arXiv subjects

Huan Yang

Publications and source records attributed to Huan Yang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Emergent Turbulence in Nonlinear Gravity

Gravity in nonlinear and dynamical regimes underpins spectacular astrophysical phenomena and observable consequences, from the early universe to black hole collisions. In these extreme environments, inverse energy cascades - mediated by nonlinear interactions - may help explain the near scale-invariance of cosmic structure and the simplicity of gravitational waves from binary black hole mergers. Yet the presence, characteristics, and generality of such interactions in full General Relativity remain largely unexplored. Here we show that two types of nonlinear interactions - a four-mode and a three-mode interaction - emerge in the fully nonlinear regime, and can indeed channel inverse energy cascades by inducing resonant and anti-damping instabilities. This establishes what was previously only hinted at in highly specialized perturbative contexts. We further demonstrate a ``laminar'' to ``turbulent'' transition for the largest-possible angular structure in General Relativity, whereas finer structures remain persistently turbulent. Our results reveal the impact and generality of these nonlinear interactions (instabilities), which can be key to understanding observations ranging from cosmological to kilometer scales. We anticipate that our work will shed new light on nonlinear gravitational phenomena and their consequences, such as constructing gravitational wave templates and testing General Relativity in the most extreme regime. Moreover, our work is a starting point for addressing nonlinear gravitational interactions using ideas and methods inspired by fluid dynamics.

gr-qc↗

arXiv2026

SemanticALLI: Caching Reasoning, Not Just Responses, in Agentic Systems

Agentic AI pipelines suffer from a hidden inefficiency: they frequently reconstruct identical intermediate logic, such as metric normalization or chart scaffolding, even when the user's natural language phrasing is entirely novel. Conventional boundary caching fails to capture this inefficiency because it treats inference as a monolithic black box. We introduce SemanticALLI, a pipeline-aware architecture within Alli (PMG's marketing intelligence platform), designed to operationalize redundant reasoning. By decomposing generation into Analytic Intent Resolution (AIR) and Visualization Synthesis (VS), SemanticALLI elevates structured intermediate representations (IRs) to first-class, cacheable artifacts. The impact of caching within the agentic loop is substantial. In our evaluation, baseline monolithic caching caps at a 38.7% hit rate due to linguistic variance. In contrast, our structured approach allows for an additional stage, the Visualization Synthesis stage, to achieve an 83.10% hit rate, bypassing 4,023 LLM calls with a median latency of just 2.66 ms. This internal reuse reduces total token consumption, offering a practical lesson for AI system design: even when users rarely repeat themselves, the pipeline often does, at stable, structured checkpoints where caching is most reliable.

cs.AI↗

arXiv2026

Science Opportunities of Wet Extreme Mass-Ratio Inspirals

Wet extreme mass-ratio inspirals (wet EMRIs), which arise from stellar-mass black holes inspiral into supermassive black holes (SMBHs) within the gas-rich environments of Active Galactic Nuclei (AGN), are primary sources of gravitational waves (GWs) for space-borne detectors like LISA, TianQin, and Taiji. Unlike "dry EMRIs", which form through gravitational scattering in nuclear star clusters, wet EMRIs are naturally accompanied by interactions with accretion disks, offering rich multi-messenger science opportunities. They are distinct in generating transient electromagnetic (EM) signals, such as quasi-periodic eruptions (QPEs), which serve as valuable probes of accretion disk physics and SMBH environments. Their GW signals provide an unprecedented precision of the order of $O(10^{-4}\sim 10^{-6})$ in measuring SMBH mass and spin, enabling the calibration of traditional EM techniques and offering insights into jet formation models. Additionally, wet EMRIs serve as bright and dark sirens for cosmology, facilitating percent-level precision measurements of Hubble parameter through AGN host identification or statistical association. These systems hold immense potential for advancing our understanding of black hole dynamics, accretion physics, and cosmology.

astro-ph.HE↗

arXiv2026

ResTok: Learning Hierarchical Residuals in 1D Visual Tokenizers for Autoregressive Image Generation

Existing 1D visual tokenizers for autoregressive (AR) generation largely follow the design principles of language modeling, as they are built directly upon transformers whose priors originate in language, yielding single-hierarchy latent tokens and treating visual data as flat sequential token streams. However, this language-like formulation overlooks key properties of vision, particularly the hierarchical and residual network designs that have long been essential for convergence and efficiency in visual models. To bring "vision" back to vision, we propose the Residual Tokenizer (ResTok), a 1D visual tokenizer that builds hierarchical residuals for both image tokens and latent tokens. The hierarchical representations obtained through progressively merging enable cross-level feature fusion at each layer, substantially enhancing representational capacity. Meanwhile, the semantic residuals between hierarchies prevent information overlap, yielding more concentrated latent distributions that are easier for AR modeling. Cross-level bindings consequently emerge without any explicit constraints. To accelerate the generation process, we further introduce a hierarchical AR generator that substantially reduces sampling steps by predicting an entire level of latent tokens at once rather than generating them strictly token-by-token. Extensive experiments demonstrate that restoring hierarchical residual priors in visual tokenization significantly improves AR image generation, achieving a gFID of 2.34 on ImageNet-256 with only 9 sampling steps. Code is available at https://github.com/Kwai-Kolors/ResTok.

cs.CV↗

arXiv2025

SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder

Visual generation grounded in Visual Foundation Model (VFM) representations offers a highly promising unified pathway for integrating visual understanding, perception, and generation. Despite this potential, training large-scale text-to-image diffusion models entirely within the VFM representation space remains largely unexplored. To bridge this gap, we scale the SVG (Self-supervised representations for Visual Generation) framework, proposing SVG-T2I to support high-quality text-to-image synthesis directly in the VFM feature domain. By leveraging a standard text-to-image diffusion pipeline, SVG-T2I achieves competitive performance, reaching 0.75 on GenEval and 85.78 on DPG-Bench. This performance validates the intrinsic representational power of VFMs for generative tasks. We fully open-source the project, including the autoencoder and generation model, together with their training, inference, evaluation pipelines, and pre-trained weights, to facilitate further research in representation-driven visual generation.

cs.CV↗

arXiv2025

Micro-Tidal Disruption Events at Galactic Centers

This work explores a scenario for micro-tidal disruption events (TDEs) triggered by close encounters between high-speed white dwarfs (WDs) and stellar-mass black holes (sBHs) in galactic centers. In this model, a WD orbiting the central massive black hole (MBH) is scattered by an sBH during the sBH's early extreme mass-ratio inspiral phase. We conservatively estimate these events occur a few times per year within $z\leq 3$. Significant disruption of the WD occurs when the impact parameter is comparable to the WD's radius. We derive a mathematical criterion and confirm numerically by hydrodynamical simulations. With the increase of the impact parameter and the collision speed, the WD material captured by the sBH decreases while the material remain self-gravitating increases. A part of the WD material becomes unbound from the sBH-WD system, and its mass ranges from nearly zero to $\ge 50\%$, reaching the peak value when the impact parameter is comparable to the WD's radius. We expect the subsequent capture of WD material by the sBH to produce a prompt X-ray burst (a micro-TDE), and the accretion of unbound debris onto the MBH can power a fainter, delayed optical flare. The properties of certain transient X-ray bursts observed by Einstein Probe are consistent with this micro-TDE picture.

astro-ph.HE↗

arXiv2025

YingVideo-MV: Music-Driven Multi-Stage Video Generation

While diffusion model for audio-driven avatar video generation have achieved notable process in synthesizing long sequences with natural audio-visual synchronization and identity consistency, the generation of music-performance videos with camera motions remains largely unexplored. We present YingVideo-MV, the first cascaded framework for music-driven long-video generation. Our approach integrates audio semantic analysis, an interpretable shot planning module (MV-Director), temporal-aware diffusion Transformer architectures, and long-sequence consistency modeling to enable automatic synthesis of high-quality music performance videos from audio signals. We construct a large-scale Music-in-the-Wild Dataset by collecting web data to support the achievement of diverse, high-quality results. Observing that existing long-video generation methods lack explicit camera motion control, we introduce a camera adapter module that embeds camera poses into latent noise. To enhance continulity between clips during long-sequence inference, we further propose a time-aware dynamic window range strategy that adaptively adjust denoising ranges based on audio embedding. Comprehensive benchmark tests demonstrate that YingVideo-MV achieves outstanding performance in generating coherent and expressive music videos, and enables precise music-motion-camera synchronization. More videos are available in our project page: https://giantailab.github.io/YingVideo-MV/ .

cs.CV↗

arXiv2025

Distinct Modulation Behavior of Superconducting Coherence Peaks Associated with Sign-Reversal Gaps in FeTe$_{0.55}$Se$_{0.45}$

Using high-resolution scanning tunneling microscopy, we reveal two distinct types of superconducting (SC) gap modulations in bulk superconductor FeTe$_{0.55}$Se$_{0.45}$. By analyzing the phase relation between modulations at positive and negative bias, we identify in-phase (particle-hole asymmetric) and anti-phase (particle-hole symmetric) oscillations, corresponding to sign-reversing and sign-preserving scattering processes, respectively. The observed features are consistent with predictions from pair-breaking scattering interference (PBSI) theory and are distinguishable from other alternative mechanisms such as pair density waves. Our results provide compelling evidence that PBSI is the dominant mechanism behind the SC gap modulations in FeTe$_{0.55}$Se$_{0.45}$, offering new insights into the role of impurity scattering in iron-based superconductors.

cond-mat.supr-con↗

arXiv2025

Relativistic excitation of compact stars

In this work, we study the excitation of a compact star under the influence of external gravitational driving in the relativistic regime. Using a model setup in which a wave with constant frequency is injected from past null infinity and scattered by the star to future null infinity, we show that the scattering coefficient encodes rich information of the star. For example, the analytical structure of the scattering coefficient implies that the decay rate of a mode generally plays the role of ``star excitation factor'', similar to the ``black hole excitation factor'' previously defined for describing black hole mode excitations. With this star excitation factor we derive the transient mode excitation as a binary system crosses a generic mode resonance of a companion star during the inspiral stage. This application is useful because previous description of resonant mode excitation of stars still relies on the mode and driving force decomposition based on the Newtonian formalism. In addition, we show that the scattering phase is intimately related to the total energy of spacetime and matter under the driving of a steady input wave from infinity. We also derive the relevant tidal energy of a star under steady driving and compare that with the dynamic tide formula. We estimate that the difference may lead to $\mathcal{O}(0.5)$ radian phase modulation in the late stage of the binary neutron star inspiral waveform.

gr-qc↗

arXiv2025

DialogGraph-LLM: Graph-Informed LLMs for End-to-End Audio Dialogue Intent Recognition

Recognizing speaker intent in long audio dialogues among speakers has a wide range of applications, but is a non-trivial AI task due to complex inter-dependencies in speaker utterances and scarce annotated data. To address these challenges, an end-to-end framework, namely DialogGraph-LLM, is proposed in the current work. DialogGraph-LLM combines a novel Multi-Relational Dialogue Attention Network (MR-DAN) architecture with multimodal foundation models (e.g., Qwen2.5-Omni-7B) for direct acoustic-to-intent inference. An adaptive semi-supervised learning strategy is designed using LLM with a confidence-aware pseudo-label generation mechanism based on dual-threshold filtering using both global and class confidences, and an entropy-based sample selection process that prioritizes high-information unlabeled instances. Extensive evaluations on the proprietary MarketCalls corpus and the publicly available MIntRec 2.0 benchmark demonstrate DialogGraph-LLM's superiority over strong audio and text-driven baselines. The framework demonstrates strong performance and efficiency in intent recognition in real world scenario audio dialogues, proving its practical value for audio-rich domains with limited supervision. Our code is available at https://github.com/david188888/DialogGraph-LLM.

cs.SD↗

arXiv2025

Broad Feshbach resonance with a large background scattering length in a fermionic atom-molecule mixture

We report the observation of a broad magnetic Feshbach resonance with a large background scattering length in an ultracold fermionic mixture of $^{23}$Na$^{40}$K molecules and $^{40}$K atoms, with both species prepared in their lowest hyperfine states. The Feshbach resonance is characterized by measuring resonantly enhanced loss rates and elastic scattering cross sections via cross-species thermalization. The large background scattering length can drive the atom-molecule mixture into the hydrodynamic regime when the magnetic field is far from the resonance. We observe that the center-of-mass motions of the atoms and molecules are phase-locked and oscillate with a common frequency due to hydrodynamic drag effects. This broad atom-molecule Feshbach resonance with its large background scattering length opens up a new avenue towards studying strongly interacting fermionic gases with mass imbalance.

cond-mat.quant-gas↗

arXiv2025

SEGA: A Stepwise Evolution Paradigm for Content-Aware Layout Generation with Design Prior

In this paper, we study the content-aware layout generation problem, which aims to automatically generate layouts that are harmonious with a given background image. Existing methods usually deal with this task with a single-step reasoning framework. The lack of a feedback-based self-correction mechanism leads to their failure rates significantly increasing when faced with complex element layout planning. To address this challenge, we introduce SEGA, a novel Stepwise Evolution Paradigm for Content-Aware Layout Generation. Inspired by the systematic mode of human thinking, SEGA employs a hierarchical reasoning framework with a coarse-to-fine strategy: first, a coarse-level module roughly estimates the layout planning results; then, another refining module performs fine-level reasoning regarding the coarse planning results. Furthermore, we incorporate layout design principles as prior knowledge into the model to enhance its layout planning ability. Besides, we present GenPoster-100K that is a new large-scale poster dataset with rich meta-information annotation. The experiments demonstrate the effectiveness of our approach by achieving the state-of-the-art results on multiple benchmark datasets. Our project page is at: https://brucew91.github.io/SEGA.github.io/

cs.CV↗

arXiv2025

Evolving extreme mass-ratio inspirals in a perturbed Schwarzschild spacetime

In this work, we develop the modified Teukolsky formalism that describes the GW radiation from a point mass orbiting around a perturbed Schwarzschild BH. This perturbation of the background spacetime induces a secular change in the orbital phase of the point mass. In turn, this causes a modification in the GW flux, which can be used to probe the background spacetime. We explicitly apply this formalism to a bumpy Schwarzschild spacetime as a proof of principle. The results pave the way for the description of EMRIs in generic perturbed Kerr spacetime in future developments.

gr-qc↗

arXiv2025

Diffusion Model as a Noise-Aware Latent Reward Model for Step-Level Preference Optimization

Preference optimization for diffusion models aims to align them with human preferences for images. Previous methods typically use Vision-Language Models (VLMs) as pixel-level reward models to approximate human preferences. However, when used for step-level preference optimization, these models face challenges in handling noisy images of different timesteps and require complex transformations into pixel space. In this work, we show that pre-trained diffusion models are naturally suited for step-level reward modeling in the noisy latent space, as they are explicitly designed to process latent images at various noise levels. Accordingly, we propose the Latent Reward Model (LRM), which repurposes components of the diffusion model to predict preferences of latent images at arbitrary timesteps. Building on LRM, we introduce Latent Preference Optimization (LPO), a step-level preference optimization method conducted directly in the noisy latent space. Experimental results indicate that LPO significantly improves the model's alignment with general, aesthetic, and text-image alignment preferences, while achieving a 2.5-28x training speedup over existing preference optimization methods. Our code and models are available at https://github.com/Kwai-Kolors/LPO.

cs.CV↗

arXiv2025

Probing Formation Channels of Extreme Mass-Ratio Inspirals

The population study of stellar-mass black hole (sBH) binaries with ground-based gravitational wave detection has achieved tremendous success in recent years. Future observation of extreme mass-ratio inspirals will similarly require proper population analysis that identify the formation channels, measuring the branch ratio(s) and characterizing major properties within each major channel. In this work, we propose that the measurement of eccentricity, inclination, and component mass provides critical information to distinguish different formation channels and probe detailed formation mechanisms. Focusing on the dry and wet extreme mass-ratio inspirals, we establish the theoretical expectation of these observables in each formation channel. We also discuss how their distributions can be used to probe lifetime and turbulence level of active galactic nuclei disks, accretion patterns of supermassive black holes and population properties of sBHs within nuclear star clusters.

gr-qc↗

arXiv2025

Probing Direct Waves in Black Hole Ringdowns

Merger gravitational waves from binary black hole coalescence carry rich information about the underlying spacetime dynamics. We analyze merger waves from comparable-mass and extreme-mass-ratio binaries, obtained from numerical relativity and black-hole perturbation theory, respectively, and argue that they are dominated by the prompt wave emissions as the black holes collide. This signal, which we refer to as the direct wave, is modulated by the plunging motion and selectively screened by the gravitational potential of the remnant black hole. The direct wave typically exhibits a time-dependent frequency and decay rate, but for high-spin remnants $(\gtrsim0.7)$ the ergosphere renders it mode-like, with a quasi-stable instantaneous oscillation frequency close to the superradiant frequency. We further estimate its detectability in a GW150914-like system and find that the signal-to-noise ratio can exceed $\sim 10$ with the current ground-based detector network. Our results therefore identify the direct wave as a robust observable for analyzing black hole ringdowns in current and future gravitational wave events.

gr-qc↗

arXiv2025

Prospects for Time-Domain and Multi-Messenger Science with eXTP

In this new era of time-domain and multi-messenger astronomy, various new transients and new phenomena are constantly being discovered thanks to the rapid advances in observations, which provide the excellent opportunity to study the physics in the extreme environments. The enhanced X-ray Timing and Polarimetry mission (eXTP), planned to be launched in 2030, has several key advantages, including advanced polarimetry, high sensitivity & large effective area, and wide energy range coverage, which make it a groundbreaking project in high-energy astrophysics. In this article, we briefly introduce the potential time-domain and multi-messenger targets for eXTP, including gravitational-wave (GW) counterparts, gamma-ray bursts (GRBs), magnetars and fast radio bursts (FRBs), tidal disruption events (TDEs), supernovae, high energy neutrinos and TeV active galactic nucleus (AGNs), and so on. We discuss the advantages of future eXTP observations for detecting these sources, their detection capabilities, the abilities to distinguish theoretical models, and their applications in gravity and cosmology.

astro-ph.HE↗

arXiv2025

Align-then-Slide: A complete evaluation framework for Ultra-Long Document-Level Machine Translation

Large language models (LLMs) have ushered in a new era for document-level machine translation (\textit{doc}-mt), yet their whole-document outputs challenge existing evaluation methods that assume sentence-by-sentence alignment. We introduce \textit{\textbf{Align-then-Slide}}, a complete evaluation framework for ultra-long doc-mt. In the Align stage, we automatically infer sentence-level source-target correspondences and rebuild the target to match the source sentence number, resolving omissions and many-to-one/one-to-many mappings. In the n-Chunk Sliding Evaluate stage, we calculate averaged metric scores under 1-, 2-, 3- and 4-chunk for multi-granularity assessment. Experiments on the WMT benchmark show a Pearson correlation of 0.929 between our method with expert MQM rankings. On a newly curated real-world test set, our method again aligns closely with human judgments. Furthermore, preference data produced by Align-then-Slide enables effective CPO training and its direct use as a reward model for GRPO, both yielding translations preferred over a vanilla SFT baseline. The results validate our framework as an accurate, robust, and actionable evaluation tool for doc-mt systems.

cs.CL↗