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

arXiv: https://arxiv.org/abs/2505.02888 · HTML: https://mlsanity.com/paper/2505.02888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [Debating with More Persuasive LLMs Leads to More Truthful Answers](https://mlsanity.com/paper/2402.06782/) (2024)
- [Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://mlsanity.com/paper/2407.18219/) (2024)
- [IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems](https://mlsanity.com/paper/2501.11067/) (2025)
- [LADDER: Self-Improving LLMs Through Recursive Problem Decomposition](https://mlsanity.com/paper/2503.00735/) (2025)
- [How to Correctly do Semantic Backpropagation on Language-based Agentic Systems](https://mlsanity.com/paper/2412.03624/) (2024)
- [System Integrated Information](https://mlsanity.com/paper/2212.14537/) (2022)
- [Multi-Level Attention Aggregation for Language-Agnostic Speaker Replication](https://mlsanity.com/paper/2403.04111/) (2024)
- [Bayesian Effect Selection in Additive Models with an Application to Time-to-Event Data](https://mlsanity.com/paper/2401.00840/) (2024)

## Built on by (most cited first, 1 of 1)

- [Emotion-Gradient Metacognitive RSI (Part I): Theoretical Foundations and Single-Agent Architecture](https://mlsanity.com/paper/2505.07757/) (2025)

## Abstract (arXiv)

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.