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Several explanations for hallucination exist, but perhaps the main reason is that they are trained to be plausible. There is no element of truth-seeking in their construction. The training content
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of theoretical work asks whether such a system could serve as a runtime guardrail, deriving probabilistic bounds on the harm probability of a candidate agent action by reasoning over a Bayesian
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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing