most citedLongCat-Flash Technical Report

1 citations · 2 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2025★ 1 cited

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…

cs.AI2025

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…

cs.SE2025

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…

cs.AI2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CV2025

Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content

Zicheng Zhang, Tengchuan Kou, Shushi Wang +9

Evaluating text-to-vision content hinges on two crucial aspects: visual quality and alignment. While significant progress has been made in developing objective models to assess the…

cs.AI2025★ 1 cited

Understanding and Optimizing Agentic Workflows via Shapley value

Yingxuan Yang, Bo Huang, Siyuan Qi +14

Agentic workflows have become the dominant paradigm for building complex AI systems, orchestrating specialized components, such as planning, reasoning, action execution, and reflec…