OpenRaiser/Intern-Atlas huggingface dataset:
Real-Time Privacy Risk Measurement with Privacy Tokens for Gradient Leakage
Venue: arXiv cs.LG 2025
Year: 2025

The widespread deployment of deep learning models in privacy-sensitive domains has amplified concerns regarding privacy risks, particularly those stemming from gradient leakage during training. Current privacy assessments primarily rely on post-training attack simulations. However, these methods are inherently reactive, unable to encompass all potential attack scenarios, and often based on idealized adversarial assumptions. These limitations underscore the need for proactive approaches to privacy risk assessment during the training process. To address this gap, we propose the concept of privacy tokens, which are derived directly from private gradients during training. Privacy tokens encapsulate gradient features and, when combined with data features, offer valuable insights into the extent of private information leakage from training data, enabling real-time measurement of privacy risks without relying on adversarial attack simulations. Additionally, we employ Mutual Information (MI) as a robust metric to quantify the relationship between training data and gradients, providing precise and continuous assessments of privacy leakage throughout the training process. Extensive experiments validate our framework, demonstrating the effectiveness of privacy tokens and MI in identifying and quantifying privacy risks. This proactive approach marks a significant advancement in privacy monitoring, promoting the safer deployment of deep learning models in sensitive applications.
LLM-KT: Aligning Large Language Models with Knowledge Tracing using a Plug-and-Play Instruction
Venue: arXiv cs.CL 2025
Year: 2025

The knowledge tracing (KT) problem is an extremely important topic in personalized education, which aims to predict whether students can correctly answer the next question based on their past question-answer records. Prior work on this task mainly focused on learning the sequence of behaviors based on the IDs or textual information. However, these studies usually fail to capture students' sufficient behavioral patterns without reasoning with rich world knowledge about questions. In this paper, we propose a large language models (LLMs)-based framework for KT, named \texttt{\textbf{LLM-KT}}, to integrate the strengths of LLMs and traditional sequence interaction models. For task-level alignment, we design Plug-and-Play instruction to align LLMs with KT, leveraging LLMs' rich knowledge and powerful reasoning capacity. For modality-level alignment, we design the plug-in context and sequence to integrate multiple modalities learned by traditional methods. To capture the long context of history records, we present a plug-in context to flexibly insert the compressed context embedding into LLMs using question-specific and concept-specific tokens. Furthermore, we introduce a plug-in sequence to enhance LLMs with sequence interaction behavior representation learned by traditional sequence models using a sequence adapter. Extensive experiments show that \texttt{\textbf{LLM-KT}} obtains state-of-the-art performance on four typical datasets by comparing it with approximately 20 strong baselines.
(Neural-Symbolic) Machine Learning for Inconsistency Measurement
Venue: arXiv cs.AI 2025
Year: 2025

We present machine-learning-based approaches for determining the \emph{degree} of inconsistency -- which is a numerical value -- for propositional logic knowledge bases. Specifically, we present regression- and neural-based models that learn to predict the values that the inconsistency measures $\incmi$ and $\incat$ would assign to propositional logic knowledge bases. Our main motivation is that computing these values conventionally can be hard complexity-wise. As an important addition, we use specific postulates, that is, properties, of the underlying inconsistency measures to infer symbolic rules, which we combine with the learning-based models in the form of constraints. We perform various experiments and show that a) predicting the degree values is feasible in many situations, and b) including the symbolic constraints deduced from the rationality postulates increases the prediction quality.
MobileA3gent: Training Mobile GUI Agents Using Decentralized Self-Sourced Data from Diverse Users
Venue: arXiv cs.AI 2025
Year: 2025

The advancement of mobile GUI agents has opened new opportunities for automating tasks on mobile devices. Training these agents requires large-scale high-quality data, which is prohibitively expensive when relying on human labor. Given the vast population of global mobile phone users, if automated data collection from them becomes feasible, the resulting data volume and the subsequently trained mobile agents could reach unprecedented levels. Nevertheless, two major challenges arise: (1) extracting user instructions without human intervention and (2) utilizing distributed user data while preserving privacy. To tackle these challenges, we propose MobileA3gent, a collaborative framework that trains mobile GUI Agents using decentralized self-sourced data from diverse users. The framework comprises two components, each targeting a specific challenge: (1) Auto-Annotation, which enables the automatic collection of high-quality datasets during users' routine phone usage with minimal cost. (2) FedVLM-A, which enhances federated VLM training under non-IID distributions by incorporating adapted global aggregation based on both episode-level and step-level variability. Extensive experiments prove that MobileA3gent achieves superior performance over traditional approaches at only 1% of the cost, highlighting its potential for real-world applications
The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation
Venue: arXiv cs.AI 2025
Year: 2025

In a widely popular analogy by Turing Award Laureate Yann LeCun, machine intelligence has been compared to cake - where unsupervised learning forms the base, supervised learning adds the icing, and reinforcement learning is the cherry on top. We expand this 'cake that is intelligence' analogy from a simple structural metaphor to the full life-cycle of AI systems, extending it to sourcing of ingredients (data), conception of recipes (instructions), the baking process (training), and the tasting and selling of the cake (evaluation and distribution). Leveraging our re-conceptualization, we describe each step's entailed social ramifications and how they are bounded by statistical assumptions within machine learning. Whereas these technical foundations and social impacts are deeply intertwined, they are often studied in isolation, creating barriers that restrict meaningful participation. Our re-conceptualization paves the way to bridge this gap by mapping where technical foundations interact with social outcomes, highlighting opportunities for cross-disciplinary dialogue. Finally, we conclude with actionable recommendations at each stage of the metaphorical AI cake's life-cycle, empowering prospective AI practitioners, users, and researchers, with increased awareness and ability to engage in broader AI discourse.
A Framework for IoT-Enabled Smart Manufacturing for Energy and Resource Optimization
Venue: arXiv cs.NI 2025
Year: 2025

The increasing demands for sustainable and efficient manufacturing systems have driven the integration of Internet of Things (IoT) technologies into smart manufacturing. This study investigates IoT-enabled systems designed to enhance energy efficiency and resource optimization in the manufacturing sector, focusing on a multi-layered architecture integrating sensors, edge computing, and cloud platforms. MATLAB Simulink was utilized for modeling and simulation, replicating typical manufacturing conditions to evaluate energy consumption, machine uptime, and resource usage. The results demonstrate an 18% reduction in energy consumption, a 22% decrease in machine downtime, and a 15% improvement in resource utilization. Comparative analyses highlight the superiority of the proposed framework in addressing operational inefficiencies and aligning with sustainability goals. The study underscores the potential of IoT in transforming traditional manufacturing into interconnected, intelligent systems, offering practical implications for industrial stakeholders aiming to optimize operations while adhering to global sustainability standards. Future work will focus on addressing identified challenges such as high deployment costs and data security concerns, aiming to facilitate the broader adoption of IoT in industrial applications. Keywords: IoT (Internet of Things), Smart Manufacturing, Energy Efficiency, Resource Optimization, Manufacturing
Scientometric Analysis of the German IR Community within TREC & CLEF
Venue: arXiv cs.IR 2025
Year: 2025

Within this study, the influence of the German Information Retrieval community on the retrieval campaigns Text Retrieval Conference (TREC) and Conference and Labs of the Evaluation Forum (CLEF) between 2000 and 2022 was analyzed based on metadata provided by OpenAlex and further metadata extracted with the GROBID framework from the publication's full texts. The analysis was conducted at the institutional and researcher levels. It was found that the German IR community, both on the author and institution level, mainly contributed to CLEF. Furthermore, it was shown that productivity follows the assumptions made by Lotka's Law.
Policies and Evaluation for Online Meeting Summarization
Venue: arXiv cs.CL 2025
Year: 2025

With more and more meetings moving to a digital domain, meeting summarization has recently gained interest in both academic and commercial research. However, prior academic research focuses on meeting summarization as an offline task, performed after the meeting concludes. In this paper, we perform the first systematic study of online meeting summarization. For this purpose, we propose several policies for conducting online summarization. We discuss the unique challenges of this task compared to the offline setting and define novel metrics to evaluate latency and partial summary quality. The experiments on the AutoMin dataset show that 1) online models can produce strong summaries, 2) our metrics allow a detailed analysis of different systems' quality-latency trade-off, also taking into account intermediate outputs and 3) adaptive policies perform better than fixed scheduled ones. These findings provide a starting point for the wider research community to explore this important task.
Solar Synergy: Innovative Strategies for Data Centers Energy Efficiency and Sustainability
Venue: arXiv cs.NI 2025
Year: 2025

One of the current trends related to data centers is providing it with renewable energy sources. This paper suggests an analysis technique for a model uses solar panels energy to power a data center consists of 100 traditional servers, physical infrastructures, 5 backup batteries. The analysis passes through three phases: Initially, the power consumption model of the data center is proposed to show the variation in traffic and total energy consumed. Then, a solar system model is designed according to the power needed. At the last phase, the desired battery capacity is chosen as (10000Ah-48V) to accommodate the solar energy production.
Improved Debordering of Waring Rank
Venue: arXiv cs.CC 2025
Year: 2025

We prove that if a degree-$d$ homogeneous polynomial $f$ has border Waring rank $\underline{\mathrm{WR}}({f}) = r$, then its Waring rank is bounded by \[ {\mathrm{WR}}({f}) \leq d \cdot r^{O(\sqrt{r})}. \] This result significantly improves upon the recent bound ${\mathrm{WR}}({f}) \leq d \cdot 4^r$ established in [Dutta, Gesmundo, Ikenmeyer, Jindal, and Lysikov, STACS 2024], which itself was an improvement over the earlier bound ${\mathrm{WR}}({f}) \leq d^r$.
Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Despite their impressive capacities, Large language models (LLMs) often struggle with the hallucination issue of generating inaccurate or fabricated content even when they possess correct knowledge. In this paper, we extend the exploration of the correlation between hidden-state prediction changes and output factuality into a deeper, token-wise level. Based on the insights , we propose cross-layer Entropy eNhanced Decoding (END), a decoding method that mitigates hallucinations without requiring extra training. END leverages inner probability changes across layers to individually quantify the factual knowledge required for each candidate token, and adjusts the final predicting distribution to prioritize tokens with higher factuality. Experiments on both hallucination and QA benchmarks demonstrate that END significantly enhances the truthfulness and informativeness of generated content while maintaining robust QA accuracy. Moreover, our work provides a deeper perspective on understanding the correlations between inherent knowledge and output factuality.
A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion
Venue: arXiv cs.RO 2025
Year: 2025

Locomotion is a fundamental skill for humanoid robots. However, most existing works make locomotion a single, tedious, unextendable, and unconstrained movement. This limits the kinematic capabilities of humanoid robots. In contrast, humans possess versatile athletic abilities-running, jumping, hopping, and finely adjusting gait parameters such as frequency and foot height. In this paper, we investigate solutions to bring such versatility into humanoid locomotion and thereby propose HugWBC: a unified and general humanoid whole-body controller for versatile locomotion. By designing a general command space in the aspect of tasks and behaviors, along with advanced techniques like symmetrical loss and intervention training for learning a whole-body humanoid controlling policy in simulation, HugWBC enables real-world humanoid robots to produce various natural gaits, including walking, jumping, standing, and hopping, with customizable parameters such as frequency, foot swing height, further combined with different body height, waist rotation, and body pitch. Beyond locomotion, HugWBC also supports real-time interventions from external upper-body controllers like teleoperation, enabling loco-manipulation with precision under any locomotive behavior. Extensive experiments validate the high tracking accuracy and robustness of HugWBC with/without upper-body intervention for all commands, and we further provide an in-depth analysis of how the various commands affect humanoid movement and offer insights into the relationships between these commands. To our knowledge, HugWBC is the first humanoid whole-body controller that supports such versatile locomotion behaviors with high robustness and flexibility.
COLP: Scaffolding Children's Online Long-term Collaborative Learning
Venue: arXiv cs.HC 2025
Year: 2025

Online collaborative learning and working are important for everyone including children. However, children still face a lot of difficulties communicating and working together while online, which keeps them from engaging in long-term project-based teamwork. We aim to investigate online long-term collaborative learning opportunities to address this gap. We design COLP, an online, 16-week, project-based learning program, as an educational intervention based on multiple learning theories for primary school students. We conducted this program with 67 primary school students ages 8-13, across more than five provinces of China. We found that this program could engage more than one-third of children in teamwork after long-term study. Furthermore, we interview children and their parents to help us understand the communication channel, benefits, and challenges of this program. Interestingly, we discovered that parents play multiple roles in their children's collaborative learning, particularly modeling and guiding the children's collaborative skills. Given the lack of programs designed for children's long-term online collaboration, this study may inspire intervention design in computer-supported collaborative learning communities.
Efficient extraction of medication information from clinical notes: an evaluation in two languages
Venue: arXiv cs.CL 2025
Year: 2025

Objective: To evaluate the accuracy, computational cost and portability of a new Natural Language Processing (NLP) method for extracting medication information from clinical narratives. Materials and Methods: We propose an original transformer-based architecture for the extraction of entities and their relations pertaining to patients' medication regimen. First, we used this approach to train and evaluate a model on French clinical notes, using a newly annotated corpus from Hôpitaux Universitaires de Strasbourg. Second, the portability of the approach was assessed by conducting an evaluation on clinical documents in English from the 2018 n2c2 shared task. Information extraction accuracy and computational cost were assessed by comparison with an available method using transformers. Results: The proposed architecture achieves on the task of relation extraction itself performance that are competitive with the state-of-the-art on both French and English (F-measures 0.82 and 0.96 vs 0.81 and 0.95), but reduce the computational cost by 10. End-to-end (Named Entity recognition and Relation Extraction) F1 performance is 0.69 and 0.82 for French and English corpus. Discussion: While an existing system developed for English notes was deployed in a French hospital setting with reasonable effort, we found that an alternative architecture offered end-to-end drug information extraction with comparable extraction performance and lower computational impact for both French and English clinical text processing, respectively. Conclusion: The proposed architecture can be used to extract medication information from clinical text with high performance and low computational cost and consequently suits with usually limited hospital IT resources
Leveraging Creativity as a Problem Solving Tool in Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

Today's software engineering (SE) complexities require a more diverse tool set going beyond technical expertise to be able to successfully tackle all challenges. Previous studies have indicated that creativity is a prime indicator for overcoming these hurdles. In this paper, we port results from creativity research in the field of cognitive psychology to the field of SE. After all, programming is a highly creative endeavour. We explore how to leverage creativity as a practical problem solving tool to wield for software developers. The seven distinct but intertwined creative problem solving themes unfolded in this paper are accompanied with practical perspectives, specifically geared for software professionals. Just like technical skills such as knowledge of programming languages, we believe that creativity can be learned and improved with practice.
Deep Learning-based Event Data Coding: A Joint Spatiotemporal and Polarity Solution
Venue: arXiv cs.CV 2025
Year: 2025

Neuromorphic vision sensors, commonly referred to as event cameras, have recently gained relevance for applications requiring high-speed, high dynamic range and low-latency data acquisition. Unlike traditional frame-based cameras that capture 2D images, event cameras generate a massive number of pixel-level events, composed by spatiotemporal and polarity information, with very high temporal resolution, thus demanding highly efficient coding solutions. Existing solutions focus on lossless coding of event data, assuming that no distortion is acceptable for the target use cases, mostly including computer vision tasks. One promising coding approach exploits the similarity between event data and point clouds, thus allowing to use current point cloud coding solutions to code event data, typically adopting a two-point clouds representation, one for each event polarity. This paper proposes a novel lossy Deep Learning-based Joint Event data Coding (DL-JEC) solution adopting a single-point cloud representation, thus enabling to exploit the correlation between the spatiotemporal and polarity event information. DL-JEC can achieve significant compression performance gains when compared with relevant conventional and DL-based state-of-the-art event data coding solutions. Moreover, it is shown that it is possible to use lossy event data coding with its reduced rate regarding lossless coding without compromising the target computer vision task performance, notably for event classification. The use of novel adaptive voxel binarization strategies, adapted to the target task, further enables DL-JEC to reach a superior performance.
Conditional Prediction by Simulation for Automated Driving
Venue: arXiv cs.RO 2025
Year: 2025

Modular automated driving systems commonly handle prediction and planning as sequential, separate tasks, thereby prohibiting cooperative maneuvers. To enable cooperative planning, this work introduces a prediction model that models the conditional dependencies between trajectories. For this, predictions are generated by a microscopic traffic simulation, with the individual traffic participants being controlled by a realistic behavior model trained via Adversarial Inverse Reinforcement Learning. By assuming various candidate trajectories for the automated vehicle, we generate predictions conditioned on each of them. Furthermore, our approach allows the candidate trajectories to adapt dynamically during the prediction rollout. Several example scenarios are available at https://conditionalpredictionbysimulation.github.io/.
Optimal Orthogonal Drawings in Linear Time
Venue: arXiv cs.CG 2025
Year: 2025

A planar orthogonal drawing Γ of a connected planar graph G is a geometric representation of G such that the vertices are drawn as distinct points of the plane, the edges are drawn as chains of horizontal and vertical segments, and no two edges intersect except at common end-points. A bend of Γ is a point of an edge where a horizontal and a vertical segment meet. Drawing Γ is bend-minimum if it has the minimum number of bends over all possible planar orthogonal drawings of G. Its curve complexity is the maximum number of bends per edge. In this paper we present a linear-time algorithm for the computation of planar orthogonal drawings of 3-graphs (i.e., graphs with vertex-degree at most three), that minimizes both the total number of bends and the curve complexity. The algorithm works in the so-called variable embedding setting, that is, it can choose among the exponentially many planar embeddings of the input graph. While the time complexity of minimizing the total number of bends of a planar orthogonal drawing of a 3-graph in the variable embedding settings is a long standing, widely studied, open question, the existence of an orthogonal drawing that is optimal both in the total number of bends and in the curve complexity was previously unknown. Our result combines several graph decomposition techniques, novel data-structures, and efficient approaches to re-rooting decomposition trees.
An 'Experimental Mathematics' Approach to Stolarsky Interspersions via Automata Theory
Venue: arXiv cs.FL 2025
Year: 2025

We look at the Stolarsky interspersions (such as the Wythoff array) one more time, this time using tools from automata theory. These tools allow easy verification of many of the published results on these arrays, as well as proofs of new results.