most citedLongCat-Flash Technical Report

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Dual Consensus: Escaping from Spurious Majority in Unsupervised RLVR via Two-Stage Vote Mechanism

Kaixuan Du, Meng Cao, Hang Zhang +3

Current label-free RLVR approaches for large language models (LLMs), such as TTRL and Self-reward, have demonstrated effectiveness in improving the performance of LLMs on complex r…

cs.AI2026

LongCat-Flash-Thinking-2601 Technical Report

Meituan LongCat Team, Anchun Gui, Bei Li +162

We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thi…

cs.CL2026

Efficient Context Scaling with LongCat ZigZag Attention

Chen Zhang, Yang Bai, Jiahuan Li +19

We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited c…

cs.AI2025

Autoformalizer with Tool Feedback

Qi Guo, Jianing Wang, Jianfei Zhang +8

Autoformalization addresses the scarcity of data for Automated Theorem Proving (ATP) by translating mathematical problems from natural language into formal statements. Efforts in r…

cs.CL20251 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…