arXiv subjects

Gang Li

Publications and source records attributed to Gang Li.

At least 73 records · Page 4Linked to original sources

arXiv2026

DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense

Large Language Models (LLMs) show promise for supporting decision-making in cybersecurity, but their reliability in high-stakes, time-evolving environments remains limited due to hallucinations, poor temporal reasoning, and shallow grounding in system context. We introduce DEFENGRAPH, an LLM-driven assistant designed to support human defenders during cybersecurity incidents. DEFENGRAPH improves contextual reasoning by integrating a dual-layer Static-Dynamic Knowledge Graph (KG) with graph-based path retrieval, LLM-driven contextual filtering, and reasoning-based re-ranking. The framework grounds LLM outputs in both long-term domain knowledge and evolving event context, enabling faithful and temporally aware decision support. We evaluate DEFENGRAPH in a cyber defense setting using knowledge graphs constructed from heterogeneous security artifacts, including SIEM alerts, system topology, attacker behaviors, and prior defensive actions. The evaluation uses data collected during live Red vs. Blue team cyber range exercises simulating attacks on critical infrastructure, which generate realistic and noisy datasets reflecting real-world defender workflows and system dynamics. Evaluations across four prevalent LLMs show that DEFENGRAPH sets a new state-of-the-art: on GPT-4o it boosts reasoning-recall from 61.45\% to 73.49\% and ticket-action recall from 52.17% to 72.46% (precision 24.49\% to 29.24\%), with similar gains on LLaMA-3 (46.99\% to 61.45\%), DeepSeek-R1 (45.78\% to 56.63\%) and QWen-3 (51.81\% to 59.04\%), while surfacing up to 50 correct defense actions versus 36 for the next best baseline and holding fault rates steady.

cs.CR↗

arXiv2026

CKM determination from $W$ decays with jet flavor tagging at CEPC

This study investigates the sensitivity of the CEPC to CKM matrix elements $|V_{ij}|$ in semileptonic $W$-pair decays using simulated $e^+e^- \to W^+W^- \to μν\bar{q}q^\prime$ samples corresponding to $21.6\,\mathrm{ab}^{-1}$ at $\sqrt{s}=240\,\mathrm{GeV}$, with $|V_{ub}|$ excluded because of its negligible contribution. The projected statistical precisions reach $0.59\,\%$ for $|V_{cb}|$ and $0.01\,\%$ for $|V_{cs}|$, indicating that the CEPC can provide direct, high-precision, and largely model-independent determinations of CKM matrix elements from hadronic $W$ decays. The main sources of systematic uncertainty are discussed, and further improvements are expected from both experimental and theoretical developments, including dedicated data-driven calibrations of detector response and jet flavor tagging performance, as well as more precise higher-order calculations of the relevant electroweak and QCD corrections. Such measurements would provide stringent consistency tests of the Standard Model charged-current flavor structure and offer sensitivity to possible new physics effects through deviations from the Standard Model expectations.

hep-ex↗

arXiv2026

Gravity-mode main-sequence pulsators in the open clusters NGC 3532 and NGC 2516: Instability strip, near-core rotation, and internal structure

Context. Studying pulsating stars in clusters opens a new window onto stellar physics. Gravity-mode (g-mode) pulsators in open clusters allow us to measure their near-core rotation rates and, together with cluster age and mass constraints, test angular momentum transport in stars above about 1.5 solar masses. Aims. We aim to detect g-mode pulsations in member stars of the young open cluster NGC 3532, about 300 Myr old, and to measure their near-core rotation rates and internal properties. Methods. We used TESS photometry to extract light curves of NGC 3532 members. The observed g-mode period spacings allow us to measure near-core rotation rates and the asymptotic period spacing, Pi0. We also fitted isochrones to the colour-magnitude diagrams of NGC 3532 and NGC 2516 to refine the cluster ages and obtain stellar masses. Results. We constrain the observed instability region of young gamma Doradus members from a blue edge at about 7760 K to a red edge at about 7070 K, while some hotter g-mode pulsators are also seen. The near-core rotation rates show a rotation-mass relation similar to that in NGC 2516: below 1.6 solar masses, the rotation rate increases with mass, whereas above 1.6 solar masses the NGC 3532 stars reach a plateau at about 2.8 d^-1. This mass may mark a threshold between different rotational spin-down mechanisms. Existing evolutionary models with angular momentum transport imply that these g-mode pulsators were born rotating above 55 percent of the critical value. Our simplified models, assuming spherical symmetry, angular momentum conservation, and rigid internal rotation, suggest that either mass-dependent initial rotation rates are required, or that minor angular momentum loss still operates above 1.6 solar masses. Finally, our Pi0 measurements reveal a discrepancy with theoretical predictions for some pulsators, as also found in the younger cluster NGC 2516.

astro-ph.SR↗

arXiv2026

Personalized 3D Spatiotemporal Trajectory Privacy Protection with Differential and Distortion Geo-Perturbation

The rapid advancement of location-based services (LBSs) in three-dimensional (3D) domains, such as smart cities and intelligent transportation, has raised concerns over 3D spatiotemporal trajectory privacy protection. However, existing research has not fully addressed the risk of attackers exploiting the spatiotemporal correlation of 3D spatiotemporal trajectories and the impact of height information, both of which can potentially lead to significant privacy leakage. To address these issues, this paper proposes a personalized 3D spatiotemporal trajectory privacy protection mechanism, named 3DSTPM. First, we analyze the characteristics of attackers that exploit spatiotemporal correlations between locations in a trajectory and present the attack model. Next, we exploit the complementary characteristics of 3D geo-indistinguishability (3D-GI) and distortion privacy to find a protection location set (PLS) that obscures the real location for all possible locations. To address the issue of privacy accumulation caused by continuous trajectory queries, we propose a Window-based Adaptive Privacy Budget Allocation (W-APBA), which dynamically allocates privacy budgets to all locations in the current PLS based on their predictability and sensitivity. Finally, we perturb the real location using the allocated privacy budget by the PF (Permute-and-Flip) mechanism, effectively balancing privacy protection and Quality of Service (QoS). Simulation results demonstrate that the proposed 3DSTPM effectively reduces QoS loss while meeting the user's personalized privacy protection needs.

cs.CR↗

arXiv2026

Supermoiré Chern mosaic in helical trilayer WSe2

Helically twisted multilayers offer access to moiré physics beyond the single-superlattice paradigm, yet their correlated and topological transport properties remain largely unexplored in semiconductor moiré materials. Here we report magnetotransport measurements of helical trilayer WSe2, in which two coupled moiré patterns relax into a supermoiré landscape composed of inequivalent local topological domains with distinct electronic structures and unequal spatial areas. By electrostatic tuning, we identify a trilayer-hybridized regime where interactions and real-space reconstruction combine to generate a plethora of magnetic and topological states absent in the twisted bilayers. At moiré filling factor $ν$ = -1, we observe a ferromagnetic insulating state that is robust against magnetic field and accompanied by a non-quantized anomalous Hall response ~-4 kOhms. This behaviour is consistent with a time-reversal-symmetry-breaking supermoiré Chern mosaic, in which the Hall response arises from the non-cancelling contributions of local domains with opposite Chern character arranged by the relaxed structure. Under strong magnetic fields, a symmetry-broken Chern insulating state (C = 1) emerges near $ν$ = -2/3, displaying a much larger positive Hall response together with strongly enhanced longitudinal resistance, suggestive of field-reconstructed topological minibands and domain-boundary scattering. These results establish relaxed supermoiré semiconductor trilayers as a platform for spatially organized magnetism and topology beyond the bilayer limit.

cond-mat.str-el↗

arXiv2026

Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines

Large-scale Visual-Language Models (LVLMs) have achieved remarkable success in natural visual tasks, yet their application to industrial defect detection remains challenging due to two fundamental limitations: (i) the scarcity of large-scale industrial datasets that cover diverse defect categories across multiple domains, and (ii) the reliance on manual prompts (points, boxes, masks) that introduce subjective noise and lack text-visual interaction for fine-grained understanding. To address these challenges, we introduce a Large-Scale Multi-Modal Industrial Open-Closed benchmark (MMIOC-1M) containing over one million samples across $14$ super-categories, $29$ industrial scenes, and $351$ defect subcategories. To our knowledge, MMIOC-1M is the first unified largest benchmark supporting both open-vocabulary and closed-set industrial detection, providing valuable pre-training data for LVLMs in industrial scenarios. Furthermore, we propose a Refined Text-Visual Prompt Network (RTVPNet) that incorporates three key innovations: (1) an expert-assisted domain projection mechanism that enables rapid adaptation of general vision models to industrial domains, (2) an energy-based sparse sampling strategy that automatically generates refined visual prompts without manual intervention, and (3) a bidirectional text-visual interaction module that enhances cross-modal semantic alignment and understanding. Extensive experiments demonstrate that RTVPNet achieves state-of-the-art performance on MMIOC-1M, LVIS, and COCO benchmarks while maintaining computational efficiency. The dataset and code are available at https://github.com/hellozzk/MMIO.

cs.AI↗

arXiv2026

Final-state rescattering mechanism of the $Δ(1232)^{++}$ production in $Λ^+_c \to K^- π^+ p$ decay

We investigate the production of the $Δ(1232)^{++}$ resonance in the charmed baryon weak decay $Λ^+_c \to K^- π^+ p$, focusing on the $π^+ p$ final-state rescattering mechanism. The direct $W^+$ exchange diagram is expected to be suppressed, hence we adopt the $W^+$ internal emission process $Λ^+_c \to p \bar K^{*0}(892)$ followed by the subsequent decay $\bar{K}^{*0} \to K^- π^+$ as the dominant source of the final state particles. The $Δ(1232)^{++}$ resonance is then generated via $π^+ p$ rescattering within a triangle loop mechanism. Our calculations incorporate both the tree-level $\bar K^{*0}(892)$ and the dynamically generated $\bar{K}^*_0(700)$ state arising from the $S$-wave $K π$ final state interaction. We find that our theoretical results can reproduce the bump and peak structures in the $K^- π^+$ invariant mass distributions for the $\bar{K}^*_0(700)$ and $\bar{K}^{*0}(892)$, respectively. Meanwhile, the peak for the $Δ(1232)^{++}$ in the $π^+ p$ invariant mass distributions is also well described. The $Δ(1232)^{++}$ signal naturally emerges from rescattering effects, and adopting the pole parameters of $Δ(1232)$ resonance yields an improved description of the experimental data. In addition, we obtain a branching fraction ratio $\mathcal{B}[Λ_c^+ \to Δ(1232)^{++} K^-] / \mathcal{B}[Λ_c^+ \to p \bar{K}^{*0}(892)] \approx 0.5$, which is lower than the experimentally measured value. This discrepancy suggests that interference effects are likely significant in this decay process. Future high-precision measurements will further verify the proposed rescattering mechanism.

hep-ph↗

arXiv2026

Repurposing Adversarial Perturbations for Continual Learning: From Defense to Active Alignment

In dynamic environments, large language models need to keep adapting to new tasks, but continual learning often suffers from forgetting, limited transfer, and vulnerability to adversarial perturbations. To address this, we present AdvCL, which repurposes adversarial perturbations as a geometric control signal for stable continual adaptation. AdvCL combines three plug-in modules: Intra-Smooth promotes local smoothness via small adversarial perturbations; Proto-Clip uses similarity clipping to prevent excessive alignment to current task prototype; and Inter-Align applies directional alignment toward previous task prototype to reduce representational gaps. Experiments show consistent gains in both standard performance and robustness, with lower forgetting and stronger transfer. We further analyze key mechanisms by quantifying the sensitivity of Intra-Smooth to perturbation settings and the effect of Inter-Align on task similarity and geometric distance. In summary, the modules provide complementary gains when combined, and each can also be integrated individually into diverse CL paradigms, including replay, regularization, and dynamic architectures, thereby offering a geometric control mechanism for continual learning.

cs.LG↗

arXiv2026

Where to Refine, When to Stop: Rethinking Redundancy via Latent Discrepancy for Efficient Visual Autoregressive Generation

Visual Autoregressive (VAR) models deliver high-quality image generation but suffer from significant inference latency at high resolutions. Recent acceleration approaches most rely on heuristic measures with layer features to prune tokens. Such heuristics are sensitive to complex contextual semantics, leading to inaccurate identification of redundant computation and poor adaptability across prompts. We rethink redundancy in VAR from the perspective of its impact on pixel-space generation and introduce Latent Discrepancy. This unified metric quantifies a token's contribution by measuring the change in model states during generation. Our analysis shows that redundancy is more accurately identified when guided by image latent or pixel-space signals. We further observed that in classifier-free guidance (CFG), the convergence trend of the discrepancy between conditional and unconditional branches exhibits high dynamics with different prompts. Based on these findings, we propose LD-Pruning (Latent Discrepancy Pruning), a training-free framework that removes redundancy via latent discrepancy by integrating decoding-free region selection and adaptive unconditional-branch skipping. Extensive experiments show that LD-Pruning substantially reduces inference latency while maintaining high generation quality, achieving up to 2.35x speedup on Infinity-8B.

cs.CV↗

arXiv2026

Dasheng AudioGen: A Unified Model for Generating Coherent Audio Scenes from Text

Audio generation has long been fragmented, with speech, music, and sound effects produced by domain-specific models that fail to jointly generate coherent audio scenes from a single description. The key obstacles are insufficient fine-grained supervision for real-world mixed audio and limited acoustic representations for modeling concurrent audio components. We present Dasheng AudioGen, a unified framework for generating general mixed-audio scenes from text. Dasheng AudioGen introduces structured multi-view captions, which explicitly decouple complex acoustic scenes into complementary description views, thereby enabling fine-grained control over audio layers. Furthermore, we employ a high-dimensional unified semantic-acoustic representation as the shared latent space. It injects semantic priors that facilitate cross-modal training convergence, while its high-dimensional feature space provides sufficient capacity to disentangle and fuse concurrent audio components effectively. With these designs, a simple flow-matching DiT achieves high-quality end-to-end audio scene generation. We also establish a comprehensive evaluation pipeline for audio scene generation. Experiments demonstrate that Dasheng AudioGen achieves performance approaching real-world recordings in mixed-audio categories, while remaining competitive with specialized models in single-type generation tasks. Demos are available at https://nieeim.github.io/Dasheng-AudioGen-Web/.

cs.SD↗

arXiv2026

Probing Solar Wind Structures with Solar Energetic Particle Observations from Solar Orbiter

The propagation of solar energetic particles (SEPs) through the heliosphere is primarily guided by the interplanetary magnetic field (IMF) which is embedded in the solar wind plasma. Large-scale IMF structures can drive transient variations in SEP intensities. Using Solar Orbiter observations, we identify a distinct class of SEP variations: SEP flux deflections (SFDs), which are commonly detected in SEP events and frequently recur multiple times within a single event. SFDs are characterized by a sudden change in SEP flux directions where the intensities drop in one direction and increase in another direction, without a significant net change in total flux magnitude. These deflections occur dispersionlessly across a broad energy range-from tens of keV to over 100 MeV-and exhibit steep intensity gradients. SFDs are typically associated with magnetic flux tubes with boundary features consistent with tangential discontinuities. We further show that the solar wind inside these structures exhibits distinct plasma properties, and that the SEP streaming direction within SFDs aligns closely to the flux-tube axis. These observations suggest that magnetic flux tubes are a prevalent structural element of the solar wind, and demonstrate that SEPs can serve as an effective diagnostic tool for probing the topology and dynamics of solar wind structures.

astro-ph.SR↗

arXiv2026

ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold

Tabular data remains prevalent in high-stakes domains such as healthcare and finance, where predictive models are expected to provide both high accuracy and faithful, human-understandable reasoning. While symbolic models offer verifiable logic, they lack semantic expressiveness. Meanwhile, general-purpose LLMs often require specialized fine-tuning to master domain-specific tabular reasoning. To address the dual challenges of scalable data curation and reasoning consistency, we propose ReSS, a systematic framework that bridges symbolic and neural reasoning models. ReSS leverages a decision-tree model to extract instance-level decision paths as symbolic scaffolds. These scaffolds, alongside input features and labels, guide an LLM to generate grounded natural-language reasoning that strictly adheres to the underlying decision logic. The resulting high-quality dataset is used to fine-tune a pretrained LLM into a specialized tabular reasoning model, further enhanced by a scaffold-invariant data augmentation strategy to improve generalization and explainability. To rigorously assess faithfulness, we introduce quantitative metrics including hallucination rate, explanation necessity, and explanation sufficiency. Experimental results on medical and financial benchmarks demonstrate that ReSS-trained models improve traditional decision trees and standard fine-tuning approaches up to $10\%$ while producing faithful and consistent reasoning

cs.AI↗

arXiv2026

WARC-Bench: Web Archive Based Benchmark for GUI Subtask Executions

Training web agents to navigate complex, real-world websites requires them to master $\textit{subtasks}$ - short-horizon interactions on multiple UI components (e.g., choosing the correct date in a date picker, or scrolling in a container to extract information). We introduce WARC-Bench (Web Archive Benchmark), a novel web navigation benchmark featuring 438 tasks designed to evaluate multimodal AI agents on subtasks. WARC-Bench enables sandboxed interactions with dynamic and realistic webpages using Web ARChive files. We show that WARC-Bench is challenging for leading computer-use models, with the highest observed success rate being 64.8%. To improve open source models on subtask, we explore two common training techniques: supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). Experiments show that SFT models obtain a 48.8% success rate on the benchmark. Training with RLVR over SFT checkpoints, even in data-scarce settings, improves the score to 52.8% on WARC-Bench, outperforming many frontier models. Our analysis concludes that mastering these subtasks is essential for robust web planning and navigation, and is a capability not extensively evaluated by existing benchmarks.

cs.LG↗

arXiv2026

Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains

Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance the reasoning capabilities of large language models (LLMs) in domains such as mathematics and coding. However, its applications on knowledge-intensive domains have not been effectively explored due to the scarcity of high-quality verifiable data. Furthermore, current RLVR focuses solely on the correctness of final answers, leading to the limitations of flawed reasoning and sparse reward signals. In this work, we propose Knowledge-to-Verification (K2V), a framework that extends RLVR to knowledge-intensive domains through automated verifiable data synthesis, while enabling verification of the LLM's reasoning process. Extensive experiments demonstrate that K2V enhances the reasoning of LLM in knowledge-intensive domains without significantly compromising the model's general capabilities. This study also suggests that integrating automated data synthesis with reasoning verification is a promising direction to enhance model capabilities in these broader domains. Code is available at https://github.com/SeedScientist/K2V.

cs.CL↗

arXiv2026

A Loewner-Nirenberg phenomena for Ricci flow on compact manifolds with boundary.II

This is a continuation of the research in [16]. Let $(\overline{M},g_{-1})$ be a closed geodesic $r_0$-ball in the hyperbolic space $(\mathbb{H}^n,g_{-1})$. Let $m\neq1$ be a positive constant. In this paper, we show that for $n\geq3$, starting from the metric $m g_{-1}$ on $\overline{M}$, with certain prescribed non-decreasing rotationally symmetric mean curvature and the fixed conformal class $[g_{\mathbb{S}^{n-1}}]$ on the boundary $\partial M$, the solution $g(t)$ to the normalized Ricci flow $(1.2)$ which is continuous up to the boundary, exists for all $t>0$, and converges locally uniformly in the interior $M$ of $\overline{M}$ to a complete hyperbolic metric as $t\to\infty$(see Theorem 1.1 for details). Under some additional conditions, we show the same conclusion holds for $n=2$.

math.DG↗

arXiv2026

PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming

4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must download in full before any frame can be rendered, causing black-screen waits of tens to hundreds of seconds on mobile bandwidth and leaving 4DGS incompatible with modern adaptive-bitrate delivery. Progressive 3DGS compression alleviates this for static scenes, but it acts only on spatial anchors and cannot partition the temporal deformation networks that dominate dynamic-scene size. We present PD-4DGS, the first framework for progressive compression and on-demand transmission of 4DGS. Hierarchical Deformation Decomposition (HDD) externalises the coarse-to-fine motion hierarchy already latent in 4DGS into three independently transmittable layers -- a static scaffold, a global deformation, and a local refinement -- so that any prefix of the bitstream is already renderable, turning a single training run into a scalable, DASH/HLS-compatible bitstream. A Gaussian-entropy attribute rate-distortion loss together with a temporal mask consistency regulariser shrink the base layer while suppressing low-bitrate flicker; a capacity-weighted rollout schedule, gated online by a learnt activation rate rho, then prevents deformation-network under-training without any per-scene hyperparameter. On the Dycheck iPhone benchmark, PD-4DGS cuts the streamed bitstream by >60% at matched rendering fidelity and reduces first-frame latency from 73--930 s to ~1.7 s on a 2 Mbps link, uniquely enabling true on-demand progressive streaming for 4DGS.

cs.CV↗

arXiv2026

MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks

While large audio-language models have advanced open-ended audio understanding, they still fall short of nuanced human-level comprehension. This gap persists largely because current benchmarks, limited by data annotations and evaluation metrics, fail to reliably distinguish between generic and highly detailed model outputs. To this end, this work introduces MECAT, a Multi-Expert Constructed Benchmark for Fine-Grained Audio Understanding Tasks. Generated via a pipeline that integrates analysis from specialized expert models with Chain-of-Thought large language model reasoning, MECAT provides multi-perspective, fine-grained captions and open-set question-answering pairs. The benchmark is complemented by a novel metric: DATE (Discriminative-Enhanced Audio Text Evaluation). This metric penalizes generic terms and rewards detailed descriptions by combining single-sample semantic similarity with cross-sample discriminability. A comprehensive evaluation of state-of-the-art audio models is also presented, providing new insights into their current capabilities and limitations. The data and code are available at https://github.com/xiaomi-research/mecat

eess.AS↗

arXiv2026

Data-Constrained Modeling of Electron Transport and Asymmetric Precipitation in the 2011 August 4 Solar Flare

Energetic electrons accelerated at coronal reconnection sites during solar flares precipitate into the lower solar atmosphere, generating nonthermal emissions and regulating energy deposition. However, how their transport and precipitation are jointly governed by the three-dimensional (3D) magnetic topology, turbulent scattering, and Coulomb collisions remains unclear. Here, we aim to disentangle these physical processes by using a data-constrained 3D particle transport model for the 2011 August 4 flare. The simulated distribution of precipitated electrons aligns closely with photospheric quasi-separatrix layers and reproduces the observed two-ribbon morphology in 1700~Å. We reveal a strong polarity asymmetry, with the 10~s precipitation fraction about six times higher in the weak positive polarity. This arises primarily from distinct mirror ratios of different polarities under the 3D magnetic configuration and can be understood via a modified escape probability for an asymmetric magnetic bottle. Varying strengths of turbulent scattering lead to a rise-then-fall trend and a pronounced energy dependence in the precipitation fraction. Coulomb collisions globally suppress precipitation, especially at low energies, and further amplify the polarity asymmetry. This integrated modeling framework bridges detailed transport physics to observable flare emissions and advances the development of quantitative models for realistic solar flare events.

astro-ph.SR↗