GraphDC applies divide-and-conquer multi-agent LLM reasoning to graph algorithms by decomposing graphs into subgraphs for local agents and integrating via a master agent, outperforming direct methods especially on large scales.
CoRRabs/2410.05130(2024)
4 Pith papers cite this work. Polarity classification is still indexing.
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A self-correcting multi-agent LLM pipeline parses floor plans into graphs and generates accessible routes, outperforming single LLM calls with success rates up to 92% on short paths in a real university building.
A survey of LLMs for graph computation introduces a role-based taxonomy of executors versus planners and concludes that current models suit simple small-scale tasks but remain unreliable for large-scale exact computation.
The paper synthesizes three synergies between LLMs and graphs—augmented retrieval/reasoning, bidirectional KG integration, and graph-enhanced agents—plus LLM uses in graph data management and ML.
citing papers explorer
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GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning
GraphDC applies divide-and-conquer multi-agent LLM reasoning to graph algorithms by decomposing graphs into subgraphs for local agents and integrating via a master agent, outperforming direct methods especially on large scales.
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LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People
A self-correcting multi-agent LLM pipeline parses floor plans into graphs and generates accessible routes, outperforming single LLM calls with success rates up to 92% on short paths in a real university building.
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Are Large Language Models Suitable for Graph Computation? Progress and Prospects
A survey of LLMs for graph computation introduces a role-based taxonomy of executors versus planners and concludes that current models suit simple small-scale tasks but remain unreliable for large-scale exact computation.
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LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems
The paper synthesizes three synergies between LLMs and graphs—augmented retrieval/reasoning, bidirectional KG integration, and graph-enhanced agents—plus LLM uses in graph data management and ML.