A CVaR-aware agentic framework for 6G network slicing eliminates URLLC SLA violations by shifting LLM decisions from mean latency to tail-risk distributions predicted by digital twins.
Fairness in agentic ai: A unified framework for ethical and equitable multi-agent system,
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Randomized Weibull anchors and debiased collective memory with decay and inflection bonuses let agentic AI in 6G cut anchoring, temporal, and confirmation biases, doubling energy savings to 25% and reducing latency by 5x in simulations.
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LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk
A CVaR-aware agentic framework for 6G network slicing eliminates URLLC SLA violations by shifting LLM decisions from mean latency to tail-risk distributions predicted by digital twins.
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A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks
Randomized Weibull anchors and debiased collective memory with decay and inflection bonuses let agentic AI in 6G cut anchoring, temporal, and confirmation biases, doubling energy savings to 25% and reducing latency by 5x in simulations.