Type-safe decision frameworks improve 5G network control reliability
Type-Safe Decision Frameworks for Agentic 5G Control: A Theory-Driven Testbed Characterization of Where They Can Be Applied
Networking and Internet Architecture
Summary
Deciding how to control 5G networks can be tricky and errors happen if decisions are not clearly defined. This paper looks at frameworks where decisions must fit into specific categories, making mistakes less likely. The authors tested three different approaches on a 5G system, finding big trade-offs between speed, accuracy, and the complexity of the methods. Some frameworks made almost no wrong decisions but were slower or needed large models, while others were faster but much less accurate.
What this means in practice
- •For 5g network operators: Improve decision reliability in automated 5G network control by using type-safe frameworks to reduce format errors and improve decision conformity.
- •For cloud platform engineers: Deploy type-safe decision frameworks to balance between decision accuracy and system responsiveness in cloud-based 5G control systems.
Authors
Michail-Alexandros Kourtis, George Xilouris
Abstract
This paper presents a theory-driven characterization of type-safe decision frameworks for the agentic control of 5G networks, where every decision must be an element of a declared option set rather than free text. Three design points are evaluated on an Open5GS/UERANSIM testbed with a closed core-policy loop, namely a hosted typed model (Jev), an open fine-tunable typed encoder (Laya), and a zero-label retrofit of a general language model (AnyJev). The proposed theoretical framework turns timeliness, type conformance, certification cost and cardinality into checkable applicability predicates, supported by an optimal act/escalate/abstain gate, an escalation-feasibility floor, a co-location stability condition, per-type conformal risk control with a certification label floor, and a type-mismatch bound. Measuring every predicate yields an applicability map from framework to 5G decision class. Type safety removes format failures but not the question: the fine-tuned typed encoder returned its training answer for 98-99.5% of changed questions, and its calibrated gate then acted wrongly on up to 80% of them, whereas the question-reading frameworks acted wrongly on at most 0.143 (Jev) and 0.137 (AnyJev) of any changed question, but were either hosted and 11-29 times slower (Jev) or reliant on an 8B language model (AnyJev).