PRGA gates wireless intent execution with progressive evidence stages, cutting time-to-first-safe-action by 23-27% and control-plane bytes by 52-54% on 3GPP benchmarks while rejecting all stale inputs and staying within a 0.5pp unsafe-action margin.
Multi-agentic ai for conflict- aware rapp policy orchestration in open ran
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6G networks need LLM-based agents in a layered semantic control plane to achieve autonomous intelligence, with empirical results showing that heterogeneous deployment across device-edge-core is required due to inherent tradeoffs in reasoning, latency, and efficiency.
citing papers explorer
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Executor-Side Progressive Risk-Gated Actuation for Agentic AI in Wireless Supervisory Control
PRGA gates wireless intent execution with progressive evidence stages, cutting time-to-first-safe-action by 23-27% and control-plane bytes by 52-54% on 3GPP benchmarks while rejecting all stale inputs and staying within a 0.5pp unsafe-action margin.
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6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence
6G networks need LLM-based agents in a layered semantic control plane to achieve autonomous intelligence, with empirical results showing that heterogeneous deployment across device-edge-core is required due to inherent tradeoffs in reasoning, latency, and efficiency.