6 papers · 1 filter
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
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…
Arrows of Math Reasoning Data Synthesis for Large Language Models: Diversity, Complexity and Correctness
Sirui Chen, Changxin Tian, Binbin Hu +4
Enhancing the mathematical reasoning of large language models (LLMs) demands high-quality training data, yet conventional methods face critical challenges in scalability, cost, and…
Unlocking General Long Chain-of-Thought Reasoning Capabilities of Large Language Models via Representation Engineering
Xinyu Tang, Xiaolei Wang, Zhihao Lv +5
Recent advancements in long chain-of-thoughts(long CoTs) have significantly improved the reasoning capabilities of large language models(LLMs). Existing work finds that the capabil…
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking
Yichi Zhang, Zhuo Chen, Lingbing Guo +8
Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud h…
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
Junjie Wang, Mingyang Chen, Binbin Hu +10
Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enha…