AIBearisharXiv – CS AI · Jun 107/10
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Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners
Researchers demonstrate that Large Language Models used for graph reasoning lack robustness to common graph representation variations like node reindexing and edge reordering, producing inconsistent outputs. Fine-tuning worsens sensitivity to structural and formatting changes while failing to improve generalization on unseen tasks, raising concerns about LLM-based graph reasoners' reliability in production environments.