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