tai/research/agent-memory-recency-tracking/mem0-introducing-memory-decay.md

# Introducing Memory Decay in Mem0

## Metadata

| Field | Value |
|---|---|
| Institute | Molodetz Institute — Research & Retrieval Division |
| Subject | agent-memory-recency-tracking |
| Document type | Source Dossier |
| Tier | 1 |
| Source title | Introducing Memory Decay in Mem0 |
| Source URL | https://mem0.ai/blog/introducing-memory-decay-in-mem0 |
| Source publisher / author | Mem0 (vendor blog) |
| Source type | blog post / vendor docs |
| Source date (published) | Unknown — not stated |
| Date researched | 2026-10-09 |
| Researched by | tai (agent), model: Claude Sonnet 5, on behalf of retoor |
| Research method | web search synthesis |
| Confidence | Medium |
| Status | Active |

## Summary

Mem0's "Memory Decay" feature is a search-time re-ranking multiplier based on recency since last access — not a deletion/forgetting mechanism. It is the closest thing in current commercial agent-memory products to what tai is being asked to build the tracking substrate for.

## Key findings

- Each memory receives a recency-based scaling factor between 0.3x and 1.5x: recently accessed memories get boosted up to 1.5x, idle ones drift down toward a 0.3x floor.
- Nothing is ever deleted by this mechanism — the 0.3x floor means an idle-but-genuinely-relevant memory can still surface for the right query, just lower in the ranking.
- Access to a memory resets/raises its decay score — i.e. "used" directly and immediately affects future ranking.
- Mem0's own A/B test is reported to have moved the correct memory to rank 1 in 3 of 4 test cases, and widened score separation from a ~0.0001 margin to a 0.15+ gap.

## Pros / strengths

- Exactly the "soft re-ranking, not hard eviction" pattern this subject's conclusion recommends tai consider for a *future* phase (explicitly not being built now) — recording this now means the next phase doesn't have to rediscover this precedent.
- The 0.3x–1.5x framing is simple enough to reason about and reimplement if/when tai decides to build a ranking-integration phase.

## Cons / limitations / caveats

- The "3 of 4 test cases" and "0.15+ gap" figures are vendor-reported, from a vendor's own blog post, on an unstated and likely small sample size — this is marketing evidence, not an independently reproduced benchmark, and should not be treated as a general guarantee.
- No exact decay formula (e.g. half-life, decay constant) is disclosed in the material available to this research pass — the mechanism's existence and rough behavior are known; its precise math is not.

## Relevance to tai

Directly relevant precedent for a possible future phase of tai's `last_used`/`use_count` work (ranking integration), explicitly out of scope for the current proposal, which is tracking-only. Cited in the subject's conclusion document as the main real-world analogue for "recency as a soft ranking signal, not a deletion trigger."

## Changelog

| Date | Change |
|---|---|
| 2026-10-09 | Initial dossier created. |