arXiv subjects

Kai Zhou

Publications and source records attributed to Kai Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation

Open-set test-time adaptation (TTA) aims to adapt source-trained models to unlabeled test streams where covariate-shifted in-distribution (csID) samples and covariate-shifted out-of-distribution (csOOD) samples may coexist. Existing TTA methods are typically designed under a closed-set assumption and therefore lack explicit mechanisms for reliably distinguishing csID from csOOD samples. Consequently, csOOD samples may be mistakenly included in confidence-enhancing adaptation, producing harmful gradients that degrade known-class performance. To address this, we propose Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation (UAPA), a plug-and-play method for reliable open-set adaptation. Specifically, we first introduce Uncertainty-Driven ID/OOD Separation, which combines a classifier-prototype score with a mini-batch-normalized evidence-derived uncertainty score to distinguish csID from csOOD samples. We then propose a Dynamic Prototype Refinement strategy that periodically refines class-specific target prototypes using buffered csID features to progressively follow the estimated class directions. Lastly, we introduce Contrastive Prototype Entropy Adaptation, which applies entropy minimization to csID samples and entropy maximization to csOOD samples, while prototype-level contrastive learning aligns csID features with their corresponding target prototypes and reduces the maximum similarity between csOOD features and known-class prototypes. Empirically, UAPA outperforms the strongest baselines on the CIFAR benchmarks and Tiny-ImageNet-C, improving average OSCR by 2.08 and 1.28 percentage points, respectively.

cs.LG↗

arXiv2026

On blow-up for the cut-off Boltzmann equation with very hard potentials

We construct smooth, strictly positive solutions of the three-dimensional cut-off Boltzmann equation with very hard potentials whose densities become unbounded in finite time. A main ingredient is a new velocity weight that is Gaussian on a ball and has a polynomial tail of fixed order. We establish coercive estimates for the linearized collision operator and bilinear estimates for the collision term, with constants independent of the radius of the Gaussian region. Combining these weighted estimates with an imploding solution of the compressible Euler equations, we develop an energy framework that controls the Boltzmann solution up to the blow-up time. The initial data agree with a constant Maxwellian outside a bounded spatial region and have finite relative entropy.

math.AP↗

arXiv2026

Inferring Nuclear Radial Parameters from Initial-State Information in Heavy-Ion Collisions Using Deep Neural Networks

The geometric structure of atomic nuclei plays an important role in shaping the initial conditions of high-energy nuclear collisions and the subsequent evolution of the quark--gluon plasma. In this work, we develop a multimodal deep-learning framework to infer the Woods--Saxon radius ($R_0$) and diffuseness parameter ($a$) from initial-state information. Using the TRENTo model for relativistic $^{238}$U+$^{238}$U collisions, we construct a dataset covering multiple combinations of $R_0$ and $a$, and perform parameter regression by combining two-dimensional entropy-density distributions with event-level scalar observables, including multiplicity and eccentricities $\varepsilon_2$--$\varepsilon_5$. The results show that the proposed framework can effectively recover the Woods--Saxon parameters from initial-state information. Joint multi-event inputs suppress statistical fluctuations and improve regression performance by enhancing the extraction of collective information. The inversion accuracy exhibits a pronounced centrality dependence: $R_0$ is best constrained in central collisions, whereas $a$ shows the strongest sensitivity in the mid-peripheral region and becomes less well constrained in ultra-central and peripheral collisions. These findings indicate that the initial-state geometry retains useful information about nuclear radial structure and provide a baseline for future studies that incorporate transport evolution and experimental final-state observables.

nucl-th↗

arXiv2026

Laser with $7\times10^{-18}$ stability

We report a laser with record frequency stability, based on a 21 cm cryogenic silicon cavity. For best short-term stability, the cavity operates at 6 K to minimize Brownian thermal noise and achieves a fractional stability of $7\times10^{-18}$ between 2 to 50 s of averaging time and 2.5 mHz linewidth. For best long-term stability the cavity operates at 16 K, where silicon has a zero-crossing of thermal expansion. Slightly increased thermal noise limits the performance to $9\times10^{-18}$, consistent with the expected temperature scaling, but the long-term performance is improved. The laser then has a linewidth of 2.9 mHz and long-term frequency drift less than 4 μHz s$^{-1}$. The laser is used to enhance the performance of a state-of-the-art Sr lattice clock, operating with Rabi and Ramsey interrogation times beyond 10 s.

physics.optics↗

arXiv2026

Physical Operating-Condition-Guided Vision-Language Matching for Cavitation Diagnosis in Industrial Pipeline Systems

Cavitation diagnosis in industrial pipeline systems is challenging because acoustic responses vary substantially with operating conditions, while similar time-frequency patterns may correspond to different cavitation states. To address this issue, we propose a physical operating-condition-guided vision-language matching (POC-VLM) for cavitation diagnosis. POC-VLM first constructs physical operating-condition-aware semantic descriptions by explicitly incorporating sample-specific operating variables into candidate cavitation texts. A time-frequency-conditioned text adaptation module then dynamically aligns these physical semantics with the observed acoustic response, followed by global-local vision-language matching to capture both holistic and localized discriminative patterns. In parallel, a K-nearest neighbours-based non-parametric physical operating-condition prior estimates the physical plausibility of each candidate state and is fused with the vision-language semantic evidence at the decision level. Experiments on three real-world cavitation datasets show that POC-VLM consistently outperforms representative data-driven, multimodal and domain-specific diagnostic methods. Further analyses verify the complementary roles of vision-language semantic matching and physical operating-condition priors, while demonstrating robust performance under varying sampling rates, noise levels and observation durations.

cs.AI↗

arXiv2026

Physical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport Systems

Industrial fluid transport systems are monitored through heterogeneous sensing streams, but robust fault diagnosis remains difficult because response signals vary with operating conditions and differ from physical variables in sampling rate, observation duration and data granularity. To address these challenges, we propose a physical-variable-informed deep learning framework (PhyNet) for robust fault diagnosis under real-world sampling heterogeneity. PhyNet treats physical variables as global operating constraints and response signals as local diagnostic evidence. Its physical-to-response cross-attention mechanism connects sequence-level physical-variable tokens with a learnable global response token, enabling interaction without strict temporal alignment. Across four benchmarks, including three real-world cavitation datasets and one public two-phase-flow dataset, PhyNet achieves the best performance across all evaluated metrics. Class-wise analyses, together with attention and masking analyses, show improved discrimination of ambiguous states and indicate that physical variables meaningfully constrain local response interpretation. These findings demonstrate a deployment-oriented approach to robust diagnosis in heterogeneous industrial environments.

cs.LG↗

arXiv2026

Task-State Governance: A Virtual Machine for Verifiable and Recoverable GUI Agent Execution

Graphical user interface (GUI) agents now execute workflows across real applications, but when execution drifts, users must determine what changed, what should have remained untouched, and what can still be recovered. Plan-centric interfaces leave users supervising procedure, but users live with changes to application state. We introduce task-state governance, an interaction paradigm that represents a task as an editable contract linking desired outcomes, facts that must be preserved, and observed application state. We instantiate this paradigm in TaskVM, which projects live state into editable task controls, executes user edits as genuine interface actions, verifies outcomes through independent observation, and marks each action as reversible, compensatable, or irreversible. Across an automated contract benchmark, a controlled user study, and an open live-model exploration, the paradigm strengthened contract compliance and users' recovery control and trust, while execution outcomes were held constant. Task contracts open a design space for governable agent interfaces.

cs.HC↗

arXiv2026

Bayesian Inference of the Critical Endpoint in 2+1-Flavor System from Holographic QCD

We present a Bayesian holographic model constructed by integrating the equation of state and baryon number susceptibility at zero chemical potential from lattice QCD. The model incorporates error estimates derived from lattice data. With this model, we systematically investigate the thermodynamic properties of the 2+1-flavor QCD system. Using Bayesian Inference, we perform precise calibration of the model parameters and determined the critical endpoint (CEP) position under the maximum a posterior (MAP) estimation to be $(T^{c},μ_{B}^{c})=(0.0859\;\mathrm{GeV},0.742\;\mathrm{GeV})$. Additionally, we predict the CEP positions within 68\% and 95\% confidence levels, yielding $(T^{c},\; μ_{B}^{c})_{68\%}$=$(0.0820\text{--}0.0889, 0.71\text{--}0.77)\;\mathrm{GeV}$ and $(T^{c},\; μ_{B}^{c})_{95\%}$=$(0.0816\text{--}0.0898,\; 0.71\text{--}0.79)\;\mathrm{GeV}$, respectively. Moreover, to validate the reliability and predictive power of our approach, we conduct a comprehensive comparison between our predictions and potential CEP locations proposed by other theoretical models. This work not only establishes a novel Bayesian framework for holographic modeling but also provides valuable insights and theoretical support for exploring phase transitions in strongly-interacting matter under extreme conditions.

hep-ph↗

arXiv2026

Systematic Bayesian investigation of the (2+1)-flavor QCD phase transition in a holographic model

In this study, we construct a bayesian holographic QCD model by integrating the Einstein-Maxwell-Dilaton (EMD) framework with lattice quantum chromodynamics (QCD) data at zero chemical potential, specifically entropy, the square of the speed of sound, and baryon number susceptibilities, while systematically incorporating error estimates from the lattice QCD (LQCD) results. Leveraging a bayesian inference framework, we first achieve a precise calibration of the model parameters, then perform a comprehensive investigation into the thermodynamic properties of $(2+1)$-flavor QCD at both zero and finite chemical potentials, and finally provide a prediction for the location of the critical end point (CEP) in the QCD phase diagram. Our results indicate that, under the maximum a posteriori (MAP) estimation, the CEP is located at $(T, μ)_{\mathrm{MAP}} = (0.119, 0.615)\mathrm{GeV}$. Furthermore, we provide the predicted regions for the CEP at $68\%$ and $95\%$ confidence levels (CL), yielding $(T, μ)_{68\% \mathrm{CL}} = (0.1176\text{--}0.1204,0.59\text{--}0.63)\mathrm{GeV}$ and $(T, μ)_{95\% \mathrm{CL}} = (0.1172\text{--}0.1206,0.58\text{--}0.63)\mathrm{GeV}$, respectively. A thorough comparison with predictions from other theoretical models validates the robustness and predictive power of our approach. This work not only establishes a novel analytical framework for holographic modeling but also provides valuable theoretical insights into the phase transitions of strongly interacting matter under extreme conditions.

hep-ph↗

arXiv2026

Stochastic Path Sampler For Lattice Field Theory

In lattice field theory, target distributions are known only up to normalization, (\tildeπ(ϕ)\propto e^{-S(ϕ)}), while the partition function is intractable. Markov chain Monte Carlo simulations often become inefficient near phase transitions or the continuum limit due to critical slowing down. In this work, we propose a novel sampler based on nonequilibrium thermodynamics, called Stochastic Path Sampler (SPS), which can generate configurations for the unnormalized target distribution without requiring training data. The central idea of SPS is to establish a trajectory-level balance for learnable forward and backward stochastic dynamics between two equilibrium states, namely the prior and target distributions. This is achieved by minimizing the path-space variational free energy, equivalently an entropy-production upper bound, defined by the log-ratio of forward and auxiliary backward trajectory measures, thereby enhancing the reversibility of the forward and backward processes. The learned forward process provides independent proposals, which are subsequently corrected by an extended-space Independence Metropolis--Hastings step. In two-dimensional (ϕ^4) theory, we demonstrate that our neural sampler can achieve the same sampling quality as HMC but with a much shorter autocorrelation time in the critical region. This sampler offers a stochastic-quantization-inspired route to data-free proposal construction for lattice field theory by leveraging a variational free-energy principle derived from path-space irreversibility.

hep-lat↗

arXiv2026

Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery

Machine learning (ML) in high-energy nuclear physics (HENP) is entering a new stage in which physical knowledge is incorporated more directly into data analysis, simulation, and physics inference. This mini-review focuses on developments that have matured in the past several years. Whereas earlier applications emphasized event classification, pattern recognition, and surrogate models for selected observables, recent work has moved toward physics-integrated workflows: calibrated Bayesian extraction of QCD matter properties, dense-matter equation-of-state inference from heavy-ion and neutron-star data, generative event modeling, neural unfolding of weak physical signals, differentiable inverse solvers, gauge-equivariant and diffusion-based lattice-field samplers, and neural reconstruction of model functions in holographic QCD. We survey recent applications of ML in heavy-ion collisions, neutron-star physics, lattice QFT, and holographic or continuum QCD. The emphasis is not on ML architectures alone, but on how they enter concrete physics workflows, how physical constraints such as symmetries, conservation laws, causality, thermodynamic stability, and topology are imposed, and how uncertainty quantification and validation determine whether an AI-assisted result can support a reliable physics conclusion.

hep-ph↗

arXiv2026

Structure-aware Keypoint Localization for Videofluoroscopic Swallowing Study

Videofluoroscopic Swallowing Study (VFSS) is one of the gold standard for diagnosing swallowing disorders, providing dynamic X-ray imaging of the swallowing process. Automated kinematic analysis in VFSS relies fundamentally on precise anatomical keypoint localization. However, existing studies focus on limited keypoints (e.g., cervical vertebrae or the hyoid) and overlook critical regions such as the soft palate, while annotating only active swallowing segments and ignoring abundant non-swallowing data, resulting in poor data efficiency. Moreover, leveraging this unlabeled data via standard semi-supervised learning is suboptimal, as generic methods are prone to spatial bias. In medical X-rays with fixed layouts, models tend to memorize absolute coordinates rather than understanding anatomical structures. To tackle these challenges, we introduce VFSSKep, a novel dataset that extends annotations to the soft palate and incorporates large-scale unlabeled data. We further propose S$^3$KL, a Structure-aware Semi-Supervised Keypoint Localization framework designed to overcome spatial bias. It integrates a Structure-Aware Learning strategy to extract high-resolution structural cues for structure-aware representation learning, and a Structural Representation Consistency Learning strategy with block shuffling to enforce invariant structural recognition. Experiments show our method achieves state-of-the-art semi-supervised performance, even with unlabeled and 25% labeled data surpassing fully supervised learning with 100% labeled data. Code and data will be made publicly available at: https://github.com/kaai520/S3KL.

cs.CV↗

arXiv2026

PulseInfer: I/O-Centric Sparse KV Cache Offloading for Efficient Long-Context LLM Decoding

Long-context LLM serving is increasingly bottlenecked by decode, where large KV caches limit batch size and underutilize GPUs. Sparse KV cache offloading expands effective capacity by storing most historical KV blocks in CPU DRAM and recalling only selected blocks on demand. However, we find that existing offloading systems shift the bottleneck to CPU-GPU recall I/O: recall volume varies widely across layers, decode steps and requests, while headwise sparse selection fragments recalls into many small PCIe transfers. This paper presents PulseInfer, an I/O-centric sparse KV cache offloading system. PulseInfer hides variable recall latency with interruptible layer-wise scheduling, adapts offloading decisions with IO-Adaptive Offloading Admission, and coalesces fragmented transfers using SoloHead sparse selection and a gather-scatter I/O engine. Implemented on SGLang, PulseInfer improves decode throughput by up to 4.7x over SGLang and 2.6x over the best existing offloading baseline, while reducing TPOT by up to 76% and preserving near-lossless accuracy.

cs.LG↗

arXiv2026

Sensitivity of Neutron Star Observables to Transition Density in Hybrid Equation-of-State Models

We investigate how the transition density $ρ_{tr}$ affects hybrid constructions of the neutron-star equation of state (EoS) in which a nucleonic description at low densities is matched to a model-agnostic high-density extension based on a speed-of-sound parametrization. Using four representative nucleonic models--Taylor expansion, $\frac{n}{3}$ expansion, Skyrme, and relativistic mean-field--built from identical nuclear matter parameters, we isolate the impact of the low-density EoS and the transition density on neutron star observables. We find that, within the present smooth-matching prescription, neutron star properties such as radii and tidal deformabilities retain significant sensitivity to the choice of low-density EoS for commonly adopted transition densities around $ρ_{tr} \approx 2ρ_0$, even when the same high-density parametrization is employed. This residual dependence arises from differences in the matching conditions at $ρ_{tr}$, which propagate into the high-density extension, so different low-density inputs lead to different effective high-density EoSs. These findings are robust across two distinct speed-of-sound parametrizations. Quantitatively, the model spread in radius and tidal deformability at $1.4\,M_\odot$ exceeds the current observational uncertainty by factors of $\sim 1.8$ and $\sim 1.4$ at $ρ_{\mathrm{tr}} \approx 2ρ_0$, whereas these factors reduce to $\sim 1.05$ and $\sim 0.4$ at $ρ_{\mathrm{tr}} = ρ_0$. Lowering the transition density, therefore, systematically diminishes the spread among models and leads to more consistent predictions. Our results demonstrate that the widely used choice $ρ_{tr} \approx 2ρ_0$ does not guarantee model independence in hybrid EoS constructions, and should be treated as an explicit source of systematic uncertainty when inferring dense matter properties from neutron star observations.

nucl-th↗

arXiv2026

Singular Cholesky Fibers over Finite Fields

For a finite field $\F_q$, consider the triangular Cholesky map $Γ_{n,q}(U)=U^TU$ from upper triangular matrices to symmetric matrices. Generalized Cholesky theory describes the regular locus on which all leading principal minors are nonzero, but it does not determine the multiplicities or root ranks in a fiber over a singular target. We develop a fixed-target fiber theory for this singular boundary and answer three questions posed by Cooper and Whitlatch. Our principal results concern the zero fiber. Over $\F_2$ we construct an explicit, invertible, rank-preserving recursive bijection between square-zero upper triangular matrices and upper triangular matrices satisfying $U^TU=0$. Over every finite field we determine the entire rank distribution of this zero fiber: in even characteristic the square-zero recurrence, and hence the rank-refined equinumerosity, persists over every $\F_{2^e}$, whereas in odd characteristic its failure is measured by an explicit quadratic-character correction. We place these results in a uniform framework by proving an exact first-pivot recursion for the fiber cardinality over an arbitrary symmetric target. Its regular specialization gives the constant fiber sizes on leading-principal-minor cones, while its zero-pivot branch explains the new singular behavior. For binary diagonal targets we further compress the rank-refined count to $O(n^2)$ integer-arithmetic transitions and show that it depends on the order, not merely the number, of the diagonal entries. Executable implementations and exhaustive low-order checks are collected in an appendix.

math.RA↗

arXiv2026

DSPrompt: Dynamic Soft Prompt Defense Against M-RAG Corruption

Multimodal Retrieval Augmented Generation (M-RAG) is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce embeddings that align with benign entries in the vector space, deceiving retrieval and inducing harmful outputs. Existing defenses primarily operate at query time, relying on auxiliary detectors, similarity re-ranking, or feature-consistency checks. However, these approaches suffer from non-trivial inference overhead, generalize poorly to unseen attack strategies, and often assume specific attack distributions. To address this, we propose DSPrompt, a Dynamic Soft Prompt defense framework that directly reshapes the retriever's embedding semantics, without modifying the retrieval pipeline. It inserts few learnable soft prompts into each layer of the visual and textual encoders of a frozen retriever, utilizing a shallow-to-deep length schedule that is adaptive to the capacity in the model layers. These prompts are trained under a dynamic min-max scheme: an online multimodal attacker continually crafts hard adversarial documents against the current retriever, while the defender is updated to push such documents out of the top-k while preserving the ranking and diversity of benign evidence. Because the defended encoder can be pre-computed and indexed exactly as in standard dense retrieval, DSPrompt incurs no additional per-query optimization and introduces fewer than 1% additional parameters. Extensive experiments across four benchmarks and three representative poisoning attacks show that DSPrompt substantially reduces the attack success rate and poison retrieval rate while maintaining near-lossless retrieval utility and generation fidelity, consistently outperforming existing defense baselines at a fraction of their computational cost.

cs.CR↗

arXiv2026

First Subconstituents of Orthogonal Graphs in Even Characteristic:Automorphisms, Cliques, Cores, and Binary Triple Transitivity

In even characteristic, the first subconstituent of an orthogonal graph at a singular point is shown to be the complement of an affine polar graph. This identification yields the full automorphism group as an affine semisimilarity group and provides explicit extensions to the ambient graph. Maximum independent sets are affine cosets of maximal totally singular subspaces, giving the independence and chromatic numbers. In plus type, Delsarte cliques correspond bijectively to ovoids through the base point, leading to a sharp rank threshold. Over $\mathbb{F}_2$, we determine the exact maximum clique number in every dimension, prove that all maximum cliques form a single orbit, and obtain the core of every first subconstituent. A uniform Witt-extension argument gives a short proof of triple transitivity for the binary local graphs.

math.CO↗

arXiv2026

Phase-continuous comparison of three all-optical time scales over 20 days

Optical frequency standards have progressed rapidly over the past two decades, leading to the anticipated redefinition of the SI second by an optical frequency. However, time scales have not yet significantly improved despite this development because they are still fully reliant on rf flywheel oscillators, mostly hydrogen masers, which impose a performance limit related to incompletely sampled noise known as the Dick effect. To best benefit from the exceptional stability and accuracy of optical frequency standards, time scales must employ optical flywheels with orders-of-magnitude better short-term (<$10^4$ s) stability than masers. Here, we introduce three optical flywheel oscillators (two cryogenic silicon cavities and one iodine optical clock) with superior short-term stability than hydrogen masers and long-term stability on par with masers. Steering each optical flywheel with a high-uptime Sr optical frequency standard generates three parallel all-optical time scales with continuous operation over >20 days. When compared with each other, these all-optical time scales achieve <$10^{-16}$ relative instability after just a few days of averaging. During typical steering gaps of ~6 hours, the accumulated time difference is ~20 ps, leading to the total time difference of <100 ps over the full measurement period. With the proliferation of long-distance optical fiber links and commercialization of optical flywheels and frequency standards, we anticipate all-optical time scales to be the future of timekeeping.

physics.atom-ph↗