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20242026
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cs.CL2026

Kimi K3: Open Frontier Intelligence

Kimi Team, Tongtong Bai, Yifan Bai +398

We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…

cs.CL2026

Attention Residuals

Kimi Team, Guangyu Chen, Yu Zhang +34

Residual connections with PreNorm are standard in modern LLMs, yet they accumulate all layer outputs with fixed unit weights. This uniform aggregation causes uncontrolled hidden-st…

cs.CL2026

Kimi K2.5: Visual Agentic Intelligence

Kimi Team, Tongtong Bai, Yifan Bai +339

We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…

cs.CL2025

Kimi Linear: An Expressive, Efficient Attention Architecture

Kimi Team, Yu Zhang, Zongyu Lin +57

We introduce Kimi Linear, a hybrid linear attention architecture that, for the first time, outperforms full attention under fair comparisons across various scenarios -- including s…

cs.CL2025

Baichuan 2: Open Large-scale Language Models

Aiyuan Yang, Bin Xiao, Bingning Wang +52

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing…

cs.CL2025

Baichuan-M1: Pushing the Medical Capability of Large Language Models

Bingning Wang, Haizhou Zhao, Huozhi Zhou +39

The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, especially in vertical fields like…