OpenRaiser/Intern-Atlas huggingface dataset:
MASTER: A Multi-Agent System with LLM Specialized MCTS
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLM) are increasingly being explored for problem-solving tasks. However, their strategic planning capability is often viewed with skepticism. Recent studies have incorporated the Monte Carlo Tree Search (MCTS) algorithm to augment the planning capacity of LLM. Despite its potential, MCTS relies on extensive sampling simulations to approximate the true reward distribution, which leads to two primary issues. Firstly, MCTS is effective for tasks like the Game of Go, where simulation results can yield objective rewards (e.g., 1 for a win and 0 for a loss). However, for tasks such as question answering, the result of a simulation is the answer to the question, which cannot yield an objective reward without the ground truth. Secondly, obtaining statistically significant reward estimations typically requires a sample size exceeding 30 simulations, resulting in excessive token usage and time consumption. To address these challenges, we present the Multi-Agent System with Tactical Execution and Reasoning using LLM Specialized MCTS (MASTER), a novel framework that coordinates agent recruitment and communication through LLM specialized MCTS. This system autonomously adjusts the number of agents based on task complexity and ensures focused communication among them. Comprehensive experiments across various tasks demonstrate the effectiveness of our proposed framework. It achieves 76% accuracy on HotpotQA and 80% on WebShop, setting new state-of-the-art performance on these datasets.
An Efficient Real Time DDoS Detection Model Using Machine Learning Algorithms
Venue: arXiv cs.LG 2025
Year: 2025

Distributed Denial of Service attacks have become a significant threat to industries and governments leading to substantial financial losses. With the growing reliance on internet services, DDoS attacks can disrupt services by overwhelming servers with false traffic causing downtime and data breaches. Although various detection techniques exist, selecting an effective method remains challenging due to trade-offs between time efficiency and accuracy. This research focuses on developing an efficient real-time DDoS detection system using machine learning algorithms leveraging the UNB CICDDoS2019 dataset including various traffic features. The study aims to classify DDoS and non-DDoS traffic through various ML classifiers including Logistic Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Naive Bayes. The dataset is preprocessed through data cleaning, standardization and feature selection techniques using Principal Component Analysis. The research explores the performance of these algorithms in terms of precision, recall and F1-score as well as time complexity to create a reliable system capable of real-time detection and mitigation of DDoS attacks. The findings indicate that RF, AdaBoost and XGBoost outperform other algorithms in accuracy and efficiency, making them ideal candidates for real-time applications.
A Ground Truth Approach for Assessing Process Mining Techniques
Venue: arXiv cs.DB 2025
Year: 2025

The assessment of process mining techniques using real-life data is often compromised by the lack of ground truth knowledge, the presence of non-essential outliers in system behavior and recording errors in event logs. Using synthetically generated data could leverage ground truth for better evaluation. Existing log generation tools inject noise directly into the logs, which does not capture many typical behavioral deviations. Furthermore, the link between the model and the log, which is needed for later assessment, becomes lost. We propose a ground-truth approach for generating process data from either existing or synthetic initial process models, whether automatically generated or hand-made. This approach incorporates patterns of behavioral deviations and recording errors to produce a synthetic yet realistic deviating model and imperfect event log. These, together with the initial model, are required to assess process mining techniques based on ground truth knowledge. We demonstrate this approach to create datasets of synthetic process data for three processes, one of which we used in a conformance checking use case, focusing on the assessment of (relaxed) systemic alignments to expose and explain deviations in modeled and recorded behavior. Our results show that this approach, unlike traditional methods, provides detailed insights into the strengths and weaknesses of process mining techniques, both quantitatively and qualitatively.
In System Alignments we Trust! Explainable Alignments via Projections
Venue: arXiv cs.AI 2025
Year: 2025

Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation - either a log or a model - but not in the other. Since for processes in which multiple entities, like objects and resources, are involved in the activities, their interactions affect the behavior and are therefore essential to take into account in the alignments. Additionally, both logged and modeled representations of reality may be imprecise and only partially represent some of these entities, but not all. In this paper, we introduce the concept of "relaxations" through projections for alignments to deal with partially correct models and logs. Relaxed alignments help to distinguish between trustworthy and untrustworthy content of the two representations (the log and the model) to achieve a better understanding of the underlying process and expose quality issues.
Streamlining Plug-and-Charge Authorization for Electric Vehicles with OAuth2 and OIDC
Venue: arXiv cs.CR 2025
Year: 2025

The Plug-and-Charge (PnC) process defined by ISO 15118 standardizes automated Electric Vehicle (EV) charging by enabling automatic installation of credentials and use for authentication between EV and Charge Point (CP). However, the current credential installation process is non-uniform, relies on a complex Public Key Infrastructure (PKI), lacks support for fine-grained authorization parameters, and is not very user-friendly. In this paper, we propose a streamlined approach to the initial charging authorization process by leveraging the OAuth Device Authorization Grant and Rich Authorization Requests. The proposed solution reduces technical complexity, simplifies credential installation, introduces flexible authorization constraints (e.g., time- and cost-based), and facilitates payment through OpenID Connect (OIDC). We present a proof-of-concept implementation along with performance evaluations and conduct a symbolic protocol verification using the Tamarin prover. Furthermore, our approach solves the issue of OAuth's cross-device authorization, making it suitable as a formally proven blueprint in contexts beyond EV charging.
DeepServe: Serverless Large Language Model Serving at Scale
Venue: arXiv cs.DC 2025
Year: 2025

In this paper, we propose DEEPSERVE, a scalable and serverless AI platform designed to efficiently serve large language models (LLMs) at scale in cloud environments. DEEPSERVE addresses key challenges such as resource allocation, serving efficiency, and cold start latencies through four main design components. First, DEEPSERVE uses a simple serverless abstraction called the request-job-task model, which helps manage diverse AI workloads across posttraining and model-serving tasks. Second, DEEPSERVE integrates an in-house serving engine named FLOWSERVE using a microkernel-inspired design, NPU-centric execution, and SPMD-based parallelism to optimize LLM serving. Third, DEEPSERVE includes novel scheduling policies tailored for a configuration with both PD-disaggregated and PD-colocated instances. Fourth, DEEPSERVE includes optimizations such as pre-warmed pods, DRAM pre-loading, and NPU-fork, which allow DEEPSERVE to scale up to 64 instances in seconds. DEEPSERVE has been in production for over a year, operating on a large Ascend NPU cluster and providing industrystandard APIs for fine-tuning, agent serving, and model serving to our customers.
Thunderdome: Timelock-Free Rationally-Secure Virtual Channels
Venue: arXiv cs.CR 2025
Year: 2025

Payment channel networks (PCNs) offer a promising solution to address the limited transaction throughput of deployed blockchains. However, several attacks have recently been proposed that stress the vulnerability of PCNs to timelock and censoring attacks. To address such attacks, we introduce Thunderdome, the first timelock-free PCN. Instead, Thunderdome leverages the design rationale of virtual channels to extend a timelock-free payment channel primitive, thereby enabling multi-hop transactions without timelocks. Previous works either utilize timelocks or do not accommodate transactions between parties that do not share a channel. At its core, Thunderdome relies on a committee of non-trusted watchtowers, known as wardens, who ensure that no honest party loses funds, even when offline, during the channel closure process. We introduce tailored incentive mechanisms to ensure that all participants follow the protocol's correct execution. Besides a traditional security proof that assumes an honest majority of the committee, we conduct a formal game-theoretic analysis to demonstrate the security of Thunderdome when all participants, including wardens, act rationally. We implement a proof of concept of Thunderdome on Ethereum to validate its feasibility and evaluate its costs. Our evaluation shows that deploying Thunderdome, including opening the underlying payment channel, costs approximately \$15 (0.0089 ETH), while the worst-case cost for closing a channel is about \$7 (0.004 ETH).
CAMEO: Autocorrelation-Preserving Line Simplification for Lossy Time Series Compression
Venue: arXiv cs.DB 2025
Year: 2025

Time series data from a variety of sensors and IoT devices need effective compression to reduce storage and I/O bandwidth requirements. While most time series databases and systems rely on lossless compression, lossy techniques offer even greater space-saving with a small loss in precision. However, the unknown impact on downstream analytics applications requires a semi-manual trial-and-error exploration. We initiate work on lossy compression that provides guarantees on complex statistical features (which are strongly correlated with the accuracy of the downstream analytics). Specifically, we propose a new lossy compression method that provides guarantees on the autocorrelation and partial-autocorrelation functions (ACF/PACF) of a time series. Our method leverages line simplification techniques as well as incremental maintenance of aggregates, blocking, and parallelization strategies for effective and efficient compression. The results show that our method improves compression ratios by 2x on average and up to 54x on selected datasets, compared to previous lossy and lossless compression methods. Moreover, we maintain -- and sometimes even improve -- the forecasting accuracy by preserving the autocorrelation properties of the time series. Our framework is extensible to multivariate time series and other statistical features of the time series.
Convergence of gradient based training for linear Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) are powerful tools for addressing learning problems on graph structures, with a wide range of applications in molecular biology and social networks. However, the theoretical foundations underlying their empirical performance are not well understood. In this article, we examine the convergence of gradient dynamics in the training of linear GNNs. Specifically, we prove that the gradient flow training of a linear GNN with mean squared loss converges to the global minimum at an exponential rate. The convergence rate depends explicitly on the initial weights and the graph shift operator, which we validate on synthetic datasets from well-known graph models and real-world datasets. Furthermore, we discuss the gradient flow that minimizes the total weights at the global minimum. In addition to the gradient flow, we study the convergence of linear GNNs under gradient descent training, an iterative scheme viewed as a discretization of gradient flow.
Design and Implementation of a Psychiatry Resident Training System Based on Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

Mental disorders have become a significant global public health issue, while the shortage of psychiatrists and inefficient training systems severely hinder the accessibility of mental health services. This paper designs and implements an artificial intelligence-based training system for psychiatrists. By integrating technologies such as large language models, knowledge graphs, and expert systems, the system constructs an intelligent and standardized training platform. It includes six functional modules: case generation, consultation dialogue, examination prescription, diagnostic decision-making, integrated traditional Chinese and Western medicine prescription, and expert evaluation, providing comprehensive support from clinical skill training to professional level assessment.The system adopts a B/S architecture, developed using the Vue.js and Node.js technology stack, and innovatively applies deep learning algorithms for case generation and doctor-patient dialogue. In a clinical trial involving 60 psychiatrists at different levels, the system demonstrated excellent performance and training outcomes: system stability reached 99.95%, AI dialogue accuracy achieved 96.5%, diagnostic accuracy reached 92.5%, and user satisfaction scored 92.3%. Experimental data showed that doctors using the system improved their knowledge mastery, clinical thinking, and diagnostic skills by 35.6%, 28.4%, and 23.7%, respectively.The research results provide an innovative solution for improving the efficiency of psychiatrist training and hold significant importance for promoting the standardization and scalability of mental health professional development.
Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research
Venue: arXiv cs.CV 2025
Year: 2025

This paper investigates the novel application of Large Language Models (LLMs) with vision capabilities to analyze satellite imagery for village-level poverty prediction. Although LLMs were originally designed for natural language understanding, their adaptability to multimodal tasks, including geospatial analysis, has opened new frontiers in data-driven research. By leveraging advancements in vision-enabled LLMs, we assess their ability to provide interpretable, scalable, and reliable insights into human poverty from satellite images. Using a pairwise comparison approach, we demonstrate that ChatGPT can rank satellite images based on poverty levels with accuracy comparable to domain experts. These findings highlight both the promise and the limitations of LLMs in socioeconomic research, providing a foundation for their integration into poverty assessment workflows. This study contributes to the ongoing exploration of unconventional data sources for welfare analysis and opens pathways for cost-effective, large-scale poverty monitoring.
Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation
Venue: arXiv cs.LG 2025
Year: 2025

Ensembling is commonly regarded as an effective way to improve the general performance of models in machine learning, while also increasing the robustness of predictions. When it comes to algorithmic fairness, heterogeneous ensembles, composed of multiple model types, have been employed to mitigate biases in terms of demographic attributes such as sex, age or ethnicity. Moreover, recent work has shown how in multi-class problems even simple homogeneous ensembles may favor performance of the worst-performing target classes. While homogeneous ensembles are simpler to implement in practice, it is not yet clear whether their benefits translate to groups defined not in terms of their target class, but in terms of demographic or protected attributes, hence improving fairness. In this work we show how this simple and straightforward method is indeed able to mitigate disparities, particularly benefiting under-performing subgroups. Interestingly, this can be achieved without sacrificing overall performance, which is a common trade-off observed in bias mitigation strategies. Moreover, we analyzed the interplay between two factors which may result in biases: sub-group under-representation and the inherent difficulty of the task for each group. These results revealed that, contrary to popular assumptions, having balanced datasets may be suboptimal if the task difficulty varies between subgroups. Indeed, we found that a perfectly balanced dataset may hurt both the overall performance and the gap between groups. This highlights the importance of considering the interaction between multiple forces at play in fairness.
On the Cross-type Homophily of Heterogeneous Graphs: Understanding and Unleashing
Venue: arXiv cs.SI 2025
Year: 2025

Homophily, the tendency of similar nodes to connect, is a fundamental phenomenon in network science and a critical factor in the performance of graph neural networks (GNNs). While existing studies primarily explore homophily in homogeneous graphs, where nodes share the same type, real-world networks are often more accurately modeled as heterogeneous graphs (HGs) with diverse node types and intricate cross-type interactions. This structural diversity complicates the analysis of homophily, as traditional homophily metrics fail to account for distinct label spaces across node types. To address this limitation, we introduce the Cross-Type Homophily Ratio (CHR), a novel metric that quantifies homophily based on the similarity of target information across different node types. Additionally, we propose Cross-Type Homophily-guided Graph Editing (CTHGE), a novel method for improving heterogeneous graph neural networks (HGNNs) performance by optimizing cross-type connectivity using Cross-Type Homophily Ratio. Extensive experiments on five HG datasets with nine HGNNs validate the effectiveness of CTHGE, which delivers a maximum relative performance improvement of over 25% for HGNNs on node classification tasks, offering a fresh perspective on cross-type homophily in HGs learning.
3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Domain generalization aims to find ways for deep learning models to maintain their performance despite significant domain shifts between training and inference datasets. This is particularly important for models that need to be robust or are costly to train. LiDAR perception in autonomous driving is impacted by both of these concerns, leading to the emergence of various approaches. This work addresses the challenge by proposing a geometry-based approach, leveraging the sequential structure of LiDAR sensors, which sets it apart from the learning-based methods commonly found in the literature. The proposed method, called 3DLabelProp, is applied on the task of LiDAR Semantic Segmentation (LSS). Through extensive experimentation on seven datasets, it is demonstrated to be a state-of-the-art approach, outperforming both naive and other domain generalization methods.
Channel Independent Precoder for OFDM-based Systems over Fading Channels
Venue: arXiv cs.IT 2025
Year: 2025

In this paper we propose an independent channel precoder for orthogonal frequency division multiplexing (OFDM) systems over fading channels. The design of the precoder is based on the information redistribution of the input modulated symbols amongst the output precoded symbols. The proposed precoder decreases the variance of the instantaneous noise power at the receiver produced by the channel variability. The employment of an interleaver together with a precoding matrix whose size does not depend on the number of data carriers in an OFDM symbol allows different configurations of time-frequency diversity which can be easily adapted to the channel conditions. The precoder is evaluated with a modified Zero Forcing (ZF) equalizer whose maximum gain is constrained by means of a clipping factor. Thus, the clipping factor limits the noise power transfer in the receiver deprecoding block in low SNR conditions.
Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics
Venue: arXiv cs.AI 2025
Year: 2025

We present a novel approach for recommending actionable strategies by integrating strategic frameworks with decision heuristics through semantic analysis. While strategy frameworks provide systematic models for assessment and planning, and decision heuristics encode experiential knowledge,these traditions have historically remained separate. Our methodology bridges this gap using advanced natural language processing (NLP), demonstrated through integrating frameworks like the 6C model with the Thirty-Six Stratagems. The approach employs vector space representations and semantic similarity calculations to map framework parameters to heuristic patterns, supported by a computational architecture that combines deep semantic processing with constrained use of Large Language Models. By processing both primary content and secondary elements (diagrams, matrices) as complementary linguistic representations, we demonstrate effectiveness through corporate strategy case studies. The methodology generalizes to various analytical frameworks and heuristic sets, culminating in a plug-and-play architecture for generating recommender systems that enable cohesive integration of strategic frameworks and decision heuristics into actionable guidance.
Optimizing Privacy-Utility Trade-off in Decentralized Learning with Generalized Correlated Noise
Venue: arXiv cs.LG 2025
Year: 2025

Decentralized learning enables distributed agents to collaboratively train a shared machine learning model without a central server, through local computation and peer-to-peer communication. Although each agent retains its dataset locally, sharing local models can still expose private information about the local training datasets to adversaries. To mitigate privacy attacks, a common strategy is to inject random artificial noise at each agent before exchanging local models between neighbors. However, this often leads to utility degradation due to the negative effects of cumulated artificial noise on the learning algorithm. In this work, we introduce CorN-DSGD, a novel covariance-based framework for generating correlated privacy noise across agents, which unifies several state-of-the-art methods as special cases. By leveraging network topology and mixing weights, CorN-DSGD optimizes the noise covariance to achieve network-wide noise cancellation. Experimental results show that CorN-DSGD cancels more noise than existing pairwise correlation schemes, improving model performance under formal privacy guarantees.