6 citations · 17 across the 10 of their papers we have counts for
10 papers
AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling
Jiajun Liang, Yucheng Liao, Yukang Cao +12
Language remains an outlier in generative modeling: while images, video, and audio are increasingly modeled in continuous latent spaces, text generation still relies predominantly…
Simply Stabilizing the Loop via Fully Looped Transformer
Rao Fu, Zixuan Yang, Jiankun Zhang +4
Scaling model performance typically requires increasing model size. Looped Transformer offers a compelling alternative by iteratively reusing the same Transformer blocks, trading a…
Paper2Data: Large-Scale LLM Extraction and Metadata Structuring of Global Urban Data from Scientific Literature
Runwen You, Tong Xia, Jingzhi Wang +5
Urban data support a wide range of applications across multiple disciplines. However, at the global scale, there is no unified platform for urban data discovery. As a result, resea…
Fix Before Search: Benchmarking Agentic Query Visual Pre-processing in Multimodal Retrieval-augmented Generation
Jiankun Zhang, Shenglai Zeng, Kai Guo +4
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a key paradigm for grounding MLLMs with external knowledge. While query pre-processing (e.g., rewriting) is standard…
AI Urban Scientist: Multi-Agent Collaborative Automation for Urban Research
Tong Xia, Jiankun Zhang, Ruiwen You +8
Urban research aims to understand how cities operate and evolve as complex adaptive systems. With the rapid growth of urban data and analytical methodologies, the central challenge…
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation
Jiankun Zhang, Shenglai Zeng, Jie Ren +4
Multimodal Retrieval-Augmented Generation (MRAG) systems enhance LMMs by integrating external multimodal databases, but introduce unexplored privacy vulnerabilities. While text-bas…
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
Shenglai Zeng, Jiankun Zhang, Bingheng Li +8
Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively…
Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data
Shenglai Zeng, Jiankun Zhang, Pengfei He +7
Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. However, when the retrie…
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)
Shenglai Zeng, Jiankun Zhang, Pengfei He +8
Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model with proprietary and private data, where data privacy is a pivotal concern. Whereas extens…
Copyright Protection in Generative AI: A Technical Perspective
Jie Ren, Han Xu, Pengfei He +10
Generative AI has witnessed rapid advancement in recent years, expanding their capabilities to create synthesized content such as text, images, audio, and code. The high fidelity a…