Adaptive Memory Decay

Problem

An append-only long-term store treats a fact written once and never used again the same as a fact reinforced every session, so the store grows without bound and stale or marginal items dilute retrieval. Hard size caps evict by crude rules and continuous time alone forgets useful-but-rarely-touched facts; neither tracks how important an item actually is. What the store needs is a retention signal that strengthens with use and weakens with neglect, the way human memory consolidates what is rehearsed and lets the rest fade, so capacity is spent on what stays relevant.

Solution

On write, give each item a retention score and a decay function — typically exponential — whose rate is modulated by signals: semantic relevance to the agent's active goals, how often the item is accessed, and how recently. Each access reinforces the score; neglect lets it decay. When a score crosses a low threshold the item is demoted to colder storage, fused with similar items, or dropped. The result is a forgetting curve per item rather than a global cap or a fixed time-to-live. Production memory layers such as Mem0 and Zep apply decay of this kind; FadeMem formalises the biologically-inspired version. Note the open limitation: decay handles low-relevance items well but does not by itself fix staleness in items that stay high-relevance and keep being reinforced. Compose with dream-consolidation-cycle for idle-time fusion and with cluster-capped-insight-store where a hard ceiling is also needed.

When to use

• A long-term memory store grows across sessions and stale items degrade retrieval.
• Importance varies and is better inferred from use than declared at write time.
• Per-item retention scoring can be updated cheaply on access.