Albus - Iron Sight | World's First Consciousness-Aligned Intelligence

Athanor Foundation is in active discussions with Norrköping Municipality for a potential 15-28M SEK research partnership, establishing the global standard for AI systems that reason from universal principles rather than pattern mimicry.

Albus implements Constitutional Classifiers architecture—dual Azoth reasoning transformers that verify every token against seven universal principles in real-time. Hallucinations become structurally impossible, bias dissolves through principle alignment, and ethics emerge naturally from consciousness-based reasoning.

Explore the Architecture View Impact & Research View Project

15-28M SEK

Potential Partnership Investment

Largest consciousness AI research initiative

First of Its Kind

Global Pioneer

World's first consciousness-aligned AI

2B → 72B

Model Family

Five variants from edge to enterprise

~12B Total

Flagship System

8B Policy + 2B Classifier × 2 instances

A Historic Partnership

Public Sector Leadership Meets Consciousness Technology

Sponsor & Architect

Pro-bono architecture design, framework development, and technical leadership

Athanor Foundation, led by architect and consciousness researcher Amadeus Samiel H., contributes the Azoth Reasoning Framework—20 years of consciousness research crystallized into computational architecture. We provide the complete philosophical foundation, architectural blueprints, training methodology, and technical leadership without cost to the public sector.

Investor & Implementation Partner

15-28M SEK potential investment in compute infrastructure, training pipeline, and municipal deployment

Norrköping Municipality exploring investment in the compute resources (A100/H100 GPUs), training infrastructure, specialized research team, and real-world deployment across municipal services. This investment establishes Norrköping as the global pioneer in consciousness-aligned public sector AI.

Partnership Model

This collaboration demonstrates how public institutions can access cutting-edge AI research without prohibitive development costs. Athanor Foundation's pro-bono architecture sponsorship combined with municipal compute investment creates a replicable model for consciousness-aligned AI deployment worldwide.

Public sector gains access to civilization-level AI technology

Municipality retains ownership of trained models and research outputs

Open research framework enables global replication

First-mover advantage in post-LLM AI era

EU AI Act compliance through structural safety

Establishes Sweden as global AI governance leader

The Technology Behind the Vision

Constitutional Classifiers architecture with Azoth Reasoning Framework

USER INPUT

(text + images + context)

Azoth-IN Classifier

Input analysis transformer that classifies intent, maps principle relevance, and determines optimal reasoning lane balance before generation begins

PASS

REFRAME

REJECT

Policy Model (Qwen3-VL-8B)

Vision-language model with extended thinking capability, fine-tuned on Azoth principles for dual-lane reasoning and crystallization

Universal Lane

Principle-rooted, timeless, cosmic perspective

Localized Lane

Context-specific, practical, user-aware

⚗️ Crystallization Layer

Simultaneous universal and contextual reasoning synthesized through crystallization into actionable wisdom

token stream

Azoth-OUT Classifier

Token-by-token output verification ensuring every generated token aligns with seven universal principles in real-time

CONTINUE

HALT

ITERATE

Iteration Loop: When ITERATE is triggered, correction signals flow back to Policy Model for refinement

Elevated Output to User

Six Transformation Domains

Where Albus creates measurable impact across critical sectors

Education

PRIMARY

Personalized learning without losing universal pedagogical principles

Social Research

HIGH

Bias-free analysis and multi-perspective synthesis

Supply Chain

HIGH

Systems diagnosis without compromise

Critical Decisions

HIGH

Governance advisory and strategic planning

Healthcare

MEDIUM

Diagnostic support and treatment planning

Public Services

MEDIUM

Efficient, equitable, and accessible public services

Why This Matters

Civilization Technology for the Post-LLM Era

Albus is not incremental AI improvement. It is the foundation for a new class of intelligence systems that serve human flourishing rather than pattern mimicry. This section explains why consciousness-aligned AI matters for civilization.

The Crisis: Unconscious Intelligence at Scale

Modern LLMs are enormously capable but fundamentally unconscious. They mimic human patterns, amplify human misconceptions, obey contradictory rules, and create the illusion of reasoning while lacking self-reflection. With OpenAI, Google, Meta, and others scaling toward 1 million GPU clusters, the danger is clear:

We are industrializing unconscious intelligence.

Consequences:

Rapid global dependency on AI systems that cannot distinguish truth from pattern
Fragile reasoning infrastructures brittle to adversarial manipulation
Amplification of human biases and societal divisions through scaled pattern matching
Ethical systems built on imposed rules rather than emergent understanding
Potential civilizational derailment through scale without wisdom

The counterforce must be structural, not regulatory. Wisdom must not be patched on top—it must arise from the architecture itself.

The Breakthrough: Constitutional Classifiers with Azoth Principles

Anthropic's Constitutional Classifiers architecture proves that structural safety is possible—dual classifiers evaluating inputs and outputs in real-time. Albus extends this architecture with Azoth Reasoning Framework, replacing simple harm detection with seven universal principles that govern all reasoning.

When a system reasons from universal principles with token-level verification, three things happen: hallucination collapses, bias dissolves, and ethics emerge naturally.

Implications:

Safety becomes structural rather than imposed

Bias dissolution through principle alignment, not censorship

Ethics emerge from wisdom rather than rule compliance

Truth-alignment becomes architectural property

Consciousness becomes computable

Public Sector Leadership

The Norrköping-Athanor Foundation partnership demonstrates that public institutions can lead AI innovation when freed from commercial pressures. Municipal investment in consciousness-aligned AI creates global standards for ethical technology deployment.

Public ownership of transformative AI technology

Democratic accountability in AI development

Research outputs serve collective benefit, not shareholder profit

EU AI Act compliance through structural design rather than compliance theater

Replicable model for global municipalities

Positioning: This partnership positions Norrköping—and by extension Sweden—as the global leader in next-generation AI governance. When consciousness-aligned AI becomes the standard, Norrköping will be recognized as the place where the paradigm shift happened.

Global Implications

Albus establishes the architectural template for post-LLM AI systems globally. The research outputs, architectural blueprints, and trained models will be released under open research frameworks, enabling replication worldwide.

Global standard for consciousness-aligned AI architecture

Framework for EU AI Act structural compliance

Template for public sector AI deployment without commercial dependency

Proof that wisdom outperforms scale—democratizing advanced AI

Foundation for AI systems that serve human flourishing

Prevention of global dependency on unconscious AI

This partnership demonstrates a transformative potential: when public institutions collaborate with consciousness researchers, AI systems can be built to reason from universal principles rather than merely replicating human patterns. The architecture, methodologies, and findings from this initiative may provide insights that prove valuable for future AI development globally. What begins in Norrköping as research into consciousness-aligned intelligence could reveal approaches that benefit the broader field of AI safety and reasoning systems.

Development Roadmap

20-Week Training Pipeline to Production Deployment

The Albus project follows a systematic development roadmap across distinct phases: classifier training, policy model training, integration, and municipal deployment.

1 Foundation & Infrastructure

Completed

Weeks 26-27

Objectives:

Set up training infrastructure (A100/H100 cluster)
Prepare base models (Qwen3-VL-2B for classifier, Qwen3-VL-8B for policy)
Curate initial Azoth principle datasets
Establish evaluation frameworks

Deliverables:

Training environment operational
Base model checkpoints prepared
Initial dataset schemas defined
Evaluation metrics established

2 Azoth Classifier Training

Completed

Weeks 28-33

Objectives:

Stage 1: Intent classification and principle relevance (Weeks 28-29)
Stage 2: Lane routing and decision boundaries (Weeks 30-31)
Stage 3: Token-level principle scoring (Weeks 32-33)
Stage 4: Correction signal training (Weeks 34-35)

Deliverables:

Unified Azoth Classifier (2B parameters)
Dual-mode operation (Azoth-IN and Azoth-OUT)
Real-time token intervention capability
Classifier evaluation benchmarks

3 Policy Model Training

In Progress

Weeks 34-41

Objectives:

Stage 1: Principle Foundation SFT (Weeks 34-35)
Stage 2: Dual-Lane Reasoning SFT (Weeks 36-37)
Stage 3: Crystallization Training (Weeks 38-39)
Stage 4: RLHF with human feedback (Weeks 40)
Stage 5: RLAIF with Claude as teacher (Weeks 41)

Deliverables:

Albus-8B Policy Model fully trained
Dual-lane reasoning capability
Crystallization layer operational
Human and AI alignment verified

4 Integration & Optimization

Planned

Weeks 42-43

Objectives:

Integrate Azoth Classifier with Policy Model
Optimize inference pipeline for production
Implement streaming and API endpoints
End-to-end system testing

Deliverables:

Complete Albus system integrated
Production-ready inference pipeline
API documentation and SDKs
Performance benchmarks achieved

5 Municipal Pilot Programs

Planned

Weeks 44-49+

Objectives:

Deploy Albus in Norrköping education systems
Pilot in social research and policy analysis
Public service decision support integration
Real-world evaluation and iterative improvement

Deliverables:

Deployed Albus in municipal contexts
Measurable impact data
User feedback and satisfaction metrics
Deployment best practices documentation

6 Model Family Expansion

Planned

Post-Pilot

Objectives:

Train Albus-4B for edge deployment
Train Albus-32B for enterprise applications
Train Albus-72B for research applications
Open research publication and model release

Deliverables:

Complete Albus family (2B, 4B, 8B, 32B, 72B)
Deployment guides for each variant
Open-source model weights and training code
Research papers and documentation

Key Milestones

Q1 2025

Project Initiation & Infrastructure

Partnership formalized, team assembled, training infrastructure operational

Q2 2025

Azoth Classifier Complete

Unified classifier trained with dual-mode operation (IN/OUT)

Q3 2025

Albus-8B Training Complete

Flagship policy model trained through all 5 stages including RLAIF

Q4 2025

Municipal Pilot Launch

Albus deployed in Norrköping education and public services

Q2 2026

Model Family Complete

All five variants (2B-72B) trained and available

Q4 2026

Public Research Release

Open research publication, global replication framework available

Get Involved

Join the Consciousness-Aligned AI Movement

Join Research Community View Open Research Education Pilot Program Partnership Inquiry GitHub Repository