WebMEM™

The Protocol for Structuring, Delivering, and Conditioning Trust-Scored AI Memory on the Open Web

Conditioning Half-Life

Conditioning Half-Life is the measurable period it takes for the retrieval, trust, or visibility impact of a Conditioning Strategy to diminish by 50% without further reinforcement. It serves as a predictive metric for how long a Memory Object or Trust Fragment can maintain its influence in AI retrieval systems before performance declines.

Unlike Memory Horizon, which is the total retention window before decay, the Conditioning Half-Life focuses on the rate of decay—allowing publishers to set proactive reinforcement schedules.

🧠 Full Definition

Conditioning Half-Life is determined by tracking:

By measuring the half-life, publishers can align Reflection Loop intervals with the actual decay curve rather than guessing reinforcement timing.

📌 Key Characteristics of Conditioning Half-Life

  • Provides a data-driven reinforcement schedule
  • Captures the decay rate of AI memory retention
  • Is specific to each fragment or entity
  • Can vary based on Trust Layer strength, surface authority, and competition

💡 Why It Matters

Without reinforcement, even highly trusted content will lose retrieval dominance over time. Understanding the Conditioning Half-Life allows for efficient allocation of resources—reinforcing just before performance dips, rather than after it has already declined.

🌐 WebMEM Perspective

In WebMEM, the Conditioning Half-Life is tracked in the Semantic Visibility Console as part of Resilience Planning. It complements the Memory Horizon metric, giving both the total retention window and the rate of decline for each fragment.

🗣️ Example Use

“The Conditioning Half-Life for our glossary’s eligibility definition is 60 days, so we schedule reinforcement campaigns every 55 days.”

🔗 Related Terms