A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper introduces M^3KG-RAG, a multimodal RAG framework that leverages a knowledge graph to deliver query-relevant and answer-useful context for multi-hop multimodal question answering.
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This paper investigates why smaller open-source long-context language models underperform proprietary ones in multi-hop reasoning with attributions.
Yasaman Zarinkia, Venkatesh Srinivasan, Alex Thomo
This paper identifies and addresses the reasoning bottleneck in Graph-RAG systems for multi-hop QA through structured prompting and context compression.