activity
20242026
collaborators

8 papers

cs.IR2026

CompRank: Efficient LLM Reranking via Token-Level Compression and Decoding-Free Scoring

Xuan Lu, Haohang Huang, Yingqi Fan +5

Large language model (LLM) rerankers have become an important component of modern retrieval and retrieval-augmented generation pipelines, but their high computational cost limits t…

cs.CV2026

Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini

Madhuri Shanbhogue, Zhe Li, Shanfeng Zhang +86

We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage t…

cs.AI2026

ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence

Rui Meng, Bhavana Dalvi Mishra, Jiefeng Chen +10

Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evalua…

cs.IR2026

MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models

Haohang Huang, Xuan Lu, Mingyi Su +9

Multimodal embedding models aim to map heterogeneous inputs, such as text, images, videos, and audio, into a shared semantic space. However, existing methods and benchmarks remain…

cs.CV2026

Beyond Global Similarity: Towards Fine-Grained, Multi-Condition Multimodal Retrieval

Xuan Lu, Kangle Li, Haohang Huang +3

Recent advances in multimodal large language models (MLLMs) have substantially expanded the capabilities of multimodal retrieval, enabling systems to align and retrieve information…

cs.IR2025

Tools are under-documented: Simple Document Expansion Boosts Tool Retrieval

Xuan Lu, Haohang Huang, Rui Meng +3

Large Language Models (LLMs) have recently demonstrated strong capabilities in tool use, yet progress in tool retrieval remains hindered by incomplete and heterogeneous tool docume…

cs.IR2025

Rethinking Reasoning in Document Ranking: Why Chain-of-Thought Falls Short

Xuan Lu, Haohang Huang, Rui Meng +3

Document reranking is a key component in information retrieval (IR), aimed at refining initial retrieval results to improve ranking quality for downstream tasks. Recent studies--mo…

cs.IR2024

Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models

Jianqun Zhou, Yuanlei Zheng, Wei Chen +5

Instruction-following capabilities in LLMs have progressed significantly, enabling more complex user interactions through detailed prompts. However, retrieval systems have not matc…