arXiv subjects

Tao Zhou

Publications and source records attributed to Tao Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Martingale deep neural network for very high-dimensional stochastic optimal controls

We propose a martingale deep learning method for very high-dimensional stochastic optimal control problems (SOCPs) via their associated Hamilton--Jacobi--Bellman equations and the verification theory for the optimal control. The method decomposes the problem into two coupled components: a martingale formulation for the value function and an integral optimality condition for the feedback control. For the value function, we extend DeepMartNet by introducing a pilot process for state-space exploration and system processes for martingale property enforcement.This yields a derivative-free formulation that avoids computing PDE solution's derivatives and online simulations of full trajectories, while enabling parallel loss evaluation in both time and space. Also, the martingale condition is imposed in a weak Galerkin form realized with adversarial learning, which avoids direct computation of conditional expectations. The control is learned by minimizing an integral residual of the optimality condition over the explored state space, thereby avoiding state-space point-wise optimization over the control set. Numerical results demonstrate accurate and efficient performance for SOCPs with complex patterns in value function and controls in dimensions up to 10,000.

math.OC↗

arXiv2026

Learning Informative Prior with Infinite-Dimensional Continuous Normalizing Flow for Bayesian Inverse Problem

This paper addresses infinite-dimensional Bayesian inference for inverse problem of partial differential equations with model parameters in infinite-dimensional Hilbert space. To effectively incorporate prior information, we propose a novel continuous normalizing flows based infinite-dimensional model. Specifically, by introducing a well-defined neural ordinary differential equation in infinite-dimensional space, a simple reference measure can be transformed into a more complex measure which encodes the prior information. A corresponding theoretical framework is established to ensure the well-posedness of our proposed Bayesian prior in infinite-dimensional space. We also provide training methods of the prior for two distinct data settings, along with two sampling algorithms for the resulting Bayesian posterior. The proposed framework is applied to three representative inverse problems: the simple smooth inverse problem, inverse scattering problem, and the inverse heat conduction problem. Numerical experiments support the theoretical analysis and demonstrate the efficiency of the proposed algorithms.

math.NA↗

arXiv2026

DeepSPoC: A Deep Learning Based Sequential Propagation of Chaos

Classical particle methods based on propagation of chaos (PoC) have been developed for solving mean-field stochastic differential equations and their associated nonlinear Fokker--Planck equations. However, direct PoC implementations are difficult to apply to high-dimensional problems because they require simulating and storing large numbers of interacting particles, often with high particle-particle interaction costs. Motivated by these limitations, we build on the recently proposed sequential propagation of chaos (SPoC) framework, which replaces the fully interacting particle system in PoC with a sequential interaction mechanism. Based on this structure, we present DeepSPoC, a neural particle method that embeds a neural density representation into the sequential particle dynamics. DeepSPoC simulates particles batch by batch, while the neural network represents the evolving empirical law and is substituted into the coefficients of the mean-field SDE, thereby replacing direct particle-particle interactions with particle-network interactions. In DeepSPoC, a recently developed normalizing flow model called KRnet is used to approximate the empirical measure of particles. Compared with direct particle implementations, DeepSPoC substantially reduces memory consumption and evaluates interaction terms more efficiently, thereby improving scalability for high-dimensional problems. We apply DeepSPoC to a wide range of mean-field equations and verify its effectiveness and computational advantages.

cs.LG↗

arXiv2026

Co-occurrence Patterns of LoRA Adapters in Production Diffusion Model Inference Services

Low-rank adaptation (LoRA) has become a key technology for serving large-scale personalized large language models and diffusion models in the cloud. However, the co-occurrence patterns, resource contention relationships, and evolutionary regularities of adapters under production inference workloads have not been systematically or quantitatively studied. Based on GenTD26, Alibaba's production diffusion model inference dataset, this paper adopts a graph-theoretic framework to construct an adapter co-occurrence network and conducts a characterization from both static structure and dynamic evolution. Our main findings are as follows. (1) The co-occurrence network is extremely sparse, and adapter usage frequency follows a significant heavy-tailed distribution. (2) Introducing the first adapter incurs a 66.1% execution-latency overhead, with diminishing marginal costs afterwards. (3) Co-occurrence relationships are driven by base models: in 90.6% of multi-adapter requests, all adapters share the same dominant base model; 66.2% of significant co-occurrence edges connect same-model adapter pairs; and in 85.8% of multi-adapter requests, all adapter pairs form significant co-occurrence edges. (4) The adapter ecosystem exhibits a core-periphery bipolar structure, with a weekly Jaccard similarity of 0.696 at the model level and a churn rate of 54.5% for the top-10 hottest models within a 12-hour window. Based on these findings, we propose a preloading strategy built on top-k co-occurrence statistics; offline experiments show that it covers 81.0% of test-set co-occurrence pairs at k=3, and sensitivity analyses across frequency thresholds and time windows verify the robustness of the conclusions. These results provide a data-driven basis for cache preloading, adaptive scheduling, and GPU memory management in LoRA inference services.

cs.DC↗

arXiv2026

Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning

Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities under reinforcement learning (RL) paradigm. However, most existing multimodal medical reasoning models focus on basic reasoning, which refers to shallow inference based on visual feature matching. In contrast, real-world clinical diagnosis extends beyond basic reasoning, demanding complex reasoning that integrates heterogeneous clinical information (such as chief complaints and medical history) with multimodal medical imaging data. To bridge this gap, we introduce MM-Retinal-Reason, an ophthalmic multimodal dataset covering the full spectrum of perception and reasoning. Specifically, it is the first dataset in ophthalmology to encompass both basic and complex reasoning tasks with Chain-of-Thought (CoT) trajectories, aiming to enhance visual-centric reasoning and emulate realistic clinical decision-making. Building upon MM-Retinal-Reason, we propose OphthaReason, the first RL-enhanced ophthalmic multimodal reasoning model with step-by-step reasoning traces. To enable flexible adaptation to both basic and complex reasoning tasks, we further introduce Uncertainty-Aware Dynamic Thinking (UADT), which estimates sample-level uncertainty via entropy and dynamically modulates exploration depth through a shaped advantage mechanism. Comprehensive experiments demonstrate the effectiveness of our model on both basic and complex reasoning tasks, outperforming general-purpose MLLMs, medical MLLMs, RL-based medical MLLMs, and ophthalmic MLLMs by at least 15.47\%. Project Page: \href{https://github.com/lxirich/OphthaReason}{link}.

cs.AI↗

arXiv2026

Amortized Filtering and Smoothing with Conditional Normalizing Flows

Bayesian filtering and smoothing are central to data assimilation in nonlinear dynamical systems. Recent advances in deep generative models provide flexible approximations of the associated non-Gaussian posterior distributions. However, several existing approaches require the score to be re-estimated or a transport map to be constructed at each assimilation step. We propose an amortized framework for filtering and smoothing that reuses trained conditional models across observation sequences and assimilation times. The framework jointly learns a shared recurrent summary network and two conditional normalizing flows from simulated state and observation trajectories. The recurrent network represents each observation history by a fixed-dimensional summary that conditions the filtering approximation and, together with the next state, the backward transition approximation. An information-theoretic analysis shows that, under the Markov assumption, a summary sufficient for filtering is also sufficient for the backward kernel. Combining the terminal filtering approximation with the backward kernels yields an approximate joint smoothing distribution. Numerical experiments on advection-diffusion, Burgers, and Lorenz systems demonstrate the accuracy of the proposed filtering and smoothing approximations and characterize the evolution of their errors beyond the training horizon.

stat.ML↗

arXiv2026

Phonon-Bottleneck-Governed Ultrafast Hot-Carrier Super-Diffusion in Transition Metal Dichalcogenides

Two-dimensional transition metal dichalcogenides (TMDCs) are promising for low-power optoelectronics, yet their operational speed is widely considered constrained by low room-temperature mobilities and carrier transit delays. Here, by combining on-chip terahertz optoelectronic sampling with thermally evaporated Ohmic contacts, we eliminate external parasitic delays and directly capture the intrinsic interfacial photoresponse in unencapsulated TMDCs under zero bias. The devices achieve ultrafast relaxation lifetimes of 48.5 ps in MoS2/Au and 14.2 ps in MoSe2/Ag, translating to intrinsic 3-dB bandwidths of 4.4 GHz and 7.5 GHz, respectively. Spatial scanning and bias-dependent measurements show that this response is position-independent and bias-immune, ruling out conventional drift-limited transport and identifying hot-carrier super-diffusion driven by an interfacial electron temperature gradient as the operative mechanism. Furthermore, ultrafast pump-probe spectroscopy reveals that the macroscopic response time is quantitatively synchronized with the microscopic optical-to-acoustic phonon scattering lifetime governed by the intrinsic phonon bottleneck. Our findings establish phonon engineering as a viable paradigm to tailor non-equilibrium optoelectronic dynamics, offering a blueprint for zero-bias, ultrafast, self-powered devices.

cond-mat.mes-hall↗

arXiv2026

AgenticGen: Reward-Guided Agentic Video Generation for Advertising

Advertising video generation is not only a video synthesis task, but also a product-conditioned reasoning problem whose success is measured by online business metrics. Recent video foundation models can generate realistic clips from multimodal conditions, yet they do not optimize how a product should be transformed into an effective advertisement or how future generation should be improved from online business feedback. To close this loop, we propose AgenticGen, a reward-guided agentic framework that decomposes advertising video generation into two trainable reasoning stages, strategy selection and draft generation, thereby exposing optimization targets that online business feedback can supervise. AgenticGen learns a performance-based reward from accumulated online feedback and a complementary rubric-based reward aligned with human quality standards, then uses them to supervise policy optimization. DPO first moves the agentic policies toward online preferences, and GRPO further refines both stages with process and outcome rewards. Offline experiments validate the reward models and successive policy optimization. Online A/B experiments in the TikTok advertising system show that AgenticGen after DPO and GRPO improves CTR by 2.72%, CVR by 2.63%, and Advv by 9.61% over the SFT baseline.

cs.CV↗

arXiv2026

An OTFS Waveform-Based Delay-Doppler Domain Channel Measurement Method for High-Mobility Scenarios

Channel measurements are the prerequisite for applying emerging transmission technologies and designing communication systems. Conventional time or frequency domain channel measurement methods cannot directly obtain Doppler information induced by high-mobility scenarios. The channel spreading function (CSF) simultaneously captures delay and Doppler information while naturally characterizing the propagation environment in the delay-Doppler (DD) domain. However, DD domain channel measurement methods remain underexplored. This paper presents an orthogonal time frequency space (OTFS) waveform-based DD domain channel measurement method for high-mobility scenarios. A native OTFS waveform, employed as the sounding signal, is designed for the first time, and its sounding capability is comprehensively analyzed. Next, we detail the methodology of DD domain channel measurement, including synchronization and CSF estimation. To enhance measurement precision, a joint fractional delay and Doppler shift estimation algorithm is proposed, and the overall performance of the proposed method is evaluated. Subsequently, a practical DD domain channel measurement system is established, followed by system calibration and verification. Finally, DD domain channel measurements are conducted in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scenarios. Measurement results, including the CSF and other small-scale fading characteristics, confirm the effectiveness of the proposed method and offer valuable insights for advancing research on high-mobility communications.

eess.SP↗

arXiv2026

Extreme mass-ratio inspirals around rotating accelerating black holes

Extreme mass-ratio inspirals (EMRIs) can magnify small departures from Kerr dynamics into appreciable gravitational-wave phase shifts accumulated over many orbital cycles. We exploit this sensitivity to investigate the imprint of a rotating black hole's acceleration on an EMRI waveform. The spinning C metric poses two obstacles to the standard Kerr flux framework: the spacetime is not asymptotically flat, and the acceleration breaks the reflection symmetry that supports exactly equatorial circular timelike orbits. For sufficiently small acceleration $AM$, we therefore formulate the calculation in an intermediate Kerr-like wave zone satisfying $M/r\ll1$ and $Ar\ll1$, and construct a near-equatorial circular orbit by examining its coupled radial--polar stability. We derive the separated point-particle source for the spin$-2$ radial Teukolsky equation, construct a regular normalized angular solution, solve the radial equation using the Sasaki--Nakamura transformation and the Green function method, and couple the resulting horizon and far-zone fluxes to the adiabatic evolution of stable near-equatorial circular orbits. The framework recovers the Kerr limit and reproduces the dominant $l=2$ Kerr fluxes with relative errors of order $10^{-7}$. Acceleration modifies both radiation reaction and the orbital frequency, producing a characteristic nonmonotonic accumulated dephasing. For $M=10^6M_\odot$, $m_s/M=10^{-5}$, $a/M=0.7$, and $AM=3\times10^{-7}$, the dominant-mode dephasing slightly exceeds $1$ rad over one year. Thus even weak acceleration can generate an order-radian secular phase imprint on long-duration EMRIs within the controlled regime of the present approximation.

gr-qc↗

arXiv2026

Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda

Large Language Models (LLMs) are moving from code completion toward repository-scale agents that retrieve context, edit files, execute tools, and participate in security-sensitive workflows. The evidence for these systems, however, remains divided between software engineering evaluations centered on functional task completion and software security evaluations centered on vulnerability detection, secure generation, or exploit-oriented validation. This evidence-centered structured survey synthesizes representative work available through May 31, 2026 across software engineering tasks, software security tasks, adaptation mechanisms, artifact granularity, and evaluation design. In addition to a task taxonomy, we introduce an assurance framework that separates functional correctness, security, operational reliability, evidence provenance, and agent authority. The review shows that execution feedback and repository access can substantially improve engineering task completion, but do not by themselves establish security; conversely, static-analysis labels or vulnerability-classification scores rarely establish deployable correctness. We identify recurring validity threats--weak test oracles, duplicated and temporally leaked data, changing agent harnesses, proxy-only security checks, and under-reported budgets and human intervention--and derive a minimum reporting protocol for cross-study comparison. The resulting research agenda prioritizes jointly secure-and-functional benchmarks, repository-scale threat models, calibrated human oversight, longitudinal maintainability evidence, and reproducible agent evaluation. The central conclusion is that model capability should be judged as an assurance case supported by task-appropriate evidence, rather than by a single benchmark score.

cs.AI↗

arXiv2026

The Third Restructuring of Software Form: From the Three-Tier Architecture to Storage, Models, and Agents

Software form has undergone two paradigm shifts since its inception: Software 1.0, in which instructions determine behavior, and Software 2.0, in which data determines behavior (machine learning). This paper argues that a third shift - Software 3.0, in which context and reasoning determine behavior - is now underway, and contends that its terminal form converges to three elements: a generalized database (the unified abstraction of all persistent state and memory), a large model (the intelligence core that performs reasoning and generation), and an agent (the execution loop connecting the first two). The core argument is as follows: in the traditional three-tier architecture, the user-interface layer will be absorbed by the model's ability to generate interfaces on demand, the business-logic layer will be re-partitioned along "expressibility x criticality" into model reasoning and storage constraints (with residual deterministic logic retained as tools), and only the data layer will be elevated into the sole persistent infrastructure. We formalize this convergence thesis, present a minimal reference architecture, report evidence from real prototypes and a live model, and systematically analyze both the conditions under which it holds and the boundaries where it fails - determinism, cost, security, and verifiability delimit the thesis's domain of applicability. We argue that the thesis holds in task domains that are expressible, verifiable, externally stateful, and tool-complete, and that it will reshape the roles of developers, the database industry, and the software-engineering discipline.

cs.AI↗

arXiv2026

A semi-generating function approach to the stability of implicit-explicit multistep methods for nonlinear parabolic equations

The rigorous stability analysis of high-order implicit-explicit linear multistep (IELM) methods for nonlinear parabolic equations by using discrete energy arguments is a long standing open issue due to their non-A-stability property. A novel semi-generating function approach combined with a global discrete energy analysis is suggested for the stability and convergence of general IELM methods in solving nonlinear parabolic equations. Inspired from the Grenander-Szegő theorem for Toeplitz matrices, the semi-generating function approach is used to handle the three groups of discrete coefficients via three complex polynomials on the unit circle. A unified theoretical framework is then presented to establish the unconditional stability of IELM methods if the minimum eigenvalue of composite convolution kernels for the implicit part is properly large and the spectral norm bound of composite convolution kernels for the explicit part is properly small. An indicator, called implicit-explicit controllability intensity, is then introduced to evaluate the degree of controllability of the implicit part over the explicit part. Some of the existing IELM methods, up to fifth-order time accuracy, are revisited and compared by computing the associated implicit-explicit controllability intensities such that one can choose an IELM method or proper parameter to maintain the unconditional stability for a specific nonlinear parabolic model. We also propose a new parameterized class of IELM methods, up to the ninth-order time accuracy, which satisfy the a priori settings of our theory and have a large value of the implicit-explicit controllability intensity by choosing a proper parameter so that they would be well suited for a wide class of nonlinear parabolic problems.

math.NA↗

arXiv2026

Economic Distance Structures Urban Mobility in 109 U.S. Cities

Urban mobility promises social integration, yet daily movement is systematically constrained by socioeconomic hierarchies. Introducing "economic distance"--the continuous income gap between origin and destination--as a unified lens, we analyze large-scale mobility records across 109 U.S. cities to reveal how urban flows are structured. We identify a universal structural boundary: flows concentrate intensely within a narrow economic distance of 0.25 quantiles, defining the effective "economic radius" of routine mobility. This boundary exhibits profound asymmetry; upward mobility faces a uniform structural ceiling across cities, whereas downward mobility drives cross-city heterogeneity. Mechanistically, the boundary is physically anchored by meso-scale residential clustering but is further tightened by an independent economic-distance friction, validated via gravity modeling. These interactions yield four distinct mobility regimes, with "affluent-confined" systems exhibiting the strongest stratification. These findings establish economic distance as a fundamental, asymmetric, and multi-scale filter shaping urban inequality, offering new theoretical grounds for interventions targeting structural barriers to cross-class interaction.

physics.soc-ph↗

arXiv2026

Embodied Multimodal Grounding for Open-Vocabulary Mobile Manipulation via Semantic 3D Gaussian Splatting

Embodied mobile manipulation requires language, visual observations, three-dimensional scene structure, and action feasibility to be aligned before execution. We study open-vocabulary target grounding with few-shot manipulation in local household workspaces and present an embodied multimodal grounding framework that integrates active multi-view Semantic 3D Gaussian Splatting (Semantic-3DGS), reachability-aware base positioning, and a diffusion-based vision-language-action policy. A task-driven local Semantic-3DGS serves as a shared interface across active sensing, language-conditioned 3D localization, obstacle-aware scene reasoning, base preparation, and semantic conditioning of the action model. To preserve pretrained action priors, the 3D semantic cues are injected only into the late action-expert blocks. In expanded 50-trial real-robot evaluations against representative vision-language-action (VLA) approaches, the full system achieves 60% long-horizon success compared with 40% for PointVLA and 28% for DexVLA, and reaches 74% success in heavily cluttered manipulation compared with 52% for the single-view variant and 46% for PointVLA. It also maintains 75% success under a 75 cm height shift and eliminates photo-induced false grasps. These results indicate that explicit, refreshable 3D semantic grounding can improve robustness under clutter, occlusion, viewpoint variation, and embodiment constraints.

cs.RO↗

arXiv2026

WebRider: Persona-Conditioned Intent Controllers for Live-Web Assistance

Delegating a web task involves more than asking a question; it requires transferring a policy: what to verify, how to handle uncertainty, which preferences matter, and when to stop. Yet, current live-web agents are evaluated solely on the final answer, ignoring the policy constraints that define the delegation. A plausible final answer can conceal violations of that policy. Our full live audit reveals this critical gap: a strong controller completes 99.2% of tasks but honors all policy constraints in only 38.8% of cases. Finishing does not imply fidelity. WebRider bridges this gap by formalizing the delegated policy as an intent contract---an operational record of goals, constraints, evidence obligations, answer form, and task-local persona controls that must hold even as web pages change. WebRider employs a hierarchical architecture: a top-layer controller maintains the contract, a middle layer realizes intentions as guarded executable actions, and a tool layer executes these actions via browser, search, and maps tools. Our benchmark, RiderBench, evaluates this design on 4,096 live-web contracts across 42 public websites, auditing both the internal contract state and the visible user experience to determine if a rollout preserved its policy and if the steps were persona-consistent. The guarded middle interface also serves as a high-quality training signal; an 8B action-policy model trained through this interface outperforms executable-only baselines under a fixed controller. By making the browsing path a first-class object, WebRider enables a system that is auditable, human-judgeable, and learnable without conflating action realization with final-answer decisions. Dataset URL: hf.co/datasets/WebRider/WebRider.

cs.AI↗

arXiv2026

Functional normalizing flow for statistical inverse problems of partial differential equations

Inverse problems of partial differential equations are ubiquitous across various scientific disciplines and can be formulated as statistical inference problems using Bayes' theorem. To address large-scale problems, it is crucial to develop discretization-invariant algorithms, which can be achieved by formulating methods directly in infinite-dimensional space. We propose a novel normalizing flow based infinite-dimensional variational inference method (NF-iVI) to extract posterior information efficiently. Specifically, by introducing well-defined transformations, the prior in Bayes' formula is transformed into post-transformed measures that approximate the posterior. To circumvent the issue of mutually singular probability measures, we formulate general conditions for the employed transformations. As guiding principles, these conditions yield four concrete transformations. Additionally, to minimize computational demands, we have developed a conditional normalizing flow variant, termed CNF-iVI, which is adapt at processing measurement data of varying dimensions while requiring minimal computational resources. We apply the proposed algorithms to three typical inverse problems governed by the simple smooth equation, the steady-state Darcy flow equation, and the electric impedance tomography. Numerical results confirm our theoretical findings, illustrate the efficiency of our algorithms, and verify the discretization-invariant property.

math.NA↗

arXiv2026

Depth-Resolved Lattice Distortions in a Silicon-Germanium Qubit Host

Semiconductor qubits, promising for quantum computation, inherit properties from their host lattice. Quantum dot spins, occupying the local lowest energy states in the conduction band, necessarily couple to structural disorder and interfaces. While silicon-based systems promise low noise alongside industrially compatible manufacturing, the growth of SiGe---a leading platform---unavoidably introduces lattice dislocations, inhomogeneous strain, and crosshatch patterns, expected to cause fluctuations between devices, qubit failure, and subsequently higher operational overhead. Through X-ray nano-structural mapping of an Intel Si/SiGe chip, we reveal, with 30$~$nm lateral and 200$~$nm functional depth resolution, how extended lattice defects introduced during growth propagate through the heterostructure, creating permanently distorted lattice planes and strain. We correlate these at the $\approx1~μ$m scale of a quantum dot device and calculate the impact on qubit energy spectra. We observe crosshatch fine structure and find that substrate miscut and growth correlate with the final crosshatch pattern.

cond-mat.mes-hall↗