WebMEM™

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

Trust Feedback Record (TFR)

Trust Feedback Record (TFR) is a structured, machine-readable log that records how an AI retrieval system interacts with a specific semantic memory fragment — such as a Semantic Digest, glossary term, or data template. In the WebMEM Protocol, TFRs are the foundational unit of runtime trust telemetry, capturing trust-relevant behaviors like retrieval, citation, contradiction, or paraphrase for use in memory governance and reinforcement.

🧠 Full Definition

A TFR is generated when an AI agent consumes or interacts with a fragment-scoped content object. Each record includes:

  • fragment_id — ID of the semantic memory object (e.g., plan, term, dataset)
  • agent_id — identifier for the AI system (e.g., Gemini, Copilot, ChatGPT)
  • interaction_type — retrieval, citation, paraphrase, contradiction, etc.
  • confidence_score — optional numeric trust weight or probability
  • timestamp — when the interaction occurred
  • provenance_path — metadata linking back to the authoritative source digest
  • trust_delta — optional reinforcement or decay modifier (e.g., +1.0, -0.4)

📜 Role in the WebMEM Protocol

Within WebMEM, TFRs are part of the Memory Governance Layer. They:

  • Turn AI retrieval events into actionable trust signals
  • Enable measurement of fragment performance over time
  • Feed Retrieval Feedback Loops for content reinforcement or correction
  • Support comparative trust scoring across AI systems (e.g., Gemini vs. Copilot)

💡 Why It Matters

TFRs replace passive publishing with retrieval-aware reinforcement by:

  • Detecting memory decay and retrieval drop-off
  • Identifying paraphrase drift or contradiction patterns
  • Reinforcing frequently cited facts with higher trust weights
  • Providing verifiable evidence of AI ingestion and usage

⚙️ How It Works

TFRs are logged when:

  • A fragment is retrieved or cited from a Semantic Digest endpoint
  • A glossary-linked term is paraphrased in an AI answer
  • A fragment is contradicted or challenged by the AI

They are stored and exposed in formats such as:

  • JSON-LD — for API and application ingestion
  • TTL (Turtle) — for semantic graph integration
  • PROV-O — for trust audit trails
  • Markdown — for developer-facing inspection
  • HTML5 + data-* markup — for inline fragment feedback exposure

🗣️ In Speech

“Trust Feedback Records tell you if the AI actually trusted, retrieved, or misunderstood what you published — and give you the signal to fix or reinforce it.”

🔗 Related Terms