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Kai Wang

Publications and source records attributed to Kai Wang.

At least 37 records · Page 2Linked to original sources
arXiv2026

Correlation of galactic very-high-energy gamma-ray sources detected by LHAASO with high-energy neutrinos

We conducted an investigation into the possible correlation between very-high-energy (VHE, $E>$ 0.1 TeV) gamma-ray sources, as identified by the Large High Altitude Air Shower Observatory (LHAASO), and high-energy neutrino events captured by the IceCube Neutrino Observatory. For this analysis, we utilized the First LHAASO catalog, which comprises 90 VHE gamma-ray sources, and the IceCube Event Catalog of Alert Tracks (IceCat-1), which includes 348 track-like neutrino events. Through Monte Carlo simulations, we assessed the correlations between the neutrino events and LHAASO sources, achieving the best p-values of 0.237, 0.312, and 0.115, respectively, for all LHAASO sources, KM2A sources, and WCDA sources. These findings imply that the spatial coincidence between LHAASO sources and neutrino alerts is relatively weak. Furthermore, under the assumption that gamma-rays are exclusively generated through hadronic interactions, we estimated the most optimistic theoretical neutrino fluxes from LHAASO sources and compared them with the IceCube observed fluxes. The results indicate that the majority of these sources are improbable counterparts to the detected neutrinos. In conclusion, LHAASO sources could be potential galactic neutrino emitters but currently do not exhibit compelling evidence, thereby underscoring the necessity for elevated sensitivity levels in forthcoming detection instruments like IceCube-Gen2.

astro-ph.HE↗
arXiv2026

What Matters for World Modeling in Vision-Language-Action Models?

Integrating world models into Vision-Language-Action models (VLAs) has emerged as a promising direction, enabling policies to anticipate future dynamics beyond reactive observation-to-action mapping. However, existing approaches vary simultaneously in their world representations, integration paradigms, and policy backbones, making it difficult to determine what truly matters for effective world model integration. To disentangle these factors, we categorize existing efforts into four representative paradigms, namely Implicit Future Alignment, Explicit Future Query, MoT Future Expert, and External World Predictor. We instantiate each within a unified framework and systematically evaluate these paradigms with diverse world representations and policy backbones across in-domain, out-of-domain, long-horizon, dynamic, few-shot, and real-world settings. Our study yields four main findings: 1) world representation effectiveness is paradigm-dependent, whereas 2D semantic representations offer robust, cross-paradigm gains; 2) no single paradigm is universally optimal, yet the decoupled MoT Future Expert offers competitive overall performance, particularly in OOD robustness; 3) world modeling consistently benefits policies built on standard VLMs, while decoupled designs exhibit greater compatibility with pretrained VLAs; and 4) world modeling improves performance over action-only baselines across multiple settings, with notable gains on long-horizon and dynamic tasks. Project page and video demonstrations are available at https://dylanmqs.github.io/WM-in-VLAs/.

cs.RO↗
arXiv2026

MorphAct: Robot Policies Morphing with the Worlds They Act In

Progress toward general intelligence in the physical world requires scaling robot learning across diverse environments and interactions. However, existing robot policies use shared parameters that remain fixed across interaction contexts. When different operations favor competing parameter adjustments, fitting their mixture can leave individual operations insufficiently adapted. Our atomic-task diagnostics reveal competing update directions in both vision-language and action parameters, with source-improving updates increasing loss on other tasks. This motivates MorphAct, a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change. Two coupled generators produce context-dependent low-rank updates for the vision-language and action modules. Their coupling follows the policy's computational dependency: vision-language updates shape the representations that condition action-side parameter generation. Both generators are trained jointly through the native action objective, without operation labels or expert-adapter targets. MorphAct improves performance across three VLA backbones on LIBERO and Meta-World, with gains of up to 16.30%. In real-world evaluation, it achieves gains of up to 14% over $π_{0.5}$. Ablations support the benefits of contextual parameter generation and representation coupling.

cs.RO↗
arXiv2026

Ensemble-based Deep Multilayer Community Search

Multilayer graphs, consisting of multiple interconnected layers, are widely used to model diverse relationships in the real world. A community is a cohesive subgraph that offers valuable insights for analyzing (multilayer) graphs. Recently, there has been an emerging trend focused on searching query-driven communities within the multilayer graphs. However, existing methods for multilayer community search are either 1) rule-based, which suffer from structure inflexibility; or 2) learning-based, which rely on labeled data or fail to capture layer-specific characteristics. To address these, we propose EnMCS, an Ensemble-based unsupervised (i.e., label-free) Multilayer Community Search framework. EnMCS contains two key components, i.e., HoloSearch which identifies potential communities in each layer while integrating both layer-shared and layer-specific information, and EMerge which is an Expectation-Maximization (EM)-based method that synthesizes the potential communities from each layer into a consensus community. Specifically, HoloSearch first employs a graph-diffusion-based model that integrates three label-free loss functions to learn layer-specific and layer-shared representations for each node. Communities in each layer are then identified based on nodes that exhibit high similarity in layer-shared representations while demonstrating low similarity in layer-specific representations w.r.t. the query nodes. To account for the varying layer-specific characteristics of each layer when merging communities, EMerge models the error rates of layers and true community as latent variables. It then employs the EM algorithm to simultaneously minimize the error rates of layers and predict the final consensus community through iterative maximum likelihood estimation. Experiments over 10 real-world datasets highlight the superiority of EnMCS in terms of both efficiency and effectiveness.

cs.SI↗
arXiv2026

Mergers may not be the main channel for galaxy stellar mass growth at redshift $z\gtrsim5$: Predictions from cosmological hierarchical models and simulations

Mergers are a crucial channel of galaxy growth, but their role relative to star formation remains uncertain at high redshift. Here we investigate merger rates and merger-driven stellar mass assembly for galaxies with stellar mass $M_\star \gtrsim 10^8 \, {\rm M_{\odot}}$ at $z\simeq 5-11$, by combining complementary theoretical approaches within $Λ$CDM. These include the DECODE semi-empirical model, the TNG100 and EAGLE hydrodynamical simulations, and the \textsc{L-Galaxies} semi-analytical model, together with the Millennium and TNG100-Dark simulations used to examine dark matter subhalo statistics and merger timescales. This compares models with different treatments of baryonic physics and galaxy-halo connections. We find that dark matter-only simulations predict merger timescales $\sim 30\%$ longer than those inferred from cosmological simulations including baryons. Galaxy major merger ($M_{\rm \star,sat} / M_{\rm \star,cen} > 0.25$) rates are high at cosmic dawn and increase towards earlier epochs, reaching $\sim 1/{\rm Gyr}$ at $z\sim 5$ and peaking at $\sim 3/{\rm Gyr}$ at $z\sim 8$. Despite these high rates, mergers contribute only $\lesssim 10\%$ of the total cosmic stellar mass density evolution. The ex-situ stellar mass fraction ranges from a few percent to $\sim 20\%$ for all galaxies at $z\gtrsim 5$, across different models and simulations. Their broad agreement implies that stellar mass assembly at cosmic dawn is dominated by in-situ star formation rather than mergers. These findings suggest that the high pair fractions identified by JWST may not directly trace efficient ex-situ stellar mass growth, highlighting the need for physically motivated merger timescales to interpret galaxy assembly in the early Universe.

astro-ph.GA↗
arXiv2026

LoopREG: Representation Supervision Enables Test-Time Recurrent Depth in Diffusion Transformers

Reusing Transformer blocks offers a way to increase diffusion-model computation at test time, but naive recurrence can degrade generation beyond its training depth. We introduce LoopREG, a representation-supervised recurrent diffusion Transformer that repeatedly applies a shared block at the state supervised by a pretrained visual encoder. Alignment is applied after the final recurrent visit during training; inference extends the same learned operator to additional visits. On ImageNet 256 x 256, LoopREG reduces SiT-B/2 FID from the local REG baseline's 15.08 to 4.91 at 400K training steps, a 67.41% reduction. Within a checkpoint trained with two visits, one extra inference visit improves FID from 14.71 to 5.34 with unchanged weights. The benefit persists on SiT-L/2 and SiT-XL/2. Controlled interventions show that useful recurrence follows the location of representation supervision, while REPA alignment produces a smaller extrapolation benefit. These results make representation-supervised recurrence a practical route to improving diffusion generation through additional inference depth.

cs.CV↗
arXiv2026

Dynamical Insights into the Current Control Strategies for Chikungunya

This paper addresses the challenge that, under China's current mosquito-control policy for chikungunya---\textit{clearing stagnant water, killing adult mosquitoes, and preventing bites}---epidemics can still recur and continue to rise. By constructing a vector--host dynamical model that incorporates asymptomatic infections and a screening rate, we derive approximate analytical expressions for the epidemic peak value and peak time, and reveal the intrinsic link between short-term transients and long-term outcomes. Simulations across three scenarios---human-seeded, mosquito-seeded, and joint human--mosquito-seeded---show that (i) even when the vector population is effectively suppressed, asymptomatic carriers can still sustain transmission; (ii) the effect of the screening rate on the peak time is non-monotonic, and there exists a critical threshold that maximizes the delay of the epidemic; and (iii) moderate screening can both suppress the first wave and avoid leaving an excessively large susceptible pool that would amplify subsequent waves. Accordingly, we upgrade the original policy to a \textit{Twelve-character} strategy for prevention, mitigation, and control---\textit{clearing stagnant water, killing adult mosquitoes, preventing bites, and screening cases early}---providing theoretical support for chikungunya and other mosquito-borne diseases.

q-bio.PE↗
arXiv2026

Totem: Object-Centric 3D Memory for World-Consistent Video Generation

Generating coherent videos grounded in a unified underlying world remains a major challenge, particularly under large viewpoint changes, where existing methods often struggle to maintain stable representations of individual objects. To this end, we introduce Totem, an object-centric 3D memory framework for world-consistent video generation. Totem first represents each selected object as a persistent 3D memory aligned to a shared world frame, assigning it a dedicated state. Based on these memories, we design a dual-stream 3D conditioning architecture that combines object memories with scene geometry for target-view synthesis. Moreover, these object memories constrain the model's attention with 3D structural information, thereby preserving object surfaces and relative spatial layouts. Extensive experiments on world-generation and object-centric benchmarks demonstrate that Totem generates videos depicting a more object-consistent 3D world under challenging camera trajectories, while supporting versatile applications.

cs.CV↗
arXiv2026

DiffNash: Finding Nash Equilibrium in High-Dimensional Continuous Action Spaces with Diffusion Policies

Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something conventional neural policies struggle to achieve. Diffusion models offer a natural solution, but standard diffusion training requires direct samples from a fixed target distribution, which are generally unavailable under iterative game-solving updates. We propose a diffusion-based framework for equilibrium computation that enables diffusion-based variants of Online Mirror Descent (OMD) and Fictitious Play (FP). Our approach derives a reweighted score-matching objective that trains diffusion policies without requiring direct samples from each updated policy. Our main method, DiffNash, combines the fast convergence of OMD with the sample efficiency of FP by reusing past actions through a replay buffer. To correct the resulting distribution shift from sample reuse, we derive an unbiased estimator of the difference between current and past policies' Evidence Lower Bounds, whose exponential provides a tractable proxy for the corresponding importance weight without evaluating diffusion likelihoods. We evaluate our approach on forest protection games and continuous Colonel Blotto games, reducing the Nash gap over the strongest baseline by 35.6% and 24.7%, respectively, with minimal hyperparameter tuning.

cs.LG↗
arXiv2026

HarnessWAM: Bridging Prediction and Deliberation in World Action Models

World Action Models~(WAMs) jointly learn environmental dynamics and robot actions, introducing priors over physical evolution into embodied control. However, finite-horizon prediction and action generation are insufficient for complex embodied tasks that require global planning, cross-stage state maintenance, execution verification, and failure recovery. We refer to this mismatch as the prediction-deliberation gap. To address this gap, we propose HarnessWAM, an agentic framework for WAMs. HarnessWAM employs a vision-language-model-based Task Manager to maintain an evidence-grounded scene belief and organize the global instruction into a structured task graph. Capability-conditioned executable-space projection then compiles the graph into WAM-supported skill sequences that satisfy task dependencies and embodiment-state constraints. During execution, HarnessWAM couples a lightweight progress estimator with event-triggered Task Manager deliberation. The estimator supplies frequent execution cues, while the Task Manager uses observations, task state, and interaction history to verify outcomes and decide whether to advance, acquire evidence, revise the plan, or recover. Local recovery preserves acquired scene knowledge so that execution can resume after failure. HarnessWAM achieves state-of-the-art average performance on RoboMemArena, with full-task and subtask success rates of 64.4% and 76.8%, respectively, and reaches 23.7% success on RoboCerebra Ideal. On a physical UR3 arm, full-task success reaches 95% for shape-conditioned tabletop tidying and 90% for memory-dependent box exploration. These results demonstrate the effectiveness of model-external agentic orchestration in bridging the prediction-deliberation gap for complex embodied tasks.

cs.RO↗
arXiv2026

Inflow-driven galaxy evolution - II: A hierarchy in the scaling relations of star-forming galaxies

The star-forming main sequence (SFMS; $M_\star$--sSFR), the gas-to-stellar mass relation ($M_\star$--$M_{\rm gas}/M_\star$), and the mass--metallicity relation (MZR; $M_\star$--$Z_{\rm gas}$) are three core scaling relations for star-forming galaxies, and together they encode the information about the baryon cycle that operates during galaxy evolution. We investigate how these relations emerge by modelling the gas flows that regulate the baryon cycle, using both an ideal and a cosmological gas flow model, and identify a hierarchy among the three relations, with one further parameter entering the scatter budget at each step. In our model, the SFMS and its scatter are set primarily by the formation time of the host halo, with a weak dependence on the star formation efficiency and the mass-loading factor. The gas-to-stellar mass relation follows from the ratio of the sSFR to the star formation efficiency, and inherits the scatter of both. The MZR in turn follows from the gas-to-stellar mass relation together with the mass-loading factor, their relative contributions depending on the galaxy evolutionary state between the inflow-driven regime and equilibrium. This hierarchy naturally introduces further correlations among the three relations, such as the fundamental plane of star formation ($M_\star$--$M_{\rm gas}$--SFR), the fundamental metallicity relation ($M_\star$--SFR--$Z_{\rm gas}$) and its gaseous counterpart ($M_\star$--$M_{\rm gas}$--$Z_{\rm gas}$), and the correlations of each relation with galaxy size, which traces the star formation efficiency through the star formation law. Together, the three core scaling relations and the higher-dimensional correlations among them follow from a single framework, providing a foundation for linking the observables of star-forming galaxies to the underlying baryon cycle.

astro-ph.GA↗
arXiv2026

OmniCapBench: A Deep-Structured Evaluation Framework for Fine-Grained Audio-Visual Captioning

Multimodal large language models (MLLMs) are rapidly evolving toward continuous audio--visual reasoning, creating an urgent need for evaluations that expose their capability limits. Audio--visual captioning is an ideal diagnostic task, yet current benchmarks face a coupled trade-off: whole-caption scores provide coverage without localization, local probes provide localization without coverage, and unconstrained LLM judges introduce instability. We introduce OmniCapBench (Omni-Video Caption Benchmark), a benchmark that reframes audio--visual caption evaluation as a deep-structured diagnostic framework. OmniCapBench shifts the prediction target from free-form text to sets of atomic, verifiable evaluation units across three tracks: entity references, visual shots, and audio events, enabling reliable scoring with deterministic constraint checks and localized LLM-based semantic comparisons. With 786 densely annotated videos, OmniCapBench effectively distinguishes MLLM perception errors, including temporal grounding failures, identity drift, cross-modal misalignment, and hallucinated descriptions. Evaluating frontier MLLMs reveals strong local perception but weak long-horizon audio--visual reasoning, particularly in identity drift and cross-modal misalignment, providing a fine-grained roadmap for omnimodal development.

cs.CV↗
arXiv2026

Joint Projective Spectrum of $D_{\infty n}$

We compute the joint spectrum of $D_{\infty n}$ with respect to the left regular representation, and finds two generators of the De Rham cohomology group of joint resolvent set which is induced by different central linear functionals.

math.FA↗
arXiv2026

FaceKit: a Toolkit for Interpretable Facial Phenotyping, Synthetic Image Generation and Privacy Analysis in Rare Diseases

Many rare genetic diseases are associated with recognizable craniofacial features. However, traditional approaches for describing facial morphology rely largely on qualitative clinical observation and free-text descriptions, which are often subjective, non-standardized, and difficult to reproduce across observers and institutions. Although the Human Phenotype Ontology (HPO) provides controlled terms for describing facial features, these terms are typically categorical rather than quantitative and may vary depending on examiner experience and interpretation. Here, we present FaceKit, a computational framework for quantitative facial phenotyping from frontal facial photographs. FaceKit extracts standardized measurements of facial landmarks and derived 120 morphological features, then reports feature-level z-scores representing deviation from population reference distributions. The reference distributions are built from the FairFace dataset spanning diverse ancestral groups. We evaluated FaceKit on a curated subset of the GestaltMatcher Database covering 50 rare-disease cohorts. In addition to quantitative facial analysis, FaceKit includes synthetic facial image generation to support rare disease model development and data augmentation. We also performed privacy evaluation to assess whether synthetic images reveal identifiable information from real patient photographs and could compromise patient privacy. Across disease case studies, FaceKit-derived quantitative measurements captured known facial features associated with rare genetic disorders and provided objective support for clinical phenotyping. Together, these results establish FaceKit as a useful tool for quantitative phenotyping, and has the potential to improve rare disease diagnosis, support genotype-phenotype studies, and enable more reproducible clinical characterization across diverse patient populations.

q-bio.QM↗
arXiv2026

Besov spaces and product BMO over polydisc

We prove that the analytic Besov space $B_{p,p}^{1/p}(\C_+^n)$ embeds continuously into Chang--Fefferman product BMO for every finite $n$ and $1\le p<\infty$. Consequently, every small Hankel operator in a finite Schatten class has a product BMO symbol. The proof uses smooth product wavelets and an estimate for dyadic rectangles. We also give a condition on Fejér means that yields a BMO symbol for a bounded Hankel operator on the polydisc.

math.FA↗
arXiv2026

Budgeted Multi-Source Counterfactual Annotation for Off-Policy Evaluation

Off-policy evaluation (OPE) estimates the value of a target policy from logged data, but limited behavior-policy coverage can force high-variance reweighting or reward-model extrapolation. Counterfactual annotations can add evidence about unobserved actions, yet practical sources, including domain experts and large language models (LLMs), may be costly, biased, or noisy. We study budgeted acquisition of such annotations for contextual-bandit OPE. Given source-specific costs and error profiles, we formulate an integer allocation problem over context-action pairs and annotation sources to minimize the component of estimator variance that depends on the annotation plan. We characterize when annotations are valuable through a first-annotation threshold and local annotation-value regimes. For the coupled multi-source problem, we develop a majorization-minimization algorithm with dynamic-programming subroutines that monotonically improves the objective. Experiments in synthetic clinical and LLM-annotated education bandits show that our allocation method reduces fixed-profile mean squared error (MSE) by 20.58% and 10.77%, respectively, relative to no annotation.

cs.LG↗
arXiv2026

ViDAL: A Visual Dynamics-Grounded Action Latent Space for Vision-Language-Action Models

Vision-Language-Action (VLA) models have become a central paradigm for robot policy learning, which predict actions in three forms: raw action chunks, discrete action tokens, or continuous action latents. However, existing action representations primarily model action trajectories, with limited consideration of the visual dynamics induced by these actions. We introduce ViDAL, a Visual Dynamics-grounded Action Latent Space that anchors continuous action latents in the future visual dynamics of the scene. Specifically, ViDAL learns action latent space by training an Action Variational Autoencoder (Action VAE) to reconstruct action chunks while aligning its latent with future scene dynamics. When integrated into downstream robot policies, the proposed Action VAE serves as a plug-in action interface compatible with multiple VLA architectures and enables optional future-video prediction as an additional capability. Empirically, ViDAL outperforms competitive baselines on LIBERO with 98.1% average success, improves a multi-task $π_{0.5}$ policy on RoboTwin 2.0 from 54.3% to 65.5% (Clean) and from 33.2% to 43.1% (Random) success rates over 50 dual-arm tasks, and yields 20.0% and 23.4% absolute success-rate gains on real-world single-arm Franka and dual-arm ARX robot platforms.

cs.RO↗
arXiv2026

Decision-Focused Learning in MDPs: An Occupancy Measure Approach

In this work, we consider decision-focused learning (DFL) for a Markov decision process (MDP), where existing methods differentiate through the KKT conditions of the Bellman equation and require solving a linear system over all state-action pairs, limiting its scalability. We address this by reformulating the MDP as an occupancy measure-based linear program (LP), whose feasible region is induced by predicted dynamics, and we derive a closed-form gradient by identifying the active constraints in the feasible polyhedron via the pivoting algorithm. This occupancy measure-based LP layer raises two challenges: (1) LP's solution gradient is discontinuous when active constraints change, and (2) the LP backward cost still scales with the state size, which is costly for large or continuous state spaces. We address the challenges with an augmented Lagrangian surrogate and smooth the boundary jumps by random row sketching of the constraints, and a learnable soft state-aggregation layer and its function-approximation generalization that scales the LP to large finite and continuous-state MDPs. Across multiple tasks, our methods reach lower regret than KKT-based DFL and two-stage baselines with significantly lower computation cost. The source code for all experiments is available at https://github.com/A-Eshragh/State_Aggregation_Project.

cs.LG↗