AI RESEARCH
DECK: A Consistency x Confidence Taxonomy of LLM Hallucinations
arXiv CS.CL
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ArXi:2606.02289v1 Announce Type: new Existing hallucination taxonomies classify LLM errors by what is wrong with the output -- memorised misconceptions, reasoning failures, fluent fabrications. These taxonomies are useful for diagnosis but cannot answer a different question: which uncertainty scorer would have caught this error? We propose a complementary taxonomy that classifies errors by their detectability signature -- the signal a scorer family would read.