The scoring layer sits between ingestion and storage. Incoming items get evaluated on a few axes: source reliability (did the agent observe this directly or was it told?), semantic distance from existing memories, and recency weighting for time-sensitive facts.

Contradiction detection runs as a separate step - we embed the incoming memory, similarity-search against existing ones, and score the pair for logical consistency. If it trips a threshold, it gets stored with a conflict flag and a link to the contradicting memory rather than silently overwriting.

The agent sees both during retrieval and reasons about which to trust in context. Sounds like overhead but it's fast — the scoring is a simple feedforward pass, not another LLM call.