arXiv subjects

Wenqi Xu

Publications and source records attributed to Wenqi Xu.

9 recordsLinked to original sources

arXiv2026

Evidence of Crystal-Field-Mediated Anisotropic Orbital Rashba Effect in Epitaxial Ru/FM Heterostructure

Electrical generation of orbital angular momentum provides a promising route to current-induced torques, yet effective control of orbital Rashba textures still remains challenging, particularly because the role of the interfacial crystal field remains largely unexplored. Here, we report experimental evidence for a crystal-field-mediated interfacial anisotropic orbital Rashba effect (AORE) in epitaxial Ru/ferromagnet heterostructures. Total orbital torque was disentangled into an isotropic bulk contribution and an in-plane anisotropic interfacial contribution. The latter was strongly suppressed by degrading the crystallinity of either constituent and by inserting a Cu spacer, highlighting the essential roles of coherent interfacial orbital hybridization and direct Ru/ferromagnet contact. These results identify interfacial crystal-field coherency as a key ingredient in manipulating orbital Rashba textures and establish a route toward engineering the symmetry and directionality of orbital torques.

cond-mat.mes-hall↗

arXiv2026

Effects of Impurity Scattering on Orbital Hall Conductivity and Orbital Transport in Ru-based Alloys

The role of impurity scattering in the generation and transport of orbital current remains less established than in conventional spin Hall systems. Here we investigate Ru-based nonmagnet/ferromagnet bilayers in which the impurity scattering is tuned by Cu or Ti alloying. According to SOT measurement and thickness-dependent drift-diffusion analysis, we extract the effective orbital Hall conductivity and the orbital diffusion length. We find that the orbital Hall effect in polycrystalline Ru is dominated by intrinsic mechanism that is moderately robust against weak disorder but suppressed by stronger alloy disorder. However, the orbital diffusion length remains nearly unchanged at approximately 14 nm over the investigated impurity range. This behavior indicates that orbital transport is not governed simply by an impurity scattering. Together with previous temperature-dependent measurements, our results show that static impurities and dynamic lattice disorder affect orbital transport through distinct microscopic channels. These results provide new insight into how disorder governs orbital generation and transport, and offer experimental guidance for developing high-efficient orbitronic materials.

cond-mat.mes-hall↗

arXiv2026

AdaSR: Adaptive Streaming Reasoning with Hierarchical Relative Policy Optimization

Large reasoning models typically follow a read-then-think paradigm: they observe the complete input, reason over a static context, and then produce the answer. Yet many real-world scenarios are inherently dynamic, such as audio and video stream, where information arrives as a continuous stream and models must reason, update, and respond under partial observations. Recent streaming reasoning methods allow models to think while reading, but they largely rely on supervised imitation of pre-constructed trajectories, which limits their flexibility. In this paper, we propose AdaSR, an adaptive streaming reasoning framework that enables models to reason during input streaming and perform final deliberation once the stream is complete, learning when to think, and how much computation to allocate across different stages. To optimize this hierarchical reasoning process, we introduce Hierarchical Relative Policy Optimization (HRPO), which decomposes policy optimization into streaming reasoning and deep reasoning phases, providing more fine-grained advantage assignment instead of uniformly distributing a single sequence-level advantage over all tokens. HRPO integrates format, accuracy, and adaptive thinking rewards to enforce valid reasoning protocols, preserve final task performance, and encourage latency-aware computation allocation. Experiments show that AdaSR achieves a better balance among reasoning accuracy, computational efficiency, and streaming latency compared with supervised fine-tuning baseline. We release our code at https://github.com/EIT-NLP/StreamingLLM/tree/main/AdaSR.

cs.CL↗

arXiv2026

Absence of Orbital Hall Magnetoresistance in Nonmagnet/Ferromagnet Bilayers with Large Orbital Torque

We report the absence of orbital Hall magnetoresistance (OMR) in nonmagnet/ferromagnet bilayers, challenging the general assumption that orbital transport mimics spin transport. Despite the observation of giant orbital torques, confirming the generation of orbital currents, thickness-dependent magnetoresistance measurements reveal that the signal is dominated by the intrinsic magnetoresistance of the ferromagnet and current shunting, with no discernible OMR contribution. We attribute this contradiction to the distinct transport properties of orbital compared with spin. Orbital currents undergo isotropic bulk absorption in the ferromagnet rather than anisotropic interfacial reflection required for OMR. Furthermore, we find that texture-induced magnetoresistance and self-torques in Ni-based bilayers can generate misleading signals, suggesting that caution is required when employing Ni in orbitronic studies. These findings clarify the distinct physical rules governing orbital transport and provide a simple method to distinguish spin and orbital currents.

cond-mat.mes-hall↗

arXiv2026

ConFoThinking: Consolidated Focused Attention Driven Thinking for Visual Question Answering

Thinking with Images improves fine-grained VQA for MLLMs by emphasizing visual cues. However, tool-augmented methods depend on the capacity of grounding, which remains unreliable for MLLMs. In parallel, attention-driven methods to crop the Region of Interest (ROIs) are proposed but they are constrained by (1) fragmented attention signals scattered across layers, leading to suboptimal localization and (2) relying on question- or redundant-text-conditioned attention extraction. Our analysis reveals three patterns: MLLMs may attend to the correct region yet generate incorrect coordinates, where-to-look attention is often fragmented across layers, and attention extraction is query-sensitive. Motivated by these, We propose ConFoThinking, a Consolidated-Focused-Attention-Driven Thinking framework that learns to aggregate attention into a designated intermediate layer, from which we mine and zoom in salient regions for downstream visual understanding. Moreover, we extract attention using concise semantic cues of what to look into, which mitigates the semantic noise introduced by question- or redundant-text-based attention extraction. Experiments across five VQA benchmarks demonstrate ConFoThinking significantly improves perception performance. The code, checkpoints, and dataset will be released after being accepted.

cs.CV↗

arXiv2025

STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions

Recent adaptations of Large Language Models (LLMs) for time series forecasting often fail to effectively enhance information for raw series, leaving LLM reasoning capabilities underutilized. Existing prompting strategies rely on static correlations rather than generative interpretations of dynamic behavior, lacking critical global and instance-specific context. To address this, we propose STELLA (Semantic-Temporal Alignment with Language Abstractions), a framework that systematically mines and injects structured supplementary and complementary information. STELLA employs a dynamic semantic abstraction mechanism that decouples input series into trend, seasonality, and residual components. It then translates intrinsic behavioral features of these components into Hierarchical Semantic Anchors: a Corpus-level Semantic Prior (CSP) for global context and a Fine-grained Behavioral Prompt (FBP) for instance-level patterns. Using these anchors as prefix-prompts, STELLA guides the LLM to model intrinsic dynamics. Experiments on eight benchmark datasets demonstrate that STELLA outperforms state-of-the-art methods in long- and short-term forecasting, showing superior generalization in zero-shot and few-shot settings. Ablation studies further validate the effectiveness of our dynamically generated semantic anchors.

cs.AI↗

arXiv2025

Uni-PrevPredMap: Extending PrevPredMap to a Unified Framework of Prior-Informed Modeling for Online Vectorized HD Map Construction

Safety constitutes a foundational imperative for autonomous driving systems, necessitating maximal incorporation of accessible prior information. This study establishes that temporal perception buffers and cost-efficient high-definition (HD) maps inherently form complementary prior sources for online vectorized HD map construction. We present Uni-PrevPredMap, a pioneering unified framework systematically integrating previous predictions with corrupted HD maps. Our framework introduces a tri-mode paradigm maintaining operational consistency across non-prior, temporal-prior, and temporal-map-fusion modes. This tri-mode paradigm simultaneously decouples the framework from ideal map assumptions while ensuring robust performance in both map-present and map-absent scenarios. Additionally, we develop a tile-indexed 3D vectorized global map processor enabling efficient 3D prior data refreshment, compact storage, and real-time retrieval. Uni-PrevPredMap achieves state-of-the-art map-absent performance across established online vectorized HD map construction benchmarks. When provided with corrupted HD maps, it exhibits robust capabilities in error-resilient prior fusion, empirically confirming the synergistic complementarity between temporal predictions and imperfect map data. Code is available at https://github.com/pnnnnnnn/Uni-PrevPredMap.

cs.CV↗

arXiv2024

All-Optical Manipulation of Band Gap Dynamics via Electron-Phonon Coupling

The electron-phonon coupling (EPC) is a ubiquitous interaction in condensed systems and plays a vital role in shaping the electronic properties of materials. Yet, achieving coherent manipulation of electron-phonon coupling has posed a considerable challenge. Here, employing time-resolved high-harmonic generation (tr-HHG) spectroscopy, we demonstrate the coherent manipulation of bandgap dynamics in a BaF2 crystal by precisely controlling the EPC using ultrashort light pulses. The tr-HHG spectrum perturbed by a triply degenerate phonon mode T2g, exhibits simultaneously a remarkable two-dimensional (2D) sensitivity, namely intensity domain in addition to the previously reported energy domain. The dynamic compression and enhancement of the harmonics in the intensity domain showed a π/2 phase shift compared to the manifestation of shifts of the harmonics in the energy domain, an astounding example of a physical phenomenon being observed simultaneously in two different perspectives. To complement our experimental observations, we employed a quantum model that incorporates the EPC, successfully reproducing the results. In addition, we demonstrated complete control over the EPC strength and initial phase of the coherent phonon oscillations by varying the incident electric field polarization over crystal orientation. Our findings lay a foundation for future investigations aiming to harness and exploit the remarkable potential of EPC in solid-state systems.

cond-mat.mtrl-sci↗

arXiv2023

Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline

Although deep learning has significantly improved Face Recognition (FR), dramatic performance deterioration may occur when processing Low Resolution (LR) faces. To alleviate this, approaches based on unified feature space are proposed with the sacrifice under High Resolution (HR) circumstances. To deal with the huge domain gap between HR and LR domains and achieve the best on both domains, we first took a closer look at the impacts of several resolution augmentations and then analyzed the difficulty of LR samples from the perspective of the model gradient produced by different resolution samples. Besides, we also find that the introduction of some resolutions could help the learning of lower resolutions. Based on these, we divide the LR samples into three difficulties according to the resolution and propose a more effective Multi-Resolution Augmentation. Then, due to the rapidly increasing domain gap as the resolution decreases, we carefully design a novel and effective metric loss based on a LogExp distance function that provides decent gradients to prevent oscillation near the convergence point or tolerance to small distance errors; it could also dynamically adjust the penalty for errors in different dimensions, allowing for more optimization of dimensions with large errors. Combining these two insights, our model could learn more general knowledge in a wide resolution range of images and balanced results can be achieved by our extremely simple framework. Moreover, the augmentations and metrics are the cornerstones of LRFR, so our method could be considered a new baseline for the LRFR task. Experiments on the LRFR datasets: SCface, XQLFW, and large-scale LRFR dataset: TinyFace demonstrate the effectiveness of our methods, while the degradation on HRFR datasets is significantly reduced.

cs.CV↗