arXiv subjects

Jing Wang

Publications and source records attributed to Jing Wang.

At least 109 records · Page 6Linked to original sources

arXiv2026

Strong counterexamples to Mubayi's supersaturation conjecture in every uniformity

The supersaturation problem asks, for a fixed $r$-graph $\mathcal F$, for the minimum number of copies of $\mathcal F$ in an $n$-vertex $r$-graph with $\ex(n,\mathcal F)+q$ edges. Mubayi conjectured a local form of supersaturation under a stability hypothesis: if $\mathcal F$ is non-$r$-partite and stable, meaning roughly that the extremal $\mathcal F$-free construction is unique and all near-extremal $\mathcal F$-free $r$-graphs are close to it, then this minimum should be at least $q c(n,\mathcal F)$, where $c(n,\mathcal F)$ is the minimum number of copies created by adding one edge to the extremal $\mathcal F$-free $r$-graph. We disprove this conjectured local lower bound in every uniformity. For every $r\ge2$ and every $K>1$, we construct a stable $r$-graph $\mathcal F$ such that, for all sufficiently large $n$ and every $1\le q\le δn$, there is an $n$-vertex $r$-graph with $\ex(n,\mathcal F)+q$ edges and at most $K^{-1}q c(n,\mathcal F)$ copies of $\mathcal F$. Thus the conjectured lower bound can already fail at $q=1$, and the failure can be by an arbitrarily large constant factor in every uniformity.

math.CO↗

arXiv2026

Insights into Jet-Induced Cloud Disruption in NGC 1316: ALMA Reveals a Spatially Extended Molecular Gas

We present ALMA CO($J=1-0$) observations of a nearby radio galaxy NGC1316 at a 100-pc resolution to investigate the impact of AGN jets on the molecular gas. The molecular gas exhibits complex spatial and kinematic distributions, with broad CO line widths ($>50$ km s$^{-1}$) observed in several regions. The interferometric CO flux is only 34%-38% compared to single-dish data, indicating a large fraction of spatially extended molecular gas, especially in the central regions. We identified 24 Giant Molecular Clouds Associations (GMAs) primarily within the ``NW Shell'' and the ``SE Blob''; these GMAs show velocity dispersions approximately twice as high as those in typical star-forming galaxies for their sizes. Analysis of archival ALMA CO($J=2-1$) and CO($J=3-2$) data reveals elevated line ratios ($R_{21} \sim 1$ and $R_{31} \sim 1$) in gas near the jet, whereas, away from the jet, typical values ($R_{21} \sim 0.7$, $R_{31} \sim 0.3$). A multi-wavelength comparison reveals a $\sim$5 kpc warm ionized gas shell that encompasses the molecular NW Shell. The observed energetics and bubble morphology are consistent with an expanding bubble model driven by the jet assuming a jet power of $1.6\times10^{43}$~erg~s$^{-1}$. We propose that the high extended gas fraction results from the destruction of molecular clouds due to interactions with the jet plasma. NGC1316 may be a good example of jet-induced negative feedback through the ablation, dispersal, and rarification of dense molecular clouds through jet-ISM interactions.

astro-ph.GA↗

arXiv2026

AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems

Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remains blocked by a structural execution bottleneck: the idea-to-launch cycle still depends on human engineers to generate hypotheses, modify production code, launch A/B experiments, and attribute online results. Innovation therefore scales linearly with headcount rather than compounding with evidence, compute, and accumulated experimental knowledge. We present AgentX, a production-deployed multi-agent system that fundamentally restructures this production function. AgentX operates as a self-evolving development engine: it autonomously generates, implements, evaluates, and learns from recommendation experiments at a scale and pace that no manual workflow can sustain. The system orchestrates four tightly coupled stages in a closed loop. A Brainstorm Agent synthesizes evidence from historical experiments, system architecture, data analysis, and external research into ranked, executable proposals. A Developing Agent translates each proposal into production-ready code through repository-grounded generation and multi-dimensional reliability verification. An Evaluation Agent conducts safe online rollout with guardrail-vetoed A/B judgment, converting both successes and failures into structured knowledge assets. A Harness Evolution layer (SGPO) then distills execution trajectories into semantic-gradient updates that continuously sharpen the agents themselves -- making the system not merely automated, but self-improving.

cs.AI↗

arXiv2026

Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety

As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. However, existing general-purpose model development largely overlook this adversarial nature, and often remain insufficient for realistic safety scenarios involving planning, tool use, and multi-step reasoning, causing measured safety performance to overestimate real deployment robustness. To address this gap, we present Yuvion LLM, a large language model built for adversarially robust content safety and broader AI safety. Yuvion LLM treats adversarial robustness and agentic capability as first-class objectives. Its pipeline combines adversarially aware data construction, knowledge-enhanced continued pretraining, and policy-grounded multi-task safety post-training, including risk-aware supervised fine-tuning and reinforcement learning-based policy optimization, together with safety-aware agentic reinforcement learning for tool use and multi-step reasoning in complex safety scenarios. We further introduce the Yuvion LLM RiskEval (YLRE), a collection of 93 benchmarks across four evaluation categories, covering diverse open and internal evaluations with a focus on safety, adversarial robustness, and real-world capability requirements. Across these evaluations, Yuvion LLM demonstrates clear advantages on safety-focused benchmarks and particularly strong robustness under adversarial conditions, while maintaining solid overall capability. Notably, Yuvion-8B outperforms most state-of-the-art baselines, including substantially larger models such as GPT-5.4 and Qwen3-MAX, on several safety tasks.

cs.CL↗

arXiv2026

What is the True HI Gas Content in Massive Quiescent Galaxies in the Local Universe?

While massive quiescent galaxies are known to be poor in atomic hydrogen (HI), their true HI content remains poorly constrained due to the limited sensitivity and morphological biases of existing surveys. We present deep HI observations using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) for a representative sample of 78 low-redshift massive quiescent galaxies, selected by stellar mass ($M_\ast > 10^{10} M_\odot$), color (NUV$-r > 5$), and specific star formation rate ($\rm \log sSFR < -11\ yr^{-1}$). Our observations reach a remarkable sensitivity of $\log(M_{HI}/M_\ast) = -2.6$ for 55 targets and $\log(M_{HI}/M_\ast) = -3.2$ for 23 targets. We find that one-third of the sample follows the HI scaling relation derived from previous surveys, while the remaining two-thirds exhibit significantly lower HI content. The HI mass fraction shows no clear correlation with specific star formation rate, NUV$-r$ color index, stellar surface mass density, and concentration. Our FAST sample shows remarkable similarity to the $ATLAS^{3D}$ sample which only includes early-type galaxies, both in its high fraction of HI-poor galaxies and its high satellite fraction among HI-poor galaxies. These results suggest that while both early-type morphology and environment may contribute to the extreme HI deficiency, neither factor alone fully explains the observed gas depletion, indicating that additional physical mechanisms must be responsible for the extreme HI deficiency prevalent in massive quiescent galaxies.

astro-ph.GA↗

arXiv2026

HI Galaxy Science with the SKA

This chapter introduces the contributions of the HI galaxy science in this volume reviewing the latest developments and urgent questions in HI galaxy science, providing guiding principles for a layered set of future key science projects. The key science will include: a complete censuses of HI morphologies and kinematics at sub-kpc and 1 km/s resolution within and around galaxies in the nearby Universe; a measurement of the cosmic HI mass density and HI mass function evolution at least up to z~1; an improved understanding of the Universe at z>1, particularly the balance between cold molecular and cool atomic gas. We also provide a view of the synergistic multi-wavelength surveys available in 2028+ in the southern hemisphere. This effort will improve our understanding of the baryon cycle across a significant fraction of the cosmic history, including the processes of gas accretion, consumption and removal as well as AGN and star formation feedback. Based on these science goals, the earlier proposed three-tiered survey strategy remains, but survey parameters and predictions are adjusted according to AA* and AA4 developments. This chapter is an update of the earlier "Advancing Astrophysics with the Square Kilometre Array" chapter 'HI Science with the SKA' by Staveley-Smith & Oosterloo.

astro-ph.GA↗

arXiv2026

Text Distance from Nested and Hierarchical Repetitions: A Compression-Based Perspective

We present a new method for structural sequence analysis grounded in Algorithmic Information Theory (AIT). At its core is the Ladderpath approach, which extracts nested and hierarchical relationships among repeated substructures in linguistic sequences -- an instantiation of AIT's principle of describing data through minimal generative programs. These structures are then used to define three distance measures: a normalized compression distance (NCD), and two alternative distances derived directly from the Ladderpath representation. Integrated with a $k$-nearest neighbor classifier, these distances achieve strong and consistent performance across in-distribution, out-of-distribution (OOD), and few-shot text classification tasks. In particular, all three methods outperform both gzip-based NCD and BERT under OOD and low-resource settings. These results demonstrate that the structured representations captured by Ladderpath preserve intrinsic properties of sequences and provide a lightweight, interpretable, and training-free alternative for text modeling. This work highlights the potential of AIT-based approaches for structural and domain-agnostic sequence understanding.

cs.CL↗

arXiv2026

Towards Practical Lossless Neural Compression for LiDAR Point Clouds

LiDAR point clouds are fundamental to various applications, yet the extreme sparsity of high-precision geometric details hinders efficient context modeling, thereby limiting the compression speed and performance of existing methods. To address this challenge, we propose a compact representation for efficient predictive lossless coding. Our framework comprises two lightweight modules. First, the Geometry Re-Densification Module iteratively densifies encoded sparse geometry, extracts features at a dense scale, and then sparsifies the features for predictive coding. This module avoids costly computation on highly sparse details while maintaining a lightweight prediction head. Second, the Cross-scale Feature Propagation Module leverages occupancy cues from multiple resolution levels to guide hierarchical feature propagation, enabling information sharing across scales and reducing redundant feature extraction. Additionally, we introduce an integer-only inference pipeline to enable bit-exact cross-platform consistency, which avoids the entropy-coding collapse observed in existing neural compression methods and further accelerates coding. Experiments demonstrate competitive compression performance at real-time speed. Code will be released upon acceptance. Code is available at https://github.com/pengpeng-yu/FastPCC.

cs.CV↗

arXiv2026

Highly filamentary H{\,\small I} gas in the circumgalactic medium and intragalactic medium around NGC 4631

Neutral hydrogen (H{\,\small I}) in the circumgalactic medium (CGM) and intergalactic medium (IGM) traces baryon cycling and galaxy evolution, yet fine filamentary substructures in diffuse CGM/IGM H{\,\small I} remain poorly constrained observationally. We combine FAST FEASTS single-dish and VLA THINGS interferometric H{\,\small I} data of the NGC 4631 group to report the first robust detection of kpc-scale filamentary structures in the CGM/IGM, with widths of $0.5$-$3.3\ \mathrm{kpc}$ and lengths of $6.1$-$49.8\ \mathrm{kpc}$. These features confirm that low-density CGM/IGM gas hosts velocity coherent substructure. From position-velocity kinematic analysis, we identify three filament classes (U-shaped, linear, and wavy), implying diverse formation mechanisms. Our results establish a structural bridge connecting pc-scale interstellar filaments, kpc-scale CGM/IGM filaments, and Mpc-scale cosmic-web filaments, providing key observational support for multiscale gaseous coupling in cosmic ecosystem.

astro-ph.GA↗

arXiv2026

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models

Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for ensuring predictive credibility. While traditional uncertainty taxonomy paradigms, such as the dichotomy of aleatoric and epistemic uncertainties, provide conceptual foundations, they often fail to capture the multi-component and multi-stage nature of LLM generation and struggle to evaluate the effectiveness of various Uncertainty Quantification (UQ) methods. In this paper, we propose a granular uncertainty taxonomy that systematically attributes LLM uncertainty into input-level, parameter-level, token-level, and decoding-process sources. Correspondingly, we categorize existing UQ methods into Bayesian, ensemble, consensus-based, and single-pass approaches. Furthermore, we introduce a comprehensive evaluation framework covering diverse generation settings and metrics. We empirically evaluate 21 typical UQ methods across three prominent LLM families, including Qwen3, Llama 3.2, and DeepSeek-V3, on benchmarks such as TriviaQA, GSM8K, and HumanEval. Our experimental results demonstrate that (i) the effectiveness of UQ methods is sensitive to task types and generation settings; (ii) consensus-based methods, typed Deg and EigV, consistently outperform other UQ approaches; and (iii) larger model scales correlate with lower uncertainty estimates, suggesting an empirical scaling law for LLM uncertainty. This work bridges the gap between theoretical origins and practical deployment, providing a versatile diagnostic tool for systematically quantifying uncertainty in LLM applications.

cs.AI↗

arXiv2026

Promise and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation: a feasibility study

Purpose: To evaluate the feasibility and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation and deep learning-based segmentation. Approach: We developed ChameleonNet, a framework utilizing the Contrastive Unpaired Translation (CUT) network with decoupled contrastive learning (DCL) loss to synthesize non-contrast CT from contrast CT scans. Using annotations of four heart chambers (left atrium (LA), left ventricle (LV), right atrium (RA), and right ventricle (RV)) from contrast scans, we trained a Hausdorff distance loss-enhanced nnU-Net on synthesized non-contrast images. The translation model was trained with 35,538 contrast-enhanced and 37,197 non-contrast CT slices. The segmentation model was trained with 292 synthesized non-contrast scans. Performance was evaluated using Dice similarity coefficient (DSC) and 95th Hausdorff distance (HD95) on 36 synthesized non-contrast scans, and volume agreement on 36 real non-contrast CT scans was assessed using Pearson correlation, mean absolute percentage error (MAPE), and mean percentage error (MPE). Results: The segmentation model achieved DSC of 0.94 (0.01), 0.91 (0.04), 0.92 (0.03), 0.93 (0.02), and HD95 of 3.63 (1.49), 5.74 (4.08), 5.18 (1.77), 5.51 (3.21) mm on synthesized non-contrast images for LA, LV, RA, and RV, respectively. On real non-contrast CT scans, Pearson correlations were 0.93, 0.82, 0.87, and 0.89 (all p<0.001), with MAPE ranging from 9.22% to 20.79%, and MPE ranging from -12.52% to 4.67%. Conclusions: ChameleonNet demonstrated feasibility for heart chamber segmentation from non-contrast CT without manual non-contrast annotations. However, volume errors, particularly for LV and RV, indicate that further refinement and validation are needed before clinical use.

eess.IV↗

arXiv2026

T-Rex: Tactile-Reactive Dexterous Manipulation

The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) models for robotic manipulation generally either overlook the tactile modality or are limited to encoders with static cues, due in part to the scarcity of diverse training data and standardized evaluation, architectural constraints in current VLA models, and limitations of static tactile encoders. In this paper, we push the frontier of tactile-reactive manipulation by addressing all of these limitations. We propose a large-scale, 100-hour tactile-rich dataset collected via a novel, data-efficient recipe that prioritizes elementary motor primitives. To effectively exploit naturally high-frequency touch signals without sacrificing the existing capabilities of existing VLAs, we introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder. We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation, achieving over 30% higher average success rate than the strongest baseline.

cs.RO↗

arXiv2026

Vesta: A Generalist Embodied Reasoning Model

Robots operating in open-world environments must seamlessly integrate localization, spatial reasoning, navigation, and long-horizon planning. While specialist models excel at individual tasks, deploying a multi-model stack is computationally expensive and prone to cascading errors. We present Vesta, a unified embodied generalist that consolidates these capabilities into a single foundation model. Our approach combines a diverse and massive curated corpus designed to induce spatial grounding and a simple multimodal memory harness that enables reasoning over extended time horizons. Across diverse benchmarks, Vesta on average beats individual SOTA baselines by >$20\%$ and beats an ensemble of per-category-best baselines by $>10\%$ -- thus demonstrating that a generalist model can match or exceed specialists. On real-world robotic tasks requiring memory and reasoning, Vesta improves task success by >35\%. Our work thus demonstrates that a single generalist is a feasible, scalable, and arguably preferable alternative to combining specialists.

cs.RO↗

arXiv2026

iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance

Video Virtual Try-On (VVT) aims to seamlessly replace a garment on a person in a video with a new one. While existing methods have made significant strides in maintaining temporal consistency, they are predominantly confined to non-interactive scenarios where models merely showcase garments. This limitation overlooks a crucial aspect of real-world apparel presentation: active human-garment interaction. To bridge this gap, we introduce and formalize a new challenging task: Interactive Video Virtual Try-On (Interactive VVT), where subjects in the video actively engage with their clothing. This task introduces unique challenges beyond simple texture preservation, including: (1) resolving the semantic ambiguity of interactions from standard pose information, and (2) learning complex garment deformations from video where interactive moments are sparse and brief. To address these challenges, we propose iTryOn, a novel framework built upon a large-scale video diffusion Transformer. iTryOn pioneers a multi-level interaction injection mechanism to guide the generation of complex dynamics. At the spatial level, we introduce a garment-agnostic 3D hand prior to provide fine-grained guidance for precise hand-garment contact, effectively resolving spatial ambiguity. At the semantic level, iTryOn leverages global captions for overall context and time-stamped action captions for localized interactions, synchronized via our novel Action-aware Rotational Position Embedding (A-RoPE). Extensive experiments demonstrate that iTryOn not only achieves state-of-the-art performance on traditional VVT benchmarks but also establishes a commanding lead in the new interactive setting, marking a significant step towards more dynamic and controllable virtual try-on experiences.

cs.CV↗

arXiv2026

GRB 250424A: A Case Study of Energy Injection with Multiwavelength Observations

We present a comprehensive multiwavelength analysis of the long-duration gamma-ray burst (GRB) 250424A. Our dataset spans from the prompt gamma-ray emission to late-time optical monitoring, including spectra obtained with the Keck 10\,m telescope. We find that the afterglow light curves display a prominent, simultaneous shallow decay phase in both X-ray and optical bands, followed by an achromatic transition to a standard decay regime. The broadband spectral energy distributions are well-modeled by a single power-law function, indicating a common synchrotron origin for the emission across frequencies. We interpret the afterglow evolution within the framework of a relativistic forward shock refreshed by continuous energy injection. This scenario successfully reproduces the observed temporal and spectral behavior, yielding an isotropic equivalent kinetic energy of $E_{\rm K,iso} \approx 5.5 \times 10^{52}$ erg and an injection index of $q\approx 0.34$ in a constant-density circumburst environment. The shallow decay phase is consistent with sustained energy injection lasting $\sim$ 9 ks. Despite the relatively low redshift, late-time optical observations reveal no distinct supernova component; however, our derived upper limits do not strictly rule out the presence of a typical GRB-associated supernova.

astro-ph.HE↗

arXiv2026

IMPart: Integration of Memetic Operations into Multi-Level Framework for Large-k-Way Hypergraph Partitioning

The problem of k-way hypergraph partitioning is fundamental with significant applications in various fields, including VLSI design and scientific computing. State-of-the-art hypergraph partitioners commonly employ a multi-level framework encompassing coarsening, initial partitioning, uncoarsening, and refinement phases. However, many existing methods do not scale well to problems requiring a large number of partitions (i.e., large k). In pursuit of exceptionally high solution quality, existing memetic approaches often execute their two key operations, recombination and mutation, by invoking separate, standalone multi-level partitioners. This design choice, however, renders them significantly more time-consuming than standard multi-level partitioners. To make such memetic approaches more practical, we propose an advanced memetic framework, IMPart, which introduces novel recombination and mutation operators and integrates them directly into the uncoarsening phase of a single multi-level framework. This transforms the local searches of different granularities in the traditional multi-level framework into a sophisticated, collaborative search. Experimental results on multiple standard benchmarks demonstrate our framework more effectively escapes local optima and explores the global solution space for higher-quality solutions, substantially outperforming all existing hypergraph partitioners for large-$k$-way hypergraph partitioning. Our framework highlights a new paradigm for the development of advanced hypergraph partitioners.

cs.AR↗

arXiv2026

ComPart: Community-Guided Post-Coarsening for High-Quality Hypergraph Partitioning

Hypergraph partitioning is a critical step in the design of complex embedded systems, essential for optimizing task mapping on heterogeneous MPSoCs and enabling multi-FPGA prototyping. Many existing methods rely on community detection to identify modules with dense internal and sparse external connections, typically utilizing them to constrain the coarsening phase--a widely adopted paradigm. In this work, we propose ComPart, a generalized framework that integrates diverse community detection methods to uncover high-quality clusterings throughout the post-coarsening stages (i.e., initial partitioning and uncoarsening). These discovered clusterings serve as distinct structural guides, enabling the refinement process to identify superior partitioning solutions. Our framework offers two key advantages: (1) it establishes a new paradigm that leverages community structures detected during uncoarsening to escape local optima and explore globally meaningful solution subspaces, transcending the limitations of standard local refinements; and (2) it flexibly accommodates both existing and future community detection methods. Furthermore, we theoretically generalize locally-dense decomposition--originally from graphs--to the hypergraph domain. We provide the formal extension and necessary proofs to apply this technique to hypergraphs, marking its first application in hypergraph partitioning. Specifically, we utilize this rigorously derived decomposition to guide the initial partitioning phase toward superior starting points. Experimental results on standard benchmarks demonstrate that our method consistently outperforms state-of-the-art methods in solution quality.

cs.AR↗

arXiv2026

A field-equation semi-local transformation for compressible wall turbulence

Compressibility and wall heat transfer change the inner scaling of wall turbulence through the mean density and viscosity fields. Most semi-local transformations are applied after a wall-normal profile has been selected. Here the transformed coordinate and transformed velocity are defined by wall-anchored field equations before any profile is extracted. Wall density and friction velocity enter as boundary data for auxiliary field equations; the resulting reference density and friction velocity, together with the local density and viscosity, set the viscous scaling in the wall layer. The local logarithmic density contrast is introduced as a bounded correction to the coordinate-stretching factor. For weak density variation the corrected stretch follows the local inverse kinematic-viscosity variation, while the bounded form limits the influence of finite density contrasts. The transformed coordinate $Y^+$ and transformed velocity $\bm{U}^+$ are obtained from field equations using the corrected stretching and the mean viscous shear. In the constant-property limit the density contrast vanishes, $Y^+$ reduces to the ordinary wall coordinate and each transformed velocity component reduces to the conventional mean velocity component in wall units. For channel flows, the field equations are solved with wall boundary data and the extracted profiles retain their own wall origins. A cooled shock/boundary-layer interaction examines the response when the wall density and friction velocity vary in the streamwise direction. Across the cooled high-speed boundary layers considered here, the bounded density correction narrows the inner- and buffer-layer profile spread...

physics.flu-dyn↗