AI RESEARCH
Counting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation
arXiv CS.AI
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ArXi:2511.22565v2 Announce Type: replace Neural methods for Complex Query Answering (CQA) over knowledge graphs (KGs) are widely believed to learn patterns that generalize beyond explicit graph structure, allowing them to infer answers that are unreachable through symbolic query processing. In this work, we critically examine this assumption through a systematic analysis comparing neural CQA models with an alternative