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Ying Tang

Publications and source records attributed to Ying Tang.

At least 19 recordsLinked to original sources
arXiv2026

Universal sampling of spin systems across quenched disorder

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

cond-mat.stat-mech↗
arXiv2026

Black Hole-Galaxy Correlations in Cluster Zoomed-in Simulations: GIZMO-SIMBA and TNG-Cluster

We investigate the co-evolution of supermassive black holes (SMBHs) and central galaxies in massive clusters using the GIZMO-SIMBA and TNG-Cluster zoom-in simulations at $z=0-5$. We find that the distinct subgrid physics of these two models suggest fundamentally different evolutionary pathways. On the one hand, GIZMO-SIMBA, employs torque-limited accretion and predicts a supply-driven scenario where the SMBHs rapidly assemble synchronized with dark matter halo ($M_{200c}$) growth (i.e. the halo mass-BH mass relation is set by $z=3.0$ and similar to the present-day relationship). On the other hand, TNG-Cluster, exhibits a feedback-regulated growth phase delayed by an early thermal suppression. While both models successfully reproduce some local black hole-galaxy scaling relations, they imply significantly different evolution for these relations. Analysis of the BH mass-gas mass ratio relations suggests that TNG-Cluster's isotropic kinetic winds efficiently deplete cold gas, resulting in a "hard quench" of star formation. In the black hole accretion rate (BHAR)-star formation rate (SFR) relation we find that both simulations successfully reproduce the decoupling of BHAR and star formation observed in recent massive cluster ellipticals. The divergent evolutionary trends emphasize the importance of the multiphase intracluster medium; while these subgrid models do not have the necessary resolution and employ distinct formalisms, the sustained BHAR in quenched systems resemble outcomes broadly consistent with modern multiphase feeding paradigms such as chaotic cold accretion in turbulent cluster cores. Furthermore, we demonstrate that for both models, black hole mass is a primary regulator of atomic and molecular gas depletion in galaxy clusters.

astro-ph.GA↗
arXiv2026

Characterizing Full Nonequilibrium Dynamics of Simple Exclusion Processes

The simple exclusion process (SEP) is a paradigmatic model for nonequilibrium transport, yet the rich dynamics of its time-dependent joint distribution over an exponentially large configuration space remain notoriously intractable. Here, we leverage variational autoregressive networks to systematically characterize the nonequilibrium dynamics of symmetric (SSEP), asymmetric (ASEP), and totally asymmetric (TASEP) cases from one to three dimensions. We first validate the approach by reproducing the previous finite-time results for the 1D SSEP and long-time tensor-network results for the 2D SSEP, and then provide richer finite-time dynamics of the SSEP, ASEP, and TASEP in 1D and 2D, and a new finite-time analysis in 3D. Specifically, in 1D, we reveal that finite-time dynamical-activity maps directly correspond to the classical three-phase TASEP steady-state organization, and, in the long-time limit, boundary and bulk effects separately govern the dynamical susceptibility during the crossover from diffusive to ballistic transport. In 2D, we establish a mean-field directional-density criterion, supported by our neural-network calculations, and show that long-time boundary and bulk effects mirror their 1D counterparts. In 3D, we uncover new finite-time scaling relations for the active-inactive phase transition of the SSEP, and reveal a broadly consistent scaling exponent of the phase-transition point versus system size, implying that the phase-transition point is asymptotically controlled by the characteristic length scale ($s_c\sim L^{-2}$) regardless of dimension. This work thus establishes a unified framework for characterizing the nonequilibrium dynamics of representative transport systems.

cond-mat.stat-mech↗
arXiv2026

Response Morphologies of a Canonical Fluctuation Diagnostic Across Ehrenfest Phase Transitions

Microcanonical inflection-point analysis identifies MIPA-type higher-order transition structures from derivatives of the microcanonical entropy. Until recently, however, there was no canonical formulation for probing the corresponding fluctuation-level behavior directly from measurable energy fluctuations without reconstructing the density of states. Previous work addressed this limitation by introducing a canonical fluctuation diagnostic that quantifies energy-fluctuation asymmetry. Here, we investigate how this diagnostic behaves across representative first-, second-, and third-order phase transitions in the Ehrenfest classification. We consider the eight-state Potts model, the two-dimensional Ising model, and ideal three-dimensional Bose--Einstein condensation. The Potts and Ising systems exhibit a robust paired-extremum structure of the diagnostic near their respective transition regions, whereas ideal Bose--Einstein condensation exhibits a discontinuous response at the condensation temperature in the thermodynamic limit. These results show that a single fluctuation-based observable can display qualitatively distinct response morphologies across thermodynamic regimes, reflecting sensitivity to the nature of the underlying phase transition rather than serving as a direct classifier of Ehrenfest order.

cond-mat.stat-mech↗
arXiv2026

Energy-guided Recursive Model

Recursive reasoning models address structured problems by repeatedly updating latent states of small neural networks. However, their test-time scaling lacks a principled inference mechanism: increasing depth or stochastic breadth generates more trajectories without a clear criterion for selection, and existing methods predominantly rely on additional q-heads or heuristic voting. Here, we develop the Energy-guided Recursive Model (ERM), which introduces an intrinsic selection principle based on explicit Hopfield energies. ERM leverages Hopfield-type memories of valid local or global structures to define the selector over candidate trajectories. The resulting energy seamlessly integrates with energy-based techniques such as parallel tempering to enhance sampling efficiency and ranking. With $D=64$ recurrent steps and $K=128$ candidates, ERM reaches optimal solutions on Sudoku ($98.97\%$), Pencil Puzzle Bench (PPBench, $88.04\%$) and Maze ($99.30\%$), improving upon recent Probabilistic Tiny Recursive Model and Equilibrium Reasoners. These results suggest that incorporating explicit energy functions into recursive reasoning offers a principled path toward more effective inference.

cs.LG↗
arXiv2026

Scalable Physics-Inspired Transformers for Spin Glasses

Efficient sampling of the Boltzmann distribution in frustrated spin glasses is central to statistical mechanics and combinatorial optimization. Despite advances in machine-learning-based approaches, two issues persist: limited understanding of why variational models fail to benefit from increased scale, unlike the monotonic scaling law of large language models; and high computational cost on large systems that negates advantages over classical sampling methods. Here, we develop a physics-inspired transformer with interpretable sparse attention and spin-tailored positional embeddings to address these challenges. By further leveraging FlashAttention for parallel ancestral sampling, it achieves up to two orders of magnitude speedup over vanilla variational autoregressive networks, enabling neural-network simulations of spin-glass systems to unprecedented sizes on a single GPU. It can resolve full probability distributions, free energies, and overlap statistics across temperatures, for Sherrington-Kirkpatrick and 2D or 3D Edwards-Anderson models, where existing machine-learning methods encounter limitations at certain temperatures. This framework thus establishes a scalable paradigm for frustrated spin-glass systems.

cond-mat.dis-nn↗
arXiv2026

Irreversibility Enhances Quantum-Enhanced Markov-Chain Monte Carlo

Detailed balance underlies conventional Markov-chain Monte Carlo (MCMC) algorithms. Yet in classical systems, breaking detailed balance generates irreversible probability currents and can accelerate sampling. Whether irreversibility can similarly enhance quantum MCMC remains an intriguing question. Here we show that irreversibility provides a new route to improving the recent quantum-enhanced MCMC (QEMC), which combines quantum proposals with classical acceptance. By introducing state-dependent proposals that break detailed balance while preserving the target stationary distribution, we develop an irreversible quantum-enhanced Monte Carlo (IQEMC). Guided by Landau-Zener transitions, IQEMC promotes large energy descents from high-energy states while maintaining stable transitions near low-energy states. On spin-glass benchmarks, IQEMC outperforms QEMC without increasing computational complexity and, unlike the annealing baseline, exhibits a spectral gap that increases with system size and annealing speed. These results establish irreversibility as a physically grounded mechanism for enhancing quantum MCMC.

cond-mat.stat-mech↗
arXiv2026

Kolmogorov-Arnold Reservoir Computing

Reservoir computing offers a lightweight framework for forecasting dynamical systems but may struggle to capture long-range dependencies due to limited representational capacity. Conventional reservoir computing recurrently uses fixed reservoirs with hyperparameter sensitivity, while the next generation reservoir computing removes recurrence at the cost of rapidly growing feature dimensions. Here, we develop Kolmogorov-Arnold Reservoir Computing (KARC), which replaces reservoirs with explicit basis-function expansions inspired by the Kolmogorov-Arnold representation theorem. We rigorously show that KARC is a lightweight design of Kolmogorov-Arnold networks (KANs), preserving the potential expressive capacity of KANs while admitting efficient closed-form training of reservoir computing. At comparable cost, KARC outperforms existing reservoir computing methods on challenging benchmarks including partial differential equations. It can also be integrated with generative diffusion models for facilitating text-to-image generation. This work thus establishes a principled bridge between reservoir computing and KANs, yielding a unified framework for efficient dynamical forecasting and generative modeling.

cs.LG↗
arXiv2026

Dynamical Partition Functions of Stochastic Dynamics via Variational Flows

Nonequilibrium thermodynamics is governed by the dynamical partition function, and its computation in high-dimensional continuous-state dynamics is a longstanding challenge. The Feynman-Kac formalism provides a rigorous representation for generating functions of arbitrary path observables; however, practical evaluation beyond low dimensions or the weak-noise limit is hindered by the curse of dimensionality and the exponentially growing replica demands of trajectory-based methods. Here we develop a mesh-free neural variational framework that realizes the Feynman-Kac theorem with generative flow models, recasting tilted stochastic evolution as a time-dependent optimization problem. This approach enables the direct computation of both finite-time and asymptotic trajectory thermodynamics in a unified manner. The method applies to general observables, enabling the evaluation of work, entropy production, and current fluctuations. We demonstrate the accuracy and scalability of this method in various nonequilibrium systems including high-dimensional cases.

cond-mat.stat-mech↗
arXiv2026

PRISM: Synergizing Vision Foundation Models via Self-organized Expert Specialization

Unifying the complementary strengths of diverse Vision Foundation Models (VFMs) into a single efficient model is highly desirable but challenged by the negative transfer inherent in monolithic distillation. To address these feature conflicts, we introduce \textbf{PRISM}, a novel dual-stream Mixture-of-Experts (MoE) framework that synergizes VFMs via modular specialization. We propose a two-stage paradigm: (1) expertise deconstruction, where a teacher-conditional router guides experts to specialize in distinct representational subspaces to mitigate interference, followed by (2) dynamic recomposition, where the router learns to assemble these experts into tailored computational pathways for downstream tasks. Experiments on PASCAL-Context and NYUD-v2 show that \textbf{PRISM} establishes a new state of the art, validating that sparse, emergent specialization is a scalable approach for integrating diverse visual knowledge.

cs.CV↗
arXiv2026

Turbulence-Robust Dynamic Object Segmentation with Multi-Signal Priors and SAM2 Refinement

This technical report presents our solution for the CVPR 2026 UG2+ Challenge Track 3: Dynamic Object Segmentation in Turbulence (DOST). We design a training-free multi-signal segmentation pipeline that combines pretrained motion estimation, self-supervised semantic priors, background anomaly modeling, manually calibrated proposal fusion, and SAM2-based mask refinement. The method uses RAFT for dense motion responses, DINOv2 for semantic objectness priors, ViBe for training-free background modeling, and pretrained SAM2 for box-prompt mask refinement. Instead of optimizing an end-to-end segmentation network, our system operates entirely in inference mode. This design is suitable for the DOST setting, where severe atmospheric turbulence produces pseudo-motion, blur, and intermittent target visibility, making a single motion cue unreliable. The final submitted masks are evaluated by the official leaderboard, which reports 0.425041 mIoU and 0.457206 mDice. Since no task-specific model training or fine-tuning is performed, stronger learned temporal association, adaptive proposal selection, or task-specific adaptation may further improve the system.

cs.CV↗
arXiv2026

Threshold-Controlled Geometric Reorganization in 2D Bootstrap Percolation

Two-dimensional bootstrap percolation is usually characterized by bulk observables, but whether increasing the activation threshold qualitatively reorganizes the geometry of the absorbing state has remained unclear. Here we show that the response undergoes a threshold-controlled geometric crossover. At low thresholds, the extrema of bulk and boundary-sensitive observables remain confined to a single collective low-$p$ window. At high thresholds, they split into distinct branches, revealing multiple geometric response scales. Over the accessible system sizes, the dominant finite-size signatures shift from fluctuations of the final active density to non-singleton boundary observables, while the fluctuation peak itself decreases. Time-resolved mechanism traces show that this crossover is accompanied by a progression from extended collective propagation to frontier exhaustion and, at the highest threshold, to quasi-one-step stabilization. Our results identify boundary organization as the dominant structural signature of high-threshold bootstrap percolation and show that conventional bulk observables alone do not capture the full reorganization of the absorbing state.

cond-mat.stat-mech↗
arXiv2026

Forecasting Return Time of Extreme Precipitation by Large Deviation Theory

Forecasting extreme precipitation is essential yet challenging due to its rarity and complexity. We develop a large deviation framework to estimate the return times of extreme precipitation events. We first find that the Landau distribution, originally introduced in plasma physics, accurately captures extreme precipitation at approximately 93% of global locations, outperforming conventional extreme value distributions with 76% matched locations under the same accuracy criterion. Enriching rare event samples by the fitted Landau distribution, we obtain more accurate estimates of large deviation rate functions and return times, enabling forecasts beyond historically observed precipitation intensities. Mapping historical return times to future projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show that return time curves under different emission scenarios collapse onto a unified relation, revealing a sharply increased lifetime exposure to extreme precipitation for 21st-century birth cohorts under most future emission scenarios.

cond-mat.stat-mech↗
arXiv2026

Third-order transitions in Ising and Potts models on Watts--Strogatz small-world networks

We study third-order transitions in the two-dimensional Ising and Potts model on regular lattices and Watts--Strogatz small-world networks. Cluster observables are used to track post-critical boundary reorganization and pre-critical cluster breakup. For the Ising model, the critical temperature $T_c$ is calibrated independently from Binder-cumulant crossings and susceptibility peaks, whereas for the Potts model on small-world networks it is identified operationally from the dominant critical peak of $\mathrm d\langle P\rangle/\mathrm dT$. The independent and dependent third-order transitions are identified from the isolated-spin peak and the post-critical structural extremum, respectively. For both lattice and small-world topologies, we find the robust ordering $T_{\mathrm{ind}}<T_c<T_{\mathrm{dep}}$. Increasing the rewiring probability shifts all three characteristic temperatures upward and enhances the visibility of the post-critical transition. The effect is especially clear in the Potts model, where perimeter-based observables are more sensitive to multistate boundary fluctuations. The systematic persistence of the characteristic temperature hierarchy across topologies and finite sizes argues against interpreting these features as incidental finite-size irregularities. Instead, our results support their interpretation as genuine third-order transitions whose structural detectability can be amplified by network topology.

cond-mat.stat-mech↗
arXiv2026

Canonical Criterion for Third-Order Transitions

Microcanonical inflection-point analysis (MIPA) identifies third-order transitions from derivatives of the microcanonical entropy, but whether such transitions admit a direct canonical formulation has remained unclear. Here we establish a fluctuation-based canonical framework for third-order transitions through a cumulant-ratio criterion whose signed extrema define their canonical counterparts and, in the single-saddle regime, are asymptotically linked to microcanonical classification. Because the criterion depends only on energy cumulants, it avoids explicit density-of-states reconstruction and remains operational in nonequilibrium steady states. Physically, it reveals dependent and independent third-order transitions as fluctuation reorganizations around low-order transitions, namely disordered-side precursors and ordered-side restructuring. Benchmarks on Onsager's two-dimensional Ising solution, finite size Potts models, and a driven nonreciprocal Ising model show that the framework is theoretically grounded and broadly applicable.

cond-mat.stat-mech↗
arXiv2026

Steering Dynamical Regimes of Diffusion Models by Breaking Detailed Balance

We show that deliberately breaking detailed balance in generative diffusion processes can accelerate the reverse process without changing the stationary distribution. Considering the Ornstein--Uhlenbeck process, we decompose the dynamics into a symmetric component and a non-reversible anti-symmetric component that generates rotational probability currents. We then construct an exponentially optimal non-reversible perturbation that improves the long-time relaxation rate while preserving the stationary target. We analyze how such non-reversible control reshapes the macroscopic dynamical regimes of the phase transitions recently identified in generative diffusion models. We derive a general criterion for the speciation time and show that suitable non-reversible perturbations can accelerate speciation. In contrast, the collapse transition is governed by a trace-controlled phase-space contraction mechanism that is fixed by the symmetric component, and the corresponding collapse time remains unchanged under anti-symmetric perturbations. Numerical experiments on Gaussian mixture models support these findings.

cond-mat.stat-mech↗
arXiv2026

DB-MSMUNet:Dual Branch Multi-scale Mamba UNet for Pancreatic CT Scans Segmentation

Accurate segmentation of the pancreas and its lesions in CT scans is crucial for the precise diagnosis and treatment of pancreatic cancer. However, it remains a highly challenging task due to several factors such as low tissue contrast with surrounding organs, blurry anatomical boundaries, irregular organ shapes, and the small size of lesions. To tackle these issues, we propose DB-MSMUNet (Dual-Branch Multi-scale Mamba UNet), a novel encoder-decoder architecture designed specifically for robust pancreatic segmentation. The encoder is constructed using a Multi-scale Mamba Module (MSMM), which combines deformable convolutions and multi-scale state space modeling to enhance both global context modeling and local deformation adaptation. The network employs a dual-decoder design: the edge decoder introduces an Edge Enhancement Path (EEP) to explicitly capture boundary cues and refine fuzzy contours, while the area decoder incorporates a Multi-layer Decoder (MLD) to preserve fine-grained details and accurately reconstruct small lesions by leveraging multi-scale deep semantic features. Furthermore, Auxiliary Deep Supervision (ADS) heads are added at multiple scales to both decoders, providing more accurate gradient feedback and further enhancing the discriminative capability of multi-scale features. We conduct extensive experiments on three datasets: the NIH Pancreas dataset, the MSD dataset, and a clinical pancreatic tumor dataset provided by collaborating hospitals. DB-MSMUNet achieves Dice Similarity Coefficients of 89.47%, 87.59%, and 89.02%, respectively, outperforming most existing state-of-the-art methods in terms of segmentation accuracy, edge preservation, and robustness across different datasets. These results demonstrate the effectiveness and generalizability of the proposed method for real-world pancreatic CT segmentation tasks.

cs.CV↗
arXiv2025

Tracking large chemical reaction networks and rare events by neural networks

Chemical reaction networks are widely used to model stochastic dynamics in chemical kinetics, systems biology and epidemiology. Solving the chemical master equation that governs these systems poses a significant challenge due to the large state space exponentially growing with system sizes. The development of autoregressive neural networks offers a flexible framework for this problem; however, its efficiency is limited especially for high-dimensional systems and in scenarios with rare events. Here, we push the frontier of neural-network approach by exploiting faster optimizations such as natural gradient descent and time-dependent variational principle, achieving a 5- to 22-fold speedup, and by leveraging enhanced-sampling strategies to capture rare events. We demonstrate reduced computational cost and higher accuracy over the previous neural-network method in challenging reaction networks, including the mitogen-activated protein kinase (MAPK) cascade network, the hitherto largest biological network handled by the previous approaches of solving the chemical master equation. We further apply the approach to spatially extended reaction-diffusion systems, the Schl\"ogl model with rare events, on two-dimensional lattices, beyond the recent tensor-network approach that handles one-dimensional lattices. The present approach thus enables efficient modeling of chemical reaction networks in general.

q-bio.MN↗