arXiv subjects

Qian Wang

Publications and source records attributed to Qian Wang.

At least 55 records · Page 3Linked to original sources

arXiv2026

InstructVVT: Instruction-Driven Video Virtual Try-On without Auxiliary Spatial Priors

Video virtual try-on is a highly constrained editing task requiring the precise replacement of a target person's clothing while strictly preserving the original video's spatial structure and temporal dynamics. Existing methods heavily rely on auxiliary handcrafted spatial priors (e.g., masks, poses) for editing control. However, these priors are prone to failure in unconstrained real-world videos and often compress rich visual context into incomplete structural signals. Furthermore, standard reconstruction objectives fail to fully capture try-on-specific human preferences. To address these challenges, we propose InstructVVT, an instruction-driven and reference-guided video virtual try-on framework based on a Diffusion Transformer (DiT) that operates without inference-time spatial priors. Our core insight is to recover fine-grained control directly from the input triplet (source video, reference garment, and instruction) via a dual-level reference conditioning scheme. Specifically, an MLLM infers semantic edit tokens for target disambiguation and structural preservation, while a lightweight conditioning pathway explicitly injects fine-grained visual garment details. Finally, we design a try-on-specific reward and utilize the DiffusionNFT algorithm to align the model with human preferences. Extensive experiments on ViViD-S and TripVVT-Bench demonstrate that InstructVVT outperforms state-of-the-art open-source methods in garment fidelity, structural preservation, and temporal consistency, despite requiring fewer inference-time controls.

cs.CV↗

arXiv2026

Concept Unlearning by Modeling Key Steps of Diffusion Process

Text-to-image diffusion models remain susceptible to generating undesirable or harmful content. Although concept unlearning mitigates this risk, existing methods struggle with a critical optimization dilemma: thorough semantic erasure frequently induces the catastrophic forgetting of unrelated generative capabilities. To overcome this challenge, we propose Key Step Concept Unlearning (KSCU). Serving as an integrated methodological refinement deeply motivated by information theory, KSCU explores the profound impact of step scheduling order and reveals that traditional randomized timestep sampling severely disrupts trajectory dependency. We demonstrate that indiscriminately targeting the entire diffusion process is inefficient, as the optimal step range for unlearning inherently varies across different concepts. Rather than globally fine-tuning all timesteps, KSCU explicitly integrates a sequential-scheduling-based Key Step Table, CFG-aware leakage compensation, and prompt augmentation to dynamically isolate optimization to a concept-specific active region. This localized strategy successfully eradicates the target concept while preventing the structural collapse caused by early-step over-optimization. Consequently, KSCU significantly reduces computational overhead and establishes a state-of-the-art trade-off between concept erasure and utility retention. Comprehensive evaluations demonstrate that KSCU consistently delivers superior performance across diverse unlearning tasks, including nudity, style, object classes, and mass instance concepts. For example, in nudity removal, KSCU yields a 96.5% unlearning accuracy alongside a state-of-the-art FID of 14.1.

cs.CV↗

arXiv2026

KOOPMAN-Luenberger Observer Design for Nonlinear Systems with Application to the Monitoring of a Latent Thermal Energy Storage

State estimation for nonlinear dynamical systems remains a fundamental challenge, particularly when measurements are sparse and internal states are inaccessible. This work presents a KOOPMAN-based Linear State Observer (KOOPMAN-LSO) design framework that enables linear observer synthesis for nonlinear systems through KOOPMAN operator theory. The nonlinear dynamics are lifted into a higher-dimensional observable space using physics-informed basis functions, where a linear predictor with control is identified via extended dynamic mode decomposition with control (eDMDc). A discrete-time Luenberger observer is then constructed in the lifted space, and the observer gain is obtained through a dual linear - quadratic regulator (LQR) formulation to ensure stable and tunable estimation error dynamics. The proposed framework combines the representational capability of KOOPMAN lifting with the simplicity and computational efficiency of linear observer design, providing a systematic approach for nonlinear state estimation under limited sensing. Its effectiveness is demonstrated on a latent thermal energy storage (LTES) system based on phase-change materials (PCM), where internal temperature states are not directly measurable. Experimental results under varying operating conditions show accurate reconstruction of unmeasured states from limited output measurements, illustrating the potential of KOOPMAN-LSO design for practical nonlinear systems. The proposed approach achieves high-fidelity reconstruction with an RMSE as low as 0.0819 °C for the LTES outlet temperature and generally below 1.0 °C for observable internal PCM temperatures.

eess.SY↗

arXiv2026

Inevitable shock formation for 3-D compressible Euler flows

We prove that solutions arising from smooth, sufficiently small, compactly supported perturbations of non-vacuum constant states in three-dimensional irrotational compressible flow must blow up in finite time, without any symmetry assumptions or other restrictions on the initial data. Moreover, we prove that shock inevitably forms at the boundary of the maximal Cauchy development, and that its formation time agrees, in the small-data asymptotic regime, with the lifespan predicted by the radiation-field analysis.

math.AP↗

arXiv2026

SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model

VAR has gained widespread popularity due to its next-scale prediction paradigm. However, it faces substantial performance bottlenecks when handling complex scenes with multiple objects and attributes. Existing diffusion-based enhancement methods fail to adequately address the unique challenge of cross-scale error propagation and accumulation in VAR. To this end, we propose SynVAR, the first training-free enhancement framework specifically tailored for the VAR paradigm, which introduces a spatial-semantic collaborative control strategy to effectively suppress propagation error and improve generation quality. SynVAR comprises three key components: (1) Global guidance to ensure reasonable spatial structure in the early stages, (2) Receptive field constraints to mitigate early-stage semantic confusion, (3) High-frequency compensation to recover fine-grained details. Extensive quantitative and qualitative experiments demonstrate the significant improvements in the ability of SynVAR to enhance the VAR's capability for complex scene modeling.

cs.CV↗

arXiv2026

Physics-Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Artificial intelligence (AI) is transforming materials discovery, yet conventional data-driven approaches often suffer from limited interpretability, poor extrapolation, and inconsistency with physical laws. Since materials behavior is fundamentally governed by thermodynamics, kinetics, electronic structure, transport processes, and operating environments, the next generation of materials intelligence must move beyond correlation-based prediction toward physics-grounded reasoning. In this Perspective, we systematically discuss Physics-Grounded Materials AI (PhysMat AI) as a unifying perspective for integrating physical knowledge into materials intelligence through five complementary roles: physics as prior knowledge, descriptors, constraints, verifiers, and infrastructure. Using representative examples from catalysis, solid-state electrolytes in solid-state battery, and hydrogen-storage materials, we illustrate how physical principles guide data representation, model reasoning, validation workflows, and knowledge management. We further present how AI agents can leverage these physics-aware components to perform mechanism-guided discovery within physically feasible search spaces. Finally, we outline a developmental roadmap from physics-aware AI to physics-reasoning AI and ultimately physics-autonomous AI. Looking forward, materials intelligence should evolve from predictive models toward autonomous scientific systems capable of integrating physical reasoning, multiscale simulations, experimental validation, and continuous knowledge updating for reliable materials discovery.

physics.chem-ph↗

arXiv2026

MMAligner: Safeguarding Multimodal Large Language Models through Representation Calibration

Multimodal large language models (MLLMs) often refuse unsafe text prompts yet generate harmful responses to semantically equivalent multimodal inputs. Existing defenses either rely on external guardrails, which add inference overhead without repairing intrinsic flaws, or safety fine-tuning, which treats alignment as black-box optimization and may sacrifice utility or require large multimodal datasets. To identify the cause of this safety disparity, we analyze MLLM representations geometrically. We find that safety mechanisms learned from text persist across modalities: a shared safety subspace and refusal boundary remain effective, and representations inside this boundary consistently trigger refusals. However, unsafe multimodal inputs undergo a representation shift that places most of them outside the boundary, allowing them to bypass the model's intrinsic safety mechanism. This indicates that multimodal safety degradation stems from representation misalignment rather than the absence of safety capability. Based on this finding, we propose MMAligner, a safeguarding method that calibrates unsafe multimodal representations into the pre-existing refusal region. MMAligner applies a hard lower bound to ensure refusal, a soft upper bound to avoid excessive modification, and a preservation objective for benign inputs. Experiments across multiple open-source MLLMs show that MMAligner raises the average refusal rate on unsafe multimodal inputs to 99% with less than 2% utility degradation and minimal training data, substantially improving the safety-utility trade-off over existing baselines. (*Due to the notification from arXiv, "The Abstract field cannot be longer than 1,920 characters", the Abstract that appeared is shortened.)

cs.CR↗

arXiv2026

Dilated coordinate method for solving nuclear lattice effective field theory

We introduce a dilated coordinate method to address computational challenges in nuclear lattice effective field theory (NLEFT) for weakly-bound few-body systems. The approach employs adaptive mesh refinement via analytic coordinate transformations, dynamically adjusting spatial resolution to resolve short-range nuclear interactions with fine grids while efficiently capturing long-range wave function tails with coarse grids. Numerical demonstrations for two- and three-body systems confirm accelerated convergence towards infinite-volume limit compared to uniform lattices, particularly beneficial for accessing highly excited states and shallow bound states near the continuum threshold. This method establishes a foundation for \textit{ab initio} studies of exotic nuclear systems near the dripline and light hypernuclei, with direct extensions to scattering and reaction processes.

nucl-th↗

arXiv2026

RayViT: Ray-Conditioned Visual Representations for Viewpoint-Robust Imitation Learning

Visual imitation learning enables robots to acquire visuomotor skills directly from images, yet RGB observations lack explicit geometric cues, making learned policies brittle to camera perturbations. To address this, we propose \textbf{Ray-conditioned Vision Transformer Encoder (RayViT)}, a lightweight architecture that injects camera geometry into pretrained ViT backbones. RayViT represents camera geometry as a Plücker ray map, patchifies it into ray features, and uses gated cross-attention to produce a ray-conditioned class token. These ray features are added as dense positional embeddings, while the ray class token replaces the original ViT class token to provide a geometry-aware summary representation. We combine this approach with an auxiliary cosine similarity loss to consistently improve the performance and robustness for geometry-aware tokens. Experiments on sim- and real-robot tasks demonstrate that RayViT improves robustness by approximately 13 percentage points under camera perturbations in multi-task RoboCasa benchmark and by 1.78 average completed stages in real-world multi-task success rate compared to baselines.

cs.RO↗

arXiv2026

ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification

The misuse of deep learning-based facial manipulation poses a serious threat to civil rights. To prevent such fraud at its source, proactive defense methods have been proposed that embed invisible adversarial perturbations into images, disrupting the manipulation process and rendering the forged output unconvincing to observers. However, non-targeted disruption of the output may leave identifiable facial features intact, potentially leading to the stigmatization of individuals. In this work, we propose a universal framework for combating facial manipulation, termed ID-Guard. The framework employs a single forward pass of an encoder-decoder network to generate cross-model transferable adversarial perturbations. We introduce a novel Identity Destruction Module (IDM) to suppress identifiable features in manipulated faces. The perturbation generation is optimized by formulating the disruption of various manipulation types as a multi-task learning problem, with a dynamic weighting strategy designed to enhance cross-model performance. Experimental results show that ID-Guard effectively defends against diverse facial manipulation models while degrading identifiable regions in manipulated images. It also enables disrupted images to evade facial inpainting and facial recognition systems. Moreover, ID-Guard can be seamlessly integrated as a plug-and-play component into other tasks, such as adversarial training. The source code is publicly available at https://github.com/ZOMIN28/ID-Guard.

cs.CV↗

arXiv2026

Genie Sim PanoWorld: An Infinite Indoor 3D World Generation Pipeline via Panoramic Scene Modeling and Simulation

We address the problem of reconstructing a high-fidelity, freely navigable 3D scene from a single $360^\circ$ panorama, without per-scene optimization or multi-view capture. Existing methods either lack metric trajectory control, which hinders reliable downstream 3D reconstruction, or struggle with large disocclusions under long-range camera motion while requiring high-end multi-GPU servers.We present Genie Sim PanoWorld, a two-stage feed-forward pipeline that bridges generation and reconstruction via an explicit, trajectory-controllable panoramic video. A NavMesh-planned $\mathrm{SE}(3)$ roaming trajectory is injected into a latent video diffusion model through dense geometry-warped conditioning; long--short trajectory mixed training and a self-consistency objective based on shortcut models together yield high-fidelity video in four CFG-free denoising steps. A feed-forward panoramic reconstructor then lifts the generated video into a high-fidelity 3D Gaussian scene that supports real-time, free-viewpoint roaming and can be directly used as a simulation-ready asset for embodied AI applications. Experiments show that Genie Sim PanoWorld outperforms geometry-conditioned baselines in both panoramic video generation and downstream 3D reconstruction, while generalizing zero-shot to unseen indoor scenes.

cs.CV↗

arXiv2026

Detector Development for HUBS I: Initial Testing of Small-Area TES Microcalorimeters

We report progress on the ongoing development of microcalorimeter detector technology for the Hot Universe Baryon Surveyor (HUBS) mission. We show the results from testing and characterizing selected pixels in a 10$\times$10 microcalorimeter array. The microcalorimeter is based on a Mo/Cu transition-edge sensor (TES) coupled to an Au absorber. To better understand the properties of the devices, we have first measured the energy resolution of a selected pixel in a TES array of the same design with a pulsed laser system that produces 3 eV photons, and found that individual photon peaks are easily resolved with the TES, indicating good performance. We have then exposed the microcalorimeter array to radiation from a $^{55}$Fe source, and found that the pixels tested show energy resolutions as good as 3.7$\pm$0.1 eV at 5.9 keV. The energy resolution is found to vary monotonically with the bias point for all the devices, showing little evidence for the presence of the so-called excess noise. This is consistent with the results from modeling the measured noise spectrum. The effects of thermal crosstalk are evident, leading to the degradation of energy resolution.

astro-ph.IM↗

arXiv2026

Structured Spectral Compression based Low-Bitrate Secure Speech Communications for Internet of Things assisted Non-Terrestrial Networks

This paper focuses on the Low-Bitrate Secure Speech Communications based on the Structured Spectral Compression (LB-S2C2). Specifically, the Mel spectral matrix of the speech signal is first encoded at the transmitter side through compressive sensing based on waveform segmentation and data quantization. Then, the Automatic Repeat Request (ARQ) is combined with forward error correction to achieve reliable transmission of speech signals over wireless channels. Thirdly, the received signals are recovered as the speech at the receiver side. Finally, we conduct a series of simulation experiments for the performance evaluation of LB-S2C2. Our simulations reveal that the dictionary matrix used for the speech reconstruction is different from the one used for the high-order matrix sparsification by even only approximately 0.1%, and then the accurate speech recovery fails. It implies that the speech data can be securely transmitted when the dictionary matrix is preserved. More importantly, the LB-S2C2 exhibits a very high privacy protection capability with the average voiceprint similarity to be only 0.3, which is much lower than the 0.8 of the semantic speech communication scheme DeepSC-S, and even lower than the 0.33 of the latest speech communication scheme OFI-OFCNB. In addition, our simulations reveal that the proposed structured speech coding boasts a time complexity of merely O(n), and the proposed speech recovery scheme requires the 12-bit memory storage only, which outperforms the traditional encryption algorithms proposed for speech communications. In comparison with the conventional compression techniques, our spectral compression method renders the coding rate of only 3.9kbps, which is lower than the current lowest speech coding rate of 6.3kbps achieved by G.723.

cs.NI↗

arXiv2026

When Literature Data Mislead Artificial Intelligence in Materials Discovery

Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tracing values from source articles to curated datasets, we identify recurrent text-figure mismatches, ambiguous axis annotations, unit inconsistencies, and missing measurement context. These discrepancies are often numerically plausible and therefore difficult to detect through routine preprocessing, but they can propagate as structured label noise during database construction and machine-learning reuse. A cross-database example shows how ambiguous reporting can create a 100-fold conductivity error. Our analysis reframes data accuracy as an infrastructure requirement for artificial-intelligence-driven discovery and motivates traceable reporting, curation, and validation practices for reusable scientific data. Keywords: AI for science; Data reliability; Scientific databases; Structured label noise; Literature-derived data; Materials informatics; Solid electrolytes

cs.IR↗

arXiv2026

Articulate Intuition or Genuine Analysis? Benchmarking Epistemic Reliability in LLM-as-a-Judge Peer Reviews

When an LLM judge calls a peer review analytical and a human committee calls another review high quality, are they tracking the same thing? We argue they are not, and that the difference matters philosophically. We operationalise Kahneman's dual-process theory into a structured rubric for peer review and release Kahneman4Review, a benchmark of 3,563 rated reviews scored along nine theoretically motivated textual dimensions, eight bias diagnostics, and a continuous reasoning-quality score. Three findings bear on trustworthiness: decision tier is not detectably aligned with the rubric's text-grounded epistemic-quality proxy; public-showcase agentic reviews receive higher raw scores than pooled human reviews, but length and venue explain most of the gap and the samples are not paper-paired; and ICLR review-text diagnostics shift at the 2022--2023 transition, temporally coincident with widespread LLM availability but without identifying its cause. A matched function-probe pilot further shows that the rubric distinguishes textual probes designed to contrast genuine fault-finding with surface fluency. We argue that a trustworthy reliability benchmark for LLM judges must separate analytical form from epistemic function, and propose concrete design choices toward that goal. An interactive demo is available at https://huggingface.co/spaces/nuojohnchen/Kahneman4Review.

cs.CL↗

arXiv2026

Precision masses of neutron-rich platinum and gold nuclei reveal enhanced $N=126$ shell strength below doubly-magic $^{208}$Pb

The heaviest stable nuclei in the universe owe their existence to quantum shell structure, the grouping of protons and neutrons into discrete energy levels separated by gaps. The largest known neutron shell gap in stable nuclei, at $N=126$, stabilizes doubly-magic $^{208}$Pb and is responsible for the characteristic abundance peak of heavy elements near gold and platinum produced by the rapid neutron-capture process (r-process). Whether this shell gap persists as protons are removed from lead is a question central to both nuclear structure and the modeling of heavy-element synthesis, yet it has remained unanswered due to the extraordinary difficulty of producing the relevant neutron-rich nuclei. Direct experimental knowledge in this region was essentially absent. Here we report the first precision mass measurements of $^{203,204}$Pt and $^{204,205,206}$Au, performed at GSI using a novel combination of Schottky and isochronous mass spectrometry in a heavy-ion storage ring. The $N=126$ isotones $^{204}$Pt and $^{205}$Au are more strongly bound than the extrapolated trend of the previously known mass surface by 403 and 464~keV, respectively, revealing an unexpectedly enhanced $N=126$ shell strength below doubly-magic $^{208}$Pb. Furthermore, the proton-neutron interaction strength exhibits a hitherto unobserved bifurcation at $N=126$ as protons are removed from $^{208}$Pb. Our results redefine the nuclear mass surface in the neutron-rich heavy-element region and provide direct experimental benchmarks for theoretical models whose extrapolations toward more exotic nuclei are essential for r-process nucleosynthesis calculations.

nucl-ex↗

arXiv2026

Finite-temperature effects on the threshold cusps in $ππ$ and $D\bar{D}^{\ast}$ scatterings from relativistic heavy-ion collisions

We investigate how the temperature influences the threshold cusps in meson-meson scatterings, i.e., $ππ$ and $D\bar{D}^\ast$ (or $D^{\ast}\bar{D}$) scatterings, using the production rates and propagators obtained at finite temperature. The lineshape of production rate of $ππ$ at different temperatures demonstrates that the cusp structure in $ππ$ scattering is mildly enhanced as the temperature increases. As for the $ππ$ propagator, which includes the isospin symmetry breaking, its lineshape displays a unique plateau-like structure and this structure will also be enhanced as the temperature increases. For comparison, the lineshape of the $D\bar{D}^\ast$ propagator including the isospin symmetry breaking is also investigated at different temperatures. As the temperature increases, its lineshape shows a similar plateau-like structure but with some different properties when the temperature modifications to the masses and widths of $D$ and $\bar{D}^\ast$ are considered.

hep-ph↗

arXiv2026

LLM-based Human Simulations Have Not Yet Been Reliable

Large Language Models (LLMs) are increasingly employed for simulating human behaviors across diverse domains. However, our position is that current LLM-based human simulations remain insufficiently reliable, as evidenced by significant discrepancies between their outcomes and authentic human actions. Our investigation begins with a systematic review of LLM-based human simulations in social, economic, policy, and psychological contexts, identifying their common frameworks, recent advances, and persistent limitations. This review reveals that such discrepancies primarily stem from inherent limitations of LLMs and flaws in simulation design, both of which are examined in detail. Building on these insights, we propose a systematic solution framework that emphasizes enriching data foundations, advancing LLM capabilities, and ensuring robust simulation design to enhance reliability. Finally, we introduce a structured algorithm that operationalizes the proposed framework, aiming to guide credible and human-aligned LLM-based simulations. To facilitate further research, we provide a curated list of related literature and resources at https://github.com/Persdre/awesome-llm-human-simulation.

cs.CL↗