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Marginal Log-Likelihood Increments under Dirichlet-Smoothed Markov Estimation

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

For a Dirichlet-smoothed transition model, the effect of adding one workflow trace to the training archive is an exact change in reference-weighted log likelihood. We derive that change and show that it is a weighted reduction of Kullback--Leibler divergence between the reference conditionals and the model. From this form we obtain an upper bound on the gain available to any acquisition, which expresses a millinat difference as a share of what is attainable, an exact covariance identity for the effect of the reference weighting, and a sign criterion for the interaction between two candidates,

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