Canonical Corpus–Based Governance for Enforcing Novelty and Deterministic Reasoning in AI-Assisted Invention

This paper presents a governance-oriented framework for AI-assisted invention that addresses systemic failure modes in large language model (LLM)–driven research and intellectual property workflows. As probabilistic AI systems are increasingly used to generate, refine, and evaluate inventive ideas, unresolved issues such as hallucinated novelty, self-collision with prior work, nondeterministic reasoning, and silent portfolio contamination pose significant risks.

The work introduces the concept of canonical corpus–based governance, in which novelty and authority are enforced externally through a user-defined, hash-verified corpus of prior patents, publications, and research artifacts. Rather than relying on model memory, prompt heuristics, or internal safeguards, the proposed approach externalizes authority from the AI system and constrains reasoning through mandatory interaction protocols, explicit corpus acknowledgment, and hallucination-resistant citation rules.

The framework reframes AI systems as probabilistic reasoning components embedded within deterministic control architectures. By binding AI outputs to an explicit and immutable corpus of prior work, the approach enables reproducible novelty assessments, auditable reasoning trails, and deterministic enforcement of invention boundaries independent of model choice.

This paper establishes architectural design principles for safe, auditable, and reproducible AI-assisted invention systems and is intended for researchers, system architects, and practitioners working at the intersection of AI, intellectual property, and long-horizon R&D governance.