OpenRaiser/Intern-Atlas huggingface dataset:
Multiparty Selective Disclosure using Attribute-Based Encryption
Venue: arXiv cs.CR 2025
Year: 2025

This study proposes a mechanism for encrypting SD-JWT (Selective Disclosure JSON Web Token) Disclosures using Attribute-Based Encryption (ABE) to enable flexible access control on the basis of the Verifier's attributes. By integrating Ciphertext-Policy ABE (CP-ABE) into the existing SD-JWT framework, the Holder can assign decryption policies to Disclosures, ensuring information is selectively disclosed. The mechanism's feasibility was evaluated in a virtualized environment by measuring the processing times for SD-JWT generation, encryption, and decryption with varying Disclosure counts (5, 10, 20). Results showed that SD-JWT generation is lightweight, while encryption and decryption times increase linearly with the number of Disclosures. This approach is suitable for privacy-sensitive applications like healthcare, finance, and supply chain tracking but requires optimization for real-time use cases such as IoT. Future research should focus on improving ABE efficiency and addressing scalability challenges.
Unencrypted Flying Objects: Security Lessons from University Small Satellite Developers and Their Code
Venue: arXiv cs.CR 2025
Year: 2025

Satellites face a multitude of security risks that set them apart from hardware on Earth. Small satellites may face additional challenges, as they are often developed on a budget and by amateur organizations or universities that do not consider security. We explore the security practices and preferences of small satellite teams, particularly university satellite teams, to understand what barriers exist to building satellites securely. We interviewed 8 university satellite club leaders across 4 clubs in the U.S. and perform a code audit of 3 of these clubs' code repositories. We find that security practices vary widely across teams, but all teams studied had vulnerabilities available to an unprivileged, ground-based attacker. Participants foresee many risks of unsecured small satellites and indicate security shortcomings in industry and government. Lastly, we identify a set of considerations for how to build future small satellites securely, in amateur organizations and beyond.
Positioning Monocular Optical See Through Head Worn Displays in Glasses for Everyday Wear
Venue: arXiv cs.HC 2025
Year: 2025

Head-worn displays for everyday wear in the form of regular eyeglasses are technically feasible with recent advances in waveguide technology. One major design decision is determining where in the user's visual field to position the display. Centering the display in the principal point of gaze (PPOG) allows the user to switch attentional focus between the virtual and real images quickly, and best performance often occurs when the display is centered in PPOG or is centered vertically below PPOG. However, these positions are often undesirable in that they are considered interruptive or are associated with negative social perceptions by users. Offsetting the virtual image may be preferred when tasks involve driving, walking, or social interaction. This paper consolidates findings from recent studies on monocular optical see-through HWDs (OST-HWDs), focusing on potential for interruption, comfort, performance, and social perception. For text-based tasks, which serve as a proxy for many monocular OST-HWD tasks, we recommend a 15° horizontal field of view (FOV) with the virtual image in the right lens vertically centered but offset to +8.7° to +23.7° toward the ear. Glanceable content can be offset up to +30° for short interactions.
Variational Prefix Tuning for Diverse and Accurate Code Summarization Using Pre-trained Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Recent advancements in source code summarization have leveraged transformer-based pre-trained models, including Large Language Models of Code (LLMCs), to automate and improve the generation of code summaries. However, existing methods often focus on generating a single high-quality summary for a given source code, neglecting scenarios where the generated summary might be inadequate and alternative options are needed. In this paper, we introduce Variational Prefix Tuning (VPT), a novel approach that enhances pre-trained models' ability to generate diverse yet accurate sets of summaries, allowing the user to choose the most suitable one for the given source code. Our method integrates a Conditional Variational Autoencoder (CVAE) framework as a modular component into pre-trained models, enabling us to model the distribution of observed target summaries and sample continuous embeddings to be used as prefixes to steer the generation of diverse outputs during decoding. Importantly, we construct our method in a parameter-efficient manner, eliminating the need for expensive model retraining, especially when using LLMCs. Furthermore, we employ a bi-criteria reranking method to select a subset of generated summaries, optimizing both the diversity and the accuracy of the options presented to users. We present extensive experimental evaluations using widely used datasets and current state-of-the-art pre-trained code summarization models to demonstrate the effectiveness of our approach and its adaptability across models.
S-DAT: A Multilingual, GenAI-Driven Framework for Automated Divergent Thinking Assessment
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces S-DAT (Synthetic-Divergent Association Task), a scalable, multilingual framework for automated assessment of divergent thinking (DT) -a core component of human creativity. Traditional creativity assessments are often labor-intensive, language-specific, and reliant on subjective human ratings, limiting their scalability and cross-cultural applicability. In contrast, S-DAT leverages large language models and advanced multilingual embeddings to compute semantic distance -- a language-agnostic proxy for DT. We evaluate S-DAT across eleven diverse languages, including English, Spanish, German, Russian, Hindi, and Japanese (Kanji, Hiragana, Katakana), demonstrating robust and consistent scoring across linguistic contexts. Unlike prior DAT approaches, the S-DAT shows convergent validity with other DT measures and correct discriminant validity with convergent thinking. This cross-linguistic flexibility allows for more inclusive, global-scale creativity research, addressing key limitations of earlier approaches. S-DAT provides a powerful tool for fairer, more comprehensive evaluation of cognitive flexibility in diverse populations and can be freely assessed online: https://sdat.iol.zib.de/.
Imitation Learning for Adaptive Control of a Virtual Soft Exoglove
Venue: arXiv cs.RO 2025
Year: 2025

The use of wearable robots has been widely adopted in rehabilitation training for patients with hand motor impairments. However, the uniqueness of patients' muscle loss is often overlooked. Leveraging reinforcement learning and a biologically accurate musculoskeletal model in simulation, we propose a customized wearable robotic controller that is able to address specific muscle deficits and to provide compensation for hand-object manipulation tasks. Video data of a same subject performing human grasping tasks is used to train a manipulation model through learning from demonstration. This manipulation model is subsequently fine-tuned to perform object-specific interaction tasks. The muscle forces in the musculoskeletal manipulation model are then weakened to simulate neurological motor impairments, which are later compensated by the actuation of a virtual wearable robotics glove. Results shows that integrating the virtual wearable robotic glove provides shared assistance to support the hand manipulator with weakened muscle forces. The learned exoglove controller achieved an average of 90.5\% of the original manipulation proficiency.
Air-Ground Collaboration for Language-Specified Missions in Unknown Environments
Venue: arXiv cs.RO 2025
Year: 2025

As autonomous robotic systems become increasingly mature, users will want to specify missions at the level of intent rather than in low-level detail. Language is an expressive and intuitive medium for such mission specification. However, realizing language-guided robotic teams requires overcoming significant technical hurdles. Interpreting and realizing language-specified missions requires advanced semantic reasoning. Successful heterogeneous robots must effectively coordinate actions and share information across varying viewpoints. Additionally, communication between robots is typically intermittent, necessitating robust strategies that leverage communication opportunities to maintain coordination and achieve mission objectives. In this work, we present a first-of-its-kind system where an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) are able to collaboratively accomplish missions specified in natural language while reacting to changes in specification on the fly. We leverage a Large Language Model (LLM)-enabled planner to reason over semantic-metric maps that are built online and opportunistically shared between an aerial and a ground robot. We consider task-driven navigation in urban and rural areas. Our system must infer mission-relevant semantics and actively acquire information via semantic mapping. In both ground and air-ground teaming experiments, we demonstrate our system on seven different natural-language specifications at up to kilometer-scale navigation.
Sequential Treatment Effect Estimation with Unmeasured Confounders
Venue: arXiv cs.LG 2025
Year: 2025

This paper studies the cumulative causal effects of sequential treatments in the presence of unmeasured confounders. It is a critical issue in sequential decision-making scenarios where treatment decisions and outcomes dynamically evolve over time. Advanced causal methods apply transformer as a backbone to model such time sequences, which shows superiority in capturing long time dependence and periodic patterns via attention mechanism. However, even they control the observed confounding, these estimators still suffer from unmeasured confounders, which influence both treatment assignments and outcomes. How to adjust the latent confounding bias in sequential treatment effect estimation remains an open challenge. Therefore, we propose a novel Decomposing Sequential Instrumental Variable framework for CounterFactual Regression (DSIV-CFR), relying on a common negative control assumption. Specifically, an instrumental variable (IV) is a special negative control exposure, while the previous outcome serves as a negative control outcome. This allows us to recover the IVs latent in observation variables and estimate sequential treatment effects via a generalized moment condition. We conducted experiments on 4 datasets and achieved significant performance in one- and multi-step prediction, supported by which we can identify optimal treatments for dynamic systems.
PreCare: Designing AI Assistants for Advance Care Planning (ACP) to Enhance Personal Value Exploration, Patient Knowledge, and Decisional Confidence
Venue: arXiv cs.HC 2025
Year: 2025

Advance Care Planning (ACP) allows individuals to specify their preferred end-of-life life-sustaining treatments before they become incapacitated by injury or terminal illness (e.g., coma, cancer, dementia). While online ACP offers high accessibility, it lacks key benefits of clinical consultations, including personalized value exploration, immediate clarification of decision consequences. To bridge this gap, we conducted two formative studies: 1) shadowed and interviewed 3 ACP teams consisting of physicians, nurses, and social workers (18 patients total), and 2) interviewed 14 users of ACP websites. Building on these insights, we designed PreCare in collaboration with 6 ACP professionals. PreCare is a website with 3 AI-driven assistants designed to guide users through exploring personal values, gaining ACP knowledge, and supporting informed decision-making. A usability study (n=12) showed that PreCare achieved a System Usability Scale (SUS) rating of excellent. A comparative evaluation (n=12) showed that PreCare's AI assistants significantly improved exploration of personal values, knowledge, and decisional confidence, and was preferred by 92% of participants.
AMSnet 2.0: A Large AMS Database with AI Segmentation for Net Detection
Venue: arXiv cs.CV 2025
Year: 2025

Current multimodal large language models (MLLMs) struggle to understand circuit schematics due to their limited recognition capabilities. This could be attributed to the lack of high-quality schematic-netlist training data. Existing work such as AMSnet applies schematic parsing to generate netlists. However, these methods rely on hard-coded heuristics and are difficult to apply to complex or noisy schematics in this paper. We therefore propose a novel net detection mechanism based on segmentation with high robustness. The proposed method also recovers positional information, allowing digital reconstruction of schematics. We then expand AMSnet dataset with schematic images from various sources and create AMSnet 2.0. AMSnet 2.0 contains 2,686 circuits with schematic images, Spectre-formatted netlists, OpenAccess digital schematics, and positional information for circuit components and nets, whereas AMSnet only includes 792 circuits with SPICE netlists but no digital schematics.
Theoretical and Experimental Assessment of Large Beam Codebook at mmWave Devices: How Much is Enough?
Venue: arXiv cs.IT 2025
Year: 2025

Modern millimeter wave (mmWave) transceivers come with a large number of antennas, each of which can support thousands of phase shifter configurations. This capability enables beam sweeping with fine angular resolution, but results in large codebook sizes that can span more than six orders of magnitude. On the other hand, the mobility of user terminals and their randomly changing orientations require constantly adjusting the beam direction. A key focus of recent research has been on the design of beam sweeping codebooks that balance a trade-off between the achievable gain and the beam search time, governed by the codebook size. In this paper, we investigate the extent to which a large codebook can be reduced to fewer steering vectors while covering the entire angular space and maintaining performance close to the maximum array gain. We derive a closed-form expression for the angular coverage range of a steering vector, subject to maintaining a gain loss within \(γ\) dB (e.g., 2\, dB) with respect to the maximum gain achieved by an infinitely large codebook. We demonstrate, both theoretically and experimentally, that a large beam-steering codebooks (such as the \(1024^{16}\) set considered in our experiment) can be reduced to just a few steering vectors. This framework serves as a proof that only a few steering vectors are sufficient to achieve near-maximum gain, challenging the common belief that a large codebook with fine angular resolution is essential to fully reap the benefits of an antenna array.
Educational impacts of generative artificial intelligence on learning and performance of engineering students in China
Venue: arXiv cs.HC 2025
Year: 2025

With the rapid advancement of generative artificial intelligence(AI), its potential applications in higher education have attracted significant attention. This study investigated how 148 students from diverse engineering disciplines and regions across China used generative AI, focusing on its impact on their learning experience and the opportunities and challenges it poses in engineering education. Based on the surveyed data, we explored four key areas: the frequency and application scenarios of AI use among engineering students, its impact on students' learning and performance, commonly encountered challenges in using generative AI, and future prospects for its adoption in engineering education. The results showed that more than half of the participants reported a positive impact of generative AI on their learning efficiency, initiative, and creativity, with nearly half believing it also enhanced their independent thinking. However, despite acknowledging improved study efficiency, many felt their actual academic performance remained largely unchanged and expressed concerns about the accuracy and domain-specific reliability of generative AI. Our findings provide a first-hand insight into the current benefits and challenges generative AI brings to students, particularly Chinese engineering students, while offering several recommendations, especially from the students' perspective, for effectively integrating generative AI into engineering education.
Towards Efficient Verification of Parallel Applications with Mc SimGrid
Venue: arXiv cs.DC 2025
Year: 2025

Assessing the correctness of distributed and parallel applications is notoriously difficult due to the complexity of the concurrent behaviors and the difficulty to reproduce bugs. In this context, Dynamic Partial Order Reduction (DPOR) techniques have proved successful in exploiting concurrency to verify applications without exploring all their behaviors. However, they may lack of efficiency when tracking non-systematic bugs of real size applications. In this paper, we suggest two adaptations of the Optimal Dynamic Partial Order Reduction (ODPOR) algorithm with a particular focus on bug finding and explanation. The first adaptation is an out-of-order version called RFS ODPOR which avoids being stuck in uninteresting large parts of the state space. Once a bug is found, the second adaptation takes advantage of ODPOR principles to efficiently find the origins of the bug.
Approximate Cartesian Tree Matching with One Difference
Venue: arXiv cs.DS 2025
Year: 2025

Cartesian tree pattern matching consists of finding all the factors of a text that have the same Cartesian tree than a given pattern. There already exist theoretical and practical solutions for the exact case. In this paper, we propose the first algorithms for solving approximate Cartesian tree pattern matching with one difference given a pattern of length m and a text of length n. We present a generic algorithm that find all the factors of the text that have the same Cartesian tree of the pattern with one difference, using different notions of differences. We show that this algorithm has a O(nM) worst-case complexity and that, for several random models, the algorithm has a linear average-case complexity. We also present an automaton based algorithm, adapting [PALP19], that can be generalized to deal with more than one difference.
Stable and Convexified Information Bottleneck Optimization via Symbolic Continuation and Entropy-Regularized Trajectories
Venue: arXiv cs.LG 2025
Year: 2025

The Information Bottleneck (IB) method frequently suffers from unstable optimization, characterized by abrupt representation shifts near critical points of the IB trade-off parameter, beta. In this paper, I introduce a novel approach to achieve stable and convex IB optimization through symbolic continuation and entropy-regularized trajectories. I analytically prove convexity and uniqueness of the IB solution path when an entropy regularization term is included, and demonstrate how this stabilizes representation learning across a wide range of \b{eta} values. Additionally, I provide extensive sensitivity analyses around critical points (beta) with statistically robust uncertainty quantification (95% confidence intervals). The open-source implementation, experimental results, and reproducibility framework included in this work offer a clear path for practical deployment and future extension of my proposed method.
Zero-Shot Multi-modal Large Language Model v.s. Supervised Deep Learning: A Comparative Study on CT-Based Intracranial Hemorrhage Subtyping
Venue: arXiv cs.CV 2025
Year: 2025

Introduction: Timely identification of intracranial hemorrhage (ICH) subtypes on non-contrast computed tomography is critical for prognosis prediction and therapeutic decision-making, yet remains challenging due to low contrast and blurring boundaries. This study evaluates the performance of zero-shot multi-modal large language models (MLLMs) compared to traditional deep learning methods in ICH binary classification and subtyping. Methods: We utilized a dataset provided by RSNA, comprising 192 NCCT volumes. The study compares various MLLMs, including GPT-4o, Gemini 2.0 Flash, and Claude 3.5 Sonnet V2, with conventional deep learning models, including ResNet50 and Vision Transformer. Carefully crafted prompts were used to guide MLLMs in tasks such as ICH presence, subtype classification, localization, and volume estimation. Results: The results indicate that in the ICH binary classification task, traditional deep learning models outperform MLLMs comprehensively. For subtype classification, MLLMs also exhibit inferior performance compared to traditional deep learning models, with Gemini 2.0 Flash achieving an macro-averaged precision of 0.41 and a macro-averaged F1 score of 0.31. Conclusion: While MLLMs excel in interactive capabilities, their overall accuracy in ICH subtyping is inferior to deep networks. However, MLLMs enhance interpretability through language interactions, indicating potential in medical imaging analysis. Future efforts will focus on model refinement and developing more precise MLLMs to improve performance in three-dimensional medical image processing.
Moving towards informative and actionable social media research
Venue: arXiv cs.SI 2025
Year: 2025

Social media is nearly ubiquitous in modern life, raising concerns about its societal impacts-from mental health and polarization to violence and democratic disruption. Yet research on its causal effects remains inconclusive: observational studies often find concerning associations, while randomized controlled trials (RCTs) tend to yield small, conflicting, or null results. Literature summaries tend to causally prioritize findings from RCTs, often arguing that concerns about social media are overstated. However, like observational studies, RCTs rely on assumptions that can easily be violated in the context of social media, especially regarding societal outcomes at scale. Here, we enumerate and examine the features of social media as a complex system that challenge our ability to infer causality at societal scales. Drawing on insight from disciplines that have faced similar challenges, like climate-science or epidemiology, we propose a path forward that combines the strength of observational and experimental approaches while acknowledging the limitations of each.
A drone that learns to efficiently find objects in agricultural fields: from simulation to the real world
Venue: arXiv cs.RO 2025
Year: 2025

Drones are promising for data collection in precision agriculture, however, they are limited by their battery capacity. Efficient path planners are therefore required. This paper presents a drone path planner trained using Reinforcement Learning (RL) on an abstract simulation that uses object detections and uncertain prior knowledge. The RL agent controls the flight direction and can terminate the flight. By using the agent in combination with the drone's flight controller and a detection network to process camera images, it is possible to evaluate the performance of the agent on real-world data. In simulation, the agent yielded on average a 78% shorter flight path compared to a full coverage planner, at the cost of a 14% lower recall. On real-world data, the agent showed a 72% shorter flight path compared to a full coverage planner, however, at the cost of a 25% lower recall. The lower performance on real-world data was attributed to the real-world object distribution and the lower accuracy of prior knowledge, and shows potential for improvement. Overall, we concluded that for applications where it is not crucial to find all objects, such as weed detection, the learned-based path planner is suitable and efficient.