Search arXiv⌕ Search

arXiv subjects

Xin Wang

Publications and source records attributed to Xin Wang.

At least 397 records · Page 22Linked to original sources
arXiv2026

SMMAN: quasi-Simultaneous Multi-wavelength Monitoring of gamma-ray-loud AGNs with the Nanshan 26-m radio telescope

Active Galactic Nuclei (AGNs) are characterized by strong temporal flux density variability across the electromagnetic spectrum, offering insights into the complex physical processes governing accretion and plasma outflows. To systematically investigate AGNs flux density variability in radio bands, a long-term program was initiated in late 2016: quasi-Simultaneous Multiwavelength Monitoring of gamma-ray-loud AGNs with the Nanshan 26-m radio telescope (SMMAN). This work presents the first data release of the SMMAN program, spanning over eight years from 2016 to 2024 with observations at 4.8 and 23.6 GHz. The SMMAN sample includes 131 northern ($δ>\sim0^{\circ}$) sources selected from the Fermi Large Area Telescope third source catalog. The characteristics of variability, spectral index, luminosity, and $γ$-ray loudness factor are examined for different AGN classes within the sample. Target sources exhibit stronger variability at 23.6 GHz compared to 4.8 GHz, with BL Lac objects being more variable than flat-spectrum radio quasars (FSRQs). BL Lacs generally have flatter radio spectra, while FSRQs, blazar candidates of uncertain type (BCUs), and radio galaxies (RDGs) span a wider range from flat to steep. FSRQs are more radio-luminous than BL Lacs and other classes, with BCUs intermediate and RDGs generally fainter. FSRQs and BL Lacs have higher $γ$-ray loudness factors than RDGs, while BCUs have intermediate values. The SMMAN dataset, incorporated with other historical and ongoing monitoring programs, will provide a unique opportunity to investigate the evolution of spectral energy distributions, search for quasi-periodic oscillations, and analyze supermassive black hole binary systems.

astro-ph.GA↗
arXiv2026

On $E_{7+1/2}$ gauge theory

We study gauge theory based on the intermediate Lie algebra $E_{7+1/2}$, interpolating between $E_7$ and $E_8$. We propose a concrete UV completion via a 6d SCFT whose tensor branch description contains a pure $E_{7+1/2}$ gauge sector. The proposal is tested by 6d anomaly cancellation and by the 5d $\mathcal N=1$ Coulomb branch prepotential from the associated M-theory geometry. As a nonperturbative check, we determine the elliptic genus of the single-string worldsheet CFT using modular bootstrap. The result matches the vacuum character of the corresponding VOA for $E_{7+1/2}$, completing the elliptic genus/VOA correspondence along the Deligne-Cvitanović series.

hep-th↗
arXiv2026

DSDR: Dual-Scale Diversity Regularization for Exploration in LLM Reasoning

Reinforcement learning with verifiers (RLVR) is a central paradigm for improving large language model (LLM) reasoning, yet existing methods often suffer from limited exploration. Policies tend to collapse onto a few reasoning patterns and prematurely stop deep exploration, while conventional entropy regularization introduces only local stochasticity and fails to induce meaningful path-level diversity, leading to weak and unstable learning signals in group-based policy optimization. We propose DSDR, a Dual-Scale Diversity Regularization reinforcement learning framework that decomposes diversity in LLM reasoning into global and coupling components. Globally, DSDR promotes diversity among correct reasoning trajectories to explore distinct solution modes. Locally, it applies a length-invariant, token-level entropy regularization restricted to correct trajectories, preventing entropy collapse within each mode while preserving correctness. The two scales are coupled through a global-to-local allocation mechanism that emphasizes local regularization for more distinctive correct trajectories. We provide theoretical support showing that DSDR preserves optimal correctness under bounded regularization, sustains informative learning signals in group-based optimization, and yields a principled global-to-local coupling rule. Experiments on multiple reasoning benchmarks demonstrate consistent improvements in accuracy and pass@k, highlighting the importance of dual-scale diversity for deep exploration in RLVR. Code is available at https://github.com/SUSTechBruce/DSDR.

cs.LG↗
arXiv2026

Event-Triggered Gossip for Distributed Learning

While distributed learning offers a new learning paradigm for distributed network with no central coordination, it is constrained by communication bottleneck between nodes. We develop a new event-triggered gossip framework for distributed learning to reduce inter-node communication overhead. The framework introduces an adaptive communication control mechanism that enables each node to autonomously decide in a fully decentralized fashion when to exchange model information with its neighbors based on local model deviations. We analyze the ergodic convergence of the proposed framework under noconvex objectives and interpret the convergence guarantees under different triggering conditions. Simulation results show that the proposed framework achieves substantially lower communication overhead than the state-of-the-art distributed learning methods, reducing cumulative point-to-point transmissions by \textbf{71.61\%} with only a marginal performance loss, compared with the conventional full-communication baseline.

eess.SP↗
arXiv2026

LMFPPO-UBP: Local Mean Field Proximal Policy Optimization with Unbalanced Punishment for Spatial Public Goods Games

Spatial public goods games are characterized by high-dimensional state spaces and localized externalities, which pose significant challenges for achieving stable and widespread cooperation. Traditional approaches often struggle to effectively capture neighborhood-level strategic interactions and dynamically align individual incentives with collective welfare. To resolve this issue, this paper introduces a novel intelligent decision-making framework called Local Mean-Field Proximal Policy Optimization with Unbalanced Punishment (LMFPPO-UBP). The conventional mean field concept is reformulated as a socio-statistical sensor embedded directly into the policy gradient space of deep reinforcement learning, allowing agents to adapt their strategies based on mesoscale neighborhood dynamics. Additionally, an unbalanced punishment mechanism is integrated to penalize defectors proportionally to the local density of cooperators, thereby reshaping the payoff structures without imposing direct costs on cooperative agents. Experimental results demonstrate that the LMFPPO-UBP promotes rapid and stable global cooperation even under low enhancement factors, consistently outperforming baseline methods such as Q-learning and Fermi update rules. Statistical analyses further validate the framework's effectiveness in lowering the cooperation threshold and achieving better coordinated outcomes.

cs.GT↗
arXiv2026

Think with Grounding: Curriculum Reinforced Reasoning with Video Grounding for Long Video Understanding

Long video understanding is challenging due to rich and complicated multimodal clues in long temporal range.Current methods adopt reasoning to improve the model's ability to analyze complex video clues in long videos via text-form reasoning.However,the existing literature suffers from the fact that the text-only reasoning under fixed video context may exacerbate hallucinations since detailed crucial clues are often ignored under limited video context length due to the temporal redundancy of long videos.To address this gap,we propose Video-TwG,a curriculum reinforced framework that employs a novel Think-with-Grounding paradigm,enabling video LLMs to actively decide when to perform on-demand grounding during interleaved text-video reasoning, selectively zooming into question-relevant clips only when necessary.Video-TwG can be trained end-to-end in a straightforward manner, without relying on complex auxiliary modules or heavily annotated reasoning tracesIn detail,we design a Two-stage Reinforced Curriculum Strategy, where the model first learns think-with-grounding behavior on a small short-video GQA dataset with grounding labels,and then scales to diverse general QA data with videos of diverse domains to encourage generalization. Further, to handle complex think-with-grounding reasoning for various kinds of data,we propose TwG-GRPO algorithm which features the fine-grained grounding reward, self-confirmed pseudo reward and accuracy-gated mechanism.Finally,we propose to construct a new TwG-51K dataset that facilitates training. Experiments on Video-MME, LongVideoBench, and MLVU show that Video-TwG consistently outperforms strong LVU baselines.Further ablation validates the necessity of our Two-stage Reinforced Curriculum Strategy and shows our TwG-GRPO better leverages diverse unlabeled data to improve grounding quality and reduce redundant groundings without sacrificing QA performance.

cs.CV↗
arXiv2026

Nonlinear Public Goods Game in Dynamical Environments

The evolutionary mechanisms of cooperative behavior represent a fundamental topic in complex systems and evolutionary dynamics. Real-world collective interactions, particularly in multi-agent systems, are often characterized by behavior-dependent mechanism switching where the environmental state is endogenously shaped by group strategies. However, existing models typically treat such environmental variations as static stochasticity and neglect the closed-loop feedback between environmental states and cooperative behaviors. Here, we introduce a dynamic environmental feedback mechanism into a nonlinear public goods game framework to establish a coevolutionary model that couples environmental states and individual cooperative strategies. Our results demonstrate that the interplay among environmental feedback, nonlinear effects, and environmental randomness can drive the system toward a wide variety of steady-state structures, including full defection, full cooperation, stable coexistence, and periodic limit cycles. Further analysis reveals that asymmetric nonlinear parameters and environmental feedback rates exert significant regulatory effects on cooperation levels and system dynamics. This study not only enriches the theoretical framework of evolutionary game theory but also provides a foundation for modeling environmental feedback loops in scenarios ranging from ecological management to the design of cooperative mechanisms in autonomous systems.

nlin.AO↗
arXiv2026

Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders

Graph data is becoming increasingly prevalent due to the growing demand for relational insights in AI across various domains. Organizations regularly use graph data to solve complex problems involving relationships and connections. Causal learning is especially important in this context, since it helps to understand cause-effect relationships rather than mere associations. Since many real-world systems are inherently causal, graphs can efficiently model these systems. However, traditional graph machine learning methods including graph neural networks (GNNs), rely on correlations and are sensitive to spurious patterns and distribution changes. On the other hand, causal models enable robust predictions by isolating true causal factors, thus making them more stable under such shifts. Causal learning also helps in identifying and adjusting for confounders, ensuring that predictions reflect true causal relationships and remain accurate even under interventions. To address these challenges and build models that are robust and causally informed, we propose CCAGNN, a Confounder-Aware causal GNN framework that incorporates causal reasoning into graph learning, supporting counterfactual reasoning and providing reliable predictions in real-world settings. Comprehensive experiments on six publicly available datasets from diverse domains show that CCAGNN consistently outperforms leading state-of-the-art models.

cs.LG↗
arXiv2026

LQA: A Lightweight Quantized-Adaptive Framework for Vision-Language Models on the Edge

Deploying Vision-Language Models (VLMs) on edge devices is challenged by resource constraints and performance degradation under distribution shifts. While test-time adaptation (TTA) can counteract such shifts, existing methods are too resource-intensive for on-device deployment. To address this challenge, we propose LQA, a lightweight, quantized-adaptive framework for VLMs that combines a modality-aware quantization strategy with gradient-free test-time adaptation. We introduce Selective Hybrid Quantization (SHQ) and a quantized, gradient-free adaptation mechanism to enable robust and efficient VLM deployment on resource-constrained hardware. Experiments across both synthetic and real-world distribution shifts show that LQA improves overall adaptation performance by 4.5\%, uses less memory than full-precision models, and significantly outperforms gradient-based TTA methods, achieving up to 19.9$\times$ lower memory usage across seven open-source datasets. These results demonstrate that LQA offers a practical pathway for robust, privacy-preserving, and efficient VLM deployment on edge devices.

cs.AI↗
arXiv2026

HiFloat4 Format for Language Model Inference

This paper introduces HiFloat4 (HiF4), a block floating-point data format tailored for deep learning. Each HiF4 unit packs 64 4-bit elements with 32 bits of shared scaling metadata, averaging 4.5 bits per value. The metadata specifies a three-level scaling hierarchy, capturing inter- and intra-group dynamic range while improving the utilization of the representational space. In addition, the large 64-element group size enables matrix multiplications to be executed in a highly fixed-point manner, significantly reducing hardware area and power consumption. To evaluate the proposed format, we conducted inference experiments on several language models, including LLaMA, Qwen, Mistral, DeepSeek-V3.1 and LongCat. Results show that HiF4 achieves higher average accuracy than the state-of-the-art NVFP4 format across multiple models and diverse downstream tasks.

cs.LG↗
arXiv2026

Testing the cosmological principle on gigaparsec scales

Recent observational analyses have suggested possible evidence of hemisphere asymmetry in cosmological datasets. Parameterizations of this kind place observers in a privileged position-specifically on the plane that divides the two hemispheres. To quantify potential deviations from the cosmological principle without presuming a special location, we develop a stochastic framework that parametrizes departures from statistical homogeneity and isotropy. The near-uniform temperature of the cosmic microwave background indicates that anisotropy is negligible (at the $\lesssim 10^{-5}$ level) on the last scattering surface. This serves as a zero boundary condition, enabling the construction of an orthogonal basis of functions below the recombination redshift. Within this basis, we expand the relative deviation from the Hubble diagram of isotropic models (such as $Λ$CDM or $w_0w_a$CDM) in a hierarchy of increasing resolution. Applying this approach, we test the cosmological principle using Type Ia supernovae, strong lensing time delays, and gravitational-wave standard sirens. For the class of large-scale anisotropies and low-order radial variations described by this framework, the current datasets are found to be consistent with statistical homogeneity and isotropy on gigaparsec scales.

astro-ph.CO↗
arXiv2026

The faint end of the UV luminosity function at $0.4 < z < 0.7$ from the Hubble Frontier Fields

By extending the Hubble Frontier Fields (HFF) observations to the F225W band using HST WFC3/UVIS, we measure the rest-frame UV luminosity function (LF) of galaxies at $0.4 < z < 0.7$, pushing into the low-luminosity galaxy regime. In this first paper of a series, we describe the HST Cycle-27 GO-15940 F225W observations and data reduction, and present a corresponding catalog for the Abell 2744 field, which is the most data-rich HFF cluster field. Combining deep Near-UV imaging and the high magnification from strong gravitational lensing of the foreground cluster, we identify 152 faint galaxies with $-19.5 < M_{UV} < -12.1$ at $0.4 < z < 0.7$ through hybrid photometric-spectroscopic redshift selection from the Abell 2744 F225W catalog. Using a sample defined by a $50\%$ completeness cut and applying the maximum likelihood estimation, we derive the best-fit Schechter parameters for the UV LF at $z \sim 0.55$ down to $M_\text{UV} < -13.5$ mag, including a faint-end slope of $α= -1.324^{+0.072}_{-0.074}$. We incorporate a curvature parameter $δ$ in parameter estimation to account for a possible turn-over at the faint end of the UV LF, leveraging the exceedingly low luminosities probed by our sample. Our results rule out a turn-over brighter than $M_{UV} = -15.5$ at the $3σ$ confidence level.

astro-ph.GA↗
arXiv2026

AssetFormer: Modular 3D Assets Generation with Autoregressive Transformer

The digital industry demands high-quality, diverse modular 3D assets, especially for user-generated content~(UGC). In this work, we introduce AssetFormer, an autoregressive Transformer-based model designed to generate modular 3D assets from textual descriptions. Our pilot study leverages real-world modular assets collected from online platforms. AssetFormer tackles the challenge of creating assets composed of primitives that adhere to constrained design parameters for various applications. By innovatively adapting module sequencing and decoding techniques inspired by language models, our approach enhances asset generation quality through autoregressive modeling. Initial results indicate the effectiveness of AssetFormer in streamlining asset creation for professional development and UGC scenarios. This work presents a flexible framework extendable to various types of modular 3D assets, contributing to the broader field of 3D content generation. The code is available at https://github.com/Advocate99/AssetFormer.

cs.CV↗
arXiv2026

More on 5d Wilson Loops in Higher-Rank Theories and Blowup Equations

In this article, we further explore the construction and computation of expectation values for Wilson loops in higher-rank 5d $\mathcal{N} = 1$ gauge theories on $\mathbb{C}_2 \times S_1$, by explicitly computing the Wilson loops via Chern-character insertion and qq-characters, including cases with the exceptional gauge group $G_2$. In particular, we propose a systematic way to write down the general blowup equations for Wilson loops by using the constraints from the one-form symmetry and low-instanton data from the instanton partition function. In addition, for one-instanton contributions in a large family of Wilson loop representations, we observe that they admit a $q_1q_2$-expansion, similar to the Hilbert-series structure of instanton partitions in pure gauge theories.

hep-th↗
arXiv2026

Enhancing Circuit Fidelity in Transmon Qubit Rings via Operation Duration Tuning under Strong Connectivity Noise

Superconducting transmon qubits are a promising platform for quantum computation, yet they face significant fidelity degradation due to connectivity noise, particularly in the intermediate coupling regime where noise levels are substantial. While prior works suggest that high fidelity requires operating in regimes with strongly suppressed noise, maintaining such conditions under practical experimental constraints remains challenging. To address this, we investigate quantum gate operations in fully connected transmon rings, examining both SWAP and general circuits. Our study reveals that fidelity can be significantly enhanced by tuning gate operation durations, with local maxima emerging even under strong noise conditions. These fidelity enhancements occur consistently across different qubit numbers and operation types, and for specific initial states -- particularly those with favorable symmetry or entanglement properties -- the achieved fidelities approach quantum error correction thresholds. Furthermore, we develop a supervised machine learning model that accurately predicts the optimal operation durations for new devices, enabling efficient optimization without extensive experimental simulations. These results provide a pathway toward robust quantum circuit design in noisy experimental environments.

quant-ph↗
arXiv2026

Heat dissipation in marginally stable linear time-delayed Langevin systems

The thermodynamic properties of time-delayed dynamics remain largely unexplored, especially for systems that exhibit asymptotically non-stationary behavior. Here, we investigate heat dissipation in two classes of marginally stable linear time-delayed Langevin dynamics: (i) diffusive criticality, which asymptotically manifests as scaled Brownian diffusion, and (ii) oscillatory criticality, which shows oscillation with diffusive amplitude. By analytical derivations, we find fundamentally different thermodynamic signatures: the average heat dissipation rate asymptotically approaches a constant for diffusive criticality but diverges linearly with oscillations for oscillatory criticality, despite both showing linearly growing variance over time. We discuss in detail how the heat dissipation rate behaves differently as the dynamics asymptotically approaches these two criticality classes from the stable regime. We also numerically study the probability distributions of heat dissipation rates for both types of critical dynamics. Our results demonstrate that non-stationary time-delayed dynamics with similar scaling of variance can yield qualitatively distinct heat dissipation behaviors, depending on the underlying dynamical details. This work provides a concrete foundation for future investigations into thermodynamic properties of general nonlinear time-delayed systems.

cond-mat.stat-mech↗
arXiv2026

Efficient implementation of arbitrary Hermitian-preserving and trace-preserving maps

Quantum control has been a cornerstone of quantum information science, driving major advances in quantum computing, quantum communication, and quantum sensing. Over the years, it has enabled the implementation of arbitrary completely positive and trace-preserving (CPTP) maps; an important next step is to extend control to Hermitian-preserving and trace-preserving (HPTP) maps, which underpin applications such as entanglement detection, quantum error mitigation, quantum simulation, and quantum machine learning. Here we present an efficient and fully constructive method for implementing arbitrary HPTP maps. Unlike existing methods that decompose an HPTP map into multiple CPTP maps or approximate it using bipartite Hamiltonians with large Hilbert spaces, our approach compiles a target HPTP map into a single executable CPTP map whose Kraus rank is guaranteed to be no larger than the intrinsic rank of the target HPTP map plus one, followed by simple classical post-processing. Numerical results for inverse noise channels used in quantum error mitigation, including bosonic photon loss, confirm substantial reductions in resources and highlight scalability in higher-dimensional settings. Together with our numerical benchmarks, these results validate the efficiency and versatility of the proposed framework, opening a route to broader quantum-information applications enabled by HPTP processing.

quant-ph↗
arXiv2026

Simulation of Adjoints and Petz Recovery Maps for Unknown Quantum Channels

Transformations of quantum channels, such as the transpose, complex conjugate, and adjoint, are fundamental to quantum information theory. Given access to an unknown channel, a central problem is whether these transformations can be implemented physically with quantum supermaps. While such supermaps are known for unitary operations, the situation for general quantum channels is fundamentally different. In this work, we establish a strict hierarchy of physical realizability for the transposition, complex conjugation, and adjoint transformation of an unknown quantum channel. We present a probabilistic protocol that exactly implements the transpose with a single query. In contrast, we prove no-go theorems showing that neither the complex conjugate nor the adjoint can be implemented by any completely positive supermap, even probabilistically. We then overcome this impossibility by designing a virtual protocol for the complex conjugate based on quasi-probability decomposition, and show its optimality in terms of the diamond norm. As a key application, we propose a protocol to estimate the expectation values resulting from the Petz recovery map of an unknown channel, achieving an improved query complexity compared to existing methods.

quant-ph↗