arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 289 records · Page 16Linked to original sources

arXiv2025

Precision Measurement of Large Shear Signals

So far, estimators of galaxy shape distortions are only carefully studied perturbatively in the case of small shear signals, mainly for weak lensing science. However, in the neighborhood of massive foreground clusters, a large number of background galaxies can be significantly distorted. The measurement of such large shear signals could be quite nontrivial under general observing conditions, i.e., in the presence of the point spread function (PSF) and noise. In this work, we propose a non-perturbative method to exactly recover large shear signals ($\gtrsim 0.5$) under general conditions. We test the method on simulated galaxy images, and find that it is accurate down to the very faint end. This new method is particularly useful for more accurate recovery of the shear distribution in the neighborhood of massive foreground clusters, thereby improving the modeling of the underlying dark matter halo properties.

astro-ph.CO↗

arXiv2025

WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research

This paper tackles \textbf{open-ended deep research (OEDR)}, a complex challenge where AI agents must synthesize vast web-scale information into insightful reports. Current approaches are plagued by dual-fold limitations: static research pipelines that decouple planning from evidence acquisition and monolithic generation paradigms that include redundant, irrelevant evidence, suffering from hallucination issues and low citation accuracy. To address these challenges, we introduce \textbf{WebWeaver}, a novel dual-agent framework that emulates the human research process. The planner operates in a dynamic cycle, iteratively interleaving evidence acquisition with outline optimization to produce a comprehensive, citation-grounded outline linking to a memory bank of evidence. The writer then executes a hierarchical retrieval and writing process, composing the report section by section. By performing targeted retrieval of only the necessary evidence from the memory bank via citations for each part, it effectively mitigates long-context issues and citation hallucinations. Our framework establishes a new state-of-the-art across major OEDR benchmarks, including DeepResearch Bench, DeepConsult, and DeepResearchGym. These results validate our human-centric, iterative methodology, demonstrating that adaptive planning and focused synthesis are crucial for producing comprehensive, trusted, and well-structured reports.

cs.CL↗

arXiv2025

SCI-Reason: A Dataset with Chain-of-Thought Rationales for Complex Multimodal Reasoning in Academic Areas

Large Language Models (LLMs) and Large Multimodal Models (LMMs) demonstrate impressive problem-solving skills in many tasks and domains. However, their ability to reason with complex images in academic domains has not been systematically investigated. To bridge this gap, we present SCI-Reason, a dataset for complex multimodel reasoning in academic areas. SCI-Reason aims to test and improve the reasoning ability of large multimodal models using real complex images in academic domains. The dataset contains 12,066 images and 12,626 question-answer pairs extracted from PubMed, divided into training, validation and test splits. Each question-answer pair also contains an accurate and efficient inference chain as a guide to improving the inference properties of the dataset. With SCI-Reason, we performed a comprehensive evaluation of 8 well-known models. The best performing model, Claude-3.7-Sonnet, only achieved an accuracy of 55.19%. Error analysis shows that more than half of the model failures are due to breakdowns in multi-step inference chains rather than errors in primary visual feature extraction. This finding underscores the inherent limitations in reasoning capabilities exhibited by current multimodal models when processing complex image analysis tasks within authentic academic contexts. Experiments on open-source models show that SCI-Reason not only enhances reasoning ability but also demonstrates cross-domain generalization in VQA tasks. We also explore future applications of model inference capabilities in this domain, highlighting its potential for future research.

cs.MM↗

arXiv2025

Observation of non-Hermitian topology in cold Rydberg quantum gases

The pursuit of topological phenomena in non-Hermitian systems has unveiled new physics beyond the conventional Hermitian paradigm, yet their realization in interacting many-body platforms remains a critical challenge. Exploring this interplay is essential to understand how strong interactions and dissipation collectively shape topological phases in open quantum systems. Here, we experimentally demonstrate non-Hermitian spectra topology in a dissipative Rydberg atomic gas and characterize parameters-dependent winding numbers. By increasing the interaction strength, the system evolves from Hermitian to non-Hermitian regime, accompanying emergence of trajectory loop in the complex energy plane. As the scanning time is varied, the spectra topology becomes twisted in the complex energy plane manifesting as a topology phase transition with the sign winding number changed. When preparing the system in different initial states, we can access a nontrivial fractional phase within a parameter space that globally possesses an integer winding. Furthermore, by changing the scanning direction, we observe the differentiated loops, revealing the breaking of chirality symmetry. This work establishes cold Rydberg gases as a versatile platform for exploring the rich interplay between non-Hermitian topology, strong interactions, and dissipative quantum dynamics.

cond-mat.quant-gas↗

arXiv2025

Quantum Zeno Effect in the Spatial Evolution of a Single Atom

The quantum Zeno effect (QZE) reveals that frequent measurements can suppress quantum evolution, but the detailed dynamics of the system under finite-duration measurements in experiments remain insufficiently explored. Here, we employ an optical dipole trap as a projective measurement to study the motion of a single cold atom in free space. By monitoring atomic loss, we directly observe the QZE in single-atom motion in free space and find that the effect of dipole measurements on the atom comprises a short-time collapse followed by subsequent periodic unitary evolution, thereby providing an intuitive physical picture of measurement backaction across different timescales. We further investigate the effects of measurement frequency, strength, and spatial position, demonstrating that measurements not only suppress the spreading of quantum states but also enable deterministic preparation of distinct motional states. Furthermore, by dynamically controlling the measurement position, we achieve measurement-induced directional transport of a single atom without imparting additional momentum. Our results provide a direct experimental demonstration of the QZE in real space and establish a versatile framework for measurement-based control of atomic motion, paving the way for motional-state engineering in cold-atom platforms.

quant-ph↗

arXiv2025

PixelCraft: A Multi-Agent System for High-Fidelity Visual Reasoning on Structured Images

Structured images (e.g., charts and geometric diagrams) remain challenging for multimodal large language models (MLLMs), as perceptual slips can cascade into erroneous conclusions. Intermediate visual cues can steer reasoning; however, existing cue-based methods are constrained with low-fidelity image processing and linear, rigid reasoning patterns, limiting their effectiveness on complex structured-image tasks. In this paper, we propose PixelCraft, a novel multi-agent system for high-fidelity image processing and flexible visual reasoning on structured images. The system comprises a dispatcher, a planner, a reasoner, critics, and a set of visual tool agents. To achieve high-fidelity processing, we construct a high-quality corpus and fine-tune an MLLM into a grounding model, whose pixel-level localizations are integrated with traditional computer vision (CV) algorithms in tool agents. Building on this foundation, PixelCraft facilitates flexible visual reasoning through a dynamic three-stage workflow of tool selection, agent discussion, and self-criticism. Moreover, unlike prior linear reasoning patterns that simply append historical images, PixelCraft maintains an image memory to allow the planner to adaptively revisit earlier visual steps, explore alternative reasoning branches, and dynamically adjust the reasoning trajectory during discussion. Extensive experiments on challenging chart and geometry benchmarks demonstrate that PixelCraft significantly improves visual reasoning performance for advanced MLLMs, setting a new standard for structured image reasoning. Our code will be available at https://github.com/microsoft/PixelCraft.

cs.CV↗

arXiv2025

Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments

Foundation model-based semantic transmission has recently shown great potential in wireless image communication. However, existing methods exhibit two major limitations: (i) they overlook the varying importance of semantic components for specific downstream tasks, and (ii) they insufficiently exploit wireless domain knowledge, resulting in limited robustness under dynamic channel conditions. To overcome these challenges, this paper proposes a foundation model-based adaptive semantic image transmission system for dynamic wireless environments, such as autonomous driving. The proposed system decomposes each image into a semantic segmentation map and a compressed representation, enabling task-aware prioritization of critical objects and fine-grained textures. A task-adaptive precoding mechanism then allocates radio resources according to the semantic importance of extracted features. To ensure accurate channel information for precoding, a channel estimation knowledge map (CEKM) is constructed using a conditional diffusion model that integrates user position, velocity, and sparse channel samples to train scenario-specific lightweight estimators. At the receiver, a conditional diffusion model reconstructs high-quality images from the received semantic features, ensuring robustness against channel impairments and partial data loss. Simulation results on the BDD100K dataset with multi-scenario channels generated by QuaDRiGa demonstrate that the proposed method outperforms existing approaches in terms of perceptual quality (SSIM, LPIPS, FID), task-specific accuracy (IoU), and transmission efficiency. These results highlight the effectiveness of integrating task-aware semantic decomposition, scenario-adaptive channel estimation, and diffusion-based reconstruction for robust semantic transmission in dynamic wireless environments.

eess.IV↗

arXiv2025

Joint Beamforming and Antenna Position Optimization for Fluid Antenna-Assisted MU-MIMO Networks

The fluid antenna system (FAS) is a disruptive tech-nology for future wireless communication networks. This paper considers the joint optimization of beamforming matrices and antenna positions for weighted sum rate (WSR) maximization in fluid antenna (FA)-assisted multiuser multiple-input multiple-output (MU-MIMO) networks, which presents significant chal-lenges due to the strong coupling between beamforming and FA positions, the non-concavity of the WSR objective function, and high computational complexity. To address these challenges, we first propose a novel block coordinate ascent (BCA)-based method that employs matrix fractional programming techniques to reformulate the original complex problem into a more tractable form. Then, we develop a parallel majorization maximization (MM) algorithm capable of optimizing all FA positions simul-taneously. To further reduce computational costs, we propose a decentralized implementation based on the decentralized base-band processing (DBP) architecture. Simulation results demon-strate that our proposed algorithm not only achieves significant WSR improvements over conventional MIMO networks but also outperforms the existing method. Moreover, the decentralized implementation substantially reduces computation time while maintaining similar performance compared with the centralized implementation.

cs.IT↗

arXiv2025

Observation of Discrete Time Quasicrystal in Rydberg Atomic Gases

Discrete time quasicrystals (DTQC) constitute a class of non-equilibrium matter characterized by temporal order without strict periodicity, in contrast to conventional time crystals. Investigating these phenomena is essential for expanding our fundamental understanding of far-from-equilibrium quantum matter and spontaneous symmetry breaking beyond periodic regimes. Here, we experimentally observe a DTQC in a driven-dissipative ensemble of strongly interacting Rydberg atoms, displaying non-equilibrium dynamical response with a different finite Abelian group symmetry $\mathbb{Z}{_m} \times \mathbb{Z}{_n}$. By applying a quasi-periodic drive using a dual-frequency drive with incommensurate frequencies, we demonstrate that the system exhibits a robust subharmonic response at multiple incommensurate frequencies, signifying the emergence of a DTQC phase. We map the full phase diagram of the system, which includes the DTQC phase, and demonstrated its rigidity against perturbations in both RF field intensity and laser detuning. Moreover, we observe a cyclic group symmetry effect that constrains the construction of $\mathbb{Z}{_2} \times \mathbb{Z}{_3}$-symmetric DTQC. This work establishes a versatile platform for studying non-equilibrium phases of matter and provides insights into the dynamics of time-translation symmetry breaking in quantum many-body systems.

cond-mat.quant-gas↗

arXiv2025

Magnetic Dip Found in a Quiescent Prominence Foot via Observation and Simulation

Solar prominences (or filaments) are cooler and denser plasma suspended in the much hotter and rarefied solar corona. When viewed on the solar disc filament barbs or feet protrude laterally from filament spine. When observed at the limb of the Sun, they reach into the chromosphere or even further down. For a long time, the magnetic field orientation of barbs has remained a mystery due to the paradox that the barbs possess vertical fine structures and flows but are likely to be supported in a horizontal magnetic field. Here we present unambiguous observations of a magnetic dip in a quiescent prominence foot with an upward-curved field. That is indicated by the horizontal bidirectional outflows probably produced by magnetic reconnection between the fields of a tiny erupting filament and those in a prominence foot. The altitude at the bottom of the dip is about 30 Mm. At the edge of the prominence foot, the angle between the dip field and the local horizontal is about 4 degrees. Additionally, the curvature radius of the dip bottom is estimated to be around 73 Mm. We also conduct magnetofrictional simulation to self-consistently form a large-scale magnetic flux rope with magnetic dips resembling the spine and feet of the quiescent prominence. The observations shed light on the field structure of prominences which is crucial for the instability that accounts for the eruption of prominences and coronal mass ejections.

astro-ph.SR↗

arXiv2025

Real-time vacuum-state quantum random number generator on a chip

Quantum random number generators (QRNGs) produce true random numbers, which are guaranteed by the fundamental principles of quantum physics. Miniaturization of QRNGs is crucial for a wide range of communication and cryptography applications. Here, we first report a fully functional QRNG chip based on vacuum-state fluctuations, with dimensions of 16.6 mm x 7.8 mm. The quantum entropy source, which is achieved via hybrid photonic integration with a SiO2 waveguide, generates raw quantum random numbers. The hybrid photonic and electrical components are assembled into a compact ceramic package using system-in-package technology. A microcontroller unit acquires the raw data and outputs the processed quantum random numbers via a serial peripheral interface. According to the characterization results, the QRNG chip achieves a constant real-time output rate of 5.2 Mbps across the industrial temperature range of -40°C to 85°C, making it suitable for practical applications.

quant-ph↗

arXiv2025

Deep holes of a class of twisted Reed-Solomon codes

The deep hole problem is a fundamental problem in coding theory, and it has many important applications in code constructions and cryptography. The deep hole problem of Reed-Solomon codes has gained a lot of attention. As a generalization of Reed-Solomon codes, we investigate the problem of deep holes of a class of twisted Reed-Solomon codes in this paper. Firstly, we provide the necessary and sufficient conditions for $\boldsymbol{a}=(a_{0},a_{1},\cdots,a_{n-k-1})\in\mathbb{F}_{q}^{n-k}$ to be the syndrome of some deep hole of $TRS_{k}(\mathcal{A},l,η)$. Next, we consider the problem of determining all deep holes of the twisted Reed-Solomon codes $TRS_{k}(\mathbb{F}_{q}^{*},k-1,η)$. Specifically, we prove that there are no other deep holes of $TRS_{k}(\mathbb{F}_{q}^{*},k-1,η)$ for $\frac{3q+2\sqrt{q}-8}{4}\leq k\leq q-5$ when q is even, and $\frac{3q+3\sqrt{q}-5}{4}\leq k\leq q-5$ when q is odd. We also completely determine their deep holes for $q-4\leq k\leq q-2$ when $q$ is even.

cs.IT↗

arXiv2025

FlashRecovery: Fast and Low-Cost Recovery from Failures for Large-Scale Training of LLMs

Large language models (LLMs) have made a profound impact across various fields due to their advanced capabilities. However, training these models at unprecedented scales requires extensive AI accelerator clusters and sophisticated parallelism strategies, which pose significant challenges in maintaining system reliability over prolonged training periods. A major concern is the substantial loss of training time caused by inevitable hardware and software failures. To address these challenges, we present FlashRecovery, a fast and low-cost failure recovery system comprising three core modules: (1) Active and real-time failure detection. This module performs continuous training state monitoring, enabling immediate identification of hardware and software failures within seconds, thus ensuring rapid incident response; (2) Scale-independent task restart. By employing different recovery strategies for normal and faulty nodes, combined with an optimized communication group reconstruction protocol, our approach ensures that the recovery time remains nearly constant, regardless of cluster scale; (3) Checkpoint-free recovery within one step. Our novel recovery mechanism enables single-step restoration, completely eliminating dependence on traditional checkpointing methods and their associated overhead. Collectively, these innovations enable FlashRecovery to achieve optimal Recovery Time Objective (RTO) and Recovery Point Objective (RPO), substantially improving the reliability and efficiency of long-duration LLM training. Experimental results demonstrate that FlashRecovery system can achieve training restoration on training cluster with 4, 800 devices in 150 seconds. We also verify that the time required for failure recovery is nearly consistent for different scales of training tasks.

cs.DC↗

arXiv2025

Scalar-Gauss-Bonnet gravity: Infrared causality and detectability of GW observations

We investigate time delays of wave scatterings around black hole backgrounds in scalar-tensor effective field theories of gravity. The scalar-Gauss-Bonnet (sGB) couplings, being corrections of the lowest orders, can give rise to hairy black holes. By requiring infrared causality, we impose lower bounds on the cutoff scales of the theories. With these bounds, we further discuss the detectability of sGB gravity in gravitational waves from binary black hole mergers. Compared with the gravitational effective field theories that contain only the two tensor modes, adding extra degrees of freedom, such as adding a scalar, opens up a detectable window in the planned observations.

hep-th↗

arXiv2025

SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning

Video large language models (Vid-LLMs) have shown strong capabilities in understanding video content. However, their reliance on dense video token representations introduces substantial memory and computational overhead in both prefilling and decoding. To mitigate the information loss of recent video token reduction methods and accelerate the decoding stage of Vid-LLMs losslessly, we introduce SpecVLM, a training-free speculative decoding (SD) framework tailored for Vid-LLMs that incorporates staged video token pruning. Building on our novel finding that the draft model's speculation exhibits low sensitivity to video token pruning, SpecVLM prunes up to 90% of video tokens to enable efficient speculation without sacrificing accuracy. To achieve this, we performs a two-stage pruning process: Stage I selects highly informative tokens guided by attention signals from the verifier (target model), while Stage II prunes remaining redundant ones in a spatially uniform manner. Extensive experiments on four video understanding benchmarks demonstrate the effectiveness and robustness of SpecVLM, which achieves up to 2.68$\times$ decoding speedup for LLaVA-OneVision-72B and 2.11$\times$ speedup for Qwen2.5-VL-32B. Code is available at https://github.com/zju-jiyicheng/SpecVLM.

cs.CV↗

arXiv2025

${C}^{3}$-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splatting

Generalizable Gaussian Splatting aims to synthesize novel views for unseen scenes without per-scene optimization. In particular, recent advancements utilize feed-forward networks to predict per-pixel Gaussian parameters, enabling high-quality synthesis from sparse input views. However, existing approaches fall short in encoding discriminative, multi-view consistent features for Gaussian predictions, which struggle to construct accurate geometry with sparse views. To address this, we propose $\mathbf{C}^{3}$-GS, a framework that enhances feature learning by incorporating context-aware, cross-dimension, and cross-scale constraints. Our architecture integrates three lightweight modules into a unified rendering pipeline, improving feature fusion and enabling photorealistic synthesis without requiring additional supervision. Extensive experiments on benchmark datasets validate that $\mathbf{C}^{3}$-GS achieves state-of-the-art rendering quality and generalization ability. Code is available at: https://github.com/YuhsiHu/C3-GS.

cs.CV↗

arXiv2025

Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System

Recent advances in speech large language models (SLMs) have improved speech recognition and translation in general domains, but accurately generating domain-specific terms or neologisms remains challenging. To address this, we propose Attention2Probability: attention-driven terminology probability estimation for robust speech-to-text system, which is lightweight, flexible, and accurate. Attention2Probability converts cross-attention weights between speech and terminology into presence probabilities, and it further employs curriculum learning to enhance retrieval accuracy. Furthermore, to tackle the lack of data for speech-to-text tasks with terminology intervention, we create and release a new speech dataset with terminology to support future research in this area. Experimental results show that Attention2Probability significantly outperforms the VectorDB method on our test set. Specifically, its maximum recall rates reach 92.57% for Chinese and 86.83% for English. This high recall is achieved with a latency of only 8.71ms per query. Intervening in SLMs' recognition and translation tasks using Attention2Probability-retrieved terms improves terminology accuracy by 6-17%, while revealing that the current utilization of terminology by SLMs has limitations.

cs.CL↗

arXiv2025

Quantitative contact Hamiltonian dynamics

This paper presents a systematic quantitative study of contact rigidity phenomena based on the contact Hamiltonian Floer theory established by Merry-Uljarević. Our quantitative approach applies to arbitrary admissible contact Hamiltonian functions on the contact boundary $M = \partial W$ of a ${\rm weakly}^{+}$-monotone symplectic manifold $W$. From a theoretical standpoint, we develop a comprehensive contact spectral invariant theory. As applications, the properties of these invariants enable us to establish several fundamental results: the contact big fiber theorem, sufficient conditions for orderability, and the existence results of translated points. Furthermore, we uncover a non-traditional filtration structure on contact Hamiltonian Floer groups, which we formalize through the introduction of a novel type of persistence modules, called gapped modules, that are only parametrized by a partially ordered set. Among the various properties of contact spectral invariants, we highlight that the triangle inequality is derived through an innovative analysis of a pair-of-pants construction in the contact-geometric framework.

math.SG↗