OpenRaiser/Intern-Atlas huggingface dataset:
Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
Venue: arXiv cs.RO 2025
Year: 2025

Research in robotic planning with temporal logic specifications, such as Linear Temporal Logic (LTL), has relied on single formulas. However, as task complexity increases, LTL formulas become lengthy, making them difficult to interpret and generate, and straining the computational capacities of planners. To address this, we introduce a hierarchical structure for a widely used specification type -- LTL on finite traces (LTL$_f$). The resulting language, termed H-LTL$_f$, is defined with both its syntax and semantics. We further prove that H-LTL$_f$ is more expressive than its standard "flat" counterparts. Moreover, we conducted a user study that compared the standard LTL$_f$ with our hierarchical version and found that users could more easily comprehend complex tasks using the hierarchical structure. We develop a search-based approach to synthesize plans for multi-robot systems, achieving simultaneous task allocation and planning. This method approximates the search space by loosely interconnected sub-spaces, each corresponding to an LTL$_f$ specification. The search primarily focuses on a single sub-space, transitioning to another under conditions determined by the decomposition of automata. We develop multiple heuristics to significantly expedite the search. Our theoretical analysis, conducted under mild assumptions, addresses completeness and optimality. Compared to existing methods used in various simulators for service tasks, our approach improves planning times while maintaining comparable solution quality.
CCNETS: A Modular Causal Learning Framework for Pattern Recognition in Imbalanced Datasets
Venue: arXiv cs.LG 2025
Year: 2025

Handling class imbalance remains a central challenge in machine learning, particularly in pattern recognition tasks where rare but critical events-such as fraudulent transactions or medical anomalies-must be identified accurately. Traditional generative models offer a potential remedy through data augmentation but often treat generation and classification as independent processes, leading to distribution mismatch and limited classifier benefit. To address these shortcomings, we propose Causal Cooperative Networks (CCNETS), a modular learning framework that integrates generation, inference, and reconstruction within a unified causal paradigm. CCNETS comprises three cooperative modules: an Explainer for latent feature abstraction, a Reasoner for label prediction, and a Producer for context-aware data generation. These components interact through a causal feedback loop, where classification results guide targeted sample synthesis. A key innovation, the Zoint mechanism, enables adaptive fusion of latent and observable features, enhancing semantic richness and enabling robust decision-making under uncertainty. We evaluate CCNETS on a real-world credit card fraud detection dataset with extreme imbalance (fraud cases < 0.2%). Across three experimental setups-including synthetic training, amplified generation, and direct classifier comparison-CCNETS outperforms baseline methods, achieving higher F1 scores, precision, and recall. Models trained on CCNETS-generated data also demonstrate superior generalization under limited data conditions. These results establish CCNETS as a scalable, interpretable, and hybrid soft computing framework. By causally aligning synthetic data with classifier objectives, CCNETS advances imbalanced pattern recognition and opens new directions for robust, modular learning in real-world applications.
SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Framework
Venue: arXiv cs.CV 2025
Year: 2025

Deepfakes have rapidly emerged as a serious threat to society due to their ease of creation and dissemination, triggering the accelerated development of detection technologies. However, many existing detectors rely on labgenerated datasets for validation, which may not prepare them for novel, real-world deepfakes. This paper extensively reviews and analyzes state-of-the-art deepfake detectors, evaluating them against several critical criteria. These criteria categorize detectors into 4 high-level groups and 13 finegrained sub-groups, aligned with a unified conceptual framework we propose. This classification offers practical insights into the factors affecting detector efficacy. We evaluate the generalizability of 16 leading detectors across comprehensive attack scenarios, including black-box, white-box, and graybox settings. Our systematized analysis and experiments provide a deeper understanding of deepfake detectors and their generalizability, paving the way for future research and the development of more proactive defenses against deepfakes.
DRL-based Latency-Aware Network Slicing in O-RAN with Time-Varying SLAs
Venue: arXiv cs.NI 2025
Year: 2025

The Open Radio Access Network (Open RAN) paradigm, and its reference architecture proposed by the O-RAN Alliance, is paving the way toward open, interoperable, observable and truly intelligent cellular networks. Crucial to this evolution is Machine Learning (ML), which will play a pivotal role by providing the necessary tools to realize the vision of self-organizing O-RAN systems. However, to be actionable, ML algorithms need to demonstrate high reliability, effectiveness in delivering high performance, and the ability to adapt to varying network conditions, traffic demands and performance requirements. To address these challenges, in this paper we propose a novel Deep Reinforcement Learning (DRL) agent design for O-RAN applications that can learn control policies under varying Service Level Agreement (SLAs) with heterogeneous minimum performance requirements. We focus on the case of RAN slicing and SLAs specifying maximum tolerable end-to-end latency levels. We use the OpenRAN Gym open-source environment to train a DRL agent that can adapt to varying SLAs and compare it against the state-of-the-art. We show that our agent maintains a low SLA violation rate that is 8.3x and 14.4x lower than approaches based on Deep Q- Learning (DQN) and Q-Learning while consuming respectively 0.3x and 0.6x fewer resources without the need for re-training.
Strategic Client Selection to Address Non-IIDness in HAPS-enabled FL Networks
Venue: arXiv cs.NI 2025
Year: 2025

The deployment of federated learning (FL) in non-terrestrial networks (NTN) that are supported by high-altitude platform stations (HAPS) offers numerous advantages. Due to its large footprint, it facilitates interaction with a large number of line-of-sight (LoS) ground clients, each possessing diverse datasets along with distinct communication and computational capabilities. The presence of many clients enhances the accuracy of the FL model and speeds up convergence. However, the variety of datasets among these clients poses a significant challenge, as it leads to pervasive non-independent and identically distributed (non-IID) data. The data non-IIDness results in markedly reduced training accuracy and slower convergence rates. To address this issue, we propose a novel weighted attribute-based client selection strategy that leverages multiple user-specific attributes, including historical traffic patterns, instantaneous channel conditions, computational capabilities, and previous-round learning performance. By combining these attributes into a composite score for each user at every FL round and selecting users with higher scores as FL clients, the framework ensures more uniform and representative data distributions, effectively mitigating the adverse effects of non-IID data. Simulation results corroborate the effectiveness of the proposed client selection strategy in enhancing FL model accuracy and convergence rate, as well as reducing training loss, by effectively addressing the critical challenge of data non-IIDness in large-scale FL system implementations.
A Closer Look at AUROC and AUPRC under Class Imbalance
Venue: arXiv cs.LG 2025
Year: 2025

In machine learning (ML), a widespread claim is that the area under the precision-recall curve (AUPRC) is a superior metric for model comparison to the area under the receiver operating characteristic (AUROC) for tasks with class imbalance. This paper refutes this notion on two fronts. First, we theoretically characterize the behavior of AUROC and AUPRC in the presence of model mistakes, establishing clearly that AUPRC is not generally superior in cases of class imbalance. We further show that AUPRC can be a harmful metric as it can unduly favor model improvements in subpopulations with more frequent positive labels, heightening algorithmic disparities. Next, we empirically support our theory using experiments on both semi-synthetic and real-world fairness datasets. Prompted by these insights, we conduct a review of over 1.5 million scientific papers to understand the origin of this invalid claim, finding that it is often made without citation, misattributed to papers that do not argue this point, and aggressively over-generalized from source arguments. Our findings represent a dual contribution: a significant technical advancement in understanding the relationship between AUROC and AUPRC and a stark warning about unchecked assumptions in the ML community.
Holey graphs: very large Betti numbers are testable
Venue: arXiv cs.DS 2025
Year: 2025

We show that the graph property of having a (very) large $k$-th Betti number $β_k$ for constant $k$ is testable with a constant number of queries in the dense graph model. More specifically, we consider a clique complex defined by an underlying graph and prove that for any $\varepsilon>0$, there exists $δ(\varepsilon,k)>0$ such that testing whether $β_k \geq (1-δ) d_k$ for $δ\leq δ(\varepsilon,k)$ reduces to tolerantly testing $(k+2)$-clique-freeness, which is known to be testable. This complements a result by Elek (2010) showing that Betti numbers are testable in the bounded-degree model. Our result combines the Euler characteristic, matroid theory and the graph removal lemma.
CCFC: Bridging Federated Clustering and Contrastive Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated clustering, an essential extension of centralized clustering for federated scenarios, enables multiple data-holding clients to collaboratively group data while keeping their data locally. In centralized scenarios, clustering driven by representation learning has made significant advancements in handling high-dimensional complex data. However, the combination of federated clustering and representation learning remains underexplored. To bridge this, we first tailor a cluster-contrastive model for learning clustering-friendly representations. Then, we harness this model as the foundation for proposing a new federated clustering method, named cluster-contrastive federated clustering (CCFC). Benefiting from representation learning, the clustering performance of CCFC even double those of the best baseline methods in some cases. Compared to the most related baseline, the benefit results in substantial NMI score improvements of up to 0.4155 on the most conspicuous case. Moreover, CCFC also shows superior performance in handling device failures from a practical viewpoint.
How Resilient is QUIC to Security and Privacy Attacks?
Venue: arXiv cs.CR 2025
Year: 2025

QUIC has rapidly evolved into a cornerstone transport protocol for secure, low-latency communications, yet its deployment continues to expose critical security and privacy vulnerabilities, particularly during connection establishment phases and via traffic analysis. This paper systematically revisits a comprehensive set of attacks on QUIC and emerging privacy threats. Building upon these observations, we critically analyze recent IETF mitigation efforts, including TLS Encrypted Client Hello (ECH), Oblivious HTTP (OHTTP) and MASQUE. We analyze how these mechanisms enhance privacy while introducing new operational risks, particularly under adversarial load. Additionally, we discuss emerging challenges posed by post-quantum cryptographic (PQC) handshakes, including handshake expansion and metadata leakage risks. Our analysis highlights ongoing gaps between theoretical defenses and practical deployments, and proposes new research directions focused on adaptive privacy mechanisms. Building on these insights, we propose future directions to ensure long-term security of QUIC and aim to guide its evolution as a robust, privacy-preserving, and resilient transport foundation for the next-generation Internet.
Always-Sparse Training by Growing Connections with Guided Stochastic Exploration
Venue: arXiv cs.LG 2025
Year: 2025

The excessive computational requirements of modern artificial neural networks (ANNs) are posing limitations on the machines that can run them. Sparsification of ANNs is often motivated by time, memory and energy savings only during model inference, yielding no benefits during training. A growing body of work is now focusing on providing the benefits of model sparsification also during training. While these methods greatly improve the training efficiency, the training algorithms yielding the most accurate models still materialize the dense weights, or compute dense gradients during training. We propose an efficient, always-sparse training algorithm with excellent scaling to larger and sparser models, supported by its linear time complexity with respect to the model width during training and inference. Moreover, our guided stochastic exploration algorithm improves over the accuracy of previous sparse training methods. We evaluate our method on CIFAR-10/100 and ImageNet using ResNet, VGG, and ViT models, and compare it against a range of sparsification methods.
Asymptotic performance of double and four circulant codes with small hull dimension
Venue: arXiv cs.IT 2025
Year: 2025

We study the asymptotic behavior of double and four circulant codes, which are quasi-cyclic codes of index two and four respectively. Exact enumeration results are derived for these families of codes with the prescribed hull dimension. These formulas, in turn, are the most used tools to prove the good behavior of double circulant and four circulant codes asymptotically. Computational results on the code families in consideration are provided as well.
Meta-Learning for Resource Allocation in Uplink Multi-Active STAR-RIS-aided NOMA System
Venue: arXiv cs.IT 2025
Year: 2025

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a novel technology which enables the full-space coverage. In this letter, a multi-active STAR-RIS-aided system using non-orthogonal multiple access in an uplink transmission is considered, where the second-order reflections among multiple active STAR-RISs assist the transmission from the single-antenna users to the multi-antenna base station. Specifically, the total sum rate maximization problem is solved by jointly optimizing the active beamforming, power allocation, transmission and reflection beamforming at the active STAR-RISs, and user-active STAR-RIS assignment. To solve the non-convex optimization problem, a novel deep reinforcement learning algorithm is proposed which integrates Meta-learning and deep deterministic policy gradient (DDPG), denoted by Meta-DDPG. Numerical results reveal that our proposed Meta-DDPG algorithm outperforms the DDPG algorithm with $19\%$ improvement, while second-order reflections among multi-active STAR-RISs provide $74.1\%$ enhancement in the total data rate.
Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering Tasks
Venue: arXiv cs.LG 2025
Year: 2025

Computational efficiency and robustness are essential in process modeling, optimization, and control for real-world engineering applications. While neural network-based approaches have gained significant attention in recent years, conventional neural networks often fail to address these two critical aspects simultaneously or even independently. Inspired by natural physical systems and established literature, input convex architectures are known to enhance computational efficiency in optimization tasks, whereas Lipschitz-constrained architectures improve robustness. However, combining these properties within a single model requires careful review, as inappropriate methods for enforcing one property can undermine the other. To overcome this, we introduce a novel network architecture, termed Input Convex Lipschitz Recurrent Neural Networks (ICLRNNs). This architecture seamlessly integrates the benefits of convexity and Lipschitz continuity, enabling fast and robust neural network-based modeling and optimization. The ICLRNN outperforms existing recurrent units in both computational efficiency and robustness. Additionally, it has been successfully applied to practical engineering scenarios, such as modeling and control of chemical process and the modeling and real-world solar irradiance prediction for solar PV system planning at LHT Holdings in Singapore. Source code is available at https://github.com/killingbear999/ICLRNN.
Centrality of shortest paths: Algorithms and complexity results
Venue: arXiv cs.DS 2025
Year: 2025

The degree centrality of a node, defined as the number of nodes adjacent to it, is often used as a measure of importance of a node to the structure of a network. This metric can be extended to paths in a network, where the degree centrality of a path is defined as the number of nodes adjacent to it. In this paper, we reconsider the problem of finding the most degree-central shortest path in an unweighted network. We propose a polynomial algorithm with the worst-case running time of $O(|E||V|^2Δ(G))$, where $|V|$ is the number of vertices in the network, $|E|$ is the number of edges in the network, and $Δ(G)$ is the maximum degree of the graph. We conduct a numerical study of our algorithm on synthetic and real-world networks and compare our results to the existing literature. In addition, we show that the same problem is NP-hard when a weighted graph is considered. Furthermore, we consider other centrality measures, such as the betweenness and closeness centrality, showing that the problem of finding the most betweenness-central shortest path is solvable in polynomial time and finding the most closeness-central shortest path is NP-hard, regardless of whether the graph is weighted or not.
SpecGen: Automated Generation of Formal Program Specifications via Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Formal program specifications play a crucial role in various stages of software development. However, manually crafting formal program specifications is rather difficult, making the job time-consuming and labor-intensive. It is even more challenging to write specifications that correctly and comprehensively describe the semantics of complex programs. To reduce the burden on software developers, automated specification generation methods have emerged. However, existing methods usually rely on predefined templates or grammar, making them struggle to accurately describe the behavior and functionality of complex real-world programs. To tackle this challenge, we introduce SpecGen, a novel technique for formal program specification generation based on Large Language Models. Our key insight is to overcome the limitations of existing methods by leveraging the code comprehension capability of LLMs. The process of SpecGen consists of two phases. The first phase employs a conversational approach that guides the LLM to generate appropriate specifications for a given program. The second phase, designed for where the LLM fails to generate correct specifications, applies four mutation operators to the model-generated specifications and selects verifiable specifications from the mutated ones through a novel heuristic selection strategy. We evaluate SpecGen on two datasets, including the SV-COMP Java category benchmark and a manually constructed dataset. Experimental results demonstrate that SpecGen succeeds in generating verifiable specifications for 279 out of 385 programs, outperforming the existing purely LLM-based approaches and conventional specification generation tools like Houdini and Daikon. Further investigations on the quality of generated specifications indicate that SpecGen can comprehensively articulate the behaviors of the input program.
From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions
Venue: arXiv cs.HC 2025
Year: 2025

Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utilizing a two-phase method, we first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. Drawing on the survey insights, we developed a GPT-4 powered personalized productivity agent that utilizes telemetry data gathered via Viva Insights from information workers to provide tailored assistance. We compared its performance with alternative productivity-assistive tools, such as dashboard and narrative, in a study involving 40 participants. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools. By building on these insights, our work provides important guidance for developing more effective productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.
Machines Do See Color: A Guideline to Classify Different Forms of Racist Discourse in Large Corpora
Venue: arXiv cs.CL 2025
Year: 2025

Current methods to identify and classify racist language in text rely on small-n qualitative approaches or large-n approaches focusing exclusively on overt forms of racist discourse. This article provides a step-by-step generalizable guideline to identify and classify different forms of racist discourse in large corpora. In our approach, we start by conceptualizing racism and its different manifestations. We then contextualize these racist manifestations to the time and place of interest, which allows researchers to identify their discursive form. Finally, we apply XLM-RoBERTa (XLM-R), a cross-lingual model for supervised text classification with a cutting-edge contextual understanding of text. We show that XLM-R and XLM-R-Racismo, our pretrained model, outperform other state-of-the-art approaches in classifying racism in large corpora. We illustrate our approach using a corpus of tweets relating to the Ecuadorian indígena community between 2018 and 2021.