[2505.02888] When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger

Computer Science > Machine Learning arXiv:2505.02888 (cs) This paper has been withdrawn by Rintaro Ando [Submitted on 5 May 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger Authors:Rintaro Ando View a PDF of the paper titled When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger, by Rintaro Ando No PDF available, click to view other formats Abstract: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, Gödelian 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. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) MSC classes: 68T05, 68Q85 ACM classes: I.2.0; I.2.3; I.2.6 Cite as: arXiv:2505.02888 [cs.LG]   (or arXiv:2505.02888v2 [cs.LG] for this version)   https://d...