OpenRaiser/Intern-Atlas huggingface dataset:
A sandbox study proposal for private and distributed health data analysis
Venue: arXiv cs.CR 2025
Year: 2025

This paper presents a sandbox study proposal focused on the distributed processing of personal health data within the Vinnova-funded SARDIN project. The project aims to develop the Health Data Bank (Hälsodatabanken in Swedish), a secure platform for research and innovation that complies with the European Health Data Space (EHDS) legislation. By minimizing the sharing and storage of personal data, the platform sends analysis tasks directly to the original data locations, avoiding centralization. This approach raises questions about data controller responsibilities in distributed environments and the anonymization status of aggregated statistical results. The study explores federated analysis, secure multi-party aggregation, and differential privacy techniques, informed by real-world examples from clinical research on Parkinson's disease, stroke rehabilitation, and wound analysis. To validate the proposed study, numerical experiments were conducted using four open-source datasets to assess the feasibility and effectiveness of the proposed methods. The results support the methods for the proposed sandbox study by demonstrating that differential privacy in combination with secure aggregation techniques significantly improves the privacy-utility trade-off.
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks
Venue: arXiv cs.LG 2025
Year: 2025

Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall.
Inverse Evolution Data Augmentation for Neural PDE Solvers
Venue: arXiv cs.LG 2025
Year: 2025

Neural networks have emerged as promising tools for solving partial differential equations (PDEs), particularly through the application of neural operators. Training neural operators typically requires a large amount of training data to ensure accuracy and generalization. In this paper, we propose a novel data augmentation method specifically designed for training neural operators on evolution equations. Our approach utilizes insights from inverse processes of these equations to efficiently generate data from random initialization that are combined with original data. To further enhance the accuracy of the augmented data, we introduce high-order inverse evolution schemes. These schemes consist of only a few explicit computation steps, yet the resulting data pairs can be proven to satisfy the corresponding implicit numerical schemes. In contrast to traditional PDE solvers that require small time steps or implicit schemes to guarantee accuracy, our data augmentation method employs explicit schemes with relatively large time steps, thereby significantly reducing computational costs. Accuracy and efficacy experiments confirm the effectiveness of our approach. Additionally, we validate our approach through experiments with the Fourier Neural Operator and UNet on three common evolution equations that are Burgers' equation, the Allen-Cahn equation and the Navier-Stokes equation. The results demonstrate a significant improvement in the performance and robustness of the Fourier Neural Operator when coupled with our inverse evolution data augmentation method.
Strong Converse Exponent for Remote Lossy Source Coding
Venue: arXiv cs.IT 2025
Year: 2025

Past works on remote lossy source coding studied the rate under average distortion and the error exponent of excess distortion probability. In this work, we look into how fast the excess distortion probability converges to 1 at small rates, also known as exponential strong converse. We characterize its exponent by establishing matched upper and lower bounds. From the exponent, we also recover two previous results on lossy source coding and biometric authentication.
Extracting Problem Structure with LLMs for Optimized SAT Local Search
Venue: arXiv cs.AI 2025
Year: 2025

Local search preprocessing makes Conflict-Driven Clause Learning (CDCL) solvers faster by providing high-quality starting points and modern SAT solvers have incorporated this technique into their preprocessing steps. However, these tools rely on basic strategies that miss the structural patterns in problems. We present a method that applies Large Language Models (LLMs) to analyze Python-based encoding code. This reveals hidden structural patterns in how problems convert into SAT. Our method automatically generates specialized local search algorithms that find these patterns and use them to create strong initial assignments. This works for any problem instance from the same encoding type. Our tests show encouraging results, achieving faster solving times compared to baseline preprocessing systems.
A Paired Autoencoder Framework for Inverse Problems via Bayes Risk Minimization
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we describe a new data-driven approach for inverse problems that exploits technologies from machine learning, in particular autoencoder network structures. We consider a paired autoencoder framework, where two autoencoders are used to efficiently represent the input and target spaces separately and optimal mappings are learned between latent spaces, thus enabling forward and inverse surrogate mappings. We focus on interpretations using Bayes risk and empirical Bayes risk minimization, and we provide various theoretical results and connections to existing works on low-rank matrix approximations. Similar to end-to-end approaches, our paired approach creates a surrogate model for forward propagation and regularized inversion. However, our approach outperforms existing approaches in scenarios where training data for unsupervised learning are readily available but training pairs for supervised learning are scarce. Furthermore, we show that cheaply computable evaluation metrics are available through this framework and can be used to predict whether the solution for a new sample should be predicted well.
The Karp Dataset
Venue: arXiv cs.LG 2025
Year: 2025

Understanding the mathematical reasoning capabilities of Large Language Models (LLMs) is a central topic in the study of artificial intelligence. This new domain necessitates the creation of datasets of reasoning tasks for both training and benchmarking the performance of LLMs. To this end, we introduce the Karp dataset: The first dataset composed of detailed proofs of NP-completeness reductions. The reductions vary in difficulty, ranging from simple exercises of undergraduate courses to more challenging reductions from academic papers. We compare the performance of state-of-the-art models on this task and demonstrate the effect of fine-tuning with the Karp dataset on reasoning capacity.
Do LLMs Provide Consistent Answers to Health-Related Questions across Languages?
Venue: arXiv cs.CL 2025
Year: 2025

Equitable access to reliable health information is vital for public health, but the quality of online health resources varies by language, raising concerns about inconsistencies in Large Language Models (LLMs) for healthcare. In this study, we examine the consistency of responses provided by LLMs to health-related questions across English, German, Turkish, and Chinese. We largely expand the HealthFC dataset by categorizing health-related questions by disease type and broadening its multilingual scope with Turkish and Chinese translations. We reveal significant inconsistencies in responses that could spread healthcare misinformation. Our main contributions are 1) a multilingual health-related inquiry dataset with meta-information on disease categories, and 2) a novel prompt-based evaluation workflow that enables sub-dimensional comparisons between two languages through parsing. Our findings highlight key challenges in deploying LLM-based tools in multilingual contexts and emphasize the need for improved cross-lingual alignment to ensure accurate and equitable healthcare information.
MLPs at the EOC: Concentration of the NTK
Venue: arXiv cs.LG 2025
Year: 2025

We study the concentration of the Neural Tangent Kernel (NTK) $K_θ: \mathbb{R}^{m_0} \times \mathbb{R}^{m_0} \to \mathbb{R}^{m_l \times m_l}$ of $l$-layer Multilayer Perceptrons (MLPs) $N : \mathbb{R}^{m_0} \times Θ\to \mathbb{R}^{m_l}$ equipped with activation functions $ϕ(s) = a s + b \vert s \vert$ for some $a,b \in \mathbb{R}$ with the parameter $θ\in Θ$ being initialized at the Edge Of Chaos (EOC). Without relying on the gradient independence assumption that has only been shown to hold asymptotically in the infinitely wide limit, we prove that an approximate version of gradient independence holds at finite width. Showing that the NTK entries $K_θ(x_{i_1},x_{i_2})$ for $i_1,i_2 \in [1:n]$ over a dataset $\{x_1,\cdots,x_n\} \subset \mathbb{R}^{m_0}$ concentrate simultaneously via maximal inequalities, we prove that the NTK matrix $K(θ) = [\frac{1}{n} K_θ(x_{i_1},x_{i_2}) : i_1,i_2 \in [1:n]] \in \mathbb{R}^{nm_l \times nm_l}$ concentrates around its infinitely wide limit $\overset{\scriptscriptstyle\infty}{K} \in \mathbb{R}^{nm_l \times nm_l}$ without the need for linear overparameterization. Our results imply that in order to accurately approximate the limit, hidden layer widths have to grow quadratically as $m_k = k^2 m$ for some $m \in \mathbb{N}+1$ for sufficient concentration. For such MLPs, we obtain the concentration bound $\mathbb{P}( \Vert K(θ) - \overset{\scriptscriptstyle\infty}{K} \Vert \leq O((Δ_ϕ^{-2} + m_l^{\frac{1}{2}} l) κ_ϕ^2 m^{-\frac{1}{2}})) \geq 1-O(m^{-1})$ modulo logarithmic terms, where we denoted $Δ_ϕ= \frac{b^2}{a^2+b^2}$ and $κ_ϕ= \frac{\vert a \vert + \vert b \vert}{\sqrt{a^2 + b^2}}$. This reveals in particular that the absolute value ($Δ_ϕ=1$, $κ_ϕ=1$) beats the ReLU ($Δ_ϕ=\frac{1}{2}$, $κ_ϕ=\sqrt{2}$) in terms of the concentration of the NTK.
Optimizing LPB Algorithms using Simulated Annealing
Venue: arXiv cs.NE 2025
Year: 2025

Learner Performance-based Behavior using Simulated Annealing (LPBSA) is an improvement of the Learner Performance-based Behavior (LPB) algorithm. LPBSA, like LPB, has been proven to deal with single and complex problems. Simulated Annealing (SA) has been utilized as a powerful technique to optimize LPB. LPBSA has provided results that outperformed popular algorithms, like the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and even LPB. This study outlines the improved algorithm's working procedure by providing a main population and dividing it into Good and Bad populations and then applying crossover and mutation operators. When some individuals are born in the crossover stage, they have to go through the mutation process. Between these two steps, we have applied SA using the Metropolis Acceptance Criterion (MAC) to accept only the best and most useful individuals to be used in the next iteration. Finally, the outcomes demonstrate that the population is enhanced, leading to improved efficiency and validating the performance of LPBSA.
LEISA: A Scalable Microservice-based System for Efficient Livestock Data Sharing
Venue: arXiv cs.DC 2025
Year: 2025

In the livestock sector, the fragmented data landscape across isolated systems presents a significant challenge, necessitating interoperability and integration. In this article, we introduce the Livestock Event Information Sharing Architecture (LEISA), a novel architecture designed to facilitate the sharing of livestock event information among various stakeholders, such as farmers and service providers. We comprehensively analysed both functional and non-functional requirements of such an architecture to ensure data integrity, standardisation, validation, ownership, and real-time data dissemination across various stakeholders while maintaining reliability, scalability, and interoperability. We designed and implemented LEISA using a cloud-based microservice architecture incorporating advanced technologies such as RESTful APIs and message brokers. We evaluated the efficiency and effectiveness of LEISA in terms of performance, scalability, and reliability, and examined its applicability through several real-world use-case scenarios. LEISA facilitates seamless data sharing, system interoperability, and enhanced decision-making capabilities, promoting synergy among stakeholders.
Persistent HyTM via Fast Path Fine-Grained Locking
Venue: arXiv cs.DC 2025
Year: 2025

Utilizing hardware transactional memory (HTM) in conjunction with non-volatile memory (NVM) to achieve persistence is quite difficult and somewhat awkward due to the fact that the primitives utilized to write data to NVM will abort HTM transactions. We present several persistent hybrid transactional memory (HyTM) that, perhaps counterintuitively, utilize an HTM fast path primarily to read or acquire fine-grained locks which protect data items. Our implementations guarantee durable linearizable transactions and the STM path satisfies either weak progressiveness or strong progressiveness. We discuss the design choices related to the differing progress guarantees and we examine how these design choices impact performance. We evaluate our persistent HyTM implementations using various microbenchmarks. Our implementations achieve improved performance especially for read dominant workloads compared to state of the art persistent STMs and persistent HyTMs despite the challenges and apparent awkwardness of using current implementation HTM to achieve persistence.
Characterizing Mobile SoC for Accelerating Heterogeneous LLM Inference
Venue: arXiv cs.DC 2025
Year: 2025

With the rapid advancement of artificial intelligence technologies such as ChatGPT, AI agents, and video generation, contemporary mobile systems have begun integrating these AI capabilities on local devices to enhance privacy and reduce response latency. To meet the computational demands of AI tasks, current mobile SoCs are equipped with diverse AI accelerators, including GPUs and Neural Processing Units (NPUs). However, there has not been a comprehensive characterization of these heterogeneous processors, and existing designs typically only leverage a single AI accelerator for LLM inference, leading to suboptimal use of computational resources and memory bandwidth. In this paper, we first summarize key performance characteristics of heterogeneous processors, SoC memory bandwidth, etc. Drawing on these observations, we propose different heterogeneous parallel mechanisms to fully exploit both GPU and NPU computational power and memory bandwidth. We further design a fast synchronization mechanism between heterogeneous processors that leverages the unified memory architecture. By employing these techniques, we present HeteroInfer, the fastest LLM inference engine in mobile devices which supports GPU-NPU heterogeneous execution. Evaluation shows that HeteroInfer delivers a 1.34x to 6.02x end-to-end speedup over state-of-the-art GPU-only and NPU-only LLM engines, while maintaining negligible interference with other applications.
Sharp exact recovery threshold for two-community Euclidean random graphs
Venue: arXiv cs.SI 2025
Year: 2025

This paper considers the problem of label recovery in random graphs and matrices. Motivated by transitive behavior in real-world networks (i.e., ``the friend of my friend is my friend''), a recent line of work considers spatially-embedded networks, which exhibit transitive behavior. In particular, the Geometric Hidden Community Model (GHCM), introduced by Gaudio, Guan, Niu, and Wei, models a network as a labeled Poisson point process where every pair of vertices is associated with a pairwise observation whose distribution depends on the labels and positions of the vertices. The GHCM is in turn a generalization of the Geometric SBM (proposed by Baccelli and Sankararaman). Gaudio et al. provided a threshold below which exact recovery is information-theoretically impossible. Above the threshold, they provided a linear-time algorithm that succeeds in exact recovery under a certain ``distinctness-of-distributions'' assumption, which they conjectured to be unnecessary. In this paper, we partially resolve the conjecture by showing that the threshold is indeed tight for the two-community GHCM. We provide a two-phase, linear-time algorithm that explores the spatial graph in a data-driven manner in Phase I to yield an almost exact labeling, which is refined to achieve exact recovery in Phase II. Our results extend achievability to geometric formulations of well-known inference problems, such as the planted dense subgraph problem and submatrix localization, in which the distinctness-of-distributions assumption does not hold.
BEST: A Unified Business Process Enactment via Streams and Tables for Service Computing
Venue: arXiv cs.SE 2025
Year: 2025

Business process models are essential for the representation, analysis, and execution of organizational processes, serving as orchestration blueprints while relying on (web) services to implement individual tasks. At the representation level, there are two dominant paradigms: procedural (imperative) notations that specify the sequential flows within a process and declarative notations that capture the process as a set of constraints. Although each notation offers distinct advantages in representational clarity and cognitive effectiveness, they are seldom integrated, leading to compatibility challenges. In this paper, we set aside the imperative-declarative dichotomy to focus on orchestrating services that execute the underlying tasks. We propose an execution semantics based on the Continuous Query Language (CQL), where CQL statements respond dynamically to streams of events. As events unfold, these CQL statements update the execution state (tables) and can generate new events, effectively triggering (web) services that implement specific process tasks. By defining all executions around a unified event model, we achieve cross-language and cross-paradigm process enactment. We showcase how industrial process modeling languages, such as BPMN and DCR graphs, can be enacted through CQL queries, allowing seamless orchestration and execution of services across diverse modeling paradigms.
On the Optimality of Gaussian Code-books for Signaling over a Two-Users Weak Gaussian Interference Channel
Venue: arXiv cs.IT 2025
Year: 2025

This article shows that the capacity region of a two users weak Gaussian interference channel can be achieved using single letter Gaussian code-books. The approach relies on traversing the boundary in incremental steps. Starting from a corner point with Gaussian code-books, and relying on calculus of variation, it is shown that the end point in each step is achieved using Gaussian code-books. Optimality of Gaussian code-books is first established by limiting the random coding to independent and identically distributed scalar (single-letter) samples. Then, it is shown that the value of any optimum solution for vector inputs does not exceed that of the single-letter case. It is also shown that the maximum number of phases needed to realize the optimum time-sharing is two. It is established that the solution to the Han-Kobayashi achievable rate region, with single letter Gaussian code-books, achieves the optimum boundary. Even though the article focuses on weak interference, the results are applicable to the general case.
MISCON: A Mission-Driven Conversational Consultant for Pre-Venture Entrepreneurs in Food Deserts
Venue: arXiv cs.AI 2025
Year: 2025

This work-in-progress report describes MISCON, a conversational consultant being developed for a public mission project called NOURISH. With MISCON, aspiring small business owners in a food-insecure region and their advisors in Community-based organizations would be able to get information, recommendation and analysis regarding setting up food businesses. MISCON conversations are modeled as state machine that uses a heterogeneous knowledge graph as well as several analytical tools and services including a variety of LLMs. In this short report, we present the functional architecture and some design considerations behind MISCON.
Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

Algorithmic modeling relies on limited information in data to extrapolate outcomes for unseen scenarios, often embedding an element of arbitrariness in its decisions. A perspective on this arbitrariness that has recently gained interest is multiplicity-the study of arbitrariness across a set of "good models", i.e., those likely to be deployed in practice. In this work, we systemize the literature on multiplicity by: (a) formalizing the terminology around model design choices and their contribution to arbitrariness, (b) expanding the definition of multiplicity to incorporate underrepresented forms beyond just predictions and explanations, (c) clarifying the distinction between multiplicity and other lenses of arbitrariness, i.e., uncertainty and variance, and (d) distilling the benefits and potential risks of multiplicity into overarching trends, situating it within the broader landscape of responsible AI. We conclude by identifying open research questions and highlighting emerging trends in this young but rapidly growing area of research.
Personalized Layer Selection for Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) combine node attributes over a fixed granularity of the local graph structure around a node to predict its label. However, different nodes may relate to a node-level property with a different granularity of its local neighborhood, and using the same level of smoothing for all nodes can be detrimental to their classification. In this work, we challenge the common fact that a single GNN layer can classify all nodes of a graph by training GNNs with a distinct personalized layer for each node. Inspired by metric learning, we propose a novel algorithm, MetSelect1, to select the optimal representation layer to classify each node. In particular, we identify a prototype representation of each class in a transformed GNN layer and then, classify using the layer where the distance is smallest to a class prototype after normalizing with that layer's variance. Results on 10 datasets and 3 different GNNs show that we significantly improve the node classification accuracy of GNNs in a plug-and-play manner. We also find that using variable layers for prediction enables GNNs to be deeper and more robust to poisoning attacks. We hope this work can inspire future works to learn more adaptive and personalized graph representations.