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NOMA treats multi-agent LLM pipelines as a query optimization problem, jointly choosing topology, model and execution engine against latency, cost and accuracy instead of hand-wiring fixed agent graphs.

Specializing the tokenizer vocabulary of a fine-tuned small model cut token counts and inference cost by 77% in a production essay grader.