Over the past two years, Recursive Self-Improvement (RSI) in the AI field has primarily remained at the first-order self-improvement stage, where the improvement program itself has not evolved alongside model enhancements. Recently, CosmosMind, in collaboration with researchers from multiple universities, released MetaRSI-v1, introducing the world's first meta-recursive self-improvement architecture that unifies Model-RSI, Data-RSI, and Harness-RSI. This innovation applies the recursive self-improvement mechanism to RSI itself, propelling self-improvement into the 'squared era.' Experimental results show that, without the involvement of external teacher models, MetaRSI-v1 enabled a small model with 3 billion active parameters to achieve an average improvement of 10.9 points across four benchmark tests, while several cutting-edge flagship models saw an average improvement of 7.3 points. The architecture integrates the unified Loop Kernel paradigm, three operators—Data-RSI, Harness-RSI, and Model-RSI—horizontal orchestration and vertical optimization strategies, as well as a three-tier RSI nesting with four collaborative agents, and distills five laws. Additionally, the research team has open-sourced RSI-Harness and established the Genome open community to lower the barrier to entry for new fields and promote the formation of self-improvement loops across various domains.