This research program studies deterministic, constraint-based generative architectures that transform structured fact substrates into verified informational artifacts through pre-inference control mechanisms.
Unlike prompt-constrained or post-generation filtering approaches, these architectures introduce mandatory intermediate representations, executable constraint specifications, and state-controlled validation gates that operate prior to and during generative execution.
The objective is not to regulate probabilistic model behavior directly, but to govern the semantic inputs and structural execution contracts that generative systems consume.
Research Focus
- Structured fact compilation into canonical master representations
- Executable constraint specifications derived from authoritative policy definitions
- Mandatory intermediate representations as non-bypassable staging artifacts
- State-controlled generative pipelines (advance, block, revert, terminate)
- Pre-inference validation gates that prevent unsupported output
- Generator-agnostic rendering architectures
- Audit-by-design through versioning, provenance binding, and integrity verification
This program examines how deterministic semantic substrates reduce agentic drift, prevent semantic blending, improve reproducibility, and support compliance obligations in regulated environments without requiring modification of model internals.
Published Studies
Working Papers
- Deterministic Semantic Substrates in Non-Device AI:
A Structural Governance Framework for Regulated Generative Systems