Anchored Confabulation: Partial Evidence Non-Monotonically Amplifies Confident Hallucination in LLMs

• URL: http://arxiv.org/abs/2604.25931v1
• Date: Thu, 02 Apr 2026 02:13:45 GMT
• Title: Anchored Confabulation: Partial Evidence Non-Monotonically Amplifies Confident Hallucination in LLMs
• Abstract summary: We identify a previously unknown property of large language models.<n>It increases the confident-wrong rate before full evidence eliminates it.<n>We commit it as Parametric Hallucination Confidence (PHC) and establish it across six lines of evidence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We identify a previously unknown calibration property of large language models: providing one confirmed intermediate fact toward a multi-step reasoning chain increases the model's confident-wrong-answer rate before full evidence eliminates it. We call this anchored confabulation: a partial anchor commits the model to confident parametric completion of remaining reasoning steps. We formalize it as Parametric Hallucination Confidence (PHC) and establish it across six lines of evidence including a causal injection experiment (PHC 0.613 to 0.656 to 0.595 to 0.536, N=160) and capability scaling across five model families (Spearman rho=0.900, p=0.037). The Anchoring Threshold Law k*(n)=floor(n/3) predicts PHC amplification by hop depth with four confirmed predictions. Applied to RAG routing, a LearnedRouter exploiting PHC closes 81.1% of the oracle performance gap (macro F1=0.426, p<1e-6) on 1,800 queries across four benchmarks with no model fine-tuning and 50x fewer labels than prior RL-based work. An epistemic humility prompt reduces the PHC spike by -0.118; explicit self-rating (PHC=0.684, p<0.001) outperforms lexical confidence as a routing signal.

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