GraphARC is a scalable benchmark for few-shot graph transformation learning that exposes a comprehension-execution gap in language models on abstract reasoning tasks.
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EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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.
citing papers explorer
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GraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
GraphARC is a scalable benchmark for few-shot graph transformation learning that exposes a comprehension-execution gap in language models on abstract reasoning tasks.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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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.