activity
20232026
most citedOptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud

14 citations · 14 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2026

NaviCache: Test-Time Self-Calibration Caching for Video Generation

Zheqi Lv, Zhibo Zhu, Jinke Wang +6

Video Diffusion Models (VDMs) is constrained by immense computational costs. While offline calibration-based acceleration suffers from calibration data dependency, prohibitive cali…

cs.CL2026

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,…

cs.CL2025

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…

cs.CV2025

Xihe: Scalable Zero-Shot Time Series Learner Via Hierarchical Interleaved Block Attention

Yinbo Sun, Yuchen Fang, Zhibo Zhu +7

The rapid advancement of time series foundation models (TSFMs) has been propelled by migrating architectures from language models. While existing TSFMs demonstrate impressive perfo…

cs.CV2025

FMCE-Net++: Feature Map Convergence Evaluation and Training

Zhibo Zhu, Renyu Huang, Lei He

Deep Neural Networks (DNNs) face interpretability challenges due to their opaque internal representations. While Feature Map Convergence Evaluation (FMCE) quantifies module-level c…

cs.CL2025

Robust Preference Optimization via Dynamic Target Margins

Jie Sun, Junkang Wu, Jiancan Wu +5

The alignment of Large Language Models (LLMs) is crucial for ensuring their safety and reliability in practical applications. Direct Preference Optimization (DPO) has emerged as an…