reaperdoesntknow/SharperSwarm

SAGI V3.2 - SELF-AWARE AGI

SAGI (Swarm AGI) is a novel causal language model that integrates swarm intelligence dynamics with transformer architecture. The model treats cognition as a dynamic, adaptive system where multiple internal "agents" collaborate through differentiable routing, trust mechanisms, and shared memory.

V3.2 introduces a revolutionary Self-Assessment Layer, allowing the system to predict its own performance, identify skill gaps, and autonomously design its own learning curriculum.

🌟 Architecture Evolution: Swarm-8 V3.2

┌─────────────────────────────────────────────────────────────────────────┐
│                    Swarm-8 V3.2 - SELF-AWARE AGI                        │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌────────────────────────────────────────────────────────────────┐   │
│  │                    SELF-ASSESSMENT LAYER                       │   │
│  ├────────────────────────────────────────────────────────────────┤   │
│  │  • Performance Predictor     • Skill Gap Analyzer              │   │
│  │  • Auto-Curriculum Gen       • Real-Time Error Detector        │   │
│  │  • Capability Boundary Detector                                 │   │
│  └────────────────────────────────────────────────────────────────┘   │
│                                                                         │
│  ┌────────────────────────────────────────────────────────────────┐   │
│  │                  AGI CORE (7 Subsystems)                       │   │
│  ├────────────────────────────────────────────────────────────────┤   │
│  │  • Hierarchical Memory       • Causal World Model              │   │
│  │  • Meta-Learner              • Concept Library                 │   │
│  │  • Reflection Engine         • Uncertainty Reasoner            │   │
│  │  • Adversarial Self-Play                                       │   │
│  └────────────────────────────────────────────────────────────────┘   │
│                                                                         │
│  ┌────────────────────────────────────────────────────────────────┐   │
│  │                 SWARM CORE (20 Agents)                         │   │
│  ├────────────────────────────────────────────────────────────────┤   │
│  │  • Vectorized Agents         • Differentiable Routing          │   │
│  │  • Dynamic Resource Mgmt     • Trust-Based Activation          │   │
│  └────────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────┘

🚀 Key V3.2 Enhancements

• Predictive Self-Awareness: Estimates success probability and identifies risks before attempting a task.
• Skill Taxonomy: Systematic tracking of 24 core skills across Cognition, Knowledge, Code, Creativity, and Planning.
• Autonomous Learning: Self-designed, personalized learning paths via the Auto-Curriculum Generator.
• Real-Time Correction: Proactive error detection during the generation process.
• Boundary Mapping: Precise identification of capability edges with expansion strategies.

💻 Usage

Installation

pip install torch transformers datasets sagi-swarm

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/SAGI")
tokenizer = AutoTokenizer.from_pretrained("reaperdoesntknow/SAGI")

# Generate text
prompt = "Explain the concept of emergence in swarm intelligence:"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📊 Skill Taxonomy (24 Core Skills)

• Cognition: Pattern recognition, Causal reasoning, Concept formation.
• Knowledge: Fact retrieval, Knowledge integration, Common sense.
• Code: Syntax understanding, Algorithm design, Debugging, Optimization.
• Creativity: Divergent thinking, Novel combination, Generative synthesis.
• Planning: Goal decomposition, Dependency analysis, Resource allocation.
• Meta-Cognition: Self-monitoring, Error detection, Strategy selection, Uncertainty quantification.

🧠 Decision Flow (V3.2)

1. Pre-Assessment: Predict success, identify risks, recommend strategy.
2. Execution: Generate with selected strategy.
3. Real-Time Monitoring: Catch and correct errors during generation.
4. Post-Assessment: Update skill proficiencies, check boundaries, refine future predictions.
5. Learning: Update internal models and curricula.

⚠️ Safety & Limitations

• Experimental Research Prototype: Not intended for production use.
• Code Execution: Model includes tool-use capabilities (Python sandbox). Use with caution.
• Intrinsic Motivation: Self-improving systems may exhibit unpredictable growth patterns.

📄 License

Apache License 2.0

📝 Citation

@software{sagi2026,
  title={SAGI: Self-Aware General Intelligence System},
  author={Reaperdoesntknow},
  year={2026},
  url={https://huggingface.co/reaperdoesntknow/SAGI},
  version={3.2.0}
}

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Convergent Intelligence Portfolio

By Convergent Intelligence LLC: Research Division

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Total Portfolio: 41 models | 2,781 total downloads

Last updated: 2026-03-28 12:57 UTC

---

From the Convergent Intelligence Portfolio

DistilQwen Collection — Our only BF16 series. Proof-weighted distillation from Qwen3-30B-A3B → 1.7B and 0.6B on H100. Three teacher variants (Instruct, Thinking, Coder), nine models, 2,788 combined downloads. The rest of the portfolio proves structure beats scale on CPU. This collection shows what happens when you give the methodology real hardware.

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Discrepancy Calculus Foundation

This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces.

DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the learning problem. Key concepts:

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For the full mathematical treatment, see Discrepancy Calculus: Foundations and Core Theory (DOI: 10.57967/hf/8194).

Citation chain: Structure Over Scale (DOI: 10.57967/hf/8165) → Three Teachers to Dual Cognition (DOI: 10.57967/hf/8184) → Discrepancy Calculus (DOI: 10.57967/hf/8194)