2 citations · 3 across the 8 of their papers we have counts for
8 papers
DLLM-Searcher: Adapting Diffusion Large Language Model for Search Agents
Jiahao Zhao, Shaoxuan Xu, Zhongxiang Sun +6
Recently, Diffusion Large Language Models (dLLMs) have demonstrated unique efficiency advantages, enabled by their inherently parallel decoding mechanism and flexible generation pa…
Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation
Ling Team, Ang Li, Ben Liu +138
We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of bi…
Every Attention Matters: An Efficient Hybrid Architecture for Long-Context Reasoning
Ling Team, Bin Han, Caizhi Tang +25
In this technical report, we present the Ring-linear model series, specifically including Ring-mini-linear-2.0 and Ring-flash-linear-2.0. Ring-mini-linear-2.0 comprises 16B paramet…
CoDefend: Cross-Modal Collaborative Defense via Diffusion Purification and Prompt Optimization
Fengling Zhu, Boshi Liu, Jingyu Hua +1
Multimodal Large Language Models (MLLMs) have achieved remarkable success in tasks such as image captioning, visual question answering, and cross-modal reasoning by integrating vis…
GLaVE-Cap: Global-Local Aligned Video Captioning with Vision Expert Integration
Wan Xu, Feng Zhu, Yihan Zeng +4
Video detailed captioning aims to generate comprehensive video descriptions to facilitate video understanding. Recently, most efforts in the video detailed captioning community hav…
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs
Ling Team, Bin Hu, Cai Chen +43
We present Ring-lite, a Mixture-of-Experts (MoE)-based large language model optimized via reinforcement learning (RL) to achieve efficient and robust reasoning capabilities. Built…
Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
Ling Team, Binwei Zeng, Chao Huang +71
In this technical report, we tackle the challenges of training large-scale Mixture of Experts (MoE) models, focusing on overcoming cost inefficiency and resource limitations preval…
Mindalogue: LLM-Powered Nonlinear Interaction for Effective Learning and Task Exploration
Rui Zhang, Ziyao Zhang, Fengliang Zhu +2
Current generative AI models like ChatGPT, Claude, and Gemini are widely used for knowledge dissemination, task decomposition, and creative thinking. However, their linear interact…