arXiv subjects

Tao Xiang

Publications and source records attributed to Tao Xiang.

At least 55 records · Page 3Linked to original sources

arXiv2025

HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming

High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To address this, we introduce HiStream, an efficient autoregressive framework that systematically reduces redundancy across three axes: i) Spatial Compression: denoising at low resolution before refining at high resolution with cached features; ii) Temporal Compression: a chunk-by-chunk strategy with a fixed-size anchor cache, ensuring stable inference speed; and iii) Timestep Compression: applying fewer denoising steps to subsequent, cache-conditioned chunks. On 1080p benchmarks, our primary HiStream model (i+ii) achieves state-of-the-art visual quality while demonstrating up to 76.2x faster denoising compared to the Wan2.1 baseline and negligible quality loss. Our faster variant, HiStream+, applies all three optimizations (i+ii+iii), achieving a 107.5x acceleration over the baseline, offering a compelling trade-off between speed and quality, thereby making high-resolution video generation both practical and scalable.

cs.CV↗

arXiv2025

Extreme Strain Controlled Correlated Metal-Insulator Transition in the Altermagnet CrSb

Correlated flat bands and altermagnetism are two important directions in quantum materials, centred respectively on interaction-dominated phases and symmetry-enforced spin-textured states, yet both derive from lattice symmetry and orbital hybridization. This common origin implies that extreme crystal distortion, by narrowing bandwidths, enhancing correlations and reshaping the symmetries of altermagnetic spin splittings, could unify flat-band and altermagnetic physics in a single material; in practice, however, achieving such large distortions in a crystalline altermagnet is a formidable challenge. Here we combine a dedicated strain device with a tailored single-crystal mounting scheme to impose a highly tensile strain gradient in bulk CrSb, a prototypical altermagnet, creating a near-surface layer in which the in-plane lattice is strongly distorted relative to the weakly strained bulk, while the average bulk distortion remains small. Angle-resolved photoemission reveals a reversible regime at moderate strain, where a deeper flat-band feature, attributed to a strain-gradient-driven suppression of Cr-Sb hybridization, coexists with a correlation-enhanced Cr 3d flat band, and an irreversible regime at larger strain where partial bond decoupling drives a predominantly insulating spectral response. Density-functional calculations show that an orbital-selective altermagnetic spin texture persists across this correlated regime despite strong bandwidth renormalisation. These results define a strain-symmetry-correlation map for CrSb and establish extreme tensile strain as a route to co-engineer flat-band tendencies and spin-textured, zero-net-moment correlated states in altermagnets, pointing toward strain-adaptive, spin-selective Mott filtering and related device concepts.

cond-mat.str-el↗

arXiv2025

OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory

Storytelling in real-world videos often unfolds through multiple shots -- discontinuous yet semantically connected clips that together convey a coherent narrative. However, existing multi-shot video generation (MSV) methods struggle to effectively model long-range cross-shot context, as they rely on limited temporal windows or single keyframe conditioning, leading to degraded performance under complex narratives. In this work, we propose OneStory, enabling global yet compact cross-shot context modeling for consistent and scalable narrative generation. OneStory reformulates MSV as a next-shot generation task, enabling autoregressive shot synthesis while leveraging pretrained image-to-video (I2V) models for strong visual conditioning. We introduce two key modules: a Frame Selection module that constructs a semantically-relevant global memory based on informative frames from prior shots, and an Adaptive Conditioner that performs importance-guided patchification to generate compact context for direct conditioning. We further curate a high-quality multi-shot dataset with referential captions to mirror real-world storytelling patterns, and design effective training strategies under the next-shot paradigm. Finetuned from a pretrained I2V model on our curated 60K dataset, OneStory achieves state-of-the-art narrative coherence across diverse and complex scenes in both text- and image-conditioned settings, enabling controllable and immersive long-form video storytelling.

cs.CV↗

arXiv2025

Hidden Moiré Topology of Low-Symmetry Weyl Surfaces

Topological materials are defined by the correspondence between bulk topology and boundary states, yet this correspondence becomes enigmatic on low-symmetry surfaces where bulk and surface periodicities are inherently mismatched. Here we reveal a hidden moiré topology emerging on the (103) surface of the Weyl semimetal NdAlSi. Angle-resolved photoemission spectroscopy uncovers closed Fermi-arc loops and momentum-space moiré modulations, phenomena unanticipated in conventional topological theory. We show that these emerge from incomplete bulk projection and multi-cell interference governed by a least-common-multiple framework. Least-common-multiple guided DFT and Green's-function calculations quantitatively reproduce the observed spectra, establishing the universality of this commensuration rule. These findings transform a long-standing paradox of bulk-boundary correspondence into a new paradigm of momentum-space moiré reconstruction, bridging crystalline and quasicrystalline topologies and opening routes to flat-band engineering on complex surfaces.

cond-mat.mtrl-sci↗

arXiv2025

Scaling Zero-Shot Reference-to-Video Generation

Reference-to-video (R2V) generation aims to synthesize videos that align with a text prompt while preserving the subject identity from reference images. However, current R2V methods are hindered by the reliance on explicit reference image-video-text triplets, whose construction is highly expensive and difficult to scale. We bypass this bottleneck by introducing Saber, a scalable zero-shot framework that requires no explicit R2V data. Trained exclusively on video-text pairs, Saber employs a masked training strategy and a tailored attention-based model design to learn identity-consistent and reference-aware representations. Mask augmentation techniques are further integrated to mitigate copy-paste artifacts common in reference-to-video generation. Moreover, Saber demonstrates remarkable generalization capabilities across a varying number of references and achieves superior performance on the OpenS2V-Eval benchmark compared to methods trained with R2V data.

cs.CV↗

arXiv2025

TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models

Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA's unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.

cs.CV↗

arXiv2025

Stackelberg Game-Driven Defense for ISAC Against Channel Attacks in Low-Altitude Networks

The increasing saturation of terrestrial resources has driven economic activities into low-altitude airspace. These activities, such as air taxis, rely on low-altitude wireless networks, and one key enabling technology is integrated sensing and communication (ISAC). However, in low-altitude airspace, ISAC is vulnerable to channel-access attacks, thereby degrading performance and threatening safety. To address this, we propose a defense framework based on a Stackelberg game. Specifically, we first model the system under attack, deriving metrics for the communication and the sensing to quantify performance. Then, we formulate the interaction as a three-player game where a malicious attacker acts as the leader, while the legitimate drone and ground base station act as followers. Using a backward induction algorithm, we obtain the Stackelberg equilibrium, allowing the defenders to dynamically adjust their strategies to mitigate the attack. Simulation results verify that the proposed algorithm converges to a stable solution and outperforms existing baselines, ensuring reliable ISAC performance for critical low-altitude applications.

eess.SP↗

arXiv2025

Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks

As terrestrial resources become increasingly saturated, the research attention is shifting to the low-altitude airspace, with many emerging applications such as urban air taxis and aerial inspection. Low-Altitude Wireless Networks (LAWNs) are the foundation for these applications, with integrated sensing, communications, and computing (ISCC) being one of the core parts of LAWNs. However, the openness of low-altitude airspace exposes communications to security threats, degrading ISCC performance and ultimately compromising the reliability of applications supported by LAWNs. To address these challenges, this paper studies joint performance optimization of ISCC while considering secrecyness of the communications. Specifically, we derive beampattern error, secrecy rate, and age of information (AoI) as performance metrics for sensing, secrecy communication, and computing. Building on these metrics, we formulate a multi-objective optimization problem that balances sensing and computation performance while keeping the probability of communication being detected below a required threshold. We then propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives, thereby leading to more effective solutions. Extensive simulations show that the proposed method achieves a superior balance among sensing accuracy, communication secrecyness, and information freshness compared with baseline algorithms, thereby safeguarding ISCC performance and LAWN-supported low-altitude applications.

cs.CR↗

arXiv2025

Possible Observation of Quadrupole Waves in Spin Nematics

Discovery of new states of matter is a key objective in modern condensed matter physics, which often leads to revolutionary technological advancements such as superconductivity. Quantum spin nematic, a ``hidden order'' that evades conventional magnetic probes, is one such state. Na$_2$BaNi(PO$_4$)$_2$ is a potential spin nematic material, suggested by the observation of a two-magnon Bose-Einstein condensation from above the saturation field. However, direct confirmation of the spin nematicity remains elusive. This Letter presents inelastic neutron scattering spectra from the putative spin nematic phases of Na$_2$BaNi(PO$_4$)$_2$, revealing low-energy quadrupole waves that are absent in the neighboring conventional magnetic phases. A spin-one model quantitatively captures the full details of the spin excitation spectra across all low-temperature phases, providing direct evidence of the spin nematic orders. Additionally, we show evidence of the three-magnon continuum and two-magnon bound states in the $1/3$-magnetization plateau, revealing condensation of the two-magnon bound state as the origin of the low-field spin nematic supersolid phase.

cond-mat.str-el↗

arXiv2025

Bridging conformal field theory and parton approaches to SU(n)_k chiral spin liquids

We employ the $\mathrm{SU}(n)_k$ Wess-Zumino-Witten (WZW) model in conformal field theory to construct lattice wave functions in both one and two dimensions. The spins on all lattice sites are chosen to transform under the $\mathrm{SU}(n)$ irreducible representation with a single row and $k$ boxes in the Young tableau. It is demonstrated that the wave functions can be reinterpreted as parton states, which enables efficient conversion to matrix product states such that many physical properties can be evaluated directly. In one dimension, these wave functions describe critical spin chains whose universality classes are in one-to-one correspondence with the WZW models used in the construction. In two dimensions, our constructions yield model wave functions for chiral spin liquids, and we show how to find all topological sectors of them in a systematic way. Using the null vectors of Kac-Moody algebras, parent Hamiltonians of the $\mathrm{SU}(3)_k$ series are derived. The $\mathrm{SU}(3)_k$ chiral spin liquids are lattice analogs of non-Abelian spin-singlet fractional quantum Hall states, and the $k=2$ member hosts Fibonacci anyons.

cond-mat.str-el↗

arXiv2025

Deconfined Quantum Critical Point in Quantum Hall Bilayers

Deconfined quantum critical points (DQCPs) represent an unconventional class of quantum criticality beyond the Landau-Ginzburg-Wilson-Fisher paradigm. Nevertheless, both their theoretical identification and experimental realization remain challenging. Here we report compelling evidence of a DQCP in quantum Hall bilayers with half-filled $n=2$ Landau levels in each layer, based on large-scale variational uniform matrix product state (VUMPS) simulations and exact diagonalization (ED). By systematically analyzing the ground-state fidelity, low-lying energy spectra, exciton superfluid and stripe order parameters, and ground-state energy derivatives, we identify a direct and continuous quantum phase transition between two distinct symmetry-breaking phases by tuning the layer separation: an exciton superfluid phase with spontaneous $U(1)$ symmetry breaking at small separation, and a unidirectional charge density wave with broken translational symmetry at large separation. Our results highlight quantum Hall bilayers as an ideal platform for realizing and experimentally probing DQCPs under precisely tunable interactions.

cond-mat.mes-hall↗

arXiv2025

Pixel-Optimization-Free Patch Attack on Stereo Depth Estimation

Stereo Depth Estimation (SDE) is essential for scene perception in vision-based systems such as autonomous driving. Prior work shows SDE is vulnerable to pixel-optimization attacks, but these methods are limited to digital, static, and view-specific settings, making them impractical. This raises a central question: how to design deployable, adaptive, and transferable attacks under realistic constraints? We present two contributions to answer it. First, we build a unified framework that extends pixel-optimization attacks to four stereo-matching stages: feature extraction, cost-volume construction, cost aggregation, and disparity regression. Through systematic evaluation across nine SDE models with realistic constraints like photometric consistency, we show existing attacks suffer from poor transferability. Second, we propose PatchHunter, the first pixel-optimization-free attack. PatchHunter casts patch generation as a search in a structured space of visual patterns that disrupt core SDE assumptions, and uses a reinforcement learning policy to discover effective and transferable patterns efficiently. We evaluate PatchHunter on three levels: autonomous driving dataset, high-fidelity simulator, and real-world deployment. On KITTI, PatchHunter outperforms pixel-level attacks in both effectiveness and black-box transferability. Tests in CARLA and on vehicles with industrial-grade stereo cameras confirm robustness to physical variations. Even under challenging conditions such as low lighting, PatchHunter achieves a D1-all error above 0.4, while pixel-level attacks remain near 0.

cs.CV↗

arXiv2025

Safeguarding ISAC Performance in Low-Altitude Wireless Networks Under Channel Access Attack

The increasing saturation of terrestrial resources has driven the exploration of low-altitude applications such as air taxis. Low altitude wireless networks (LAWNs) serve as the foundation for these applications, and integrated sensing and communication (ISAC) constitutes one of the core technologies within LAWNs. However, the openness nature of low-altitude airspace makes LAWNs vulnerable to malicious channel access attacks, which degrade the ISAC performance. Therefore, this paper develops a game-based framework to mitigate the influence of the attacks on LAWNs. Concretely, we first derive expressions of communication data's signal-to-interference-plus-noise ratio and the age of information of sensing data under attack conditions, which serve as quality of service metrics. Then, we formulate the ISAC performance optimization problem as a Stackelberg game, where the attacker acts as the leader, and the legitimate drone and the ground ISAC base station act as second and first followers, respectively. On this basis, we design a backward induction algorithm that achieves the Stackelberg equilibrium while maximizing the utilities of all participants, thereby mitigating the attack-induced degradation of ISAC performance in LAWNs. We further prove the existence and uniqueness of the equilibrium. Simulation results show that the proposed algorithm outperforms existing baselines and a static Nash equilibrium benchmark, ensuring that LAWNs can provide reliable service for low-altitude applications.

cs.NI↗

arXiv2025

Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications

AI-enabled wireless communications have attracted tremendous research interest in recent years, particularly with the rise of novel paradigms such as low-altitude integrated sensing and communication (ISAC) networks. Within these systems, feature engineering plays a pivotal role by transforming raw wireless data into structured representations suitable for AI models. Hence, this paper offers a comprehensive investigation of feature engineering techniques in AI-driven wireless communications. Specifically, we begin with a detailed analysis of fundamental principles and methodologies of feature engineering. Next, we present its applications in wireless communication systems, with special emphasis on ISAC networks. Finally, we introduce a generative AI-based framework, which can reconstruct signal feature spectrum under malicious attacks in low-altitude ISAC networks. The case study shows that it can effectively reconstruct the signal spectrum, achieving an average structural similarity index improvement of 4%, thereby supporting downstream sensing and communication applications.

eess.SP↗

arXiv2025

Solving the Hubbard model with Neural Quantum States

The rapid development of neural quantum states (NQS) has established it as a promising framework for studying quantum many-body systems. In this work, by leveraging the cutting-edge transformer-based architectures and developing highly efficient optimization algorithms, we achieve the state-of-the-art results for the doped two-dimensional (2D) Hubbard model, arguably the minimum model for high-Tc superconductivity. Interestingly, we find different attention heads in the NQS ansatz can directly encode correlations at different scales, making it capable of capturing long-range correlations and entanglements in strongly correlated systems. With these advances, we establish the half-filled stripe in the ground state of 2D Hubbard model with the next nearest neighboring hoppings, consistent with experimental observations in cuprates. Our work establishes NQS as a powerful tool for solving challenging many-fermions systems.

cond-mat.str-el↗

arXiv2025

Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset

Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals. To develop socially intelligent AI technologies, it is crucial to develop models that can both comprehend and generate dyadic behavioral dynamics. To this end, we introduce the Seamless Interaction Dataset, a large-scale collection of over 4,000 hours of face-to-face interaction footage from over 4,000 participants in diverse contexts. This dataset enables the development of AI technologies that understand dyadic embodied dynamics, unlocking breakthroughs in virtual agents, telepresence experiences, and multimodal content analysis tools. We also develop a suite of models that utilize the dataset to generate dyadic motion gestures and facial expressions aligned with human speech. These models can take as input both the speech and visual behavior of their interlocutors. We present a variant with speech from an LLM model and integrations with 2D and 3D rendering methods, bringing us closer to interactive virtual agents. Additionally, we describe controllable variants of our motion models that can adapt emotional responses and expressivity levels, as well as generating more semantically-relevant gestures. Finally, we discuss methods for assessing the quality of these dyadic motion models, which are demonstrating the potential for more intuitive and responsive human-AI interactions.

cs.CV↗

arXiv2025

Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation

Embodied agents exhibit immense potential across a multitude of domains, making the assurance of their behavioral safety a fundamental prerequisite for their widespread deployment. However, existing research predominantly concentrates on the security of general large language models, lacking specialized methodologies for establishing safety benchmarks and input moderation tailored to embodied agents. To bridge this gap, this paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents. This framework encompasses the entire pipeline, including taxonomy definition, dataset curation, moderator architecture, model training, and rigorous evaluation. Notably, we introduce EAsafetyBench, a meticulously crafted safety benchmark engineered to facilitate both the training and stringent assessment of moderators specifically designed for embodied agents. Furthermore, we propose Pinpoint, an innovative prompt-decoupled input moderation scheme that harnesses a masked attention mechanism to effectively isolate and mitigate the influence of functional prompts on moderation tasks. Extensive experiments conducted on diverse benchmark datasets and models validate the feasibility and efficacy of the proposed approach. The results demonstrate that our methodologies achieve an impressive average detection accuracy of 94.58%, surpassing the performance of existing state-of-the-art techniques, alongside an exceptional moderation processing time of merely 0.002 seconds per instance.

cs.AI↗

arXiv2025

Efficient Blockchain-based Steganography via Backcalculating Generative Adversarial Network

Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose a generic blockchain-based steganography framework (GBSF). The sender generates the required fields such as amount and fees, where the additional covert data is embedded to enhance the channel capacity. Based on GBSF, we design a reversible generative adversarial network (R-GAN) that utilizes the generative adversarial network with a reversible generator to generate the required fields and encode additional covert data into the input noise of the reversible generator. We then explore the performance flaw of R-GAN. To further improve the performance, we propose R-GAN with Counter-intuitive data preprocessing and Custom activation functions, namely CCR-GAN. The counter-intuitive data preprocessing (CIDP) mechanism is used to reduce decoding errors in covert data, while it incurs gradient explosion for model convergence. The custom activation function named ClipSigmoid is devised to overcome the problem. Theoretical justification for CIDP and ClipSigmoid is also provided. We also develop a mechanism named T2C, which balances capacity and concealment. We conduct experiments using the transaction amount of the Bitcoin mainnet as the required field to verify the feasibility. We then apply the proposed schemes to other transaction fields and blockchains to demonstrate the scalability. Finally, we evaluate capacity and concealment for various blockchains and transaction fields and explore the trade-off between capacity and concealment. The results demonstrate that R-GAN and CCR-GAN are able to enhance the channel capacity effectively and outperform state-of-the-art works.

cs.CR↗