arXiv subjects

Fan Zhang

Publications and source records attributed to Fan Zhang.

At least 253 records · Page 14Linked to original sources

arXiv2026

Boost+: Equitable, Incentive-Compatible Block Building

Block space on the blockchain is scarce and must be allocated efficiently through block building. However, Ethereum's current block-building ecosystem, MEV-Boost, has become highly centralized due to integration, which distorts competition, reduces blockspace efficiency, and obscures MEV flow transparency. To guarantee equitability and economic efficiency in block building, we propose $\mathrm{Boost+}$, a system that decouples the process into collecting and ordering transactions, and ensures equal access to all collected transactions. The core of $\mathrm{Boost+}$ is the mechanism $\mathit{M}_{\mathrm{Boost+}}$, built around a default algorithm. $\mathit{M}_{\mathrm{Boost+}}$ aligns incentives for both searchers (intermediaries that generate or route transactions) and builders: Truthful bidding is a dominant strategy for all builders. For searchers, truthful reporting is dominant whenever the default algorithm dominates competing builders, and it remains dominant for all conflict-free transactions, even when builders may win. We further show that even if a searcher can technically integrate with a builder, non-integration combined with truthful bidding still dominates any deviation for conflict-free transactions. We also implement a concrete default algorithm informed by empirical analysis of real-world transactions and evaluate its efficacy using historical transaction data.

cs.CR↗

arXiv2026

MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering

Multimodal large language models (MLLMs) have demonstrated remarkable capabilities across vision-language tasks, yet their large-scale deployment raises pressing concerns about memorized private data, outdated knowledge, and harmful content. Existing unlearning approaches for MLLMs typically adapt training-based strategies such as gradient ascent or preference optimization, but these methods are computationally expensive, irreversible, and often distort retained knowledge. In this work, we propose MLLMEraser, an input-aware, training-free framework for test-time unlearning. Our approach leverages activation steering to enable dynamic knowledge erasure without parameter updates. Specifically, we construct a multimodal erasure direction by contrasting adversarially perturbed, knowledge-recall image-text pairs with knowledge-erasure counterparts, capturing both textual and visual discrepancies. To prevent unnecessary interference, we further design an input-aware steering mechanism that adaptively determines when and how the erasure direction should be applied, preserving utility on retained knowledge while enforcing forgetting on designated content. Experiments on LLaVA-1.5 and Qwen-2.5-VL demonstrate that MLLMEraser consistently outperforms state-of-the-art MLLM unlearning baselines, achieving stronger forgetting performance with lower computational cost and minimal utility degradation.

cs.LG↗

arXiv2026

Revisiting neutrino-driven magnetogenesis during stellar core collapse

The literature has not converged onto a precise depiction of the magnetogenesis process for pulsars, and it is profitable to preliminarily but exhaustively assess the viability of the plethora of alternative proposals, before substantial efforts are invested into simulating them in detail. In this note, we tackle one of them, taking notice of an earlier work that suggests neutrino ponderomotive force could spawn a magnetic field not so far off from pulsar strengths. We reexamine this mechanism with more modern technology, accounting for actual core collapse dynamics, and show that this mechanism is likely less powerful than originally envisioned.

astro-ph.HE↗

arXiv2026

Ebisu: Benchmarking Large Language Models in Japanese Finance

Japanese finance combines agglutinative, head-final linguistic structure, mixed writing systems, and high-context communication norms that rely on indirect expression and implicit commitment, posing a substantial challenge for LLMs. We introduce Ebisu, a benchmark for native Japanese financial language understanding, comprising two linguistically and culturally grounded, expert-annotated tasks: JF-ICR, which evaluates implicit commitment and refusal recognition in investor-facing Q&A, and JF-TE, which assesses hierarchical extraction and ranking of nested financial terminology from professional disclosures. We evaluate a diverse set of open-source and proprietary LLMs spanning general-purpose, Japanese-adapted, and financial models. Results show that even state-of-the-art systems struggle on both tasks. While increased model scale yields limited improvements, language- and domain-specific adaptation does not reliably improve performance, leaving substantial gaps unresolved. Ebisu provides a focused benchmark for advancing linguistically and culturally grounded financial NLP. All datasets and evaluation scripts are publicly released.

cs.CL↗

arXiv2026

Design Perspective on Materials Experience: A CiteSpace-Based Bibliometric and Visual Analysis of Interdisciplinary Research

Based on a bibliometric analysis of literature from 2005 to 2024, this study reveals that material experience is undergoing a profound transformation characterized by evolving material definitions, methodological advances, and increasing interdisciplinary integration. Material types now extend beyond traditional substances to encompass virtual and biological media, underscoring a growing emphasis on perception and interaction. Methodologically, the field has transitioned from subjective descriptions to data-driven, quantifiable models focused on objective sensory analysis and multisensory integration to enhance immersion. Key drivers, including human-machine perception convergence, material-driven interface interactions, and the embedding of intelligent interactive functions, propel the discipline toward an experience-centered paradigm reflecting a deep convergence of design, science, and technology. At the national/regional level, the United States, China, Japan, Germany, and the Netherlands lead in contributions, while France, the United Kingdom, and Romania demonstrate significant interdisciplinary progress. At the institutional level, Delft University of Technology, Justus Liebig University Giessen, and the Centre National de la Recherche Scientifique show significant advantages. In particular, the Material-Driven Design theory has established a foundational impact on the discipline, while, regarding general research trends, scholars from the United States, the Netherlands, and Germany maintain the highest academic visibility. Overall, material experience research is at a critical juncture, its future development will depend on progress in material innovation, technological integration, and perceptual quantification, as well as the establishment of socio-cultural values, all of which must be effectively unified through design to address complex evolving needs.

cs.HC↗

arXiv2026

EEO-TFV: Escape-Explore Optimizer for Web-Scale Time-Series Forecasting and Vision Analysis

Transformer-based foundation models have achieved remarkable progress in tasks such as time-series forecasting and image segmentation. However, they frequently suffer from error accumulation in multivariate long-sequence prediction and exhibit vulnerability to out-of-distribution samples in image-related tasks. Furthermore, these challenges become particularly pronounced in large-scale Web data analysis tasks, which typically involve complex temporal patterns and multimodal features. This complexity substantially increases optimization difficulty, rendering models prone to stagnation at saddle points within high-dimensional parameter spaces. To address these issues, we propose a lightweight Transformer architecture in conjunction with a novel Escape-Explore Optimizer (EEO). The optimizer enhances both exploration and generalization while effectively avoiding sharp minima and saddle-point traps. Experimental results show that, in representative Web data scenarios, our method achieves performance on par with state-of-the-art models across 11 time-series benchmark datasets and the Synapse medical image segmentation task. Moreover, it demonstrates superior generalization and stability, thereby validating its potential as a versatile cross-task foundation model for Web-scale data mining and analysis.

cs.LG↗

arXiv2026

Time-TK: A Multi-Offset Temporal Interaction Framework Combining Transformer and Kolmogorov-Arnold Networks for Time Series Forecasting

Time series forecasting is crucial for the World Wide Web and represents a core technical challenge in ensuring the stable and efficient operation of modern web services, such as intelligent transportation and website throughput. However, we have found that existing methods typically employ a strategy of embedding each time step as an independent token. This paradigm introduces a fundamental information bottleneck when processing long sequences, the root cause of which is that independent token embedding destroys a crucial structure within the sequence - what we term as multi-offset temporal correlation. This refers to the fine-grained dependencies embedded within the sequence that span across different time steps, which is especially prevalent in regular Web data. To fundamentally address this issue, we propose a new perspective on time series embedding. We provide an upper bound on the approximate reconstruction performance of token embedding, which guides our design of a concise yet effective Multi-Offset Time Embedding method to mitigate the performance degradation caused by standard token embedding. Furthermore, our MOTE can be integrated into various existing models and serve as a universal building block. Based on this paradigm, we further design a novel forecasting architecture named Time-TK. This architecture first utilizes a Multi-Offset Interactive KAN to learn and represent specific temporal patterns among multiple offset sub-sequences. Subsequently, it employs an efficient Multi-Offset Temporal Interaction mechanism to effectively capture the complex dependencies between these sub-sequences, achieving global information integration. Extensive experiments on 14 real-world benchmark datasets, covering domains such as traffic flow and BTC/USDT throughput, demonstrate that Time-TK significantly outperforms all baseline models, achieving state-of-the-art forecasting accuracy.

cs.LG↗

arXiv2026

A Unified SPD Token Transformer Framework for EEG Classification: Systematic Comparison of Geometric Embeddings

Spatial covariance matrices of EEG signals are Symmetric Positive Definite (SPD) and lie on a Riemannian manifold, yet the theoretical connection between embedding geometry and optimization dynamics remains unexplored. We provide a formal analysis linking embedding choice to gradient conditioning and numerical stability for SPD manifolds, establishing three theoretical results: (1) BWSPD's $\sqrtκ$ gradient conditioning (vs $κ$ for Log-Euclidean) via Daleckii-Kre\uın matrices provides better gradient conditioning on high-dimensional inputs ($d \geq 22$), with this advantage reducing on low-dimensional inputs ($d \leq 8$) where eigendecomposition overhead dominates; (2) Embedding-Space Batch Normalization (BN-Embed) approximates Riemannian normalization up to $O(\varepsilon^2)$ error, yielding $+26\%$ accuracy on 56-channel ERP data but negligible effect on 8-channel SSVEP data, matching the channel-count-dependent prediction; (3) bi-Lipschitz bounds prove BWSPD tokens preserve manifold distances with distortion governed solely by the condition ratio $κ$. We validate these predictions via a unified Transformer framework comparing BWSPD, Log-Euclidean, and Euclidean embeddings within identical architecture across 1,500+ runs on three EEG paradigms (motor imagery, ERP, SSVEP; 36 subjects). Our Log-Euclidean Transformer achieves state-of-the-art performance on all datasets, substantially outperforming classical Riemannian classifiers and recent SPD baselines, while BWSPD offers competitive accuracy with similar training time.

cs.LG↗

arXiv2026

AGFS-Tractometry: A Novel Atlas-Guided Fine-Scale Tractometry Approach for Enhanced Along-Tract Group Statistical Comparison Using Diffusion MRI Tractography

Diffusion MRI (dMRI) tractography is currently the only method for in vivo mapping of the brain's white matter (WM) connections. Tractometry is an advanced tractography analysis technique for along-tract profiling to investigate the morphology and microstructural properties along the fiber tracts. Tractometry has become an essential tool for studying local along-tract differences between different populations (e.g., health vs disease). In this study, we propose a novel atlas-guided fine-scale tractometry method, namely AGFS-Tractometry, that leverages tract spatial information and permutation testing to enhance the along-tract statistical analysis between populations. There are two major contributions in AGFS-Tractometry. First, we create a novel atlas-guided tract profiling template that enables consistent, fine-scale, along-tract parcellation of subject-specific fiber tracts. Second, we propose a novel nonparametric permutation testing group comparison method to enable simultaneous analysis across all along-tract parcels while correcting for multiple comparisons. We perform experimental evaluations on synthetic datasets with known group differences and in vivo real data. We compare AGFS-Tractometry with two state-of-the-art tractometry methods, including Automated Fiber-tract Quantification (AFQ) and BUndle ANalytics (BUAN). Our results show that the proposed AGFS-Tractometry obtains enhanced sensitivity and specificity in detecting local WM differences. In the real data analysis experiments, AGFS-Tractometry can identify more regions with significant differences, which are anatomically consistent with the existing literature. Overall, these demonstrate the ability of AGFS-Tractometry to detect subtle or spatially localized WM group-level differences. The created tract profiling template and related code are available at: https://github.com/ZhengRuixi/AGFS-Tractometry.git.

q-bio.QM↗

arXiv2026

iFAN Ecosystem: A Unified AI, Digital Twin, Cyber-Physical Security, and Robotics Environment for Advanced Nuclear Simulation and Operations

As nuclear facilities experience digital transformation and advanced reactor development, AI integration, cyber-physical security, and other emerging technologies such as autonomous robot operations are increasingly developed. However, evaluation and deployment is challenged by the lack of dedicated virtual testbeds. The Immersive Framework for Advanced Nuclear (iFAN) ecosystem is developed, a comprehensive digital twin framework with a realistic 3D environment with physics-based simulations. The iFAN ecosystem serves as a high-fidelity virtual testbed for plant operation, cybersecurity, physical security, and robotic operation, as it provides real-time data exchange for pre-deployment verification. Core features include virtual reality, reinforcement learning, radiation simulation, and cyber-physical security. In addition, the paper investigates various applications through potential operational scenarios. The iFAN ecosystem provides a versatile and secure architecture for validating the next generation of autonomous and cyber-resilient nuclear operations.

cs.RO↗

arXiv2026

PocketDVDNet: Realtime Video Denoising for Real Camera Noise

Live video denoising under realistic, multi-component sensor noise remains challenging for applications such as autofocus, autonomous driving, and surveillance. We propose PocketDVDNet, a lightweight video denoiser developed using our model compression framework that combines sparsity-guided structured pruning, a physics-informed noise model, and knowledge distillation to achieve high-quality restoration with reduced resource demands. Starting from a reference model, we induce sparsity, apply targeted channel pruning, and retrain a teacher on realistic multi-component noise. The student network learns implicit noise handling, eliminating the need for explicit noise-map inputs. PocketDVDNet reduces the original model size by 74% while improving denoising quality and processing 5-frame patches in real-time. These results demonstrate that aggressive compression, combined with domain-adapted distillation, can reconcile performance and efficiency for practical, real-time video denoising.

eess.IV↗

arXiv2026

Advances in Artificial Intelligence: A Review for the Creative Industries

Artificial intelligence (AI) has undergone transformative advances since 2022, particularly through generative AI, large language models (LLMs), and diffusion models, fundamentally reshaping the creative industries. However, existing reviews have not comprehensively addressed these recent breakthroughs and their integrated impact across the creative production pipeline. This paper addresses this gap by providing a systematic review of AI technologies that have emerged or matured since our 2022 review, examining their applications across content creation, information analysis, post-production enhancement, compression, and quality assessment. We document how transformers, LLMs, diffusion models, and implicit neural representations have established new capabilities in text-to-image/video generation, real-time 3D reconstruction, and unified multi-task frameworks-shifting AI from support tool to core creative technology. Beyond technological advances, we analyze the trend toward unified AI frameworks that integrate multiple creative tasks, replacing task-specific solutions. We critically examine the evolving role of human-AI collaboration, where human oversight remains essential for creative direction and mitigating AI hallucinations. Finally, we identify emerging challenges including copyright concerns, bias mitigation, computational demands, and the need for robust regulatory frameworks. This review provides researchers and practitioners with a comprehensive understanding of current AI capabilities, limitations, and future trajectories in creative applications.

cs.AI↗

arXiv2026

Beyond Visual Realism: Toward Reliable Financial Time Series Generation

Generative models for financial time series often create data that look realistic and even reproduce stylized facts such as fat tails or volatility clustering. However, these apparent successes break down under trading backtests: models like GANs or WGAN-GP frequently collapse, yielding extreme and unrealistic results that make the synthetic data unusable in practice. We identify the root cause in the neglect of financial asymmetry and rare tail events, which strongly affect market risk but are often overlooked by objectives focusing on distribution matching. To address this, we introduce the Stylized Facts Alignment GAN (SFAG), which converts key stylized facts into differentiable structural constraints and jointly optimizes them with adversarial loss. This multi-constraint design ensures that generated series remain aligned with market dynamics not only in plots but also in backtesting. Experiments on the Shanghai Composite Index (2004--2024) show that while baseline GANs produce unstable and implausible trading outcomes, SFAG generates synthetic data that preserve stylized facts and support robust momentum strategy performance. Our results highlight that structure-preserving objectives are essential to bridge the gap between superficial realism and practical usability in financial generative modeling.

q-fin.ST↗

arXiv2026

Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality

Expressive behaviors in robots are critical for effectively conveying their emotional states during interactions with humans. In this work, we present a framework that autonomously generates realistic and diverse robotic emotional expressions based on expert human demonstrations captured in Mixed Reality (MR). Our system enables experts to teleoperate a virtual robot from a first-person perspective, capturing their facial expressions, head movements, and upper-body gestures, and mapping these behaviors onto corresponding robotic components including eyes, ears, neck, and arms. Leveraging a flow-matching-based generative process, our model learns to produce coherent and varied behaviors in real-time in response to moving objects, conditioned explicitly on given emotional states. A preliminary test validated the effectiveness of our approach for generating autonomous expressions.

cs.RO↗

arXiv2026

MuseAgent-1: Interactive Grounded Multimodal Understanding of Music Scores and Performance Audio

Despite recent advances in multimodal large language models (MLLMs), their ability to understand and interact with music remains limited. Music understanding requires grounded reasoning over symbolic scores and expressive performance audio, which general-purpose MLLMs often fail to handle due to insufficient perceptual grounding. We introduce MuseAgent, a music-centric multimodal agent that augments language models with structured symbolic representations derived from sheet music images and performance audio. By integrating optical music recognition and automatic music transcription modules, MuseAgent enables multi-step reasoning and interaction over fine-grained musical content. To systematically evaluate music understanding capabilities, we further propose MuseBench, a benchmark covering music theory reasoning, score interpretation, and performance-level analysis across text, image, and audio modalities. Experiments show that existing MLLMs perform poorly on these tasks, while MuseAgent achieves substantial improvements, highlighting the importance of structured multimodal grounding for interactive music understanding.

cs.MM↗

arXiv2026

Brightest GRB flare observed in GRB 221009A: bridge the last gap between flare and prompt emission in GRB

Flares are usually observed during the afterglow phase of Gamma-Ray Bursts (GRBs) in soft X-ray, optical and radio bands, but rarely in gamma-ray band. Despite the extraordinary brightness, GECAM-C has accurately measured both the bright prompt emission and flare emission of GRB 221009A without instrumental effects, offering a good opportunity to study the relation between them. In this work, we present a comprehensive analysis of flare emission of GRB 221009A, which is composed of a series of flares. Among them, we identify an exceptionally bright flare with a record-breaking isotropic energy $E_{\rm iso} = 1.82 \times 10^{53}$ erg of GRB flares. It exhibits the highest peak energy ever detected in GRB flares, $E_{\rm peak} \sim 300$ keV, making it a genuine gamma-ray flare. It also shows rapid rise and decay timescales, significantly shorter than those of typical X-ray flares observed in soft X-ray or optical band, but comparable to those observed in prompt emissions. Despite these exceptional properties, the flare shares several common properties with typical GRB flares. We note that this is the first observation of a GRB flare in the keV-MeV band with sufficiently high temporal resolution and high statistics, which bridges the last gap between prompt emission and flare.

astro-ph.HE↗

arXiv2026

BrainSegNet: A Novel Framework for Whole-Brain MRI Parcellation Enhanced by Large Models

Whole-brain parcellation from MRI is a critical yet challenging task due to the complexity of subdividing the brain into numerous small, irregular shaped regions. Traditionally, template-registration methods were used, but recent advances have shifted to deep learning for faster workflows. While large models like the Segment Anything Model (SAM) offer transferable feature representations, they are not tailored for the high precision required in brain parcellation. To address this, we propose BrainSegNet, a novel framework that adapts SAM for accurate whole-brain parcellation into 95 regions. We enhance SAM by integrating U-Net skip connections and specialized modules into its encoder and decoder, enabling fine-grained anatomical precision. Key components include a hybrid encoder combining U-Net skip connections with SAM's transformer blocks, a multi-scale attention decoder with pyramid pooling for varying-sized structures, and a boundary refinement module to sharpen edges. Experimental results on the Human Connectome Project (HCP) dataset demonstrate that BrainSegNet outperforms several state-of-the-art methods, achieving higher accuracy and robustness in complex, multi-label parcellation.

cs.CV↗

arXiv2026

Absence of magnetic order in epitaxial RuO2 revealed by X-ray linear dichroism

Recently, the topic of altermagnetism has attracted tremendous attention and RuO2 have been demonstrated to be one of the most promising altermagnetic candidates. However, disputes still remain on the existence of magnetic order in RuO2. Here in this work, we employ X-ray linear dichroism (XLD), a widely utilized technique for characterizing antiferromagnets, in conjunction with photoemission electron microscopy and multiple scattering calculation to provide clear evidence of the absence of magnetic order in epitaxial RuO2 films. The observed XLD signal is nearly invariant with temperature and independent on cooling field direction, in stark contrast to the substantial magnetic order-related XLD signal predicted by multiple scattering calculation. This finding strongly suggests a nonmagnetic origin for RuO2. Furthermore, we observed significantly distinct XLD signals at the Ru M3 and O K edges in RuO2 films grown on TiO2 substrate with different surface orientations, which can be attributed to the low-symmetry crystal field. These results unequivocally demonstrate the absence of magnetic order in RuO2 and establishes XLD measurement as a robust technique for probing the low-symmetry magnetic materials.

cond-mat.mtrl-sci↗