1 citations · 2 across the 7 of their papers we have counts for
7 papers
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
CIAR: Interval-based Collaborative Decoding for Image Generation Acceleration
Keming Ye, Zhou Zhao, Fan Wu +1
Auto-regressive (AR) models have recently made notable progress in image generation, achieving performance comparable to diffusion-based approaches. However, their computational in…
EC-Diff: Fast and High-Quality Edge-Cloud Collaborative Inference for Diffusion Models
Jiajian Xie, Shengyu Zhang, Zhou Zhao +2
Diffusion Models have shown remarkable proficiency in image and video synthesis. As model size and latency increase limit user experience, hybrid edge-cloud collaborative framework…
Device-Cloud Collaborative Correction for On-Device Recommendation
Tianyu Zhan, Shengyu Zhang, Zheqi Lv +4
With the rapid development of recommendation models and device computing power, device-based recommendation has become an important research area due to its better real-time perfor…
ThinkRec: Thinking-based recommendation via LLM
Qihang Yu, Kairui Fu, Zheqi Lv +6
Recent advances in large language models (LLMs) have enabled more semantic-aware recommendations through natural language generation. Existing LLM for recommendation (LLM4Rec) meth…
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…
Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation
Yuhang Liu, Xueyu Hu, Shengyu Zhang +3
Retrieval-Augmented Generation (RAG) has proven to be an effective method for mitigating hallucination issues inherent in large language models (LLMs). Previous approaches typicall…