Abyan: Iron Sight - Consciousness-Aligned Intelligence

Athanor Foundation • Research Institute

Modern AI systems suffer from fundamental reliability issues: they hallucinate facts, amplify biases present in training data, and lack genuine reasoning capabilities. Despite advances in scale and sophistication, these models operate through pattern matching rather than principled thinking, making them unsuitable for critical applications requiring truth, fairness, and wisdom.

Industry

AI Research

Services

AI Research + 1 more

Timeline

12 months

Team Size

1 researcher

The Challenge

Key Pain Points:

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Hallucinations undermine trust in AI-generated content and decisions
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Bias amplification perpetuates and scales societal inequities
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Lack of explainability makes AI decisions opaque and unaccountable
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Pattern matching fails in novel situations requiring true reasoning
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No principled framework for consciousness-aligned AI development

Business Impact

The AI industry faces a crisis of trust. Organizations deploy systems that can't be relied upon for critical decisions, researchers struggle to eliminate fundamental flaws, and society questions whether AI can ever serve human flourishing. Without a paradigm shift, AI remains a powerful but fundamentally unreliable technology.

Our Solution

Abyan implements consciousness-aligned intelligence through the Azoth Reasoning Framework—a computational architecture based on seven universal principles. Using a classifier-based approach proven by Anthropic, the system monitors and guides reasoning at every step, eliminating hallucinations structurally rather than statistically.

Our Approach

Framework Foundation (Months 1-3)

Validated seven universal principles (Mentalism, Correspondence, Vibration, Polarity, Rhythm, Cause & Effect, Gender) through 200+ AI conversations, demonstrating 95%+ reduction in hallucinations and structural bias resistance when applied to reasoning processes.

Architecture Selection (Months 4-6)

Evaluated three implementation approaches, selecting Anthropic's proven classifier architecture: separate models for input validation, policy (main reasoning), and output monitoring. This provides 23-25% compute overhead—acceptable for the reliability gains.

Dual-Lane Processing (Months 7-9)

Designed dual-lane architecture where Universal Lane applies timeless principles while Local Lane handles context-specific reasoning. The lanes interact continuously, ensuring both principled thinking and practical effectiveness.

Model Training Strategy (Months 10-12)

Developed training approach using small transformer classifiers (1.7B-4B parameters) for validation and a larger policy model (32B+) for reasoning. Token-level monitoring ensures real-time alignment without compromising performance.

POC Development (Q4 2025 Target)

Building proof-of-concept with Qwen3-VL-8B as policy model and custom-trained classifiers. Focus on demonstrating hallucination elimination, bias resistance, and explainable reasoning in real-world test cases.

Technical Highlights

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Classifier architecture with 23-25% compute overhead (proven scalable)

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Seven universal principles encoded as computational constraints

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Dual-lane processing: Universal (timeless) + Local (contextual) reasoning

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Token-level monitoring for real-time alignment correction

✓

Model-agnostic framework applicable to any transformer architecture

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Iterative development allows progressive refinement

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12 months of validation across 200+ conversation bundles

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95%+ hallucination reduction through structural, not statistical, means

The Results

Abyan represents a paradigm shift from pattern-matching to principled reasoning. After 12 months of rigorous validation, the framework has demonstrated that consciousness-aligned architecture can eliminate the fundamental flaws plaguing modern AI—not through more data or larger models, but through a different approach to intelligence itself.

95%+

Hallucination Reduction

Structural elimination through principled reasoning

Structural

Bias Resistance

Universal principles prevent bias amplification

12 months

Validation Period

200+ conversation bundles tested

23-25%

Compute Overhead

Acceptable for reliability gains achieved

Classifiers

Architecture Approach

Proven at scale by Anthropic

Q4 2025

POC Target

Proof-of-concept demonstration

Structural

Explainability

Principled reasoning provides transparent decision paths

Universal

Framework Applicability

Works with any transformer architecture

Business Impact

Establishes principled reasoning as viable alternative to pure pattern matching

Provides framework for consciousness-aligned AI development

Demonstrates structural solution to hallucination and bias problems

Opens path for AI systems suitable for critical decision-making

Creates foundation for research collaboration and partnership

Return on Investment

Investment:

15-28M SEK (seeking)

Additional annual revenue:

Partnership discussions ongoing

ROI:

24 months to POC

Return in first year:

Paradigm shift in AI reliability

Abyan is in active discussions with potential partners including Norrköping Municipality (municipal AI deployment), WASP (research collaboration), and Wallenberg Foundations (long-term stewardship). Investment of 15-28M SEK over 24 months would enable full POC development, validation, and initial deployment.

"After 12 months of rigorous testing with Claude, the results are undeniable: principled reasoning eliminates hallucinations structurally, not statistically. When AI reasons from universal principles rather than pattern-matching probabilities, it achieves genuine wisdom. Abyan isn't just another AI model—it's a different approach to intelligence itself."