OpenRaiser/Intern-Atlas huggingface dataset:
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding and the emergence of large language models capable of processing document-based questions. While various methods have been proposed to tackle the complexities of VsTP, existing solutions often rely on task-specific architectures and objectives for individual tasks. This leads to modal isolation and complex workflows due to the diversified targets and heterogeneous schemas. In this paper, we introduce OmniParser V2, a universal model that unifies VsTP typical tasks, including text spotting, key information extraction, table recognition, and layout analysis, into a unified framework. Central to our approach is the proposed Structured-Points-of-Thought (SPOT) prompting schemas, which improves model performance across diverse scenarios by leveraging a unified encoder-decoder architecture, objective, and input\&output representation. SPOT eliminates the need for task-specific architectures and loss functions, significantly simplifying the processing pipeline. Our extensive evaluations across four tasks on eight different datasets show that OmniParser V2 achieves state-of-the-art or competitive results in VsTP. Additionally, we explore the integration of SPOT within a multimodal large language model structure, further enhancing text localization and recognition capabilities, thereby confirming the generality of SPOT prompting technique. The code is available at \href{https://github.com/AlibabaResearch/AdvancedLiterateMachinery}{AdvancedLiterateMachinery}.
Teardown Analysis of Samsung S20 Exynos 990 SoC
Venue: arXiv cs.AR 2025
Year: 2025

The mobile phone has evolved from a simple communication device to a complex and highly integrated system with heterogeneous devices, thanks to the rapid technological developments in the semiconductor industry. Understanding the new technology is indeed a time-consuming and challenging task. Therefore, this study performs a teardown analysis of the Samsung Exynos S20 990 System-on-Chip (SoC), a flagship mobile processor that features a three-dimensional (3D) package on-package (PoP) solution with flip chip interconnect (fcPoP). The fcPoP design integrates the SoC and the memory devices in a single package, reducing the interconnection length and improving signal integrity and power efficiency. The study reveals the complex integration of various components and the advanced features of the SoC. The study also examines the microstructure of the chip and the package using X-ray, SEM, and optical microscopy techniques. Moreover, it demonstrates how the fcPoP design enables the SoC to meet the demands of higher performance, higher bandwidth, lower power consumption, and smaller form factor, especially in 5G mobile applications. The study contributes to understanding advanced packaging methodologies and indicates potential directions for future semiconductor innovations.
PersGuard: Preventing Malicious Personalization via Backdoor Attacks on Pre-trained Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models (DMs) have revolutionized data generation, particularly in text-to-image (T2I) synthesis. However, the widespread use of personalized generative models raises significant concerns regarding privacy violations and copyright infringement. To address these issues, researchers have proposed adversarial perturbation-based protection techniques. However, these methods have notable limitations, including insufficient robustness against data transformations and the inability to fully eliminate identifiable features of protected objects in the generated output. In this paper, we introduce PersGuard, a novel backdoor-based approach that prevents malicious personalization of specific images. Unlike traditional adversarial perturbation methods, PersGuard implant backdoor triggers into pre-trained T2I models, preventing the generation of customized outputs for designated protected images while allowing normal personalization for unprotected ones. Unfortunately, existing backdoor methods for T2I diffusion models fail to be applied to personalization scenarios due to the different backdoor objectives and the potential backdoor elimination during downstream fine-tuning processes. To address these, we propose three novel backdoor objectives specifically designed for personalization scenarios, coupled with backdoor retention loss engineered to resist downstream fine-tuning. These components are integrated into a unified optimization framework. Extensive experimental evaluations demonstrate PersGuard's effectiveness in preserving data privacy, even under challenging conditions including gray-box settings, multi-object protection, and facial identity scenarios. Our method significantly outperforms existing techniques, offering a more robust solution for privacy and copyright protection.
Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers
Venue: arXiv cs.SE 2025
Year: 2025

This paper presents linear DML models for causal inference using the simplest Python code on a Jupyter notebook based on an Anaconda platform and compares the performance of different DML models. The results show that current Library API technology is not yet sufficient to enable novice Python users to build qualified and high-quality DML models with the simplest coding approach. Novice users attempting to perform DML causal inference using Python still have to improve their mathematical and computer knowledge to adapt to more flexible DML programming. Additionally, the issue of mismatched outcome variable dimensions is also widespread when building linear DML models in Jupyter notebook.
An End-to-End Homomorphically Encrypted Neural Network
Venue: arXiv cs.CR 2025
Year: 2025

Every commercially available, state-of-the-art neural network consume plain input data, which is a well-known privacy concern. We propose a new architecture based on homomorphic encryption, which allows the neural network to operate on encrypted data. We show that Homomorphic Neural Networks (HNN) can achieve full privacy and security while maintaining levels of accuracy comparable to plain neural networks. We also introduce a new layer, the Differentiable Soft-Argmax, which allows the calibration of output logits in the encrypted domain, raising the entropy of the activation parameters, thus improving the security of the model, while keeping the overall noise below the acceptable noise budget. Experiments were conducted using the Stanford Sentiment Treebank (SST-2) corpora on the DistilBERT base uncased finetuned SST-2 English sentiment analysis model, and the results show that the HNN model can achieve up to 82.5% of the accuracy of the plain model while maintaining full privacy and security.
SalM$^{2}$: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention
Venue: arXiv cs.CV 2025
Year: 2025

Driver attention recognition in driving scenarios is a popular direction in traffic scene perception technology. It aims to understand human driver attention to focus on specific targets/objects in the driving scene. However, traffic scenes contain not only a large amount of visual information but also semantic information related to driving tasks. Existing methods lack attention to the actual semantic information present in driving scenes. Additionally, the traffic scene is a complex and dynamic process that requires constant attention to objects related to the current driving task. Existing models, influenced by their foundational frameworks, tend to have large parameter counts and complex structures. Therefore, this paper proposes a real-time saliency Mamba network based on the latest Mamba framework. As shown in Figure 1, our model uses very few parameters (0.08M, only 0.09~11.16% of other models), while maintaining SOTA performance or achieving over 98% of the SOTA model's performance.
Privacy-Aware White and Black List Searching for Fraud Analysis
Venue: arXiv cs.CR 2025
Year: 2025

In many areas of cybersecurity, we require access to Personally Identifiable Information (PII), such as names, postal addresses and email addresses. Unfortunately, this can lead to data breaches, especially in relation to data compliance regulations such as GDPR. An Internet Protocol (IP) address is an identifier that is assigned to a networked device to enable it to communicate over networks that use IP. Thus, in applications which are privacy-aware, we may aim to hide the IP address while aiming to determine if the address comes from a blacklist. One solution to this is to use homomorphic encryption to match an encrypted version of an IP address to a blacklisted network list. This matching allows us to encrypt the IP address and match it to an encrypted version of a blacklist. In this paper, we use the OpenFHE library [1] to encrypt network addresses with the BFV homomorphic encryption scheme. In order to assess the performance overhead of BFV, we implement a matching method using the OpenFHE library and compare it against partial homomorphic schemes, including Paillier, Damgard-Jurik, Okamoto-Uchiyama, Naccache-Stern and Benaloh. The main findings are that the BFV method compares favourably against the partial homomorphic methods in most cases.
Beyond Trusting Trust: Multi-Model Validation for Robust Code Generation
Venue: arXiv cs.SE 2025
Year: 2025

This paper explores the parallels between Thompson's "Reflections on Trusting Trust" and modern challenges in LLM-based code generation. We examine how Thompson's insights about compiler backdoors take on new relevance in the era of large language models, where the mechanisms for potential exploitation are even more opaque and difficult to analyze. Building on this analogy, we discuss how the statistical nature of LLMs creates novel security challenges in code generation pipelines. As a potential direction forward, we propose an ensemble-based validation approach that leverages multiple independent models to detect anomalous code patterns through cross-model consensus. This perspective piece aims to spark discussion about trust and validation in AI-assisted software development.
A Purely Geometric Variant of the Gale-Berlekamp Switching Game
Venue: arXiv cs.CG 2025
Year: 2025

We introduce the following variant of the Gale-Berlekamp switching game. Let $P$ be a set of n noncollinear points in the plane, each of them having weight $+1$ or $-1$. At each step, we pick a line $\ell$ passing through at least two points of $P$, and switch the sign of every point $p \in P\cap\ell$. The objective is to maximize the total weight of the elements of $P$. We show that one can always achieve that this quantity is at least $n - o(n)$, as $n\rightarrow\infty$, and at least $n/3$, for every $n$. Moreover, these can be attained by a polynomial time algorithm.
Towards a GPU-Native Adaptive Mesh Refinement Scheme for the Lattice Boltzmann Method in Complex Geometries
Venue: arXiv cs.CG 2025
Year: 2025

We present a GPU-native mesh adaptation procedure that incorporates a complex geometry represented with a triangle mesh within a primary Cartesian computational grid organized as a forest of octrees. A C++/CUDA program implements the procedure for execution on a single GPU as part of a new module with the AGAL framework, which was originally developed for GPU-native adaptive mesh refinement (AMR) and fluid flow simulation with the Lattice Boltzmann Method (LBM). Traditional LBM is limited to grids with regular prismatic cells with domain boundaries aligned with the cell faces. This work is a first step towards an implementation of the LBM that can simulate flow over irregular surfaces while retaining both adaptation of the mesh and the temporal integration routines entirely on the GPU. Geometries can be inputted as a text file (which generates primitive objects such as circles and spheres) or as an STL file (which can be generated by most 3D modeling software). The procedure is divided into three steps: 1) an import step where the geometry is loaded into either an index list arrangement or directly as a face-vertex coordinates list, 2) a spatial binning step where the faces are distributed to a set of bins with user-defined density, and 3) a near-wall refinement step where the cells of the computational grid detect adjacency to the faces stored in the appropriate bin to form the links between the geometry and the boundary nodes. We validate the implementation and assess its performance in terms of total execution time and speedup relative to a serial CPU implementation using a 2D circle and a 3D Stanford bunny.
Development of a Cloud-Based Payroll Management System
Venue: arXiv cs.DC 2025
Year: 2025

Cloud computing is continually evolving, enhancing hardware technologies, improving software and enhancing business processes. A payroll management system deployed on the Cloud harnesses on-demand of delivery of computational power and database storage using cloud computing technologies. This project aims to develop and deploy a cloud-based payroll management system. The objectives of this study are: to carry out a study on the existing cloud-based payroll management system, to design a payroll data model for calculating basic salary and enables retrieval of payroll history when needed from the database, to develop and deploy a payroll management system, on the Cloud that generates earning statements, filling the gap between security infrastructure and optimal system performance harnessing cloud computing technologies. The focus was on the design, implementation and deployment, using UML diagrams to illustrate the payroll application and Google App Engine for deployment. The system analysis in comparison of a conventional payroll system and the cloud-based system is endless in terms of speed, processing power, storage capacity, universalization and pricing. The cloud-based payroll has an infinite number of advantages; all conventional payroll system is rendered obsolete as it mends all the cons.
Multi-Party Data Pricing for Complex Data Trading Markets: A Rubinstein Bargaining Approach
Venue: arXiv cs.GT 2025
Year: 2025

With the rapid development of Internet of Things (IoT) and artificial intelligence technologies, data has become an important strategic resource in the new era. However, the growing demand for data has exacerbated the issue of \textit{data silos}. Existing data pricing models primarily focus on single factors such as data quality or market demand, failing to adequately address issues such as data seller monopolies and the diverse needs of buyers, resulting in biased pricing that cannot meet the complexities of evolving transaction scenarios. To address these problems, this paper proposes a multi-party data pricing model based on the Rubinstein bargaining model. The model introduces buyer data utility indicators and data quality assessments, comprehensively considering factors such as the utility, accuracy, and timeliness of data sets, to more accurately evaluate their value to buyers. To overcome the limitations of single-factor models, this paper innovatively introduces the buyer data set satisfaction indicator, which reflects the overall satisfaction of buyers with data sets by integrating both data utility and quality assessments. Based on this, the model uses the Rubinstein bargaining model to simulate the pricing process between multiple sellers and multiple buyers, yielding pricing results that better align with market demands. Experimental results show that the proposed model effectively addresses the pricing imbalance caused by data monopolies and demonstrates good applicability and accuracy in multi-seller, multi-buyer transaction environments. This research provides an effective pricing mechanism for complex data trading markets and has significant theoretical and practical value in solving pricing issues in actual data transactions.
Study of Constrained Precoding with Zero-Crossing Modulation for Channels with 1-Bit Quantization and Oversampling
Venue: arXiv cs.IT 2025
Year: 2025

Future wireless communications systems are expected to operate at bands above 100GHz. The high energy consumption of analog-to-digital converters, due to their high resolution represents a bottleneck for future wireless communications systems which require low-energy consumption and low-complexity devices at the receiver. In this work, we derive a novel precoding method based on quality of service constraints for a multiuser multiple-input multiple-output downlink system with 1-bit quantization and oversampling. For this scenario, the time-instance zero-crossing modulation which conveys the information into the zero-crossings is considered. Unlike prior studies, the constraint is given regarding the symbol error probability related to the minimum distance to the decision threshold. Numerical results illustrate the performance of the proposed precoding method evaluated under different parameters
COMPASS: Cross-embodiment Mobility Policy via Residual RL and Skill Synthesis
Venue: arXiv cs.RO 2025
Year: 2025

As robots are increasingly deployed in diverse application domains, enabling robust mobility across different embodiments has become a critical challenge. Classical mobility stacks, though effective on specific platforms, require extensive per-robot tuning and do not scale easily to new embodiments. Learning-based approaches, such as imitation learning (IL), offer alternatives, but face significant limitations on the need for high-quality demonstrations for each embodiment. To address these challenges, we introduce COMPASS, a unified framework that enables scalable cross-embodiment mobility using expert demonstrations from only a single embodiment. We first pre-train a mobility policy on a single robot using IL, combining a world model with a policy model. We then apply residual reinforcement learning (RL) to efficiently adapt this policy to diverse embodiments through corrective refinements. Finally, we distill specialist policies into a single generalist policy conditioned on an embodiment embedding vector. This design significantly reduces the burden of collecting data while enabling robust generalization across a wide range of robot designs. Our experiments demonstrate that COMPASS scales effectively across diverse robot platforms while maintaining adaptability to various environment configurations, achieving a generalist policy with a success rate approximately 5X higher than the pre-trained IL policy on unseen embodiments, and further demonstrates zero-shot sim-to-real transfer.
Preserving Simultaneity and Chronology for Sensing in Perceptive Wireless Networks
Venue: arXiv cs.IT 2025
Year: 2025

We address the challenge of preserving the simultaneity and chronology of sensing events in multisensor systems with wireless links. The network uses temporal windows of integration (TWIs), borrowed from human multisensory perception, to preserve the temporal structure of the sensing data at the application side. We introduce a composite latency model for propagation, sensing, and communication that leads to the derivation of the probability of simultaneity violation. This is used to select the TWI duration aiming to achieve the desired degrees of chronological preservation, while maintaining the throughput of events. The letter provides important insights and analytical tools about the TWI impact on the event registration.
Automatic Detection of Research Values from Scientific Abstracts Across Computer Science Subfields
Venue: arXiv cs.CL 2025
Year: 2025

The field of Computer science (CS) has rapidly evolved over the past few decades, providing computational tools and methodologies to various fields and forming new interdisciplinary communities. This growth in CS has significantly impacted institutional practices and relevant research communities. Therefore, it is crucial to explore what specific research values, known as basic and fundamental beliefs that guide or motivate research attitudes or actions, CS-related research communities promote. Prior research has manually analyzed research values from a small sample of machine learning papers. No prior work has studied the automatic detection of research values in CS from large-scale scientific texts across different research subfields. This paper introduces a detailed annotation scheme featuring ten research values that guide CS-related research. Based on the scheme, we build value classifiers to scale up the analysis and present a systematic study over 226,600 paper abstracts from 32 CS-related subfields and 86 popular publishing venues over ten years.
Efficient Semantic-aware Encryption for Secure Communications in Intelligent Connected Vehicles
Venue: arXiv cs.CR 2025
Year: 2025

Semantic communication (SemCom) significantly improves inter-vehicle interactions in intelligent connected vehicles (ICVs) within limited wireless spectrum. However, the open nature of wireless communications introduces eavesdropping risks. To mitigate this, we propose the Efficient Semantic-aware Encryption (ESAE) mechanism, integrating cryptography into SemCom to secure semantic transmission without complex key management. ESAE leverages semantic reciprocity between source and reconstructed information from past communications to independently generate session keys at both ends, reducing key transmission costs and associated security risks. Additionally, ESAE introduces a semantic-aware key pre-processing method (SA-KP) using the YOLO-v10 model to extract consistent semantics from bit-level diverse yet semantically identical content, ensuring key consistency. Experimental results validate ESAE's effectiveness and feasibility under various wireless conditions, with key performance factors discussed.
Network Tomography with Path-Centric Graph Neural Network
Venue: arXiv cs.LG 2025
Year: 2025

Network tomography is a crucial problem in network monitoring, where the observable path performance metric values are used to infer the unobserved ones, making it essential for tasks such as route selection, fault diagnosis, and traffic control. However, most existing methods either assume complete knowledge of network topology and metric formulas-an unrealistic expectation in many real-world scenarios with limited observability-or rely entirely on black-box end-to-end models. To tackle this, in this paper, we argue that a good network tomography requires synergizing the knowledge from both data and appropriate inductive bias from (partial) prior knowledge. To see this, we propose Deep Network Tomography (DeepNT), a novel framework that leverages a path-centric graph neural network to predict path performance metrics without relying on predefined hand-crafted metrics, assumptions, or the real network topology. The path-centric graph neural network learns the path embedding by inferring and aggregating the embeddings of the sequence of nodes that compose this path. Training path-centric graph neural networks requires learning the neural netowrk parameters and network topology under discrete constraints induced by the observed path performance metrics, which motivates us to design a learning objective that imposes connectivity and sparsity constraints on topology and path performance triangle inequality on path performance. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of DeepNT in predicting performance metrics and inferring graph topology compared to state-of-the-art methods.