Study ID: TPI-DGA-2026-01
Submitted To: U.S. Department of Health and Human Services
Docket: HHS-ONC-2026-0001
Comment ID: HHS-ONC-2026-0001-0010
Publication Date: January 2026
Status: Federal RFI Comment
Official Record: View on Regulations.gov
Abstract
This submission responds to the HHS Health Sector Artificial Intelligence Request for Information (RFI) by identifying a structural governance gap in non-device AI systems operating in regulated benefit environments: the absence of a deterministic semantic memory layer capable of anchoring generative outputs to authoritative policy meaning.
The comment introduces the concept of a deterministic semantic substrate as a model-agnostic, vendor-neutral governance layer designed to reduce agentic drift, improve auditability, and strengthen compliance integrity without constraining model innovation.
This submission serves as the baseline policy articulation for the Deterministic, Constraint-Based Generative Architectures research program and establishes the real-world governance need that subsequent studies will examine empirically.
Problem Statement: Agentic Drift
Agentic drift refers to the tendency of AI systems to diverge from authoritative policy definitions, benefit rules, and regulatory intent when operating without a persistent, verifiable semantic substrate.
In regulated healthcare and federal benefit programs, this manifests as:
- Inconsistent coverage explanations
- Hallucinated or outdated benefit interpretations
- Divergence between regulatory documents and AI-generated communications
- Limited reproducibility of automated outputs
The submission argues that data transport standards and post-deployment monitoring alone are insufficient to resolve this failure mode.
Proposed Governance Layer
The deterministic semantic substrate described in the submission functions as a structured, machine-readable semantic control layer embedded within authoritative content surfaces, designed to bind AI-generated outputs to versioned, policy-scoped definitions prior to inference.
Key architectural characteristics include:
- Explicit semantic structure rather than inferred meaning
- Stable identifiers and versioned definitions
- Provenance metadata tied to authoritative sources
- Integrity verification through cryptographic checksums
- Pre-inference retrieval of policy-bound fragments
- Generator-agnostic compatibility
This approach shifts trust from probabilistic model behavior to content-level governance, enabling audit-by-design without requiring inspection of model internals.
Significance for Regulated AI
The submission positions deterministic semantic substrates as a structural complement to existing interoperability frameworks, introducing a semantic governance layer that operates independently of model internals while enabling algorithmic transparency, reproducibility, and reduced interpretive variance.
The proposal does not advocate model-level regulation. It instead identifies a missing semantic infrastructure layer necessary for responsible AI deployment in high-trust domains.
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PDF · 16 Pages · 196Kb
Citation
Trust Publishing Institute (2026). Comment on HHS Health Sector AI Request for Information: Addressing Agentic Drift in Non-Device AI Through Deterministic Semantic Memory. TPI-DGA-2026-01.
Version History
- v1.0 — January 2026 — Submitted to HHS under Docket HHS-ONC-2026-0001