arXiv subjects

Jian Wang

Publications and source records attributed to Jian Wang.

At least 163 records · Page 9Linked to original sources

arXiv2026

Observation of flat-bottom U-shaped energy gap in high-Tc nickelate (La,Pr)3Ni2O7 thin films

The discovery of high transition temperature (high-Tc) superconductivity in Ruddlesden-Popper (R-P) bilayer nickelates under high pressure has stimulated extensive work to understand the underlying mechanism and search for superconductors with higher Tc. The recent realization of superconductivity in R-P bilayer nickelate thin films with onset Tc above 40 K at ambient-pressure enables the use of a wide array of powerful experimental tools to investigate the unconventional high-Tc superconductivity in bilayer nickelates. Here, using ultra-low temperature scanning tunneling microscopy/spectroscopy (STM/S) and electrical transport study, we report the first successful observation of an energy-symmetric, flat-bottom U-shaped gap with zero residual density of states around the Fermi level in the high-Tc nickelate (La,Pr)3Ni2O7 thin film grown on SrLaAlO4 substrate. Before and after STM/S studies, transport measurements on the same sample reveal consistent superconducting behaviors showing zero resistance, with an onset Tc above 40 K and zero resistance Tc above 20 K. The tunneling spectra exhibit highly unconventional temperature evolution, characterized by a rapid filling of the U-shaped energy gap to a V-shaped gap as the temperature increases. Furthermore, the U-shaped energy gap is reduced under a c-axis magnetic field of 14 T. The energy-symmetric U-shaped gap, taken together with its dependence on magnetic field and temperature, is consistent with the behavior of a superconducting gap, suggesting a nodeless gap function at ultra-low temperatures. Our findings shed new lights on the nature of high-Tc superconductivity and provide an encouraging and thought-provoking hint for a local superconductivity with Tc above liquid nitrogen boiling temperature in nickelate superconductors at ambient or zero pressure.

cond-mat.supr-con↗

arXiv2026

Constraining the Higgs potential using multi-Higgs production

The Higgs self-couplings remain only weakly constrained by current Large Hadron Collider (LHC) measurements, leaving ample room for physics beyond the Standard Model that could modify the structure of the Higgs potential. Multi-Higgs production processes provide a particularly sensitive probe of deviations in both the Higgs trilinear and quartic self-couplings. In this note, we summarize the current status of next-to-leading-order electroweak (EW) corrections to double-Higgs production computed within the Standard Model Effective Field Theory and Higgs Effective Field Theory frameworks, emphasizing how these calculations introduce sensitivity to the Higgs self-couplings beyond what is accessible at leading order. We discuss the key conceptual and technical differences between the two effective field theory approaches, including their treatment of higher-dimensional operators, renormalization procedures, and the structure of EW~two-loop amplitudes. Despite these differences, both approaches yield broadly consistent constraints, illustrating the complementarity of double- and triple-Higgs measurements. With the high-luminosity LHC and future high-energy colliders on the horizon, these developments and further advances provide an essential foundation for extracting increasingly precise information on the dynamics of EW symmetry breaking.

hep-ph↗

arXiv2026

FFAvatar: Few-Shot, Feed-Forward, and Generalizable Avatar Reconstruction

Avatar reconstruction has traditionally relied on per-subject optimization that requires hours of computation or on expensive preprocessing that limits scalability. We introduce FFAvatar, a generalizable feed-forward framework that reconstructs high-quality, animatable 3D Gaussian head avatars from few-shot unposed portrait images in seconds. FFAvatar fuses information from multiple source images into a unified canonical Gaussian representation through Multi-View Query-Former, which is animated via FLAME parameters predicted end-to-end directly from pixels, eliminating the overhead of offline FLAME extraction. We further propose a three-stage training curriculum that achieves both broad generalization and high-fidelity reconstruction: (i) scalable pretraining on extensive monocular video data with over 1M identities to learn strong generalizable priors; (ii) multi-view fine-tuning on a small but high-quality dataset of 360-degree captures to enhance geometric fidelity and extreme-view awareness; and (iii) optional personalization that adapts to specific identities for maximum fidelity within 500 optimization steps. Extensive experiments demonstrate that FFAvatar sets a new standard for identity preservation, geometric consistency, and animation fidelity. On the NeRSemble benchmark, it outperforms the state-of-the-art LAM by a substantial 5.5 PSNR gain. Furthermore, FFAvatar enables real-time deployment, reconstructing avatars in 2 seconds without personalization and 10 seconds with personalization, while supporting 49 FPS animation on a single NVIDIA A100 GPU.

cs.GR↗

arXiv2026

Good Agentic Friends Do Not Just Give Verbal Advice: They Can Update Your Weights

Multi-agent LLM systems usually collaborate by exchanging natural-language messages. This interface is simple and interpretable, but it forces each sender's intermediate computation to be serialized into tokens and then reprocessed by the receiver, thereby increasing the generated-token cost, prefill overhead, and KV-cache memory. We study an alternative communication interface: instead of appending a sender's message to the receiver's context, compile the sender's hidden states into a transient, receiver-specific weight perturbation. We introduce TFlow (Thought Flow), a weight-space communication framework for a known and fixed receiver architecture. For each query, frozen role-prompted sender agents process the input, and a learned parameter generator maps their internal activations into low-rank LoRA perturbations targeting the receiver's modules. These perturbations are fused and applied only during the receiver's generation, enabling instance-level adaptation without permanently changing the model or enlarging the receiver's text context. With three Qwen3-4B agents, TFlow improves over a standalone receiver by up to 8.5 accuracy points across five benchmarks while reducing processed tokens by up to 32.69%. Compared with a text-based three-agent baseline, it reduces total processed tokens by up to 83.27% and the wall-clock inference time by up to 4.6$\times$, while maintaining competitive accuracy on four of five benchmarks. These results suggest that transient low-rank weight perturbations can serve as an executable communication medium for efficient multi-agent LLM collaboration.

cs.CL↗

arXiv2026

An MRI Atlas of the Human Fetal Brain: Reference and Segmentation Tools for Fetal Brain MRI Analysis

Characterizing in-utero brain development is essential for understanding typical and atypical neurodevelopment. Building on prior spatiotemporal fetal brain MRI atlases, we present the CRL-2025 fetal brain atlas, a spatiotemporal (4D) atlas of the developing fetal brain between 21 and 37 gestational weeks. This atlas is constructed from MRI scans of 159 fetuses with typically developing brains using a diffeomorphic deformable registration framework integrated with kernel regression on age. CRL-2025 uniquely includes detailed tissue segmentations, transient white matter compartments, and parcellation into 126 anatomical regions. It offers significantly enhanced anatomical details over the CRL-2017 atlas and is presented along with a re-release of the CRL diffusion MRI atlas featuring newly created tissue segmentation and labels. We release de-identified, processed subject-level fetal MRI datasets used to generate CRL-2025, providing input-output transparency and reproducibility. We also provide FetalSEG, a deep learning-based multiclass segmentation tool to facilitate automatic fetal brain MRI segmentation. The CRL-2025 atlas and its tools enable scalable fetal brain MRI segmentation, analysis, and neurodevelopmental research for the broader community.

q-bio.QM↗

arXiv2026

A product version of the Hilton-Milner Theorem II

Two families $\mathcal{F},\mathcal{G}$ of $k$-subsets of $\{1,2,\ldots,n\}$ are called {\it non-trivial cross-intersecting} if $F\cap G\neq \emptyset$ for all $F\in \mathcal{F}, G\in \mathcal{G}$ and $\cap \{F\colon F\in \mathcal{F}\}=\emptyset=\cap \{G\colon G\in\mathcal{G}\}$. In this note, we establish the product version of the Hilton-Milner Theorem for $k\geq 8$ in the full range. That is, if $\mathcal{F},\mathcal{G}\subset \binom{[n]}{k}$ are non-trivial cross-intersecting, $n\geq 2k+1$ and $k\geq 8$, then \[ |\mathcal{F}||\mathcal{G}|\leq \left(\binom{n-1}{k-1}- \binom{n-k-1}{k-1} +1\right)^2. \]

math.CO↗

arXiv2026

Intersecting families with covering number three II

A family $\mathcal{F}\subset \binom{[n]}{k}$ is called intersecting if $F\cap F'\neq \emptyset$ for all $F,F'\in \mathcal{F}$. The covering number of a family $\mathcal{F}$ is defined as the minimum size of $T\subset [n]$ such that $T\cap F\neq \emptyset$ for all $F\in \mathcal{F}$. In 1980, the first author proved that for sufficiently large $n$, any intersecting $k$-graph $\mathcal{F}$ with covering number at least three, satisfies $|\mathcal{F}|\leq \binom{n-1}{k-1}-\binom{n-k}{k-1}-\binom{n-k-1}{k-1}+\binom{n-2k}{k-1}+\binom{n-k-2}{k-3}+3$. There was very little progress during more than forty years but recently (cf. \cite{FW25}) with a completely different approach we proved the same result for the full range $n\geq 2k$ and $k\geq 7$. In this short paper we prove the same inequality for all the remaining cases.

math.CO↗

arXiv2026

WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning

Deep Research systems based on web agents have shown strong potential in solving complex information-seeking tasks, yet their search efficiency remains underexplored. We observe that many state-of-the-art open-source web agents rely on long tool-call trajectories with cyclic reasoning loops and exploration of unproductive branches. To address this, we propose WebClipper, a framework that compresses web agent trajectories via graph-based pruning. Concretely, we model the agent's search process as a state graph and cast trajectory optimization as a minimum-necessary Directed Acyclic Graph (DAG) mining problem, yielding pruned trajectories that preserve essential reasoning while eliminating redundant steps. Continued training on these refined trajectories enables the agent to evolve toward more efficient search patterns and reduces tool-call rounds by about 20% while improving accuracy. Furthermore, we introduce a new metric called F-AE Score to measure the model's overall performance in balancing accuracy and efficiency. Experiments demonstrate that WebClipper compresses tool-call rounds under excellent performance, providing practical insight into balancing effectiveness and efficiency in web agent design.

cs.AI↗

arXiv2026

RecGPT-Mobile: On-Device Large Language Models for User Intent Understanding in Taobao Feed Recommendation

Predicting a user's next search query from recent interaction behaviors is a critical problem in modern e-commerce systems, particularly in scenarios where user intent evolves rapidly. Large Language Models (LLMs) offer strong semantic reasoning capabilities and have recently been adopted to enhance training data construction for next-query prediction. However, due to resource constraints on mobile devices, existing applications are deployed on cloud servers, resulting in high inference costs. In this paper, we propose RecGPT-Mobile, a framework that designs a lightweight LLM-based intent understanding agent to improve recommendation quality in mobile e-commerce scenarios. By deploying LLMs directly on mobile devices, our approach can capture evolving interests of users more quickly and adjust the recommendation results in real time. Extensive offline analyses and online experiments demonstrate that our method significantly improves the accuracy of recommendation results, laying a practical path for LLM deployment in production-scale recommendation systems on mobile devices, as well as a scalable solution for integrating LLMs into real-world next-query prediction systems.

cs.IR↗

arXiv2026

Spin Dynamics in the van der Waals Ferromagnet CrTe2 Engineered by Niobium Doping

Understanding and controlling spin dynamics in two-dimensional (2D) van der Waals (vdW) ferromagnets is essential for their application in magnonics and hybrid quantum platforms. Here, we investigate the spin dynamics of the vdW ferromagnet 1T-CrTe_{2} and demonstrate their systematic tunability via niobium (Nb) substitution in Cr_{1-x}Nb_{x}Te_{2}(x=0-0.2). Ferromagnetic resonance (FMR) spectroscopy reveals that Nb doping enables wide-band tuning of the resonance frequency from 40 GHz down to the few-GHz regime, accompanied by a moderate increase in the Gilbert damping constant from ~0.066 to ~0.14, while preserving robust room-temperature ferromagnetism. Complementary magnetometry shows a concurrent reduction of the Curie temperature and saturation magnetization with increasing Nb content. Density functional theory calculations attribute the observed spin-dynamic trends to Nb-induced modifications of magnetic anisotropy and magnetic exchange interactions. Furthermore, CrTe_{2} flakes (~80 nm thick) exhibit lower resonance frequencies and damping than bulk crystals, consistent with thickness, surface/interface, and shape-dependent magnetic anisotropy. These results establish Nb-doped CrTe_{2} as a tunable vdW ferromagnet with controllable spin dynamics, extending its functionality from spintronics to broadband magnonics and quantum magnonics.

cond-mat.mtrl-sci↗

arXiv2026

Nonreciprocal superconducting critical currents with normal state field trainability in kagome superconductor CsV3Sb5

Determining time-reversal symmetry (TRS) and chirality in the superconducting state and its relation to normal-state symmetry and topology are important issues in condensed matter physics. Here, we report nonreciprocal superconducting critical currents (Ic) at zero magnetic field in kagome superconductor CsV3Sb5 nanodevices: Ic differs for opposite directions, indicating spontaneous TRS and inversion symmetry breakings. The polarity of Ic asymmetry changes randomly in repeated thermal cycling to 300 K, consistent with spontaneous TRS breaking. Crucially, on applying a perpendicular magnetic field above the charge density wave (CDW) transition temperature and then removing it to zero above the superconducting onset temperature (Tc), the polarity of Ic asymmetry follows the field direction, ascertaining that the CDW state has a macroscopic and trainable TRS-breaking directionality. The symmetry breaking continues into the superconducting state and generates the nonreciprocal critical currents. These results provide evidence for the loop-current CDW normal state with TRS breaking in CsV3Sb5.

cond-mat.supr-con↗

arXiv2026

Multi-TRP Assisted UAV Detection in 3GPP 5G-Advanced ISAC Network

ISAC is currently being standardized within the 3GPP New Radio (NR) to enable cellular infrastructure to perform sensing using existing communication waveforms. While standardization is progressing, practical deployment may be limited by scenario-dependent observability constraints. For example, in UMa-AV scenarios, sensing with a single TRP can be affected by restricted angular coverage, partial blockage, and limited field of view, which may degrade detection reliability in three-dimensional UAV environments. For this reason, multi-TRP solutions have been suggested to improve spatial diversity and sensing robustness. In this paper, we present a system-level investigation of multi-TRP assisted monostatic sensing for UAV detection under standardized 3GPP UMa-AV channel assumptions and Release 19 evaluation parameters. We propose a spatial diversity fusion framework and evaluate the achievable performance of a 3GPP network by combining the measurements obtained independently at different TRP. Extensive evaluations demonstrate that multi-TRP assistance improves target observability, reduces spurious detections, and tightens localization error distributions at the cost of additional sensing overhead due to the need for multiple TRPs to periodically allocate radio resources for sensing measurements. In the evaluated scenario, results show that a voting threshold of two assisting TRPs achieves an optimal trade-off between miss detection probability and false alarm suppression, meeting 3GPP performance objectives. Furthermore, we quantify the sensing overhead and show that proper system design, tuned to the application requirements, can substantially reduce its impact: for example, extending the sensing refresh interval beyond the 128 ms coherent processing interval to 1 s reduces the effective overhead from 29 % to approximately 3.7 %, enabling more scalable network deployment.

cs.IT↗

arXiv2026

Spectroscopic evidence of disorder-induced quantum phase transitions in monolayer Fe(Te,Se) superconductor

The superconductor-insulator transition as a paradigm of quantum phase transitions has attracted tremendous interest over the past three decades. While the magnetic field and carrier density can be tuned to drive the transition, the role of disorder in the transition is not well understood due to the complicated interplay between superconductivity and electron localization. In this work, we controllably introduce disorder in a two-dimensional high-temperature superconductor by depositing iron clusters onto the superconducting monolayer Fe(Te,Se) crystalline film. The spectral evolution from superconducting gaps to insulating gaps with increasing disorder is detected by scanning tunneling spectroscopy measurements. When the disorder is strong, large U-shaped gaps are observed and attributed to the localization-enhanced Cooper pair correlation. Our observations provide the insight into the emergent phases of low-dimensional and high-temperature superconductors with disorder.

cond-mat.supr-con↗

arXiv2026

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs

Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasoning capabilities to address the limitations of conventional models on complex queries. However, current LLM-based reranking paradigms are fundamentally constrained by an efficiency-accuracy trade-off: (1) pointwise methods are efficient but ignore inter-document comparison, yielding suboptimal accuracy; (2) listwise methods capture global context but suffer from context-window constraints and prohibitive inference latency. To address these issues, we propose GroupRank, a novel paradigm that balances flexibility and context awareness. To unlock the full potential of groupwise reranking, we propose an answer-free data synthesis pipeline that fuses local pointwise signals with global listwise rankings. These samples facilitate supervised fine-tuning and reinforcement learning, with the latter guided by a specialized group-ranking reward comprising ranking-utility and group-alignment. These complementary components synergistically optimize document ordering and score calibration to reflect intrinsic query-document relevance. Experimental results show GroupRank achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED, while delivering a 6.4$\times$ inference speedup.

cs.IR↗

arXiv2026

Propagation of nonlinear pulses near diffractive points of any order

We construct pulse-type approximate solutions to nonlinear hyperbolic equations near diffractive points, allowing arbitrary (even infinite) order of grazing. We show that in low regularity spaces and the high frequency limit, such solutions can be approximated by a sum of incoming and reflected pulses constructed using incoming and reflected phases and profiles that satisfy transport equations. New low-regularity estimates comparing the size of pulses to the size of their profiles are required. Earlier geometric optics results for pulses assumed much higher regularity, and considered only propagation in free space or transversal reflection at boundaries.

math.AP↗

arXiv2026

Third-order intrinsic anomalous Hall effect as a transport fingerprint of altermagnets

The intrinsic anomalous Hall effect (IAHE) provides a powerful transport fingerprint of quantum magnets, with its linear and second-order responses distinguishing ferromagnets and $\mathcal{P}\mathcal{T}$-symmetric antiferromagnets, respectively. Altermagnets, as an emergent class of quantum magnets, have recently been shown to host a third-order extrinsic anomalous Hall effect, raising a question of whether an \textit{intrinsic} counterpart can serve as a diagnostic of altermagnetic order. Based on spin-group symmetry analysis, we demonstrate that the third-order IAHE is generically allowed in the ten spin Laue groups relevant to altermagnets when spin-orbit coupling (SOC) is taken into account. By combining these symmetry constraints with the anomalous velocity induced by the second-order Berry curvature, we uncover a resonant third-order IAHE arising near the altermagnetic band crossings at generic momenta in both the Lieb-lattice altermagnet and the experimentally realized altermagnet V$_2$Se$_2$O. Notably, we identify the Berry curvature quadrupole, encoded in the second-order Berry curvature and activated by finite SOC, as the microscopic quantum geometric origin of this resonance. Our results establish the third-order IAHE as an intrinsic quantum geometric transport fingerprint of altermagnets and extend the hierarchy of IAHE across collinear quantum magnets.

cond-mat.mtrl-sci↗

arXiv2026

Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution

Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to VSR can result in redundancy, since low-quality videos already preserve substantial content information. Such redundancy leads to increased computational overhead and learning burden, as the model performs superfluous operations and must learn to filter out irrelevant information. To address this problem, we propose OASIS, an efficient $\textbf{o}$ne-step diffusion model with $\textbf{a}$ttention $\textbf{s}$pecialization for real-world v$\textbf{i}$deo $\textbf{s}$uper-resolution. OASIS incorporates an attention specialization routing that assigns attention heads to different patterns according to their intrinsic behaviors. This routing mitigates redundancy while effectively preserving pretrained knowledge, allowing diffusion models to better adapt to VSR and achieve stronger performance. Moreover, we propose a simple yet effective progressive training strategy, which starts with temporally consistent degradations and then shifts to inconsistent settings. This strategy facilitates learning under complex degradations. Extensive experiments demonstrate that OASIS achieves state-of-the-art performance on both synthetic and real-world datasets. OASIS also provides superior inference speed, offering a $\textbf{6.2$\times$}$ speedup over one-step diffusion baselines such as SeedVR2. The code will be available at \href{https://github.com/jp-guo/OASIS}{https://github.com/jp-guo/OASIS}.

cs.CV↗

arXiv2026

Sub-Nyquist Sampling for Reaching Theoretical Minimal Sampling Rate Boundary

Wideband spectrum sensing motivates sub-Nyquist sampling architectures that exploit spectral sparsity, yet in blind scenarios where subband locations are unknown, existing schemes require sampling rates at least twice the theoretical minimum. To this end, we propose a dual-frequency aliasing wideband converter (DAWC), which partitions the multiband spectrum into non-uniform frequency intervals and selectively samples only a subset of them, requiring no prior knowledge of subband locations. We demonstrate that under mild conditions on the signal and the system, DAWC achieves perfect subband localization and waveform reconstruction at the theoretical minimum rate. Moreover, we introduce an innovative side-information-aided subspace pursuit (MSSP) algorithm exploiting the common support structure inherent in the signal column submatrices for exact recovery of the spectrum support set. Based on the restricted isometry property (RIP), we provide stable recovery guarantees for MSSP in the presence of noise. Numerical simulations show that the proposed scheme achieves superior spectrum recovery accuracy compared to state-of-the-art methods.

cs.IT↗