Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management

arXiv:2509.26200v2 Announce Type: replace-cross Abstract: Agentic artificial intelligence is a candidate enabler of Level-4 autonomy in sixth-generation (6G) networks, but agents reasoning over a shared memory inherit its distortions. We study cross-domain radio access network (RAN)--edge orchestration in which a RAN agent minimizing energy and an edge agent minimizing latency negotiate, validate proposals against a digital twin (DT), and share a collective memory. Such a memory is a sampling device: what agents retrieve, not what they store, determines what they do. We define the Retrieval Bias Index, a divergence between the induced retrieval law and a representative reference, decomposed along temporal, confirmation and availability axes, and show that additive retrieval scoring is an exponential tilt of the undebiased law, yielding a closed form for the failure-amplification weight and a saturation floor set by the store's own composition; a further result bounds excess service level agreement (SLA) violation additively in retrieval bias and DT mismatch. On a tandem queue for which stochastic network calculus supplies a feasible region, an oracle and a Nash bargaining reference, the operator attains a $12.7\times$ bias reduction over a recency store and the smallest distance to the bargaining solution among six memory architectures.

Abstract:Agentic artificial intelligence is a candidate enabler of Level-4 autonomy in sixth-generation (6G) networks, but agents reasoning over a shared memory inherit its distortions. We study cross-domain radio access network (RAN)--edge orchestration in which a RAN agent minimizing energy and an edge agent minimizing latency negotiate, validate proposals against a digital twin (DT), and share a collective memory. Such a memory is a sampling device: what agents retrieve, not what they store, determines what they do. We define the Retrieval Bias Index, a divergence between the induced retrieval law and a representative reference, decomposed along temporal, confirmation and availability axes, and show that additive retrieval scoring is an exponential tilt of the undebiased law, yielding a closed form for the failure-amplification weight and a saturation floor set by the store's own composition; a further result bounds excess service level agreement (SLA) violation additively in retrieval bias and DT mismatch. On a tandem queue for which stochastic network calculus supplies a feasible region, an oracle and a Nash bargaining reference, the operator attains a $12.7\times$ bias reduction over a recency store and the smallest distance to the bargaining solution among six memory architectures.

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

arXiv:2509.26200 [cs.NI]

(or arXiv:2509.26200v2 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2509.26200 Focus to learn more arXiv-issued DOI via DataCite

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Toward an Unbiased Collective Memory for Efficient LLM-Based Agentic 6G Cross-Domain Management

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