From the 2 of 22 linked papers with an AI index.
22 papers
Does More Retrieved Evidence Help Visual Retrieval-Augmented Generation with Diffusion Language Models?
Jiankun Wang, Yisen Gao, Ziwei Zhang +3
Visual retrieval-augmented generation (RAG) commonly expands the retrieved evidence set to improve answer-page coverage, implicitly assuming that all available evidence should be p…
Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite
Xiawei Yue, Boran Wang, Xiaoqing Zhang +2
Agents for long term reasoning require a memory that can be efficiently and effectively updated over time, as new facts and external feedback continue to arrive. Recently, graph me…
ToolLIFT: Lifting Tool-Specific Trajectories into Function-Level Graphs for Generalizable Tool Planning
Xiuhui You, Jiayi Luo, Zichao Shen +2
Historical tool-use trajectories provide valuable experience for large language model (LLM) agents to plan and coordinate tool usage. Existing approaches directly construct tool-le…
DualG-MRAG: Decoupling Macro-Reasoning and Micro-Matching for Multimodal Retrieval-Augmented Generation
Jiacheng Tao, Qingyun Sun, Haonan Yuan +2
The paper introduces DualG-MRAG, a framework that separates global reasoning and fine-grained evidence matching using macro and micro graphs to improve multimodal retrieval-augment…
Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment
Chunyu Hu, Tianyin Liao, Ge Lan +4
The paper introduces GTAlign, a simple framework that aligns graph structures to tabular representations, enabling a text-free Graph Foundation Model that uses community-guided con…
CRITIC-R1: Learning Structured Critics for Retrieval-Augmented Generation
Wenhan Xiao, Ziwei Zhang, Chuanyue Yu +4
Retrieval-augmented generation (RAG) improves knowledge-intensive question answering by incorporating external evidence. However, existing RAG methods still suffer from hallucinati…