5 papers
Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization
Chenghao Liu, Yu Zhang, Zhongtao Jiang +7
Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items. In many production settings this…
ResAdapt: Adaptive Resolution for Efficient Multimodal Reasoning
Huanxuan Liao, Zhongtao Jiang, Yupu Hao +6
Multimodal Large Language Models (MLLMs) achieve stronger visual understanding by scaling input fidelity, yet the resulting visual token growth makes jointly sustaining high spatia…
WideSeek: Advancing Wide Research via Multi-Agent Scaling
Ziyang Huang, Haolin Ren, Xiaowei Yuan +6
Search intelligence is evolving from Deep Research to Wide Research, a paradigm essential for retrieving and synthesizing comprehensive information under complex constraints in par…
Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment
Jingcheng Deng, Zhongtao Jiang, Liang Pang +5
A new trend uses LLMs as dense text encoders via contrastive learning. However, since LLM embeddings predict the probability distribution of the next token, they are inherently gen…
Shuttle Between the Instructions and the Parameters of Large Language Models
Wangtao Sun, Haotian Xu, Huanxuan Liao +5
The interaction with Large Language Models (LLMs) through instructions has been extensively investigated in the research community. While instructions have been widely used as the…