mlsanityData to 7 Oct 2026

When Corrective Hints Hurt: Prompt Design in Reasoner-Guided Repair of LLM Overcaution on Entailed Negations under OWL~2~DL

arXiv 2604.23398 ↗First posted 25 Apr 2026cs.AIcited by 1

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Abstract (arXiv)

We report a reproducible error pattern in GPT-5.4 on OWL~2~DL compliance queries: the model frequently answers ``unknown'' when the reasoner-entailed answer is ``no'' under FunctionalProperty closure or class disjointness. Using 180 reasoner-audited queries from a procedural expansion of the observed pattern plus 18 hand-authored held-out queries in two unrelated domains (insurance and clinical), we compare four interaction modes under matched query budget: single-shot, three rounds of generic ``you-are-wrong'' retry, three rounds of reasoner-verdict repair with an open-world-assumption (OWA) hint, and the same repair without the hint. Direct faithfulness is 43.9,% (Wilson 95,% CI [36.8,51.2]); generic retry reaches 81.7,% ([75.4,86.6]); the verdict-with-hint variant is worse at 67.2,% ([60.1,73.7]); the verdict-only variant reaches 97.8,% ([94.4,99.1]). All pairwise comparisons remain significant under McNemar's exact test with Bonferroni correction (α = 0.01; all p < 10-5). The same fingerprint accounts for 4/4 errors on the held-out queries. Our interpretation is bounded: prompt framing can matter more than corrective content, and reasoner-guided wrappers should be ablated explicitly.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 25 Apr 2026.

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7 of 17 references.

References · 7

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2025Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Datacited by 11
  3. 2025Structured Memory Mechanisms for Stable Context Representation in Large Language Modelscited by 9
  4. 2024Benchmarking Large Language Models for Image Classification of Marine Mammalscited by 4
  5. 2026Large Language Model for OWL Proofscited by 3
  6. 2026Coverage-Aware Web Crawling for Domain-Specific Supplier Discovery via a Web--Knowledge--Web Pipelinecited by 3
  7. 2026Detecting High-Potential SMEs with Heterogeneous Graph Neural Networkscited by 1

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