AI RESEARCH
LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk
arXiv CS.AI
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ArXi:2511.19175v2 Announce Type: replace-cross A critical barrier to the trustworthiness of sixth-generation (6G) agentic autonomous networks is the uncertainty neglect bias; a cognitive tendency for large language model (LLM)-powered agents to make high-stakes decisions based on simple averages while ignoring the tail risk of extreme events. This paper proposes an unbiased, risk-aware framework for agentic negotiation, designed to ensure robust resource allocation in 6G network slicing.