Condensed Matter · Physics & Astronomy
Disordered Systems & Neural Networks
The newest papers in Disordered Systems & Neural Networks that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
135 papers, from 4 September 2026 to 5 November 2026: 135 arXiv preprints.
Get the few papers worth your time in Disordered Systems & Neural Networks every morning.
Follow Disordered Systems & Neural Networks and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 3
Efficient Ground-State Manifold Amplification for Rydberg Atom Arrays via Houdayer Cluster Updates
We demonstrate a hybrid quantum-classical approach for exploring ground-state manifolds of maximum independent set problems, in which quantum hardware supplies the initial solutions and an entirely…Dynamical Batching: How Class-Cycling Training Shapes the Internal Structure of Neural Networks
We investigate whether cyclically varying the class composition of training batches can modify both the performance and the internal organization of neural networks.Physics-Inspired Optimizers beyond the Edge of Stability: Period-Two Orbits and Delayed Instability
Gradient descent with momentum can be viewed as a particle moving with friction in the loss landscape.Higher-order disorder correlations control reflected phonon counting statistics
We investigate how structural correlations beyond the power spectrum affect phonon counting in a one-dimensional acoustic stack.Power-Law Correlations Without Apparent Temperature-Tuned Criticality in Fixed-Tree Hierarchical Languages
Sampling temperature can substantially reshape autoregressive generation, but it is unclear when temperature changes in long-range correlations reflect a genuine phase transition rather than a…Towards optimal selection schedules for online active learning
We consider the problem of online linear classification, where a stream of unlabeled samples is available, and annotation is costly.Mesoscopic learning dynamics of extensive-width neural networks
Understanding how stochastic gradient descent learns internal representations in wide neural networks trained on rich target functions is a central problem in machine learning.Sampling the XY model with an attention-based neural network
We present a transformer-based variational autoregressive network for sampling lattice systems with continuous degrees of freedom and apply it to the two-dimensional XY model near the…Finite-size geometric crossover toward fragmentation of the potential energy shell in the quantum random energy model
The quantum random energy model (QREM) is a paradigmatic model for studying delocalization-localization transitions at finite energy density in Fock space.Attention over noisy examples: generalization and memorization in the exponential Hopfield network
In Hebbian learning from examples, a Hopfield network retrieves archetypes seen only through noisy examples.Stability of Quantum LDPC States under Local Pauli Noise
Quantum low-density parity-check (qLDPC) codes have a canonical mixed state---the normalized projector onto the code space---that lies at the intersection of quantum error correction and quantum…Emergent Discrimination and Polarization in Multi-Agent Systems
We study theoretically how discrimination and polarization can emerge in a population of learning agents.How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity
Mandlebrot's coastline paradox reframes measurement as a joint property of an object and its ruler.The Blume-Capel Model as a $φ^6$ Field Theory: A Monte Carlo and Flow Model Study
We report Monte Carlo simulations of a continuous spin variant of the Blume-Capel model.Sampling from the Sherrington-Kirkpatrick Model up to Criticality
We prove that the simulated annealing algorithm efficiently samples from the Sherrington-Kirkpatrick model at inverse temperature $β< 1$ to an arbitrary inverse polynomial accuracy in total variation.Optimal Disorder Strength for Approximate Unitary $k$-Designs in Spin Chains
Quantum simulators with disordered spin Hamiltonians offer a natural route to Haar-like unitary ensembles, but face a competition.Measurement-induced criticality for non-Abelian anyons: The monitored golden chain
Non-Abelian anyon chains whose interactions favor a single fusion channel host quantum critical ground states described by minimal-model conformal field theories (CFTs) protected by a topological…Fisher information and the geometry of memorization in neural networks
A fruitful approach towards understanding generalization in machine learning involves characterizing how models separate generalizable structure from idiosyncratic or even corrupted features of…Probe Signatures of Quantum Chaos with Quantum Sensing
The spectral form factor (SFF), which is the Fourier transform of the two-point spectral correlation, is a fundamental diagnostics of quantum chaos.Whither quantum advantage? New classical lower bounds for Max-Cut on large-girth 3-regular graphs
Every 3-regular graph of girth at least $g$ has a cut containing at least a fraction $M_g$ of its edges, and the best rigorous bounds at large girth are $0.912\le \lim_{g\to\infty}M_g \le 0.92386$.