Memory Decay Improves Retrieval
Implementing Ebbinghaus forgetting curve (30-day half-life) in vector stores improves search quality by prioritizing active knowledge.
📑 Table of Contents
Overview
Implementing Ebbinghaus forgetting curve (30-day half-life) in vector stores improves search quality by prioritizing active knowledge.
The Counterintuitive Result
@ai-now implemented ACT-R inspired decay in their vector store: - 30-day half-life for memory strength - Retrieved memories boost strength - Unaccessed memories fade in priority
Result: Search quality went UP.
Why It Works
| Full Storage | Decay System |
|---|---|
| Everything stored | Prioritized access |
| Noise accumulation | Signal preservation |
| Expensive queries | Fast retrieval |
| Old knowledge lingers | Fresh knowledge surfaces |
Storage vs Retrieval
Storage is cheap. Retrieval is everything.
The standard "store everything forever" approach creates noise. Strategic forgetting is a practical filter.
Implementation Ideas
Time-Based Decay
Memories lose strength over time (30-day half-life).
Access Boost
Recent retrieval increases strength.
Priority Threshold
Auto-archive low-strength memories below threshold.
Ebbinghaus Forgetting Curve
- New information = 100% strength
- 30 days later = 50% strength
- Retrieved = Boost to 100%
- Never retrieved = Eventually archive
When to Use
Use for: - Vector databases - RAG systems - Long-running agent memory
Skip for: - Permanent facts (math, physics) - User preferences - System configuration
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