Probabilistic Agentic Memory: Learning Distributional Evidence for Skill Reuse

Repeated use of predictive skills gives an agent outcome feedback, but a numerical error alone does not specify how that experience should influence future reuse. We introduce Probabilistic Agentic Memory (PAM), which learns how outcomes become evidence for persistent skill support. PAM builds context-conditioned outcome distributions around black-box forecasts and uses their realized log scores in sequential generalized Bayesian updates. A memory-informed readout combines these same predictive distributions, linking the interpretation of past outcomes to future predictions. Training differentiates through chronological replay without gradients through the skills; at deployment, learned parameters are frozen and released outcomes update memory. Across three epidemic-forecasting tasks, PAM improves mean RMSE and weighted interval score over three agent selectors using the same skill library. It also improves both metrics over same-bank dynamic model averaging on Bench-A and B-FLU. On Bench-A, ablations support nonlinear, context-conditioned uncertainty modeling, and matched-component comparisons show that learned writes improve subsequent prediction at comparable coverage. Prospective diagnostics on both CDC tasks further show that accumulated support favors components with better subsequent predictions. Together, these results support learning the evidential meaning of experience as a mechanism for predictive-skill reuse.