From the 1 of 6 linked papers with an AI index.
6 papers
VIG-RL: Learning to Search and Insert for Verified Image Grounding
Qinhan Yu, Jun Guang, Chong Chen +1
The paper introduces VIG-RL, a reinforcement‑learning based agent that dynamically decides when to retrieve, select, and insert authentic images into text responses, improving veri…
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
DataFlow Team, Bohan Zeng, Daili Hua +39
World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we…
M2IO-R1: An Efficient RL-Enhanced Reasoning Framework for Multimodal Retrieval Augmented Multimodal Generation
Zhiyou Xiao, Qinhan Yu, Binghui Li +3
Current research on Multimodal Retrieval-Augmented Generation (MRAG) enables diverse multimodal inputs but remains limited to single-modality outputs, restricting expressive capaci…
QAEncoder: Towards Aligned Representation Learning in Question Answering Systems
Zhengren Wang, Qinhan Yu, Shida Wei +6
Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders…
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation
Hao Liu, Zhengren Wang, Xi Chen +4
Retrieval-Augmented Generation (RAG) systems often struggle with imperfect retrieval, as traditional retrievers focus on lexical or semantic similarity rather than logical relevanc…
MRAMG-Bench: A Comprehensive Benchmark for Advancing Multimodal Retrieval-Augmented Multimodal Generation
Qinhan Yu, Zhiyou Xiao, Binghui Li +3
Recent advances in Retrieval-Augmented Generation (RAG) have significantly improved response accuracy and relevance by incorporating external knowledge into Large Language Models (…