AI Governance Should Prioritize Control and Knowledge Boundaries Over Limiting Intelligence

Computer Science > Artificial Intelligence

[Submitted on 6 Nov 2026]

Title:AI Governance Should Prioritize Control and Knowledge Boundaries Over Limiting Intelligence

View PDF HTML (experimental)Abstract:The rapid proliferation of autonomously acting AI agents makes the governance of agentic systems urgent. We propose the competence circuit, summarized as IKC + KC + C, that decomposes an agent's real-world impact into three pathways: intelligence-mediated discovery, procedural know-how, and direct control. Using the canonical fast-takeoff debate as a stress test, we formalize the conditions for intelligence-mediated fast takeoff as a conjunction jointly sufficient for fast catastrophic supremacy, showing that most rely on rapid knowledge acquisition or control attainment rather than intelligence alone. We operationalize epistemic intelligence as hypothesis-selection efficiency and validate this framework via stochastic simulation on NK fitness landscapes across six scenarios spanning the conjunctive chain. An analytic bound supported by the simulations shows that intelligence-driven speedup is capped by the ratio of competing intelligence levels regardless of task difficulty. The simulations also expose a fast-takeoff dilemma: a large intelligence-driven speedup multiplier materializes only when the hypothesis space is noisy, but the absolute timescale remains constrained by the time required for capability-critical tests, so a large multiplier need not imply rapid progress. Simulations also indicate that institutional chokepoints disproportionately slow down superintelligent agents when mandatory authorization delays are large relative to the negligible time these agents spend on unproductive search. We argue that the dominant safety levers are restricting control and enforcing knowledge boundaries rather than limiting intelligence alone, and this applies to agentic systems at any capability level. Purely digital domains weaken these bottlenecks, strengthening the case for architectures that intentionally "put physics in the loop."

Additional Features

References & Citations

Loading...

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)

Connected Papers (What is Connected Papers?)

Litmaps (What is Litmaps?)

scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub (What is DagsHub?)

Gotit.pub (What is GotitPub?)

Hugging Face (What is Huggingface?)

ScienceCast (What is ScienceCast?)

Demos

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)

CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.