arXiv subjects

Kai Wang

Publications and source records attributed to Kai Wang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Functionalization via Structure Completion and Motion Rectification

Acquisition and creation of 3D assets have been largely view- or appearance-driven. As a result, existing digital 3D models often lack the requisite structural components to function as intended, such as joints, supports, interiors, or interaction elements. At the same time, even human-annotated motions are frequently error-prone, leading to physically implausible behavior. We introduce object functionalization, a novel task aimed at transforming visually plausible but non-functional 3D models into functional and physically operable ones. We formulate functionalization as a graph completion problem over a new functional graph representation, where labeled nodes represent object parts, labeled edges encode functional and contact relations, and movable nodes carry motion attributes, so that structural functional deficiencies manifest as missing nodes or incorrect edges. We develop a neural Graph Functionalizer (GraFu) to complete an incomplete graph representing a non-functional 3D object. The completed graph then drives a geometry realization stage that instantiates predicted connectors and structural elements in 3D, with the compelling side effect of rectifying erroneous human-annotated and predicted motions. To support training and evaluation, focusing on furniture as a rich and challenging target category, we introduce FurFun-233, a dataset of 233 paired non-functional and functionalized furniture models. On PartNet-Mobility ("zero-shot") and HSSD test sets, our method matches state-of-the-art methods in motion prediction accuracy while substantially improving functionality in terms of collision and connectivity. Project page: https://mingrui-zhao.github.io/Functionalization/

cs.CV↗

arXiv2026

Fisher-information training of optical sensing front ends from natural fluctuations

Fisher-information-based optimization of programmable optical front ends in situ generally requires a response model or parameter-labeled measurements. We show that natural parameter fluctuations can instead provide the training signal. We construct proxies for the Fisher information (FI) of one parameter and the prior-weighted FI spectrum of multiple parameters from measured covariances after subtracting conditional detection noise, assuming locally affine responses. We use simulated photon counts from two incoherent point sources to train a mode sorter that approaches the quantum limit for separation and the Nagaoka--Hayashi single-copy bound for joint centroid--separation estimation. We demonstrate count-only updates that require no instrument response model and achieve performance comparable to model-based gradients. Our results provide a route to adaptive optical measurements without controlled parameter scans.

physics.optics↗

arXiv2026

The Grothendieck Ring of Strictly Weakly Integral Fusion Categories of Rank 6

In this paper, we study strictly weakly integral fusion categories of rank 6 and give a complete classification of their Grothendieck rings. We obtain twelve candidate Grothendieck rings. Eight of them can be realized by fusion categories, while the remaining four are ruled out by necessary obstructions to categorification. Together with previously known results, this completes the classification of Grothendieck rings of strictly weakly integral fusion categories of rank at most 6.

math.QA↗

arXiv2026

Linearisation, splitting property and homotopy algebras

We study the linearisation of formal vector fields and homotopy algebras using graded coalgebras. We prove that a formal vector field is linearisable if and only if it satisfies a splitting property, and we give an explicit recursive construction of a linearising coordinate transformation. This provides a direct coalgebraic proof of Bandiera's characterisation of linearisable, equivalently homotopy abelian, $L_{\infty}[1]$ algebras. We establish an associative analogue, characterising linearisability of $A_{\infty}[1]$ algebras by a splitting property for one-sided Hochschild cochains. We also examine the corresponding splitting property for standard Hochschild cochains and show that it is equivalent to $A_{\infty}[1]$ commutativity in the sense of Briggs and Gélinas. Finally, we establish a corresponding criterion for formal endomorphisms and give sufficient conditions for splitting that recover Basto-Gonçalves' linearisation theorem for admissible formal vector fields.

math.DG↗

arXiv2026

Fountain pattern of baryon cycle revealed in galaxy ecosystems

Baryons in galaxy ecosystems are believed to undergo continuous cycles of inflow and outflow, forming fountain-like patterns that encode key information about how galaxies acquire matter from their environments and respond through feedback. The presence of such baryon cycles has been inferred from pieces of observational evidence, but a concrete understanding remains elusive because individual galaxy ecosystems are diverse and dynamic. Here we introduce a stacking method that combines baryonic fields across ensembles of individual galaxy ecosystems to suppress irregularities and reveal the underlying pattern. Applied to a cosmological hydrodynamic simulation, this approach unveils strikingly regular patterns in gas properties across the full spatial extent of galaxy ecosystems, in close agreement with those inferred from observations. This method is straightforward to implement, allowing the processes shaping the gas-cycling pattern to be fully understood within the structure-formation paradigm, and a solid framework to be constructed for linking simulated galaxy ecosystems with observations.

astro-ph.GA↗

arXiv2026

Data Efficient Any Transformer-to-Mamba Distillation via Attention Bridge

State-space models (SSMs) have emerged as promising alternatives to Transformers for sequence modeling. However, training competitive SSMs from scratch remains computationally intensive, and the ecosystem around them is far less mature than that of Transformers. Moreover, the architectural differences between SSMs and Transformers make it challenging to efficiently transfer knowledge from pretrained Transformers. In this work, we propose Cross-architecture distillation via Attention Bridge (CAB), a distillation framework that transfers attention-related representations from Transformer teachers to state-space student models. Unlike conventional knowledge distillation that supervises only final predictions, CAB enables token-level intermediate supervision through a lightweight bridge and flexible layer-wise alignment. By aligning Transformer attention-related representations with Mamba's token-dependent state projections, CAB facilitates efficient cross-architecture knowledge transfer without inference-time overhead. Experiments on image classification and language modeling demonstrate that CAB improves Transformer-to-SSM distillation, particularly under limited-supervision settings. Overall, CAB provides an efficient pathway for transferring Transformer representations to SSM-based models, helping bridge the gap between mature Transformer ecosystems and emerging SSM ecosystems.

cs.LG↗

arXiv2026

Neutrino Emission from Gamma-ray Burst Jet inside the Cavity within Active Galactic Nucleus Accretion Disks

Active galactic nucleus (AGN) accretion disks are promising sites for compact binary mergers. Short gamma-ray burst (SGRB) jets from binary neutron star coalescence can propagate within low-density cavities carved by circumbinary outflows. We investigate the high-energy neutrino emission and detectability of such SGRB jets, adopting three seed photon components: prompt GRB emission, AGN disk thermal radiation, and external inverse Compton (EIC) scattered photons. Our results show that neutrino emission is dominated by proton-photon ($pγ$) interactions, with prompt GRB photons and AGN disk photons serving as the dominant target components. The inclusion of AGN disk photons significantly reshapes the neutrino spectrum, shifting the emission peak to lower energies. This effect becomes more pronounced as the mass of the central supermassive black hole decreases and the burst location approaches the black hole. For a fiducial model with a $10^6~ M_\odot$ central black hole and the burst at 10 Schwarzschild radii, IceCube and IceCube-Gen2 achieve maximum luminosity distances of $\sim$180 Mpc and $\sim$400 Mpc, respectively. Next-generation high-sensitivity neutrino observatories, combined with multi-messenger observations, hold promise for identifying such GRBs in AGN environments.

astro-ph.HE↗

arXiv2026

Massive Galaxy Halos Contain Less Inner Dark Matter Than Predicted

The mass profiles of galaxy halos encode how baryons reshape dark matter distribution, yet direct observational constraints across the full radial range remain scarce. Here we combine stellar kinematics from MaNGA, H I dynamical measurements from ALFALFA, and independently calibrated halo masses of SDSS groups to statistically reconstruct the mass distribution of central galaxies over nearly two orders of magnitude in radius. We demonstrate that H I data alone do not provide reliable total halo mass estimates, necessitating an independent group-based halo-mass scale. Compared to the IllustrisTNG and EAGLE simulations, the observational profiles of low-mass halos are broadly consistent; in contrast, massive observed halos exhibit systematically lower dynamical masses at the H I radius, lower inner dark-matter masses, and lower central dark-matter fractions (about 4$σ$ difference in units of population scatter) at fixed total halo mass. After subtracting baryonic contributions, the inferred dark-matter profiles remain broadly consistent with an NFW form, but with lower effective concentrations than predicted for massive halos. These results suggest that the inner dark-matter content of massive halos has been reduced more significantly than predicted by current hydrodynamical simulations, plausibly due to long-term baryonic halo heating in massive systems.

astro-ph.GA↗

arXiv2026

Reconstructing the Projected Dark Matter Field across 0.1-100 Mpc Scales from the SDSS Survey

Dark matter sets the gravitational environment in which galaxies form and evolve, but cannot be observed directly. We present a conditional diffusion model that reconstructs the projected dark matter density field from the galaxy stellar-mass density field for direct application to galaxy surveys. The model is trained on CAMELS and validated on the independent IllustrisTNG300-1 simulation. Halo masses inferred from the reconstructed projected-aperture measurements agree well with the corresponding true values, with a scatter below 0.2 dex. On 100 kpc scales, reconstructed surface densities show a typical scatter of ~0.3 dex in the regime most relevant for observations. We apply the model to SDSS galaxies with $M_\star\ge10^9\,M_\odot$ in a contiguous low-redshift region. Averaging over 100 stochastic realizations, we reconstruct and publicly release a projected dark matter field covering $90\times90\,(h^{-1}\mathrm{Mpc})^2$ with a pixel size of $0.097\,h^{-1}\mathrm{Mpc}$. This pixel area corresponds to the characteristic projected area of halos with masses of ~$10^{10.6}\,h^{-1}\,M_\odot$. The map reveals the multiscale projected cosmic web, including cluster-scale overdensities, filaments and voids. Projected-aperture masses are statistically consistent with SDSS group-catalog masses, while the derived halo mass function broadly matches mock-catalog expectations. The reconstructed projected potential places Coma in one of the deepest wells and near a convergence region of the inferred projected acceleration field, suggesting that the reconstruction retains both local overdensities and coherent large-scale projected gravitational structure. This work shows that diffusion-based dark matter reconstruction can be applied to real galaxy surveys, enabling halo-mass- and spatially resolved dark-matter-environment-based studies of galaxy evolution in SDSS and future wide-area surveys.

astro-ph.GA↗

arXiv2026

Critical quantum metrology in the output of an optical parametric oscillator

Many quantum sensors infer parameters from continuously emitted fields, yet in the presence of unmonitored loss, it is difficult to determine how much critical enhancement survives in the accessible output. In this work, we calculate the full frequency-resolved output quantum Fisher information (QFI) of a stationary vacuum-seeded optical parametric oscillator for cavity-detuning estimation. We show that, for any fixed nonzero unmonitored loss, the QFI of correlated sideband pairs grows quadratically with the mean intracavity photon number near threshold, but only within a spectral window that narrows inversely with that number; consequently, the spectrum-integrated output QFI rate scales asymptotically only linearly. We identify a far-detuned, near-threshold regime in which the monitored output alone asymptotically approaches the loss-imposed upper bound on the joint cavity-output QFI. We further determine the operating points that maximize the output QFI rate per intracavity photon and introduce a frequency-resolved homodyne strategy that maximizes the Fisher information obtainable from a single record. Our results establish an operational connection between critical enhancement and metrological information accessible in the output field, while providing concrete guidance for the design and readout of dissipative parametric sensors.

physics.optics↗

arXiv2026

Forged in Quenching: Morphological Transformation across Star-forming and Quiescent Galaxies in EAGLE

The connection between morphology and quenching in central galaxies is well established, but its physical origin remains widely debated. We address this by tracing the main progenitor branches of $z=0$ star-forming and quiescent central galaxies in the EAGLE cosmological simulation from $z\gtrsim4$. Their disc-to-total ratio and triaxiality tracks are indistinguishable until $z\approx 1$-$2$, when both diverge concurrently with the onset of quenching, whereas the size and supermassive black hole (SMBH) mass differences are established earlier. We identify four physically distinct channels linking galaxy morphology and quenching. First, mergers cause size growth, rotation suppression, triaxiality increase, and SMBH growth, with the accumulated SMBH mass subsequently causes the quenching of galaxies. Second, with merger history controlled, galaxy morphology modulates SMBH growth throughout the star-forming phase: compact, dispersion-dominated galaxies grow their SMBHs faster and are preferentially quenched, producing the size and morphology differences between star-forming and quiescent galaxies. Third, at fixed stellar mass and SMBH mass, compactness further facilitates the quenching of galaxies. Fourth, disc instability transforms compact oblate discs into prolate systems, with substantial size growth and suppressed rotation but negligible stellar mass growth. This secular channel contributes about half of the prolate galaxy population around $M_{\rm star}\approx 10^{10.6}\,\rm M_\odot$. Prior to quenching, the progenitors of quiescent galaxies already have smaller sizes, lower disc-to-total ratios, and more massive SMBHs than star-forming galaxies at the same epoch, by amounts comparable to their differences at $z=0$. Morphology therefore plays an active role in growing the SMBH and quenching the galaxy, rather than being passively inherited through progenitor bias.

astro-ph.GA↗

arXiv2026

No Detectable One-halo Galactic Conformity Signal with Halo-mass Estimates Consistent with Weak-lensing Constraints

One-halo galactic conformity is the tendency for satellites in halos with quenched centrals to have lower star-formation activity than those in halos with star-forming centrals at fixed halo mass. It is an important probe of the galaxy--halo connection and halo-wide quenching processes that may couple central and satellite evolution. However, its existence remains controversial, because conformity must be measured at fixed halo mass, while halo masses are difficult to estimate accurately. In this Letter, we measure one-halo conformity in SDSS using five stellar-mass-complete samples and three halo-mass estimates: an ML estimate whose star-forming and quenched stellar mass--halo mass relations (SHMRs) agree with independent weak-lensing constraints, and two conventional abundance-matching (AM) estimates. We quantify conformity as the difference in median $\log({\rm sSFR})$ between satellites of star-forming and quenched centrals, using both satellite-level and halo-level statistics. The two AM estimates produce strong positive conformity signals, consistent with previous AM-based measurements, but these signals are not reproduced with the ML halo masses. For the halo-level statistic, the representative AM-based signals are $+0.38\pm0.04$ dex and $+0.23\pm0.04$ dex for the luminosity-ranking and mass-ranking AM halo masses, detected relative to no conformity at about $10σ$ and $6σ$, respectively. In contrast, the ML result is consistent with no conformity, $+0.00\pm0.03$ dex; the satellite-level statistic gives a similar result. Thus, with halo-mass estimates consistent with weak-lensing constraints, we find no detectable one-halo conformity signal in the present SDSS sample, suggesting that the strong AM-based signal is largely driven by halo-mass estimation biases.

astro-ph.GA↗

arXiv2026

SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation

Single-view 3D generative models have achieved impressive visual quality, yet they are not designed to satisfy structural or functional requirements, and in practice, often fall short. Symmetry is one such requirement: violations, even subtle ones, on symmetry can render a model physically unusable. We present SymTRELLIS, a method that enforces arbitrary finite point group symmetries (rotational, reflectional, and polyhedral) during the flow-based 3D generation of TRELLIS.2, without retraining the underlying VAE or flow model. Our key idea is to approximate the latent-space action of spatial transformations as a learned linear operator on voxel latents, implemented as a lightweight spatial-transform latent mapper trained on generic, non-symmetric 3D data. At generation time, we enforce symmetry by averaging predicted flow velocities across all symmetry-equivalent transformations at each ODE step, a process we call velocity symmetrization. The symmetry specification can be estimated automatically from an initial TRELLIS.2 generation or supplied by the user, enabling deliberate fold manipulation beyond what the input image suggests. On a curated benchmark of 266 strictly symmetric objects spanning 2- to 20-fold rotations and polyhedral symmetry groups, SymTRELLIS substantially reduces all symmetry error metrics compared to TRELLIS.2, Hunyuan3D-2.1, and TripoSG, while maintaining reconstruction accuracy comparable to the base model.

cs.GR↗

arXiv2026

A Hybrid POD-Autoencoder Framework for Reduced Order Modeling of Turbulent Flow via Strategic Field Decomposition

This study proposes a hybrid reduced-order modeling (ROM) framework for the simulation of turbulent flow. The central idea is to decompose flow dynamics according to their temporal characteristics and predict the resulting components individually. The full field is first divided into a sub-field represented by a limited number of proper orthogonal decomposition (POD) modes (named as POD-retained field) and the corresponding residual sub-field (named as POD-truncated field). A frequency-informed POD strategy identifies the retained modes by considering both modal energy and dominant frequency. The evolution of retained POD coefficients, which feature similar temporal scales, is described using a vector autoregressive (VAR) model. In parallel, the POD-truncated field is compressed into a low-dimensional latent space using a Fourier-neural-operator-based Koopman $β$-variational autoencoder (FK-$β$-VAE), with the latent variables subsequently predicted by a switching-VAR model. Turbulent statistics of the full field are recovered by combining the contributions from the two components. The framework is assessed using turbulent channel flow at a friction Reynolds number of $110$. The predicted Reynolds-stress components, turbulent kinetic energy (TKE), and dominant wavenumber spectra show good agreement with the reference. Moreover, in comparison with an alternative framework of full-field modeling (i.e., without field decomposition), the proposed framework yields more accurate and robust long-term statistical predictions.

physics.flu-dyn↗

arXiv2026

CHAMP: Cross-domain Hybrid Architecture for Matchmaking and Prediction in Online Multi-Player Games

Multiplayer Online Battle Arena (MOBA) games rely on matchmaking to maintain competitive balance. Our prior work, CUPID, framed matchmaking as an assignment re-optimization problem and showed that a single-mode win-rate predictor can meaningfully rebalance teams. However, deploying such a system across diverse player populations exposes three practical bottlenecks: most queueing players lack sufficient in-mode match history (cold start), skill distributions shift drastically across rank tiers (distribution inconsistency), and extreme skill segments are severely data-starved. We present CHAMP, a cross-domain matchmaking framework that resolves these deployment bottlenecks. To address data sparsity and cold starts, CHAMP replaces the target-mode-only player profile with a hybrid domain feature collection: a timestamp-ordered cross-mode short-term sequence whose slices are annotated with target-domain features, plus per-mode breakdowns of long-term, real-time and team statistics. We further propose the Domain-Aware Win-rate Network (DAWN): a Domain-aware Knowledge Extractor (DAKE) compiles target-mode attributes into learnable representations that feed Domain-Aware Temporal/Spatial/Permutation OmniNet Encoders (DATOE/DASOE/DAPOE), so that mode-conditioned representations and per-mode debiasing are learned jointly inside a single shared network. Online, one trained DAWN serves every supported mode, with per-mode position-satisfaction thresholds as the only mode-specific knob. Offline, DAWN achieves 67.73% win-rate prediction accuracy, outperforming all evaluated attention and sequence baselines. Online A/B tests across the entire League ladder of a large-scale MOBA game, from novice players up to the top-expert players served by Elite Mode, demonstrate consistent drops in imbalanced matches. For lower-tier players, CHAMP reduces the 5-minute kill crushing rate by up to 20.73%.

cs.AI↗

arXiv2026

ReCAST: Restoration-aware Cascaded Stage-wise Training for Obfuscated SMS Risk Classification

Fraudulent messages sent via Short Message Service (SMS) are increasingly obfuscated to evade cost-conscious classifiers in production systems. In Chinese SMS, attackers can exploit a wide range of carefully crafted obfuscation strategies to hide risk-bearing phrases while preserving human readability, making direct classification brittle under real-world latency and throughput constraints. We propose ReCAST, a Restoration-aware Cascaded Stage-wise Training framework for robust obfuscated Chinese SMS classification. ReCAST distills a large teacher model's de-obfuscation ability into a smaller deployable student model by supervising obfuscated span detection, obfuscation type prediction, and text restoration, and then uses the restoration-aware student for downstream risk classification. Experiments on an internally constructed real-world Chinese SMS benchmark show that ReCAST substantially improves classification performance over directly trained baselines under obfuscation. The results suggest that restoration-aware distillation offers a practical path toward robust SMS risk classification with smaller deployable models under production-oriented constraints.

cs.CR↗

arXiv2026

GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims

Visual geolocation benchmarks typically ask a model where an image was captured without accounting for the location context that users often provide. We introduce GeoContext, a resource supporting two complementary tasks: GeoHint, open-ended localization given a true but coarse location hint, and GeoVerify, binary verification of whether an image was taken within 150 m of a claimed place. GeoContext constructs a context ladder by stratifying nearby reference points according to distance and referenceability, allowing the image to remain fixed while the supplied context varies. The benchmark covers 109 sites in 30 cities and evaluates five vision-language models using 21,933 GeoHint responses and 6,270 GeoVerify responses. Our evaluation reveals three main patterns. First, hint repetition varies by only 1.5 percentage points across referenceability tiers and by less than 3 points across distance bands, while the resulting localization error increases steadily with hint distance. Second, behavior depends strongly on no-context performance: at sites with low no-context accuracy, the median ratio between localization error and hint distance is approximately 1.00, whereas at higher-accuracy sites it ranges from 0.24 to 0.69. After correcting for bias introduced by the site grouping procedure, only one of the five models retains a negative accuracy estimate when given a nearby hint. Third, in GeoVerify, no model reaches d' = 1 for decoys immediately beyond the 150 m tolerance. Model rankings also change when sensitivity is separated from response bias, and 83.8% of false acceptances are reported with confidence of at least 0.8. We release the benchmark, construction pipeline, audit decisions, and scoring code.

cs.CV↗

arXiv2026

Attend to Evidence: Evidence-Anchored Spatial Attention Supervision for Multimodal RLVR

Reinforcement learning with verifiable rewards (RLVR) improves vision-language models (VLMs) by optimizing outcome rewards derived from final answers. However, such outcome-only rewards do not tell the model which image regions justify an answer. For questions that require visual grounding, these rewards cannot distinguish responses supported by relevant visual evidence from those produced by language-prior shortcuts or lucky guesses. We introduce EASE (Evidence-Anchored Spatial Attention), which augments multimodal RLVR with visual-evidence process supervision. EASE converts annotated evidence regions into a smoothed visual-token target and uses it to guide response-to-image attention during RL training, but only on high-reward trajectories. The annotations are used solely as privileged training labels, while inference requires only the original image and question. Across Qwen2.5-VL-7B, Qwen3-VL-4B, and Qwen3-VL-8B, EASE raises average scores over DAPO by 2.5 to 3.1 points on perception, hallucination, visual math, and multimodal reasoning benchmarks. Diagnostics and ablations show that EASE better aligns visual attention with annotated evidence regions.

cs.CV↗