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This work aims to demonstrate limitations in systems that verify conclusions based on learned norms and structural choices.

July 28, 2026Open Access

Scope-Blind Verification, and What a Learned System Cannot Represent

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This work aims to demonstrate limitations in systems that verify conclusions based on learned norms and structural choices.
Analyzed verification mechanisms across different domains such as healthcare, law, and marketing.
Demonstrated scope-blindness through an executable example showing conflicting conclusions from identical facts.
Introduced the Cryptotype LLM Probe dataset to assess the impact of learned norms on cultural representation.
Identical facts produced opposing conclusions under different domain carvings, indicating scope-blindness.
The assessment revealed that systems trained on observed conduct tend to flatten diverse cultural differences, favoring a single norm.
Proposed remedy included adding metadata fields to improve verification specificity without inference engines.

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Discusses verification limits in systems applying learned norms, suggesting significant implications.

László Fazakas (2026) studied this question.

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