arXiv subjects

Qianying Wang

Publications and source records attributed to Qianying Wang.

At least 19 recordsLinked to original sources

arXiv2026

EgoIntrospect: An Egocentric Dataset and Benchmark for User-Centric Internal State Reasoning

Despite extensive efforts on egocentric video datasets and benchmarks, understanding users' internal states, which is crucial for enabling seamless AI assistant experiences, remains largely overlooked. In this work, we introduce EgoIntrospect, the first egocentric dataset captured in user-driven scenarios with self-annotations that explicitly reveal users' interactive intentions with AI assistants. EgoIntrospect was collected using a cross-device setup, providing synchronized video, audio, gaze, motion, and physiological signals. It consists of 180 hours of recordings from 60 subjects, with an average recording duration of 3 hours per subject. Leveraging EgoIntrospect, we formalize a suite of tasks centered on user internal states, including affective experience, interactive intent, and cognitive memory. We further process the annotations to construct benchmarks that evaluate the ability of modern multimodal large language models to reason about users' internal states from egocentric observations. Experiments on our benchmark suggest that existing multimodal large language models struggle to effectively leverage multimodal signals to infer users' subjective internal states. The dataset and annotations will be made publicly available to advance research in egocentric vision and wearable AI assistants. Project page: https://ego-introspect.github.io/

cs.CV↗

arXiv2026

OmniHands: Towards Robust 4D Hand Mesh Recovery via A Versatile Transformer

In this paper, we introduce OmniHands, a universal approach to recovering interactive hand meshes and their relative movement from monocular or multi-view inputs. Our approach addresses two major limitations of previous methods: lacking a unified solution for handling various hand image inputs and neglecting the positional relationship of two hands within images. To overcome these challenges, we develop a universal architecture with novel tokenization and contextual feature fusion strategies, capable of adapting to a variety of tasks. Specifically, we propose a Relation-aware Two-Hand Tokenization (RAT) method to embed positional relation information into the hand tokens. In this way, our network can handle both single-hand and two-hand inputs and explicitly leverage relative hand positions, facilitating the reconstruction of intricate hand interactions in real-world scenarios. As such tokenization indicates the relative relationship of two hands, it also supports more effective feature fusion. To this end, we further develop a 4D Interaction Reasoning (FIR) module to fuse hand tokens in 4D with attention and decode them into 3D hand meshes and relative temporal movements. The efficacy of our approach is validated on several benchmark datasets. The results on in-the-wild videos and real-world scenarios demonstrate the superior performances of our approach for interactive hand reconstruction. More video results can be found on the project page: https://OmniHand.github.io.

cs.CV↗

arXiv2026

IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inference

Large language models (LLMs) have become integral to modern Human-AI collaboration workflows, where accurately understanding user intent serves as a crucial step for generating satisfactory responses. Context-aware intent understanding, which involves inferring user intentions from situational environments, is inherently challenging because it requires reasoning over both the immediate context and the user's underlying motivations that drive their behavior. Moreover, existing approaches often treat intent understanding as a static recognition task, overlooking users' accumulated intent patterns that could provide valuable references for more accurate and generalizable understanding. To address this gap, we propose IntPro, a proxy agent that learns to adapt to individual users via retrieval-conditioned intent inference. We design intent explanations that abstract how contextual signals connect to expressed intents, and store them in an individual intent history library for retrieval. We train IntPro through supervised fine-tuning on retrieval-conditioned trajectories and multi-turn Group Relative Policy Optimization (GRPO) with tool-aware reward functions, enabling the agent to learn when to leverage historical intent patterns and when to infer directly. Experiments across three diverse scenarios (Highlight-Intent, MIntRec2.0, and Weibo Post-Sync) demonstrate that IntPro achieves strong intent understanding performance with effective context-aware reasoning capabilities across different scenarios and model types.

cs.CL↗

arXiv2026

SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking

Social cues, which convey others' presence, behaviors, or identities, play a crucial role in human information seeking by helping individuals judge relevance and trustworthiness. However, existing LLM-based search systems primarily rely on semantic features, creating a misalignment with the socialized cognition underlying natural information seeking. To address this gap, we explore how the integration of social cues into LLM-based search influences users' perceptions, experiences, and behaviors. Focusing on social media platforms that are beginning to adopt LLM-based search, we integrate design workshops, the implementation of the prototype system (SoulSeek), a between-subjects study, and mixed-method analyses to examine both outcome- and process-level findings. The workshop informs the prototype's cue-integrated design. The study shows that social cues improve perceived outcomes and experiences, promote reflective information behaviors, and reveal limits of current LLM-based search. We propose design implications emphasizing better social-knowledge understanding, personalized cue settings, and controllable interactions.

cs.HC↗

arXiv2025

Observation of square-like moire lattice and quasicrystalline order in twisted rock-salt nitrides

Twistronics, which exploits moire modulation of lattice and electronic structures in twisted bilayers, has emerged as a powerful approach to engineer novel quantum states. Recent efforts have expanded beyond two dimensional van der Waals (vdWs) crystals to more complex, strongly correlated materials, where interfacial moire effects can dominate physical properties. Here we demonstrate a generalizable route to fabricate twisted bilayers of transition metal nitrides with vdWs like interfaces, using freestanding CrN membranes as a model system. Twisted bilayer CrN (tCrN) is realized by employing cubic alkaline earth metal monoxides as sacrificial layers, enabling the assembly of clean, controllable interfaces. Electron ptychography reveals well defined, periodic square moire superlattices in tCrN. For a twist angle of 16.3 degree, we identify a nearly commensurate moire lattice with coincident Cr columns, whereas at 45 degree we uncover localized octagonal quasicrystalline order with clear self-similarity. These results establish a practical platform for twisted TMNs and open avenues to explore moire-induced atomic configurations and emergent correlated phenomena in nitride based heterostructures.

cond-mat.mtrl-sci↗

arXiv2025

Unusual strain relaxation and Dirac semimetallic behavior in epitaxial antiperovskite nitrides

Antiperovskite nitrides (X3AN) are the structural analogues to perovskite oxides, while their epitaxial growth and electronic properties remain largely unexplored. We report the successful synthesis of Ni3InN thin films on substrates with different lattice constants. First-principles phonon calculations confirm the dynamical stability of cubic phase Ni3InN, providing the basis for epitaxial synthesis. High-resolution scanning transmission electron microscopy reveals coherent (001)-oriented interfaces when Ni3InN is grown on LaAlO3 and SrTiO3, while an unexpected (011)-orientation forms on DyScO3, aligning with surface-energy predictions. Transport measurements highlight a strain-controlled Fermi-liquid behavior, correlated with variations in the Ni-3d bandwidth and hybridization. Band structure calculations reveal a dual character near the Fermi level: a high-mobility Dirac-like band and a Ni-3d manifold that drives strange-metal transport with a reduced slope compared to oxide perovskites. The formal Ni valence (+2/3) places Ni3InN in an overdoped correlated-metal regime, distinguishing from most perovskite oxides. This positions antiperovskite nitrides as a promising platform for investigating overdoped Fermi liquids and strange-metal behavior.

cond-mat.mtrl-sci↗

arXiv2025

Material Synthesis 2025 (MatSyn25) Dataset for 2D Materials

Two-dimensional (2D) materials have shown broad application prospects in fields such as energy, environment, and aerospace owing to their unique electrical, mechanical, thermal and other properties. With the development of artificial intelligence (AI), the discovery and design of novel 2D materials have been significantly accelerated. However, due to the lack of basic theories of material synthesis, identifying reliable synthesis processes for theoretically designed materials is a challenge. The emergence of large language model offers new approaches for the reliability prediction of material synthesis processes. However, its development is limited by the lack of publicly available datasets of material synthesis processes. To address this, we present the Material Synthesis 2025 (MatSyn25), a large-scale open dataset of 2D material synthesis processes. MatSyn25 contains 163,240 pieces of synthesis process information extracted from 85,160 high-quality research articles, each including basic material information and detailed synthesis process steps. Based on MatSyn25, we developed MatSyn AI which specializes in material synthesis, and provided an interactive web platform that enables multifaceted exploration of the dataset (https://matsynai.stpaper.cn/). MatSyn25 is publicly available, allowing the research community to build upon our work and further advance AI-assisted materials science.

cond-mat.mtrl-sci↗

arXiv2025

Advancing Antiferromagnetic Nitrides via Metal Alloy Nitridation

Nitride materials, valued for their structural stability and exceptional physical properties, have garnered significant interest in both fundamental research and technological applications. The fabrication of high-quality nitride thin films is essential for advancing their use in microelectronics and spintronics. Yet, achieving single-crystal nitride thin films with excellent structural integrity remains a challenge. Here, we introduce a straightforward yet innovative metallic alloy nitridation technique for the synthesis of stable single-crystal nitride thin films. By subjecting metal alloy thin films to a controlled nitridation process, nitrogen atoms integrate into the lattice, driving structural transformations while preserving high epitaxial quality. Combining nanoscale magnetic imaging with a diamond nitrogen-vacancy (NV) probe, X-ray magnetic linear dichroism, and comprehensive transport measurements, we confirm that the nitridated films exhibit a robust antiferromagnetic character with a zero net magnetic moment. This work not only provides a refined and reproducible strategy for the fabrication of nitride thin films but also lays a robust foundation for exploring their burgeoning device applications.

cond-mat.mtrl-sci↗

arXiv2025

A single-phase epitaxially grown ferroelectric perovskite nitride

The integration of ferroelectrics with semiconductors is crucial for developing functional devices, such as field-effect transistors, tunnel junctions, and nonvolatile memories. However, the synthesis of high-quality single-crystalline ferroelectric nitride perovskites has been limited, hindering a comprehensive understanding of their switching dynamics and potential applications. Here we report the synthesis and characterizations of epitaxial single-phase ferroelectric cerium tantalum nitride (CeTaN3) on both oxides and semiconductors. The polar symmetry of CeTaN3 was confirmed by observing the atomic displacement of central ions relative to the center of the TaN6 octahedra, as well as through optical second harmonic generation. We observed switchable ferroelectric domains in CeTaN3 films using piezo-response force microscopy, complemented by the characterization of square-like polarization-electric field hysteresis loops. The remanent polarization of CeTaN3 reaches approximately 20 uC/cm2 at room temperature, consistent with theoretical calculations. This work establishes a vital link between ferroelectric nitride perovskites and their practical applications, paving the way for next-generation information and energy-storage devices with enhanced performance, scalability, and manufacturability.

cond-mat.mtrl-sci↗

arXiv2025

Designing Optimal Distorted-Octahedra Superlattices for Strong Topological Hall Effect

Topologically protected spin states hold great promise for applications in next generation of memory circuits and spintronic devices. These intriguing textures typically emerge in bulk materials or heterostructures with broken inversion symmetry, accompanied by an enhanced Dzyaloshinskii-Moriya interaction (DMI). In this study, we successfully induced the topological Hall effect (THE) in atomically designed (DyScO3)n/(SrRuO3)n (DnSn) superlattices over a significant range of temperatures (10~120K) and thicknesses (16~40nm). Using magnetic force microscopy (MFM), we observed the formation and stability of magnetic domains, such as topological skyrmions. By precisely controlling the interlayer thickness (n) and biaxial strain, we elucidated the mechanisms underlying the modulation and induction of magnetic topological states. Supporting evidence was provided by scanning transmission electron microscopy (STEM) and X-ray absorption spectroscopy (XAS), thereby lending further credence to our conclusions. These heterostructures offer a universal method for exploring topological phenomena driven by distorted octahedra, while enhancing the integrability and addressability of topologically protected functional devices.

cond-mat.mtrl-sci↗

arXiv2025

Deteriorated Interlayer Coupling in Twisted Bilayer Cobaltites

A wealth of remarkable behaviors is observed at the interfaces between magnetic oxides due to the coexistence of Coulomb repulsion and interatomic exchange interactions. While previous research has focused on bonded oxide heterointerfaces, studies on magnetism in van der Waals interfaces remain rare. In this study, we stacked two freestanding cobaltites with precisely controlled twist angles. Scanning transmission electron microscopy revealed clear and ordered moiré patterns, which exhibit an inverse relationship with the twist angle. We found that the Curie temperature in the twisted region is reduced by approximately 13 K compared to the single-layer region using nitrogen-vacancy (NV) magnetometry. This phenomenon may be related to the weakening of the orbital hybridization between oxygen ions and transition metal ions in the unbonded interfaces. Our findings suggest a potential avenue for modulating magnetic interactions in correlated systems through twist, providing opportunities for the discovery of unknown quantum states.

cond-mat.mtrl-sci↗

arXiv2025

Charge transfer induced insulating state at antiperovskite/perovskite heterointerfaces

Heterointerfaces have been pivotal in unveiling extraordinary interfacial properties and enabling multifunctional material platforms. Despite extensive research on all-oxide interfaces, heterointerfaces between different material classes, such as oxides and nitrides, remain underexplored. Here we present the fabrication of a high-quality Dirac metal antiperovskite Ni3InN, characterized by an extremely low temperature coefficient of resistivity, approximately 1.8*10^-8 Ω*cm/K, over a broad temperature range. Atomically sharp heterointerfaces between Ni3InN and SrVO3 were constructed, revealing intriguing interfacial phenomena. Leveraging layer-resolved scanning transmission electron microscopy and electron energy loss spectroscopy, we identified pronounced charge transfer across the well-ordered interface. Remarkably, this interfacial electron transfer from Ni3InN to SrVO3 induces an insulating interfacial layer and an emergent magnetic moment within the Ni3InN layer, consistent with first-principles calculations. These findings pave the way for novel electronic and spintronic applications by enabling tunable interfacial properties in nitride/oxide systems.

cond-mat.mtrl-sci↗

arXiv2024

Unleashing the Potential of Model Bias for Generalized Category Discovery

Generalized Category Discovery is a significant and complex task that aims to identify both known and undefined novel categories from a set of unlabeled data, leveraging another labeled dataset containing only known categories. The primary challenges stem from model bias induced by pre-training on only known categories and the lack of precise supervision for novel ones, leading to category bias towards known categories and category confusion among different novel categories, which hinders models' ability to identify novel categories effectively. To address these challenges, we propose a novel framework named Self-Debiasing Calibration (SDC). Unlike prior methods that regard model bias towards known categories as an obstacle to novel category identification, SDC provides a novel insight into unleashing the potential of the bias to facilitate novel category learning. Specifically, the output of the biased model serves two key purposes. First, it provides an accurate modeling of category bias, which can be utilized to measure the degree of bias and debias the output of the current training model. Second, it offers valuable insights for distinguishing different novel categories by transferring knowledge between similar categories. Based on these insights, SDC dynamically adjusts the output logits of the current training model using the output of the biased model. This approach produces less biased logits to effectively address the issue of category bias towards known categories, and generates more accurate pseudo labels for unlabeled data, thereby mitigating category confusion for novel categories. Experiments on three benchmark datasets show that SDC outperforms SOTA methods, especially in the identification of novel categories. Our code and data are available at \url{https://github.com/Lackel/SDC}.

cs.LG↗

arXiv2024

Confined Magnetization at the Sublattice-Matched Ruthenium Oxide Heterointerface

Creating a heterostructure by combining two magnetically and structurally distinct ruthenium oxides is a crucial approach for investigating their emergent magnetic states and interactions. Previously, research has predominantly concentrated on the intrinsic properties of the ferromagnet SrRuO3 and recently discovered altermagnet RuO2 solely. Here, we engineered an ultrasharp sublattice-matched heterointerface using pseudo-cubic SrRuO3 and rutile RuO2, conducting an in-depth analysis of their spin interactions. Structurally, to accommodate the lattice symmetry mismatch, the inverted RuO2 layer undergoes an in-plane rotation of 18 degrees during epitaxial growth on SrRuO3 layer, resulting in an interesting and rotational interface with perfect crystallinity and negligible chemical intermixing. Performance-wise, the interfacial layer of 6 nm in RuO2 adjacent to SrRuO3 exhibits a nonzero magnetic moment, contributing to an enhanced anomalous Hall effect (AHE) at low temperatures. Furthermore, our observations indicate that, in contrast to SrRuO3 single layers, the AHE of [(RuO2)15/(SrRuO3)n] heterostructures shows nonlinear behavior and reaches its maximum when the SrRuO3 thickness reaches tens of nm. These results suggest that the interfacial magnetic interaction surpasses that of all-perovskite oxides (~5-unit cells). This study underscores the significance and potential applications of magnetic interactions based on the crystallographic asymmetric interfaces in the design of spintronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing

Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires inverting the source image into a latent space, a process often hindered by prediction errors inherent in DDIM inversion. These errors accumulate during the diffusion process, resulting in inferior content preservation and edit fidelity, especially with conditional inputs. We address these challenges by investigating the primary contributors to error accumulation in DDIM inversion and identify the singularity problem in traditional noise schedules as a key issue. To resolve this, we introduce the Logistic Schedule, a novel noise schedule designed to eliminate singularities, improve inversion stability, and provide a better noise space for image editing. This schedule reduces noise prediction errors, enabling more faithful editing that preserves the original content of the source image. Our approach requires no additional retraining and is compatible with various existing editing methods. Experiments across eight editing tasks demonstrate the Logistic Schedule's superior performance in content preservation and edit fidelity compared to traditional noise schedules, highlighting its adaptability and effectiveness.

cs.CV↗

arXiv2024

Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery

Recent advancements have shown promise in applying traditional Semi-Supervised Learning strategies to the task of Generalized Category Discovery (GCD). Typically, this involves a teacher-student framework in which the teacher imparts knowledge to the student to classify categories, even in the absence of explicit labels. Nevertheless, GCD presents unique challenges, particularly the absence of priors for new classes, which can lead to the teacher's misguidance and unsynchronized learning with the student, culminating in suboptimal outcomes. In our work, we delve into why traditional teacher-student designs falter in open-world generalized category discovery as compared to their success in closed-world semi-supervised learning. We identify inconsistent pattern learning across attention layers as the crux of this issue and introduce FlipClass, a method that dynamically updates the teacher to align with the student's attention, instead of maintaining a static teacher reference. Our teacher-student attention alignment strategy refines the teacher's focus based on student feedback from an energy perspective, promoting consistent pattern recognition and synchronized learning across old and new classes. Extensive experiments on a spectrum of benchmarks affirm that FlipClass significantly surpasses contemporary GCD methods, establishing new standards for the field.

cs.CV↗

arXiv2024

DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation

While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editing": precisely modifying specific features of a subject while maintaining its inherent identity and context. Existing personalization methods either require time-consuming optimization or learning additional encoders, adept in "identity re-contextualization". However, they often struggle with detailed and sensitive tasks like human face editing. To address these challenges, we introduce DreamSalon, a noise-guided, staged-editing framework, uniquely focusing on detailed image manipulations and identity-context preservation. By discerning editing and boosting stages via the frequency and gradient of predicted noises, DreamSalon first performs detailed manipulations on specific features in the editing stage, guided by high-frequency information, and then employs stochastic denoising in the boosting stage to improve image quality. For more precise editing, DreamSalon semantically mixes source and target textual prompts, guided by differences in their embedding covariances, to direct the model's focus on specific manipulation areas. Our experiments demonstrate DreamSalon's ability to efficiently and faithfully edit fine details on human faces, outperforming existing methods both qualitatively and quantitatively.

cs.CV↗

arXiv2023

Transfer and Alignment Network for Generalized Category Discovery

Generalized Category Discovery is a crucial real-world task. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute the poor performance to two reasons: biased knowledge transfer between labeled and unlabeled data and noisy representation learning on the unlabeled data. To mitigate these two issues, we propose a Transfer and Alignment Network (TAN), which incorporates two knowledge transfer mechanisms to calibrate the biased knowledge and two feature alignment mechanisms to learn discriminative features. Specifically, we model different categories with prototypes and transfer the prototypes in labeled data to correct model bias towards known categories. On the one hand, we pull instances with known categories in unlabeled data closer to these prototypes to form more compact clusters and avoid boundary overlap between known and novel categories. On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later. After knowledge transfer, we further propose two feature alignment mechanisms to acquire both instance- and category-level knowledge from unlabeled data by aligning instance features with both augmented features and the calibrated prototypes, which can boost model performance on both known and novel categories with less noise. Experiments on three benchmark datasets show that our model outperforms SOTA methods, especially on novel categories. Theoretical analysis is provided for an in-depth understanding of our model in general. Our code and data are available at https://github.com/Lackel/TAN.

cs.CL↗