| _version_ | 1866901972346470400 |
|---|---|
| author | Baumgart, Oliver |
| author_facet | Baumgart, Oliver |
| contents | <p>This paper introduces the Core Signal Hypothesis: that intelligence in large language models (LLMs) emerges from the multiplicative interaction of Compression (C), Generalization (G), and Structural Coherence (S).<br><br>We propose that LLMs achieve intelligence through coherent navigation of semantic geometry, the mathematical structure underlying human meaning-making, and that optimizing for structural coherence can dramatically enhance measured intelligence output. Through controlled experiments with Claude 4 Sonnet across complex reasoning tasks, we provide preliminary evidence that coherence optimization produces significant intelligence improvements: 44-52% overall enhancement, 150-186% improvement in systems thinking integration, and 5.6x to 12.9x multiplicative effects when applying our heuristic framework. Coherence-optimized responses exhibit qualitatively different reasoning architecture, replacing fragmented analysis with unified frameworks and additive logic with multiplicative systems thinking.<br> <br> Our findings reveal measurable intelligence indicators including framework consistency, pattern compression ratios, and integration versus fragmentation language patterns. These results suggest that structural coherence acts as a genuine intelligence multiplier, and that current LLM architectures may suffer from "fragmentation problems" where competing objectives reduce effective intelligence by disrupting coherent semantic navigation. The Core Signal Hypothesis offers immediate practical applications for LLM development through coherence-focused optimization strategies, while providing a foundation for objective intelligence evaluation that transcends traditional benchmark limitations. Our preliminary experimental validation suggests that enhanced intelligence through coherence optimization may be domain-general, replicable, and measurable, opening new directions for AI development focused on architectural coherence rather than computational scale alone.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18546473 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Core Signal Hypothesis - A Framework for Understanding Coherence-Based Intelligence Enhancement in LLM Baumgart, Oliver <p>This paper introduces the Core Signal Hypothesis: that intelligence in large language models (LLMs) emerges from the multiplicative interaction of Compression (C), Generalization (G), and Structural Coherence (S).<br><br>We propose that LLMs achieve intelligence through coherent navigation of semantic geometry, the mathematical structure underlying human meaning-making, and that optimizing for structural coherence can dramatically enhance measured intelligence output. Through controlled experiments with Claude 4 Sonnet across complex reasoning tasks, we provide preliminary evidence that coherence optimization produces significant intelligence improvements: 44-52% overall enhancement, 150-186% improvement in systems thinking integration, and 5.6x to 12.9x multiplicative effects when applying our heuristic framework. Coherence-optimized responses exhibit qualitatively different reasoning architecture, replacing fragmented analysis with unified frameworks and additive logic with multiplicative systems thinking.<br> <br> Our findings reveal measurable intelligence indicators including framework consistency, pattern compression ratios, and integration versus fragmentation language patterns. These results suggest that structural coherence acts as a genuine intelligence multiplier, and that current LLM architectures may suffer from "fragmentation problems" where competing objectives reduce effective intelligence by disrupting coherent semantic navigation. The Core Signal Hypothesis offers immediate practical applications for LLM development through coherence-focused optimization strategies, while providing a foundation for objective intelligence evaluation that transcends traditional benchmark limitations. Our preliminary experimental validation suggests that enhanced intelligence through coherence optimization may be domain-general, replicable, and measurable, opening new directions for AI development focused on architectural coherence rather than computational scale alone.</p> |
| title | Core Signal Hypothesis - A Framework for Understanding Coherence-Based Intelligence Enhancement in LLM |
| url | https://doi.org/10.5281/zenodo.18546473 |