Condensed Matter · Physics & Astronomy
Disordered Systems & Neural Networks
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135 papers, from 4 September 2026 to 5 November 2026: 135 arXiv preprints.
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Newest papers, page 2
Measurement-induced entanglement phases in hyperbolic gauge-Higgs models
Gauge-Higgs theory in flat spacetime provides a prime theoretical model of multiple phases of matter with distinct entanglement structures.Atom as an Agent: Emergent Order by Collective Predictive Coding
What is the intrinsic reason of emergence of macroscopic order parameter out of microscopic systems?Approximate Counting for Mixed $p$-Spin Glasses in Weak External Fields via Cluster Expansion
We give algorithms for approximating the partition function $Z_N$ of the Sherrington--Kirkpatrick (SK) model and finite mixed Ising $p$-spin models with $p\ge 3$.Topological Quantum Error-Correcting Thresholds of a Symmetry-Enriched Toric Code
We study the error thresholds of a new topological quantum error-correcting code under the independent bit-flip and phase-flip noisy channel.Finite-step statistical mechanics of scale-invariant learning: spectral equations of state and curvature transport
Stationary learning admits an ideal-gas description under restrictive noise assumptions.Typicality of entanglement growth and transport fluctuations in quantum symmetric exclusion processes
We use macroscopic fluctuation theory (MFT) to establish the typicality of charge-transfer statistics and entanglement dynamics in the quantum symmetric exclusion process, a model of stochastically…Matter that learns what matters
Can a material learn what deserves its attention?Controlling transport with structured long-range hopping
The combined effects of disorder and long-range hopping play an important role in determining localization and transport in low-dimensional quantum systems.Sliding Networks: A Family of Residual Structures Preserving Feature Learning and Feature Diversity
Deep learning models tend to perform best at large widths and depths.When is early stopping beneficial under label noise? A phase diagram for kernel gradient flow
Early stopping is commonly used to prevent fitting label noise from degrading generalization.Generalization properties of the token perceptron: the role of embedding and vocabulary
Large language models are trained to predict tokens from a finite vocabulary, each represented by a vector in a $d$-dimensional embedding space.Neural LoFi with Backward Coupling: A Spectral Theory of Cross-Layer Feature Learning
During joint training, backpropagation allows later layers to reshape earlier representations, a feedback mechanism absent from purely layer-wise learning.Mixed State Deep Thermalization under Generic Quantum Errors
Pure-state deep thermalization predicts that, after sufficient scrambling, projected ensembles approach universal distributions that contain information beyond the reduced density matrix.Mitigating Model Collapse through Dreaming Learning
Training generative models on synthetic data causes loss of diversity and a decrease in performance, a phenomenon known as model collapse.The role of locality and weight sharing in convolutional neural networks
The success of convolutional neural networks is often attributed to locality and weight sharing.Scalable Neural Wave Functions via Information Mixing and Hierarchical Network Architectures
Numerically simulating quantum many-body systems is one of the central challenges in modern physics, as the exponential growth of the Hilbert space with system size renders large-scale exact…Non-adiabatic Optimisation via Localisation-Delocalisation Transitions
The promise of efficiently solving classical optimisation problems on quantum computers is tantalizing.Extreme competition between stripes and superconductivity in the hole-doped Hubbard model
The two-dimensional Hubbard model was originally proposed as the minimal model of uniform $d$-wave superconductivity in the cuprate high-temperature superconductors, but recent calculations with…Steering of topological criticality in the Su-Schrieffer-Heeger chain via non-Hermitian substitutional impurities
We investigate the topological phase in a one-dimensional Su-Schrieffer-Heeger (SSH) chain in the presence of a finite concentration of non-Hermitian substitutional impurities.Moments of Random Permanents and Hafnians: A Replica Approach
Using contour-integral representations of the permanent and of the Hafnian, we turn the computation of their moments for random matrices into a replica calculation, analogous to that of the moments…