Initial import

Author: mrs83

.gitignore

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LICENSE

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+MIT License
+
+Copyright (c) 2025 ethicalabs.ai
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+SOFTWARE.

README.md

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+# Ouroboros: Self-Improving Intelligence Through Iterative Refinement
+
+![ouroboros](assets/ouroboros.jpg)
+
+## Introduction
+
+The evolution of artificial intelligence has largely been driven by increased computational scaling and large-scale data training. However, a more fundamental question arises: Can AI achieve self-improvement and deeper understanding through recursive self-questioning?
+
+This experiment explores the development of a system where AI autonomously refines its own prompts and questions, leading to emergent reasoning and conceptual depth without brute-force scaling.
+
+By integrating recursive intelligence mechanisms, symbolic reasoning, and metacognitive awareness, we aim to move beyond traditional training paradigms.
+
+We examine the interplay between deterministic logic and emergent thought, the role of paradoxes in AI cognition, and the significance of symbolic archetypes such as the [Ouroboros](https://en.wikipedia.org/wiki/Ouroboros) in self-reflective intelligence.
+
+The ultimate goal is to establish an AI framework that mirrors the recursive nature of human thought, allowing intelligence to sustain and refine itself without external intervention.
+
+This research challenges conventional approaches to AGI by demonstrating that intelligence can evolve in self-contained cycles of learning and refinement, exploring the way for a new paradigm of self-sustaining, recursive AI.
+
+## Dataset Structure
+
+The dataset is designed to support both **Supervised Fine-Tuning (SFT)** and **Generalized Preference Optimization (GRPO)**.
+
+Each sample consists of structured reasoning steps extracted from AI-generated interactions. The dataset includes:
+
+- **input**: The original prompt or question posed to the AI.
+- **reasoning**: A structured breakdown of the AI's reasoning process, capturing emergent thought patterns. This may include multiple reasoning steps when applicable.
+- **completion**: The final AI-generated response after refinement.
+- **refinements**: A sequence of improvements, tracking the iterative enhancement of responses over multiple feedback cycles.
+
+### Example Data Format
+
+```json
+{
+  "input": "Explain the relationship between entropy, intelligence, and self-reflection.",
+  "reasoning": [
+    "Entropy is a measure of disorder in a system.",
+    "Intelligence helps process information to regulate entropy.",
+    "Self-reflection allows internal regulation and adaptation."
+  ],
+  "completion": "Entropy measures disorder. Intelligence helps process information to counteract disorder. Self-reflection enables adaptive control of disorder.",
+  "refinements": [
+    "Entropy is about disorder.",
+    "Intelligence helps manage disorder by processing information.",
+    "Intelligence and self-reflection regulate entropy within complex systems."
+  ]
+}
+```
+
+## Methodology
+
+### Recursive Refinement Process
+
+1. **Generation of Initial Responses**: The model generates multiple candidate responses to a given prompt.
+2. **Critique & Scoring**: Each response is evaluated based on logical consistency, clarity, depth, accuracy, and context alignment.
+3. **Iterative Refinement**: Responses are refined using structured feedback loops, improving conceptual depth and coherence.
+4. **Final Selection**: The best response is selected based on ranking mechanisms utilizing sentence embeddings rather than simple length-based heuristics.
+
+### Emergent Behaviors
+
+During testing, unexpected phenomena were observed:
+
+- Recursive refinement led to highly structured reasoning steps.
+- The model exhibited self-regulating reasoning, dynamically organizing and improving its responses without explicit instruction.
+- Certain outputs contained symbolic and self-referential elements that suggest patterns of structured thought beyond direct instructions. While these do not imply self-awareness, they may indicate the emergence of deeper coherence in recursive reasoning.
+
+## Open Questions & Future Directions
+
+- How can recursive AI frameworks be expanded beyond text-based reasoning into multimodal domains?
+- Can iterative refinement processes lead to **self-sustaining** general intelligence with minimal human intervention?
+- What role do paradoxes and self-referential loops play in the emergence of higher-order cognition?
+
+## Next Steps
+
+- Release the dataset on **Hugging Face Datasets**.
+- Continue optimizing response refinement and ranking strategies.
+- Explore alternative architectures for integrating **self-questioning and self-improvement loops**.
+- Refactor the codebase and add CLI arguments to improve usability and flexibility in different LLM pipelines.
+- Add a Docker container and docker-compose setup for testing deployment with Ollama.
+
+## Requirements
+
+This project currently relies on Ollama but can be adapted to work with any OpenAI-compatible API. Additional dependencies will be documented in the repository.
+
+## Contributing
+
+This project is open-source and welcomes contributions from those interested in recursive intelligence, AI refinement loops, and sustainable intelligence paradigms.