OpenRaiser/Intern-Atlas huggingface dataset:
Ridge Estimation-Based Vision and Laser Ranging Fusion Localization Method for UAVs
Venue: arXiv cs.CV 2025
Year: 2025

Tracking and measuring targets using a variety of sensors mounted on UAVs is an effective means to quickly and accurately locate the target. This paper proposes a fusion localization method based on ridge estimation, combining the advantages of rich scene information from sequential imagery with the high precision of laser ranging to enhance localization accuracy. Under limited conditions such as long distances, small intersection angles, and large inclination angles, the column vectors of the design matrix have serious multicollinearity when using the least squares estimation algorithm. The multicollinearity will lead to ill-conditioned problems, resulting in significant instability and low robustness. Ridge estimation is introduced to mitigate the serious multicollinearity under the condition of limited observation. Experimental results demonstrate that our method achieves higher localization accuracy compared to ground localization algorithms based on single information. Moreover, the introduction of ridge estimation effectively enhances the robustness, particularly under limited observation conditions.
A first look at common RPKI publication practices
Venue: arXiv cs.CR 2025
Year: 2025

The RPKI is crucial for securing the routing system of the Internet. With the RPKI, owners of Internet resources can make cryptographically backed claims, for example about the legitimate origin of their IP space. Thousands of networks use this information to detect malicious or accidental route hijacks. The RPKI consists out of 100 distributed repositories. However, public reports claim that some of these repositories are unreliable. A current Internet-Draft suggests best practices on how to operate these repositories, with the goal to improve deployment quality. Inspired by this draft, we take a first look at the operational practices of repositories of the RPKI. We mainly focus on the distribution of RPKI information. We find that there is a wide variety in deployment practices, of which some might risk the availability of parts of the information in the RPKI. This study creates a baseline for measuring the maturity of RPKI repositories in the future.
Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference
Venue: arXiv cs.LG 2025
Year: 2025

Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG. We propose Kascade, a training-free sparse attention method that leverages known observations such as 1) post-softmax attention is intrinsically sparse, and 2) the identity of high-weight keys is stable across nearby layers. Kascade computes exact Top-k indices in a small set of anchor layers, then reuses those indices in intermediate reuse layers. The anchor layers are selected algorithmically, via a dynamic-programming objective that maximizes cross-layer similarity over a development set, allowing easy deployment across models. The method incorporates efficient implementation constraints (e.g. tile-level operations), across both prefill and decode attention. The Top-k selection and reuse in Kascade is head-aware and we show in our experiments that this is critical for high accuracy. Kascade achieves up to 4.1x speedup in decode attention and 2.2x speedup in prefill attention over FlashAttention-3 baseline on H100 GPUs while closely matching dense attention accuracy on long-context benchmarks such as LongBench and AIME-24.
Large Language Models as a (Bad) Security Norm in the Context of Regulation and Compliance
Venue: arXiv cs.CY 2025
Year: 2025

The use of Large Language Models (LLM) by providers of cybersecurity and digital infrastructures of all kinds is an ongoing development. It is suggested and on an experimental basis used to write the code for the systems, and potentially fed with sensitive data or what would otherwise be considered trade secrets. Outside of these obvious points, this paper asks how AI can negatively affect cybersecurity and law when used for the design and deployment of security infrastructure by its developers. Firstly, the paper discusses the use of LLMs in security, either directly or indirectly, and briefly tackles other types of AI. It then lists norms in cybersecurity, then a range of legal cybersecurity obligations from the European Union, to create a frame of reference. Secondly, the paper describes how LLMs may fail to fulfil both legal obligations and best practice in cybersecurity is given, and the paper ends with some economic and practical consequences for this development, with some notions of solutions as well. The paper finds that using LLMs comes with many risks, many of which are against good security practice, and the legal obligations in security regulation. This is because of the inherent weaknesses of LLMs, most of which are mitigated if replaced with symbolic AI. Both also have issues fulfilling basic traceability obligations and practice. Solutions are secondary systems surrounding LLM based AI, fulfilment of security norms beyond legal requirements and simply not using such technology in certain situations.
Preparing Future-Ready Learners: K12 Skills Shift and GenAI EdTech Innovation Direction
Venue: arXiv cs.HC 2025
Year: 2025

Since Generative AI came out it has quickly embedded itself in our social fabric, triggering lots of discussions, predictions, and efforts from research, industry, government and capital market to experiment and embrace the technology. The question for the global K12 education is, what and how should our children learn in this fast changing world to be prepared for the changing labor market and live a happy and balanced life? Three key aspects will be discussed: 1) Skills; 2) Evaluation of Learning; 3) Strategic GenAI-powered EdTech innovation for long term educational impact.
AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research
Venue: arXiv cs.DC 2025
Year: 2025

In this paper, we describe a federated compute platform dedicated to support Artificial Intelligence in scientific workloads. Putting the effort into reproducible deployments, it delivers consistent, transparent access to a federation of physically distributed e-Infrastructures. Through a comprehensive service catalogue, the platform is able to offer an integrated user experience covering the full Machine Learning lifecycle, including model development (with dedicated interactive development environments), training (with GPU resources, annotation tools, experiment tracking, and federated learning support) and deployment (covering a wide range of deployment options all along the Cloud Continuum). The platform also provides tools for traceability and reproducibility of AI models, integrates with different Artificial Intelligence model providers, datasets and storage resources, allowing users to interact with the broader Machine Learning ecosystem. Finally, it is easily customizable to lower the adoption barrier by external communities.
Talent is Everywhere, Mobility is Not: Mapping the Topological Anchors of Educational Pathways
Venue: arXiv cs.CY 2025
Year: 2025

The relationship between socioeconomic background, academic performance, and post-secondary educational outcomes remains a significant concern for policymakers and researchers globally. While the literature often relies on self-reported or aggregate data, its ability to trace individual pathways limits these studies. Here, we analyze administrative records from over 2.7 million Chilean students (2021-2024) to map post-secondary trajectories across the entire education system. Using machine learning, we identify seven distinct student archetypes and introduce the Educational Space, a two-dimensional representation of students based on academic performance and family background. We show that, despite comparable academic abilities, students follow markedly different enrollment patterns, career choices, and cross-regional migration behaviors depending on their socioeconomic origins and position in the educational space. For instance, high-achieving, low-income students tend to remain in regional institutions, while their affluent peers are more geographically mobile. Our approach provides a scalable framework applicable worldwide for using administrative data to uncover structural constraints on educational mobility and inform policies aimed at reducing spatial and social inequality.
cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution
Venue: arXiv cs.AI 2025
Year: 2025

Optimizing CUDA kernels is a challenging and labor-intensive task, given the need for hardware-software co-design expertise and the proprietary nature of high-performance kernel libraries. While recent large language models (LLMs) combined with evolutionary algorithms show promise in automatic kernel optimization, existing approaches often fall short in performance due to their suboptimal agent designs and mismatched evolution representations. This work identifies these mismatches and proposes cuPilot, a strategy-coordinated multi-agent framework that introduces strategy as an intermediate semantic representation for kernel evolution. Key contributions include a strategy-coordinated evolution algorithm, roofline-guided prompting, and strategy-level population initialization. Experimental results show that the generated kernels by cuPilot achieve an average speed up of 3.09$\times$ over PyTorch on a benchmark of 100 kernels. On the GEMM tasks, cuPilot showcases sophisticated optimizations and achieves high utilization of critical hardware units. The generated kernels are open-sourced at https://github.com/champloo2878/cuPilot-Kernels.git.
Tri-Select: A Multi-Stage Visual Data Selection Framework for Mobile Visual Crowdsensing
Venue: arXiv cs.RO 2025
Year: 2025

Mobile visual crowdsensing enables large-scale, fine-grained environmental monitoring through the collection of images from distributed mobile devices. However, the resulting data is often redundant and heterogeneous due to overlapping acquisition perspectives, varying resolutions, and diverse user behaviors. To address these challenges, this paper proposes Tri-Select, a multi-stage visual data selection framework that efficiently filters redundant and low-quality images. Tri-Select operates in three stages: (1) metadata-based filtering to discard irrelevant samples; (2) spatial similarity-based spectral clustering to organize candidate images; and (3) a visual-feature-guided selection based on maximum independent set search to retain high-quality, representative images. Experiments on real-world and public datasets demonstrate that Tri-Select improves both selection efficiency and dataset quality, making it well-suited for scalable crowdsensing applications.
Causal-Tune: Mining Causal Factors from Vision Foundation Models for Domain Generalized Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Fine-tuning Vision Foundation Models (VFMs) with a small number of parameters has shown remarkable performance in Domain Generalized Semantic Segmentation (DGSS). Most existing works either train lightweight adapters or refine intermediate features to achieve better generalization on unseen domains. However, they both overlook the fact that long-term pre-trained VFMs often exhibit artifacts, which hinder the utilization of valuable representations and ultimately degrade DGSS performance. Inspired by causal mechanisms, we observe that these artifacts are associated with non-causal factors, which usually reside in the low- and high-frequency components of the VFM spectrum. In this paper, we explicitly examine the causal and non-causal factors of features within VFMs for DGSS, and propose a simple yet effective method to identify and disentangle them, enabling more robust domain generalization. Specifically, we propose Causal-Tune, a novel fine-tuning strategy designed to extract causal factors and suppress non-causal ones from the features of VFMs. First, we extract the frequency spectrum of features from each layer using the Discrete Cosine Transform (DCT). A Gaussian band-pass filter is then applied to separate the spectrum into causal and non-causal components. To further refine the causal components, we introduce a set of causal-aware learnable tokens that operate in the frequency domain, while the non-causal components are discarded. Finally, refined features are transformed back into the spatial domain via inverse DCT and passed to the next layer. Extensive experiments conducted on various cross-domain tasks demonstrate the effectiveness of Causal-Tune. In particular, our method achieves superior performance under adverse weather conditions, improving +4.8% mIoU over the baseline in snow conditions.
Yuan-TecSwin: A text conditioned Diffusion model with Swin-transformer blocks
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have shown remarkable capacity in image synthesis based on their U-shaped architecture and convolutional neural networks (CNN) as basic blocks. The locality of the convolution operation in CNN may limit the model's ability to understand long-range semantic information. To address this issue, we propose Yuan-TecSwin, a text-conditioned diffusion model with Swin-transformer in this work. The Swin-transformer blocks take the place of CNN blocks in the encoder and decoder, to improve the non-local modeling ability in feature extraction and image restoration. The text-image alignment is improved with a well-chosen text encoder, effective utilization of text embedding, and careful design in the incorporation of text condition. Using an adapted time step to search in different diffusion stages, inference performance is further improved by 10%. Yuan-TecSwin achieves the state-of-the-art FID score of 1.37 on ImageNet generation benchmark, without any additional models at different denoising stages. In a side-by-side comparison, we find it difficult for human interviewees to tell the model-generated images from the human-painted ones.
Don't Guess, Escalate: Towards Explainable Uncertainty-Calibrated AI Forensic Agents
Venue: arXiv cs.MA 2025
Year: 2025

AI is reshaping the landscape of multimedia forensics. We propose AI forensic agents: reliable orchestrators that select and combine forensic detectors, identify provenance and context, and provide uncertainty-aware assessments. We highlight pitfalls in current solutions and introduce a unified framework to improve the authenticity verification process.
DeContext as Defense: Safe Image Editing in Diffusion Transformers
Venue: arXiv cs.CV 2025
Year: 2025

In-context diffusion models allow users to modify images with remarkable ease and realism. However, the same power raises serious privacy concerns: personal images can be easily manipulated for identity impersonation, misinformation, or other malicious uses, all without the owner's consent. While prior work has explored input perturbations to protect against misuse in personalized text-to-image generation, the robustness of modern, large-scale in-context DiT-based models remains largely unexamined. In this paper, we propose DeContext, a new method to safeguard input images from unauthorized in-context editing. Our key insight is that contextual information from the source image propagates to the output primarily through multimodal attention layers. By injecting small, targeted perturbations that weaken these cross-attention pathways, DeContext breaks this flow, effectively decouples the link between input and output. This simple defense is both efficient and robust. We further show that early denoising steps and specific transformer blocks dominate context propagation, which allows us to concentrate perturbations where they matter most. Experiments on Flux Kontext and Step1X-Edit show that DeContext consistently blocks unwanted image edits while preserving visual quality. These results highlight the effectiveness of attention-based perturbations as a powerful defense against image manipulation. Code is available at https://github.com/LinghuiiShen/DeContext.
Stackelberg Learning from Human Feedback: Preference Optimization as a Sequential Game
Venue: arXiv cs.LG 2025
Year: 2025

We introduce Stackelberg Learning from Human Feedback (SLHF), a new framework for preference optimization. SLHF frames the alignment problem as a sequential-move game between two policies: a Leader, which commits to an action, and a Follower, which responds conditionally on the Leader's action. This approach decomposes preference optimization into a refinement problem for the Follower and an optimization problem against an adversary for the Leader. Unlike Reinforcement Learning from Human Feedback (RLHF), which assigns scalar rewards to actions, or Nash Learning from Human Feedback (NLHF), which seeks a simultaneous-move equilibrium, SLHF leverages the asymmetry of sequential play to capture richer preference structures. The sequential design of SLHF naturally enables inference-time refinement, as the Follower learns to improve the Leader's actions, and these refinements can be leveraged through iterative sampling. We compare the solution concepts of SLHF, RLHF, and NLHF, and lay out key advantages in consistency, data sensitivity, and robustness to intransitive preferences. Experiments on large language models demonstrate that SLHF achieves strong alignment across diverse preference datasets, scales from 0.5B to 8B parameters, and yields inference-time refinements that transfer across model families without further fine-tuning.
Exploiting Radio Frequency Fingerprints for Device Identification: Tackling Cross-receiver Challenges in the Source-data-free Scenario
Venue: arXiv cs.LG 2025
Year: 2025

With the rapid proliferation of edge computing, Radio Frequency Fingerprint Identification (RFFI) has become increasingly important for secure device authentication. However, practical deployment of deep learning-based RFFI models is hindered by a critical challenge: their performance often degrades significantly when applied across receivers with different hardware characteristics due to distribution shifts introduced by receiver variation. To address this, we investigate the source-data-free cross-receiver RFFI (SCRFFI) problem, where a model pretrained on labeled signals from a source receiver must adapt to unlabeled signals from a target receiver, without access to any source-domain data during adaptation. We first formulate a novel constrained pseudo-labeling-based SCRFFI adaptation framework, and provide a theoretical analysis of its generalization performance. Our analysis highlights a key insight: the target-domain performance is highly sensitive to the quality of the pseudo-labels generated during adaptation. Motivated by this, we propose Momentum Soft pseudo-label Source Hypothesis Transfer (MS-SHOT), a new method for SCRFFI that incorporates momentum-center-guided soft pseudo-labeling and enforces global structural constraints to encourage confident and diverse predictions. Notably, MS-SHOT effectively addresses scenarios involving label shift or unknown, non-uniform class distributions in the target domain -- a significant limitation of prior methods. Extensive experiments on real-world datasets demonstrate that MS-SHOT consistently outperforms existing approaches in both accuracy and robustness, offering a practical and scalable solution for source-data-free cross-receiver adaptation in RFFI.
Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance
Venue: arXiv cs.IR 2025
Year: 2025

In today's information-driven world, access to scientific publications has become increasingly easy. At the same time, filtering through the massive volume of available research has become more challenging than ever. Graph Neural Networks (GNNs) and graph attention mechanisms have shown strong effectiveness in searching large-scale information databases, particularly when combined with modern large language models. In this paper, we propose an Attention-Based Subgraph Retriever, a GNN-as-retriever model that applies attention-based pruning to extract a refined subgraph, which is then passed to a large language model for advanced knowledge reasoning.
Novel Inconsistency Results for Partial Information Decomposition
Venue: arXiv cs.IT 2025
Year: 2025

Partial Information Decomposition (PID) seeks to disentangle how information about a target variable is distributed across multiple sources, separating redundant, unique, and synergistic contributions. Despite extensive theoretical development and applications across diverse fields, the search for a unique, universally accepted solution remains elusive, with numerous competing proposals offering different decompositions. A promising but underutilized strategy for making progress is to establish inconsistency results, proofs that certain combinations of intuitively appealing axioms cannot be simultaneously satisfied. Such results clarify the landscape of possibilities and force us to recognize where fundamental choices must be made. In this work, we leverage the recently developed mereological approach to PID to establish novel inconsistency results with far-reaching implications. Our main theorem demonstrates that three cornerstone properties of classical information theory, namely non-negativity, the chain rule, and invariance under invertible transformations, become mutually incompatible when extended to the PID setting. This result reveals that any PID framework must sacrifice at least one property that seems fundamental to information theory itself. Additionally, we strengthen the classical result of Rauh et al., which showed that non-negativity, the identity property, and the Williams and Beer axioms cannot coexist.
FrameDiffuser: G-Buffer-Conditioned Diffusion for Neural Forward Frame Rendering
Venue: arXiv cs.CV 2025
Year: 2025

Neural rendering for interactive applications requires translating geometric and material properties (G-buffer) to photorealistic images with realistic lighting on a frame-by-frame basis. While recent diffusion-based approaches show promise for G-buffer-conditioned image synthesis, they face critical limitations: single-image models like RGBX generate frames independently without temporal consistency, while video models like DiffusionRenderer are too computationally expensive for most consumer gaming sets ups and require complete sequences upfront, making them unsuitable for interactive applications where future frames depend on user input. We introduce FrameDiffuser, an autoregressive neural rendering framework that generates temporally consistent, photorealistic frames by conditioning on G-buffer data and the models own previous output. After an initial frame, FrameDiffuser operates purely on incoming G-buffer data, comprising geometry, materials, and surface properties, while using its previously generated frame for temporal guidance, maintaining stable, temporal consistent generation over hundreds to thousands of frames. Our dual-conditioning architecture combines ControlNet for structural guidance with ControlLoRA for temporal coherence. A three-stage training strategy enables stable autoregressive generation. We specialize our model to individual environments, prioritizing consistency and inference speed over broad generalization, demonstrating that environment-specific training achieves superior photorealistic quality with accurate lighting, shadows, and reflections compared to generalized approaches.