arXiv subjects

Kai Wang

Publications and source records attributed to Kai Wang.

At least 91 records · Page 5Linked to original sources

arXiv2026

Adversarial Concept Distillation for One-Step Diffusion Personalization

Recent progress in accelerating text-to-image diffusion models enables high-fidelity synthesis within a single denoising step. However, customizing the fast one-step models remains challenging, as existing methods consistently fail to produce acceptable results, underscoring the need for new methodologies to personalize one-step models. Therefore, we propose One-step Personalized Adversarial Distillation (OPAD), a framework that combines teacher-student distillation with adversarial supervision. A multi-step diffusion model serves as the teacher, while a one-step student model is jointly trained with it. The student learns from alignment losses that preserve consistency with the teacher and from adversarial losses that align its output with real image distributions. Beyond one-step personalization, we further observe that the student's efficient generation and adversarially enriched representations provide valuable feedback to improve the teacher model, forming a collaborative learning stage. Extensive experiments demonstrate that OPAD is the first approach to deliver reliable, high-quality personalization for one-step diffusion models; in contrast, prior methods largely fail and produce severe failure cases, while OPAD preserves single-step efficiency.

cs.CV↗

arXiv2026

DreamStyle3D: Efficient 3D Stylized Asset Generation via Dual-Attention Disentanglement

With the growth of gaming, animation, and virtual reality industries, the demand for efficient generation of stylized 3D assets is rapidly increasing. However, existing approaches still struggle to jointly preserve style fidelity, geometric consistency, and generation efficiency, as most of them still rely on indirect 2D-to-3D stylization pipelines. This motivates a native 3D stylization framework that can explicitly disentangle style from geometry while remaining efficient. To this end, we propose DreamStyle3D, an efficient framework for stylized 3D asset generation built on a Decoupled Dual Cross-Attention mechanism. Our method explicitly separates geometric and stylistic features to enable efficient style injection while preserving structural consistency, and further adopts a lightweight training strategy to enhance style consistency and model generalization. In addition, we build an automated data pipeline and construct a dataset of about 15K content-style-stylized triplets for training and evaluation. Extensive experiments demonstrate that our DreamStyle3D can generate high-fidelity, geometrically consistent stylized 3D assets within 10 seconds, substantially improving efficiency while maintaining superior style quality and offering a new solution for 3D content creation. The project is available at https://github.com/NK-JittorCV/nk-3D/tree/main/models/DreamStyle3D.

cs.CV↗

arXiv2026

XEWorld: Can Action-Conditioned World Models Generalize to Unseen Robot Embodiments?

Action-conditioned world models are promising learned simulators for robotic manipulation, yet evaluating them exclusively on training robots fails to reveal whether they capture physical dynamics or merely memorize visual patterns. To answer whether a model can faithfully render a robot it has never seen, we introduce XEWorld, a controlled cross-embodiment testbed for world models that isolates embodiments by evaluating held-out robots within physically identical scenes. Our systematic analysis uncovers a shared architectural bottleneck: current models act primarily as 2D visual pattern matchers whose generalization is governed by visual similarity rather than physical kinematic similarity. Driven by this limitation, they struggle to translate abstract numeric joint actions into coherent visual trajectories, and fail to predict dynamic visual changes from static initial observations. Consequently, successfully rendering an unseen embodiment zero-shot strictly requires heavily grounded cues, specifically pixel-space actions and explicit spatial-temporal alignment. Even when bypassing this zero-shot barrier via few-shot adaptation, the forced appearance recovery triggers catastrophic forgetting of seen embodiments. Together, these failures expose a critical inability to apply learned physical dynamics to novel visual appearances, highlighting that achieving true cross-embodiment generalization requires architectural innovations that decouple visual appearance from underlying physical dynamics.

cs.RO↗

arXiv2026

Ruffled Feathers: Merger-driven galaxy size growth and structural transformation in EAGLE

Galaxy mergers drive both the size growth and the transformation from discs to spheroids, yet the prescriptions used to model these processes in semi-analytic frameworks have not been tested against the realistic merger population in cosmological hydrodynamical simulations. Using $\approx 4{,}500$ mergers identified in the EAGLE simulation, we test an energy-conservation estimator for post-merger galaxy sizes and quantify merger-driven morphological transformation. The predicted remnant half-stellar-mass radius matches the simulated descendant size with a scatter of $\approx 0.12$-$0.15$ dex and no significant systematic dependence on progenitor properties, while a commonly used dissipation correction applied to gas-rich mergers under-predicts the post-merger size by up to $\approx 0.4$ dex in a cosmological context and increases the overall scatter. The per-merger size growth increases monotonically with the stellar mass ratio of the merging pair, from $\lesssim 0.03$ dex for minor mergers to $\approx 0.10$ dex for equal-mass mergers. From the energy-conservation estimator, we analytically derive the size growth efficiency per unit accreted stellar mass, $η\equiv \mathrm{d}\log_{10} r_{\star}/\mathrm{d}\log_{10} M_{\star}$, and show that $η$ reaches $\approx 2$ only in the idealised limit of collisionless minor mergers with zero orbital energy; as $η$ is highly sensitive to the orbital energy at the time of merging, the minor merger channel cannot be established as the driver of the rapid size growth of massive galaxies without better constraints on this quantity. Beyond the size growth, mergers systematically reduce rotational support and increase triaxiality in proportion to mass ratio, but even the most nearly equal-mass mergers do not always fully destroy the disc, in tension with the complete disc destruction assumed in several semi-analytic models.

astro-ph.GA↗

arXiv2026

Inflow-driven galaxy evolution - I. Revealing the physics of the fundamental metallicity relation

We present a unified physical framework for the fundamental metallicity relation (FMR), based on the mass-continuity equations. The FMR is not merely the anti-correlation between star formation rate (SFR) and gas metallicity ($Z_{\rm g}$) at fixed stellar mass ($M_\star$); it is a redshift-invariant surface in the $(M_\star,{\rm SFR},Z_{\rm g})$ space. We construct a minimal cosmological gas flow model, calibrated to reproduce the mass-metallicity relation, star-forming main sequence, and stellar-to-halo mass relation at $z=0-3$, and show that the FMR emerges as a prediction of the calibrated physics. Through controlled experiments that progressively simplify the model, we reveal that in a universe where both the star formation efficiency ($ε$) and mass-loading factor ($η$) are constants, the FMR reduces to a universal scaling between $Z_{\rm g}$ and $M_\star/$SFR, whose shape traces the transition from inflow-driven regime to equilibrium. The specific parameterisation of the observed FMR is not a fundamental symmetry but a contingent consequence of how $ε$ and $η$ depend on stellar mass and redshift. We show that the gaseous FMR (gFMR), defined in the $(M_\star,M_{\rm g},Z_{\rm g})$ space, is more fundamental than the standard FMR: in the inflow-driven limit, $Z_{\rm g}$ is proportional to $M_\star/M_{\rm g}$, and the approach to equilibrium is governed by $M_\star/M_{\rm g}$ and $η$ alone. We derive an analytic solution for an idealised version of the model that provides closed-form expressions relating $Z_{\rm g}$, $M_{\rm g}/M_\star$, and $η$, and show this framework accurately reproduces the cosmological gas flow model. By establishing the physical origin of the FMR and its connection to the more fundamental gFMR, we provide the theoretical foundation to turn metallicity scaling relations into precision probes of the baryon cycle over cosmic history.

astro-ph.GA↗

arXiv2026

Quantum States Protection under Environmental Noise

All realistic quantum systems are inevitably in contact with the environment. Suppressing theimpact of environmental noise is a critical challenge in cutting-edge quantum technologies. In thiswork, we introduce and systematically analyze a scheme for the protection of quantum states againstamplitude-damping (AD) noise based on the circuit structure called the quantum filter. Filtrationcircuits employing single- and multi-control qubits are examined, and their capability to enhance stateprotection fidelity while preserving a high success probability is discussed. Moreover, for many-bodyqubit states, those with a fixed quantum Hamming weight can be perfectly protected against ADnoise, whereas states with the largest Hamming weight difference set a lower bound on the achievableprotection fidelity. Our work provides a resource-efficient route for quantum state protection withoutrequiring full quantum error correction.

quant-ph↗

arXiv2026

HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT

We present HERMES (Hybrid Ensemble for Radiotherapy-target segmentation, Malignancy staging, and Event-free Survival), a single containerized algorithm for the three HECKTOR 2026 subtasks: segmentation of the primary tumor (GTVp) and pathological lymph nodes (GTVn), radiological T/N staging, and recurrence-free survival (RFS), computed from a paired FDG-PET/CT scan and an electronic health record. A 10-fold ensemble of STU-Net Small networks produces the segmentation; the predicted mask then drives two downstream tasks. Rather than pass a generic radiomics vector to the staging models, we derive from the predicted masks a compact set of geometry features aligned with the size and number axes of AJCC/UICC 7th-edition radiological N/T staging. On internal cross-validation these features raise N-stage balanced accuracy from 0.691 to 0.720 (+0.030), our largest single design gain, at lower feature dimensionality. For prognosis we combine complementary deep and clinical risk experts in an equal-weight ensemble, and train one deep expert with a concordance-tracking survival loss of our own, whose value approximates the concordance index during training. Every component was selected on honest out-of-fold predictions under a regularization-oriented protocol, with no tuning on the public validation set, and deployed as two decorrelated submissions. On the HECKTOR 2026 validation leaderboard, HERMES achieved a weighted score of 0.6454 (Mean Dice 0.641, T balanced accuracy 0.580, N balanced accuracy 0.642, RFS C-index 0.679) and qualified for the testing phase. Team: AMC_HNC.

cs.CV↗

arXiv2026

A Unified Resolution-Conditioned Framework for Orthogonal Line-Scanning Image Fusion

Laser line-scanning microscopy enables fast volumetric imaging but produces anisotropic lateral resolution. Orthogonal line scans provide complementary directional information that can recover near-isotropic resolution, yet existing deep-learning methods require a separate model for each optical configuration. We present a unified, resolution-conditioned fusion framework based on Rank Enhanced Linear Attention (RELA). Feature-wise Linear Modulation (FiLM) conditions the network continuously on the resolving-power ratio, enabling one model to adapt across slit widths. We further introduce Adaptive RELA, which replaces fixed-kernel rank enhancement with ratio-conditioned multi-scale depthwise convolutions and uses a learnable attention temperature to adjust selectivity with degradation severity. Training data spanning multiple slit configurations are generated using a physics-grounded separable point-spread-function model verified against measured optical data at 48.3 dB accuracy. The resulting model achieves 34-40 dB PSNR across configurations, whereas unconditioned multi-slit training collapses to 24.3 dB and per-slit specialists lose 4-9 dB outside their training setting. It also generalizes smoothly to unseen intermediate configurations without interpolation artifacts. Ablations show that FiLM resolves configuration ambiguity, global linear attention captures long-range directional correspondences, and adaptive temperature yields an additional 2 dB in the challenging near-isotropic regime, where complementary signals are weak.

cs.CV↗

arXiv2026

Stochastic Choice with Advertising

We study how advertised products (e.g., Top Picks, Recommended, Featured) affect consumer choice on digital platforms and retail interfaces by extending the Luce (1959) (or multinomial logit) model. A consumer either focuses on the advertised items or considers the full menu, then chooses among the considered alternatives according to the Luce/logit rule. We characterize this model and show that its underlying primitives are uniquely identified from choice data. We also study a managerially important advertisement-design problem, in which a platform or retailer chooses the advertised subset to maximize expected profit, and we derive implementable design rules. We then introduce a richer framework in which advertising can influence both attention and preference. For this more general model, we provide a characterization and show how choice data can be used to separate the attention effect from the preference effect.

econ.GN↗

arXiv2026

Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes

Community detection is a fundamental task in graph analytics that aims to identify cohesive groups of entities with similar behaviors or interests. Classic objective-driven methods struggle with complex graph structures, while deep-learning approaches improve performance at the expense of interpretability and rely on labeled data and training. Large language models (LLMs), with strong reasoning capabilities and world knowledge, are promising for interpretable, label-free community detection. To leverage these strengths, we propose LUCID, an LLM-guided, interpretable, training-free, and unsupervised community detection method. Inspired by phase-transition kinetics in natural systems, where complex structures emerge through initialization, merging, refinement, and selection, LUCID is designed as a four-stage pipeline. Within this pipeline, the LLM induces formal rules that translate implicit knowledge into explicit and interpretable logical structures. Specifically, (1) the Local-View Community Initialization stage encodes local graph structures using k-ego contexts and unsupervised node roles; (2) the Multi-factor Community Merge stage uses LLM-induced rules to iteratively merge local communities; (3) the Multi-grain Community Refinement stage applies LLM-induced coarse-to-fine rules in parallel to reduce boundary noise; and (4) the Global-view Community Selection stage identifies high-quality communities based on topological compactness and boundary clarity. Extensive experiments on real-world datasets demonstrate that LUCID, as an unsupervised approach, achieves state-of-the-art performance and consistently outperforms leading unsupervised and semi-supervised baselines.

cs.AI↗

arXiv2026

TEngineDB-V: An OLAP-Native Vector Search System for Large-$k$ Workloads at Tencent

Vector search systems are essential infrastructure for modern data-driven applications. Large-$k$ analytical vector search, which retrieves $k=10^3$--$10^5$ results for analytics (e.g., aggregation, filtering, joins), is increasingly important for emerging workloads, including LLM data management and advertising analysis at Tencent. Existing systems remain inadequate: specialized vector databases often cap $k$ (e.g., $k \leq 10^4$) to satisfy tail-latency constraints and offer limited analytical support, while OLAP systems typically embed per-segment vector indexes as black boxes, causing severe read/compute amplification and preventing native query optimization. This paper presents TEngineDB-V, an OLAP-native vector search system for large-$k$ workloads. TEngineDB-V makes vector search a first-class analytical primitive in Tencent's OLAP engine through a global segment-decoupled index materialized as relational tables, eliminating scatter-gather execution, reducing amplification, and enabling native storage optimizations. It decomposes IVFPQ-based search into relational operators, integrates OLAP optimizations, and introduces DPPQ, which combines direction-aware quantization with hierarchical residual refinement to improve recall while preserving relational efficiency. TEngineDB-V further incorporates index-aware query rewriting and a distributed-aware cost model for efficient distributed execution. Experiments show that TEngineDB-V achieves up to a $145\times$ speedup over competitive systems such as StarRocks, and up to a $52\times$ improvement in 10-billion-scale production deployments.

cs.DB↗

arXiv2026

SeekBrain: An Autonomous Multi-Agent System for Accelerating Neuroscience Discovery

Modern neuroscience relies on integrating multi-scale, multimodal datasets to uncover the neural principles underlying intelligence. However, analytical challenges posed by highly heterogeneous data and fragmented workflows increasingly constrain discoveries. Here we introduce SeekBrain, an autonomous multi-agent framework designed to accelerate neuroscience discovery through domain-grounded hierarchical planning and cross-modal data analysis. SeekBrain dynamically constructs a repertoire of analysis recipes extracted from code-paper pairs. By coupling this codified expertise with agentic planning and execution engines, the framework scalably generates hypotheses and analytical pipelines on demand. Systematic evaluation on the expert-annotated BrainArena benchmark demonstrates that SeekBrain substantially outperforms state-of-the-art agent baselines across various analysis tasks. Crucially, when deployed in real-world research, SeekBrain integrated behavioral, neural, and anatomical data to reveal structured, distributed neural representations of larval zebrafish behavior and a shared axis of regional decoding strength across the brain in a mouse decision-making task. These results establish SeekBrain as a scalable and practical tool for accelerating data-driven discoveries in neuroscience.

cs.MA↗

arXiv2026

The Greedy Advantage in Finite-Horizon Bandits

Organizations increasingly rely on sequential experimentation to improve decision-making. While the multi-armed bandit literature has developed algorithms with strong asymptotic regret guarantees, many practical applications operate over finite and externally imposed horizons. Motivated by the finite-horizon setting, we develop a class of regularized greedy algorithms for multi-armed Bernoulli bandits. We derive the first finite-horizon regret envelopes for regularized greedy bandits, showing that finite-horizon regret decomposes into transient exploration costs and a suboptimal convergence term that decays exponentially with the regularization strength. This characterization yields principled calibration rules for the regularization parameters and, as a limiting case, sharper regret guarantees for the classical greedy policy. Across extensive numerical experiments, calibrated regularized greedy policies consistently match or outperform state-of-the-art algorithms. These results suggest that regularized greedy policies can provide an effective approach for finite-horizon bandit problems.

stat.ML↗

arXiv2026

Directional Photocurrent Generated by Quantum Interference Control

Although the absorption of light in a bulk homogeneous semiconductor produces photocarriers with non-zero momentum, it generally does not produce a current in the absence of an applied electric field because equal amounts of carriers with opposite momentum are injected. The interference of absorption processes, for example, between one-photon and two-photon absorption, can produce a current because constructive interference for carriers with one momentum can correspond to destructive interference for carriers with the opposite momentum. We show that for the interference between two-photon and three-photon absorption, the current has a narrower angular spread, i.e., a ``beam'' of electrons in a specified direction is produced in the semiconductor.

cond-mat.other↗

arXiv2026

Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Model

We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-agent, and complex tool-use tasks while maintaining highly competitive reasoning capabilities in mathematics, coding, and science. Nanbeige4.2-3B is pretrained from scratch on 28T tokens with a Looped Transformer that reuses the layer stack to increase capacity without adding parameters. For SFT data and trajectory construction, we expand the diversity of executable environments, task assets, and agentic scaffolds through real-world deployment and large-scale synthesis. Our RL pipeline applies mixed-mode RLHF over Think and Non-Think responses to improve overall model quality and reduce failure cases, length-controlled reasoning RL to balance accuracy and reasoning efficiency, and agentic RL with outcome and process rewards to stabilize long-horizon training. Extensive evaluations show that Nanbeige4.2-3B outperforms larger models, including Qwen3.5-9B and Gemma4-12B, across diverse agentic benchmarks while remaining competitive on reasoning and alignment tasks. Performance with OpenClaw further supports its use as a compact local personal assistant.

cs.AI↗

arXiv2026

Token-Operations-Oriented Inference Optimization Techniques for Large Models

Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large model services. Centered on token-oriented inference optimization technology, this paper proposes for the first time a four-layer technical architecture consisting of Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion. It systematically reviews the key technologies and current industry status across these four levels and analyzes the application value of related technologies in real-world business scenarios. This paper provides a practical technical path for reducing token production costs, improving token service efficiency, ensuring the stability of token supply, and driving the transition of large model services from being merely callable to being operable.

cs.SE↗

arXiv2026

Searching for $γ$-ray emission from a bona fide Compact Symmetric Object sample: a $γ$-ray signal near GB6 J0906+4124

As a particular subclass of active galactic nuclei (AGNs), compact symmetric objects (CSOs) have attracted significant attention due to potential role as young AGNs. Several $γ$-ray emitting CSOs have been detected with Fermi Large Area Telescope (Fermi-LAT), which motivates further searches for more $γ$-ray emitting CSOs. We perform a systematic search for $γ$-ray emission from a bona fide CSO sample using 16 yr Fermi-LAT observation data. No significant $γ$-ray signal is found to be firmly associated with any CSO. Only one $γ$-ray signal is detected near CSO GB6 J0906+4124 with TS = 28.7 ($\sim4.9σ$) in 0.1--300 GeV band. Within the 68\% containment radius of Fermi-LAT average PSF at 3 GeV, GB6 J0906+4124 remains the plausible counterpart of the $γ$-ray signal, primarily due to its classification and broad spectral coverage. This work suggests that GeV emission from CSOs may either be uncommon or simply below the current Fermi-LAT detection sensitivity. Further multi-wavelength follow-up is needed to determine the origin of the $γ$-ray signal near GB6 J0906+4124.

astro-ph.HE↗

arXiv2026

CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement

V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from multiple collaborative agents. However, existing mainstream V2X perception methods mainly focus on 2D BEV object detection. When 3D detection task is concerned, inferior results are obtained because they ignore the 3D spatial misalignment caused by differing height and attitude among the collaborators. In this paper, we propose a novel collaborative 3D object detection framework called CoGoal3D, which extracts and refines the 3D feature gradually in a two-stage pipeline. In the first stage, a multiscale 3D-aware global fusion module is designed to mitigate the 3D spatial misalignment. The resulting proposals are then refined in the second stage with an auxiliary task of 3D point reconstruction. An effective multi-agent collaborative data augmentation strategy is further proposed to enrich the training data while minimizing information loss. Extensive experiments on public real-world datasets demonstrate that our CoGoal3D achieves new state-of-the-art performance, with 3D AP@0.7 improvements of 10.86%, 10.34%, and 10.18% on the DAIR-V2X, V2V4Real, and V2X-Real datasets, respectively. Code is available at https://github.com/Megalo-f/CoGoal3D.

cs.CV↗