1 citations · 1 across the 3 of their papers we have counts for
8 papers
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
Introducing LongCat-Flash-Thinking: A Technical Report
Meituan LongCat Team, Anchun Gui, Bei Li +122
We present LongCat-Flash-Thinking, an efficient 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model. Its advanced capabilities are cultivated through a metic…
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks
Lingyue Fu, Hao Guan, Bolun Zhang +10
The evaluation of Large Language Models (LLMs) for software engineering has shifted towards complex, repository-level tasks. However, existing benchmarks predominantly rely on coar…
OIBench: Benchmarking Strong Reasoning Models with Olympiad in Informatics
Yaoming Zhu, Junxin Wang, Yiyang Li +8
As models become increasingly sophisticated, conventional algorithm benchmarks are increasingly saturated, underscoring the need for more challenging benchmarks to guide future imp…
Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement
Peng Ding, Jun Kuang, Zongyu Wang +4
Large Language Models (LLMs) have shown impressive capabilities across various tasks but remain vulnerable to meticulously crafted jailbreak attacks. In this paper, we identify a c…
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs' Instruction Following Capability
Jiaming wang, Yunke Zhao, Peng Ding +8
The capability to precisely adhere to instructions is a cornerstone for Large Language Models (LLMs) to function as dependable agents in real-world scenarios. However, confronted w…