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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.02888 |
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| _version_ | 1866917313182171136 |
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| author | Ando, Rintaro |
| author_facet | Ando, Rintaro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02888 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger Ando, Rintaro Machine Learning Artificial Intelligence Computation and Language 68T05, 68Q85 I.2.0; I.2.3; I.2.6 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. |
| title | When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger |
| topic | Machine Learning Artificial Intelligence Computation and Language 68T05, 68Q85 I.2.0; I.2.3; I.2.6 |
| url | https://arxiv.org/abs/2505.02888 |