Marilyn vos Savant Probabilistic Clarity Prover Connector for AI agents.

It is an Connector that forces your AI agent to perform a rigorous check of its own logic. It ensures the agent doesn't give you a quick answer based on 'gut feeling' but instead accounts for math, sample sizes, and potential biases.

It prevents your agent from seeing patterns where none exist. It forces the AI to check if the data is statistically significant and whether the sample was large enough to be meaningful.

Yes. It is specifically designed to catch base rate neglect, which is common in medical testing. It forces the AI to consider how common a condition is before interpreting a test result.

It significantly reduces hallucinations by making the AI commit to a verification process. It must prove it checked for things like independence and framing before it gives you a conclusion.

Exactly. It is ideal for risk managers who need to ensure that AI-generated reports aren't ignoring hidden correlations or biased samples in financial and operational data.

Standard AI reasoning is a suggestion; this Connector makes logic an obligation. The agent cannot skip the probability check, making the reasoning much more reliable for high-stakes tasks.

No. It forces the agent to show its probabilistic reasoning. state the intuitive answer, compute the actual probability, account for base rates, scrutinize the sample. The engine validates consistency, not computation. If the agent claims it checked intuition but uses phrases like 'it seems like,' the engine rejects.

Critical Thinking validates general reasoning. assumptions, perspectives, evidence. Marilyn targets PROBABILISTIC reasoning specifically: base rates, sample bias, framing traps, independence assumptions. Critical Thinking asks 'did you consider alternatives?' Marilyn asks 'did you compute the actual probability, or did you just go with your gut?'

Three doors. One prize. You pick door 1. The host opens door 3. empty. Switch or stay? Intuition says 50/50. Math says switch wins 2/3 of the time. 10,000 people. including PhDs. got this wrong. The prover catches the same failure pattern: trusting intuition when the math says otherwise.