9 citations · 9 across the 4 of their papers we have counts for
4 papers
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
Ting Bai, Yue Yu, Le Huang +2
The sparse Mixture-of-Experts (MoE) architecture of large language models (LLMs) confronts an inherent issue of load imbalance arising from the simplistic linear router strategy, w…
RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code
Jiachi Chen, Qingyuan Zhong, Yanlin Wang +6
The emergence of Large Language Models (LLMs) has significantly influenced various aspects of software development activities. Despite their benefits, LLMs also pose notable risks,…