3 citations · 4 across the 6 of their papers we have counts for
6 papers
SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring
Yuling Shi, Jinghan Xu, Kelin Fu +12
As AI coding agents take on increasingly complex, long-horizon software engineering tasks, existing benchmarks are rapidly saturating and their evaluation quality has come under se…
Scaling Coding Agents via Atomic Skills
Yue Liu
Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to task-specific overfitting and limited generalization. To addres…
Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents
Zonghan Yang, Shengjie Wang, Kelin Fu +18
Large Language Models (LLMs) are increasingly applied to software engineering (SWE), with SWE-bench as a key benchmark. Solutions are split into SWE-Agent frameworks with multi-tur…
Identify Then Recommend: Towards Unsupervised Group Recommendation
Yue Liu, Shihao Zhu, Tianyuan Yang +2
Group Recommendation (GR), which aims to recommend items to groups of users, has become a promising and practical direction for recommendation systems. This paper points out two is…
End-to-end Learnable Clustering for Intent Learning in Recommendation
Yue Liu, Shihao Zhu, Jun Xia +6
Intent learning, which aims to learn users' intents for user understanding and item recommendation, has become a hot research spot in recent years. However, existing methods suffer…
At Which Training Stage Does Code Data Help LLMs Reasoning?
Yingwei Ma, Yue Liu, Yue Yu +4
Large Language Models (LLMs) have exhibited remarkable reasoning capabilities and become the foundation of language technologies. Inspired by the great success of code data in trai…