6 citations · 7 across the 24 of their papers we have counts for
11 papers · 1 filter
Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching
Rong Shan, Tianyi Xu, Congmin Zheng +9
Image retrieval has traditionally been formulated as a point-wise matching problem, where each candidate image is scored in isolation. However, this atomic paradigm fails to captur…
Discrete Preference Learning for Personalized Multimodal Generation
Yuting Zhang, Ying Sun, Dazhong Shen +6
The emergence of generative models enables the creation of texts and images tailored to users' preferences. Existing personalized generative models have two critical limitations: l…
Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
Chenyu Zhou, Huacan Chai, Wenteng Chen +18
Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the…
OThink-SRR1: Search, Refine and Reasoning with Reinforced Learning for Large Language Models
Haijian Liang, Zenghao Niu, Junjie Wu +3
Retrieval-Augmented Generation (RAG) expands the knowledge of Large Language Models (LLMs), yet current static retrieval methods struggle with complex, multi-hop problems. While re…
Sharpness-Aware Minimization for Generalized Embedding Learning in Federated Recommendation
Fengyuan Yu, Xiaohua Feng, Yuyuan Li +3
Federated recommender systems enable collaborative model training while keeping user interaction data local and sharing only essential model parameters, thereby mitigating privacy…
Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots
Mingzhe Li, Mengyin Liu, Zekai Wu +9
Humanoid robots have achieved significant progress in motion generation and control, exhibiting movements that appear increasingly natural and human-like. Inspired by the Turing Te…
TSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings
Yebo Wu, Feng Liu, Ziwei Xie +4
Despite the exceptional reasoning capabilities of Multimodal Large Language Models (MLLMs), their adaptation into universal embedding models is significantly impeded by task confli…
PhotoBench: Beyond Visual Matching Towards Personalized Intent-Driven Photo Retrieval
Tianyi Xu, Rong Shan, Junjie Wu +11
Personal photo albums are not merely collections of static images but living, ecological archives defined by temporal continuity, social entanglement, and rich metadata, which make…
DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories
Chenlong Deng, Mengjie Deng, Junjie Wu +10
Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This paradigm overlooks the rich dep…
OSCAR: Optimization-Steered Agentic Planning for Composed Image Retrieval
Teng Wang, Rong Shan, Jianghao Lin +8
Composed image retrieval (CIR) requires complex reasoning over heterogeneous visual and textual constraints. Existing approaches largely fall into two paradigms: unified embedding…
SpecTran: Spectral-Aware Transformer-based Adapter for LLM-Enhanced Sequential Recommendation
Yu Cui, Feng Liu, Zhaoxiang Wang +4
Traditional sequential recommendation (SR) models learn low-dimensional item ID embeddings from user-item interactions, often overlooking textual information such as item titles or…