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Research on artificial intelligence, emergent coherence, and system design principles. Current work focuses on AI signal processing and coherence analysis methods.
Featured Research
October 10, 2025
Core Signal Hypothesis: A Framework for Understanding Coherence-Based Intelligence Enhancement in Large Language Models
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This paper introduces the Core Signal Hypothesis: that intelligence in large language models emerges from the multiplicative interaction of Compression (C), Generalization (G), and Structural Coherence (S), expressed as I = C × G × S. Through controlled experiments, we provide preliminary evidence that coherence optimization produces 44-52% intelligence improvements, 150-186% enhancement in systems thinking, and 5.6x to 12.9x multiplicative effects. The framework explains why tiny recursive networks (7M params) beat massive LLMs (671B params), and why comprehensive contexts prevent performance collapse. Our findings suggest that structural coherence acts as a genuine intelligence multiplier, offering immediate practical applications for AI development focused on architectural coherence rather than computational scale alone.