Not pile up forever.
Tenure treats memory as governed state. Repeated beliefs are reinforced, aliases are added, stale versions are superseded, and uncertain merges are left alone.
When a new belief matches an existing one, Tenure reinforces the existing belief instead of creating a second copy.
When a belief changes, older versions are superseded so the system can preserve what changed and why.
Tenure is conservative by default. If two beliefs might be different, they remain distinct until stronger evidence appears.
A new belief is not simply dumped into memory. Tenure checks for canonical and alias matches, fuzzy merge candidates, file context, confidence, and explicit update signals before deciding what to do.
Tenure looks for existing beliefs by canonical name, aliases, and scoped merge candidates.
File-specific beliefs stay separate when the same name means different things in different files.
Insert, reinforce, add aliases, supersede, flag conflict, or skip low-confidence input.
Changes are recorded with session, turn, model, confidence, and prior belief state.
Tenure separates weak signals from durable memory. Inferred beliefs need both repeated evidence and time before they can become active, which keeps one long session from accidentally promoting a fragile assumption.
Tenure merges beliefs when they express the same fact, preference, decision, entity, or expertise signal with enough confidence.
Tenure avoids aggressive compaction because false merges are worse than extra memory objects.
Tenure’s merge rule is intentionally boring: preserve evidence, promote slowly, and never compress uncertainty into false certainty.
Memory is only useful when the model receives the right belief at the right time. Merging is not just cleanup. It controls what future sessions are allowed to assume.
Tenure gives teams a memory layer that can accumulate evidence without losing control of provenance, confidence, scope, or lineage.