12 citations · 16 across the 15 of their papers we have counts for
15 papers
FinFIRST: Benchmarking Search Agents for Financial Information Retrieval, Sourcing and Traceability
Wenqing Wang, Haitao Xiang, Xinyi Zhao +8
Financial search is a highly demanding task for LLM agents, requiring not only a correct final answer but also temporally valid information retrieval, authoritative source selectio…
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,…
Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards
Yu Huang, Zihua Zhao, Zhaoxin Huan +9
The open-ended generation in LLMs usually requires multi-dimensional rubrics to adequately assess quality and guide the improvement of reinforcement learning. However, a critical d…
ReportLogic: Evaluating Logical Quality in Deep Research Reports
Jujia Zhao, Zhaoxin Huan, Zihan Wang +4
Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports that support understanding and action. I…
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…
Short-Path Prompting in LLMs: Analyzing Reasoning Instability and Solutions for Robust Performance
Zuoli Tang, Junjie Ou, Kaiqin Hu +6
Recent years have witnessed significant progress in large language models' (LLMs) reasoning, which is largely due to the chain-of-thought (CoT) approaches, allowing models to gener…
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…
BOSE: A Systematic Evaluation Method Optimized for Base Models
Hongzhi Luan, Changxin Tian, Zhaoxin Huan +4
This paper poses two critical issues in evaluating base models (without post-training): (1) Unstable evaluation during training: in the early stages of pre-training, the models lac…
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
Youshao Xiao, Lin Ju, Zhenglei Zhou +8
Many distributed training techniques like Parameter Server and AllReduce have been proposed to take advantage of the increasingly large data and rich features. However, stragglers…
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors
Binzong Geng, Zhaoxin Huan, Xiaolu Zhang +5
With the rise of large language models (LLMs), recent works have leveraged LLMs to improve the performance of click-through rate (CTR) prediction. However, we argue that a critical…
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems
Youshao Xiao, Shangchun Zhao, Zhenglei Zhou +5
Recently, a new paradigm, meta learning, has been widely applied to Deep Learning Recommendation Models (DLRM) and significantly improves statistical performance, especially in col…
Rethinking Memory and Communication Cost for Efficient Large Language Model Training
Chan Wu, Hanxiao Zhang, Lin Ju +8
Recently, various distributed strategies for large language model training have been proposed. However, these methods provided limited solutions for the trade-off between memory co…
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems
Zuoli Tang, Zhaoxin Huan, Zihao Li +6
Sequential recommendation systems aim to predict users' next likely interaction based on their history. However, these systems face data sparsity and cold-start problems. Utilizing…
AntMC: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li +10
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily s…
Data-Free Adversarial Perturbations for Practical Black-Box Attack
ZhaoXin Huan, Yulong Wang, Xiaolu Zhang +3
Neural networks are vulnerable to adversarial examples, which are malicious inputs crafted to fool pre-trained models. Adversarial examples often exhibit black-box attacking transf…