arXiv subjects

Ming Zhang

Publications and source records attributed to Ming Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Pitch-Angle Scattering of Cosmic Rays: Confronting Theory with Observations

Cosmic ray (CR) propagation is controlled by scattering in turbulent magnetic fields in space. In general, diffusive propagation is governed by pitch-angle diffusion in phase space. In this study, pitch-angle diffusion in the local interstellar medium (LISM) deduced from the analysis of the CR small-scale anisotropy data from the Tibet AS$γ$ experiment is compared with theoretical predictions. While it is difficult to reconcile the inferred LISM pitch angle diffusion coefficient with conventional theoretical results of particle scattering by Alfvénic turbulence, we find remarkable agreement with the predictions of particle scattering by quasi-slab fast modes whose properties are shaped by damping in the warm ionized medium. These findings offer direct evidence that CR scattering is predominantly governed by fast-mode turbulence. Furthermore, the comparison between experimental and theoretical results imposes strong constraints on plasma and magnetic field parameters within the local bubble, indicating that the LISM is in a low $β$ condition. The turbulence in the LISM should be compressible with an amplitude in the range of $0.3\lesssim δB/B_0 <1$.

astro-ph.HE↗

arXiv2026

Observational Study of Multi-wavelength Synergistic Effects in 3C 120

The energy dissipation and particle acceleration mechanisms within relativistic jets remain fundamental questions in active galactic nuclei (AGN) research. In this paper, we present a comprehensive 13-year (2012-2025) multi-wavelength study of the broad-line radio galaxy 3C 120, utilizing Fermi-LAT ($γ$-ray), ASAS-SN (optical), and high-resolution VLBA (15 GHz and 43 GHz) monitoring. Cross-correlation analyses reveal that $γ$-ray flares lead radio emission by $11.08_{-1.88}^{+4.03}$ months at 15 GHz and $8.27_{-5.55}^{+3.45}$ months at 43 GHz. This frequency-dependent temporal hierarchy positions the high-energy dissipation zone upstream of the radio core, corroborating the opacity-driven core-shift effect. By tracking the parsec-scale jet morphology during major $γ$-ray flaring epochs, we demonstrate that radio outbursts systematically coincide with compact core brightening, pronounced surges in polarized flux, abrupt electric vector position angle rotations, and the subsequent ejection of superluminal knots. Ultimately, our internal radio correlations suggest that jet dynamics are governed by a dual mechanism: long-term kinematic and flux baseline variations are geometrically modulated by a secular jet precession, while rapid, highly energetic polarimetric bursts are driven by short-lived internal shocks propagating down the jet channel.

astro-ph.GA↗

arXiv2026

VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training

Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete reward supervision, which undermines policy stability and generalization. Such noise may cause models to ignore key information or even collapse in advantage estimation. We find that a strong value model is essential for absorbing unstable signals and producing reliable advantages, offering denser and more robust supervision than the reward model. To better optimize noisy supervision, we propose VRPO, a framework that enhances value modeling for robust RL in LLM post-training. VRPO integrates (1) auxiliary losses guided by entropy and perplexity from a frozen language model, and (2) a variational information bottleneck, enabling the value model to filter noise and capture key words. This design allows the value model to correct noise rewards and generate more reliable advantage estimates, transforming it from a passive predictor into an active noise regulator. Experiments on multi-turn dialogue, math reasoning, and science QA with both rule-based and model-based rewards show that VRPO consistently outperforms baselines such as PPO and GRPO. Our work highlight the central role of the value model in Robust RL and provide a principled and practical approach to policy optimization under noisy supervision.

cs.LG↗

arXiv2026

Review of Machine Learning Models for Solar Energetic Particle Prediction

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.

astro-ph.SR↗

arXiv2026

Quantum $K$-invariants via Quot schemes II

We derive a $K$-theoretic analogue of the Vafa--Intriligator formula, computing the (virtual) Euler characteristics of vector bundles over the Quot scheme that compactifies the space of degree $d$ morphisms from a fixed projective curve to the Grassmannian $\mathrm{Gr}(r,N)$. As an application, we deduce interesting vanishing results, used in our previous work to study the quantum $K$-ring of $\mathrm{Gr}(r,N)$. In the genus-zero case, we prove a simplified formula involving Schur functions, consistent with the Borel--Weil--Bott theorem in the degree-zero setting. These new formulas offer a novel approach for computing the structure constants of quantum $K$-products.

math.AG↗

arXiv2026

A multi-agent system for spine MRI report generation from multi-sequence imaging

Spinal pathology is a leading cause of pain and disability worldwide. Spine MRI is central to clinical evaluation, yet its interpretation remains complex and time-consuming, requiring integration of information across multiple imaging sequences and anatomical regions. Despite recent advances in automated MRI analysis, effectively combining multi-sequence data while preserving sequence-specific diagnostic information remains an open challenge. Here we present SpineAgent, a multi-agent framework for spine MRI report generation built upon a multi-sequence foundation model trained on routine clinical data from 32,047 patients and 453,683 MRI series, comprising a total of 13,441,191 MRI slices. To accommodate diverse modalities of sequences, we first pre-train two DINOv3-based encoders separately on T1- and T2-weighted sequences. We then introduce a continual training strategy that learns a synthesizer to embed images of other sequences using the T1 and T2 encoders, producing patient-level embedding that integrates various signals across MRI sequences. Using these embeddings, SpineAgent achieves state-of-the-art performance, and demonstrates strong generalizability under cross-manufacturer and cross-cohort evaluation. Beyond classification, SpineAgent enables pathology localization by identifying findings-relevant slices and segmenting pathological regions. It also supports multimodal image-report retrieval, providing a solid foundation for scalable and explainable MRI report generation. We further integrate these validated capabilities of SpineAgent into 37 specialized agents. Finally, we incorporate their outputs as structured tokens within a Medical Report Agent trained end-to-end for report generation. Through both automated metrics and expert evaluation by five radiologists, SpineAgent achieves leading performance in spine MRI report generation.

cs.CV↗

arXiv2026

Polar perturbations of dilaton-Euler-Heisenberg black holes

We investigate the quasinormal modes of polar metric-dilaton perturbations around the dilaton-Euler-Heisenberg (dEH) black holes with dilaton hair. The dEH black holes are obtained from the Einstein-Maxwell-dilaton theory with two dilaton coupling parameters ($α,β$) to the nonlinear Euler-Heisenberg term. We compute the quasinormal mode spectra by making use of two numerical techniques: direct integration and matrix values continued fraction methods. An excellent agreement is found between two approaches, confirming the robustness of our computation. We present the fundamental quasinormal frequencies for both gravitational and dilaton modes and analyze their dependence on the magnetic charge ($Q_m$), angular momentum quantum number ($l$), and coupling parameter ($ε=α-β$). All negative imaginary quasinormal frequencies for polar metric-dilaton perturbations imply that the dEH black hole with dilaton hair is stable against dilaton with $l=0,1,2,3$ and gravitational modes with $l=2,3$. Also, our results reveal distinct qualitative behaviors between $ε=1$ and $ε=-1$, particularly in the damping rates near the extremality.

gr-qc↗

arXiv2026

Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction

Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics. However, the presence of label noise in real scenarios poses a significant challenge in learning robust GNNs, and their effectiveness can be severely impacted when dealing with noisy labels on graphs, often stemming from annotation errors or inconsistencies. To address this, in this paper we propose a novel approach called ICGNN that harnesses the structure information of the graph to effectively alleviate the challenges posed by noisy labels. Specifically, we first design a novel noise indicator that measures the influence contradiction score (ICS) based on the graph diffusion matrix to quantify the credibility of nodes with clean labels, such that nodes with higher ICS values are more likely to be detected as having noisy labels. Then we leverage the Gaussian mixture model to precisely detect whether the label of a node is noisy or not. Additionally, we develop a soft strategy to combine the predictions from neighboring nodes on the graph to correct the detected noisy labels. At last, pseudo-labeling for abundant unlabeled nodes is incorporated to provide auxiliary supervision signals and guide the model optimization. Experiments on benchmark datasets show the superiority of our approach over competitive baselines in noisy label scenarios.

cs.LG↗

arXiv2026

AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining

Formula alpha mining, which generates predictive signals from financial data, is critical for quantitative investment. Although various algorithmic approaches-such as genetic programming, reinforcement learning, and large language models-have significantly expanded the capacity for alpha discovery, systematic evaluation remains a key challenge. Existing evaluation metrics predominantly include backtesting and correlation-based measures. Backtesting is computationally intensive, inherently sequential, and sensitive to specific strategy parameters. Correlation-based metrics, though efficient, assess only predictive ability and overlook other crucial properties such as temporal stability, robustness, diversity, and interpretability. Additionally, the closed-source nature of most existing alpha mining models hinders reproducibility and slows progress in this field. To address these issues, we propose AlphaEval, a unified, parallelizable, and backtest-free evaluation framework for automated alpha mining models. AlphaEval assesses the overall quality of generated alphas along five complementary dimensions: predictive power, stability, robustness to market perturbations, financial logic, and diversity. Extensive experiments across representative alpha mining algorithms demonstrate that AlphaEval achieves evaluation consistency comparable to comprehensive backtesting, while providing more comprehensive insights and higher efficiency. Furthermore, AlphaEval effectively identifies superior alphas compared to traditional single-metric screening approaches. All implementations and evaluation tools are open-sourced to promote reproducibility and community engagement.

cs.AI↗

arXiv2026

Comprehensive Analysis of Optical brightness and Color Variability of Blazars in the ZTF Survey DR22

This study conducts a comprehensive analysis of brightness and color variability in blazars, utilizing over six years of quasi-simultaneous g-band and r-band data from 1149 sources in the ZTF Data Release 22 (DR22), including 589 BL Lacs and 560 FSRQs. We quantify the amplitude of variability and the fractional root mean square (rms) variability for each source and statistically assess the overall and short-term color behaviors across different subclasses; examine the distribution of brightness variability characteristics across different blazar types and investigate how the extent of variability correlates with color trends. We found BL Lacs tend to exhibit a BWB (bluer when brighter) trend, while FSRQs display a RWB (redder when brighter) trend; BL Lacs with negligible host-galaxy contamination exhibit a BWB trend fraction of 14.7% (68/462) compared to 2.3% (11/462) for RWB trend, while FSRQs show 8.8% (49/560) BWB trend versus 14.1% (79/560) RWB trend. By statistically investigating how color behavior depends on brightness state across different timescales, we find that brighter states in both BL Lacs and FSRQs are more likely to exhibit BWB trend. Our results also show that BL Lacs with a BWB trend exhibit higher variability than those with a RWB trend, whereas FSRQs with a RWB trend display significantly greater variability than those with a BWB trend. These results suggest that blazar color variability depends jointly on source type, brightness state, and variability amplitude, highlighting the complexity of color evolution in blazars.

astro-ph.CO↗

arXiv2026

Testing Cosmic Distance Duality Relation and Transparency with DESI DR2

The Cosmic Distance Duality Relation (CDDR) is a fundamental principle of standard cosmology, linking luminosity (LD) and angular diameter distances (ADD). This work investigates the validity of the CDDR and cosmic transparency by combining the latest Baryon Acoustic Oscillations (BAO) data from DESI DR2, Type Ia Supernovae from Pantheon+, and cosmic chronometers. To address the redshift mismatch between datasets, two distinct reconstruction techniques are employed: Gaussian Process Regression (GPR) and the Free-Knots Method (FKM). The analysis performs null tests on the CDDR under different cosmological priors, finding that the null hypothesis holds and the CDDR is valid within statistical uncertainties. Although mild deviations are observed from the local distance ladder prior, internal consistency calibration indicates that these discrepancies and the Hubble tension may share a common origin, possibly related to systematic effects or new physics. Using multiple phenomenological parameterizations, the deviation parameter is also found to be statistically consistent with zero (e.g., $η_1 = 0.023 \pm 0.027$ for the linear model under Planck priors). Furthermore, the study finds no statistically significant evidence for cosmic opacity. The average of the opacity derivative is compatible with zero ($\langle dτ/dz \rangle = 0.0409 \pm 0.1024$ for GPR and $0.0730 \pm 0.1607$ for FKM). Based on these null results, stringent constraints are placed on the parameter space of Axion-Like Particles (ALPs) and Mini-Charged Particles (MCPs).

astro-ph.CO↗

arXiv2026

SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents

Scientific reasoning inherently demands integrating sophisticated toolkits to navigate domain-specific knowledge. Yet, current benchmarks largely overlook agents' ability to orchestrate tools for such rigorous workflows. To bridge this gap, we introduce SciAgentGym, a scalable interactive environment featuring 1,780 domain-specific tools across four natural science disciplines, supported by a robust execution infrastructure. Complementing this, we present SciAgentBench, a tiered evaluation suite designed to stress-test agentic capabilities from elementary actions to long-horizon workflows. Our evaluation identifies a critical bottleneck: state-of-the-art models still struggle with complex scientific tool-use, and their performance degrades substantially as interaction horizons extend. To address this, we propose SciForge, a data synthesis method that models the tool action space as a dependency graph to generate logic-aware training trajectories. By fine-tuning on these trajectories, our SciAgent-8B outperforms the significantly larger Qwen3-VL-235B-Instruct while exhibiting positive cross-domain transfer of scientific tool-use capabilities. These results underscore the promising potential of next-generation autonomous scientific agents.

cs.CL↗

arXiv2026

Detached Skip-Links and $R$-Probe: Decoupling Feature Aggregation from Gradient Propagation for MLLM OCR

Multimodal large language models (MLLMs) excel at high-level reasoning yet fail on OCR tasks where fine-grained visual details are compromised or misaligned. We identify an overlooked optimization issue in multi-layer feature fusion. Skip pathways introduce direct back-propagation paths from high-level semantic objectives to early visual layers. This mechanism overwrites low-level signals and destabilizes training. To mitigate this gradient interference, we propose Detached Skip-Links, a minimal modification that reuses shallow features in the forward pass while stopping gradients through the skip branch during joint training. This asymmetric design reduces gradient interference, improving stability and convergence without adding learnable parameters. To diagnose whether fine-grained information is preserved and usable by an LLM, we introduce $R$-Probe, which measures pixel-level reconstructability of projected visual tokens using a shallow decoder initialized from the first quarter of the LLM layers. Across multiple ViT backbones and multimodal benchmarks, and at scales up to 7M training samples, our approach consistently improves OCR-centric benchmarks and delivers clear gains on general multimodal tasks.

cs.CV↗

arXiv2026

Generative structure search for efficient and diverse discovery of molecular and crystal structures

Predicting stable and metastable structures is central to molecular and materials discovery, but remains limited by the cost of searching high-dimensional energy landscapes. Deep generative models offer efficient structure sampling, yet their outputs remain shaped by training data and can underexplore minima that are rare but physically relevant. We introduce generative structure search (GSS), a unified framework that formulates diffusion-based generation and random structure search (RSS) as limiting regimes of a common sampling process driven by learned score fields and physical forces. Coupling these drivers lets GSS use data priors to accelerate sampling while retaining energy-guided exploration of local minima. Across molecular and crystalline systems, GSS recovers diverse metastable structures with more than tenfold lower sampling cost than RSS for broad coverage and remains effective for compositions outside the training distribution. The results establish a physically grounded generative search strategy for discovering structures beyond the reach of data-driven sampling alone.

cs.AI↗

arXiv2026

UniSpike: Accelerating Spiking Neural Networks on Neuromorphic Systems via Eliminating Address Redundancy

Many-core neuromorphic systems accelerate Spiking Neural Networks (SNNs), yet their packet-based spike communication can spend substantial traffic and energy repeatedly transmitting destination addresses. This overhead is amplified by the small payload of spike packets: in representative workloads, duplicate address transmissions account for up to 49% of the total traffic. This paper presents UniSpike, a hardware-software co-design that removes address redundancy by aggregating spikes destined for the same core into compact packets. UniSpike combines destination-centric spike scheduling, lightweight runtime packet assembly hardware, and destination-aware SNN partitioning. Across diverse SNN workloads, UniSpike reduces traffic by 1.93$\times$ on average, delivering 1.77$\times$ speedup and 1.50$\times$ energy efficiency improvement over state-of-the-art designs.

cs.NE↗

arXiv2026

MindLoom: Composing Thought Modes for Frontier-Level Reasoning Data Synthesis

Although LLMs have made substantial progress in reasoning, systematically producing frontier-level reasoning data remains difficult. Existing synthesis methods often have limited visibility into the structural factors that govern problem difficulty, which can result in narrow diversity and unstable difficulty control. In this work, we view the difficulty of a reasoning problem as arising from the accumulation of atomic knowledge-reasoning transformations, which we term thought modes. Building on this perspective, we propose MindLoom, a framework for synthesizing frontier-level reasoning data through compositional thought mode engineering. Given a collection of hard problems with verified solutions, MindLoom first decomposes those solutions into thought mode chains that reveal each problem's construction logic. It then trains a retrieval model that matches problem states to compatible thought modes, providing guidance on which reasoning challenges to introduce during synthesis. New problems are composed by iteratively applying retrieved thought modes to seed questions, with distribution-aligned sampling to encourage diverse reasoning coverage. Finally, a rollout-based judging stage labels generated questions by difficulty and supplies judged-correct responses for supervised fine-tuning. We evaluate MindLoom on nine benchmarks covering five STEM disciplines and four mathematical reasoning tasks across multiple model families and sizes. Models fine-tuned on MindLoom-generated data achieves favorable performances over base models, distillation, and external-data baselines across the reported benchmarks. Ablation studies indicate the contribution of each component, and further analysis suggests that MindLoom covers a broad range of reasoning patterns while maintaining useful difficulty control. We have open-sourced our implementation at https://github.com/EachSheep/MindLoom.

cs.AI↗

arXiv2026

AlphaSAGE: Structure-Aware Alpha Mining via GFlowNets for Robust Exploration

The automated mining of predictive signals, or alphas, is a central challenge in quantitative finance. While Reinforcement Learning (RL) has emerged as a promising paradigm for generating formulaic alphas, existing frameworks are fundamentally hampered by a triad of interconnected issues. First, they suffer from reward sparsity, where meaningful feedback is only available upon the completion of a full formula, leading to inefficient and unstable exploration. Second, they rely on semantically inadequate sequential representations of mathematical expressions, failing to capture the structure that determine an alpha's behavior. Third, the standard RL objective of maximizing expected returns inherently drives policies towards a single optimal mode, directly contradicting the practical need for a diverse portfolio of non-correlated alphas. To overcome these challenges, we introduce AlphaSAGE (Structure-Aware Alpha Mining via Generative Flow Networks for Robust Exploration), a novel framework is built upon three cornerstone innovations: (1) a structure-aware encoder based on Relational Graph Convolutional Network (RGCN); (2) a new framework with Generative Flow Networks (GFlowNets); and (3) a dense, multi-faceted reward structure. Empirical results demonstrate that AlphaSAGE outperforms existing baselines in mining a more diverse, novel, and highly predictive portfolio of alphas, thereby proposing a new paradigm for automated alpha mining. Our code is available at https://github.com/BerkinChen/AlphaSAGE.

q-fin.CP↗

arXiv2026

Quasinormal modes of massless scalar and electromagnetic perturbations for Euler-Heisenberg black holes surrounded by perfect fluid dark matter

We investigate the quasinormal modes of massless scalar and electromagnetic perturbations in charged Euler--Heisenberg black holes surrounded by perfect fluid dark matter. The quasinormal frequencies are calculated using the asymptotic iteration method and the sixth-order WKB approximation, and the relative deviation between the two methods is quantitatively analyzed to verify the reliability of results. The greybody factors for both perturbations are also evaluated within the sixth-order WKB framework. We systematically examine the effects of the black hole charge $Q$, nonlinear electrodynamic parameter $a$, dark matter parameter $λ$, and angular quantum number $l$ on the quasinormal frequencies and greybody factors. We find that these parameters significantly modify the structure of the effective potential barriers, and thus affect the oscillation frequencies, damping rates, and wave transmission and reflection properties of the perturbed fields.

gr-qc↗