arXiv subjects

Qian Yang

Publications and source records attributed to Qian Yang.

At least 19 recordsLinked to original sources

arXiv2026

PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation

Text-to-audio-video (T2AV) generation has advanced rapidly, but its evaluation still underestimates the audio modality. Existing benchmarks either treat audio as an auxiliary component of video quality or assess it in isolation from audiovisual grounding, making it difficult to diagnose where current systems truly succeed or fail in audio generation. We present PRISM-Bench, the first audio-centric diagnostic benchmark for T2AV generation. Built from a rigorously curated dataset of 900 human-verified samples, PRISM-Bench factorizes audio evaluation along two orthogonal axes: audio type (Speech, Music, and Sound) and sound-source visibility (On-screen vs. Off-screen). It evaluates generated content across four perceptual dimensions (Audio-Visual Coherence, Audio Quality, Audio Expressiveness, and Prompt Following) with 35 fine-grained criteria. To ensure reliable assessment, we adopt an enhanced MLLM-as-a-Judge protocol based on blind, side-by-side comparison against ground-truth references, demonstrating strong alignment (over 70% mean agreement) with human raters. Our evaluation of recent T2AV systems highlights a significant performance gap between frontier and open-source models. Furthermore, we demonstrate that current generation paradigms overfit to perceptual fidelity while struggling with complex grounding and control tasks, particularly in generating music and synchronized On-screen audio.

cs.MM↗

arXiv2026

IP Protection in the Era of Visual Generative AI: A Survey

The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction, extraction, misuse, and redistribution of protected data and model assets. To address these risks, a growing body of technical defenses has been proposed. However, existing surveys typically organize this literature by lifecycle stage or technical mechanism, which can obscure the protective intent of different methods. This survey presents a two-dimensional taxonomy for IP protection in visual generative models. The primary axis is a Control Logic View, which classifies methods into Information Exposure Control, Generative Behavior Constraint, and Attribution & Accountability according to the risk variable they regulate. The secondary axis distinguishes Data IP from Model IP as cross-cutting asset dimensions. Under this framework, we systematically review protection methods, align evaluation protocols with protection objectives, and discuss open challenges including proactive model-level safeguards, standardized evaluation, robustness against adaptive attacks, and explainable evidence. This survey aims to offer a principled, systematic, and easy-to-follow overview for both new and experienced researchers in visual generative AI IP protection.

cs.CV↗

arXiv2026

An Exploratory Analysis of New Large Gaia-informed Quasar Samples in SDSS-V

Quasars are luminous objects that provide insights into the physics and evolution of supermassive black holes (SMBHs) and their accretion flows, galaxy evolution, and even cosmology. In this study, we present an exploratory study based on the ongoing fifth generation of the Sloan Digital Sky Survey (SDSS-V) and its unique dual-hemisphere, wide-field, and multi-object spectroscopic capabilities, with the aim of creating a comprehensive, all-sky quasar sample. The targets were selected through two novel methods, GUA and Skewt-QSO, that rely primarily on data from WISE and Gaia, aiming to address gaps in previous large quasar samples. Our sample includes over 250,000 spectroscopically confirmed quasars reaching z~5, with tens of thousands of newly identified quasars in the southern hemisphere. The selection methods are highly pure, with well over 80% of the spectra collected being genuine quasars; the main contaminants are M-type stars. The detailed spectral decomposition procedure we employed shows that the quasars in the sample span a wide range of luminosities (Lbol~$10^{44}-10^{48} erg s^{-1}$), SMBH masses (MBH~$10^6-10^{10}$ Msun), and accretion rates (L/LEdd~0.01-1). The distributions of these properties are consistent with those of previous quasar catalogs, which are based on past generations of SDSS, once we account for potential selection biases related to the various survey depths. Our findings confirm that novel selection methods based on optical+IR colors and/or astrometry can yield a large, high-purity quasar sample over wide sky areas, including in cases where more nuanced multi-band photometry and/or multi-wavelength data in the X-ray or radio is not available. This SDSS-V sample, which will continue to grow, establishes a robust reference for future southern (time-domain) surveys, while enhancing and complementing our understanding of quasar demographics and SMBH evolution.

astro-ph.GA↗

arXiv2026

Symmetry-aware generative design of flat-band materials beyond known crystal-net prototypes

Flat electronic bands underlie a range of strongly correlated and topological phenomena, whose design in real materials has so far relied on a small catalogue of named geometric motifs such as kagome, Lieb, and pyrochlore nets. This discrete catalogue is by no means to exhaust the geometries that support flat bands in real compounds, as band flatness is a property of network connectivity. Here we combine a continuous geometric representation of crystal sublattices, with a symmetry-constrained generative model, to access a broader design space for materials hosting flat bands. The key step is to choose sublattice motifs that are outside the known geometric clusters, ensuring the novelty of the generated structures. We then introduce SkeleGen, which pins these unconventional skeletons to symmetry-compatible Wyckoff positions while denoising the surrounding chemistry, resulting in 9,352 crystal candidates that survive stability and flatnessscreening. Band flatness is confirmed using high throughput full DFT calculations, which agree well also with the tight-binding spectra of the isolated skeletons, supporting a geometric origin of the band flatness. We demonstrate "out-of-distribution" motifs as a new design principle to dramatically expand geometric repertoire for materials discovery, potentially beyond flat bands.

cond-mat.mtrl-sci↗

arXiv2026

Exploring Reinforcement Learning for Fluid Transitions Between Clinical Mental Healthcare and Everyday Wellness Support

Mental health struggles wax and wane, yet clinical and wellness interventions typically operate separately, causing frequent breakdowns at care transitions. We explore reinforcement learning (RL) as a means to build digital health systems that deliver clinical and wellness interventions proactively, as part of a coherent care journey. We ask: what complexities does designing such a system involve? We built a contextual bandit that dynamically selects journaling prompts from clinical and wellness repertoires to optimize for an overarching health goal (sustained journaling) and deployed it in a four-week exploratory study (N=38). We found that, first, many benefits of RL-optimized intervention sequences appeared only after interventions ended, raising the question: Should systems that offer coherent clinical-wellness care journeys include stepping-back periods? If so, when and how? Second, participants most engaged with RL-generated interventions deepened their engagement over time, while those most engaged with a constant intervention tended to burn out and drop out later. It raises the question: When should a system blending clinical and wellness interventions reduce intensity to prevent burnout in versus sustain it to maximize treatment gains?

cs.HC↗

arXiv2026

How and What to Imagine? Visual Thinking in Unified Multimodal Models for Cross-View Spatial Reasoning

Cross-view spatial reasoning remains a weak spot for vision-language models (VLMs): they often reason in language and lose the fine-grained geometry needed for the task. Thinking with images aims to address this by generating an intermediate thinking image, but recent work shows that models often ignore the visual evidence in these traces. We therefore ask how to make visual thinking matter, and what kind of visual thinking works best. We study these questions in unified multimodal models (UMMs), which natively support interleaved image-text generation. For the first question, we propose View Dropout (VDrop), a training-time intervention that hides parts of one input view from the answer span while keeping them visible to the thinking-image tokens. This encourages the model to use the thinking image when answering, instead of relying only on the input views. Once the thinking image is used for answer prediction, we study which type of visual thinking is most effective. We frame this as a learnability-informativeness tradeoff and compare three thinking-image variants: top-down, panoramic, and point-matching renderings. Trained on synthetic scenes and evaluated on five real-world out-of-domain benchmarks, panoramic visual thinking with VDrop is the only configuration that is both informative and learnable, and it achieves the best out-of-domain generalization.

cs.CV↗

arXiv2026

Attention: What Prevents Young Adults from Speaking Up Against Cyberbullying in an LLM-Powered Social Media Simulation

Interactive, multi-agent social simulation systems have shown promise for helping users practice navigating various complex social situations across domains. This paper asks: To what extent can such systems help young adult (YA) bystanders speak up publicly against cyberbullying, a task often thwarted by complex, multi-party social dynamics? We created Upstanders' Practicum, a multi-AI-agent social media simulation powered by Large Language Models (LLMs), as a probe and observed 34 YAs freely practicing public bystander intervention across three iteratively refined versions. We found that practicing public bystander intervention in the simulation was helpful, but after participants made three attention shifts: (1) from inattention to paying true attention, (2) from self-focus ("I don't usually do this'') to attending to those directly involved, and (3) from resolving the private conflict between bully and victim ("maybe I could set up the meeting between them'') to addressing the broader audience online ("public comment is about norm-setting"). Only after these shifts did practice in the simulation start to help: participants then saw a reason to speak up publicly and, through continued practice, crafted tactful public messages without explicit instruction. These findings illuminate new design and research opportunities for bystander education beyond social skill instruction, namely, designing for true attention, for fostering a vocal upstander identity, and for seeing bystander intervention as public norm setting. In addition, we open-source Truman Agents (cornell-design-aigroup.github.io/TrumanAgents/), the first-of-its-kind multi-LLM-agent social media simulation platform that Upstanders' Practicum builds upon, for future cyberbullying and social media research.

cs.HC↗

arXiv2026

Architecting mechanosensitive nanofluidic transport in graphite nanoslits

Mechanosensitive ion transport plays a central role in enabling living systems to perceive and adapt to their environment through the deformation of soft, embedded ion channels. In this work, we demonstrate that ion transport within a two-dimensional graphite nanoslit can be rationally engineered to achieve a bipolar, pressure-sensitive response without any structural deformation. The mechanosensitivity arises from the selective charging of one channel inlet, which acts as a reversible source of mobile charge carriers. These excess-ions can then be advected in or out of the channel by the pressure-driven water flow, thereby modulating the ionic conductance. This mechanism is captured through a comprehensive electrohydrodynamic model that analytically accounts for coupled diffusion, convection, surface transport, diffusio-osmosis, and interfacial slippage, both inside and outside the nanoslit. The theoretical framework quantitatively reproduces the experimental data, showing that a simple surface charge pattern can give rise to complex, pressure-dependent conductance. These findings reveal how rich nonlinear couplings at the nanoscale can be harnessed to design adaptive, bioinspired nanofluidic systems, exemplified here by ionic pressure sensors.

cond-mat.soft↗

arXiv2026

Layer-by-layer water filling in molecular-scale capillaries

Under ambient humidity, water spontaneously condenses in pores only a few nanometers in size, making nanoscale capillarity central to numerous natural phenomena and technological applications. At these dimensions, water may no longer be treated as a continuous fluid, yet the consequences of molecular discreteness for capillary condensation and filling remain poorly understood. Here we study nanocapillaries fabricated by van der Waals assembly and, using atomic force microscopy, monitor their wall deformations during humidity-driven water uptake. We observe two distinct regimes: layer-by-layer filling of flexible capillaries and abrupt filling of rigid ones. Flexible walls deform in steps of ~3 {\AA}, corresponding to the sequential entry of individual water molecular layers. The different filling regimes are explained by the competition between deformation energy and oscillatory wall-water interactions. Our findings show that the molecular discreteness of water can profoundly affect ubiquitous capillary phenomena, with wall compliance selecting between discrete and abrupt filling.

cond-mat.mes-hall↗

arXiv2026

Discovering Failure Modes in Vision-Language Models using RL

Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpret straightforward visual concepts that humans identify effortlessly, such as counting, spatial reasoning, and viewpoint understanding. Previous studies manually identified these weaknesses and found that they often stem from deficits in specific skills. However, such manual efforts are costly, unscalable, and subject to human bias, which often overlooks subtle details in favour of salient objects, resulting in an incomplete understanding of a model's vulnerabilities. To address these limitations, we propose a Reinforcement Learning (RL)-based framework to automatically discover the failure modes or blind spots of any ``candidate VLM'' on a given data distribution without human intervention. Our framework trains a questioner agent that adaptively generates queries based on the candidate VLM's responses to elicit incorrect answers. Our approach increases question complexity by focusing on fine-grained visual details and distinct skill compositions as training progresses, consequently identifying novel failure modes in which VLMs struggle. We demonstrate the broad applicability of our framework by showcasing its generalizability across various model combinations.

cs.CV↗

arXiv2026

MPC-Based Trajectory Tracking for a Quadrotor UAV with Uniform Semi-Global Asymptotic Stability Guarantees

This paper proposes a model predictive trajectory tracking approach for quadrotors subject to input constraints. Our proposed approach relies on a hierarchical control strategy with an outer-loop feedback generating the required thrust and desired attitude and an inner-loop feedback regulating the actual attitude to the desired one. For the outer-loop translational dynamics, the generation of the virtual control input is formulated as a constrained model predictive control problem with time-varying input constraints and a control strategy, endowed with uniform global asymptotic stability guarantees, is proposed. For the inner-loop rotational dynamics, a hybrid geometric controller is adopted, achieving semi-global exponential tracking of the desired attitude. Finally, we prove that the overall cascaded system is semi-globally asymptotically stable. Simulation results illustrate the effectiveness of the proposed approach.

eess.SY↗

arXiv2026

PACHA: Probing AGN Coronae with High-redshift AGN

The X-ray emission of active galactic nuclei (AGN) is generally attributed to inverse Compton scattering of accretion-disk photons by hot electrons in a compact corona. In local AGN, directly constraining coronal properties is challenging because the high-energy cutoff often lies beyond the NuSTAR bandpass. High-redshift, luminous quasars enable systematic constraints on the high-energy cutoff, as cosmological redshift shifts the spectal cutoff into the observable hard X-ray band. We present first results from the ``Probing the AGN Coronae with High-redshift AGN'' (PACHA) project, based on quasi-simultaneous NuSTAR and XMM-Newton observations of 13 radio-quiet AGN at $z>1$. We constrain the high-energy cutoff and coronal temperature at 90\% confidence level for 10 and 9 sources, respectively. The sample exhibits a mean cutoff energy of $E_{\rm cut}=80.8\pm8.1$ keV and a mean coronal temperature of $kT_{\rm e}=18.4\pm1.6$ keV, both significantly lower than those measured in local {\it Swift}-BAT AGN, while the mean optical depth ($\tau=4.8\pm0.3$) is significantly higher. The uncertainties are at 1~$\sigma$. Combining our high-redshift sample with local AGN, we find a potential anti-correlation between cutoff energy and both X-ray luminosity and black hole mass, with no significant dependence on Eddington ratio. Within a hybrid coronal framework, the inferred temperatures lie well below the pair-production limits for purely thermal coronae, indicating a substantial efficient Compton cooling and/or non-thermal electron component. The detection of low coronal temperatures in high-luminosity AGN is broadly consistent with predictions from recent radiation MHD simulations that consider purely thermal electron populations, implying that non-thermal electrons may not be the primary drivers of the observed coronal properties in these systems.

astro-ph.HE↗

arXiv2026

Transient Relativistic Iron Emission Line from an X-ray Flaring Supermassive Black Hole

We report the discovery of the first transient relativistic iron K{\alpha} line in an Active Galactic Nucleus (AGN) J1047+5907. The line was detected 21.5 days (rest-frame) after an X-ray coronal flare observed in 2008 and it exhibits significant broadening consistent with relativistic reflection from the accretion disk in the vicinity of the central supermassive black hole (SMBH). The line has a width of ~300 eV, corresponding to a Keplerian velocity of 14,000 km s-1, at a distance of 5-41 light-days from the SMBH, strongly implying that the observed coronal flare triggered the emergence of the line. This event provides rare direct evidence of the response of the accretion disk to impulsive coronal illumination and offers a new method to probe the SMBH and disk physics. The relativistic modeling favors a broadened line produced by distant reflection from an accretion disk around a rapidly spinning black hole viewed at an intermediate inclination, consistent with other observations. Systematic monitoring of type 1 AGN following strong X-ray flares may open a new observational window into the innermost regions of AGN, enabling constraints on the physics of SMBH and its accretion disk at different radii that are otherwise challenging to access.

astro-ph.HE↗

arXiv2026

Framing Responsible Design of AI for Mental Well-Being: AI as Primary Care, Nutritional Supplement, or Yoga Instructor?

Millions of people now use non-clinical Large Language Model (LLM) tools like ChatGPT for mental well-being support. This paper investigates what it means to design such tools responsibly, and how to operationalize that responsibility in their design and evaluation. By interviewing experts and analyzing related regulations, we found that designing an LLM tool responsibly involves: (1) Articulating the specific benefits it guarantees and for whom. Does it guarantee specific, proven relief, like an over-the-counter drug, or offer minimal guarantees, like a nutritional supplement? (2) Specifying the LLM tool's "active ingredients" for improving well-being and whether it guarantees their effective delivery (like a primary care provider) or not (like a yoga instructor). These specifications outline an LLM tool's pertinent risks, appropriate evaluation metrics, and the respective responsibilities of LLM developers, tool designers, and users. These analogies - LLM tools as supplements, drugs, yoga instructors, and primary care providers - can scaffold further conversations about their responsible design.

cs.HC↗

arXiv2026

The Hadronization Impact on $J/\psi$ Energy Correlators: A Pythia8 Study from Partonic to Hadronic Observables

A comprehensive study of the $J/\psi$ energy correlator as a probe of non-perturbative hadronization in color-octet $c\bar{c}$ production is performed. The energy correlator measures the energy flow as a function of the angular distance ($\chi$) from the identified $J/\psi$ meson. Using the PYTHIA 8 Monte Carlo event generator, the correlator is computed at both parton and hadron levels. At high $J/\psi$ transverse momentum ($p_T > 7\ \text{GeV}/c$), the parton-level correlator in the $\cos\chi > 0$ region is dominated by soft gluon emission during the hadronization of the color-octet state, a contribution clearly distinguishable from other partonic sources, such as underlying multi-parton interactions. The transition to the hadron level, however, introduces substantial modifications, suppressing the correlator in this region by approximately an order of magnitude and underscoring the complexity of the hadronization mapping. Further analysis reveals that the hadron-level observable exhibits notable sensitivity to model parameters: increasing the mass splitting between colored $c\bar{c}$ pre-resonances and the $J/\psi$ meson from 0.2 to 0.8 GeV/$c^{2}$ enhances the correlator by up to $60\%$, while extending the color reconnection range yields a milder enhancement of about $10\%$. These findings demonstrate that precise measurements of the hadron level $J/\psi$ energy correlator, when interpreted within robust event-generator frameworks, can provide novel constraints on hadronization dynamics and help clarify the production mechanisms of $J/\psi$ state.

hep-ph↗

arXiv2025

Rapid fabrication of clean van der Waals nanochannels using Mask and Stack method

Two-dimensional (2D) nanochannels have emerged as a pivotal platform for exploring nanoscale hydrodynamics and electrokinetics. Conventional fabrication methods to make nanochannels often introduce polymer contamination and require lengthy processing, limiting device performance and scalability. Here we introduce the Mask & Stack method, employing silicon nitride stencil mask combined with dry transfer stacking to rapidly fabricate ultraclean vdW nanochannels within hours. This polymer-free approach preserves pristine interfaces, confirmed by atomic force microscopy and Raman spectroscopy, and yields nanochannel devices exhibiting reproducible ionic transport and long-term stability. The streamlined process is compatible with diverse 2D materials and promising for upscale production. Our method advances the fabrication of nanofluidic and 2D heterostructure devices, facilitating applications in quantum transport, photonics, energy harvesting, and sensing technologies requiring high-throughput, contamination-free heterostructure architectures.

cond-mat.mes-hall↗

arXiv2025

Systematic Analysis of Changing-look AGN Variability Using ZTF Light Curves

Changing-look active galactic nuclei (CLAGNs) are a unique population of AGNs that exhibit the appearance (turn-on) or disappearance (turn-off) of broad emission lines. This study aims to explore the intrinsic mechanisms of CLAGNs by investigating their photometric variability using data from the Zwicky Transient Facility (ZTF), which has provided high-cadence observations over the past five years. By visual inspections, we construct a sample of 152 CLAGNs from the literature, all of which show spectral transitions and large optical variability in their ZTF light curves. By analyzing 90 of these CLAGNs and the control samples of Type 1 AGNs, Type 2 AGNs, and extremely variable quasars (EVQs), matched in redshift ($0.2<z<0.8$) and supermassive black hole mass, we compare the color variability, structure function (SF), and variability metric $\sigma_{\mathrm{QSO}}$, which quantifies how closely the light curves resemble a damped random walk (DRW) model. We find that while CLAGNs and EVQs differ from typical Type 1 and Type 2 AGNs in bolometric luminosity and Eddington ratio, the on/off-state CLAGNs share similar variability patterns with the overall CLAGN population, and distinct from EVQ, Type 1 and Type 2 AGNs. This suggests that 'on' and 'off' CLAGNs are not simply equivalent to Type 1 and Type 2 AGNs, respectively. Instead of undergoing genuine transitions between two AGN types, CLAGNs may inhabit a critical state where moderate fluctuations in accretion rate lead to the temporary spectral changes.

astro-ph.GA↗

arXiv2025

LongCat-Flash-Omni Technical Report

We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal model with 560 billion parameters, excelling at real-time audio-visual interaction. By adopting a curriculum-inspired progressive training strategy that transitions from simpler to increasingly complex modality sequence modeling tasks, LongCat-Flash-Omni attains comprehensive multimodal capabilities while maintaining strong unimodal capability. Building upon LongCat-Flash, which adopts a high-performance Shortcut-connected Mixture-of-Experts (MoE) architecture with zero-computation experts, LongCat-Flash-Omni integrates efficient multimodal perception and speech reconstruction modules. Despite its immense size of 560B parameters (with 27B activated), LongCat-Flash-Omni achieves low-latency real-time audio-visual interaction. For training infrastructure, we developed a modality-decoupled parallelism scheme specifically designed to manage the data and model heterogeneity inherent in large-scale multimodal training. This innovative approach demonstrates exceptional efficiency by sustaining over 90% of the throughput achieved by text-only training. Extensive evaluations show that LongCat-Flash-Omni achieves state-of-the-art performance on omni-modal benchmarks among open-source models. Furthermore, it delivers highly competitive results across a wide range of modality-specific tasks, including text, image, and video understanding, as well as audio understanding and generation. We provide a comprehensive overview of the model architecture design, training procedures, and data strategies, and open-source the model to foster future research and development in the community.

cs.MM↗