The Memory Worth (MW) metric uses a two-counter signal to track how often specific memories correlate with successful or failed outcomes. This replaces static importance scores with dynamic, outcome-based feedback. Researchers prove MW converges to conditional success probability, giving AI agents a principled way to deprecate stale or misleading data.