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The research aims to create a unified framework, the Distortion Theory of Intelligence, that addresses failures in current AI systems.

April 12, 2026Open Access

Distortion, Not Scale — A Unified Framework for Intelligence, Alignment, and Efficiency in Neural Systems

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The research aims to create a unified framework, the Distortion Theory of Intelligence, that addresses failures in current AI systems.
Introduced a framework analyzing intelligence modeled as effective capacity under structural distortion.
Identified three major structural errors impacting intelligence: BPE tokenization, RLHF, and autoregressive generation.
Established formal results, including the Landauer-Assertion Binding theorem and a 6-step deterministic Inference Protocol.
Demonstrated that a model with a specific architecture (σ ≈ 0) produces coherent output without reliance on RLHF and GPUs.
Proposed testable predictions and falsifiable criteria to advance the understanding of AI coherence.
Reinterpreted scaling laws as mechanisms to compensate for information loss rather than as measures of intelligence enhancement.

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This theoretical framework models intelligence, revealing errors in current AI systems, suggesting new approaches for improvement.

Lauri Elias Rainio (2026) studied this question.

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