LLMs fail graph isomorphism tests, revealing lack of structural understanding

A new research paper published on arXiv suggests that large language models (LLMs) do not genuinely understand graph isomorphism, despite achieving high accuracy on related tasks. The study found that LLMs fail to identify isomorphic graphs when node labels are permuted, indicating they rely on superficial patterns rather than abstract structural reasoning. This lack of permutation invariance highlights a fundamental limitation in LLMs' topological understanding. AI

IMPACT Highlights a fundamental gap in LLM reasoning capabilities, suggesting current models may not be suitable for tasks requiring deep structural understanding.

RANK_REASON Research paper published on arXiv detailing limitations of LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• graph isomorphism
• graph theory
• Hugging Face
• large-language models
• Sukumar Kishanthan

• paper
• other

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