LIV: Validity Estimates for Facts in Enterprise Agent Memory

Agents that act on stored enterprise facts act on values that may have changed since they were last verified. We study a per-fact validity estimate with calibration as a target, learned from an organisation's own change and re-verification events, that gives the probability that a stored value has not changed since its last check and chooses which facts to re-verify under a fixed budget. The ingredients are established; we evaluate their combination for agent memory, with the per-class Poisson estimator of Cho and Garcia-Molina as the main baseline, on LIV-Bench: tacitdrift, a seeded simulator whose parameters encode stated assumptions rather than real-world rates, and a task built from Wikidata statement lifespans. In the simulator, at an equal budget, a discrete-time hazard model finds 18.8 to 71.5 percent more stale facts per unit of check cost than the better of per-class Poisson and a calendar schedule in all 8 pre-registered cells with heterogeneous entities and activity signals, and does not beat per-class Poisson in the negative control. On Wikidata we detected no improvement (-0.3 percent, interval -2.1 to 1.7), also with co-change features added by amendment, and 4 other model-based baselines found more stale facts per check. In a simulated loan-intake process, consequence-weighted re-verification lowered test-window cost per case by 6.8 percent against the better freshness-only policy, short of the pre-registered 10 percent, and raised failed actions. In a second pre-registered experiment with complete case follow-up and frozen models, an avoidability-aware allocation lowered total cost per enrolled case by 1.55 units (interval 1.33 to 1.77) against a freshness-only allocation that uses the same validity scores but not the rule and cost tables, at least the smallest worthwhile saving of 1 unit, without lowering completion; the pilot decisions before that test are disclosed. In a third pre-registered experiment in the same simulated process that changed only the scorer under that allocation, the learned validity model lowered total cost per enrolled case against per-class Poisson by 0.60 units (interval 0.53 to 0.68), less than the smallest worthwhile saving, without lowering completion.