OpenRaiser/Intern-Atlas huggingface dataset:
X-Driver: Explainable Autonomous Driving with Vision-Language Models
Venue: arXiv cs.RO 2025
Year: 2025

End-to-end autonomous driving has advanced significantly, offering benefits such as system simplicity and stronger driving performance in both open-loop and closed-loop settings than conventional pipelines. However, existing frameworks still suffer from low success rates in closed-loop evaluations, highlighting their limitations in real-world deployment. In this paper, we introduce X-Driver, a unified multi-modal large language models(MLLMs) framework designed for closed-loop autonomous driving, leveraging Chain-of-Thought(CoT) and autoregressive modeling to enhance perception and decision-making. We validate X-Driver across multiple autonomous driving tasks using public benchmarks in CARLA simulation environment, including Bench2Drive[6]. Our experimental results demonstrate superior closed-loop performance, surpassing the current state-of-the-art(SOTA) while improving the interpretability of driving decisions. These findings underscore the importance of structured reasoning in end-to-end driving and establish X-Driver as a strong baseline for future research in closed-loop autonomous driving.
MDE-Edit: Masked Dual-Editing for Multi-Object Image Editing via Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Multi-object editing aims to modify multiple objects or regions in complex scenes while preserving structural coherence. This task faces significant challenges in scenarios involving overlapping or interacting objects: (1) Inaccurate localization of target objects due to attention misalignment, leading to incomplete or misplaced edits; (2) Attribute-object mismatch, where color or texture changes fail to align with intended regions due to cross-attention leakage, creating semantic conflicts (\textit{e.g.}, color bleeding into non-target areas). Existing methods struggle with these challenges: approaches relying on global cross-attention mechanisms suffer from attention dilution and spatial interference between objects, while mask-based methods fail to bind attributes to geometrically accurate regions due to feature entanglement in multi-object scenarios. To address these limitations, we propose a training-free, inference-stage optimization approach that enables precise localized image manipulation in complex multi-object scenes, named MDE-Edit. MDE-Edit optimizes the noise latent feature in diffusion models via two key losses: Object Alignment Loss (OAL) aligns multi-layer cross-attention with segmentation masks for precise object positioning, and Color Consistency Loss (CCL) amplifies target attribute attention within masks while suppressing leakage to adjacent regions. This dual-loss design ensures localized and coherent multi-object edits. Extensive experiments demonstrate that MDE-Edit outperforms state-of-the-art methods in editing accuracy and visual quality, offering a robust solution for complex multi-object image manipulation tasks.
A Weighted Byzantine Fault Tolerance Consensus Driven Trusted Multiple Large Language Models Network
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) have achieved remarkable success across a wide range of applications. However, individual LLMs often produce inconsistent, biased, or hallucinated outputs due to limitations in their training corpora and model architectures. Recently, collaborative frameworks such as the Multi-LLM Network (MultiLLMN) have been introduced, enabling multiple LLMs to interact and jointly respond to user queries. Nevertheless, MultiLLMN architectures raise critical concerns regarding the reliability and security of the generated content, particularly in open environments where malicious or compromised LLMs may be present. Moreover, reliance on centralized coordination undermines system efficiency and introduces single points of failure. In this paper, we propose a novel Trusted MultiLLMN framework, driven by a Weighted Byzantine Fault Tolerance (WBFT) blockchain consensus mechanism, to ensure the reliability, security, and efficiency of multi-LLM collaboration. In WBFT, voting weights are adaptively assigned to each LLM based on its response quality and trustworthiness, incentivizing reliable behavior, and reducing the impact of malicious nodes. Extensive simulations demonstrate that WBFT significantly improves both consensus security and efficiency compared to classical and modern consensus mechanisms, particularly under wireless network conditions. Furthermore, our evaluations reveal that Trusted MultiLLMN supported by WBFT can deliver higher-quality and more credible responses than both single LLMs and conventional MultiLLMNs, thereby providing a promising path toward building robust, decentralized AI collaboration networks.
Spatially Disaggregated Energy Consumption and Emissions in End-use Sectors for Germany and Spain
Venue: arXiv cs.DB 2025
Year: 2025

High-resolution energy consumption and emissions datasets are essential for localized policy-making, resource optimization, and climate action planning. They enable municipalities to monitor mitigation strategies and foster engagement among governments, businesses, and communities. However, smaller municipalities often face data limitations that hinder tailored climate strategies. This study generates detailed final energy consumption and emissions data at the local administrative level for Germany and Spain. Using national datasets, we apply spatial disaggregation techniques with open data sources. A key innovation is the application of XGBoost for imputing missing data, combined with a stepwise spatial disaggregation process incorporating district- and province-level statistics. Prioritizing reproducibility, our open-data approach provides a scalable framework for municipalities to develop actionable climate plans. To ensure transparency, we assess the reliability of imputed values and assign confidence ratings to the disaggregated data.
Online Velocity Profile Generation and Tracking for Sampling-Based Local Planning Algorithms in Autonomous Racing Environments
Venue: arXiv cs.RO 2025
Year: 2025

This work presents an online velocity planner for autonomous racing that adapts to changing dynamic constraints, such as grip variations from tire temperature changes and rubber accumulation. The method combines a forward-backward solver for online velocity optimization with a novel spatial sampling strategy for local trajectory planning, utilizing a three-dimensional track representation. The computed velocity profile serves as a reference for the local planner, ensuring adaptability to environmental and vehicle dynamics. We demonstrate the approach's robust performance and computational efficiency in racing scenarios and discuss its limitations, including sensitivity to deviations from the predefined racing line and high jerk characteristics of the velocity profile.
MARK: Memory Augmented Refinement of Knowledge
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) assist in specialized tasks but struggle to align with evolving domain knowledge without costly fine-tuning. Domain knowledge consists of: Knowledge: Immutable facts (e.g., 'A stone is solid') and generally accepted principles (e.g., ethical standards); Refined Memory: Evolving insights shaped by business needs and real-world changes. However, a significant gap often exists between a domain expert's deep, nuanced understanding and the system's domain knowledge, which can hinder accurate information retrieval and application. Our Memory-Augmented Refinement of Knowledge (MARK) framework enables LLMs to continuously learn without retraining by leveraging structured refined memory, inspired by the Society of Mind. MARK operates through specialized agents, each serving a distinct role: Residual Refined Memory Agent: Stores and retrieves domain-specific insights to maintain context over time; User Question Refined Memory Agent: Captures user-provided facts, abbreviations, and terminology for better comprehension; LLM Response Refined Memory Agent: Extracts key elements from responses for refinement and personalization. These agents analyse stored refined memory, detect patterns, resolve contradictions, and improve response accuracy. Temporal factors like recency and frequency prioritize relevant information while discarding outdated insights. MARK enhances LLMs in multiple ways: Ground Truth Strategy: Reduces hallucinations by establishing a structured reference; Domain-Specific Adaptation: Essential for fields like healthcare, law, and manufacturing, where proprietary insights are absent from public datasets; Personalized AI Assistants: Improves virtual assistants by remembering user preferences, ensuring coherent responses over time.
In-Situ Model Validation for Continuous Processes Using In-Network Computing
Venue: arXiv cs.NI 2025
Year: 2025

The advancing industrial digitalization enables evolved process control schemes that rely on accurate models learned through data-driven approaches. While they provide high control performance and are robust to smaller deviations, a larger change in process behavior can pose significant challenges, in the worst case even leading to a damaged process plant. Hence, it is important to frequently assess the fit between the model and the actual process behavior. As the number of controlled processes and associated data volumes increase, the need for lightweight and fast reacting assessment solutions also increases. In this paper, we propose CIVIC, an in-network computing-based solution for Continuous In-situ Validation of Industrial Control models. In short, CIVIC monitors relevant process variables and detects different process states through comparison with a priori knowledge about the desired process behavior. This detection can then be leveraged to, e.g., shut down the process or trigger a reconfiguration. We prototype CIVIC on an Intel Tofino-based switch and apply it to a lab-scale water treatment plant. Our results show that we can achieve a high detection accuracy, proving that such monitoring systems are feasible and sensible.
HQC-NBV: A Hybrid Quantum-Classical View Planning Approach
Venue: arXiv cs.CV 2025
Year: 2025

Efficient view planning is a fundamental challenge in computer vision and robotic perception, critical for tasks ranging from search and rescue operations to autonomous navigation. While classical approaches, including sampling-based and deterministic methods, have shown promise in planning camera viewpoints for scene exploration, they often struggle with computational scalability and solution optimality in complex settings. This study introduces HQC-NBV, a hybrid quantum-classical framework for view planning that leverages quantum properties to efficiently explore the parameter space while maintaining robustness and scalability. We propose a specific Hamiltonian formulation with multi-component cost terms and a parameter-centric variational ansatz with bidirectional alternating entanglement patterns that capture the hierarchical dependencies between viewpoint parameters. Comprehensive experiments demonstrate that quantum-specific components provide measurable performance advantages. Compared to the classical methods, our approach achieves up to 49.2% higher exploration efficiency across diverse environments. Our analysis of entanglement architecture and coherence-preserving terms provides insights into the mechanisms of quantum advantage in robotic exploration tasks. This work represents a significant advancement in integrating quantum computing into robotic perception systems, offering a paradigm-shifting solution for various robot vision tasks.
Overcoming the hurdle of legal expertise: A reusable model for smartwatch privacy policies
Venue: arXiv cs.SE 2025
Year: 2025

Regulations for privacy protection aim to protect individuals from the unauthorized storage, processing, and transfer of their personal data but oftentimes fail in providing helpful support for understanding these regulations. To better communicate privacy policies for smartwatches, we need an in-depth understanding of their concepts and provide better ways to enable developers to integrate them when engineering systems. Up to now, no conceptual model exists covering privacy statements from different smartwatch manufacturers that is reusable for developers. This paper introduces such a conceptual model for privacy policies of smartwatches and shows its use in a model-driven software engineering approach to create a platform for data visualization of wearable privacy policies from different smartwatch manufacturers. We have analyzed the privacy policies of various manufacturers and extracted the relevant concepts. Moreover, we have checked the model with lawyers for its correctness, instantiated it with concrete data, and used it in a model-driven software engineering approach to create a platform for data visualization. This reusable privacy policy model can enable developers to easily represent privacy policies in their systems. This provides a foundation for more structured and understandable privacy policies which, in the long run, can increase the data sovereignty of application users.
Diffusion Model Quantization: A Review
Venue: arXiv cs.CV 2025
Year: 2025

Recent success of large text-to-image models has empirically underscored the exceptional performance of diffusion models in generative tasks. To facilitate their efficient deployment on resource-constrained edge devices, model quantization has emerged as a pivotal technique for both compression and acceleration. This survey offers a thorough review of the latest advancements in diffusion model quantization, encapsulating and analyzing the current state of the art in this rapidly advancing domain. First, we provide an overview of the key challenges encountered in the quantization of diffusion models, including those based on U-Net architectures and Diffusion Transformers (DiT). We then present a comprehensive taxonomy of prevalent quantization techniques, engaging in an in-depth discussion of their underlying principles. Subsequently, we perform a meticulous analysis of representative diffusion model quantization schemes from both qualitative and quantitative perspectives. From a quantitative standpoint, we rigorously benchmark a variety of methods using widely recognized datasets, delivering an extensive evaluation of the most recent and impactful research in the field. From a qualitative standpoint, we categorize and synthesize the effects of quantization errors, elucidating these impacts through both visual analysis and trajectory examination. In conclusion, we outline prospective avenues for future research, proposing novel directions for the quantization of generative models in practical applications. The list of related papers, corresponding codes, pre-trained models and comparison results are publicly available at the survey project homepage https://github.com/TaylorJocelyn/Diffusion-Model-Quantization.
A thermoelastic plate model for shot peen forming metal panels based on effective torque
Venue: arXiv cs.CE 2025
Year: 2025

A common technique used in factories to shape metal panels is shot peen forming. The impacts between the hard steel shot and the softer metal of the panel cause localized plastic deformation used to improve the fatigue properties of the material's surface. The residual stress distribution imparted by impacts also results in bending, which suggests that a torque is associated with it. In this paper, we model shot peen forming as the application of spatially varying torques to a Kirchhoff plate, opting to use the language of thermoelasticity in order to introduce these torque distributions. First, we derive the governing equations for the thermoelastic thin plate model and show that only a torque-type resultant of the temperature distribution shows up in the bending equation. Next, to calibrate from the shot peen operation an empirical effective torque parameter used in the thermoelastic model, a simple and non-invasive test is devised. This test relies only on measuring the maximum displacement of a uniformly shot peened plate as opposed to characterizing the residual stress distribution. After discussing how to handle the unconventional fully-free boundary conditions germane for peened plates, we introduce an approach to solving the inverse problem whereby the peening distribution required to obtain a specified plate contour can be obtained. Given the non-unique relationship between peening distributions and the displacement at discrete points, we explore a regularization of the inverse problem which gives rise to shot peen distributions that match the capabilities of equipment in the factory. In order to validate our proposed model, an experiment with quantified uncertainty is designed and carried out which investigates the agreement between the predictions of the calibrated model and real shot peen forming operations.
QUIC-Exfil: Exploiting QUIC's Server Preferred Address Feature to Perform Data Exfiltration Attacks
Venue: arXiv cs.CR 2025
Year: 2025

The QUIC protocol is now widely adopted by major tech companies and accounts for a significant fraction of today's Internet traffic. QUIC's multiplexing capabilities, encrypted headers, dynamic IP address changes, and encrypted parameter negotiations make the protocol not only more efficient, secure, and censorship-resistant, but also practically unmanageable by firewalls. This opens doors for attackers who may exploit certain traits of the QUIC protocol to perform targeted attacks, such as data exfiltration attacks. Whereas existing data exfiltration techniques, such as TLS and DNS-based exfiltration, can be detected on a firewall level, QUIC-based data exfiltration is more difficult to detect, since changes in IP addresses and ports are inherent to the protocol's normal behavior. To show the feasibility of a QUIC-based data exfiltration attack, we introduce a novel method leveraging the server preferred address feature of the QUIC protocol and, thus, allows an attacker to exfiltrate sensitive data from an infected machine to a malicious server, disguised as a server-side connection migration. The attack is implemented as a proof of concept tool in Rust. We evaluated the performance of five anomaly detection classifiers - Random Forest, Multi-Layer Perceptron, Support Vector Machine, Autoencoder, and Isolation Forest - trained on datasets collected from three network traffic scenarios. The classifiers were trained on over 700K benign and malicious QUIC packets and 786 connection migration events, but were unable to detect the data exfiltration attempts. Furthermore, post-analysis of the traffic captures did not reveal any identifiable fingerprint. As part of our evaluation, we also interviewed five leading firewall vendors and found that, as of today, no major firewall vendor implements functionality capable of distinguishing between benign and malicious QUIC connection migrations.
CottonSim: A vision-guided autonomous robotic system for cotton harvesting in Gazebo simulation
Venue: arXiv cs.RO 2025
Year: 2025

Cotton is a major cash crop in the United States, with the country being a leading global producer and exporter. Nearly all U.S. cotton is grown in the Cotton Belt, spanning 17 states in the southern region. Harvesting remains a critical yet challenging stage, impacted by the use of costly, environmentally harmful defoliants and heavy, expensive cotton pickers. These factors contribute to yield loss, reduced fiber quality, and soil compaction, which collectively threaten long-term sustainability. To address these issues, this study proposes a lightweight, small-scale, vision-guided autonomous robotic cotton picker as an alternative. An autonomous system, built on Clearpath's Husky platform and integrated with the CottonEye perception system, was developed and tested in the Gazebo simulation environment. A virtual cotton field was designed to facilitate autonomous navigation testing. The navigation system used Global Positioning System (GPS) and map-based guidance, assisted by an RGBdepth camera and a YOLOv8nseg instance segmentation model. The model achieved a mean Average Precision (mAP) of 85.2%, a recall of 88.9%, and a precision of 93.0%. The GPS-based approach reached a 100% completion rate (CR) within a $(5e-6)^{\circ}$ threshold, while the map-based method achieved a 96.7% CR within a 0.25 m threshold. The developed Robot Operating System (ROS) packages enable robust simulation of autonomous cotton picking, offering a scalable baseline for future agricultural robotics. CottonSim code and datasets are publicly available on GitHub: https://github.com/imtheva/CottonSim
Timestamp Manipulation: Timestamp-based Nakamoto-style Blockchains are Vulnerable
Venue: arXiv cs.CR 2025
Year: 2025

Nakamoto consensus are the most widely adopted decentralized consensus mechanism in cryptocurrency systems. Since it was proposed in 2008, many studies have focused on analyzing its security. Most of them focus on maximizing the profit of the adversary. Examples include the selfish mining attack [FC '14] and the recent riskless uncle maker (RUM) attack [CCS '23]. In this work, we introduce the Staircase-Unrestricted Uncle Maker (SUUM), the first block withholding attack targeting the timestamp-based Nakamoto-style blockchain. Through block withholding, timestamp manipulation, and difficulty risk control, SUUM adversaries are capable of launching persistent attacks with zero cost and minimal difficulty risk characteristics, indefinitely exploiting rewards from honest participants. This creates a self-reinforcing cycle that threatens the security of blockchains. We conduct a comprehensive and systematic evaluation of SUUM, including the attack conditions, its impact on blockchains, and the difficulty risks. Finally, we further discuss four feasible mitigation measures against SUUM.
Hearing and Seeing Through CLIP: A Framework for Self-Supervised Sound Source Localization
Venue: arXiv cs.CV 2025
Year: 2025

Large-scale vision-language models demonstrate strong multimodal alignment and generalization across diverse tasks. Among them, CLIP stands out as one of the most successful approaches. In this work, we extend the application of CLIP to sound source localization, proposing a self-supervised method operates without explicit text input. We introduce a framework that maps audios into tokens compatible with CLIP's text encoder, producing audio-driven embeddings. These embeddings are used to generate sounding region masks, from which visual features are extracted and aligned with the audio embeddings through a contrastive audio-visual correspondence objective. Our findings show that alignment knowledge of pre-trained multimodal foundation model enables our method to generate more complete and compact localization for sounding objects. We further propose an LLM-guided extension that distills object-aware audio-visual scene understanding into the model during training to enhance alignment. Extensive experiments across five diverse tasks demonstrate that our method, in all variants, outperforms state-of-the-art approaches and achieves strong generalization in zero-shot settings.
InfTDA: A Simple TopDown Mechanism for Hierarchical Differentially Private Counting Queries
Venue: arXiv cs.DS 2025
Year: 2025

This paper extends $\texttt{InfTDA}$, a mechanism proposed in (Boninsegna, Silvestri, PETS 2025) for mobility datasets with origin and destination trips, in a general setting. The algorithm presented in this paper works for any dataset of $d$ categorical features and produces a differentially private synthetic dataset that answers all hierarchical queries, a special case of marginals, each with bounded maximum absolute error. The algorithm builds upon the TopDown mechanism developed for the 2020 US Census.