14 papers
Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Research Agents
Shuai Wang, Haodong Chen, Yu Yin +3
Existing deep-research agents use a Search--Visit workflow that retrieves whole webpages without considering the structure they expose through titles, headings, sections, and metad…
DiffRetriever: Parallel Representative Tokens for Retrieval with Diffusion Language Models
Shuai Wang, Yu Yin, Shengyao Zhuang +2
This paper shows how diffusion language models (DLMs) can be used as effective and efficient retrievers. Existing DLM-based retrievers (e.g., DiffEmbed) follow BERT-style encoding,…
Where Relevance Emerges: A Layer-Wise Study of Internal Attention for Zero-Shot Re-Ranking
Haodong Chen, Shengyao Zhuang, Zheng Yao +2
Zero-shot document re-ranking with Large Language Models (LLMs) has evolved from Pointwise methods to Listwise and Setwise approaches that optimize computational efficiency. Despit…
An Investigation of Prompt Variations for Zero-shot LLM-based Rankers
Shuoqi Sun, Shengyao Zhuang, Shuai Wang +1
We provide a systematic understanding of the impact of specific components and wordings used in prompts on the effectiveness of rankers based on zero-shot Large Language Models (LL…
MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed
Jiaqi Samantha Zhan, Crystina Zhang, Shengyao Zhuang +2
Effective video retrieval remains challenging due to the complexity of integrating visual, auditory, and textual modalities. In this paper, we explore unified retrieval methods usi…
Starbucks-v2: Improved Training for 2D Matryoshka Embeddings
Shengyao Zhuang, Shuai Wang, Fabio Zheng +2
2D Matryoshka training enables a single embedding model to generate sub-network representations across different layers and embedding dimensions, offering adaptability to diverse c…