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Clean Scores, Buried Evidence, and Confident Wrong: A Receipt-Based Audit of Frontier Agentic QA

Sánchez, Luis M.
arXiv (OAI Expanded) · Papers
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artificial-intelligencecomputation-and-languagegeneral-financeh-3-3i-2-7information-retrieval
Frontier models score well on shallow document/chart reading tasks. In a controlled data-room audit, moving evidence into buried conditions reduced accuracy, increased forced declarations, increased tool calls, and increased cost per correct answer. Confidence and benchmark calibration did not fully
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