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March 19, 20260 citationsOpen Access

Calibration Inversion and Data-Freshness Govern AI Reliability in Indeterminate Domains: Evidence from 2,730 Data Points across 142 Cross-Protocol Evaluation Sessions

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KPKuldeep Kumar PanditVPVatsala PanditAPAayan Pandit

Key Points

  • The research investigates the reliability of AI models in indeterminate domains, focusing on calibration inversion and data freshness failures.
  • Analyzed 2,730 data points from 142 evaluation sessions across six AI models and three protocols.
  • Assessed performance in financial markets, meteorology, sports, and cryptocurrency.
  • Computed aggregate scores and robustness using cross-protocol evaluation metrics.
  • Gemini achieved a point-estimate accuracy of 5.3% mean error but a confidence interval calibration of only 29.7%.
  • DeepSeek's S&P 500 predictions exhibited 12–13% stale error due to information access issues.
  • Spearman rank correlation of ρ = 0.619 indicated moderate ranking consistency across protocols.

Abstract

VERSION HISTORYv2 (16 March 2026): Corrected typographic error in Table 2 (page 4), Gemini S −60 pp below the 90% target)—a dissociation absent from all prior domains. A second finding is data-freshness failure: DeepSeek S V1: 96.7%, V3: 100%, V4-CI: 84.2%). Four indeterminate-domain error types are identified; Type IV (calibration inversion) demands a calibration-verification layer in GAAS.

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Cite This Study

Pandit et al. (2026) studied this question.

synapsesocial.com/papers/69bb92ae496e729e629801f4https://doi.org/10.5281/zenodo.19048635
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