OpenRaiser/Intern-Atlas huggingface dataset:
Verification Challenge: Fractional Cascading for Multi-Nuclide Grid Lookup
Venue: arXiv cs.LO 2025
Year: 2025

We present a verification challenge based on the fractional cascading (FC) technique for accelerating repeated searches across a collection of sorted arrays. The specific context is nuclear cross section lookup in a simulation code, where a material consists of many nuclides, each with its own sorted energy grid. A naive search performs a binary search in each array individually. The FC-based cascade grid structure reduces this cost by performing a single binary search followed by constant-time refinements. The challenge consists of verifying the correctness of the FC algorithm with respect to the naive approach and validating its structural properties.
Hybrid Interval Type-2 Mamdani-TSK Fuzzy System for Regression Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Regression analysis is employed to examine and quantify the relationships between input variables and a dependent and continuous output variable. It is widely used for predictive modelling in fields such as finance, healthcare, and engineering. However, traditional methods often struggle with real-world data complexities, including uncertainty and ambiguity. While deep learning approaches excel at capturing complex non-linear relationships, they lack interpretability and risk over-fitting on small datasets. Fuzzy systems provide an alternative framework for handling uncertainty and imprecision, with Mamdani and Takagi-Sugeno-Kang (TSK) systems offering complementary strengths: interpretability versus accuracy. This paper presents a novel fuzzy regression method that combines the interpretability of Mamdani systems with the precision of TSK models. The proposed approach introduces a hybrid rule structure with fuzzy and crisp components and dual dominance types, enhancing both accuracy and explainability. Evaluations on benchmark datasets demonstrate state-of-the-art performance in several cases, with rules maintaining a component similar to traditional Mamdani systems while improving precision through improved rule outputs. This hybrid methodology offers a balanced and versatile tool for predictive modelling, addressing the trade-off between interpretability and accuracy inherent in fuzzy systems. In the 6 datasets tested, the proposed approach gave the best fuzzy methodology score in 4 datasets, out-performed the opaque models in 2 datasets and produced the best overall score in 1 dataset with the improvements in RMSE ranging from 0.4% to 19%.
Rectify and Align GPS Points to Parking Spots via Rank-1 Constraint
Venue: arXiv cs.LG 2025
Year: 2025

Parking spots are essential components, providing vital mobile resources for residents in a city. Accurate Global Positioning System (GPS) points of parking spots are the core data for subsequent applications,e.g., parking management, parking policy, and urban development. However, high-rise buildings tend to cause GPS points to drift from the actual locations of parking spots; besides, the standard lower-cost GPS equipment itself has a certain location error. Therefore, it is a non-trivial task to correct a few wrong GPS points from a large number of parking spots in an unsupervised approach. In this paper, motivated by the physical constraints of parking spots (i.e., parking spots are parallel to the sides of roads), we propose an unsupervised low-rank method to effectively rectify errors in GPS points and further align them to the parking spots in a unified framework. The proposed unconventional rectification and alignment method is simple and yet effective for any type of GPS point errors. Extensive experiments demonstrate the superiority of the proposed method to solve a practical problem. The data set and the code are publicly accessible at:https://github.com/pangjunbiao/ITS-Parking-spots-Dataset.
Near-Infrared Hyperspectral Imaging Applications in Food Analysis -- Improving Algorithms and Methodologies
Venue: arXiv cs.CV 2025
Year: 2025

This thesis investigates the application of near-infrared hyperspectral imaging (NIR-HSI) for food quality analysis. The investigation is conducted through four studies operating with five research hypotheses. For several analyses, the studies compare models based on convolutional neural networks (CNNs) and partial least squares (PLS). Generally, joint spatio-spectral analysis with CNNs outperforms spatial analysis with CNNs and spectral analysis with PLS when modeling parameters where chemical and physical visual information are relevant. When modeling chemical parameters with a 2-dimensional (2D) CNN, augmenting the CNN with an initial layer dedicated to performing spectral convolution enhances its predictive performance by learning a spectral preprocessing similar to that applied by domain experts. Still, PLS-based spectral modeling performs equally well for analysis of the mean content of chemical parameters in samples and is the recommended approach. Modeling the spatial distribution of chemical parameters with NIR-HSI is limited by the ability to obtain spatially resolved reference values. Therefore, a study used bulk mean references for chemical map generation of fat content in pork bellies. A PLS-based approach gave non-smooth chemical maps and pixel-wise predictions outside the range of 0-100\%. Conversely, a 2D CNN augmented with a spectral convolution layer mitigated all issues arising with PLS. The final study attempted to model barley's germinative capacity by analyzing NIR spectra, RGB images, and NIR-HSI images. However, the results were inconclusive due to the dataset's low degree of germination. Additionally, this thesis has led to the development of two open-sourced Python packages. The first facilitates fast PLS-based modeling, while the second facilitates very fast cross-validation of PLS and other classical machine learning models with a new algorithm.
Tahakom LLM Guidelines and Recipes: From Pre-training Data to an Arabic LLM
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) have significantly advanced the field of natural language processing, enhancing capabilities in both language understanding and generation across diverse domains. However, developing LLMs for Arabic presents unique challenges. This paper explores these challenges by focusing on critical aspects such as data curation, tokenizer design, and evaluation. We detail our approach to the collection and filtration of Arabic pre-training datasets, assess the impact of various tokenizer designs on model performance, and examine the limitations of existing Arabic evaluation frameworks, for which we propose a systematic corrective methodology. To promote transparency and facilitate collaborative development, we share our data and methodologies, contributing to the advancement of language modeling, particularly for the Arabic language.
Non-Linear Precoding via Dirty Paper Coding for Near-Field Downlink MISO Communications
Venue: arXiv cs.IT 2025
Year: 2025

In 6G systems, extremely large-scale antenna arrays operating at terahertz frequencies extend the near-field region to typical user distances from the base station, enabling near-field communication (NFC) with fine spatial resolution through beamfocusing. Existing multiuser NFC systems predominantly employ linear precoding techniques such as zero-forcing (ZF), which suffer from performance degradation due to the high transmit power required to suppress interference. This paper proposes a nonlinear precoding framework based on Dirty Paper Coding (DPC), which pre-cancels known interference to maximize the sum-rate performance. We formulate and solve the corresponding sum-rate maximization problems, deriving optimal power allocation strategies for both DPC and ZF schemes. Extensive simulations demonstrate that DPC achieves substantial sum-rate gains over ZF across various near-field configurations, with the most pronounced improvements observed for closely spaced users.
DistilCLIP-EEG: Enhancing Epileptic Seizure Detection Through Multi-modal Learning and Knowledge Distillation
Venue: arXiv cs.LG 2025
Year: 2025

Epilepsy is a prevalent neurological disorder marked by sudden, brief episodes of excessive neuronal activity caused by abnormal electrical discharges, which may lead to some mental disorders. Most existing deep learning methods for epilepsy detection rely solely on unimodal EEG signals, neglecting the potential benefits of multimodal information. To address this, we propose a novel multimodal model, DistilCLIP-EEG, based on the CLIP framework, which integrates both EEG signals and text descriptions to capture comprehensive features of epileptic seizures. The model involves an EEG encoder based on the Conformer architecture as a text encoder, the proposed Learnable BERT (BERT-LP) as prompt learning within the encoders. Both operate in a shared latent space for effective cross-modal representation learning. To enhance efficiency and adaptability, we introduce a knowledge distillation method where the trained DistilCLIP-EEG serves as a teacher to guide a more compact student model to reduce training complexity and time. On the TUSZ, AUBMC, and CHB-MIT datasets, both the teacher and student models achieved accuracy rates exceeding 97%. Across all datasets, the F1-scores were consistently above 0.94, demonstrating the robustness and reliability of the proposed framework. Moreover, the student model's parameter count and model size are approximately 58.1% of those of the teacher model, significantly reducing model complexity and storage requirements while maintaining high performance. These results highlight the potential of our proposed model for EEG-based epilepsy detection and establish a solid foundation for deploying lightweight models in resource-constrained settings.
How Blind and Low-Vision Users Manage Their Passwords
Venue: arXiv cs.CR 2025
Year: 2025

Managing passwords securely and conveniently is still an open problem for many users. Existing research has examined users' password management strategies and identified pain points, such as security concerns, leading to insecure practices. We investigate how Blind and Low-Vision (BLV) users tackle this problem and how password managers can assist them. This paper presents the results of a qualitative interview study with N = 33 BLV participants. We found that all participants utilize password managers to some extent, which they perceive as fairly accessible. However, the adoption is mainly driven by the convenience of storing and retrieving passwords. The security advantages - generating strong, random passwords - were avoided mainly due to the absence of practical accessibility. Password managers do not adhere to BLV users' underlying needs for agency, which stem from experiences with inaccessible software and vendors who deprioritize accessibility issues. Underutilization of password managers leads BLV users to adopt insecure practices, such as reusing predictable passwords or resorting to 'security through obscurity' by writing important credentials in braille. We conclude our analysis by discussing the need to implement practical accessibility and usability improvements for password managers as a way of establishing trust and secure practices while maintaining BLV users' agency.
Hoecken-D Hand: A Novel Robotic Hand for Linear Parallel Pinching and Self-Adaptive Grasping
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents the Hoecken-D Hand, an underactuated robotic gripper that combines a modified Hoecken linkage with a differential spring mechanism to achieve both linear parallel pinching and a mid-stroke transition to adaptive envelope. The original Hoecken linkage is reconfigured by replacing one member with differential links, preserving straight-line guidance while enabling contact-triggered reconfiguration without additional actuators. A double-parallelogram arrangement maintains fingertip parallelism during conventional pinching, whereas the differential mechanism allows one finger to wrap inward upon encountering an obstacle, improving stability on irregular or thin objects. The mechanism can be driven by a single linear actuator, minimizing complexity and cost; in our prototype, each finger is driven by its own linear actuator for simplicity. We perform kinematic modeling and force analysis to characterize grasp performance, including simulated grasping forces and spring-opening behavior under varying geometric parameters. The design was prototyped using PLA-based 3D printing, achieving a linear pinching span of approximately 200 mm. Preliminary tests demonstrate reliable grasping in both modes across a wide range of object geometries, highlighting the Hoecken-D Hand as a compact, adaptable, and cost-effective solution for manipulation in unstructured environments.
Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization
Venue: arXiv cs.CL 2025
Year: 2025

The reasoning pattern of Large language models (LLMs) remains opaque, and Reinforcement learning (RL) typically applies uniform credit across an entire generation, blurring the distinction between pivotal and routine steps. This work positions attention as a privileged substrate that renders the internal logic of LLMs legible, not merely as a byproduct of computation, but as a mechanistic blueprint of reasoning itself. We first distinguish attention heads between locally and globally focused information processing and reveal that locally focused heads produce a sawtooth pattern near the diagonal indicating phrasal chunks, while globally focused heads expose tokens that exert broad downstream influence over future tokens. We formalize these with two metrics: 1) Windowed Average Attention Distance, which measures the extent of backward attention within a clipped window; 2) Future Attention Influence, which quantifies a token's global importance as the average attention it receives from subsequent tokens. Taken together, these signals reveal a recurring preplan-and-anchor mechanism, where the model first performs a long-range contextual reference to generate an introductory token, which is immediately followed by or coincides with a semantic anchor token that organizes subsequent reasoning. Leveraging these insights, we introduce three novel RL strategies that dynamically perform targeted credit assignment to critical nodes (preplan tokens, anchor tokens, and their temporal coupling) and show consistent performance gains across various reasoning tasks. By aligning optimization with the model's intrinsic reasoning rhythm, we aim to transform opaque optimization into an actionable structure-aware process, hoping to offer a potential step toward more transparent and effective optimization of LLM reasoning.
Steer-MoE: Efficient Audio-Language Alignment with a Mixture-of-Experts Steering Module
Venue: arXiv cs.SD 2025
Year: 2025

Aligning pretrained audio encoders and Large Language Models (LLMs) offers a promising, parameter-efficient path to building powerful multimodal agents. However, existing methods often require costly full-model finetuning or rely on static adapters that may lack expressive power. Drawing inspiration from the Platonic Representation Hypothesis, we introduce SteerMoE, a novel and modular framework for audio-language alignment. SteerMoE freezes both the audio encoder and the LLM decoder, training only a lightweight steering module integrated within the encoder's layers. This module uses a Mixture-of-Experts (MoE) router to dynamically select and apply learned steering vectors, progressively transforming continuous audio representations into a space comprehensible to the LLM. By operating entirely in the continuous embedding space, our approach requires no modifications to the LLM's vocabulary and preserves its advanced reasoning and agentic capabilities. We demonstrate through experiments on ASR, audio understanding, and a qualitative function-calling task that SteerMoE achieves strong performance while remaining highly modular and computationally efficient, offering a robust new paradigm for developing sophisticated audio-language systems.
XD-RCDepth: Lightweight Radar-Camera Depth Estimation with Explainability-Aligned and Distribution-Aware Distillation
Venue: arXiv cs.CV 2025
Year: 2025

Depth estimation remains central to autonomous driving, and radar-camera fusion offers robustness in adverse conditions by providing complementary geometric cues. In this paper, we present XD-RCDepth, a lightweight architecture that reduces the parameters by 29.7% relative to the state-of-the-art lightweight baseline while maintaining comparable accuracy. To preserve performance under compression and enhance interpretability, we introduce two knowledge-distillation strategies: an explainability-aligned distillation that transfers the teacher's saliency structure to the student, and a depth-distribution distillation that recasts depth regression as soft classification over discretized bins. Together, these components reduce the MAE compared with direct training with 7.97% and deliver competitive accuracy with real-time efficiency on nuScenes and ZJU-4DRadarCam datasets.
Towards Robust Knowledge Removal in Federated Learning with High Data Heterogeneity
Venue: arXiv cs.LG 2025
Year: 2025

Nowdays, there are an abundance of portable devices capable of collecting large amounts of data and with decent computational power. This opened the possibility to train AI models in a distributed manner, preserving the participating clients' privacy. However, because of privacy regulations and safety requirements, elimination upon necessity of a client contribution to the model has become mandatory. The cleansing process must satisfy specific efficacy and time requirements. In recent years, research efforts have produced several knowledge removal methods, but these require multiple communication rounds between the data holders and the process coordinator. This can cause the unavailability of an effective model up to the end of the removal process, which can result in a disservice to the system users. In this paper, we introduce an innovative solution based on Task Arithmetic and the Neural Tangent Kernel, to rapidly remove a client's influence from a model.
A Modular Object Detection System for Humanoid Robots Using YOLO
Venue: arXiv cs.RO 2025
Year: 2025

Within the field of robotics, computer vision remains a significant barrier to progress, with many tasks hindered by inefficient vision systems. This research proposes a generalized vision module leveraging YOLOv9, a state-of-the-art framework optimized for computationally constrained environments like robots. The model is trained on a dataset tailored to the FIRA robotics Hurocup. A new vision module is implemented in ROS1 using a virtual environment to enable YOLO compatibility. Performance is evaluated using metrics such as frames per second (FPS) and Mean Average Precision (mAP). Performance is then compared to the existing geometric framework in static and dynamic contexts. The YOLO model achieved comparable precision at a higher computational cost then the geometric model, while providing improved robustness.
Local-Global Context-Aware and Structure-Preserving Image Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion models have recently achieved significant success in various image manipulation tasks, including image super-resolution and perceptual quality enhancement. Pretrained text-to-image models, such as Stable Diffusion, have exhibited strong capabilities in synthesizing realistic image content, which makes them particularly attractive for addressing super-resolution tasks. While some existing approaches leverage these models to achieve state-of-the-art results, they often struggle when applied to diverse and highly degraded images, leading to noise amplification or incorrect content generation. To address these limitations, we propose a contextually precise image super-resolution framework that effectively maintains both local and global pixel relationships through Local-Global Context-Aware Attention, enabling the generation of high-quality images. Furthermore, we propose a distribution- and perceptual-aligned conditioning mechanism in the pixel space to enhance perceptual fidelity. This mechanism captures fine-grained pixel-level representations while progressively preserving and refining structural information, transitioning from local content details to the global structural composition. During inference, our method generates high-quality images that are structurally consistent with the original content, mitigating artifacts and ensuring realistic detail restoration. Extensive experiments on multiple super-resolution benchmarks demonstrate the effectiveness of our approach in producing high-fidelity, perceptually accurate reconstructions.
International AI Safety Report 2025: First Key Update: Capabilities and Risk Implications
Venue: arXiv cs.CY 2025
Year: 2025

Since the publication of the first International AI Safety Report, AI capabilities have continued to improve across key domains. New training techniques that teach AI systems to reason step-by-step and inference-time enhancements have primarily driven these advances, rather than simply training larger models. As a result, general-purpose AI systems can solve more complex problems in a range of domains, from scientific research to software development. Their performance on benchmarks that measure performance in coding, mathematics, and answering expert-level science questions has continued to improve, though reliability challenges persist, with systems excelling on some tasks while failing completely on others. These capability improvements also have implications for multiple risks, including risks from biological weapons and cyber attacks. Finally, they pose new challenges for monitoring and controllability. This update examines how AI capabilities have improved since the first Report, then focuses on key risk areas where substantial new evidence warrants updated assessments.
FlashWorld: High-quality 3D Scene Generation within Seconds
Venue: arXiv cs.CV 2025
Year: 2025

We propose FlashWorld, a generative model that produces 3D scenes from a single image or text prompt in seconds, 10~100$\times$ faster than previous works while possessing superior rendering quality. Our approach shifts from the conventional multi-view-oriented (MV-oriented) paradigm, which generates multi-view images for subsequent 3D reconstruction, to a 3D-oriented approach where the model directly produces 3D Gaussian representations during multi-view generation. While ensuring 3D consistency, 3D-oriented method typically suffers poor visual quality. FlashWorld includes a dual-mode pre-training phase followed by a cross-mode post-training phase, effectively integrating the strengths of both paradigms. Specifically, leveraging the prior from a video diffusion model, we first pre-train a dual-mode multi-view diffusion model, which jointly supports MV-oriented and 3D-oriented generation modes. To bridge the quality gap in 3D-oriented generation, we further propose a cross-mode post-training distillation by matching distribution from consistent 3D-oriented mode to high-quality MV-oriented mode. This not only enhances visual quality while maintaining 3D consistency, but also reduces the required denoising steps for inference. Also, we propose a strategy to leverage massive single-view images and text prompts during this process to enhance the model's generalization to out-of-distribution inputs. Extensive experiments demonstrate the superiority and efficiency of our method.