arXiv subjects

Yu Xu

Publications and source records attributed to Yu Xu.

At least 19 recordsLinked to original sources

arXiv2026

Projected Sensitivity to Slow Muonphilic Dark Matter with Accelerator Muon Beams

The nature of dark matter (DM) remains one of the most enduring open questions in modern physics, and muonphilic DM has emerged as a promising scenario that complements traditional DM candidates. Following the recently established cosmic-ray muon scattering approach, we investigate the sensitivity for probing slow muonphilic DM with accelerator muon beams. A Geant4-based simulation framework is developed, incorporating the detector geometry from the PKMu muon tomography system and a dedicated elastic $\mu$-DM scattering process. The projected sensitivity is found to be largely insensitive to both the beam energy and the transverse beam size when the beam is fully contained within the detector acceptance. For a benchmark beam intensity of $10^5/\rm{s}$, the simulated pure-muon beam surpasses the existing cosmic-ray limit of $1.61\times10^{-17}$ cm$^2$ at $m_{\rm DM}=1$ GeV within approximately 11 seconds. A realistic muon beam phase-space distribution based on simulations for the High Intensity heavy-ion Accelerator Facility (HIAF) is also implemented, yielding projected limits that improve upon the cosmic-ray results by nearly two orders of magnitude in a one-day exposure. These results demonstrate that a beam-muon scattering experiment offers a robust and promising route toward significantly improved sensitivity to slow muonphilic DM.

hep-ex↗

arXiv2026

Low-energy Muon-Nucleon scattering experiment: LUNE (White Paper)

The HIAF will provide high-intensity, high-quality muon beams with momenta from 0.5 to 7.5 GeV/c. This energy range is uniquely suited for precision muon scattering, bridging the gap between low-energy electron facilities and future high-energy lepton-ion colliders. In particular, HIAF will enable precision measurements with both positive and negative muon beams over a broad kinematic range, complementing existing electron-scattering facilities such as JLab, EicC and EIC. Based on HIAF muon source, the LUNE Collaboration has been established to address several fundamental questions in nuclear and particle physics, including the proton charge radius puzzle, nucleon electromagnetic structure, and the dynamics of quantum electrodynamics and hadronic interactions. The program proceeds in two phases, from elastic scattering to nucleon structure and beyond-Standard-Model searches. The experiment is expected to determine the proton charge radius with a precision of approximately 1.0\% using elastic muon-proton scattering. It will also perform systematic measurements of the proton electromagnetic form factors with both $\mu^+$ and $\mu^-$ beams, enabling precise studies of two-photon exchange effects and stringent tests of quantum electrodynamics. Beyond elastic scattering, LUNE will investigate TMD, gravitational form factors, and nuclear charge radii, providing new insights into the 3D structure of nucleons and nuclei. The experiment will further address important topics including Coulomb-distortion corrections, nuclear medium effects, and possible signatures of physics beyond the Standard Model. This white paper presents the scientific motivation, detector concept, expected performance, and long-term strategy of LUNE.

hep-ex↗

arXiv2026

High fidelity control of superconducting qubits with optical transmitted signal

Superconducting circuits exhibit remarkable potential for constructing large-scale quantum simulation and computation systems, featuring numerous qubits, extended coherence time, and precise control. Nevertheless, the growing number of signal cables poses a challenge in dilution refrigerators due to space and heat load constraints. To overcome this issue, we experimentally implemented an optically-assisted transmission line as an alternative to coaxial cables. By modulating microwave signals on laser intensities at room temperature and regenerating the signals at a cryogenic plate within the dilution refrigerator, we demonstrated full control of superconducting qubits using photocurrent. We demonstrate and benchmark both single-qubit and two-qubit gates on frequency tunable transmon qubits, achieving fidelities of 99.915% $\pm$ 0.005% and 99.676% $\pm$ 0.041%, respectively, which have reached the requirement of the surface code.

quant-ph↗

arXiv2026

Production of lepton-flavor-violating scalars through resonant positive-muon annihilation on atomic electrons

We investigate an invisible lepton-flavor-violating scalar $\phi$ with exclusive $e-\mu$ couplings and study its resonant production via $\mu^+e^-\to\phi$ in fixed-target experiments. Since the effective center-of-mass energy is determined by the momentum of the initial-state bound electrons, atomic effects can significantly affect the resonance behavior. We therefore employ relativistic bound-state electron wave functions to calculate the production cross section and reveal a material-dependent broadening of the resonance lineshape. For the proposed HIAF experiment, fewer than one day of data taking ($6\times10^{10}$ MOT) can probe couplings at the $10^{-5}$ level at 90\% confidence level near resonance, demonstrating that high-intensity muon fixed-target experiments provide a powerful complementary probe of lepton-flavor violation.

hep-ph↗

arXiv2026

Development and characterization of a millimeter-wave cold load prototype

Superconducting transition-edge sensors (TESs) are crucial detectors for cosmic microwave background (CMB) observations and require stable and tunable millimeter-wave cold loads for optical-efficiency calibration. This work presents the design, fabrication, and preliminary characterization of a 4-20 K millimeter-wave cold load prototype intended for integration into the 1 K stage of a dilution refrigerator and subsequent 40/90 GHz CMB TES calibration experiments. Two absorber prototypes based on commercially available CR-110 and a Stycast 2850FT composite were fabricated and studied. Simulation results show that both absorber structures exhibit small predicted steady-state temperature gradients and low normal-incidence reflectance in the target frequency bands. Room-temperature S11 measurements were used only to screen low-reflectance cold load prototype, and the measured results generally agree with the electromagnetic simulations. The measured S11 of the Stycast 2850FT composite is comparable to that of the commercial absorber TK RAM. Additionally, to explore a more readily obtainable alternative absorber material, TIE280-25AB was preliminarily evaluated by measuring its electromagnetic parameters. Based on the measured parameters, the simulated S11 of the TIE280-25AB pyramidal absorber structure is comparable to those of CR-110 and the Stycast 2850FT composite over 33-110 GHz. These results identify CR-110 and the Stycast 2850FT composite as promising absorbers for subsequent cryogenic evaluation. The absolute low-temperature emissivity, effective radiation temperature, and TES calibration performance remain to be established through future cryogenic radiometric and TES based optical-power measurements.

astro-ph.IM↗

arXiv2026

Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs

Developing seamless, high-performance, native intelligent full-duplex Spoken Language Models (SLMs) remains a critical challenge and long-standing goal for the speech and NLP community. Despite notable progress, recent endeavors are fundamentally constrained by severe modality interference, which causes substantial knowledge degradation and compromises semantic integrity -- ultimately making full-duplex SLMs feel unnatural and unintelligent. In this paper, through an exhaustive fine-grained analysis of model optimization dynamics, we uncover the root cause of such performance degradation, revealing that modality interference arises from inherent gradient conflicts between acoustic and semantic modeling when the two modalities are forced to share a deep parameter space. Guided by this key insight, we introduce Lychee-FD, a native end-to-end full-duplex framework designed to mitigate modality interference. Importantly, we propose a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers while preserving cross-modality coherence via a dedicated semantic alignment channel. Extensive experiments on multiple full-duplex benchmarks demonstrate that our method significantly advances the state of the art, yielding substantial improvements in both speech intelligence (+7.4% on Spoken QA) and full-duplex interaction fluidity (+28.5% on FullDuplexBench 1.5) without compromising inference efficiency. To the best of our knowledge, this work is the first to achieve two key advances: 1) uncovering and elucidating the root cause of modality interference in full-duplex SLMs, and 2) designing an elegant hierarchical model together with a practical solution for seamless, high-performance, native intelligent full-duplex SLMs.

cs.CL↗

arXiv2026

Surface code logical operations on a superconducting quantum processor

Fault-tolerant quantum computation requires logical operations that manipulate encoded information while preserving quantum error-correction protection. In planar surface-code architectures, code deformation and lattice surgery provide a local, measurement-based route to such operations. Here we experimentally realize key elements of patch-based surface-code logical processing on a 107-qubit superconducting quantum processor. We first implement a reusable primitive layer comprising merge and split, patch expansion and shrinkage, and deformations mediated by domain walls and twist defects. We then compose these primitives to realize logical state routing, the logical controlled-NOT gate, and the single-qubit Hadamard and phase gates, which together form a Clifford-generating set. All operations are implemented on distance-three rotated surface-code patches with multi-round syndrome extraction and neural-network decoding, without post-selection. Our results advance superconducting surface-code experiments from protected logical memory to active, patch-based fault-tolerant logical operations.

quant-ph↗

arXiv2026

T-Mem: Memory That Anticipates, Not Archives

Long-term memory is essential for conversational agents to remain coherent across extended dialogues, follow through on commitments made many sessions earlier, and adapt their behaviour to each user. Current LLM-backed long-term conversational memory, however, is reachability-bounded by the similarity between a query and stored content, both lexical and dense-vector. The approach is effective when query and memory share surface features such as wording or named entities (we call this descriptive). But it misses another, equally valuable class of cases, where query and memory do not share surface features and are tied only by a latent semantic arc (associative). On this regime prevailing long-term memory systems collectively fail. Covering this other half is what allows an assistant, for the first time, to actively draw on past dialogue as a semantic asset. On the memory side, this is the engineering counterpart of what cognitive science calls episodic future thinking: rehearsing past experience for the future contexts under which it will need to be found. We call these write-time rehearsals triggers. We propose T-Mem, the first long-term conversational memory architecture that covers both descriptive and associative recall. At each of two evidence granularities, single facts and full exchanges, T-Mem instantiates one descriptive trigger family and one associative trigger family, so that every memory remains reachable from both surface-similar and relevance-bound queries. As empirical validation, T-Mem reaches state-of-the-art on both LoCoMo and LoCoMo-Plus.

cs.CL↗

arXiv2026

Suppression of p-Wave Altermagnetism by Localized 4f Electrons in CeNiAsO

Altermagnetism, characterized by momentum-dependent spin splitting and zero net magnetization, has so far been explored mainly in weakly or moderately correlated d-electron systems. How symmetry-allowed altermagnetic band splitting manifests in heavy-fermion materials, where magnetic exchange competes with Kondo correlations, remains unclear. Here we use high-resolution angle-resolved photoemission spectroscopy (ARPES) to investigate CeNiAsO, a Kondo-lattice system that was predicted to be a candidate for p-wave altermagnetism. Fermi surface mapping and polarization-dependent ARPES show that the experimentally observed itinerant bands are mainly derived from Ni 3d orbitals, while resonant photoemission reveals that the Ce 4f states remain predominantly localized with residual c-f hybridization. Ultra-low-temperature measurements reveal no resolvable near-Fermi-level p-wave-like exchange splitting on the Ni 3d-derived conduction bands across the successive antiferromagnetic transitions. These experimental observations cannot be captured by an itinerant-4f band-structure description, which predicts a sizable p-wave splitting in the itinerant bands. When the localized Ce 4f character is incorporated, our band structure calculations indicate that the itinerant Ce 4f band weight is shifted away from the Fermi level and the p-wave-like splitting on the Ni 3d-derived bands is reduced to the few-meV scale. These results establish CeNiAsO as a strongly correlated f-electron setting in which the magnetic symmetry allows p-wave-like band splitting, but localized 4f electrons strongly suppress its observable itinerant single-particle signature.

cond-mat.str-el↗

arXiv2026

Meta-CoT: Enhancing Granularity and Generalization in Image Editing

Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a critical question remains underexplored: what forms of CoT and training strategy can jointly enhance both the understanding granularity and generalization? To address this, we propose Meta-CoT, a paradigm that performs a two-level decomposition of any single-image editing operation with two key properties: (1) Decomposability. We observe that any editing intention can be represented as a triplet - (task, target, required understanding ability). Inspired by this, Meta-CoT decomposes both the editing task and the target, generating task-specific CoT and traversing editing operations on all targets. This decomposition enhances the model's understanding granularity of editing operations and guides it to learn each element of the triplet during training, substantially improving the editing capability. (2) Generalizability. In the second decomposition level, we further break down editing tasks into five fundamental meta-tasks. We find that training on these five meta-tasks, together with the other two elements of the triplet, is sufficient to achieve strong generalization across diverse, unseen editing tasks. To further align the model's editing behavior with its CoT reasoning, we introduce the CoT-Editing Consistency Reward, which encourages more accurate and effective utilization of CoT information during editing. Experiments demonstrate that our method achieves an overall 15.8% improvement across 21 editing tasks, and generalizes effectively to unseen editing tasks when trained on only a small set of meta-tasks. Our code, benchmark, and model are released at https://shiyi-zh0408.github.io/projectpages/Meta-CoT/

cs.CV↗

arXiv2026

DREAMuS: Dark matter REsearch with Advanced Muon Source

We propose DREAMuS, a fixed-target experiment at the High Intensity Heavy-Ion Accelerator Facility (HIAF), to search for muon-philic dark matter mediated by light flavor-violating bosons. DREAMuS is designed to probe the parameter space of a muon-philic dark matter (DM) mediated by a light flavor-violating boson, specifically a vector $Z'$ (or a scalar $\phi$) which is produced in muon-nucleus interactions and decays into dark matter particles with a distinctive detector signature. Precision tracking and time-of-flight measurements are used to suppress the Standard Model backgrounds. We find that DREAMuS can achieve competitive sensitivity in the GeV-scale muon-philic dark matter parameter space, reaching sensitivity to couplings at the $10^{-4}$, especially in the few-hundred-MeV region.In addition to a $\mu^-$ run, we highlight the potential of a complementary $\mu^+$ beam option, further improving sensitivity to dark matter below 200 $\mathrm{MeV}$ by an order of magnitude.

hep-ph↗

arXiv2026

Nested Multilevel Monte Carlo with Preintegration for Efficient Risk Estimation

Nested Monte Carlo is widely used for risk estimation, but its efficiency is limited by the discontinuity of the indicator function and high computational cost. This paper proposes a nested Multilevel Monte Carlo (MLMC) method combined with preintegration for efficient risk estimation. We first use preintegration to integrate out one outer random variable, which effectively handles the discontinuity of the indicator function, then we construct the MLMC estimator with preintegration to reduce the computational cost. Our theoretical analysis proves that the strong convergence rate of the MLMC combined with preintegration reaches -1, compared with -1/2 for the standard MLMC. Consequently, we obtain a nearly optimal computational complexity. Besides, our method can also handle the high-kurtosis phenomenon caused by indicator functions. Numerical experiments verify that the smoothed MLMC with preintegration outperforms the standard MLMC and the optimal computational cost can be attained. Combining our method with quasi-Monte Carlo further improves its performance in high dimensions. Keywords: Nested simulation, Multilevel Monte Carlo, Risk estimation, Preintegration

math.NA↗

arXiv2026

Design, Fabrication and Characterization of Microwave Multiplexing SQUID Prototype

The readout system with a high multiplexing ratio has become a bottleneck limiting the application of large-scale Transition Edge Sensor (TES) detector arrays. In recent years, the microwave superconducting quantum interference device (SQUID) multiplexer has emerged as a key technology for effectively reading large-scale cryogenic detector arrays. Currently, the microwave SQUID multiplexer is being adopted by an increasing number of experiments due to its capability of achieving a multiplexing ratio of 2000:1 within the readout bandwidth. In this study, we developed and fabricated a 32-channel microwave SQUID multiplexer prototype. And we measured 8 channels of the prototype. The measured equivalent noise current of the prototype reached 42 pA/$\sqrt{Hz}$.

physics.ins-det↗

arXiv2026

Half-year Evolution of a Decaying Solar Active Region and Peripheral Dimming Regions

Using multi-wavelength observations from the Solar Dynamics Observatory (SDO), we investigated the six-month decay process of the solar active region NOAA AR 12738 from April to October 2019. We systematically analyzed the region's evolution by examining extreme ultraviolet (EUV) intensity variations, quantifying magnetic flux diffusion, and investigating thermodynamic changes via Differential Emission Measure (DEM) analysis. This study presents the first long-term tracking of a peripheral dimming region (dark moat), revealing its continuous areal decrease over time. DEM results reveal cooling plasma signatures and thermal restructuring, with the dimming region exhibiting a distinct temperature deficit in range 10$^{5.5}$ -- 10$^{5.9}$~K. Potential field extrapolation identifies two dominant magnetic configurations: low-lying loops with cool plasma ($<$10$^{5.5}$ K), and high-arching structures connecting to the AR core, contributing to localized emission reduction. We found that the dimming is dominated by high-lying loops extending from the AR core, which are heated to temperatures above the main response of the 171~\AA\ passband ($>$ 10$^{5.8}$ K), consequently lacking plasma at the typical 10$^{5.8}$~K formation temperature. The thermal deficit, not just the absence of material, is the key driver of the reduced emission. Our results demonstrate that long-duration dimming provides a valuable diagnostic for understanding active region decay, thermal evolution, and coronal magnetic restructuring.

astro-ph.SR↗

arXiv2026

Generative Visual Chain-of-Thought for Image Editing

Existing image editing methods struggle to perceive where to edit, especially under complex scenes and nuanced spatial instructions. To address this issue, we propose Generative Visual Chain-of-Thought (GVCoT), a unified framework that performs native visual reasoning by first generating spatial cues to localize the target region and then executing the edit. Unlike prior text-only CoT or tool-dependent visual CoT paradigms, GVCoT jointly optimizes visual tokens generated during the reasoning and editing phases in an end-to-end manner. This way fosters the emergence of innate spatial reasoning ability and enables more effective utilization of visual-domain cues. The main challenge of training GCVoT lies in the scarcity of large-scale editing data with precise edit region annotations; to this end, we construct GVCoT-Edit-Instruct, a dataset of 1.8M high-quality samples spanning 19 tasks. We adopt a progressive training strategy: supervised fine-tuning to build foundational localization ability in reasoning trace before final editing, followed by reinforcement learning to further improve reasoning and editing quality. Finally, we introduce SREdit-Bench, a new benchmark designed to comprehensively stress-test models under sophisticated scenes and fine-grained referring expressions. Experiments demonstrate that GVCoT consistently outperforms state-of-the-art models on SREdit-Bench and ImgEdit. We hope our GVCoT will inspire future research toward interpretable and precise image editing.

cs.CV↗

arXiv2026

Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents

Long-term conversational memory is a core capability for LLM-based dialogue systems, yet existing benchmarks and evaluation protocols primarily focus on surface-level factual recall. In realistic interactions, appropriate responses often depend on implicit constraints such as user state, goals, or values that are not explicitly queried later. To evaluate this setting, we introduce \textbf{LoCoMo-Plus}, a benchmark for assessing cognitive memory under cue--trigger semantic disconnect, where models must retain and apply latent constraints across long conversational contexts. We further show that conventional string-matching metrics and explicit task-type prompting are misaligned with such scenarios, and propose a unified evaluation framework based on constraint consistency. Experiments across diverse backbone models, retrieval-based methods, and memory systems demonstrate that cognitive memory remains challenging and reveals failures not captured by existing benchmarks. Our code and evaluation framework are publicly available at: https://github.com/xjtuleeyf/Locomo-Plus.

cs.CL↗

arXiv2026

Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning

A visual metaphor constitutes a high-order form of human creativity, employing cross-domain semantic fusion to transform abstract concepts into impactful visual rhetoric. Despite the remarkable progress of generative AI, existing models remain largely confined to pixel-level instruction alignment and surface-level appearance preservation, failing to capture the underlying abstract logic necessary for genuine metaphorical generation. To bridge this gap, we introduce the task of Visual Metaphor Transfer (VMT), which challenges models to autonomously decouple the "creative essence" from a reference image and re-materialize that abstract logic onto a user-specified target subject. We propose a cognitive-inspired, multi-agent framework that operationalizes Conceptual Blending Theory (CBT) through a novel Schema Grammar ("G"). This structured representation decouples relational invariants from specific visual entities, providing a rigorous foundation for cross-domain logic re-instantiation. Our pipeline executes VMT through a collaborative system of specialized agents: a perception agent that distills the reference into a schema, a transfer agent that maintains generic space invariance to discover apt carriers, a generation agent for high-fidelity synthesis and a hierarchical diagnostic agent that mimics a professional critic, performing closed-loop backtracking to identify and rectify errors across abstract logic, component selection, and prompt encoding. Extensive experiments and human evaluations demonstrate that our method significantly outperforms SOTA baselines in metaphor consistency, analogy appropriateness, and visual creativity, paving the way for automated high-impact creative applications in advertising and media. Project page with source code and self-contained skills is at https://yuci-gpt.github.io/Beyond-Pixels/.

cs.CV↗

arXiv2026

TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Experts

Unified image generation and editing models suffer from severe task interference in dense diffusion transformers architectures, where a shared parameter space must compromise between conflicting objectives (e.g., local editing v.s. subject-driven generation). While the sparse Mixture-of-Experts (MoE) paradigm is a promising solution, its gating networks remain task-agnostic, operating based on local features, unaware of global task intent. This task-agnostic nature prevents meaningful specialization and fails to resolve the underlying task interference. In this paper, we propose a novel framework to inject semantic intent into MoE routing. We introduce a Hierarchical Task Semantic Annotation scheme to create structured task descriptors (e.g., scope, type, preservation). We then design Predictive Alignment Regularization to align internal routing decisions with the task's high-level semantics. This regularization evolves the gating network from a task-agnostic executor to a dispatch center. Our model effectively mitigates task interference, outperforming dense baselines in fidelity and quality, and our analysis shows that experts naturally develop clear and semantically correlated specializations.

cs.CV↗