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This paper critically evaluates the reliability of semantic decision engines within network control loops, revealing a significant gap between theoretical claims and empirical evidence regarding deadline adherence and action feasibility. By analyzing 139 research families, the study demonstrates that many systems fail under queued execution times due to non-deterministic computation steps, undermining their suitability for real-time infrastructure. The work establishes essential design rules and reporting standards necessary before such AI-driven decision engines can be safely admitted into critical network architectures.
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SoK: Semantic Decision Engines in Network Control Loops

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2610.06425v1 Announce Type: cross Abstract: A semantic decision engine such as Jev can return a valid answer and still miss a network deadline, select an infeasible action or leave the service unverified. We systematize 139 paper families by decision interface, execution path and check ownership. Fifty families claim that their engine fits a control loop or time budget, but only four support the claim with matched measurement. Across all 139, four report deadline attainment. The gap concentrates where the decision has no deterministic computation step. Those 72 families make 22 of the claims, none supported, and name a coverage owner in only two. Bounded tests under one event model show that each gap can reverse an admission verdict. A decision that meets a 10 s budget for every isolated request meets it for none once decisions queue ahead of replayed execution times. The same engine passes one coverage check and fails another. We derive a minimum reporting record, design rules and a research agenda for admitting decision engines to control loops.

Abstract:A semantic decision engine such as Jev can return a valid answer and still miss a network deadline, select an infeasible action or leave the service unverified. We systematize 139 paper families by decision interface, execution path and check ownership. Fifty families claim that their engine fits a control loop or time budget, but only four support the claim with matched measurement. Across all 139, four report deadline attainment. The gap concentrates where the decision has no deterministic computation step. Those 72 families make 22 of the claims, none supported, and name a coverage owner in only two. Bounded tests under one event model show that each gap can reverse an admission verdict. A decision that meets a 10 s budget for every isolated request meets it for none once decisions queue ahead of replayed execution times. The same engine passes one coverage check and fails another. We derive a minimum reporting record, design rules and a research agenda for admitting decision engines to control loops.

Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)

arXiv:2610.06425 [cs.NI]

(or arXiv:2610.06425v1 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2610.06425 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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