42 citations · 48 across the 10 of their papers we have counts for
12 papers
InfiGFusion: Graph-on-Logits Distillation via Efficient Gromov-Wasserstein for Model Fusion
Yuanyi Wang, Zhaoyi Yan, Yiming Zhang +4
Recent advances in large language models (LLMs) have intensified efforts to fuse heterogeneous open-source models into a unified system that inherits their complementary strengths.…
InfiFPO: Implicit Model Fusion via Preference Optimization in Large Language Models
Yanggan Gu, Yuanyi Wang, Zhaoyi Yan +4
Model fusion combines multiple Large Language Models (LLMs) with different strengths into a more powerful, integrated model through lightweight training methods. Existing works on…
EcoAgent: An Efficient Device-Cloud Collaborative Multi-Agent Framework for Mobile Automation
Biao Yi, Xavier Hu, Yurun Chen +3
To tackle increasingly complex tasks, recent research on mobile agents has shifted towards multi-agent collaboration. Current mobile multi-agent systems are primarily deployed in t…
InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners
Yuhang Liu, Pengxiang Li, Congkai Xie +5
Multimodal Large Language Models (MLLMs) have powered Graphical User Interface (GUI) Agents, showing promise in automating tasks on computing devices. Recent works have begun explo…
InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via LLM Fusion
Zhaoyi Yan, Yiming Zhang, Baoyi He +7
We introduce InfiFusion, an efficient training pipeline designed to integrate multiple domain-specialized Large Language Models (LLMs) into a single pivot model, effectively harnes…
InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection
Yuhang Liu, Pengxiang Li, Zishu Wei +7
Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers an…
Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning
Ziyu Zhao, Leilei Gan, Guoyin Wang +5
Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models (LLMs). Its modular and plug-and-play nature allows the integration of various domain-specific…
LoraRetriever: Input-Aware LoRA Retrieval and Composition for Mixed Tasks in the Wild
Ziyu Zhao, Leilei Gan, Guoyin Wang +4
Low-Rank Adaptation (LoRA) provides an effective yet efficient solution for fine-tuning large language models (LLM). The modular and plug-and-play nature of LoRA enables the integr…
Leveraging Print Debugging to Improve Code Generation in Large Language Models
Xueyu Hu, Kun Kuang, Jiankai Sun +2
Large language models (LLMs) have made significant progress in code generation tasks, but their performance in tackling programming problems with complex data structures and algori…
Learning to Reweight for Graph Neural Network
Zhengyu Chen, Teng Xiao, Kun Kuang +6
Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing GNNs' generalization ability will degrade when there exist distribution shifts between testin…
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization
Zheqi Lv, Wenqiao Zhang, Shengyu Zhang +8
Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. It aims to improve the generalization ability of…
Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling
Zheqi Lv, Feng Wang, Shengyu Zhang +3
In an era of information explosion, recommendation systems play an important role in people's daily life by facilitating content exploration. It is known that user activeness, i.e.…