1 citations · 1 across the 10 of their papers we have counts for
10 papers
m3BERT: A Modern, Multi-lingual, Matryoshka Bidirectional Encoder
Yaoxiang Wang, Simiao Zuo, Qingguo Hu +4
Embedding models are pivotal in industrial information retrieval systems like search and advertising. However, existing pretrained models often exhibit fixed architectures and embe…
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment
Zhanyu Liu, Qingguo Hu, Ante Wang +5
Reinforcement Learning with Verifiable Reward (RLVR) has proven effective for training reasoning-oriented large language models, but existing methods largely assume high-resource s…
VeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation
Yaoxiang Wang, Qi Shi, ShangZhan Li +6
LLMs have recently demonstrated strong capabilities in automatic RTL code generation, achieving high syntactic and functional correctness. However, most methods focus on functional…
Can LLMs Track Their Output Length? A Dynamic Feedback Mechanism for Precise Length Regulation
Meiman Xiao, Ante Wang, Qingguo Hu +5
Precisely controlling the length of generated text is a common requirement in real-world applications. However, despite significant advancements in following human instructions, La…
Sigma-MoE-Tiny Technical Report
Qingguo Hu, Zhenghao Lin, Ziyue Yang +12
Mixture-of-Experts (MoE) has emerged as a promising paradigm for foundation models due to its efficient and powerful scalability. In this work, we present Sigma-MoE-Tiny, an MoE la…
BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models
Yujie Lin, Jiayao Ma, Qingguo Hu +4
Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation. Current fairness interventions often adopt a difference-…
Mixture of Neuron Experts
Runxi Cheng, Yuchen Guan, Yucheng Ding +6
In this work, we first explore whether the parameters activated by the MoE layer remain highly sparse at inference. We perform a sparsification study on several representative MoE…
Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization
Yaoxiang Wang, Qingguo Hu, Yucheng Ding +6
Mixture-of-Experts (MoE) has emerged as a promising paradigm for efficiently scaling large language models without a proportional increase in computational cost. However, the stand…
Boosting Visual Knowledge-Intensive Training for LVLMs Through Causality-Driven Visual Object Completion
Qingguo Hu, Ante Wang, Jia Song +3
Large Vision-Language Models (LVLMs) have experienced significant advancements in recent years. However, their performance still falls short in tasks requiring deep visual percepti…
Towards Better Document-level Relation Extraction via Iterative Inference
Liang Zhang, Jinsong Su, Yidong Chen +4
Document-level relation extraction (RE) aims to extract the relations between entities from the input document that usually containing many difficultly-predicted entity pairs whose…