2 citations · 2 across the 5 of their papers we have counts for
9 papers
IQuest-Q1: Advancing Foundation Capabilities for Agentic CLI Systems
A. Jian Yang, An Huang, Bo Dan +173
We present IQuest-Q1, a sparse Mixture-of-Experts (MoE) agentic foundation model with 320B total parameters (15B activated). IQuest-Q1 is developed through a three-stage process: (…
ALLOT: Budgeted Hybrid-Memory Routing for Knowledge Updates in LLMs
Shanfeng Huang, Zhou Fang, Song Xiao +1
For large language models (LLMs), parametric adaptation is costly when retrieval already suffices. We introduce ALLOT, a hybrid-memory routing framework that separates learned writ…
Progressive-View On-Policy Distillation for Regional-to-Global Transfer in Multimodal LLMs
Shanfeng Huang, Zhou Fang, Song Xiao +1
Regional-to-global distillation uses crop-conditioned guidance to improve full-image understanding. The challenge is to effectively transfer the teacher's crop-based advantage to t…
PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression
Shanfeng Huang, Rongqian Zhang, Zhou Fang +2
Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses severe memory challenges for de…
GRACE: Generative Recommender Acceleration Engine for Real-Time Ads Retrieval
Zhou Fang, Yuhang Huang, Ang Zhang +12
Productionizing generative recommenders for high-volume, real-time ads retrieval creates two serving challenges: eligibility, ensuring that each generated ad is eligible for the re…
GR2 Technical Report
Yufei Li, Zaiwei Zhang, Mingfu Liang +67
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step dispropo…
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Gang Liao, Hongsen Qin, Ying Wang +36
Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture div…
UI-UG: A Unified MLLM for UI Understanding and Generation
Hao Yang, Weijie Qiu, Ru Zhang +8
Although Multimodal Large Language Models (MLLMs) have been widely applied across domains, they are still facing challenges in domain-specific tasks, such as User Interface (UI) un…
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing
Xinyu Zuo, Haijin Liang, Ning Jing +3
Fine-grained entity typing (FET) aims to deduce specific semantic types of the entity mentions in text. Modern methods for FET mainly focus on learning what a certain type looks li…