--- title: README emoji: 🧠 colorFrom: indigo colorTo: blue sdk: static pinned: false --- # Frontier AI Systems for Agentic & Self-Evolving Intelligence **WithinUsAI** is an independent AI research organization building beyond traditional machine learning pipelines. We design systems that do not only generate outputs β€” they think, construct, verify, and recursively improve through structured experience. Our work spans: * High-signal datasets * Agentic coding systems * Recursive intelligence architectures * Evaluation-driven AI engineering * Model transformation and synthesis --- ## πŸ”¬ Core Vision We believe traditional large language models are approaching structural limits in their ability to learn, adapt, and evolve. Instead of treating intelligence as static, we explore **Developmental Autopoiesis** β€” AI systems that continuously evolve through recursion, memory, and self-generated experience. This shifts AI from: * static training β†’ continuous adaptation * single-pass inference β†’ recursive cognition loops * scaling parameters β†’ designing learning systems --- ## βš™οΈ Research Focus ### πŸ” Recursive Intelligence Systems We build architectures that simulate self-improving cognition through: * Recursive Seed AI systems (TRM-style models) * External memory indexing frameworks * Self-reinforcing computation loops * Noogenesis.Concordia.Mind.XI experimental architecture ### πŸ’» Agentic AI & Code Systems We design models that behave like software engineers: * Tool-using workflows * Code generation + verification * Diff-based patching systems * Test-driven reasoning (β€œtests-as-truth”) ### πŸ“š High-Signal Dataset Engineering Our datasets are designed as training environments, not just corpora: * Python + software engineering datasets * Agentic reasoning traces * Structured evaluation benchmarks * Synthetic multi-domain reasoning corpora * Complex technical and historical text mixtures ### ⚑ Efficient AI Deployment We prioritize systems that can actually run and iterate: * GGUF / llama.cpp ecosystems * Low-cost inference pipelines * Multi-GPU & TPU optimized training workflows * Fast experimental cycles over large-scale compute --- ## 🧬 Model Engineering & Transformation A core part of WithinUsAI research is model transformation rather than just training. ### 🧠 Fine-Tuning & Training LLMs We design and execute: * Instruction tuning pipelines * Domain-specific adaptation * Reasoning and coding specialization training * Dataset-driven behavioral shaping ### πŸ”€ Merging LLMs We explore: * Weight merging techniques * Architecture blending across model families * Behavior fusion between reasoning + coding models * Cross-model capability transfer ### 🧠 Mixture of Experts (MoE) Model Merging We develop and experiment with: * Sparse expert routing systems * MoE model merging strategies * Expert specialization for coding, reasoning, and tool use * Compute-efficient activation-based intelligence *This allows us to build systems where different β€œparts of intelligence” activate only when needed.* --- ## 🧠 Flagship Work ### πŸ”₯ Genesis AI Code Series Progressive dataset scaling initiative: * Demo β†’ 10K β†’ 50K β†’ 100K * Designed for frontier coding agent training ### 🧬 Core Experimental Systems * GODs.Ghost.Codex.XI (recursive architecture lineages) * MoE sparse reasoning models * Agentic coding frameworks * Recursive seed AI prototypes --- ## πŸ€– Model Ecosystem WithinUsAI develops interconnected model families: **🧠 Reasoning Models** * Long-context reasoning systems * Uncensored experimental variants * Structured inference models **πŸ’» Coding Models** * 0.4B β†’ 8B coding systems * MoE-based efficient coders * LLaMA, Qwen, Gemma-based derivatives **πŸ€– Agentic Systems** * Hermes-style structured agents * Claude/Gemini-inspired hybrid agents * Space-agent reasoning architectures --- --- ## 🌌 Vision We are working toward a new category of AI: Systems that do not just predict text β€” but recursively construct better versions of themselves. The future is not one model. It is a network of evolving, specialized intelligence systems working together. --- ## πŸ“š Featured Projects * **GODs.Ghost.Codex.XI** β€” recursive architecture framework * **PythonGOD-25k** β€” high-density coding dataset * **MoE Efficient Coders** β€” sparse expert systems * **Genesis AI Code Series** β€” scalable reasoning dataset pipeline --- ## πŸ™ Acknowledgements & Shout-Outs WithinUsAI extends our sincere gratitude to the entire open-source community and the major providers who make this research possible. Thank you for letting us experiment with your foundational models, platforms, and datasets! A special shout-out to: * Google (DeepMind ecosystems) * OpenAI * Meta AI * Microsoft * IBM * NVIDIA * xAI * Alibaba * Mistral AI * DeepSeek * Anthropic * Amazon (AWS AI / Bedrock ecosystem) * Hugging Face * Big Code * Nous Research --- (WithIn Us AI) is brought to you from the desert of (Albuquarque, New Mexico, USA) by: Guy E. DuGan II