A reflection-driven framework with scenario, solver, simulation, and reflector agents uses simulation-in-the-loop to create self-correcting agentic AI for 6G RAN, reporting 17.1% throughput gains and other improvements.
Designing network algorithms via large language models,
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Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows
A reflection-driven framework with scenario, solver, simulation, and reflector agents uses simulation-in-the-loop to create self-correcting agentic AI for 6G RAN, reporting 17.1% throughput gains and other improvements.