WebMEM™

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

Part 6: Trust Feedback Records and the Memory Governance Layer

Tracking AI Retrieval Behavior and Enforcing Semantic Trust at Runtime

While Semantic Digests provide the memory format, and Vertical Retrieval Interfaces orchestrate structured access, neither guarantees that AI systems will remember, prefer, or cite your content over time.

That’s the role of Trust Feedback Records.

A Trust Feedback Record is a machine-resolvable feedback signal that records and reinforces how an AI system interacts with a memory object. It closes the loop between exposure, retrieval, and reinforcement, allowing publishers to condition persistent recall across models and agentic systems.

More importantly, Trust Feedback Records—along with the Digest Authority Resolver—form the backbone of the Memory Governance Layer: a runtime system within the WebMEM™ Protocol for validating memory fragments before ingestion and logging model behavior after retrieval.

6.1 What Is a Trust Feedback Record?

A Trust Feedback Record (TFR) is a structured feedback artifact that logs how a particular fragment was used, cited, paraphrased, ignored, or contradicted by an AI agent or system.

Each TFR contains:

  • fragment_id — The specific memory object interacted with
  • agent_id — The name or fingerprint of the AI system involved (e.g., Gemini, ChatGPT, Claude)
  • interaction_type — Retrieval, citation, paraphrase, contradiction, etc.
  • confidence_score — Optional trust rating (computed via internal model heuristics or publisher reinforcement logic)
  • timestamp — When the event occurred
  • provenance_path — Traceable metadata showing which version or digest instance was engaged
  • trust_delta — Change in reinforcement weight based on model usage behavior (optional)

These records can be stored locally, in a public ledger, or as retrievable fragments themselves—enabling recursive trust modeling and long-term memory conditioning.

6.2 The Digest Authority Resolver

To prevent hallucination and unauthorized memory injection, Trust Feedback Records are reinforced by a runtime control system: the Digest Authority Resolver (patent pending).

This resolver performs three critical actions before an AI system or agent ingests a fragment:

  1. Term Validation
    Confirms that all defined terms map to an accepted glossary or external ontology (e.g., WikiData, DefinedTermSet)
  2. Provenance Scoring
    Evaluates the trustworthiness of each field using:

    • prov:wasDerivedFrom
    • prov:generatedAtTime
    • prov:wasAttributedTo
    • data-confidence, data-source, and other custom metadata
  3. Memory Output Assembly
    Returns a scored memory object for ingestion (or rejection), including glossary alignment status and a computed trust index.

Fragments that fail validation may be:

  • Rejected
  • Deferred for fallback resolution
  • Logged for conditional future ingestion

This positions the Digest Authority Resolver as a gatekeeper between public content and private AI memory—governing what gets retained, reused, or retrained upon.

6.3 Conditioning AI Memory Behavior

Trust Feedback Records are not just logs—they are conditioning signals.

When observed over time, they allow publishers to:

  • Detect memory decay (e.g., a term no longer retrieved or paraphrased)
  • Identify citation drift (e.g., an entity is cited but misattributed)
  • Reinforce high-value facts or definitions through targeted redistribution
  • Monitor model preference for specific glossaries, styles, or fragments
  • Trigger retrieval feedback loops that influence future fragment visibility

This transforms publishing from a one-way act into an interactive memory training protocol.

6.4 TFR Formats and Exposure

Trust Feedback Records can be exposed using the same multi-format surface as Semantic Digests:

  • JSON-LD — For machine parsing and storage
  • Turtle (TTL) — For semantic graph applications
  • Markdown — For transparency and developer-facing feedback logs
  • PROV-O — For auditability and external trust scoring
  • HTML5 Fragments — For surfacing live inline feedback and citations

In each case, the TFR operates as a retrievable verification object—creating traceable, citeable proof that content was resolved, retrieved, and trusted by a machine.

6.5 Patent Protection and Licensing Availability

The methods described in this section are protected under provisional patent application #63/848,915, filed July 22, 2025:

System and Method for Structured AI Memory Governance Using Digest Resolution, Glossary Verification, and Trust-Based Ingestion Control

This invention defines the runtime ingestion system, trust validation pipeline, and behavior reinforcement feedback loop that underpin the Semantic Digest framework and WebMEM™ Protocol.

Entities building retrieval infrastructures, vertical AI agents, or trust-based LLM systems may request licensing details at:
📩 contact@webmem.com