasir Wahed, Xiaona Zhou, Kiet A. Nguyen, Tianjiao Yu, Nirav Diwan, Gang Wang, Dilek Hakkani-Tür, Ismini Lourentzou

机构 * University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） 纠错

专题命中 代码评测 ：code generation(abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Winner Defender Team at Amazon Nova AI Challenge 2025

MLC-Agent: Cognitive Model based on Memory-Learning Collaboration in LLM Empowered Agent Simulation Environment

Ming Zhang, Yiling Xuan, Qun Ma, Yuwei Guo

专题命中 代码评测 ：repository(abstract)