2 citations · 5 across the 10 of their papers we have counts for
18 papers
ATGen: Adversarial Reinforcement Learning for Test Case Generation
Qingyao Li, Xinyi Dai, Weiwen Liu +5
Large Language Models (LLMs) excel at code generation, yet their outputs often contain subtle bugs, for which effective test cases are a critical bottleneck. Existing test generati…
Humanity's Last Code Exam: Can Advanced LLMs Conquer Human's Hardest Code Competition?
Xiangyang Li, Xiaopeng Li, Kuicai Dong +7
Code generation is a core capability of large language models (LLMs), yet mainstream benchmarks (e.g., APPs and LiveCodeBench) contain questions with medium-level difficulty and po…
Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs
Hanting Chen, Jiarui Qin, Jialong Guo +15
Large Language Models (LLMs) deliver state-of-the-art capabilities across numerous tasks, but their immense size and inference costs pose significant computational challenges for p…
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
Yunjia Xi, Jianghao Lin, Menghui Zhu +10
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding responses with retrieved information. As an emerging paradigm, Agentic RAG further enhances…
ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution
Xu Huang, Weiwen Liu, Xingshan Zeng +8
The tool-using capability of large language models (LLMs) enables them to access up-to-date external information and handle complex tasks. Current approaches to enhancing this capa…
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
Yehui Tang, Yichun Yin, Yaoyuan Wang +71
Sparse large language models (LLMs) with Mixture of Experts (MoE) and close to a trillion parameters are dominating the realm of most capable language models. However, the massive…