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perturbation-testing

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Portable, content-addressed reliability evidence for LLM systems. Capture how a model behaves under perturbation; preserve, verify, and diff the evidence across model changes.

  • Updated May 25, 2026
  • Python

yuragi — LLM Confidence Fragility Analyzer. Perturbation-driven hallucination detection with workshop-grade real benchmarks (TruthfulQA n=412 ensemble AUC 0.73, TriviaQA n=200 confidence-inversion AUC 0.75).

  • Updated May 25, 2026
  • Python

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