Researchers have developed a formalization for governing AI workflow architectures, ensuring that effect-level governance can be implemented without sacrificing internal computational expressivity. This system, built using Interaction Trees in Rocq, mediates all effectful directives, including memory access, external calls, and LLM queries. The work establishes properties such as governed Turing completeness, semantic transparency, and a decidability boundary, demonstrating that governance and expressivity are orthogonal. AI
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IMPACT Provides a theoretical framework for ensuring AI systems can be governed without compromising their computational capabilities.
RANK_REASON Academic paper detailing a formalization for AI workflow governance. [lever_c_demoted from research: ic=1 ai=1.0]