We present Noise-to-Meaning Recursive Self-Improvement (N2M-RSI), a minimal formal model showing that once an AI agent feeds its own outputs back as inputs and crosses an explicit information-integration threshold, its internal complexity will grow without bound under our assumptions. The framework unifies earlier ideas on self-prompting large language models, G\"odelian self-reference, and AutoML, yet remains implementation-agnostic. The model furthermore scales naturally to interacting swarms of agents, hinting at super-linear effects once communication among instances is permitted. For safety reasons, we omit system-specific implementation details and release only a brief, model-agnostic toy prototype in Appendix C.
@article{arxiv.2505.02888,
title = {When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger},
author = {Rintaro Ando},
journal= {arXiv preprint arXiv:2505.02888},
year = {2026}
}
Comments
Withdrawn due to a critical error discovered in the mathematical derivation and proof of Theorem 2 (Unbounded Growth) and related Lemma 2 (Compression gain lower bound). This flaw invalidates the paper's main conclusion that N2M-RSI guarantees unbounded growth, requiring a fundamental revision of the theoretical framework