OpenRaiser/Intern-Atlas huggingface dataset:
Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while preserving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained IoT devices face significant challenges due to limited energy,unreliable communication channels, and the impracticality of assuming infinite blocklength transmission. This paper proposes a federated learning framework for IoT networks that integrates finite blocklength transmission, model quantization, and an error-aware aggregation mechanism to enhance energy efficiency and communication reliability. The framework also optimizes uplink transmission power to balance energy savings and model performance. Simulation results demonstrate that the proposed approach significantly reduces energy consumption by up to 75\% compared to a standard FL model, while maintaining robust model accuracy, making it a viable solution for FL in real-world IoT scenarios with constrained resources. This work paves the way for efficient and reliable FL implementations in practical IoT deployments. Index Terms: Federated learning, IoT, finite blocklength, quantization, energy efficiency.
Gesture Evaluation in Virtual Reality
Venue: arXiv cs.HC 2025
Year: 2025

Gestures are central to human communication, enriching interactions through non-verbal expression. Virtual avatars increasingly use AI-generated gestures to enhance life-likeness, yet evaluations have largely been confined to 2D. Virtual Reality (VR) provides an immersive alternative that may affect how gestures are perceived. This paper presents a comparative evaluation of computer-generated gestures in VR and 2D, examining three models from the 2023 GENEA Challenge. Results show that gestures viewed in VR were rated slightly higher on average, with the strongest effect observed for motion-capture "true movement." While model rankings remained consistent across settings, VR influenced participants' overall perception and offered unique benefits over traditional 2D evaluation.
DiffHash: Text-Guided Targeted Attack via Diffusion Models against Deep Hashing Image Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

Deep hashing models have been widely adopted to tackle the challenges of large-scale image retrieval. However, these approaches face serious security risks due to their vulnerability to adversarial examples. Despite the increasing exploration of targeted attacks on deep hashing models, existing approaches still suffer from a lack of multimodal guidance, reliance on labeling information and dependence on pixel-level operations for attacks. To address these limitations, we proposed DiffHash, a novel diffusion-based targeted attack for deep hashing. Unlike traditional pixel-based attacks that directly modify specific pixels and lack multimodal guidance, our approach focuses on optimizing the latent representations of images, guided by text information generated by a Large Language Model (LLM) for the target image. Furthermore, we designed a multi-space hash alignment network to align the high-dimension image space and text space to the low-dimension binary hash space. During reconstruction, we also incorporated text-guided attention mechanisms to refine adversarial examples, ensuring them aligned with the target semantics while maintaining visual plausibility. Extensive experiments have demonstrated that our method outperforms state-of-the-art (SOTA) targeted attack methods, achieving better black-box transferability and offering more excellent stability across datasets.
Runge-Kutta Approximation and Decoupled Attention for Rectified Flow Inversion and Semantic Editing
Venue: arXiv cs.CV 2025
Year: 2025

Rectified flow (RF) models have recently demonstrated superior generative performance compared to DDIM-based diffusion models. However, in real-world applications, they suffer from two major challenges: (1) low inversion accuracy that hinders the consistency with the source image, and (2) entangled multimodal attention in diffusion transformers, which hinders precise attention control. To address the first challenge, we propose an efficient high-order inversion method for rectified flow models based on the Runge-Kutta solver of differential equations. To tackle the second challenge, we introduce Decoupled Diffusion Transformer Attention (DDTA), a novel mechanism that disentangles text and image attention inside the multimodal diffusion transformers, enabling more precise semantic control. Extensive experiments on image reconstruction and text-guided editing tasks demonstrate that our method achieves state-of-the-art performance in terms of fidelity and editability. Code is available at https://github.com/wmchen/RKSovler_DDTA.
MEJO: MLLM-Engaged Surgical Triplet Recognition via Inter- and Intra-Task Joint Optimization
Venue: arXiv cs.CV 2025
Year: 2025

Surgical triplet recognition, which involves identifying instrument, verb, target, and their combinations, is a complex surgical scene understanding challenge plagued by long-tailed data distribution. The mainstream multi-task learning paradigm benefiting from cross-task collaborative promotion has shown promising performance in identifying triples, but two key challenges remain: 1) inter-task optimization conflicts caused by entangling task-generic and task-specific representations; 2) intra-task optimization conflicts due to class-imbalanced training data. To overcome these difficulties, we propose the MLLM-Engaged Joint Optimization (MEJO) framework that empowers both inter- and intra-task optimization for surgical triplet recognition. For inter-task optimization, we introduce the Shared-Specific-Disentangled (S$^2$D) learning scheme that decomposes representations into task-shared and task-specific components. To enhance task-shared representations, we construct a Multimodal Large Language Model (MLLM) powered probabilistic prompt pool to dynamically augment visual features with expert-level semantic cues. Additionally, comprehensive task-specific cues are modeled via distinct task prompts covering the temporal-spatial dimensions, effectively mitigating inter-task ambiguities. To tackle intra-task optimization conflicts, we develop a Coordinated Gradient Learning (CGL) strategy, which dissects and rebalances the positive-negative gradients originating from head and tail classes for more coordinated learning behaviors. Extensive experiments on the CholecT45 and CholecT50 datasets demonstrate the superiority of our proposed framework, validating its effectiveness in handling optimization conflicts.
Cross-Layer Vision Smoothing: Enhancing Visual Understanding via Sustained Focus on Key Objects in Large Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Large Vision-Language Models (LVLMs) can accurately locate key objects in images, yet their attention to these objects tends to be very brief. Motivated by the hypothesis that sustained focus on key objects can improve LVLMs' visual capabilities, we propose Cross-Layer Vision Smoothing (CLVS). The core idea of CLVS is to incorporate a vision memory that smooths the attention distribution across layers. Specifically, we initialize this vision memory with position-unbiased visual attention in the first layer. In subsequent layers, the model's visual attention jointly considers the vision memory from previous layers, while the memory is updated iteratively, thereby maintaining smooth attention on key objects. Given that visual understanding primarily occurs in the early and middle layers of the model, we use uncertainty as an indicator of completed visual understanding and terminate the smoothing process accordingly. Experiments on four benchmarks across three LVLMs confirm the effectiveness and generalizability of our method. CLVS achieves state-of-the-art overall performance across a variety of visual understanding tasks and attains comparable results to the leading approaches on image captioning benchmarks.
All Roads Lead to Rome: Graph-Based Confidence Estimation for Large Language Model Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Confidence estimation is essential for the reliable deployment of large language models (LLMs). Existing methods are primarily designed for factual QA tasks and often fail to generalize to reasoning tasks. To address this gap, we propose a set of training-free, graph-based confidence estimation methods tailored to reasoning tasks. Our approach models reasoning paths as directed graphs and estimates confidence by exploiting graph properties such as centrality, path convergence, and path weighting. Experiments with two LLMs on three reasoning datasets demonstrate improved confidence estimation and enhanced performance on two downstream tasks.
A Graph-Based Approach to Alert Contextualisation in Security Operations Centres
Venue: arXiv cs.CR 2025
Year: 2025

Interpreting the massive volume of security alerts is a significant challenge in Security Operations Centres (SOCs). Effective contextualisation is important, enabling quick distinction between genuine threats and benign activity to prioritise what needs further analysis. This paper proposes a graph-based approach to enhance alert contextualisation in a SOC by aggregating alerts into graph-based alert groups, where nodes represent alerts and edges denote relationships within defined time-windows. By grouping related alerts, we enable analysis at a higher abstraction level, capturing attack steps more effectively than individual alerts. Furthermore, to show that our format is well suited for downstream machine learning methods, we employ Graph Matching Networks (GMNs) to correlate incoming alert groups with historical incidents, providing analysts with additional insights.
Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
Venue: arXiv cs.DC 2025
Year: 2025

Multimodal federated learning (MFL) is a distributed framework for training multimodal models without uploading local multimodal data of clients, thereby effectively protecting client privacy. However, multimodal data is commonly heterogeneous across diverse clients, where each client possesses only a subset of all modalities, renders conventional analysis results and optimization methods in unimodal federated learning inapplicable. In addition, fixed latency demand and limited communication bandwidth pose significant challenges for deploying MFL in wireless scenarios. To optimize the wireless MFL performance on modal heterogeneity, this paper proposes a joint client scheduling and bandwidth allocation (JCSBA) algorithm based on a decision-level fusion architecture with adding a unimodal loss function. Specifically, with the decision results, the unimodal loss functions are added to both the training objective and local update loss functions to accelerate multimodal convergence and improve unimodal performance. To characterize MFL performance, we derive a closed-form upper bound related to client and modality scheduling and minimize the derived bound under the latency, energy, and bandwidth constraints through JCSBA. Experimental results on multimodal datasets demonstrate that the JCSBA algorithm improves the multimodal accuracy and the unimodal accuracy by 4.06% and 2.73%, respectively, compared to conventional algorithms.
4DRadar-GS: Self-Supervised Dynamic Driving Scene Reconstruction with 4D Radar
Venue: arXiv cs.CV 2025
Year: 2025

3D reconstruction and novel view synthesis are critical for validating autonomous driving systems and training advanced perception models. Recent self-supervised methods have gained significant attention due to their cost-effectiveness and enhanced generalization in scenarios where annotated bounding boxes are unavailable. However, existing approaches, which often rely on frequency-domain decoupling or optical flow, struggle to accurately reconstruct dynamic objects due to imprecise motion estimation and weak temporal consistency, resulting in incomplete or distorted representations of dynamic scene elements. To address these challenges, we propose 4DRadar-GS, a 4D Radar-augmented self-supervised 3D reconstruction framework tailored for dynamic driving scenes. Specifically, we first present a 4D Radar-assisted Gaussian initialization scheme that leverages 4D Radar's velocity and spatial information to segment dynamic objects and recover monocular depth scale, generating accurate Gaussian point representations. In addition, we propose a Velocity-guided PointTrack (VGPT) model, which is jointly trained with the reconstruction pipeline under scene flow supervision, to track fine-grained dynamic trajectories and construct temporally consistent representations. Evaluated on the OmniHD-Scenes dataset, 4DRadar-GS achieves state-of-the-art performance in dynamic driving scene 3D reconstruction.
A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Venue: arXiv cs.IR 2025
Year: 2025

Pre-ranking plays a crucial role in large-scale recommender systems by significantly improving the efficiency and scalability within the constraints of providing high-quality candidate sets in real time. The two-tower model is widely used in pre-ranking systems due to a good balance between efficiency and effectiveness with decoupled architecture, which independently processes user and item inputs before calculating their interaction (e.g. dot product or similarity measure). However, this independence also leads to the lack of information interaction between the two towers, resulting in less effectiveness. In this paper, a novel architecture named learnable Fully Interacted Two-tower Model (FIT) is proposed, which enables rich information interactions while ensuring inference efficiency. FIT mainly consists of two parts: Meta Query Module (MQM) and Lightweight Similarity Scorer (LSS). Specifically, MQM introduces a learnable item meta matrix to achieve expressive early interaction between user and item features. Moreover, LSS is designed to further obtain effective late interaction between the user and item towers. Finally, experimental results on several public datasets show that our proposed FIT significantly outperforms the state-of-the-art baseline pre-ranking models.
xRWA: A Cross-Chain Framework for Interoperability of Real-World Assets
Venue: arXiv cs.CR 2025
Year: 2025

Real-World Assets (RWAs) have recently attracted increasing attention as a means of bridging traditional financial instruments with decentralized infrastructures. By representing assets such as bonds, commodities, and real estate on blockchains, RWAs can enhance liquidity, broaden accessibility, and extend the scope of decentralized finance. Industry forecasts further suggest rapid growth of tokenized RWAs in the coming years, underscoring their potential role in the evolution of digital financial markets. However, when deployed across multiple blockchains, RWAs face challenges such as repeated authentication on different chains and inefficiency caused by multi-step settlement protocols. To address these issues, we present a cross-chain framework for RWAs that emphasizes identity management, authentication, and interaction. The framework integrates Decentralized Identifiers and Verifiable Credentials with customized attributes to support decentralized identification, and incorporates an authentication protocol based on Simplified Payment Verification to avoid redundant verification across chains. Furthermore, we design a cross-chain channel that enables the settlement of RWAs without requiring channel closure, thereby improving operational efficiency. We implement the framework and evaluate it through simulations, which confirm its feasibility and demonstrate improvements in efficiency for RWAs in cross-chain settings.
Time-step Mixup for Efficient Spiking Knowledge Transfer from Appearance to Event Domain
Venue: arXiv cs.CV 2025
Year: 2025

The integration of event cameras and spiking neural networks holds great promise for energy-efficient visual processing. However, the limited availability of event data and the sparse nature of DVS outputs pose challenges for effective training. Although some prior work has attempted to transfer semantic knowledge from RGB datasets to DVS, they often overlook the significant distribution gap between the two modalities. In this paper, we propose Time-step Mixup knowledge transfer (TMKT), a novel fine-grained mixing strategy that exploits the asynchronous nature of SNNs by interpolating RGB and DVS inputs at various time-steps. To enable label mixing in cross-modal scenarios, we further introduce modality-aware auxiliary learning objectives. These objectives support the time-step mixup process and enhance the model's ability to discriminate effectively across different modalities. Our approach enables smoother knowledge transfer, alleviates modality shift during training, and achieves superior performance in spiking image classification tasks. Extensive experiments demonstrate the effectiveness of our method across multiple datasets. The code will be released after the double-blind review process.
The CCF AATC 2025 Speech Restoration Challenge: A Retrospective
Venue: arXiv cs.SD 2025
Year: 2025

Real-world speech communication is rarely affected by a single type of degradation. Instead, it suffers from a complex interplay of acoustic interference, codec compression, and, increasingly, secondary artifacts introduced by upstream enhancement algorithms. To bridge the gap between academic research and these realistic scenarios, we introduced the CCF AATC 2025 Challenge. This challenge targets universal blind speech restoration, requiring a single model to handle three distinct distortion categories: acoustic degradation, codec distortion, and secondary processing artifacts. In this paper, we provide a comprehensive retrospective of the challenge, detailing the dataset construction, task design, and a systematic analysis of the 25 participating systems. We report three key findings that define the current state of the field: (1) Efficiency vs. Scale: Contrary to the trend of massive generative models, top-performing systems demonstrated that lightweight discriminative architectures (<10M parameters) can achieve state-of-the-art performance, balancing restoration quality with deployment constraints. (2) Generative Trade-off: While generative and hybrid models excel in theoretical perceptual metrics, breakdown analysis reveals they suffer from "reconstruction bias" in high-SNR codec tasks and struggle with hallucination in complex secondary artifact scenarios. (3) Metric Gap: Most critically, our rank correlation analysis exposes a strong negative correlation (\r{ho}=-0.8) between widely-used reference-free metrics (e.g., DNSMOS) and human MOS when evaluating hybrid systems. This indicates that current metrics may over-reward artificial spectral smoothness at the expense of perceptual naturalness. This paper aims to serve as a reference for future research in robust speech restoration and calls for the development of next-generation evaluation metrics sensitive to generative artifacts.
Causal Discovery via Quantile Partial Effect
Venue: arXiv cs.LG 2025
Year: 2025

Quantile Partial Effect (QPE) is a statistic associated with conditional quantile regression, measuring the effect of covariates at different levels. Our theory demonstrates that when the QPE of cause on effect is assumed to lie in a finite linear span, cause and effect are identifiable from their observational distribution. This generalizes previous identifiability results based on Functional Causal Models (FCMs) with additive, heteroscedastic noise, etc. Meanwhile, since QPE resides entirely at the observational level, this parametric assumption does not require considering mechanisms, noise, or even the Markov assumption, but rather directly utilizes the asymmetry of shape characteristics in the observational distribution. By performing basis function tests on the estimated QPE, causal directions can be distinguished, which is empirically shown to be effective in experiments on a large number of bivariate causal discovery datasets. For multivariate causal discovery, leveraging the close connection between QPE and score functions, we find that Fisher Information is sufficient as a statistical measure to determine causal order when assumptions are made about the second moment of QPE. We validate the feasibility of using Fisher Information to identify causal order on multiple synthetic and real-world multivariate causal discovery datasets.
SitLLM: Large Language Models for Sitting Posture Health Understanding via Pressure Sensor Data
Venue: arXiv cs.CL 2025
Year: 2025

Poor sitting posture is a critical yet often overlooked factor contributing to long-term musculoskeletal disorders and physiological dysfunctions. Existing sitting posture monitoring systems, although leveraging visual, IMU, or pressure-based modalities, often suffer from coarse-grained recognition and lack the semantic expressiveness necessary for personalized feedback. In this paper, we propose \textbf{SitLLM}, a lightweight multimodal framework that integrates flexible pressure sensing with large language models (LLMs) to enable fine-grained posture understanding and personalized health-oriented response generation. SitLLM comprises three key components: (1) a \textit{Gaussian-Robust Sensor Embedding Module} that partitions pressure maps into spatial patches and injects local noise perturbations for robust feature extraction; (2) a \textit{Prompt-Driven Cross-Modal Alignment Module} that reprograms sensor embeddings into the LLM's semantic space via multi-head cross-attention using the pre-trained vocabulary embeddings; and (3) a \textit{Multi-Context Prompt Module} that fuses feature-level, structure-level, statistical-level, and semantic-level contextual information to guide instruction comprehension.
Efficient Compilation of Algorithms into Compact Linear Programs
Venue: arXiv cs.PL 2025
Year: 2025

Linear Programming (LP) is widely applied in industry and is a key component of various other mathematical problem-solving techniques. Recent work introduced an LP compiler translating polynomial-time, polynomial-space algorithms into polynomial-size LPs using intuitive high-level programming languages, offering a promising alternative to manually specifying each set of constraints through Algebraic Modeling Languages (AMLs). However, the resulting LPs, while polynomial in size, are often extremely large, posing challenges for existing LP solvers. In this paper, we propose a novel approach for generating substantially smaller LPs from algorithms. Our goal is to establish minimum-size compact LP formulations for problems in P having natural formulations with exponential extension complexities. Our broader vision is to enable the systematic generation of Compact Integer Programming (CIP) formulations for problems with exponential-size IPs having polynomial-time separation oracles. To this end, we introduce a hierarchical linear pipelining technique that decomposes nested program structures into synchronized regions with well-defined execution transitions -- functions of compile-time parameters. This decomposition allows us to localize LP constraints and variables within each region, significantly reducing LP size without the loss of generality, ensuring the resulting LP remains valid for all inputs of size $n$. We demonstrate the effectiveness of our method on two benchmark problems -- the makespan problem, which has exponential extension complexity, and the weighted minimum spanning tree problem -- both of which have exponential-size natural LPs. Our results show up to a $25$-fold reduction in LP size and substantial improvements in solver performance across both commercial and non-commercial LP solvers.