6 papers
K2-V2: A 360-Open, Reasoning-Enhanced LLM
K2 Team, Zhengzhong Liu, Liping Tang +36
We introduce K2-V2, a 360-open LLM built from scratch as a superior base for reasoning adaptation, in addition to functions such as conversation and knowledge retrieval from genera…
SCALAR: Scientific Citation-based Live Assessment of Long-context Academic Reasoning
Renxi Wang, Honglin Mu, Liqun Ma +5
Long-context understanding has emerged as a critical capability for large language models (LLMs). However, evaluating this ability remains challenging. We present SCALAR, a benchma…
Bi-Mamba: Towards Accurate 1-Bit State Space Models
Shengkun Tang, Liqun Ma, Haonan Li +2
The typical Selective State-Space Model (SSM) used in Mamba addresses several limitations of Transformers, such as the quadratic computational complexity with respect to sequence l…
Concise Reasoning in the Lens of Lagrangian Optimization
Chengqian Gao, Haonan Li, Taylor W. Killian +6
Concise reasoning in large language models seeks to generate only essential intermediate steps needed to arrive at a final answer, thereby alleviating issues of overthinking. Most…
K2-Think: A Parameter-Efficient Reasoning System
Zhoujun Cheng, Richard Fan, Shibo Hao +28
K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1.…
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch
Zhengzhong Liu, Bowen Tan, Hongyi Wang +22
We detail the training of the LLM360 K2-65B model, scaling up our 360-degree OPEN SOURCE approach to the largest and most powerful models under project LLM360. While open-source LL…