When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger

Researchers present N2M-RSI, a formal model showing that AI systems feeding their own outputs back as inputs can experience unbounded complexity growth once crossing an information-integration threshold. The framework applies to both individual AI agents and swarms of communicating agents, with implementation details withheld for safety reasons.