OpenRaiser/Intern-Atlas huggingface dataset:
On the Volatility of Shapley-Based Contribution Metrics in Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning (FL) is a collaborative and privacy-preserving Machine Learning paradigm, allowing the development of robust models without the need to centralize sensitive data. A critical challenge in FL lies in fairly and accurately allocating contributions from diverse participants. Inaccurate allocation can undermine trust, lead to unfair compensation, and thus participants may lack the incentive to join or actively contribute to the federation. Various remuneration strategies have been proposed to date, including auction-based approaches and Shapley-value-based methods, the latter offering a means to quantify the contribution of each participant. However, little to no work has studied the stability of these contribution evaluation methods. In this paper, we evaluate participant contributions in federated learning using gradient-based model reconstruction techniques with Shapley values and compare the round-based contributions to a classic data contribution measurement scheme. We provide an extensive analysis of the discrepancies of Shapley values across a set of aggregation strategies and examine them on an overall and a per-client level. We show that, between different aggregation techniques, Shapley values lead to unstable reward allocations among participants. Our analysis spans various data heterogeneity distributions, including independent and identically distributed (IID) and non-IID scenarios.
A Galois theorem for machine learning: Functions on symmetric matrices and point clouds via lightweight invariant features
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we present a mathematical formulation for machine learning of (1) functions on symmetric matrices that are invariant with respect to the action of permutations by conjugation, and (2) functions on point clouds that are invariant with respect to rotations, reflections, and permutations of the points. To achieve this, we provide a general construction of generically separating invariant features using ideas inspired by Galois theory. We construct $O(n^2)$ invariant features derived from generators for the field of rational functions on $n\times n$ symmetric matrices that are invariant under joint permutations of rows and columns. We show that these invariant features can separate all distinct orbits of symmetric matrices except for a measure zero set; such features can be used to universally approximate invariant functions on almost all weighted graphs. For point clouds in a fixed dimension, we prove that the number of invariant features can be reduced, generically without losing expressivity, to $O(n)$, where $n$ is the number of points. We combine these invariant features with DeepSets to learn functions on symmetric matrices and point clouds with varying sizes. We empirically demonstrate the feasibility of our approach on molecule property regression and point cloud distance prediction.
Vector Field-Guided Learning Predictive Control for Motion Planning of Mobile Robots with Uncertain Dynamics
Venue: arXiv cs.RO 2025
Year: 2025

In obstacle-dense scenarios, providing safe guidance for mobile robots is critical to improve the safe maneuvering capability. However, the guidance provided by standard guiding vector fields (GVFs) may limit the motion capability due to the improper curvature of the integral curve when traversing obstacles. On the other hand, robotic system dynamics are often time-varying, uncertain, and even unknown during the motion planning process. Therefore, many existing kinodynamic motion planning methods could not achieve satisfactory reliability in guaranteeing safety. To address these challenges, we propose a two-level Vector Field-guided Learning Predictive Control (VF-LPC) approach that improves safe maneuverability. The first level, the guiding level, generates safe desired trajectories using the designed kinodynamic GVF, enabling safe motion in obstacle-dense environments. The second level, the Integrated Motion Planning and Control (IMPC) level, first uses a deep Koopman operator to learn a nominal dynamics model offline and then updates the model uncertainties online using sparse Gaussian processes (GPs). The learned dynamics and a game-based safe barrier function are then incorporated into the LPC framework to generate near-optimal planning solutions. Extensive simulations and real-world experiments were conducted on quadrotor unmanned aerial vehicles and unmanned ground vehicles, demonstrating that VF-LPC enables robots to maneuver safely.
MTP: A Meaning-Typed Language Abstraction for AI-Integrated Programming
Venue: arXiv cs.PL 2025
Year: 2025

Software development is shifting from traditional programming to AI-integrated applications that leverage generative AI and large language models (LLMs) during runtime. However, integrating LLMs remains complex, requiring developers to manually craft prompts and process outputs. Existing tools attempt to assist with prompt engineering, but often introduce additional complexity. This paper presents Meaning-Typed Programming (MTP), a novel paradigm that abstracts LLM integration through intuitive language-level constructs. By leveraging the inherent semantic richness of code, MTP automates prompt generation and response handling without additional developer effort. We introduce the (1) by operator for seamless LLM invocation, (2) MT-IR, a meaning-based intermediate representation for semantic extraction, and (3) MT-Runtime, an automated system for managing LLM interactions. We implement MTP in Jac, a programming language that supersets Python, and find that MTP significantly reduces coding complexity while maintaining accuracy and efficiency. MTP significantly reduces development complexity, lines of code modifications needed, and costs while improving run-time performance and maintaining or exceeding the accuracy of existing approaches. Our user study shows that developers using MTP completed tasks 3.2x faster with 45% fewer lines of code compared to existing frameworks. Moreover, MTP demonstrates resilience even when up to 50% of naming conventions are degraded, demonstrating robustness to suboptimal code. MTP is developed as part of the Jaseci open-source project, and is available under the module byLLM.
Curriculum Dataset Distillation
Venue: arXiv cs.CV 2025
Year: 2025

Most dataset distillation methods struggle to accommodate large-scale datasets due to their substantial computational and memory requirements. Recent research has begun to explore scalable disentanglement methods. However, there are still performance bottlenecks and room for optimization in this direction. In this paper, we present a curriculum-based dataset distillation framework aiming to harmonize performance and scalability. This framework strategically distills synthetic images, adhering to a curriculum that transitions from simple to complex. By incorporating curriculum evaluation, we address the issue of previous methods generating images that tend to be homogeneous and simplistic, doing so at a manageable computational cost. Furthermore, we introduce adversarial optimization towards synthetic images to further improve their representativeness and safeguard against their overfitting to the neural network involved in distilling. This enhances the generalization capability of the distilled images across various neural network architectures and also increases their robustness to noise. Extensive experiments demonstrate that our framework sets new benchmarks in large-scale dataset distillation, achieving substantial improvements of 11.1\% on Tiny-ImageNet, 9.0\% on ImageNet-1K, and 7.3\% on ImageNet-21K. Our distilled datasets and code are available at https://github.com/MIV-XJTU/CUDD.
MicroPython Testbed for Federated Learning Algorithms
Venue: arXiv cs.DC 2025
Year: 2025

Recently, Python Testbed for Federated Learning Algorithms emerged as a low code and generative large language models amenable framework for developing decentralized and distributed applications, primarily targeting edge systems, by nonprofessional programmers with the help of emerging artificial intelligence tools. This light framework is written in pure Python to be easy to install and to fit into a small IoT memory. It supports formally verified generic centralized and decentralized federated learning algorithms, as well as the peer-to-peer data exchange used in time division multiplexing communication, and its current main limitation is that all the application instances can run only on a single PC. This paper presents the MicroPyton Testbed for Federated Learning Algorithms, the new framework that overcomes its predecessor's limitation such that individual application instances may run on different network nodes like PCs and IoTs, primarily in edge systems. The new framework carries on the pure Python ideal, is based on asynchronous I/O abstractions, and runs on MicroPython, and therefore is a great match for IoTs and devices in edge systems. The new framework was experimentally validated on a wireless network comprising PCs and Raspberry Pi Pico W boards, by using application examples originally developed for the predecessor framework.
A Comprehensive Survey on Data Augmentation
Venue: arXiv cs.LG 2025
Year: 2025

Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving scarce or imbalanced datasets, thereby substantially enhancing AI models' generalization capabilities. Existing literature surveys only focus on a certain type of specific modality data and categorize these methods from modality-specific and operation-centric perspectives, which lacks a consistent summary of data augmentation methods across multiple modalities and limits the comprehension of how existing data samples serve the data augmentation process. To bridge this gap, this survey proposes a more enlightening taxonomy that encompasses data augmentation techniques for different common data modalities by investigating how to take advantage of the intrinsic relationship between and within instances. Additionally, it categorizes data augmentation methods across five data modalities through a unified inductive approach.
Cross-sensor self-supervised training and alignment for remote sensing
Venue: arXiv cs.CV 2025
Year: 2025

Large-scale ''foundation models'' have gained traction as a way to leverage the vast amounts of unlabeled remote sensing data collected every day. However, due to the multiplicity of Earth Observation satellites, these models should learn ''sensor agnostic'' representations, that generalize across sensor characteristics with minimal fine-tuning. This is complicated by data availability, as low-resolution imagery, such as Sentinel-2 and Landsat-8 data, are available in large amounts, while very high-resolution aerial or satellite data is less common. To tackle these challenges, we introduce cross-sensor self-supervised training and alignment for remote sensing (X-STARS). We design a self-supervised training loss, the Multi-Sensor Alignment Dense loss (MSAD), to align representations across sensors, even with vastly different resolutions. Our X-STARS can be applied to train models from scratch, or to adapt large models pretrained on e.g low-resolution EO data to new high-resolution sensors, in a continual pretraining framework. We collect and release MSC-France, a new multi-sensor dataset, on which we train our X-STARS models, then evaluated on seven downstream classification and segmentation tasks. We demonstrate that X-STARS outperform s the state-of-the-art by a significant margin with less data across various conditions of data availability and resolutions.
CatCMA : Stochastic Optimization for Mixed-Category Problems
Venue: arXiv cs.NE 2025
Year: 2025

Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variables, categorical variables do not necessarily have a meaningful order, and the discretization approach of continuous variables does not work well. Although several Bayesian optimization methods can deal with mixed-category black-box optimization (MC-BBO), they suffer from a lack of scalability to high-dimensional problems and internal computational cost. This paper proposes CatCMA, a stochastic optimization method for MC-BBO problems, which employs the joint probability distribution of multivariate Gaussian and categorical distributions as the search distribution. CatCMA updates the parameters of the joint probability distribution in the natural gradient direction. CatCMA also incorporates the acceleration techniques used in the covariance matrix adaptation evolution strategy (CMA-ES) and the stochastic natural gradient method, such as step-size adaptation and learning rate adaptation. In addition, we restrict the ranges of the categorical distribution parameters by margin to prevent premature convergence and analytically derive a promising margin setting. Numerical experiments show that the performance of CatCMA is superior and more robust to problem dimensions compared to state-of-the-art Bayesian optimization algorithms.
Large Language Models for Automated Web-Form-Test Generation: An Empirical Study
Venue: arXiv cs.SE 2025
Year: 2025

Testing web forms is an essential activity for ensuring the quality of web applications. It typically involves evaluating the interactions between users and forms. Automated test-case generation remains a challenge for web-form testing: Due to the complex, multi-level structure of web pages, it can be difficult to automatically capture their inherent contextual information for inclusion in the tests. Large Language Models (LLMs) have shown great potential for contextual text generation. This motivated us to explore how they could generate automated tests for web forms, making use of the contextual information within form elements. To the best of our knowledge, no comparative study examining different LLMs has yet been reported for web-form-test generation. To address this gap in the literature, we conducted a comprehensive empirical study investigating the effectiveness of 11 LLMs on 146 web forms from 30 open-source Java web applications. In addition, we propose three HTML-structure-pruning methods to extract key contextual information. The experimental results show that different LLMs can achieve different testing effectiveness. Compared with GPT-4, the other LLMs had difficulty generating appropriate tests for the web forms: Their successfully-submitted rates (SSRs) decreased by 9.10% to 74.15%. Our findings also show that, for all LLMs, when the designed prompts include complete and clear contextual information about the web forms, more effective web-form tests were generated. Specifically, when using Parser-Processed HTML for Task Prompt (PH-P), the SSR averaged 70.63%, higher than the 60.21% for Raw HTML for Task Prompt (RH-P) and 50.27% for LLM-Processed HTML for Task Prompt (LH-P). Finally, this paper also highlights strategies for selecting LLMs based on performance metrics, and for optimizing the prompt design to improve the quality of the web-form tests.
Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks
Venue: arXiv cs.CV 2025
Year: 2025

The accelerated progress of artificial intelligence (AI) has popularized deep learning models across various domains, yet their inherent opacity poses challenges, particularly in critical fields like healthcare, medicine, and the geosciences. Explainable AI (XAI) has emerged to shed light on these 'black box' models, aiding in deciphering their decision-making processes. However, different XAI methods often produce significantly different explanations, leading to high inter-method variability that increases uncertainty and undermines trust in deep networks' predictions. In this study, we address this challenge by introducing a novel framework designed to enhance the explainability of deep networks through a dual focus on maximizing both accuracy and comprehensibility in the explanations. Our framework integrates outputs from multiple established XAI methods and leverages a non-linear neural network model, termed the 'explanation optimizer,' to construct a unified, optimal explanation. The optimizer evaluates explanations using two key metrics: faithfulness (accuracy in reflecting the network's decisions) and complexity (comprehensibility). By balancing these, it provides accurate and accessible explanations, addressing a key XAI limitation. Experiments on multi-class and binary classification in 2D object and 3D neuroscience imaging confirm its efficacy. Our optimizer achieved faithfulness scores 155% and 63% higher than the best XAI methods in 3D and 2D tasks, respectively, while also reducing complexity for better understanding. These results demonstrate that optimal explanations based on specific quality criteria are achievable, offering a solution to the issue of inter-method variability in the current XAI literature and supporting more trustworthy deep network predictions
Evaluation of a Multi-Molecule Molecular Communication Testbed Based on Spectral Sensing
Venue: arXiv cs.ET 2025
Year: 2025

This work presents a novel flow-based molecular communication (MC) testbed using spectral sensing and ink intensity estimation to enable real-time multi-molecule (MUMO) transmission. MUMO communication opens up crucial opportunities for increased throughput as well as implementing more complex coding, modulation, and resource allocation strategies for MC testbeds. An estimator using non-invasive spectral sensing at the receiver is proposed based on a simple absorption model. We conduct in-depth channel impulse response (CIR) measurements and a preliminary communication performance evaluation. Additionally, a simple analytical model is used to check the consistency of the CIRs. The results indicate that by utilizing MUMO transmission, on-off-keying, and a simple difference detector, the testbed can achieve up to 3 bits per second for near-error-free communication, which is on par with comparable testbeds that utilize more sophisticated coding or detection methods. Our platform lays the ground for implementing MUMO communication and evaluating various physical layer and networking techniques based on multiple molecule types in future MC testbeds in real time.
UniRAG: Universal Retrieval Augmentation for Large Vision Language Models
Venue: arXiv cs.IR 2025
Year: 2025

Recently, Large Vision Language Models (LVLMs) have unlocked many complex use cases that require Multi-Modal (MM) understanding (e.g., image captioning or visual question answering) and MM generation (e.g., text-guided image generation or editing) capabilities. To further improve the output fidelityof LVLMs we introduce UniRAG, a plug-and-play technique that adds relevant retrieved information to prompts as few-shot examples during inference. Unlike the common belief that Retrieval Augmentation (RA) mainly improves generation or understanding of uncommon entities, our evaluation results on the MSCOCO dataset with common entities show that both proprietary models like GPT-4o and Gemini-Pro and smaller open-source models like LLaVA, LaVIT, and Emu2 significantly enhance their generation quality when their input prompts are augmented with relevant information retrieved by Vision-Language (VL) retrievers like UniIR models. All the necessary code to reproduce our results is available at https://github.com/castorini/UniRAG
Dynamic Cluster Analysis to Detect and Track Novelty in Network Telescopes
Venue: arXiv cs.NI 2025
Year: 2025

In the context of cybersecurity, tracking the activities of coordinated hosts over time is a daunting task because both participants and their behaviours evolve at a fast pace. We address this scenario by solving a dynamic novelty discovery problem with the aim of both re-identifying patterns seen in the past and highlighting new patterns. We focus on traffic collected by Network Telescopes, a primary and noisy source for cybersecurity analysis. We propose a 3-stage pipeline: (i) we learn compact representations (embeddings) of hosts through their traffic in a self-supervised fashion; (ii) via clustering, we distinguish groups of hosts performing similar activities; (iii) we track the cluster temporal evolution to highlight novel patterns. We apply our methodology to 20 days of telescope traffic during which we observe more than 8 thousand active hosts. Our results show that we efficiently identify 50-70 well-shaped clusters per day, 60-70% of which we associate with already analysed cases, while we pinpoint 10-20 previously unseen clusters per day. These correspond to activity changes and new incidents, of which we document some. In short, our novelty discovery methodology enormously simplifies the manual analysis the security analysts have to conduct to gain insights to interpret novel coordinated activities.
Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
Venue: arXiv cs.CL 2025
Year: 2025

In recent years, large language models (LLMs) have driven advances in natural language processing. Still, their growing scale has increased the computational burden, necessitating a balance between efficiency and performance. Low-rank compression, a promising technique, reduces non-essential parameters by decomposing weight matrices into products of two low-rank matrices. Yet, its application in LLMs has not been extensively studied. The key to low-rank compression lies in low-rank factorization and low-rank dimensions allocation. To address the challenges of low-rank compression in LLMs, we conduct empirical research on the low-rank characteristics of large models. We propose a low-rank compression method suitable for LLMs. This approach involves precise estimation of feature distributions through pooled covariance matrices and a Bayesian optimization strategy for allocating low-rank dimensions. Experiments on the LLaMA-2 models demonstrate that our method outperforms existing strong structured pruning and low-rank compression techniques in maintaining model performance at the same compression ratio.
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios
Venue: arXiv cs.CL 2025
Year: 2025

Active learning is designed to minimize annotation efforts by prioritizing instances that most enhance learning. However, many active learning strategies struggle with a `cold-start' problem, needing substantial initial data to be effective. This limitation reduces their utility in the increasingly relevant few-shot scenarios, where the instance selection has a substantial impact. To address this, we introduce ActiveLLM, a novel active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances. We demonstrate that ActiveLLM significantly enhances the classification performance of BERT classifiers in few-shot scenarios, outperforming traditional active learning methods as well as improving the few-shot learning methods ADAPET, PERFECT, and SetFit. Additionally, ActiveLLM can be extended to non-few-shot scenarios, allowing for iterative selections. In this way, ActiveLLM can even help other active learning strategies to overcome their cold-start problem. Our results suggest that ActiveLLM offers a promising solution for improving model performance across various learning setups.