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 6
First Learn, Then Memorize: The Spectral Bias of Diffusion Models
Diffusion models trained on a finite dataset first learn to generate novel, high-quality samples and only much later collapse onto their training set.Discrete Scale Invariance of Ising Mesons
Discrete scale invariance often arises from resonant few-body or singular scale-invariant dynamics.Collectivity limits quantum entanglement
Understanding what limits many-body quantum entanglement is a central problem in physics.Interactions Protect Periodically Driven Time Crystals Against Dephasing but Destabilise Multipolar-Driven Order
We show that stronger interactions protect periodically driven time-crystal order against dephasing but destabilise order under random multipolar drives, and that coordination matters beyond the…Swapping Quantum Annealing Errors into a Cavity
Quantum annealing is a promising form of quantum computation, but it slows down at a small energy gap $Δ$ during the anneal, and the errors it makes there remain undetected.A Flatness-Generalization Relation in the Teacher-Student Tree-Committee Machine
The flatness of the loss landscape at a minimizer is a widely used heuristic for reasoning about neural-network generalization, yet evidence for this relation is mostly empirical and controversial.Connecting finite-size scaling and renormalization-group flows in Anderson localization on random graphs
We investigate the relation between two complementary descriptions of the Anderson transition on small-world random graphs: finite-size scaling of eigenfunction moments and renormalization-group…Accurate Sampling from Diffusion Models
A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using diffusion models trained on samples from the…Disorder-induced quantum Fisher information in topological quantum systems
The topological order of the Kitaev toric code is insensitive to disorder in its couplings since all star and plaquette operators commute; consequently, the stabilizer ground-state manifold is…A Mechanism for the Rényi Hierarchy of Decoherence-Induced Phase Transitions
Decoherence-induced phase transitions (DIPTs) are associated with singular changes in a quantum system's entanglement structure as the decoherence strength varies.Discrete Diffusion Models via Evolving Variational Autoregressive Networks
Conventional score-based diffusion models learn scores without representing normalized densities, whereas tractable normalized models support both sampling and direct likelihood evaluation.Optimal low-rank compression of quantum dynamics
Local quantum interactions generate dynamics in an exponentially large Hilbert space, yet locality and entanglement restrict the information that can spread and accumulate.Rank-One Signal Recovery in Sparse Wishart Noise
We study the high-dimensional recovery of a signal vector $\mathbf{x}$ in the presence of sparse Wishart-like noise.Analog neutral-atom for in-memory processing in quantum reservoir computing
Quantum Reservoir Computing (QRC) exploits the rich dynamics of quantum many-body systems to process time-dependent information with high-dimensional state spaces.Nonequilibrium Phases of Repulsive Self-Attention: Chaos, Attention Condensation, and Emergent Locality
We study the nonequilibrium dynamics of a minimal recurrent transformer with $N$ normalized tokens, $Q=K=I$, and a negative value map $V=-I$.RLVR landscapes for iterated multiplications can be benign: Insights from spin-glass theory
Despite the importance of reinforcement learning with verifiable rewards (RLVR), the extent to which it can learn new reasoning capabilities remains debated.An Exploratory Replica-Overlap Probe of the Grokking Transition
We trained 64 independently seeded networks in four configurations, continuing each to sustained convergence or a 40,000-epoch ceiling.Hyperbolic Restricted Boltzmann Machine Neural Quantum State
We construct the first type of non-Euclidean non-autoregressive neural quantum state (NQS) in the form of the hyperbolic Restricted Boltzmann Machine (HRBM), which is studied in the variational…Entanglement Embezzlement from Diffusive Hydrodynamics
Entanglement embezzlement asks how much entanglement can be borrowed from a many-body state by local operations and classical communication while returning that state with a small error.Double Descent and Malign Overfitting in Diffusion Models
Conventional wisdom in deep learning holds that overparameterization---having more parameters $p$ than training samples $n$---is benign: larger models generalize better and, even without…