arXiv subjects

Yi Yang

Publications and source records attributed to Yi Yang.

At least 253 records · Page 14Linked to original sources

arXiv2026

A Highly Magnetic Ultra Massive White Dwarf with a 23-minute Rotation Period

We present a physical characterization of TMTS J00063798+3104160 (J0006), a rapidly rotating,ultra-massive white dwarf (WD) identified in high-cadence light curves from the Tsinghua University-Ma Huateng Telescope for Survey (TMTS). A coherent 23-minute periodicity is detected in TMTS, TESS, and ZTF photometry. A time series of low-resolution spectra with the Keck-I 10 m telescope reveals broad, shallow hydrogen absorption features indicative of an extreme magnetic field and shows no evidence for radial-velocity variations. Atmospheric modeling yields a magnetic field strength of $\sim$ 250 MG, while Gaia astrometry and photometry imply a mass of 1.06 $\pm$ 0.01 M$_{\odot}$. A significant infrared excess is detected in the WISE W1 band and is well fitted by a 550 K blackbody, likely arising from residual material of a merger. We interpret the 23-minute photometric modulation as the rotation period of an isolated, massive WD formed likely through the merger of a double WD binary. With one of the shortest rotation periods known among candidate merger remnants and with constraints from a deep Einstein Probe X-ray nondetection, J0006 provides a rare and important observational window into the poorly explored intermediate stages of post-merger evolution.

astro-ph.SR↗

arXiv2026

RSATalker: Realistic Socially-Aware Talking Head Generation for Multi-Turn Conversation

Talking head generation is increasingly important in virtual reality (VR), especially for social scenarios involving multi-turn conversation. Existing approaches face notable limitations: mesh-based 3D methods can model dual-person dialogue but lack realistic textures, while large-model-based 2D methods produce natural appearances but incur prohibitive computational costs. Recently, 3D Gaussian Splatting (3DGS) based methods achieve efficient and realistic rendering but remain speaker-only and ignore social relationships. We introduce RSATalker, the first framework that leverages 3DGS for realistic and socially-aware talking head generation with support for multi-turn conversation. Our method first drives mesh-based 3D facial motion from speech, then binds 3D Gaussians to mesh facets to render high-fidelity 2D avatar videos. To capture interpersonal dynamics, we propose a socially-aware module that encodes social relationships, including blood and non-blood as well as equal and unequal, into high-level embeddings through a learnable query mechanism. We design a three-stage training paradigm and construct the RSATalker dataset with speech-mesh-image triplets annotated with social relationships. Extensive experiments demonstrate that RSATalker achieves state-of-the-art performance in both realism and social awareness. The code and dataset will be released.

cs.CV↗

arXiv2026

A binary merger product as the direct progenitor of a Type II-P supernova

Type II-P supernovae (SNe II-P) are the most common class of core-collapse SNe in the local Universe and play critical roles in many aspects of astrophysics. Since decades ago theorists have predicted that SNe II-P may originate not only from single stars but also from interacting binaries. While ~20 SNII-P progenitors have been directly detected on pre-explosion images, observational evidence still remains scarce for this speculated binary progenitor channel. In this work, we report the discovery of a red supergiant progenitor for the Type II-P SN 2018gj. While the progenitor resembles those of other SNe II-P in terms of effective temperature and luminosity, it is located in a very old environment and SN 2018gj has an abnormally short plateau in the light curve. With state-of-the-art binary evolution simulations, we find these characteristics can only be explained if the progenitor of SN 2018gj is the merger product of a close binary system, which developed a different interior structure and evolved over a longer timescale compared with single-star evolution. This work provides the first compelling evidence for the long-sought binary progenitor channel toward SNe II-P, and our methodology serves as an innovative and pragmatic tool to motivate further investigations into this previously hidden population of SNe II-P from binaries.

astro-ph.HE↗

arXiv2026

A Classification of Fractal Squares

Let $λ_K:\bbR^2\rightarrow\{0,1,\ldots\}\cup\{\infty\}$ be the lambda function of a planar comapctum $K$, as defined in MR4488162. It is known that a planar continuum is locally connected if and only if its lambda function vanishes everywhere, or equivalently, $λ_K(K)=\{0\}$. In this article we show that every fractal square $K$ satisfies $λ_K(K)\subset\{0,1\}$ and find criterions to classify when $λ_K(K)$ equals $\{0\}$, $\{1\}$ or $\{0,1\}$. Here for any integer $N\ge2$ and any set $\Dc=\left\{(i,j): 0\le i,j\le N-1\right\}$ with cardinality $\ge2$, if we set $K^{(0)}=[0,1]^2$ and $\displaystyle K^{(n)}=\left\{\frac{x+d}{N}: x\in K^{(n-1)}, d\in\Dc\right\}(n\ge1)$ then $K=\bigcap_nK^{(n)}$ is called a fractal square.

math.GN↗

arXiv2026

Unified Generation and Self-Verification for Vision-Language Models via Advantage Decoupled Preference Optimization

Parallel test-time scaling typically trains separate generation and verification models, incurring high training and inference costs. We propose Advantage Decoupled Preference Optimization (ADPO), a unified reinforcement learning framework that jointly learns answer generation and self-verification within a single policy. ADPO introduces two innovations: a preference verification reward improving verification capability and a decoupled optimization mechanism enabling synergistic optimization of generation and verification. Specifically, the preference verification reward computes mean verification scores from positive and negative samples as decision thresholds, providing positive feedback when prediction correctness aligns with answer correctness. Meanwhile, the advantage decoupled optimization computes separate advantages for generation and verification, applies token masks to isolate gradients, and combines masked GRPO objectives, preserving generation quality while calibrating verification scores. ADPO achieves up to +34.1% higher verification AUC and -53.5% lower inference time, with significant gains of +2.8%/+1.4% accuracy on MathVista/MMMU, +1.9 cIoU on ReasonSeg, and +1.7%/+1.0% step success rate on AndroidControl/GUI Odyssey.

cs.CV↗

arXiv2026

KV-Embedding: Training-free Text Embedding via Internal KV Re-routing in Decoder-only LLMs

While LLMs are powerful embedding backbones, their application in training-free settings faces two structural challenges: causal attention restricts early tokens from accessing subsequent context, and the next-token prediction objective biases representations toward generation rather than semantic compression. To address these limitations, we propose KV-Embedding, a framework that activates the latent representation power of frozen LLMs. Our method leverages the observation that the key-value (KV) states of the final token at each layer encode a compressed view of the sequence. By re-routing these states as a prepended prefix, we enable all tokens to access sequence-level context within a single forward pass. To ensure model-agnostic applicability, we introduce an automated layer selection strategy based on intrinsic dimensionality. Evaluations on MTEB across Qwen, Mistral, and Llama backbones show that KV-Embedding outperforms existing training-free baselines by up to 10%, while maintaining robust performance on sequences up to 4,096 tokens. These results demonstrate that internal state manipulation offers an efficient alternative to input modification, and we hope this work encourages further exploration of LLM internals for representation learning.

cs.CL↗

arXiv2025

To Define the Core Entropy for All Polynomials Having a Connected Julia Set

For all polynomials $f$ with ${\rm deg}(f)\ge2$ that have a connected filled Julia set $K$, we introduce a new quantity $h_{\rm GCE}(f)$, such that $h_{\rm GCE}\left(f^n\right)=n\cdot h_{\rm GCE}(f)$ for all $n\ge1$ and $h_{\rm GCE}(f)=h_{\rm GCE}(g)$ for $J$-equivalent $f$ and $g$. When the coefficients and the critical points of $f$ are real, $h_{\rm GCE}(f)=h(K\cap\mathbb{R},f)$. When $f$ is post-critically finite, $h_{\rm GCE}(f)$ equals the core entropy $h(\mathcal{H}(f),f)$, where $\mathcal{H}(f)$ is the Hubbard tree. For $f_c(z)=z^2+c$ with $c$ varying in the Mandelbrot set $\mathcal{M}$, the entropy map $c\mapsto h_{\rm GCE}(f_c)$ is not continuous. However, its lower envelope $h_{\rm core}:\mathcal{M}\rightarrow\mathbb{R}$ given by $h_{\rm core}(c)=\inf\left\{t:\ \exists\ c_n\ne c\ \text{with}\ c_n\rightarrow c\ \text{and}\ t=\lim\limits_{n\rightarrow\infty}h_{\rm GCE}\left(f_{c_n}\right)\right\}$ is continuous over $\mathcal{M}$ and has three properties. First, every $h_{\rm core}^{-1}([0,s])$ with $s\ge0$ is connected. In particular, $h_{\rm core}^{-1}(0)$ coincides with the central molecule. Second, $h_{\rm core}(c)=h(\mathbb{R},f_c)$ for $c\in[-2,\frac14]$. Third, $h_{\rm core}(c)=h(\mathcal{H}(f_c),f_c)$ for post-critically finite $f_c$.

math.DS↗

arXiv2025

DeMoGen: Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models

Human motions are compositional: complex behaviors can be described as combinations of simpler primitives. However, existing approaches primarily focus on forward modeling, e.g., learning holistic mappings from text to motion or composing a complex motion from a set of motion concepts. In this paper, we consider the inverse perspective: decomposing a holistic motion into semantically meaningful sub-components. We propose DeMoGen, a compositional training paradigm for decompositional learning that employs an energy-based diffusion model. This energy formulation directly captures the composed distribution of multiple motion concepts, enabling the model to discover them without relying on ground-truth motions for individual concepts. Within this paradigm, we introduce three training variants to encourage a decompositional understanding of motion: 1. DeMoGen-Exp explicitly trains on decomposed text prompts; 2. DeMoGen-OSS performs orthogonal self-supervised decomposition; 3. DeMoGen-SC enforces semantic consistency between original and decomposed text embeddings. These variants enable our approach to disentangle reusable motion primitives from complex motion sequences. We also demonstrate that the decomposed motion concepts can be flexibly recombined to generate diverse and novel motions, generalizing beyond the training distribution. Additionally, we construct a text-decomposed dataset to support compositional training, serving as an extended resource to facilitate text-to-motion generation and motion composition.

cs.CV↗

arXiv2025

Optical parametric free-electron--photon quantum interaction

Optical parametric processes underpin quantum photonics, while free-electron--photon interactions offer agile pathways to generate nontrivial quantum photonic states. These threads have so far largely progressed independently, whereas placing free electrons in a driven nonlinear system can potentially activate coherent parametric interaction channels for joint state engineering of both types of particles. Here we unify these paradigms by developing a general theoretical framework for parametric free-electron--photon interactions in a nonlinear optical system driven by degenerate parametric down-conversion. Unlike free electrons in a linear bath, here they can couple to Bogoliubov quasiparticles through two detuned phase-matching channels, where the parametric process and free-electron interactions can quantum amplify each other. Seeding the interaction with squeezed vacuum yields gain-only or loss-only electron energy spectra, and enables electron-heralded squeezed Fock states; with bare vacuum, postselecting electron energy sidebands generates high-fidelity Schrödinger cat states. Our results show how optical parametric interactions can quantum shape free electrons and photons, potentially enabling a quantum parametric dielectric laser accelerator that mitigates the need for temporal phase synchronization, thereby allowing acceleration probabilities to approach unity even for phase-random electrons.

physics.optics↗

arXiv2025

A graphical framework for proving holographic entanglement entropy inequalities in multipartite systems

We present a graphical method for proving holographic entanglement entropy inequalities (HEIs) in general multipartite systems. By introducing a geometric representation of the entanglement structure, we develop a systematic approach that enables one to visualize and verify the validity of HEIs for any number of subsystems $n$. Several theorems are established to formalize this method, and explicit examples are provided for systems with $n = 4$ to $7$ entangled regions.

quant-ph↗

arXiv2025

ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training

Large-scale deep learning models impose substantial communication overh ead in distributed training, particularly in bandwidth-constrained or heterogeneous clo ud-edge environments. Conventional synchronous or fixed-compression techniques o ften struggle to balance communication cost, convergence stability, and model accura cy. To address these challenges, we propose ACE-Sync, an Adaptive Cloud-Edge Sy nchronization Framework that integrates (1) an attention-based gradient importance p redictor, (2) a differentiated parameter compression strategy, and (3) a hierarchical cl oud-edge coordination mechanism. ACE-Sync dynamically selects which parameter groups to synchronize and determines appropriate compression levels under per-devic e bandwidth budgets. A knapsack-based optimization strategy is adopted to maximize important gradient preservation while reducing redundant communication. Furthermo re, residual-based error compensation and device clustering ensure long-term converg ence and cross-device personalization. Experiments show that ACE-Sync substantiall y reduces communication overhead while maintaining competitive accuracy. Compar ed with FullSync, ACE-Sync lowers communication cost from 112.5 GB to 44.7 GB (a 60% reduction) and shortens convergence from 41 to 39 epochs. Despite aggressiv e communication reduction, ACE-Sync preserves high model quality, achieving 82. 1% Top-1 accuracy-only 0.3% below the full-synchronization baseline-demonstrating its efficiency and scalability for large-scale distributed training. These results indicate that ACE-Sync provides a scalable, communication-efficient, and accuracy-preservin g solution for large-scale cloud-edge distributed model training.

cs.DC↗

arXiv2025

Strong well-filteredness of upper topology on sup-complete posets

We first introduce and investigate a new class of $T_0$ spaces -- strong R-spaces, which are stronger than both R-spaces and strongly well-filtered spaces. It is proved that any sup-complete poset equipped with the upper topology is a strong R-space and the Hoare power space of a $T_0$-space is a strong R-space. Hence the upper topology on a sup-complete poset is strongly well-filtered and the Hoare power space of a $T_0$-space is strongly well-filtered, which answers two problems recently posed by Xu.

math.GN↗

arXiv2025

See Once, Then Act: Vision-Language-Action Model with Task Learning from One-Shot Video Demonstrations

Developing robust and general-purpose manipulation policies represents a fundamental objective in robotics research. While Vision-Language-Action (VLA) models have demonstrated promising capabilities for end-to-end robot control, existing approaches still exhibit limited generalization to tasks beyond their training distributions. In contrast, humans possess remarkable proficiency in acquiring novel skills by simply observing others performing them once. Inspired by this capability, we propose ViVLA, a generalist robotic manipulation policy that achieves efficient task learning from a single expert demonstration video at test time. Our approach jointly processes an expert demonstration video alongside the robot's visual observations to predict both the demonstrated action sequences and subsequent robot actions, effectively distilling fine-grained manipulation knowledge from expert behavior and transferring it seamlessly to the agent. To enhance the performance of ViVLA, we develop a scalable expert-agent pair data generation pipeline capable of synthesizing paired trajectories from easily accessible human videos, further augmented by curated pairs from publicly available datasets. This pipeline produces a total of 892,911 expert-agent samples for training ViVLA. Experimental results demonstrate that our ViVLA is able to acquire novel manipulation skills from only a single expert demonstration video at test time. Our approach achieves over 30% improvement on unseen LIBERO tasks and maintains above 35% gains with cross-embodiment videos. Real-world experiments demonstrate effective learning from human videos, yielding more than 38% improvement on unseen tasks.

cs.RO↗

arXiv2025

3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization

Inverse design aims to design the input variables of a physical system to optimize a specified objective function, typically formulated as a search or optimization problem. However, in 3D domains, the design space grows exponentially, rendering exhaustive grid-based searches infeasible. Recent advances in deep learning have accelerated inverse design by providing powerful generative priors and differentiable surrogate models. Nevertheless, current methods tend to approximate the 3D design space using 2D projections or fine-tune existing 3D shapes. These approaches sacrifice volumetric detail and constrain design exploration, preventing true 3D design from scratch. In this paper, we propose a 3D Inverse Design (3DID) framework that directly navigates the 3D design space by coupling a continuous latent representation with a physics-aware optimization strategy. We first learn a unified physics-geometry embedding that compactly captures shape and physical field data in a continuous latent space. Then, we introduce a two-stage strategy to perform physics-aware optimization. In the first stage, a gradient-guided diffusion sampler explores the global latent manifold. In the second stage, an objective-driven, topology-preserving refinement further sculpts each candidate toward the target objective. This enables 3DID to generate high-fidelity 3D geometries, outperforming existing methods in both solution quality and design versatility.

cs.CV↗

arXiv2025

Development of a 15-Degree-of-Freedom Bionic Hand with Cable-Driven Transmission and Distributed Actuation

In robotic hand research, minimizing the number of actuators while maintaining human-hand-consistent dimensions and degrees of freedom constitutes a fundamental challenge. Drawing bio-inspiration from human hand kinematic configurations and muscle distribution strategies, this work proposes a novel 15-DoF dexterous robotic hand, with detailed analysis of its mechanical architecture, electrical system, and control system. The bionic hand employs a new tendon-driven mechanism, significantly reducing the number of motors required by traditional tendon-driven systems while enhancing motion performance and simplifying the mechanical structure. This design integrates five motors in the forearm to provide strong gripping force, while ten small motors are installed in the palm to support fine manipulation tasks. Additionally, a corresponding joint sensing and motor driving electrical system was developed to ensure efficient control and feedback. The entire system weighs only 1.4kg, combining lightweight and high-performance features. Through experiments, the bionic hand exhibited exceptional dexterity and robust grasping capabilities, demonstrating significant potential for robotic manipulation tasks.

cs.RO↗

arXiv2025

Line Polarization of Si II $λ$6355 Å in Type Ia Supernovae: A New Statistical Approach to Probe the Explosion Physics and Diversity

Spectropolarimetry provides a unique probe of ejecta asphericities, offering direct insights into the underlying explosion physics of Type Ia supernovae (SNe Ia). We analyze the statistical properties of pre-maximum spectropolarimetric data for 24 SNe Ia observed with VLT/FORS, focusing on the Si II $λ$6355 Åline. Previous studies have revealed a correlation between the peak Si II polarization degree and the expansion velocity. Here, we combine these observations with multi-dimensional non-LTE radiative transfer simulations. We consider two asphericity classes: (i) lopsided abundance distributions produced by off-center delayed-detonation transitions in near-$M_{Ch}$ white dwarfs or, for example, WD collisions (Class I), and (ii) global, axisymmetric density asphericities such as those arising from explosions of rapidly rotating WDs or mergers (Class II). Our model grid spans normal to subluminous SNe Ia and successfully reproduces the observed Si II velocity-polarization trend, with higher velocities associated with stronger asphericities. Consistent with observations, transitional SNe Ia and the faint end of the normal SNe Ia population show the highest Si II polarization and are best explained by Class I scenarios. In contrast, subluminous SNe Ia are dominated by Class II asphericities, characterized by lower Si II polarization but significant continuum polarization. The observed distribution of Si II polarization depends on both the observer's viewing angle $θ$ and the intrinsic asphericity. Statistical analysis of these spectropolarimetric snapshots enables the separation of Class I and Class II contributions and highlights the intrinsic diversity among SNe Ia. Our results imply viewing-angle-dependent luminosities in our local sample, which may have implications when using high-redshift SNe Ia as evidence for the need of non-standard cosmology.

astro-ph.HE↗

arXiv2025

3DIS: Depth-Driven Decoupled Instance Synthesis for Text-to-Image Generation

The increasing demand for controllable outputs in text-to-image generation has spurred advancements in multi-instance generation (MIG), allowing users to define both instance layouts and attributes. However, unlike image-conditional generation methods such as ControlNet, MIG techniques have not been widely adopted in state-of-the-art models like SD2 and SDXL, primarily due to the challenge of building robust renderers that simultaneously handle instance positioning and attribute rendering. In this paper, we introduce Depth-Driven Decoupled Instance Synthesis (3DIS), a novel framework that decouples the MIG process into two stages: (i) generating a coarse scene depth map for accurate instance positioning and scene composition, and (ii) rendering fine-grained attributes using pre-trained ControlNet on any foundational model, without additional training. Our 3DIS framework integrates a custom adapter into LDM3D for precise depth-based layouts and employs a finetuning-free method for enhanced instance-level attribute rendering. Extensive experiments on COCO-Position and COCO-MIG benchmarks demonstrate that 3DIS significantly outperforms existing methods in both layout precision and attribute rendering. Notably, 3DIS offers seamless compatibility with diverse foundational models, providing a robust, adaptable solution for advanced multi-instance generation. The code is available at: https://github.com/limuloo/3DIS.

cs.CV↗

arXiv2025

SR-GRPO: Stable Rank as an Intrinsic Geometric Reward for Large Language Model Alignment

Aligning Large Language Models (LLMs) with human preferences typically relies on external supervision, which faces critical limitations: human annotations are scarce and subjective, reward models are vulnerable to reward hacking, and self-evaluation methods suffer from prompt sensitivity and biases. In this work, we propose stable rank, an intrinsic, annotation-free quality signal derived from model representations. Stable rank measures the effective dimensionality of hidden states by computing the ratio of total variance to dominant-direction variance, capturing quality through how information distributes across representation dimensions. Empirically, stable rank achieves 84.04% accuracy on RewardBench and improves task accuracy by an average of 11.3 percentage points over greedy decoding via Best-of-N sampling. Leveraging this insight, we introduce Stable Rank Group Relative Policy Optimization (SR-GRPO), which uses stable rank as a reward signal for reinforcement learning. Without external supervision, SR-GRPO improves Qwen2.5-1.5B-Instruct by 10% on STEM and 19% on mathematical reasoning, outperforming both learned reward models and self-evaluation baselines. Our findings demonstrate that quality signals can be extracted from internal model geometry, offering a path toward scalable alignment without external supervision.

cs.CL↗