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Yao Yao

Publications and source records attributed to Yao Yao.

At least 37 records · Page 2Linked to original sources
arXiv2026

Stability for multiple Lamb dipoles

In the class of nonnegative vorticities on the half-plane, we establish the Lyapunov stability of finite sums of Lamb dipoles under the initial assumptions that the dipoles are sufficiently separated and that the faster dipoles are positioned to the right of the slower ones. Our approach combines sharp energy estimates near the Lamb dipoles with a Lagrangian bootstrapping scheme, enabling us to quantify the exchanges of circulation, enstrophy, impulse, and energy between various parts of the solution. The strategy of the proof is robust, and we present several potential extensions of the result.

math.AP↗
arXiv2026

On the electromagnetic effects of collisionless trapped-electron modes

We present a linear gyrokinetic theory for the electromagnetic collisionless trapped-electron mode (CTEM). It is found that the weak electromagnetic effects of CTEMs originate from the particle dynamics. Theoretical analysis reveals that the kinetic and fluid-like components of the trapped-electron parallel current cancel at leading order. The ion parallel current is also negligible due to the weak ion transit resonance. Consequently, the perturbed parallel current in the electromagnetic CTEM is dominated by passing electrons. We demonstrate that these characteristics of particle dynamics decouple the CTEM from the shear Alfvén wave branch, rendering the electromagnetic effects subdominant. Both eigenmode analyses and gyrokinetic simulations validate these findings.

physics.plasm-ph↗
arXiv2026

Non-uniqueness of weak solutions to cross-diffusion systems with advection

A cross-diffusion system with advection is considered on the whole line, describing the dynamics of two segregating population species. Starting from two initial densities supported on the half-lines $x\leq 0$ and $x\geq 0$, respectively, we construct two distinct solutions of the system: one pair of densities remains confined to their initial supports and stay completely segregated, while the second pair of densities begins to invade the opposite half-line after a finite time (mixing). To the best of our knowledge, this provides one of the first examples of non-uniqueness for this class of equations, together with an explicit demonstration of mixing phenomena. The construction produces infinitely many mixing solutions and applies throughout the full range of pressure exponents. For a certain range of exponents, we additionally obtain quantitative estimates on the mixing process.

math.AP↗
arXiv2026

Intrinsic spectral structure of bipartite nonlocal magic resource

Bipartite nonlocal magic resource (BNMR) quantifies the irreducible nonstabilizerness residing in bipartite entanglement, yet its evaluation is intractable due to the full Hilbert space optimization. Here, we introduce a canonical encoding framework that exactly confines the BNMR of an arbitrary bipartite pure state within a minimal encoding core. This dimension reduction proves that pure-state BNMR is an intrinsic function of the nonzero Schmidt spectrum, extending its invariance from local unitary transformations to local isometries. Leveraging this spectral link, we derive the leading quadratic response of BNMR under spectral perturbations around its zeros. We apply this quadratic response to Haar-random states, deriving and numerically validating the BNMR profile: its distribution is sharply localized at the symmetric bipartition and exponentially suppressed toward asymmetric cuts, in stark contrast to the broadening Page curve of entanglement. Finally, we provide a closed-form expression for the BNMR of Schmidt rank-2 states, uncovering a hierarchy collapse in generalized GHZ states where bipartite and global nonlocal magic resources coincide exactly.

quant-ph↗
arXiv2026

Communication Policy Evolution for Proactive LLM Agents

LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users' identical preferences further limit information exchange. To investigate how agents should communicate across modalities, this paper formalizes Communication Policy, establishes textual and UI-based policies, and then evaluates communication policies across diverse environments, personas, and model combinations. Building information asymmetry for proactive agents, we set up two complementary settings, User-Agent and Planner-Executor. Experimental results reveal complementary strengths between interaction channels: text-based interaction often facilitates task performance, while structured UI improves agents' response quality and persona compliance. Motivated by that, a hybrid method combines these advantages. We further propose Communication Policy Evolution (CPE), a self-evolution framework for refining communication policies through rollout and prompt-level evolving. Without model modification, CPE achieves the best task success across multiple settings using prompt refinement alone. Our findings identify communication behavior as a critical yet underexplored design dimension for LLM agents.

cs.AI↗
arXiv2026

JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions

We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions. Existing AI music systems are mainly designed for short, isolated clips and lack mechanisms to ensure narrative continuity. We present JenBridge, a modular and interpretable framework for adaptive long-form video soundtracking that ensures both high-fidelity audio generation and transition naturalness. The core architecture is a Transformer-based generative model trained with a flow-matching objective, following a two-stage paradigm: pretraining on large-scale text-audio corpora to establish robust musical priors, then adapting to the video domain with dual text-visual conditioning for precise cross-modal alignment. Crucially, to achieve long-form coherence across diverse scene changes, JenBridge incorporates a novel adaptive transition mechanism. This system features a versatile toolkit of transition styles, including a generative transition method, and uniquely employs a Large Language Model (LLM) Agent that acts as a director to select the most appropriate transition for each narrative shift intelligently. To rigorously assess this task, we propose the LVS Benchmark, a new benchmark that includes a curated dataset and novel evaluation metrics focusing on holistic and transition-aware assessment. Extensive experiments on the proposed benchmark demonstrate that JenBridge significantly outperforms existing methods in both objective and subjective metrics, particularly in terms of transition naturalness and overall narrative coherence. JenBridge represents a significant step towards fully automated, professional-quality video soundtracking.

cs.SD↗
arXiv2026

Enforcing Fair Predicted Scores on Intervals of Percentiles by Difference-of-Convex Constraints

Fairness in machine learning has become a critical concern. Existing approaches often focus on achieving full fairness across all score ranges generated by predictive models, ensuring fairness in both high- and low-percentile populations. However, this stringent requirement can compromise predictive performance and may not align with the practical fairness concerns of stakeholders. In this work, we propose a novel framework for building partially fair machine learning models that enforce fairness only within a specific percentile interval of interest while maintaining flexibility in other regions. We introduce statistical metrics to evaluate partial fairness within a given percentile interval. To achieve partial fairness, we propose an in-processing method by formulating the model training problem as constrained optimization with difference-of-convex constraints, which can be solved by an inexact difference-of-convex algorithm (IDCA). We provide the complexity analysis of IDCA for finding a nearly KKT point. Through numerical experiments on real-world datasets, we demonstrate that our framework achieves high predictive performance while enforcing partial fairness where it matters most.

cs.LG↗
arXiv2026

UNICA: A Unified Neural Framework for Controllable 3D Avatars

Controllable 3D human avatars have found widespread applications in 3D games, the metaverse, and AR/VR scenarios. The conventional approach to creating such a 3D avatar requires a lengthy, intricate pipeline encompassing appearance modeling, motion planning, rigging, and physical simulation. In this paper, we introduce UNICA (UNIfied neural Controllable Avatar), a skeleton-free generative model that unifies all avatar control components into a single neural framework. Given keyboard inputs akin to video game controls, UNICA generates the next frame of a 3D avatar's geometry through an action-conditioned diffusion model operating on 2D position maps. A point transformer then maps the resulting geometry to 3D Gaussian Splatting for high-fidelity free-view rendering. Our approach naturally captures hair and loose clothing dynamics without manually designed physical simulation, and supports extra-long autoregressive generation. To the best of our knowledge, UNICA is the first model to unify the workflow of "motion planning, rigging, physical simulation, and rendering". Code is released at https://github.com/zjh21/UNICA.

cs.CV↗
arXiv2026

High-efficiency and noise-immune quantum battery

Nowadays, quantum batteries (QBs) have been designed to outperform their classical counterparts by leveraging quantum advantages. For instance, the charging power greatly benefits from the entanglement generation of a collective charging scheme (e.g., the Dicke QB), especially in the ultrastrong coupling (USC) regime or even larger. However, apart from the fragility of the QB under intrinsic decoherence effects, another critical drawback emerges inevitably. Specifically, the non-negligible counter-rotating (CR) term in the USC regime would induce coherence in the energy basis of QB, thus remarkably degrading the charging efficiency. To tackle these challenges, we propose a high-efficiency and noise-immune QB boosted by dynamical modulation. It is demonstrated that the time-varying modulation can effectively reduce the CR coupling, resulting in a notable improvement in charging efficiency. Particularly, for a judicious choice of modulation parameters that entirely eliminate the CR interaction, the Dicke QB can be charged optimally, resembling the behavior of the Tavis-Cummings QB. In the subsequent storage process, beyond the natural robustness to pure dephasing noise, our scenario is also highly resilient to the dissipation noise and thus can achieve perfect energy storage by effective bath engineering. While feasible with current experimental platforms, our proposal offers a solid foundation for the implementation of a powerful QB and may drastically promote the development of energy storage and delivery techniques in the future.

quant-ph↗
arXiv2026

Denoise to Track: Harnessing Video Diffusion Priors for Robust Correspondence

In this work, we introduce HeFT (Head-Frequency Tracker), a zero-shot point tracking framework that leverages the visual priors of pretrained video diffusion models. To better understand how they encode spatiotemporal information, we analyze the internal representations of Video Diffusion Transformer (VDiT). Our analysis reveals that attention heads act as minimal functional units with distinct specializations for matching, semantic understanding, and positional encoding. Additionally, we find that the low-frequency components in VDiT features are crucial for establishing correspondences, whereas the high-frequency components tend to introduce noise. Building on these insights, we propose a head- and frequency-aware feature selection strategy that jointly selects the most informative attention head and low-frequency components to enhance tracking performance. Specifically, our method extracts discriminative features through single-step denoising, applies feature selection, and employs soft-argmax localization with forward-backward consistency checks for correspondence estimation. Extensive experiments on TAP-Vid benchmarks demonstrate that HeFT achieves state-of-the-art zero-shot tracking performance, approaching the accuracy of supervised methods while eliminating the need for annotated training data. Our work further underscores the promise of video diffusion models as powerful foundation models for a wide range of downstream tasks, paving the way toward unified visual foundation models.

cs.CV↗
arXiv2026

PA-SFM: Tracker-free differentiable acoustic radiation for freehand 3D photoacoustic imaging

Three-dimensional (3D) handheld photoacoustic tomography typically relies on bulky and expensive external positioning sensors to correct motion artifacts, which severely limits its clinical flexibility and accessibility. To address this challenge, we present PA-SFM, a tracker-free framework that leverages exclusively single-modality photoacoustic data for both sensor pose recovery and high-fidelity 3D reconstruction via differentiable acoustic radiation modeling. Unlike traditional structure-from-motion (SFM) methods based on visual features, PA-SFM integrates the acoustic wave equation into a differentiable programming pipeline. By leveraging a high-performance, GPU-accelerated acoustic radiation kernel, the framework simultaneously optimizes the 3D photoacoustic source distribution and the sensor array pose via gradient descent. To ensure robust convergence in freehand scenarios, we introduce a coarse-to-fine optimization strategy that incorporates geometric consistency checks and rigid-body constraints to eliminate motion outliers. We validated the proposed method through both numerical simulations and in-vivo rat experiments. The results demonstrate that PA-SFM achieves sub-millimeter positioning accuracy and restores high-resolution 3D vascular structures comparable to ground-truth benchmarks, offering a low-cost, software-defined solution for clinical freehand photoacoustic imaging. The source code is publicly available at \href{https://github.com/JaegerCQ/PA-SFM}{https://github.com/JaegerCQ/PA-SFM}.

cs.CV↗
arXiv2026

Generalized Task-Driven Design of Soft Robots via Reduced-Order FEM-based Surrogate Modeling

Task-driven design of soft robots requires models that are physically accurate and computationally efficient, while remaining transferable across actuator designs and task scenarios. However, existing modeling approaches typically face a fundamental trade-off between physical fidelity and computational efficiency, which limits model reuse across design and task variations and constrains scalable task-driven optimization. This paper presents a unified reduced-order finite element method (FEM)-based surrogate modeling pipeline for generalized task-driven soft robot design. High-fidelity FEM simulations characterize actuator behavior at the modular level, from which compact surrogate joint models are constructed for evaluation within a pseudo-rigid body model (PRBM). A meta-model maps actuator design parameters to surrogate representations, enabling rapid instantiation across a parameterized actuator family. The resulting models are embedded into a PRBM-based simulation environment, supporting task-level simulation and optimization under realistic physical constraints. The proposed pipeline is validated through sim-to-real transfer across multiple actuator types, including bellow-type pneumatic actuators and a tendon-driven soft finger, as well as two task-driven design studies: soft gripper co-design via Reinforcement Learning (RL) and 3D actuator shape matching via evolutionary optimization. The results demonstrate high accuracy, efficiency, and reliable reuse, providing a scalable foundation for autonomous task-driven soft robot design.

cs.RO↗
arXiv2026

Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web

The proliferation of ride-hailing services has fundamentally transformed urban mobility patterns, making accurate ride-hailing forecasting crucial for optimizing passenger experience and urban transportation efficiency. However, ride-hailing forecasting faces significant challenges due to geospatial heterogeneity and high susceptibility to external events. This paper proposes MVGR-Net(Multi-View Geospatial Representation Learning), a novel framework that addresses these challenges through a two-stage approach. In the pretraining stage, we learn comprehensive geospatial representations by integrating Points-of-Interest and temporal mobility patterns to capture regional characteristics from both semantic attribute and temporal mobility pattern views. The forecasting stage leverages these representations through a prompt-empowered framework that fine-tunes Large Language Models while incorporating external events. Extensive experiments on DiDi's real-world datasets demonstrate the state-of-the-art performance.

cs.LG↗
arXiv2026

Updated Metallicity Diagnostics for Precision Oxygen Abundance Measurements in High-redshift Galaxies with JWST

Recent work has demonstrated that widely used strong-line oxygen abundance indicators, such as O3N2, $\rm R23$, and $\widehat{\rm R}$, suffer from large uncertainties when applied to high-redshift galaxies. We show that this loss of precision primarily arises because, at fixed \Oabund, galaxies span a wide dynamic range in ionization parameter and nitrogen enrichment. Here we develop updated indicators that explicitly incorporate both effects via the proxies O32 and N2O2. We define ${\rm R}_{\rm u}\equiv \rm R23+α_1 O32+α_2 N2O2$, $\widehat{\rm R}_{\rm u}\equiv \rm \widehat{R}+β_1 O32+β_2 N2O2$, and ${\rm O}_{\rm u}\equiv \rm O3N2+γ_1 O32+γ_2 N2O2$, and calibrate \Oabund~as low-order polynomials in each composite indicator. Applied to a JWST sample with $T_{\rm e}$-method abundances, the updated indicators substantially tighten the correlations with \Oabund, boosting adjusted coefficients of determination from $\mathbb{R}^2\lesssim 0$ (classical indicators) to $\mathbb{R}^2\gtrsim 0.5$ for the full sample and to $\sim 0.7$ at $z>2$. The residuals reveal a redshift evolution in the mapping between \Oabund, strong lines, ionization, and nitrogen enrichment, with a pivotal turning point near the cosmic noon ($z\sim 2$). Our calibrations provide a practical, physically grounded path to precise metallicity measurements in the JWST era and a firmer basis for quantifying early chemical enrichment and feedback.

astro-ph.GA↗
arXiv2026

Advancing Open-source World Models

We present LingBot-World, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. (1) It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. (2) It enables a minute-level horizon while preserving contextual consistency over time, which is also known as "long-term memory". (3) It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public access to the code and model in an effort to narrow the divide between open-source and closed-source technologies. We believe our release will empower the community with practical applications across areas like content creation, gaming, and robot learning.

cs.CV↗
arXiv2026

Unstable Interface Dynamics for Gravity Stokes Flow

We investigate some unstable behavior of the interface given by two incompressible fluids of different densities evolving by the regular Stokes law with gravity force. In the unstable scenario, where the denser fluid lies above the lighter fluid, we prove infinite-in-time growth of the length or the curvature of the interface. We support these analytical results with numerical simulations that confirm the predicted growth phenomena. In the stable configuration, where the denser fluid lies below the lighter fluid, we show that certain initial configurations evolve into the unstable regime in finite time.

math.AP↗
arXiv2026

SlingBAG Pro: Accelerating point cloud-based iterative reconstruction for 3D photoacoustic imaging with arbitrary array geometries

High-quality three-dimensional (3D) photoacoustic imaging (PAI) is gaining increasing attention in clinical applications. To address the challenges of limited space and high costs, irregular geometric transducer arrays that conform to specific imaging regions are promising for achieving high-quality 3D PAI with fewer transducers. However, traditional iterative reconstruction algorithms struggle with irregular array configurations, suffering from high computational complexity, substantial memory requirements, and lengthy reconstruction times. In this work, we introduce SlingBAG Pro, an advanced reconstruction algorithm based on the point cloud iteration concept of the Sliding ball adaptive growth (SlingBAG) method, while extending its compatibility to arbitrary array geometries. SlingBAG Pro maintains high reconstruction quality, reduces the number of required transducers, and employs a hierarchical optimization strategy that combines zero-gradient filtering with progressively increased temporal sampling rates during iteration. This strategy rapidly removes redundant spatial point clouds, accelerates convergence, and significantly shortens overall reconstruction time. Compared to the original SlingBAG algorithm, SlingBAG Pro achieves up to a 2.2-fold speed improvement in point cloud-based 3D PA reconstruction under irregular array geometries. The proposed method is validated through both simulation and in vivo mouse experiments, and the source code is publicly available at https://github.com/JaegerCQ/SlingBAG_Pro.

cs.CV↗
arXiv2025

SpatialVID: A Large-Scale Video Dataset with Spatial Annotations

Significant progress has been made in spatial intelligence, spanning both spatial reconstruction and world exploration. However, the scalability and real-world fidelity of current models remain severely constrained by the scarcity of large-scale, high-quality training data. While several datasets provide camera pose information, they are typically limited in scale, diversity, and annotation richness, particularly for real-world dynamic scenes with ground-truth camera motion. To this end, we collect SpatialVID, a dataset consists of a large corpus of in-the-wild videos with diverse scenes, camera movements and dense 3D annotations such as per-frame camera poses, depth, and motion instructions. Specifically, we collect more than 21,000 hours of raw videos, and process them into 2.7 million clips through a hierarchical filtering pipeline, totaling 7,089 hours of dynamic content. A subsequent annotation pipeline enriches these clips with detailed spatial and semantic information, including camera poses, depth maps, dynamic masks, structured captions, and serialized motion instructions. Analysis of SpatialVID's data statistics reveals a richness and diversity that directly fosters improved model generalization and performance, establishing it as a key asset for the video and 3D vision research community.

cs.CV↗