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papersTODAY 04:00 UTC

GraMRAG combines graph memory and reinforcement learning for multi-agent RAG

A new arXiv paper introduces GraMRAG, a framework that coordinates multi-agent, multi-step reasoning using a graph-based memory structure trained with reinforcement learning. The authors argue that current multi-agent retrieval-augmented generation systems are limited in reasoning depth and memory organisation, and position the graph memory approach as a way to address those gaps. The work focuses on complex multimodal reasoning tasks.

GraMRAG graph memory multi-agent RAG multimodal reasoning reinforcement-learning retrieval-augmented generation

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIGraMRAG: Orchestrating Multi-Agent Multi-Step Reasoning via Graph Memory with Reinforcement Learning ↗TODAY 04:00 UTC

arXiv cs.CLGraMRAG: Orchestrating Multi-Agent Multi-Step Reasoning via Graph Memory with Reinforcement Learning ↗TODAY 04:00 UTC