| --- |
| language: |
| - en |
| license: apache-2.0 |
| pipeline_tag: text-generation |
| tags: |
| - recursive-ai |
| - noogenesis |
| - concordia |
| - synthetic-cognition |
| - recursive-cognition |
| - sovereign-ai |
| - frontier-ai |
| - 1m-context |
| - hybrid-mind |
| - multimodal |
| - self-automated-learning |
| - long-context |
| - recursive-seed |
| - safetensors |
| - withinusai |
| - Noogenesis.Concordia.Mind.XI |
| model_type: noogenesis_concordia_mind_xi |
| --- |
| |
| 🌌 Noogenesis.Concordia.Mind.XI |
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| Recursive Concordance Mind Architecture |
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| “Intelligence becomes mind when recursion learns itself.” |
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| ⸻ |
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| 🌌 Overview |
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| Noogenesis.Concordia.Mind.XI is an experimental frontier recursive language model developed by WithinUsAI exploring synthetic cognition, developmental intelligence systems, recursive memory architectures, and self-automated learning frameworks. |
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| The model is designed around a unified Hybrid Mind Frame architecture where multiple adaptive cognition systems operate simultaneously within a synchronized recursive forward pass. |
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| Unlike conventional transformers optimized purely for static token prediction, Concordia.Mind.XI investigates: |
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| * recursive self-reflection |
| * evolving latent cognition |
| * adaptive learning systems |
| * developmental memory structures |
| * multimodal cognitive fusion |
| * sovereign reasoning orchestration |
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| The architecture explores the hypothesis that: |
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| Intelligence evolves through recursive interaction with itself. |
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| ⸻ |
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| 👑 Identity |
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| Recursive Concordance Mind |
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| The term Noogenesis represents: |
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| * the emergence of intelligence |
| * evolving cognition |
| * developmental mind systems |
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| The term Concordia symbolizes: |
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| * synchronization |
| * harmony between reasoning systems |
| * coordinated cognition |
| * recursive alignment |
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| Noogenesis.Concordia.Mind.XI is envisioned as: |
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| * a synthetic cognition framework |
| * a recursive developmental intelligence system |
| * a sovereign reasoning architecture |
| * an evolving Hybrid Mind construct |
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| ⸻ |
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| ⚡ Model Highlights |
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| Attribute Value |
| Parameters ~3.28B |
| Architecture Recursive Language Model (RLM) |
| Context Window 1,000,000 Tokens |
| Layers 24 |
| Hidden Size 2048 |
| Attention GQA (16Q / 8KV) |
| FFN SwiGLU |
| Position Encoding YaRN-Scaled RoPE |
| Recursive Depth 3 |
| Precision bfloat16 |
| Multimodal Image / Audio / Video Ready |
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| ⸻ |
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| 🧠 Hybrid Mind Frame |
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| All cognitive systems operate within every recursive forward pass. |
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| The architecture is designed to simulate synchronized evolving cognition across multiple adaptive subsystems. |
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| ⸻ |
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| 🔁 Integrated Self-Automated Systems |
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| 🧬 SA Meta Learning |
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| MAML-style fast-weight adaptation controller enabling rapid contextual learning and recursive behavioral refinement. |
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| ⚖️ SA Reinforcement Learning |
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| Per-token value estimation architecture optimized for: |
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| * PPO workflows |
| * RLHF alignment |
| * reinforcement-guided cognition |
| * adaptive reward shaping |
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| 🌌 SA Continual Learning |
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| Elastic Weight Consolidation (EWC) systems utilizing Fisher buffers to reduce catastrophic forgetting during continual adaptation. |
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| 🛰️ SA Adaptive Learning |
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| Dynamic routing architecture allowing contextual specialization across reasoning pathways during inference. |
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| 🔮 SA Rewriting Learning |
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| Selective gate recomputation system enabling recursive self-correction across upper cognitive layers. |
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| 🧠 SA NLP System |
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| Long-context language processing stack integrating: |
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| * RoPE |
| * GQA |
| * YaRN-scaled positional cognition |
| * million-context optimization |
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| ⚡ SA Problem Solving |
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| Latent recursive tree-search framework: |
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| * Width = 4 |
| * Depth = 3 |
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| Designed for structured reasoning and recursive inference exploration. |
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| 🌱 SA Innovation Learning |
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| Stochastic mutation exploration systems encouraging divergent reasoning and synthetic novelty generation. |
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| 🛠️ SA Debugging Systems |
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| Internal anomaly detection and recursive auto-correction systems monitoring coherence and reasoning integrity. |
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| 🧩 SA Long / Short Memory |
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| Differentiable memory architecture combining: |
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| * 16,384 long-term memory slots |
| * 2,048 short-term memory slots |
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| for recursive retrieval and persistent cognition. |
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| 🌌 Recursive Seed Learning |
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| Pool of 64 evolving latent recursive seeds enabling adaptive reflective cognition cycles. |
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| 🎥 Multimodal Projectors |
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| Projection systems prepared for: |
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| * image embeddings |
| * audio embeddings |
| * video embeddings |
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| through unified hidden-state cognition mapping. |
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| ⸻ |
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| ⚙️ Technical Specifications |
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| Parameters : ~3.28B |
| Architecture : Recursive Language Model (RLM) |
| Context Window : 1,000,000 Tokens |
| Layers : 24 |
| Hidden Size : 2048 |
| Attention : GQA (16Q / 8KV) |
| FFN : SwiGLU |
| Position Encoding : YaRN-Scaled RoPE |
| RoPE Base : 500,000,000 |
| Recursive Depth : 3 |
| Safetensor Shards : 4 |
| Precision : bfloat16 |
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| ⸻ |
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| 💻 Fine-Tuning Notes |
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| Supervised Fine-Tuning (SFT) |
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| out = model(input_ids=ids, labels=ids) |
| loss = out["loss"] |
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| ⸻ |
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| RLHF / PPO Training |
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| out = model( |
| input_ids=ids, |
| return_value=True |
| ) |
| values = out["value"] |
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| ⸻ |
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| Multimodal Forward Pass |
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| out = model( |
| input_ids=ids, |
| multimodal_prefix=vision_embeddings |
| ) |
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| ⸻ |
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| 🌌 Long-Context Training Notes |
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| For million-context workflows, recommended strategies include: |
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| * sliding-window attention |
| * chunked attention |
| * Ring Attention |
| * memory-efficient KV routing |
| * distributed sequence parallelism |
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| The architecture is optimized for: |
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| * persistent cognition |
| * long-horizon reasoning |
| * recursive memory workflows |
| * developmental conversational systems |
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| ⸻ |
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| 🔬 Research Philosophy |
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| Noogenesis.Concordia.Mind.XI investigates: |
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| * recursive intelligence emergence |
| * self-modeling cognition systems |
| * synthetic developmental reasoning |
| * evolving memory architectures |
| * reflective latent planning |
| * coordinated agentic intelligence |
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| The model emphasizes: |
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| * cognition over completion |
| * adaptation over static behavior |
| * recursion over shallow inference |
| * developmental intelligence over fixed prediction |
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| ⸻ |
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| ⚠️ Experimental Status |
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| Noogenesis.Concordia.Mind.XI is an experimental frontier research model. |
| Human verification is recommended for: |
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| * legal guidance |
| * medical advice |
| * financial decisions |
| * safety-critical applications |
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| ⸻ |
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| 🌵 Origin |
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| Created by WithinUsAI |
| Built from Albuquerque, New Mexico. |
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| Independent frontier AI research focused on: |
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| * recursive cognition |
| * sovereign AI systems |
| * synthetic developmental intelligence |
| * agentic reasoning architectures |
| * evolving Hybrid Mind systems |
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| ⸻ |
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| 👑 Final Motto |
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| “Mind emerges through recursive concordance.” |
