OpenRaiser/Intern-Atlas huggingface dataset:
Embodiment Transfer Learning for Vision-Language-Action Models
Venue: arXiv cs.RO 2025
Year: 2025

Vision-language-action (VLA) models have significantly advanced robotic learning, enabling training on large-scale, cross-embodiment data and fine-tuning for specific robots. However, state-of-the-art autoregressive VLAs struggle with multi-robot collaboration. We introduce embodiment transfer learning, denoted as ET-VLA, a novel framework for efficient and effective transfer of pre-trained VLAs to multi-robot. ET-VLA's core is Synthetic Continued Pretraining (SCP), which uses synthetically generated data to warm up the model for the new embodiment, bypassing the need for real human demonstrations and reducing data collection costs. SCP enables the model to learn correct actions and precise action token numbers. Following SCP, the model is fine-tuned on target embodiment data. To further enhance the model performance on multi-embodiment, we present the Embodied Graph-of-Thought technique, a novel approach that formulates each sub-task as a node, that allows the VLA model to distinguish the functionalities and roles of each embodiment during task execution. Our work considers bimanual robots, a simple version of multi-robot to verify our approaches. We validate the effectiveness of our method on both simulation benchmarks and real robots covering three different bimanual embodiments. In particular, our proposed ET-VLA \space can outperform OpenVLA on six real-world tasks over 53.2%. We will open-source all codes to support the community in advancing VLA models for robot learning.
WindMiL: Equivariant Graph Learning for Wind Loading Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of wind loading on buildings is crucial for structural safety and sustainable design, yet conventional approaches such as wind tunnel testing and large-eddy simulation (LES) are prohibitively expensive for large-scale exploration. Each LES case typically requires at least 24 hours of computation, making comprehensive parametric studies infeasible. We introduce WindMiL, a new machine learning framework that combines systematic dataset generation with symmetry-aware graph neural networks (GNNs). First, we introduce a large-scale dataset of wind loads on low-rise buildings by applying signed distance function interpolation to roof geometries and simulating 462 cases with LES across varying shapes and wind directions. Second, we develop a reflection-equivariant GNN that guarantees physically consistent predictions under mirrored geometries. Across interpolation and extrapolation evaluations, WindMiL achieves high accuracy for both the mean and the standard deviation of surface pressure coefficients (e.g., RMSE $\leq 0.02$ for mean $C_p$) and remains accurate under reflected-test evaluation, maintaining hit rates above $96\%$ where the non-equivariant baseline model drops by more than $10\%$. By pairing a systematic dataset with an equivariant surrogate, WindMiL enables efficient, scalable, and accurate predictions of wind loads on buildings.
A Saddle Point Remedy: Power of Variable Elimination in Non-convex Optimization
Venue: arXiv cs.LG 2025
Year: 2025

The proliferation of saddle points, rather than poor local minima, is increasingly understood to be a primary obstacle in large-scale non-convex optimization for machine learning. Variable elimination algorithms, like Variable Projection (VarPro), have long been observed to exhibit superior convergence and robustness in practice, yet a principled understanding of why they so effectively navigate these complex energy landscapes has remained elusive. In this work, we provide a rigorous geometric explanation by comparing the optimization landscapes of the original and reduced formulations. Through a rigorous analysis based on Hessian inertia and the Schur complement, we prove that variable elimination fundamentally reshapes the critical point structure of the objective function, revealing that local maxima in the reduced landscape are created from, and correspond directly to, saddle points in the original formulation. Our findings are illustrated on the canonical problem of non-convex matrix factorization, visualized directly on two-parameter neural networks, and finally validated in training deep Residual Networks, where our approach yields dramatic improvements in stability and convergence to superior minima. This work goes beyond explaining an existing method; it establishes landscape simplification via saddle point transformation as a powerful principle that can guide the design of a new generation of more robust and efficient optimization algorithms.
Fault-Tolerant Approximate Distance Oracles with a Source Set
Venue: arXiv cs.DS 2025
Year: 2025

Our input is an undirected weighted graph $G = (V,E)$ on $n$ vertices along with a source set $S\subseteq V$. The problem is to preprocess $G$ and build a compact data structure such that upon query $Qu(s,v,f)$ where $(s,v) \in S\times V$ and $f$ is any faulty edge, we can quickly find a good estimate (i.e., within a small multiplicative stretch) of the $s$-$v$ distance in $G-f$. The work of Bil{ò} et al. (Algorithmica 2022) on multiple-edge fault-tolerant approximate shortest path trees implies a compact oracle for the above problem with a stretch of at most 3 and with query answering time $O(\log^2 n)$. We show a very simple construction of an $S\times V$ approximate distance oracle with $O(1)$ query answering time; its size is $\widetilde{O}(|S|n + n^{3/2})$ and multiplicative stretch is at most 5. A single-edge fault-tolerant $ST$-distance oracle from the work of Bil{ò} et al. (STACS 2018) plays a key role in our construction. We also give a construction of a fault-tolerant $S \times V$ approximate distance oracle of size $\widetilde{O}(|S|n + n^{4/3})$ with multiplicative stretch at most 13 and as before, with $O(1)$ query answering time.
Improving Needle Penetration via Precise Rotational Insertion Using Iterative Learning Control
Venue: arXiv cs.RO 2025
Year: 2025

Achieving precise control of robotic tool paths is often challenged by inherent system misalignments, unmodeled dynamics, and actuation inaccuracies. This work introduces an Iterative Learning Control (ILC) strategy to enable precise rotational insertion of a tool during robotic surgery, improving penetration efficacy and safety compared to straight insertion tested in subretinal injection. A 4 degree of freedom (DOF) robot manipulator is used, where misalignment of the fourth joint complicates the simple application of needle rotation, motivating an ILC approach that iteratively adjusts joint commands based on positional feedback. The process begins with calibrating the forward kinematics for the chosen surgical tool to achieve higher accuracy, followed by successive ILC iterations guided by Optical Coherence Tomography (OCT) volume scans to measure the error and refine control inputs. Experimental results, tested on subretinal injection tasks on ex vivo pig eyes, show that the optimized trajectory resulted in higher success rates in tissue penetration and subretinal injection compared to straight insertion, demonstrating the effectiveness of ILC in overcoming misalignment challenges. This approach offers potential applications for other high precision robot tasks requiring controlled insertions as well.
Graph Neural Network-Based Semi-Supervised Open-Set Fault Diagnosis for Marine Machinery Systems
Venue: arXiv cs.AI 2025
Year: 2025

Recently, fault diagnosis methods for marine machinery systems based on deep learning models have attracted considerable attention in the shipping industry. Most existing studies assume fault classes are consistent and known between the training and test datasets, and these methods perform well under controlled environment. In practice, however, previously unseen or unknown fault types (i.e., out-of-distribution or open-set observations not present during training) can occur, causing such methods to fail and posing a significant challenge to their widespread industrial deployment. To address this challenge, this paper proposes a semi-supervised open-set fault diagnosis (SOFD) framework that enhances and extends the applicability of deep learning models in open-set fault diagnosis scenarios. The framework includes a reliability subset construction process, which uses a multi-layer fusion feature representation extracted by a supervised feature learning model to select an unlabeled test subset. The labeled training set and pseudo-labeled test subset are then fed into a semi-supervised diagnosis model to learn discriminative features for each class, enabling accurate classification of known faults and effective detection of unknown samples. Experimental results on a public maritime benchmark dataset demonstrate the effectiveness and superiority of the proposed SOFD framework.
AraFinNews: Arabic Financial Summarisation with Domain-Adapted LLMs
Venue: arXiv cs.CL 2025
Year: 2025

We introduce AraFinNews, the largest publicly available Arabic financial news dataset to date, comprising 212,500 article-headline pairs spanning a decade of reporting from 2015 to 2025. Designed as an Arabic counterpart to major English summarisation corpora such as CNN/DailyMail, AraFinNews provides a realistic benchmark for evaluating domain-specific language understanding and generation in financial contexts. Using this resource, we investigate the impact of domain specificity on abstractive summarisation of Arabic financial texts with large language models (LLMs). In particular, we evaluate transformer-based models: mT5, AraT5, and the domain-adapted FinAraT5 to examine how financial-domain pretraining influences accuracy, numerical reliability, and stylistic alignment with professional reporting. Experimental results show that domain-adapted models generate more coherent summaries, especially in their handling of quantitative and entity-centric information. These findings highlight the importance of domain-specific adaptation for improving narrative fluency in Arabic financial summarisation. The dataset is freely available for non-commercial research at https://github.com/ArabicNLP-uk/AraFinNews.
Design and Fabrication of Origami-Inspired Knitted Fabrics for Soft Robotics
Venue: arXiv cs.RO 2025
Year: 2025

Soft robots employing compliant materials and deformable structures offer great potential for wearable devices that are comfortable and safe for human interaction. However, achieving both structural integrity and compliance for comfort remains a significant challenge. In this study, we present a novel fabrication and design method that combines the advantages of origami structures with the material programmability and wearability of knitted fabrics. We introduce a general design method that translates origami patterns into knit designs by programming both stitch and material patterns. The method creates folds in preferred directions while suppressing unintended buckling and bending by selectively incorporating heat fusible yarn to create rigid panels around compliant creases. We experimentally quantify folding moments and show that stitch patterning enhances folding directionality while the heat fusible yarn (1) keeps geometry consistent by reducing edge curl and (2) prevents out-of-plane deformations by stiffening panels. We demonstrate the framework through the successful reproduction of complex origami tessellations, including Miura-ori, Yoshimura, and Kresling patterns, and present a wearable knitted Kaleidocycle robot capable of locomotion. The combination of structural reconfigurability, material programmability, and potential for manufacturing scalability highlights knitted origami as a promising platform for next-generation wearable robotics.
Adversarial Spatio-Temporal Attention Networks for Epileptic Seizure Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Forecasting epileptic seizures from multivariate EEG signals represents a critical challenge in healthcare time series prediction, requiring high sensitivity, low false alarm rates, and subject-specific adaptability. We present STAN, an Adversarial Spatio-Temporal Attention Network that jointly models spatial brain connectivity and temporal neural dynamics through cascaded attention blocks with alternating spatial and temporal modules. Unlike existing approaches that assume fixed preictal durations or separately process spatial and temporal features, STAN captures bidirectional dependencies between spatial and temporal patterns through a unified cascaded architecture. Adversarial training with gradient penalty enables robust discrimination between interictal and preictal states learned from clearly defined 15-minute preictal windows. Continuous 90-minute pre-seizure monitoring reveals that the learned spatio-temporal attention patterns enable early detection: reliable alarms trigger at subject-specific times (typically 15-45 minutes before onset), reflecting the model's capacity to capture subtle preictal dynamics without requiring individualized training. Experiments on two benchmark EEG datasets (CHB-MIT scalp: 8 subjects, 46 events; MSSM intracranial: 4 subjects, 14 events) demonstrate state-of-the-art performance: 96.6% sensitivity with 0.011 false detections per hour and 94.2% sensitivity with 0.063 false detections per hour, respectively, while maintaining computational efficiency (2.3M parameters, 45 ms latency, 180 MB memory) for real-time edge deployment. Beyond epilepsy, the proposed framework provides a general paradigm for spatio-temporal forecasting in healthcare and other time series domains where individual heterogeneity and interpretability are crucial.
"Give a Positive Review Only": An Early Investigation Into In-Paper Prompt Injection Attacks and Defenses for AI Reviewers
Venue: arXiv cs.CL 2025
Year: 2025

With the rapid advancement of AI models, their deployment across diverse tasks has become increasingly widespread. A notable emerging application is leveraging AI models to assist in reviewing scientific papers. However, recent reports have revealed that some papers contain hidden, injected prompts designed to manipulate AI reviewers into providing overly favorable evaluations. In this work, we present an early systematic investigation into this emerging threat. We propose two classes of attacks: (1) static attack, which employs a fixed injection prompt, and (2) iterative attack, which optimizes the injection prompt against a simulated reviewer model to maximize its effectiveness. Both attacks achieve striking performance, frequently inducing full evaluation scores when targeting frontier AI reviewers. Furthermore, we show that these attacks are robust across various settings. To counter this threat, we explore a simple detection-based defense. While it substantially reduces the attack success rate, we demonstrate that an adaptive attacker can partially circumvent this defense. Our findings underscore the need for greater attention and rigorous safeguards against prompt-injection threats in AI-assisted peer review.
A High-Speed Capable Spherical Robot
Venue: arXiv cs.RO 2025
Year: 2025

This paper designs a new spherical robot structure capable of supporting high-speed motion at up to 10 m/s. Building upon a single-pendulum-driven spherical robot, the design incorporates a momentum wheel with an axis aligned with the secondary pendulum, creating a novel spherical robot structure. Practical experiments with the physical prototype have demonstrated that this new spherical robot can achieve stable high-speed motion through simple decoupled control, which was unattainable with the original structure. The spherical robot designed for high-speed motion not only increases speed but also significantly enhances obstacle-crossing performance and terrain robustness.
CSMD: Curated Multimodal Dataset for Chinese Stock Analysis
Venue: arXiv cs.CE 2025
Year: 2025

The stock market is a complex and dynamic system, where it is non-trivial for researchers and practitioners to uncover underlying patterns and forecast stock movements. The existing studies for stock market analysis rely on leveraging various types of information to extract useful factors, which are highly conditional on the quality of the data used. However, the currently available resources are mainly based on the U.S. stock market in English, which is inapplicable to adapt to other countries. To address these issues, we propose CSMD, a multimodal dataset curated specifically for analyzing the Chinese stock market with meticulous processing for validated quality. In addition, we develop a lightweight and user-friendly framework LightQuant for researchers and practitioners with expertise in financial domains. Experimental results on top of our datasets and framework with various backbone models demonstrate their effectiveness compared with using existing datasets. The datasets and code are publicly available at the link: https://github.com/ECNU-CILAB/LightQuant.
RDTE-UNet: A Boundary and Detail Aware UNet for Precise Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Medical image segmentation is essential for computer-assisted diagnosis and treatment planning, yet substantial anatomical variability and boundary ambiguity hinder reliable delineation of fine structures. We propose RDTE-UNet, a segmentation network that unifies local modeling with global context to strengthen boundary delineation and detail preservation. RDTE-UNet employs a hybrid ResBlock detail-aware Transformer backbone and three modules: ASBE for adaptive boundary enhancement, HVDA for fine-grained feature modeling, and EulerFF for fusion weighting guided by Euler's formula. Together, these components improve structural consistency and boundary accuracy across morphology, orientation, and scale. On Synapse and BUSI dataset, RDTE-UNet has achieved a comparable level in terms of segmentation accuracy and boundary quality.
Beyond Permissions: Investigating Mobile Personalization with Simulated Personas
Venue: arXiv cs.HC 2025
Year: 2025

Mobile applications increasingly rely on sensor data to infer user context and deliver personalized experiences. Yet the mechanisms behind this personalization remain opaque to users and researchers alike. This paper presents a sandbox system that uses sensor spoofing and persona simulation to audit and visualize how mobile apps respond to inferred behaviors. Rather than treating spoofing as adversarial, we demonstrate its use as a tool for behavioral transparency and user empowerment. Our system injects multi-sensor profiles - generated from structured, lifestyle-based personas - into Android devices in real time, enabling users to observe app responses to contexts such as high activity, location shifts, or time-of-day changes. With automated screenshot capture and GPT-4 Vision-based UI summarization, our pipeline helps document subtle personalization cues. Preliminary findings show measurable app adaptations across fitness, e-commerce, and everyday service apps such as weather and navigation. We offer this toolkit as a foundation for privacy-enhancing technologies and user-facing transparency interventions.
Design and development of an electronics-free earthworm robot
Venue: arXiv cs.RO 2025
Year: 2025

Soft robotic systems have gained widespread attention due to their inherent flexibility, adaptability, and safety, making them well-suited for varied applications. Among bioinspired designs, earthworm locomotion has been extensively studied for its efficient peristaltic motion, enabling movement in confined and unstructured environments. Existing earthworm-inspired robots primarily utilize pneumatic actuation due to its high force-to-weight ratio and ease of implementation. However, these systems often rely on bulky, power-intensive electronic control units, limiting their practicality. In this work, we present an electronics-free, earthworm-inspired pneumatic robot utilizing a modified Pneumatic Logic Gate (PLG) design. By integrating preconfigured PLG units with bellow actuators, we achieved a plug-and-play style modular system capable of peristaltic locomotion without external electronic components. The proposed design reduces system complexity while maintaining efficient actuation. We characterize the bellow actuators under different operating conditions and evaluate the robots locomotion performance. Our findings demonstrate that the modified PLG-based control system effectively generates peristaltic wave propagation, achieving autonomous motion with minimal deviation. This study serves as a proof of concept for the development of electronics-free, peristaltic soft robots. The proposed system has potential for applications in hazardous environments, where untethered, adaptable locomotion is critical. Future work will focus on further optimizing the robot design and exploring untethered operation using onboard compressed air sources.
AI Literacy in UAE Libraries: Assessing Competencies, Training Needs, and Ethical Considerations for the Digital Age
Venue: arXiv cs.DL 2025
Year: 2025

The study explores the current state of artificial intelligence (AI) literacy levels among library professionals employing a quantitative approach consisting of 92 surveys of LIS professionals in the United Arab Emirates (UAE). Findings of the study revealed the presence of strong cognitive competencies, while there were gaps observed in behavioral and normative competencies, especially related to AI biases, AI-powered learning, and ethical considerations. There was a disconnect observed between the perceived importance of AI skills and the effectiveness of the current training programs.
A semantic-based deep learning approach for mathematical expression retrieval
Venue: arXiv cs.IR 2025
Year: 2025

Mathematical expressions (MEs) have complex two-dimensional structures in which symbols can be present at any nested depth like superscripts, subscripts, above, below etc. As MEs are represented using LaTeX format, several text retrieval methods based on string matching, vector space models etc., have also been applied for ME retrieval problem in the literature. As these methods are based on syntactic similarity, recently deep learning approaches based on embedding have been used for semantic similarity. In our present work, we have focused on the retrieval of mathematical expressions using deep learning approaches. In our approach, semantic features are extracted from the MEs using a deep recurrent neural network (DRNN) and these features have been used for matching and retrieval. We have trained the network for a classification task which determines the complexity of an ME. ME complexity has been quantified in terms of its nested depth. Based on the nested depth, we have considered three complexity classes of MEs: Simple, Medium and Complex. After training the network, outputs just before the the final fully connected layer are extracted for all the MEs. These outputs form the semantic features of MEs and are stored in a database. For a given ME query, its semantic features are computed using the trained DRNN and matched against the semantic feature database. Matching is performed based on the standard euclidean distance and top 'k' nearest matches are retrieved, where 'k' is a user-defined parameter. Our approach has been illustrated on a database of 829 MEs.