BeliefLens is hosting a practical AI Quant Risk Bootcamp on 21 October at the NYC STAC Summit covering prompt stability, calibration, stochastic evidence graphs, and open-weight models.
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Prompt engineering is not model risk management. Using frontier models is no substitute for managing and measuring their uncertainty. On 21 October at the NYC STAC Summi…
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BeliefLens
Measure belief. Govern uncertainty.
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About BeliefLens
BeliefLens turns unpredictable AI outputs into calibrated, auditable decision states—enabling organizations to measure uncertainty, detect instability and govern AI workflows quantitatively at runtime.
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Research & Knowledge
BeliefLens conducted a controlled 30-case benchmark demonstrating that the Semantic Evidence Graph (SSEG) detected and localized every injected fault without falsely rejecting healthy runs.
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BeliefLens developed Verity, an algorithm that constructs semantic evidence graphs to connect claims to supporting sources, measure prompt and output uncertainty, test stability, and show where evidence stops explaining answers.
Research & Knowledge
BeliefLens is working on constructing a semantic evidence graph from token probabilities and controlled prompt variations to examine model interpretation, uncertainty, and response stability.
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