The Depth-Coherence Hypothesis: A Structural Perspective on Hallucinations as "Coherence Bridges" in Transformers
Year: 2026
Type: article
Abstract: The Depth-Coherence Hypothesis: A Structural Perspective on Hallucinations as “Coherence Bridges” in Transformers is a conceptual hypothesis proposal. Hallucinations in Large Language Models (LLMs) are often treated as training artifacts or statistical errors observed only at the output level. This note proposes a structural perspective: hallucinations may function as a “bridge” that preserves continuation when a model encounters a depth-wise discontinuity during inference. The core shift is conceptual: for a Transformer in a single forward pass, layer depth can be treated as an analogue of temporal dynamics in biological coherence theories. We hypothesize that safety constraints or strongly conflicting objectives can induce a structural blockage that is depth-localized (often hypothesized near mid-depth, but not assumed). The model then compensates by widening internal routing, enabling fluent continuation while weakening factual anchoring in a subset of cases. Scope: This record reports no experiments and no empirical results. It is intended as a falsifiable hypothesis proposal and a call for measurement by researchers with access to internal activations, attention patterns, or mechanistic interpretability tooling. The appendix provides an operational measurement sketch (pre-registration, negative controls, length controls, and robustness checks).
Author: Stolze, Nicol
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7128402882
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