1 citations · 1 across the 9 of their papers we have counts for
9 papers
Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation
Ruofan Hu, Shengyang Xu, Minjie Hong +5
Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, existing multimodal RAG models ar…
DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark
Ruofan Hu, Menghui Zhu, Jieming Zhu +8
Multimodal documents contain diverse elements, such as tables, figures, and layouts, which can complicate retrieval tasks. While current approaches typically combine dense visual e…
DynSess: Dynamic Session-Level Evaluation and Optimization Framework for Role-Playing Agents
Rongsheng Zhang, Jiji Tang, Junnan Ren +6
Role-playing with large language models is fundamentally a session-level task, requiring agents to sustain character identity and interaction quality across extended multi-turn con…
From Facts to Insights: A Persona-Driven Dual Memory Framework and Dataset for Role-Playing Agents
Rongsheng Zhang, Ruofan Hu, Weijie Chen +7
While role-playing agents excel in short-term interactions, long-term conversations overwhelm context windows, motivating external memory frameworks. Current systems typically rely…
SpatialReward: Verifiable Spatial Reward Modeling for Fine-Grained Spatial Consistency in Text-to-Image Generation
Sashuai Zhou, Qiang Zhou, Junpeng Ma +9
Recent advances in text-to-image (T2I) generation via reinforcement learning (RL) have benefited from reward models that assess semantic alignment and visual quality. However, most…
Generative Reasoning Recommendation via LLMs
Minjie Hong, Zetong Zhou, Zirun Guo +5
Despite their remarkable reasoning capabilities across diverse domains, large language models (LLMs) face fundamental challenges in natively functioning as generative reasoning rec…
Vela: Scalable Embeddings with Voice Large Language Models for Multimodal Retrieval
Ruofan Hu, Yan Xia, Minjie Hong +5
Multimodal large language models (MLLMs) have seen substantial progress in recent years. However, their ability to represent multimodal information in the acoustic domain remains u…
OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios
Xize Cheng, Dongjie Fu, Xiaoda Yang +11
With the rapid development of large language models, researchers have created increasingly advanced spoken dialogue systems that can naturally converse with humans. However, these…
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt
Yongqi Wang, Ruofan Hu, Rongjie Huang +6
Recent singing-voice-synthesis (SVS) methods have achieved remarkable audio quality and naturalness, yet they lack the capability to control the style attributes of the synthesized…