Memories Expire at Different Rates: Calibrating Agent Memory

Agent memory can retrieve the right record, place it in time and revise a belief once a contradiction arrives. Yet none of these abilities tells whether a retrieved observation is still true before a contradiction appears. Recency heuristics discount age alone, but once memory types expire at different rates no function of age is calibrated for all types. We formalize the missing quantity as current validity, the probability that a stored proposition was correct when written and has not since been superseded, and build its estimator and decision rule together. Transition-Calibrated Memory (TCM) reads a memory’s transition context into a discrete-time invalidation hazard and integrates it over elapsed time into a probability. A cost-optimal rule then prices that probability into using or verifying the memory. We evaluate TCM on human re-query judgments, two intensive-care cohorts and an exact-event simulator. Conditioning on transition context is the robust gain: it cuts the worst group-calibration gap of age-only rules from 0.358 to 0.197 on TicToc, survives standard recalibration and largely disappears when the context is permuted. Beyond calibration, current validity also changes what an agent does. Routed through one cost-sensitive rule at a matched 20% verification budget, TCM earns 0.05 to 0.09 more harm-weighted utility than the strongest baseline on both clinical cohorts in every seed. By contrast, prompted and post-trained agents deciding alone verify out of proportion to risk, over-verifying eight probes in ten or acting on stale memories in three. Survival factorization is only one estimator of current validity, and its added value is regime-dependent, which separates the robust benefit of transition conditioning from the inductive bias used to estimate it.