arXiv subjects

Rui Chen

Publications and source records attributed to Rui Chen.

At least 19 recordsLinked to original sources

arXiv2026

Fully Coupled Nonlinear Forward-Backward Stochastic Difference Equations: Spectral Contraction and Infinite-Horizon Maximum Principle

This paper develops an explicit spectral-contraction approach to studying fully coupled nonlinear forward--backward stochastic difference equations on infinite horizon and their applications to stochastic control. The estimates for this fully coupled system are assembled into an explicit two-dimensional nonnegative matrix. Its spectral radius yields an explicit sufficient discount threshold, and a corresponding equivalent weighted product norm is constructed to establish the contraction property. Moreover, for an infinite-horizon control problem with convex control constraints and an accumulated discounted running cost, we derive a Pontryagin-type stochastic maximum principle, its equivalent pointwise normal-cone formulation, and a verification theorem. Finally, a recursive risk-adjusted portfolio example is given to demonstrate the applications of our theoretical results, and a projection-type sufficient optimality condition for this example is derived.

math.OC↗

arXiv2026

Learning 3D Editing without Paired Supervision via Generative Prior Distillation

Instruction-guided 3D editing is essential for interactive content creation, yet it faces a significant bottleneck: the severe scarcity of high-quality paired training data. Existing approaches attempt to bypass this by either relying on slow test-time optimization or training on pseudo-pairs constructed via complex pipelines, which often introduce structural drift and geometric artifacts. In this paper, we propose a novel framework that learns feed-forward 3D editing without paired 3D supervision via Generative Prior Distillation. Instead of relying on ground-truth 3D pairs, our core idea is to distill visual, semantic, and geometric knowledge from powerful foundation models directly into a 3D editing model. Specifically, through a differentiable rendering pipeline, we supervise the 3D representation using two complementary signals: a 2D visual prior from an image editing model at the main editing view, and a semantic prior from a Vision-Language Model at novel views to ensure strict instruction following and source identity preservation. Crucially, to address the geometric collapse and multi-view inconsistencies inherent in 2D projection supervision, we introduce a 3D-aware Distribution Matching regularization. Acting as a geometric prior, this term operates in the 3D latent space, constraining the edited output to remain within the manifold of realistic 3D assets defined by a pretrained image to 3D teacher model. Extensive experiments demonstrate that our method achieves superior instruction fidelity and cross-view consistency, significantly outperforming state-of-the-art baselines. Our project is available at: https://github.com/thiamine128/PriorEdit3D.

cs.CV↗

arXiv2026

Editable Visual Design

While diffusion base models such as GPT-Image-2 and Nano-Banana exhibit remarkable visual expressiveness, their end-to-end generation inherently yields flattened bitmaps with error-prone text, precluding layer-wise post-editing. Conversely, code-based visual generation via Coding Agents provides precise layout control and decoupled layers, yet remains constrained by a lack of global aesthetic intuition and the difficulty of coding complex visual assets. To address this, we propose Editable Visual Design, a new paradigm driven by a Coding Agent. We designate the VLM as the ``creative brain'' for requirement comprehension, task planning, and aesthetic judgment, while utilizing the image generation model as an on-demand ``visual world simulator'' to synthesize standalone visual assets. Operating under an ``imagine first, then act'' closed-loop workflow, the agent generates isolated assets, writes native HTML/CSS, and iteratively refines the design against visual rendering feedback. Furthermore, Agent Design Replay faithfully reproduces the creative and reasoning trajectory akin to that of professional human designers. Ultimately, the system delivers editable artifacts with decoupled layers and real text, enabling users to perform intuitive mouse dragging and layout adjustments on a graphical user interface. Validations on posters, infographics, and other scenarios show that this paradigm successfully achieves both refined aesthetics and production-grade editability.

cs.CV↗

arXiv2026

Two-point estimates for the logarithmic p-flux of the first Dirichlet p-eigenfunction

Let $u>0$ be the first Dirichlet $p$-eigenfunction on a bounded convex domain $\Omega\subset\mathbb R^N$, and set \[ X_\Omega:=|\nabla\log u|^{p-2}\nabla\log u. \] We study whether the sharp one-dimensional two-point modulus for $X_\Omega$, which for $p=2$ reduces to the logarithmic-gradient estimate of Andrews and Clutterbuck, persists for $p\neq2$. We prove sharp estimates on intervals and balls for every $p>1$, with the radial modulus on balls strictly larger than the one-dimensional one. In dimensions $N\ge2$, however, the one-dimensional modulus fails on general convex domains for every $p\neq2$. For $1 2$, on thin domains $\Omega_\varepsilon=D\times(-\varepsilon,\varepsilon)$, with $D\subset\mathbb R^{N-1}$ bounded and convex, the normalized first eigenfunctions satisfy \[ u_\varepsilon(x,\varepsilon z)\longrightarrow \frac{\phi(z)}{\phi(0)} \left(\frac{G_D(x)}{G_D(x_0)}\right)^{2/p} \] locally uniformly in $D\times(-1,1)$, where $\phi$ and $G_D$ are the first Dirichlet eigenfunctions of the $p$-Laplacian on $(-1,1)$ and of the Laplacian on $D$, respectively. This yields the failure for $p>2$. Finally, for arbitrary $C^2$ functions, positivity of the symmetric differential of the $p$-gradient implies convexity, and the converse holds universally if and only if $p=2$.

math.AP↗

arXiv2026

Cross-Architecture Knowledge Distillation from a Vision Foundation Model to a Lightweight Visual State Space Model for Tea Leaf Disease Classification

Automated tea leaf disease classification supports precision agriculture, yet deploying accurate models on edge devices remains challenging under tight compute budgets. Self-supervised vision foundation models such as DINOv2 provide strong features but are too large for field deployment, while lightweight models trained from scratch on small agricultural datasets often underfit. We study cross-architecture knowledge distillation (KD) from a fine-tuned DINOv2 teacher (Vision Transformer) to a compact bidirectional Visual State Space Model (LVSSM) student, an underexplored direction because the architectures use fundamentally different token-mixing mechanisms. We identify and fix two training-stability problems that prevent the from-scratch SSM student from learning on limited data: a single large patch-embedding convolution and a fusion layer that severs the residual path. With a progressive convolutional stem and gated bidirectional selective-scan block, the 4.45M-parameter student trains stably. Across three seeds, temperature-scaled logit distillation raises test accuracy from 92.32+/-2.14% to 95.41+/-1.17% (best single run: 96.20%; macro-F1: 94.45%), a +3.09 percentage-point mean gain. The student uses 5.0 times fewer parameters than the 22M-parameter teacher while retaining 98.3% of its accuracy. Ablations show that intermediate feature-alignment losses reduce accuracy, making simple logit-level KD the strongest configuration. A fair from-scratch comparison shows the gain is specific to students that start below the teacher. We report per-class metrics, confusion matrices, bootstrap confidence intervals, and FLOPs/latency measurements, and discuss limitations including the single-dataset scope and simplified non-official SSM implementation.

cs.CV↗

arXiv2026

SpatialCrafter: Single Image World Modeling with Generative 3D Proxies

Explorable image-to-scene generation is essential for applications in gaming, robotics, and virtual reality. Existing methods based on video diffusion model (VDM) commonly rely on incomplete conditioning signals such as sparse point clouds or 2D panoramas, leading to stochastic hallucinations, long-term drifts and suboptimal 3D consistency. We present SpatialCrafter, a novel two-stage framework that addresses these issues by introducing a global 3D proxy for high-fidelity image-to-scene generation. Specifically, we decompose the generation process into global proxy generation and appearance refinement. For proxy generation, we propose a Point-anchored Sparse Structure~(PaSS) Flow module that predicts a spatially aligned and geometrically consistent 3D proxy. For appearance refinement, we re-frame the VDM as a Generative Deferred Refiner which synthesizes high-frequency photorealistic details upon proxy-defined scene geometry. To better integrate the proxy with the pre-trained VDM, we introduce Parallel Geometry Injection and Proxy-Aware Corruption training strategies, which improve robustness to proxy artifacts without disrupting the pretrained generative manifold. Furthermore, as no suitable dataset exists for this explorable scene generation task, we construct a new large-scale dataset of 115K scenes. To the best of our knowledge, it is the first hybrid dataset for image-to-scene generation. Extensive experiments on both synthetic and real-world datasets show that SpatialCrafter outperforms state-of-the-art methods, mitigates long-term drift, and remains robust and consistent under rapid camera motion and extreme viewpoint changes. Our project page: \href{https://fangchuan.github.io/SpatialCrafter/}{fangchuan.github.io/SpatialCrafter/}

cs.CV↗

arXiv2026

R2M-Bench: Evaluating Revisit Memory via Relative Consistency in Interactive Video World Models

High similarity between first-visit and return frames does not necessarily show that a video world model remembered the scene; the intervening rollout may simply have changed very little. This ambiguity makes absolute revisit scores sensitive to rendering stability, repetitive content, and failed motion. We introduce \emph{R2M-Bench} (\textbf{R}elative \textbf{R}evisit \textbf{M}emory Benchmark), a benchmark of observable revisit-selective consistency. For every detected return, R2M-Bench compares the revisit pair with two controls from the same rollout: a gap-matched non-revisit pair that measures generic temporal stability and a short-range pair that estimates short-horizon consistency. These comparisons produce \emph{MemoryGain} (MG), the revisit advantage over the temporal baseline, and the \emph{Normalized Memory Ratio} (NMR), which normalizes this advantage by the short-to-baseline dynamic range. R2M-Bench combines 100 reference scenes with three leave-and-return trajectories to form 300 instances and evaluates appearance fidelity, scene and object identity, local geometry, and persistent state. Across seven action-conditioned video world models, Overall NMR correlates with human consistency judgments at Spearman's $\rho=0.547$ (95\% CI $[0.45,0.63]$). Its within-model correlation magnitude with generated motion is $0.072$, compared with $0.207$ for raw revisit similarity, indicating that relative calibration substantially reduces the slow-motion shortcut. DreamX-World-Memo achieves the highest Overall NMR among the evaluated video models. Together, these results support same-rollout relative calibration as a practical way to distinguish revisit-specific consistency from generic temporal stability.

cs.CV↗

arXiv2026

4DStreamCtrl: Interactive Video Generation with Online 4D Control

Generative video models now synthesize footage nearly indistinguishable from reality. Their promise as interactive tools hinges on fine-grained control of how objects and the camera move over time, yet each existing approach captures only part of this: camera-parameter methods steer the viewpoint but cannot move objects, 2D-trajectory methods act in the image plane and ignore depth and occlusion, and recent 3D methods add geometry but run only offline at a fixed length. In particular, none combines 3D-consistent control of both camera and objects with real-time, streaming generation. Here we show that camera motion, object trajectories, and depth can be unified into a single 3D point-track representation, from which one model performs joint camera and object control, depth editing, and motion transfer in a single forward pass. To learn this interface at scale, we mine in-the-wild video for 3D motion supervision, yielding OpenVidHD-Motion3D, and encode it with a lightweight Geometric Motion Head that plugs into a pretrained video diffusion model. Because this encoder is temporally separable, we distill the model into a causal streaming student that generates arbitrarily long video in four denoising steps at memory independent of length. This unified design surpasses prior camera-only, 2D, and offline-3D methods in motion-control precision while covering modalities they address only in isolation. 4DStreamCtrl runs at 20 FPS on a single high-end GPU for 480p video and stays temporally coherent over hundreds of frames, enabling, to our knowledge, interactive 4D-controllable streaming generation for the first time. More broadly, grounding generation in explicit 3D geometry with efficient causal inference points toward interactive world models with closed-loop spatiotemporal control, from controllable simulators to real-time visual imagination for embodied agents.

cs.CV↗

arXiv2026

Optical conductivity signature of Van Hove singularity in altermagnetic topological systems

We investigate the topological phases, joint density of states (JDOS), optical conductivities, and magneto-optical responses of a two-dimensional $d$-wave altermagnet with spin-orbit coupling and Zeeman splitting. The system hosts gapped Dirac points at the high-symmetry points $\Gamma$, $\textrm{M}$, $\textrm{X}$, and $\textrm{Y}$. We show that the JDOS exhibits kinks at the corresponding Dirac gap frequencies and pronounced peaks at Van Hove singularities, whose positions can be tuned by the altermagnetic order. These features are reflected in the optical conductivities, with complementary signatures in their real and imaginary parts. In particular, the Van Hove signatures in the transverse optical conductivity disappear in the absence of $d$-wave altermagnetism, revealing an altermagnet-induced optical signature of the Van Hove singularity. Finally, the Faraday and Kerr rotations exhibit characteristic features inherited from the optical conductivity. Our results establish optical and magneto-optical spectroscopy as sensitive probes of Dirac gaps and Van Hove singularities in altermagnetic topological systems.

cond-mat.mes-hall↗

arXiv2026

PRISM: Precision and contact-rich Real-world Industrial Skill dataset with Multimodal sensing

Recent progress in robotic learning has been fueled by large-scale datasets collected in everyday environments. However, most existing datasets emphasize short-horizon, low-contact tasks such as pick-and-place, and therefore do not capture the precision control, force/torque or tactile regulation, and multimodal feedback required for industrial assembly. To address this gap, we introduce PRISM, a large-scale multimodal dataset for contact-rich industrial operations. The dataset spans more than 25 manipulation tasks (e.g., electronic components plug/unplug, conveyor-based sorting) and covers diverse mechanical constraints. PRISM includes more than 5,000 trajectories totaling 45 hours of teleoperated demonstrations, recorded using synchronized multi-view RGB-D, force/torque, tactile, and robot-state measurements. In contrast to datasets collected in household or laboratory settings, PRISM provides a realistic benchmark for multimodal perception and control under high-precision industrial constraints, and serves as a foundation for contact-rich, generalizable manipulation in real-world manufacturing environments. The dataset is open-sourced at: https://tengbo-yu.github.io/PRISM/

cs.RO↗

arXiv2026

Extending the Constituent Gluon Model to Heavy-Flavour Hybrids: A Unified Study of $c\bar{c}g$ Mesons

We investigate the mass spectra and two-body strong decay properties of ground charmonium hybrids within the framework of a constituent gluon model. Based on the assumption that non-perturbative QCD endows the gluon with an effective mass, we extend the chiral quark model by introducing a single new parameter, the constituent gluon mass $m_g=450$~MeV, which is fixed from previous studies of light hybrids, while other parameters are taken directly from successful descriptions of ordinary meson spectra. We systematically compute the spectra for various quantum numbers and find good agreement with results from lattice QCD, potential models, and other approaches. The corresponding decay widths are also reasonable. For experimental searches, we recommend focusing on the exotic $1^{-+}$ and $2^{+-}$ states, which decay prominently into $D\bar{D}_1$ and $D\bar{D}_2^*$ channels, respectively. Among ordinary quantum numbers, the $0^{-+}$, $2^{-+}$, and $1^{+-}$ states with significant decays into orbitally excited charm mesons are also suggested. Our results provide a unified and consistent description of charmonium hybrids and offer clear guidance for future experimental identification.

hep-ph↗

arXiv2026

Fundamental Gaps for the Dirichlet p-Laplacian with Convex Potentials: Sharp One-Dimensional Bounds and a Higher-Dimensional Dichotomy

We study fundamental gaps for the Dirichlet $p$-Laplacian on bounded convex domains with convex potentials. In one dimension, we prove the sharp inequality \[ \lambda_{2,p}(I_D,V)-\lambda_{1,p}(I_D,V) \geq (p-1)(2^p-1)\left(\frac{\pi_p}{D}\right)^p \] for every $p>1$ and every convex potential, with equality precisely for constant potentials. For $N\geq2$, we identify a sharp transition at $p=2$ through collapsing smooth convex domains: the gap vanishes for $1 2$. In the regime $p\geq2$, we prove log-concavity of the positive first eigenfunction by a regularization and two-point maximum principle. We then establish a degenerate weighted Poincar\'e inequality, which yields quantitative stability estimates for the $L^p$-Poincar\'e inequality and, in turn, quantitative lower bounds for the fundamental gap. For zero potential, we further obtain an enhanced gap estimate involving both the first eigenvalue and the diameter. Finally, we prove existence of diameter-normalized gap minimizers for $p>2$ and show that they degenerate as $p\downarrow2$, whereas for $p=2$ the optimal gap is not attained by any bounded $N$-dimensional convex domain.

math.AP↗

arXiv2026

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

We present \textbf{DreamX-Phi 1.0}, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations. Yet realism alone does not guarantee faithfulness: a convincing rollout can still move the wrong arm or lose the manipulated object. To ensure the prediction respects each arm's commanded path, we inject per-arm $\mathrm{SE}(3)$ transformations into attention via \textbf{PRoPE-style geometric encoding}, preserving arm identity and rigid-motion structure. Action control alone does not fully constrain scene geometry or the evolution of small manipulated objects. We therefore add a lightweight \textbf{depth branch} for scene-level geometry and use \textbf{SAM3 masks} with a frozen \textbf{V-JEPA teacher} to maintain object consistency throughout grasping. We further distill the multi-step generator into a few-step student via distribution-matching distillation for efficient deployment. At the time of writing, \model{} achieves first place on Track~1 and second place on Track~2 of the WorldArena~2.0 Challenge. Our model and code will be publicly available.

cs.CV↗

arXiv2026

High second Chern number induced by long-range hopping in a four-dimensional Dirac model

Four-dimensional (4D) topological systems provide a promising platform for exploring topological phenomena beyond three dimensions. So far, extensive recent studies on 4D topological insulators have focused on the 4D Dirac model, while its second Chern number is restricted to a limited set of values. In this work, we demonstrate that introducing long-range hopping into the 4D Dirac model induces topological phases with high second Chern numbers. Furthermore, we show that the long-range hopping can transform a trivial insulator into a topological insulator with a nonzero second Chern number. Our work establishes long-range hopping as a powerful route for engineering 4D topological states and reveals new possibilities for realizing unconventional topological phases beyond minimal models.

cond-mat.mes-hall↗

arXiv2026

Topological surface altermagnets in SSH-stacked magnetic layers

Surface altermagnetism opens new avenues in spintronics by unlocking altermagnetic spin-splitting at the boundaries of conventional antiferromagnets, bypassing the strict symmetry requirements of bulk altermagnets. In this work, we propose creating topological surface altermagnet by stacking magnetic layers in a Su-Schrieffer-Heeger pattern. We show that while the bulk of the system is a standard antiferromagnet with degenerate bands protected by $PT$ symmetry, breaking the local symmetry at the boundary gives rise to a topologically protected surface altermagnetic state residing within the topological gap. Furthermore, we propose that this effect can be experimentally detected by applying a perpendicular electric field. Besides, this approach can be readily generalized to surface altermagnetism of different types. Our work establishes topological boundaries as a natural platform for surface altermagnetism, offering a distinct route for realizing and manipulating topological surface altermagnets.

cond-mat.mes-hall↗

arXiv2026

PaDoc: Layout-Grounded Parallel Decoding for Document Parsing

End-to-end document parsers provide a unified interface, but serialize page layouts and regional contents into one autoregressive sequence. This formulation forces independent regions onto a decoding path whose length grows with the total content, whereas crop-based two-stage parsers expose region-level parallelism at the cost of repeated visual prefills and fragmented page context. To retain full-page context while removing dependencies, we propose PaDoc, a layout-grounded parser that treats the predicted layout as a branching structure over a shared page representation. Under a region-sufficiency assumption, we derive a prefix-conditioned factorization in which the layout stream and regional content branches advance concurrently, reducing the decoding depth to the longest layout-content path. We realize this factorization within a single MLLM: packed variable-length ancestor attention preserves the visibility under standard next-token training, while masked parallel decoding creates branches that the evaluated vLLM backend serves as concurrent requests with cache-resident shared-prefix reuse. On OmniDocBench Full, PaDoc attains an Overall layout F1 of 91.1 and, among end-to-end parsers, a top-tier Overall score of 94.24 together with the best Text Edit (0.038) and Formula CDM (95.59). On a 384-page subset and one A800 GPU, it is the fastest end-to-end parser at five concurrency levels, improving valid-page throughput by 67.4-118% and reducing P95 latency by 39.2-54.9% relative to a same-backbone Sequential SFT baseline. Code is available at https://github.com/Longin-Yu/Padoc

cs.AI↗

arXiv2026

SieveIVF: Threshold-Aware IVF Execution for Large-Scale Training Data Deduplication

Embedding-based training data deduplication retrieves candidate duplicate edges above an application similarity threshold, but fixed-probe inverted-file (IVF) search ignores this predicate when giving every query the same partition budget. Across four Hunyuan workloads, qualifying neighbors appear early despite sharply varying search depths. We present SieveIVF, a threshold-aware IVF executor that stops after $W$ consecutive searches find no qualifying candidate. The systems challenge is to preserve partition-major batching when each query's remaining work depends on prior results. Continuous batching groups ready queries by partition. A lookahead scheduler layers on top, exposing only work committed by the stopping rule to increase concurrency without changing stopping decisions or returned results. We implement SieveIVF in Lance. At $W=8$, SieveIVF is $4.1$--$7.6\times$ faster than fixed-probe IVF on four 10M Hunyuan workloads and $6.1$--$8.4\times$ faster on two public 100M workloads under the same index and search parameters, with pooled filtered top-10 recall losses of $0.03$--$1.13$ percentage points on Hunyuan and $1.43$--$2.29$ percentage points on the public workloads. These results show how an application predicate can guide IVF work allocation without changing the index or bounded top-$k$ interface.

cs.DB↗

arXiv2026

Degradation of Proton Exchange Membrane Water Electrolyzers in Accelerated Stress Tests of Dynamic Load Cycling

Proton exchange membrane water electrolysis (PEMWE) is a promising technology for harnessing intermittent renewable energy. This study experimentally investigates the degradation of PEMWE under fluctuating power supply, characterized by dynamic load cycling under accelerated stress test (AST). We focus on the effects of key parameters of dynamic loading, including the peak voltage and cycling frequency, in a set of simplified AST protocols designed to represent selected features of dynamic load fluctuations associated with variable renewable-energy operation. The results unveil the intricate relationship between the structural characteristics of the catalyst layer and the electrochemical performance. An elevated peak voltage accelerates the degradation in the initial phase of the AST. However, a low cycling frequency can mitigate the degradation by limiting the rise in various resistance forms, whereas a higher cycling frequency exacerbates the degradation primarily by increasing mass transport resistance, suggesting a frequency-sensitive deterioration of the system's components.

physics.chem-ph↗