OpenRaiser/Intern-Atlas huggingface dataset:
IsoSched: Preemptive Tile Cascaded Scheduling of Multi-DNN via Subgraph Isomorphism
Venue: arXiv cs.DC 2025
Year: 2025

Deploying deep neural network (DNN) accelerators with Layer Temporal Scheduling (LTS) often incurs significant overheads (e.g., energy and latency), as intermediate activations must be cached in DRAM. To alleviate this, Tile Spatial Scheduling (TSS) reduces such costs by fragmenting inter-layer data into smaller tiles communicated via on-chip links.However, many emerging applications require concurrent execution of multiple DNNs with complex topologies, where critical tasks must preempt others to meet stringent latency requirements (e.g., in autonomous driving, obstacle detection must complete within tens of milliseconds). Existing TSS works lack support for preemption, while prior preemption schemes rely on LTS and thus inherit its overheads. This highlights the need for preemptive and efficient TSS-based frameworks. Yet, realizing such systems is challenging due to the complexity of enabling preemption in graphs with large-scale topologies (e.g., modern large language models may contain tens of thousands of edges). To tackle this, we present IsoSched, the first framework enabling preemptive multi-DNN scheduling on TSS architecture. IsoSched first formulates scheduling of complex-topology graphs as an integer-linear program (ILP) and subgraph isomorphism problem; second, it applies Layer Concatenate and Split (LCS) for load balancing in tile pipelines; third, it employs an Ullmann-based algorithm enhanced by Monte Carlo Tree Search (MCTS) to accelerate subgraph matching, and uses compact matrix encoding (i.e., Compressed Sparse Row, CSR) to reduce memory usage. IsoSched outperforms LTS-PRM approaches (i.e., PREMA, Planaria, CD-MSA, MoCA) in Latency-Bound Throughput (LBT), speedup, and energy efficiency, and achieves higher critical task satisfaction than TSS-NPRM (i.e., HASP) across varying task complexities.
MEUV: Achieving Fine-Grained Capability Activation in Large Language Models via Mutually Exclusive Unlock Vectors
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) enforce safety alignment to reliably refuse malicious requests, yet the same blanket safeguards also block legitimate uses in policing, defense, and other high-stakes settings. Earlier "refusal-direction" edits can bypass those layers, but they rely on a single vector that indiscriminately unlocks all hazardous topics, offering no semantic control. We introduce Mutually Exclusive Unlock Vectors (MEUV), a lightweight framework that factorizes the monolithic refusal direction into topic-aligned, nearly orthogonal vectors, each dedicated to one sensitive capability. MEUV is learned in a single epoch with a multi-task objective that blends a differential-ablation margin, cross-topic and orthogonality penalties, and several auxiliary terms. On bilingual malicious-prompt benchmarks, MEUV achieves an attack success rate of no less than 87% on Gemma-2-2B, LLaMA-3-8B, and Qwen-7B, yet cuts cross-topic leakage by up to 90% compared with the best single-direction baseline. Vectors trained in Chinese transfer almost unchanged to English (and vice versa), suggesting a language-agnostic refusal subspace. The results show that fine-grained, topic-level capability activation is achievable with minimal utility loss, paving the way for controlled LLMs deployment in security-sensitive domains.
Ratio1 -- AI meta-OS
Venue: arXiv cs.OS 2025
Year: 2025

We propose the Ratio1 AI meta-operating system (meta-OS), a decentralized MLOps protocol that unifies AI model development, deployment, and inference across heterogeneous edge devices. Its key innovation is an integrated blockchain-based framework that transforms idle computing resources (laptops, smartphones, cloud VMs) into a trustless global supercomputer. The architecture includes novel components: a decentralized authentication layer (dAuth), an in-memory state database (CSTORE), a distributed storage system (R1FS), homomorphic encrypted federated learning (EDIL), decentralized container orchestration (Deeploy) and an oracle network (OracleSync), which collectively ensure secure, resilient execution of AI pipelines and other container based apps at scale. The protocol enforces a formal circular token-economic model combining Proof-of-Availability (PoA) and Proof-of-AI (PoAI) consensus. Compared to centralized heterogeneous cloud MLOps and existing decentralized compute platforms, which often lack integrated AI toolchains or trusted Ratio1 node operators (R1OP) mechanics, Ratio1's holistic design lowers barriers for AI deployment and improves cost-efficiency. We provide mathematical formulations of its secure licensing and reward protocols, and include descriptive information for the system architecture and protocol flow. We argue that our proposed fully functional ecosystem proposes and demonstrates significant improvements in accessibility, scalability, and security over existing alternatives.
EfficientNet-Based Multi-Class Detection of Real, Deepfake, and Plastic Surgery Faces
Venue: arXiv cs.CV 2025
Year: 2025

Currently, deep learning has been utilised to tackle several difficulties in our everyday lives. It not only exhibits progress in computer vision but also constitutes the foundation for several revolutionary technologies. Nonetheless, similar to all phenomena, the use of deep learning in diverse domains has produced a multifaceted interaction of advantages and disadvantages for human society. Deepfake technology has advanced, significantly impacting social life. However, developments in this technology can affect privacy, the reputations of prominent personalities, and national security via software development. It can produce indistinguishable counterfeit photographs and films, potentially impairing the functionality of facial recognition systems, so presenting a significant risk. The improper application of deepfake technology produces several detrimental effects on society. Face-swapping programs mislead users by altering persons' appearances or expressions to fulfil particular aims or to appropriate personal information. Deepfake technology permeates daily life through such techniques. Certain individuals endeavour to sabotage election campaigns or subvert prominent political figures by creating deceptive pictures to influence public perception, causing significant harm to a nation's political and economic structure.
A Modern Look at Simplicity Bias in Image Classification Tasks
Venue: arXiv cs.CV 2025
Year: 2025

The simplicity Bias (SB) of neural networks, i.e.\ their tendency to represent simple functions, is a key factor in their generalization capabilities. Recent studies show that an excessive SB may harm performance on complex tasks, and the need for this bias varies across tasks. Many of these studies focus on simple models or synthetic tasks. It remains challenging to measure the SB in large models and little is known about the relevance of the SB to various image classification tasks. In this paper, we investigate the relationship between the SB in CLIP models and their performance across image classification tasks. First, we theoretically analyze the potential limitation of existing measures of complexity that have been used to characterize small models. To address this, we propose a frequency-aware measure capturing finer-grained SB differences. We validate this measure on CLIP models subjected to two recent SB-modulation methods, demonstrating that it is more informative and consistent than previous measures. Second, we examine the relation between the SB of those models and their performance across a range of image classification tasks, including zero-shot and fine-tuning settings. These experiments reveal a range of behaviors. For example, a stronger SB correlates with a better performance on OOD generalization than on adversarial robustness. These results highlight the benefits of aligning a model's inductive biases with the characteristics of the target task.
Integrating Attention-Enhanced LSTM and Particle Swarm Optimization for Dynamic Pricing and Replenishment Strategies in Fresh Food Supermarkets
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents a novel approach to optimizing pricing and replenishment strategies in fresh food supermarkets by combining Long Short-Term Memory (LSTM) networks with Particle Swarm Optimization (PSO). The LSTM model, enhanced with an attention mechanism, is used to predict sales volumes, pricing trends, and spoilage rates over a seven-day period. The predictions generated by the LSTM model serve as inputs for the PSO algorithm, which iteratively optimizes pricing and replenishment strategies to maximize profitability while adhering to inventory constraints. The integration of cost-plus pricing allows for dynamic adjustments based on fixed and variable costs, ensuring real-time adaptability to market fluctuations. The framework not only maximizes profits but also reduces food waste, contributing to more sustainable supermarket operations. The attention mechanism enhances the interpretability of the LSTM model by identifying key time points and factors influencing sales, improving decision-making accuracy. This methodology bridges the gap between predictive modeling and optimization, offering a scalable solution for dynamic pricing and inventory management in fresh food retail and other industries dealing with perishable goods.
Knowledge Graph Tokenization for Behavior-Aware Generative Next POI Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Generative paradigm, especially powered by Large Language Models (LLMs), has emerged as a new solution to the next point-of-interest (POI) recommendation. Pioneering studies usually adopt a two-stage pipeline, starting with a tokenizer converting POIs into discrete identifiers that can be processed by LLMs, followed by POI behavior prediction tasks to instruction-tune LLM for next POI recommendation. Despite of remarkable progress, they still face two limitations: (1) existing tokenizers struggle to encode heterogeneous signals in the recommendation data, suffering from information loss issue, and (2) previous instruction-tuning tasks only focus on users' POI visit behavior while ignore other behavior types, resulting in insufficient understanding of mobility. To address these limitations, we propose KGTB (Knowledge Graph Tokenization for Behavior-aware generative next POI recommendation). Specifically, KGTB organizes the recommendation data in a knowledge graph (KG) format, of which the structure can seamlessly preserve the heterogeneous information. Then, a KG-based tokenizer is developed to quantize each node into an individual structural ID. This process is supervised by the KG's structure, thus reducing the loss of heterogeneous information. Using generated IDs, KGTB proposes multi-behavior learning that introduces multiple behavior-specific prediction tasks for LLM fine-tuning, e.g., POI, category, and region visit behaviors. Learning on these behavior tasks provides LLMs with comprehensive insights on the target POI visit behavior. Experiments on four real-world city datasets demonstrate the superior performance of KGTB.
Data selves and identity theft in the age of AI
Venue: arXiv cs.HC 2025
Year: 2025

This chapter examines identity theft in the digital age, particularly in the context of emerging artificial intelligence (AI) technologies. It begins with a discussion of big data and selfhood, the concepts of data selves and data doubles, and the process of identification in the digital age. Next, the literature on online identity theft is reviewed, including its theoretical and empirical aspects. As is evident from that review, AI technologies have increased the speed and scale of identity crimes that were already rampant in the online world, even while they have led to new ways of detecting and preventing such crimes. As with any new technology, AI is currently fuelling an arms race between criminals and law enforcement, with end users often caught powerless in the middle. The chapter closes by exploring some emerging directions and future possibilities of identity theft in the age of AI.
Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks
Venue: arXiv cs.CR 2025
Year: 2025

Machine learning (ML) models are susceptible to various risks to security, privacy, and fairness. Most defenses are designed to protect against each risk individually (intended interactions) but can inadvertently affect susceptibility to other unrelated risks (unintended interactions). We introduce Amulet, the first Python library for evaluating both intended and unintended interactions among ML defenses and risks. Amulet is comprehensive by including representative attacks, defenses, and metrics; extensible to new modules due to its modular design; consistent with a user-friendly API template for inputs and outputs; and applicable for evaluating novel interactions. By satisfying all four properties, Amulet offers a unified foundation for studying how defenses interact, enabling the first systematic evaluation of unintended interactions across multiple risks.
DYNAMO: Dependency-Aware Deep Learning Framework for Articulated Assembly Motion Prediction
Venue: arXiv cs.CV 2025
Year: 2025

Understanding the motion of articulated mechanical assemblies from static geometry remains a core challenge in 3D perception and design automation. Prior work on everyday articulated objects such as doors and laptops typically assumes simplified kinematic structures or relies on joint annotations. However, in mechanical assemblies like gears, motion arises from geometric coupling, through meshing teeth or aligned axes, making it difficult for existing methods to reason about relational motion from geometry alone. To address this gap, we introduce MechBench, a benchmark dataset of 693 diverse synthetic gear assemblies with part-wise ground-truth motion trajectories. MechBench provides a structured setting to study coupled motion, where part dynamics are induced by contact and transmission rather than predefined joints. Building on this, we propose DYNAMO, a dependency-aware neural model that predicts per-part SE(3) motion trajectories directly from segmented CAD point clouds. Experiments show that DYNAMO outperforms strong baselines, achieving accurate and temporally consistent predictions across varied gear configurations. Together, MechBench and DYNAMO establish a novel systematic framework for data-driven learning of coupled mechanical motion in CAD assemblies.
Deep learning for 3D point cloud processing -- from approaches, tasks to its implications on urban and environmental applications
Venue: arXiv cs.CV 2025
Year: 2025

Point cloud processing as a fundamental task in the field of geomatics and computer vision, has been supporting tasks and applications at different scales from air to ground, including mapping, environmental monitoring, urban/tree structure modeling, automated driving, robotics, disaster responses etc. Due to the rapid development of deep learning, point cloud processing algorithms have nowadays been almost explicitly dominated by learning-based approaches, most of which are yet transitioned into real-world practices. Existing surveys primarily focus on the ever-updating network architecture to accommodate unordered point clouds, largely ignoring their practical values in typical point cloud processing applications, in which extra-large volume of data, diverse scene contents, varying point density, data modality need to be considered. In this paper, we provide a meta review on deep learning approaches and datasets that cover a selection of critical tasks of point cloud processing in use such as scene completion, registration, semantic segmentation, and modeling. By reviewing a broad range of urban and environmental applications these tasks can support, we identify gaps to be closed as these methods transformed into applications and draw concluding remarks in both the algorithmic and practical aspects of the surveyed methods.
Nonlocal Neural Tangent Kernels via Parameter-Space Interactions
Venue: arXiv cs.LG 2025
Year: 2025

The Neural Tangent Kernel (NTK) framework has provided deep insights into the training dynamics of neural networks under gradient flow. However, it relies on the assumption that the network is differentiable with respect to its parameters, an assumption that breaks down when considering non-smooth target functions or parameterized models exhibiting non-differentiable behavior. In this work, we propose a Nonlocal Neural Tangent Kernel (NNTK) that replaces the local gradient with a nonlocal interaction-based approximation in parameter space. Nonlocal gradients are known to exist for a wider class of functions than the standard gradient. This allows NTK theory to be extended to nonsmooth functions, stochastic estimators, and broader families of models. We explore both fixed-kernel and attention-based formulations of this nonlocal operator. We illustrate the new formulation with numerical studies.
Localized Region Guidance for Class Activation Mapping in WSSS
Venue: arXiv cs.CV 2025
Year: 2025

Weakly Supervised Semantic Segmentation (WSSS) addresses the challenge of training segmentation models using only image-level annotations. Existing WSSS methods struggle with precise object boundary localization and focus only on the most discriminative regions. To address these challenges, we propose IG-CAM (Instance-Guided Class Activation Mapping), a novel approach that leverages instance-level cues and influence functions to generate high-quality, boundary-aware localization maps. Our method introduces three key innovations: (1) Instance-Guided Refinement using object proposals to guide CAM generation, ensuring complete object coverage; (2) Influence Function Integration that captures the relationship between training samples and model predictions; and (3) Multi-Scale Boundary Enhancement with progressive refinement strategies. IG-CAM achieves state-of-the-art performance on PASCAL VOC 2012 with 82.3% mIoU before post-processing, improving to 86.6% after CRF refinement, significantly outperforming previous WSSS methods. Extensive ablation studies validate each component's contribution, establishing IG-CAM as a new benchmark for weakly supervised semantic segmentation.
Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain
Venue: arXiv cs.CE 2025
Year: 2025

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by permanent enlargement of the aorta, often detected incidentally during imaging for unrelated conditions. Current management relies primarily on aneurysm diameter and growth rate, which may not reliably predict patient-specific rupture risk. Computation of AAA wall stress and strain has the potential to improve individualized risk assessment, but these analyses depend on image-derived geometry, which is subject to segmentation uncertainty and lacks a definitive ground truth for the wall boundary. While the effect of geometric uncertainty on wall stress has been studied, its influence on wall strain remains unclear. In this study, we assessed the impact of geometric uncertainty on AAA wall strain computed using deformable image registration of time-resolved 3D computed tomography angiography (4D-CTA). Controlled perturbations were applied to the wall geometry along the surface normal, parameterized by the standard deviation for random variation and the mean for systematic inward or outward bias, both scaled relative to wall thickness. Results show that uncertainties in AAA wall geometry reduce the accuracy of computed strain, with inward bias (toward the blood lumen and intraluminal thrombus) consistently causing greater deviations than outward bias (toward regions external to the aortic wall). Peak strain is more sensitive but less robust, whereas the 99th percentile strain remains more stable under perturbations. We concluded that, for sufficiently accurate strain estimation, geometric uncertainty should remain within one wall thickness (typically 1.5 mm).
VQT-Light:Lightweight HDR Illumination Map Prediction with Richer Texture.pdf
Venue: arXiv cs.CV 2025
Year: 2025

Accurate lighting estimation is a significant yet challenging task in computer vision and graphics. However, existing methods either struggle to restore detailed textures of illumination map, or face challenges in running speed and texture fidelity. To tackle this problem, we propose a novel framework (VQT-Light) based on VQVAE and ViT architecture. VQT-Light includes two modules: feature extraction and lighting estimation. First, we take advantages of VQVAE to extract discrete features of illumination map rather than continuous features to avoid "posterior collapse". Second, we capture global context and dependencies of input image through ViT rather than CNNs to improve the prediction of illumination outside the field of view. Combining the above two modules, we formulate the lighting estimation as a multiclass classification task, which plays a key role in our pipeline. As a result, our model predicts light map with richer texture and better fidelity while keeping lightweight and fast. VQT-Light achieves an inference speed of 40FPS and improves multiple evaluation metrics. Qualitative and quantitative experiments demonstrate that the proposed method realizes superior results compared to existing state-of-the-art methods.
Channel Estimation for Rydberg Atomic Quantum Receivers
Venue: arXiv cs.IT 2025
Year: 2025

The advent of Rydberg atomic quantum receivers (RAQRs) offers a new solution for the evolution of wireless transceiver architecture, promising unprecedented sensitivity and immunity to thermal noise. However, RAQRs introduce a unique non-linear signal model based on biased phase retrieval, which complicates fundamental channel estimation tasks. Traditional iterative algorithms often struggle in low signal-to-noise regimes and fail to capture complex and non-ideal system characteristics. To address this, we propose a novel model-driven deep learning framework for channel estimation in RAQRs. Specifically, we propose a Transformer-based unrolling architecture, termed URformer, which is derived by unrolling a stabilized variant of the expectation-maximization Gerchberg-Saxton (EM-GS) algorithm. Specifically, each layer of the proposed URformer incorporates three trainable modules: 1) a learnable filter implemented by a neural network that replaces the fixed Bessel function ratio in the classic EM-GS algorithm; 2) a trainable gating mechanism that adaptively combines classic and model-based updates to ensure training stability; and 3) a efficient channel Transformer block that learns to correct residual errors by capturing non-local dependencies across the channel matrix. Numerical results demonstrate that the proposed URformer significantly outperforms classic iterative algorithms and conventional black-box neural networks with less pilot overhead.
DPCheatSheet: Using Worked and Erroneous LLM-usage Examples to Scaffold Differential Privacy Implementation
Venue: arXiv cs.HC 2025
Year: 2025

This paper explores how programmers without specialized expertise in differential privacy (DP) (i.e., novices) can leverage LLMs to implement DP programs with minimal training. We first conducted a need-finding study with 6 novices and 3 experts to understand how they utilize LLMs in DP implementation. While DP experts can implement correct DP analyses through a few prompts, novices struggle to articulate their requirements in prompts and lack the skills to verify the correctness of the generated code. We then developed DPCheatSheet, an instructional tool that helps novices implement DP using LLMs. DPCheatSheet combines two learning concepts: it annotates an expert's workflow with LLMs as a worked example to bridge the expert mindset to novices, and it presents five common mistakes in LLM-based DP code generation as erroneous examples to support error-driven learning. We demonstrated the effectiveness of DPCheatSheet with an error identification study and an open-ended DP implementation study.