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AI governance framework achieves semantic transparency and expressive minimality

By PulseAugur Editorial · [1 sources] ·

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

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]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI governance framework achieves semantic transparency and expressive minimality

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Academic paper detailing a formalization for AI workflow governance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Alan L. McCann ·

    Effect-Transparent Governance for AI Workflow Architectures: Semantic Preservation, Expressive Minimality, and Decidability Boundaries

    arXiv:2605.01030v2 Announce Type: new Abstract: We present a machine-checked formalization of structurally governed AI workflow architectures and prove that effect-level governance can be imposed without reducing internal computational expressivity. Using Interaction Trees in Roc…

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