This paper describes an LLM-driven graph neural architecture search toolkit whose performance claims are weakened by best-of-three reporting, unavailable code, and a false 'first work' statement.
Beyond low-frequency infor- mation in graph convolutional networks
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LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search
This paper describes an LLM-driven graph neural architecture search toolkit whose performance claims are weakened by best-of-three reporting, unavailable code, and a false 'first work' statement.