6G Radio Access Network

GENESIS: Agentic AI Framework for Autonomous 6G RAN Synthesis, Research, and Testing

GENESIS is an agentic AI framework designed to automate the full R&D lifecycle for 6G Radio Access Networks (RAN), addressing six structural bottlenecks that each consume months of manual engineering per iteration. The system converts high-level intents—such as specification clauses, telemetry anomalies, or research hypotheses—into solutions validated via over-the-air experiments. It is built on three composable primitives (agents, skills, hooks) and a persistent knowledge layer called SYNAPSE that accumulates artifacts across runs. The framework specifically targets known LLM failure modes in RAN contexts, including API hallucination and simulation-to-hardware transfer gaps.