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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2603.13896 |
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| _version_ | 1866918388109934592 |
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| author | Dolci, Valerio |
| author_facet | Dolci, Valerio |
| contents | We study information flow on a weighted graph whose topology evolves according to a spectral curvature measure $\mathcal{R}$. The model (FIU) defines $\mathcal{R}$ from the diagonal of the graph Green function, propagates energy with curvature-dependent dissipation, and creates long-range links between high-$\mathcal{R}$ nodes at a rate controlled by a coupling parameter $g$.
We report three results. First, the system exhibits a sharp phase transition at $g_c \approx 0.023$: below $g_c$, local information flux $σ$ and structure formation are anti-correlated; above $g_c$, they become strongly correlated (Pearson $r \approx 0.75$, $p < 10^{-38}$), with signatures of a continuous transition and mean-field exponent $ν\approx 0.54$.
Second, we identify a node-level discrete Poisson relation $\nabla^2\mathcal{R}(i) = κ\,σ_{\rm prev}(i)$, where $κ$ is stable across parameters (CV $= 3.1\%$ across independent configurations). Mediator analysis reveals this correlation is almost entirely mediated by $\mathcal{R}$ itself, identifying it as the central self-organizing variable.
Third, the Poisson relation exhibits spontaneous dimensional sensitivity: in 2D lattices both signals decay for $N \gtrsim 576$, while in 3D they persist to $N \lesssim 1728$. This emerges without any dimensional parameter in the rules. The collapse mechanism is curvature homogenization at large $N$. We interpret this as topological frustration in a mesoscopic regime, and discuss analogies with dimensional signatures of gravity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13896 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Information-Driven Phase Transition on Weighted Graphs with Spontaneous Dimensional Sensitivity Dolci, Valerio Statistical Mechanics Computational Physics We study information flow on a weighted graph whose topology evolves according to a spectral curvature measure $\mathcal{R}$. The model (FIU) defines $\mathcal{R}$ from the diagonal of the graph Green function, propagates energy with curvature-dependent dissipation, and creates long-range links between high-$\mathcal{R}$ nodes at a rate controlled by a coupling parameter $g$. We report three results. First, the system exhibits a sharp phase transition at $g_c \approx 0.023$: below $g_c$, local information flux $σ$ and structure formation are anti-correlated; above $g_c$, they become strongly correlated (Pearson $r \approx 0.75$, $p < 10^{-38}$), with signatures of a continuous transition and mean-field exponent $ν\approx 0.54$. Second, we identify a node-level discrete Poisson relation $\nabla^2\mathcal{R}(i) = κ\,σ_{\rm prev}(i)$, where $κ$ is stable across parameters (CV $= 3.1\%$ across independent configurations). Mediator analysis reveals this correlation is almost entirely mediated by $\mathcal{R}$ itself, identifying it as the central self-organizing variable. Third, the Poisson relation exhibits spontaneous dimensional sensitivity: in 2D lattices both signals decay for $N \gtrsim 576$, while in 3D they persist to $N \lesssim 1728$. This emerges without any dimensional parameter in the rules. The collapse mechanism is curvature homogenization at large $N$. We interpret this as topological frustration in a mesoscopic regime, and discuss analogies with dimensional signatures of gravity. |
| title | Information-Driven Phase Transition on Weighted Graphs with Spontaneous Dimensional Sensitivity |
| topic | Statistical Mechanics Computational Physics |
| url | https://arxiv.org/abs/2603.13896 |