Application to AI context and memory

A structured memory layer, not a claim about model internals#

VDMT can be applied to the external context orchestration around an AI system. A task induces retrieval requests; retrieved evidence reveals additional dependencies; claims are stored with provenance and scope; task-specific representations are assembled; and authorized human corrections become persistent input for later tasks.

This is a proposed application of the framework. It does not establish that a language model internally implements VDMT, or that a registry can substitute for learned reasoning.

Separate evidence from generated interpretation#

A retrieved statement, a verified observation, a model inference, and a human correction should have distinct types. A fluent generated summary must not be promoted to authoritative source merely because it has been stored and retrieved again.

A context key may include user or tenant, task, source revision, time validity, and permitted use. Retrieval must respect those boundaries. Similar text from a different project is not automatically applicable knowledge.

Bounded iterative retrieval#

An answer can reveal a missing premise and trigger another retrieval. Use a budget and a stopping criterion based on the task's evidence obligations, not an indefinite “think again” loop. A failed search is not proof that a fact does not exist.

Approximate retrieval changes the semantics: the candidate set can vary with embedding models, ranking, index state, and nondeterministic services. Include those inputs in reproducibility records or explicitly adopt a probabilistic contract. The finite deterministic closure proof does not automatically cover a stochastic retriever.

Derived comprehension blocks#

A task-specific block can combine several attributed facts into a reusable interpretation. Its dependency record should identify the premises, transformation, uncertainty, and scope. When a premise changes, invalidate or review the block rather than treating a remembered conclusion as timeless knowledge.

This gives an operational interpretation to the repeated question “what do I know about this?” The answer includes what is known, what is inferred, why it is applicable, and what remains unresolved.

Human feedback#

Corrections should enter through an authorized reconciliation step. Preserve who changed what, the affected scope, and whether the correction replaces a source assertion or merely expresses a preference. A malicious instruction embedded in a retrieved document remains document content, not authority to change the system's rules.

The editorial-loop analogy is useful, but AI memory needs additional privacy, consent, retention, and trust controls. Code-region markers alone are not a sufficient memory-governance mechanism.

Evaluation#

Compare answer quality, unsupported-claim rate, stale-memory rate, retrieval cost, context size, and correction persistence against fixed-context and ordinary retrieval baselines. Use held-out tasks and blinded evaluation where judgment is required. Report failures, not only successful examples.

A gain would support a bounded engineering hypothesis about context orchestration. It would not establish biological similarity, consciousness, or universal cognitive optimality. See cognition and hypotheses.