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Kai Liu

Publications and source records attributed to Kai Liu.

At least 91 records · Page 5Linked to original sources
arXiv2026

YUHENG-OS: A Cloud-Native Space Cluster Operating System

As industry and academia continue to advance spaceborne computing and communication capabilities, the formation of cloud-native space clusters (CNSCs) has become an increasingly evident trend. This evolution progressively exposes the resource management challenges associated with coordinating fragmented and heterogeneous onboard resources while supporting large-scale and diverse space applications. However, directly transplanting mature terrestrial cloud-native cluster operating system paradigms into space is ineffective due to the fragmentation of spaceborne computing resources and satellite mobility, which collectively impose substantial challenges on resource awareness and orchestration. This article presents YUHENG-OS, a cloud-native space cluster operating system tailored for CNSCs. YUHENG-OS provides unified abstraction, awareness, and orchestration of heterogeneous spaceborne infrastructure, enabling cluster-wide task deployment and scheduling across distributed satellites. We introduce a four-layer system architecture and three key enabling technologies: modeling of heterogeneous resource demands for space tasks, fragmented heterogeneous resource awareness under network constraints, and matching of differentiated tasks with multidimensional heterogeneous resources under temporal dependency constraints. Evaluation results show that, compared with representative terrestrial cloud-native cluster operating systems exemplified by Kubernetes, YUHENG-OS achieves a substantially higher task completion ratio, with improvements of up to 98%. This advantage is primarily attributed to its ability to reduce resource awareness delay by 71%.

cs.NI↗
arXiv2026

Space-Based Computing Networks: Trends, Architecture, Challenges, and Key Technologies

As one of the most promising hotspots in the 6G era, space remote sensing information networks play a key and irreplaceable role in areas such as emergency response and scientific research, and are expected to foster remote sensing data processing into the next generation of killer applications. However, due to the inability to deploy ground communication stations at scale and the limited satellite-to-ground link rate, the traditional model for transmitting space data back to ground stations faces significant challenges in terms of timeliness. To address this problem, we focus on the emerging paradigm of on-orbit space data processing, which reduces the volume of transmitted data by several orders of magnitude to enable faster task response, taking the first step toward building a space-based computing network. Specifically, we propose a hierarchical space-based computing network architecture, comprising the space-based cloud constellation system, the remote sensing constellation system, the network operation control center, the orchestration data center, and the user access portal. Each component is described in detail from a system design perspective to clarify its specific role and functionality. Next, we analyze three scientific challenges: the heterogeneous resource virtualization and state information synchronization, the matching of multi-priority tasks with multidimensional resources, and the fault detection and localization under extreme conditions. Finally, we discuss key technologies to address the aforementioned challenges and highlight promising research priorities for the future.

cs.NI↗
arXiv2026

World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation

Robotic manipulation policies are commonly initialized through imitation learning, but their performance is limited by the scarcity and narrow coverage of expert data. Reinforcement learning can refine polices to alleviate this limitation, yet real-robot training is costly and unsafe, while training in simulators suffers from the sim-to-real gap. Recent advances in generative models have demonstrated remarkable capabilities in real-world simulation, with diffusion models in particular excelling at generation. This raises the question of how diffusion model-based world models can be combined to enhance pre-trained policies in robotic manipulation. In this work, we propose World4RL, a framework that employs diffusion-based world models as high-fidelity simulators to refine pre-trained policies entirely in imagined environments for robotic manipulation. Unlike prior works that primarily employ world models for planning, our framework enables direct end-to-end policy optimization. World4RL is designed around two principles: pre-training a diffusion world model that captures diverse dynamics on multi-task datasets and refining policies entirely within a frozen world model to avoid online real-world interactions. We further design a two-hot action encoding scheme tailored for robotic manipulation and adopt diffusion backbones to improve modeling fidelity. Extensive simulation and real-world experiments demonstrate that World4RL provides high-fidelity environment modeling and enables consistent policy refinement, yielding significantly higher success rates compared to imitation learning and other baselines.

cs.RO↗
arXiv2026

Interfacial exchange and magnetostatic coupling in a CoFeB/Thulium Iron Garnet heterostructure

We investigate the exchange coupling between a ferrimagnetic insulator (FI) thulium iron garnet (TmIG) deposited on a gadolinium gallium garnet (GGG) substrate, which shows perpendicular magnetic anisotropy and a ferromagnetic metal (FMM) stack with oxide capping that consists of CoFeB(x)/W(0.4 nm)/CoFeB(0.8 nm)/MgO(1 nm)/W(5 nm). Vibrating sample magnetometry, magneto-optical Kerr microscopy and first-order reversal curve studies coupled with micromagnetic simulations are used to analyze the coupling between these layers. Strong interlayer exchange coupling and magnetostatic coupling are observed in the samples where the relative strength between these interactions can be controlled by varying the thickness of the CoFeB layer. Films with CoFeB thickness x <=1 nm are strongly exchange coupled, whereas the magnetostatic coupling dominates when the thickness is increased to 3 nm or above. These findings have important implications towards realizing fast and energy efficient spintronic devices using a FI, as its coupling to the FMM layer can be used for effective electrical read out of the magnetic state of the FI.

cond-mat.mtrl-sci↗
arXiv2026

Denoising diffusion and latent diffusion models for physics field simulations

Accurate prediction of physical fields is critical in various engineering applications, including thermal management in electronic systems, airfoil shape optimization in aerospace, and flow field control in hypersonic vehicles. This study employs the Denoising Diffusion Probabilistic Models (DDPMs) for predicting the temperature field caused by the thermal diffusion, and the flow fields spanning from incompressible to hypersonic regimes. A conditional DDPM framework is first validated with a steady-state thermal diffusion problem by predicting the temperature distribution around a plate with holes. Strong agreement with ground truth data is shown with an average error of approximately 0.013 for plates with a central circular hole. The model also delivers high accuracy in critical regions, such as near the inner circular or square holes. Its performance is further evaluated on incompressible flow around an airfoil and hypersonic flow over a compression ramp, confirming robust predictive capability across diverse flow conditions. Additionally, a latent-space implementation of DDPM is introduced, which employs an Autoencoder (AE) for dimensionality reduction and reconstruction of the physical data. The resulting Latent Diffusion Model (LDM) maintains reconstruction quality comparable to the standard DDPM while substantially reducing the computational cost of the diffusion training process. When applied to hypersonic flow over a compression ramp in the original parameter space, LDM predictions align well with ground truth, achieving a deviation of only 4.28% in separation length estimation. This work confirms the high predictive accuracy of the DDPM framework and highlights the efficiency gains from performing diffusion in a learned latent space. The findings establish an efficient framework for high fidelity generative modeling of complex thermal/flow fields.

physics.flu-dyn↗
arXiv2026

DVD-Quant: Data-free Video Diffusion Transformers Quantization

Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While post-training quantization (PTQ) presents a promising approach to accelerate Video DiT models, existing methods suffer from two critical limitations: (1) dependence on computation-heavy and inflexible calibration procedures, and (2) considerable performance deterioration after quantization. To address these challenges, we propose DVD-Quant, a novel Data-free quantization framework for Video DiTs. Our approach integrates three key innovations: (1) Bounded-init Grid Refinement (BGR) and (2) Auto-scaling Rotated Quantization (ARQ) for calibration data-free quantization error reduction, as well as (3) $δ$-Guided Bit Switching ($δ$-GBS) for adaptive bit-width allocation. Extensive experiments across multiple video generation benchmarks demonstrate that DVD-Quant achieves an approximately 2$\times$ speedup over full-precision baselines on advanced DiT models while maintaining visual fidelity. Notably, DVD-Quant is the first to enable W4A4 PTQ for Video DiTs without compromising video quality. Code and models will be available at https://github.com/lhxcs/DVD-Quant.

cs.CV↗
arXiv2026

A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment

Cognitive impairment is becoming a major public health challenge. Cognitive Stimulation Therapy (CST) is an effective intervention for cognitive impairment, but traditional methods are difficult to scale, and existing digital systems struggle with group dialogues and cognitive stimulation principles. While Large Language Models (LLMs) are powerful, their application in this context faces key challenges: cognitive stimulation dialogue paradigms, a lack of therapeutic reasoning, and static-only user modeling. To address these issues, we propose a principle-driven adaptive policy actualized through a Group Cognitive Stimulation Dialogue (GCSD) system. We first construct a dataset with over 500 hours of real-world CST conversations and 10,000+ simulated dialogues generated via our Principle-Guided Scenario Simulation strategy. Our GCSD system then integrates four core modules to overcome LLM limitations: (i) a multi-speaker context controller to resolve role confusion; (ii) dynamic participant cognitive state modeling for personalized interaction; (iii) a cognitive stimulation-focused attention loss to instill cognitive stimulation reasoning; and (iv) a multi-dimensional reward strategy to enhance response value. Experimental results demonstrate that GCSD significantly outperforms baseline models across various evaluation metrics. Future work will focus on long-term clinical validation to bridge the gap between computational performance and clinical efficacy.

cs.CL↗
arXiv2026

JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization

This paper introduces JavisDiT, a novel Joint Audio-Video Diffusion Transformer designed for synchronized audio-video generation (JAVG). Based on the powerful Diffusion Transformer (DiT) architecture, JavisDiT simultaneously generates high-quality audio and video content from open-ended user prompts in a unified framework. To ensure audio-video synchronization, we introduce a fine-grained spatio-temporal alignment mechanism through a Hierarchical Spatial-Temporal Synchronized Prior (HiST-Sypo) Estimator. This module extracts both global and fine-grained spatio-temporal priors, guiding the synchronization between the visual and auditory components. Furthermore, we propose a new benchmark, JavisBench, which consists of 10,140 high-quality text-captioned sounding videos and focuses on synchronization evaluation in diverse and complex real-world scenarios. Further, we specifically devise a robust metric for measuring the synchrony between generated audio-video pairs in real-world content. Experimental results demonstrate that JavisDiT significantly outperforms existing methods by ensuring both high-quality generation and precise synchronization, setting a new standard for JAVG tasks. Our code, model, and data are available at https://javisverse.github.io/JavisDiT-page/.

cs.CV↗
arXiv2026

JavisDiT++: Unified Modeling and Optimization for Joint Audio-Video Generation

AIGC has rapidly expanded from text-to-image generation toward high-quality multimodal synthesis across video and audio. Within this context, joint audio-video generation (JAVG) has emerged as a fundamental task that produces synchronized and semantically aligned sound and vision from textual descriptions. However, compared with advanced commercial models such as Veo3, existing open-source methods still suffer from limitations in generation quality, temporal synchrony, and alignment with human preferences. To bridge the gap, this paper presents JavisDiT++, a concise yet powerful framework for unified modeling and optimization of JAVG. First, we introduce a modality-specific mixture-of-experts (MS-MoE) design that enables cross-modal interaction efficacy while enhancing single-modal generation quality. Then, we propose a temporal-aligned RoPE (TA-RoPE) strategy to achieve explicit, frame-level synchronization between audio and video tokens. Besides, we develop an audio-video direct preference optimization (AV-DPO) method to align model outputs with human preference across quality, consistency, and synchrony dimensions. Built upon Wan2.1-1.3B-T2V, our model achieves state-of-the-art performance merely with around 1M public training entries, significantly outperforming prior approaches in both qualitative and quantitative evaluations. Comprehensive ablation studies have been conducted to validate the effectiveness of our proposed modules. All the code, model, and dataset are released at https://JavisVerse.github.io/JavisDiT2-page.

cs.CV↗
arXiv2026

DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI

Moving beyond the traditional paradigm of adapting internet-pretrained models to physical tasks, we present DM0, an Embodied-Native Vision-Language-Action (VLA) framework designed for Physical AI. Unlike approaches that treat physical grounding as a fine-tuning afterthought, DM0 unifies embodied manipulation and navigation by learning from heterogeneous data sources from the onset. Our methodology follows a comprehensive three-stage pipeline: Pretraining, Mid-Training, and Post-Training. First, we conduct large-scale unified pretraining on the Vision-Language Model (VLM) using diverse corpora--seamlessly integrating web text, autonomous driving scenarios, and embodied interaction logs-to jointly acquire semantic knowledge and physical priors. Subsequently, we build a flow-matching action expert atop the VLM. To reconcile high-level reasoning with low-level control, DM0 employs a hybrid training strategy: for embodied data, gradients from the action expert are not backpropagated to the VLM to preserve generalized representations, while the VLM remains trainable on non-embodied data. Furthermore, we introduce an Embodied Spatial Scaffolding strategy to construct spatial Chain-of-Thought (CoT) reasoning, effectively constraining the action solution space. Experiments on the RoboChallenge benchmark demonstrate that DM0 achieves state-of-the-art performance in both Specialist and Generalist settings on Table30.

cs.RO↗
arXiv2026

OMEGA-Avatar: One-shot Modeling of 360° Gaussian Avatars

Creating high-fidelity, animatable 3D avatars from a single image remains a formidable challenge. We identified three desirable attributes of avatar generation: 1) the method should be feed-forward, 2) model a 360° full-head, and 3) should be animation-ready. However, current work addresses only two of the three points simultaneously. To address these limitations, we propose OMEGA-Avatar, the first feed-forward framework that simultaneously generates a generalizable, 360°-complete, and animatable 3D Gaussian head from a single image. Starting from a feed-forward and animatable framework, we address the 360° full-head avatar generation problem with two novel components. First, to overcome poor hair modeling in full-head avatar generation, we introduce a semantic-aware mesh deformation module that integrates multi-view normals to optimize a FLAME head with hair while preserving its topology structure. Second, to enable effective feed-forward decoding of full-head features, we propose a multi-view feature splatting module that constructs a shared canonical UV representation from features across multiple views through differentiable bilinear splatting, hierarchical UV mapping, and visibility-aware fusion. This approach preserves both global structural coherence and local high-frequency details across all viewpoints, ensuring 360° consistency without per-instance optimization. Extensive experiments demonstrate that OMEGA-Avatar achieves state-of-the-art performance, significantly outperforming existing baselines in 360° full-head completeness while robustly preserving identity across different viewpoints.

cs.GR↗
arXiv2026

D-CORE: Incentivizing Task Decomposition in Large Reasoning Models for Complex Tool Use

Effective tool use and reasoning are essential capabilities for large reasoning models~(LRMs) to address complex real-world problems. Through empirical analysis, we identify that current LRMs lack the capability of sub-task decomposition in complex tool use scenarios, leading to Lazy Reasoning. To address this, we propose a two-stage training framework D-CORE~(\underline{\textbf{D}}ecomposing tasks and \underline{\textbf{Co}}mposing \underline{\textbf{Re}}asoning processes) that first incentivize the LRMs' task decomposition reasoning capability via self-distillation, followed by diversity-aware reinforcement learning~(RL) to restore LRMs' reflective reasoning capability. D-CORE achieves robust tool-use improvements across diverse benchmarks and model scales. Experiments on BFCLv3 demonstrate superiority of our method: D-CORE-8B reaches 77.7\% accuracy, surpassing the best-performing 8B model by 5.7\%. Meanwhile, D-CORE-14B establishes a new state-of-the-art at 79.3\%, outperforming 70B models despite being 5$\times$ smaller. The source code is available at https://github.com/alibaba/EfficientAI.

cs.CL↗
arXiv2026

Exotic vortex states at high magnetic fields in a quasi-two-dimensional FeSe-based superconductor

Owing to strong electronic correlations, high-temperature superconductivity always exhibits intricate intertwinement with various competing electronic orders in phase diagrams, such as spin/charge density waves (S/CDWs). In cuprate superconductors,the intertwinement of superconductivity and CDW order could strongly affect the fundamental properties of superconductivity, such as the critical temperature(Tc) and critical magnetic field(Hc). Recent high-field transport measurements indicate that when quantum fluctuations become important at low temperatures and high magnetic fields, the CDW order also reshapes the vortex states, which leads to fragile superconductivity with extremely low critical current(Jc). Here, by performing comprehensive high-field transport measurements, the H-T phase diagram of vortex states is mapped to H = 33 T in a quasi-two-dimensional FeSe-based superconductor (TBA+)xFeSe with a zero-resistivity transition temperature above 40 K. Our results indicate that (TBA+)xFeSe is an extremely type II superconductor with significant thermal fluctuations.At low temperatures, high magnetic fields cause the vortex solid state to exhibit similar current-dependent zero-resistance behavior as the fragile superconductivity in cuprate superconductors with CDW order. When the vortex solid state is melted with increasing temperature, a superconducting regime with vortex-like phase fluctuations emerges as an intermediate state, which features finite longitudinal resistance and vanishing Hall resistance. At higher temperatures, a vortex liquid state with finite Hall resistance eventually appears due to thermal fluctuations. All these observations suggest exotic vortex states beyond the classical paradigm of vortex matter.

cond-mat.supr-con↗
arXiv2026

Superconductivity in non-centrosymmetric rhombohedral NbSe2

Crystal stacking offers a powerful yet underexplored route to engineer symmetry in layered superconductors. Here we report superconductivity in rhombohedral-stacked NbSe2 (3R-NbSe2), a non-centrosymmetric polytype in which global inversion symmetry is removed by stacking alone. Using comprehensive structural, transport, magnetic, and thermodynamic measurements, we establish superconductivity as a bulk property of the 3R phase and find that the in-plane upper critical field exceeds the Pauli paramagnetic limit, indicating the persistence of strong Ising-type spin-orbit coupling. Unlike the thickness-dependent superconductivity in centrosymmetric 2H-NbSe2, the superconducting transition temperature in 3R-NbSe2 shows little dependence on layer number but exhibits an unusually strong sensitivity to disorder. We further observe strongly enhanced nonlinear optical and electrical responses near the superconducting transition, consistent with stacking-induced inversion-symmetry breaking. Our results identify 3R-NbSe2 as a single-phase platform in which stacking engineering reshapes superconductivity and enables nonlinear transport phenomena in layered materials.

cond-mat.supr-con↗
arXiv2026

Topological Semimetal Transport Modulated by Interstitial Fe in Ba(Fe$_{1-x}$Co$_x$)$_{2+δ}As$_2$ Superconductors

Topological semimetals are renowned for exhibiting large, unsaturated magnetoresistance arising from ultrahigh carrier mobility and electron-hole compensation. However, such behaviors remain poorly understood in iron-based superconductors that have been recently recognized to harbor rich nontrivial topology. Here, we combine angle-resolved magneto-transport measurements with first principles calculations to reveal the emergence and tunability of topological semimetals in ferropnictide Ba(Fe$_{1-x}$Co$_x$)$_{2+δ}As$_2$ epitaxial films, modulated by interstitial Fe. These states exhibit ultralow residual resistivity, coexisting high-mobility electron and hole carriers, and linear positive magnetoresistance below 110 K. Remarkably, the magnetoresistance becomes more pronounced when the magnetic field is applied parallel to the film plane, reaching an unsaturated 1206% at 56 T. Furthermore, superconductivity persists in these ferropnictide films, establishing them as a tunable platform for investigating the interplay among electron correlation, topology, and superconductivity.

cond-mat.supr-con↗
arXiv2026

Superconductivity in W3Re2C with chiral structure

We discover superconductivity in cubic W3Re2C with chiral structure and the superconducting transition temperature Tc is about 6.2 K. Detailed characterizations and analysis indicate that W3Re2C is a bulk type-II BCS superconductor with full isotropic gap. Moreover, first-principles calculations indicate that the electron-phonon coupling primarily arises from interactions between W/Re 5d electronic states and their low-frequency phonons. Furthermore, the breaking of inversion symmetry in W3Re2C facilitates the emergence of Weyl points in the electronic structure. Therefore, W3Re2C can serve as a promising platform for investigating the influences of chiral structure on both superconductivity and band topology.

cond-mat.supr-con↗
arXiv2026

Observer design and boundary output feedback stabilization for semilinear parabolic system over general multidimensional domain

This paper investigates the output feedback stabilization of parabolic equation with Lipschitz nonlinearity over general multidimensional domain using spectral geometry theories. First, a novel nonlinear observer is designed, and the error system is shown to achieve any prescribed decay rate by leveraging the Berezin-Li-Yau inequality from spectral geometry, which also provides effective guidance for sensor placement. Subsequently, a finite-dimensional state feedback controller is proposed, which ensures the quantitative rapid stabilization of the linear part. By integrating this control law with the observer, an efficient boundary output feedback control strategy is developed. The feasibility of the proposed control design is rigorously verified for arbitrary Lipschitz constants, thereby resolving a persistent theoretical challenge. Finally, a numerical case study confirms the effectiveness of the approach.

math.OC↗
arXiv2026

Symmetry-engineered and electrically tunable in-plane anomalous Hall effect in oxide heterostructures

The family of Hall effects has long served as a premier probe of how symmetry, magnetic order, and topology intertwine in solids. Recently, the in-plane anomalous Hall effect (IP-AHE), a transverse Hall response driven by in-plane magnetization, has emerged as a distinct member of this family, offering innovative spintronic functionalities and illuminating intricate interplay between mirror-symmetry breaking and in-plane magnetic order. However, practical routes to deterministically and reversibly control IP-AHE remain limited. Here, we establish a symmetry-engineered IP-AHE platform, CaRuO3/La2/3Ca1/3MnO3/CaRuO3 heterostructure on NdGaO3(110), that turns strict mirror-symmetry breaking constraints into effective tuning knobs. IP-AHE in these epitaxial trilayers unambiguously couples to the CaRuO3-buffer-induced mirror-symmetry breaking and faithfully reproduces the ferromagnetic hysteresis. Ionic liquid gating further enables reversible reconfigurations of the symmetry breaking, thereby achieving electrical modulation and ON/OFF switching of IP-AHE. This highly tunable IP-AHE platform opens pathways for exploring nontrivial magnetic order and developing programmable Hall functionalities in planar geometries.

cond-mat.str-el↗