arXiv subjects

Junyi Zhang

Publications and source records attributed to Junyi Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Observation of a structurally driven, reversible topological phase transition in a distorted square net material

Topological materials hold immense promise for exhibiting exotic quantum phenomena, yet achieving controllable topological phase transitions remains challenging. Here, we demonstrate a structurally driven, reversible topological phase transition in the distorted square net material GdPS, induced via in situ potassium dosing. Using angle-resolved photoemission spectroscopy and first principles calculations, we demonstrate a cascade of topological phases in the sub-surface P layer: from a large, topologically trivial band gap to a gapless Dirac cone state with a 2 eV dispersion, and finally to a two-dimensional topological insulator as inferred from theory. This evolution is driven by subtle structural distortions in the first P layer caused by potassium adsorption, which in turn contribute to the band gap closure and topological phase transition. Furthermore, the ability to manipulate the topology of a sub-surface layer in GdPS offers a unique route for exploring and controlling topological states in bulk materials.

cond-mat.mtrl-sci↗

arXiv2026

VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents

Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long horizons. We introduce VisGym, a gymnasium of 17 environments for evaluating and training VLMs. The suite spans symbolic puzzles, real-image understanding, navigation, and manipulation, and provides flexible controls over difficulty, input representation, planning horizon, and feedback. We also provide multi-step solvers that generate structured demonstrations, enabling supervised finetuning. Our evaluations show that all frontier models struggle in interactive settings, achieving low success rates in both the easy (46.6%) and hard (26.0%) configurations. Our experiments reveal notable limitations: models struggle to effectively leverage long context, performing worse with an unbounded history than with truncated windows. Furthermore, we find that several text-based symbolic tasks become substantially harder once rendered visually. However, explicit goal observations, textual feedback, and exploratory demonstrations in partially observable or unknown-dynamics settings for supervised finetuning yield consistent gains, highlighting concrete failure modes and pathways for improving multi-step visual decision-making. Code, data, and models can be found at: https://visgym.github.io/.

cs.CV↗

arXiv2026

Spherical Geometry Diffusion: Generating High-quality 3D Face Geometry via Sphere-anchored Representations

A fundamental challenge in text-to-3D face generation is achieving high-quality geometry. The core difficulty lies in the arbitrary and intricate distribution of vertices in 3D space, making it challenging for existing models to establish clean connectivity and resulting in suboptimal geometry. To address this, our core insight is to simplify the underlying geometric structure by constraining the distribution onto a simple and regular manifold, a topological sphere. Building on this, we first propose the Spherical Geometry Representation, a novel face representation that anchors geometric signals to uniform spherical coordinates. This guarantees a regular point distribution, from which the mesh connectivity can be robustly reconstructed. Critically, this canonical sphere can be seamlessly unwrapped into a 2D map, creating a perfect synergy with powerful 2D generative models. We then introduce Spherical Geometry Diffusion, a conditional diffusion framework built upon this 2D map. It enables diverse and controllable generation by jointly modeling geometry and texture, where the geometry explicitly conditions the texture synthesis process. Our method's effectiveness is demonstrated through its success in a wide range of tasks: text-to-3D generation, face reconstruction, and text-based 3D editing. Extensive experiments show that our approach substantially outperforms existing methods in geometric quality, textual fidelity, and inference efficiency.

cs.CV↗

arXiv2025

Selfi: Self Improving Reconstruction Engine via 3D Geometric Feature Alignment

Novel View Synthesis (NVS) has traditionally relied on models with explicit 3D inductive biases combined with known camera parameters from Structure-from-Motion (SfM) beforehand. Recent vision foundation models like VGGT take an orthogonal approach -- 3D knowledge is gained implicitly through training data and loss objectives, enabling feed-forward prediction of both camera parameters and 3D representations directly from a set of uncalibrated images. While flexible, VGGT features lack explicit multi-view geometric consistency, and we find that improving such 3D feature consistency benefits both NVS and pose estimation tasks. We introduce Selfi, a self-improving 3D reconstruction pipeline via feature alignment, transforming a VGGT backbone into a high-fidelity 3D reconstruction engine by leveraging its own outputs as pseudo-ground-truth. Specifically, we train a lightweight feature adapter using a reprojection-based consistency loss, which distills VGGT outputs into a new geometrically-aligned feature space that captures spatial proximity in 3D. This enables state-of-the-art performance in both NVS and camera pose estimation, demonstrating that feature alignment is a highly beneficial step for downstream 3D reasoning.

cs.CV↗

arXiv2025

Spontaneous $π$ flux trapping in granular rings of unconventional superconductors

We study Josephson couplings in unconventional superconductors and generalize the Sigrist-Rice formula by incorporating symmetry constraints and interface orientation disorder. Applying this framework to granular superconducting rings, we establish a no-go result that single-band chiral superconductors cannot spontaneously trap a magnetic flux. This rules out chiral $p$-wave pairing in $β$-Bi$_2$Pd, in light of the half-quantum flux observed in recent Little-Parks experiments. Incorporating the full crystalline and time-reversal symmetries, we show that a fully gapped helical equal-spin pairing state, naturally stabilized by spin-orbit coupling arising from local inversion-symmetry breaking, is instead favored. We further find that granular rings of such superconductors can trap a spontaneous $π$ flux in a manner robust against interface disorder.

cond-mat.supr-con↗

arXiv2025

Phase Diagram and Spectroscopic Signatures of a Supersolid in Quantum Ising Magnet K$_2$Co(SeO$_3$)$_2$

A supersolid is a quantum-entangled state of matter exhibiting the dual characteristics of superfluidity and solidity. Theory predicts that hard-core bosons with repulsive interactions on a triangular lattice can form supersolid phases at half filling and near complete filling. Leveraging an exact mapping between bosons and spin-$\frac{1}{2}$ degrees of freedom, we investigate these phases in the spin-$\frac{1}{2}$ triangular-lattice antiferromagnet \K212 with exchange constants $J_z = 2.96(2)$~meV and $J_{\perp} = 0.21(3)$~meV. At zero field, neutron diffraction reveals the gradual development for $T<15$~K of quasi-two-dimensional $\sqrt{3}\times\sqrt{3}$ magnetic order with $Z_3$ translational symmetry breaking (solidity) albeit with 44(5)% reduced amplitude at $T=0.3$~K indicating strong quantum fluctuations. These are apparent in equidistant bands of continuum neutron scattering for $\hslashω_n\approx n\times J_z$, where $n=0,1,2,3$. The lowest energy ($n=0$) $\bf Q$-dependent continuum has a lower resonant edge and includes a quasi-elastic component at K $(\frac{1}{3}\frac{1}{3})$ consistent with broken $U(1)$ spin rotational symmetry (boson superfluidity). Competing instabilities are apparent in soft albeit finite-energy modes at M $(\frac{1}{2}0)$ and at $\frac{1}{2}$K $(\frac{1}{6}\frac{1}{6})$. For $\bf c$-axis-oriented magnetic fields $17~{\rm T} <μ_0 H< 21~{\rm T}$ that almost saturate the magnetization, corresponding to nearly filling the lattice with bosons, we find a new phase consistent with a second supersolid. These phases are separated by a pronounced 1/3 magnetization plateau that supports coherent spin waves, from which we determine the spin Hamiltonian.

cond-mat.str-el↗

arXiv2025

Microwave Photonics for Space-Ground Connectivity

Future space-ground communication networks require a seamless fusion of technologies that combine the all-weather reliability of microwave links with the ultra-high data capacity of near-infrared optical systems. Achieving this vision demands compact, robust, and multifunctional hardware, yet monolithic integration of these fundamentally distinct domains has remained elusive. Here, we present the first monolithically integrated silicon photonic chip that bridges microwave and optical domains for dual-band free-space communications and dynamic beamforming. The chip integrates a microwave true time delay (TTD) beamforming network, an optical phased array (OPA) beamforming network, and an optical coherent transceiver, all on a silicon-on-insulator (SOI) platform. By uniting the strengths of microwave resilience, optical bandwidth, and coherent detection sensitivity, this photonic integrated circuit represents a critical step toward reconfigurable, interference-resistant, high-throughput links for satellites, UAVs, and ground stations. Experimental demonstrations confirm two-dimensional dynamic beam steering in both bands 24.9 deg x 18.5 deg at microwave frequencies and 10 deg x 4.7 deg in the optical domain. In a 5-meter free-space link, the chip achieves error-free transmission at 10 Gbps for microwave and 80 Gbps per wavelength in the near infrared band. These results establish integrated microwave photonics as a promising platform for bridging Earth and orbit through compact, dual-band, beamforming-enabled transceivers.

physics.optics↗

arXiv2025

Breaking Android with AI: A Deep Dive into LLM-Powered Exploitation

The rapid evolution of Artificial Intelligence (AI) and Large Language Models (LLMs) has opened up new opportunities in the area of cybersecurity, especially in the exploitation automation landscape and penetration testing. This study explores Android penetration testing automation using LLM-based tools, especially PentestGPT, to identify and execute rooting techniques. Through a comparison of the traditional manual rooting process and exploitation methods produced using AI, this study evaluates the efficacy, reliability, and scalability of automated penetration testing in achieving high-level privilege access on Android devices. With the use of an Android emulator (Genymotion) as the testbed, we fully execute both traditional and exploit-based rooting methods, automating the process using AI-generated scripts. Secondly, we create a web application by integrating OpenAI's API to facilitate automated script generation from LLM-processed responses. The research focuses on the effectiveness of AI-enabled exploitation by comparing automated and manual penetration testing protocols, by determining LLM weaknesses and strengths along the way. We also provide security suggestions of AI-enabled exploitation, including ethical factors and potential misuse. The findings exhibit that while LLMs can significantly streamline the workflow of exploitation, they need to be controlled by humans to ensure accuracy and ethical application. This study adds to the increasing body of literature on AI-powered cybersecurity and its effect on ethical hacking, security research, and mobile device security.

cs.SE↗

arXiv2025

Visual Imitation Enables Contextual Humanoid Control

How can we teach humanoids to climb staircases and sit on chairs using the surrounding environment context? Arguably, the simplest way is to just show them-casually capture a human motion video and feed it to humanoids. We introduce VIDEOMIMIC, a real-to-sim-to-real pipeline that mines everyday videos, jointly reconstructs the humans and the environment, and produces whole-body control policies for humanoid robots that perform the corresponding skills. We demonstrate the results of our pipeline on real humanoid robots, showing robust, repeatable contextual control such as staircase ascents and descents, sitting and standing from chairs and benches, as well as other dynamic whole-body skills-all from a single policy, conditioned on the environment and global root commands. VIDEOMIMIC offers a scalable path towards teaching humanoids to operate in diverse real-world environments.

cs.RO↗

arXiv2025

Sun-as-a-star Analysis of the Solar Eruption Source Region Using Ha Spectroscopic Observations of CHASE

Sun-as-a-star analyses serve as a bridge for comparative studies on solar and stellar activities. To investigate the typical Sun-as-a-star Ha temporal spectral characteristics in solar eruption source regions, we analyzed five different types of solar eruptions, using spectroscopic data from the Chinese Ha Solar Explorer (CHASE). Because the spatially-integral Ha spectrum of source region is mainly contributed by emission from heated plasma in flare ribbons and absorption from cold plasma in evolving filaments, we separately analyze the sub-regions of the source region dominated by different dynamical processes. It is revealed that filament eruptions show emission near Ha line center, accompanied by blueshifted/redshifted absorption, while flare ribbons show Ha line center emission with red asymmetry and line broadening. Moreover, a special spectral signature likely associated with coronal mass ejections (CMEs) is identified: prominent blueshifted absorption without a clear deceleration phase, along with redshifted absorption, which can be used as a probe when searching stellar CMEs. Furthermore, in the X9.0 flare (SOL2024-10-03T12:18) accompanied by a violent CME, the expected blueshifted signal is not visible in the spatially-integral Ha spectra. This suggests that filament-lifting signals associated with CMEs in the source region can be obscured by the simultaneous dominant flare-ribbon emission within the integration region, which may explain the relatively small number of confirmed stellar CMEs observed in Ha. We also find that comparison between the Ha and UV spectral observations can effectively reveal the velocity evolution of erupting filaments and potential existence of associated CMEs.

astro-ph.SR↗

arXiv2025

Pre-training Auto-regressive Robotic Models with 4D Representations

Foundation models pre-trained on massive unlabeled datasets have revolutionized natural language and computer vision, exhibiting remarkable generalization capabilities, thus highlighting the importance of pre-training. Yet, efforts in robotics have struggled to achieve similar success, limited by either the need for costly robotic annotations or the lack of representations that effectively model the physical world. In this paper, we introduce ARM4R, an Auto-regressive Robotic Model that leverages low-level 4D Representations learned from human video data to yield a better pre-trained robotic model. Specifically, we focus on utilizing 3D point tracking representations from videos derived by lifting 2D representations into 3D space via monocular depth estimation across time. These 4D representations maintain a shared geometric structure between the points and robot state representations up to a linear transformation, enabling efficient transfer learning from human video data to low-level robotic control. Our experiments show that ARM4R can transfer efficiently from human video data to robotics and consistently improves performance on tasks across various robot environments and configurations.

cs.RO↗

arXiv2025

MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion

Estimating geometry from dynamic scenes, where objects move and deform over time, remains a core challenge in computer vision. Current approaches often rely on multi-stage pipelines or global optimizations that decompose the problem into subtasks, like depth and flow, leading to complex systems prone to errors. In this paper, we present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes. Our key insight is that by simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes. However, this approach presents a significant challenge: the scarcity of suitable training data, namely dynamic, posed videos with depth labels. Despite this, we show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics, even without an explicit motion representation. Based on this, we introduce new optimizations for several downstream video-specific tasks and demonstrate strong performance on video depth and camera pose estimation, outperforming prior work in terms of robustness and efficiency. Moreover, MonST3R shows promising results for primarily feed-forward 4D reconstruction.

cs.CV↗

arXiv2025

St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World

Dynamic 3D reconstruction and point tracking in videos are typically treated as separate tasks, despite their deep connection. We propose St4RTrack, a feed-forward framework that simultaneously reconstructs and tracks dynamic video content in a world coordinate frame from RGB inputs. This is achieved by predicting two appropriately defined pointmaps for a pair of frames captured at different moments. Specifically, we predict both pointmaps at the same moment, in the same world, capturing both static and dynamic scene geometry while maintaining 3D correspondences. Chaining these predictions through the video sequence with respect to a reference frame naturally computes long-range correspondences, effectively combining 3D reconstruction with 3D tracking. Unlike prior methods that rely heavily on 4D ground truth supervision, we employ a novel adaptation scheme based on a reprojection loss. We establish a new extensive benchmark for world-frame reconstruction and tracking, demonstrating the effectiveness and efficiency of our unified, data-driven framework. Our code, model, and benchmark will be released.

cs.CV↗

arXiv2025

Broken symmetries associated with a Kagome chiral charge order

Chirality or handedness manifests in all fields of science, ranging from cell biology, molecular interaction, and catalysis to different branches of physics. In condensed matter physics, chirality is intrinsic to enigmatic quantum phases, such as chiral charge density waves and chiral superconductivity. Here, the underlying chiral response is subtle and leads to broken symmetries in the ground state. Detection of subtle broken symmetries is the key to understand these quantum states but they are extremely challenging to expose leading to debate and controversy. Here, using second-order optical response, we uncover the broken symmetries of a chiral charge density wave in the Kagome lattice KV3Sb5, revealing the relevant broken symmetries of its charge order. KV3Sb5 undergoes a phase transition to a charge-ordered state at low temperatures. Our polarization-dependent mid-infrared photocurrent microscopy reveals an intrinsic, longitudinal helicity-dependent photocurrent associated with the charge order. Our measurements, supported by our theoretical analysis, provide direct evidence for broken inversion and mirror symmetries at the charge order transition, indicating a chiral charge ordered state. On the other hand, we do not observe a circular photogalvanic effect along the direction perpendicular to that of the incident light, imposing stringent constraints on the rotational and point group symmetries of the charge order. Our study not only visualizes the chiral nature of the Kagome charge order revealing its broken symmetries, but also highlights the nonlinear photogalvanic effect as a sensitive probe for detecting subtle symmetry breakings.

cond-mat.str-el↗

arXiv2025

Discovery of a topological exciton insulator with tunable momentum order

Correlated topological materials often maintain a delicate balance among physical symmetries: many topological orders are symmetry protected, while most correlated phenomena arise from spontaneous symmetry breaking. It is rare to find cases where symmetry breaking induces a non-trivial topological phase. Here, we present the discovery of such a phase in Ta2Pd3Te5, where Coulomb interactions form excitons, which condense below 100 K, opening a topological gap and creating a topological excitonic insulator. Our spectroscopy reveals the full spectral bulk gap stemming from exciton condensation. This excitonic insulator state spontaneously breaks mirror symmetries but involves a very weak structural coupling, as indicated by photoemission spectroscopy, thermodynamic measurements, and a detailed structural analysis. Notably, scanning tunneling microscopy uncovers gapless boundary modes in the bulk insulating phase. Their magnetic field response, together with theoretical modeling, suggests a topological origin. These observations establish Ta2Pd3Te5 as the first confirmed topological excitonic insulator in a three-dimensional crystal. This allows to access the associated physics through bulk-sensitive techniques. Furthermore, we uncover another surprising aspect of the topological excitonic insulator, a secondary excitonic instability near 5 K that breaks the translational symmetry. The wavevector of this state shows an unprecedented magnetic field tunability. Thus, we unveil a unique sequence of topological exciton condensations in a bulk crystal, offering new opportunities to study critical behavior and excitations.

cond-mat.str-el↗

arXiv2025

A photonic integrated processor for multiple parallel computational tasks

Optical networks with parallel processing capabilities are significant in advancing high-speed data computing and large-scale data processing by providing ultra-width computational bandwidth. In this paper, we present a photonic integrated processor that can be segmented into multiple functional blocks, to enable compact and reconfigurable matrix operations for multiple parallel computational tasks. Fabricated on a silicon-on-insulator (SOI) platform, the photonic integrated processor supports fully reconfigurable optical matrix operations. By segmenting the chip into multiple functional blocks, it enables optical matrix operations of various sizes, offering great flexibility and scalability for parallel computational tasks. Specifically, we utilize this processor to perform optical convolution operations with various kernel sizes, including reconfigurable three-channel 1x1 convolution kernels and 2x2 real-valued convolution kernels, implemented within distinct segmented blocks of the chip. The multichannel optical 1x1 convolution operation is experimentally validated by using the deep residual U-Net, demonstrating precise segmentation of pneumonia lesion region in lung CT images. In addition, the capability of the 2x2 optical convolution operation is also experimentally validated by constructing an optical convolution layer and integrating an electrical fully connected layer, achieving ten-class classification of handwritten digit images. The photonic integrated processor features high scalability and robust parallel computational capability, positioning it a promising candidate for applications in optical neural networks.

physics.optics↗

arXiv2025

How to Select Pre-Trained Code Models for Reuse? A Learning Perspective

Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection. However, pretraining language models on a large-scale code corpus is computationally expensive. Fortunately, many off-the-shelf Pre-trained Code Models (PCMs), such as CodeBERT, CodeT5, CodeGen, and Code Llama, have been released publicly. These models acquire general code understanding and generation capability during pretraining, which enhances their performance on downstream code intelligence tasks. With an increasing number of these public pre-trained models, selecting the most suitable one to reuse for a specific task is essential. In this paper, we systematically investigate the reusability of PCMs. We first explore three intuitive model selection methods that select by size, training data, or brute-force fine-tuning. Experimental results show that these straightforward techniques either perform poorly or suffer high costs. Motivated by these findings, we explore learning-based model selection strategies that utilize pre-trained models without altering their parameters. Specifically, we train proxy models to gauge the performance of pre-trained models, and measure the distribution deviation between a model's latent features and the task's labels, using their closeness as an indicator of model transferability. We conduct experiments on 100 widely-used opensource PCMs for code intelligence tasks, with sizes ranging from 42.5 million to 3 billion parameters. The results demonstrate that learning-based selection methods reduce selection time to 100 seconds, compared to 2,700 hours with brute-force fine-tuning, with less than 6% performance degradation across related tasks.

cs.SE↗

arXiv2025

Polarization-controlled non-Hermitian metasurfaces for ultra-sensitive terahertz sensing

Non-Hermitian systems offer significant advantages in sensor design, especially at the exceptional points. However, the extreme sensitivity near these points poses great challenges due to fabrication errors and system noises, which degrade sensing performance. To address this, we introduce a novel approach leveraging the polarization degrees of freedom in non-Hermitian systems. In this work, we establish a direct relation between the incident polarization and the transmission phase of a coupled metasurface system and achieve the polarization-controlled phase singularity even post-fabrication. The incident polarization angle can be utilized as a sensing index, which enables indirect and accurate measurement. The theoretical approach is experimentally validated using a general design of THz non-Hermitian metasurface sensors. Our method enhances robustness and sensitivity, opening new avenues for practical applications in ultra-sensitive sensing.

physics.optics↗