OpenRaiser/Intern-Atlas huggingface dataset:
RulePilot: An LLM-Powered Agent for Security Rule Generation
Venue: arXiv cs.CR 2025
Year: 2025

The real-time demand for system security leads to the detection rules becoming an integral part of the intrusion detection life-cycle. Rule-based detection often identifies malicious logs based on the predefined grammar logic, requiring experts with deep domain knowledge for rule generation. Therefore, automation of rule generation can result in significant time savings and ease the burden of rule-related tasks on security engineers. In this paper, we propose RulePilot, which mimics human expertise via LLM-based agent for addressing rule-related challenges like rule creation or conversion. Using RulePilot, the security analysts do not need to write down the rules following the grammar, instead, they can just provide the annotations such as the natural-language-based descriptions of a rule, our RulePilot can automatically generate the detection rules without more intervention. RulePilot is equipped with the intermediate representation (IR), which abstracts the complexity of config rules into structured, standardized formats, allowing LLMs to focus on generation rules in a more manageable and consistent way. We present a comprehensive evaluation of RulePilot in terms of textual similarity and execution success abilities, showcasing RulePilot can generate high-fidelity rules, outperforming the baseline models by up to 107.4% in textual similarity to ground truths and achieving better detection accuracy in real-world execution tests. We perform a case study from our industry collaborators in Singapore, showcasing that RulePilot significantly help junior analysts/general users in the rule creation process.
Actionable Warning Is Not Enough: Recommending Valid Actionable Warnings with Weak Supervision
Venue: arXiv cs.SE 2025
Year: 2025

The use of static analysis tools has gained increasing popularity among developers in the last few years. However, the widespread adoption of static analysis tools is hindered by their high false alarm rates. Previous studies have introduced the concept of actionable warnings and built a machine-learning method to distinguish actionable warnings from false alarms. However, according to our empirical observation, the current assumption used for actionable warning(s) collection is rather shaky and inaccurate, leading to a large number of invalid actionable warnings. To address this problem, in this study, we build the first large actionable warning dataset by mining 68,274 reversions from Top-500 GitHub C repositories, we then take one step further by assigning each actionable warning a weak label regarding its likelihood of being a real bug. Following that, we propose a two-stage framework called ACWRecommender to automatically recommend the actionable warnings with high probability to be real bugs (AWHB). Our approach warms up the pre-trained model UniXcoder by identifying actionable warnings task (coarse-grained detection stage) and rerank AWHB to the top by weakly supervised learning (fine-grained reranking stage). Experimental results show that our proposed model outperforms several baselines by a large margin in terms of nDCG and MRR for AWHB recommendation. Moreover, we ran our tool on 6 randomly selected projects and manually checked the top-ranked warnings from 2,197 reported warnings, we reported top-10 recommended warnings to developers, 27 of them were already confirmed by developers as real bugs. Developers can quickly find real bugs among the massive amount of reported warnings, which verifies the practical usage of our tool.
Intermittent Rendezvous Plans with Mixed Integer Linear Program for Large-Scale Multi-Robot Exploration
Venue: arXiv cs.RO 2025
Year: 2025

Multi-Robot Exploration (MRE) systems with communication constraints have proven efficient in accomplishing a variety of tasks, including search-and-rescue, stealth, and military operations. While some works focus on opportunistic approaches for efficiency, others concentrate on pre-planned trajectories or scheduling for increased interpretability. However, scheduling usually requires knowledge of the environment beforehand, which prevents its deployment in several domains due to related uncertainties (e.g., underwater exploration). In our previous work, we proposed an intermittent communications framework for MRE under communication constraints that uses scheduled rendezvous events to mitigate such limitations. However, the system was unable to generate optimal plans and had no mechanisms to follow the plan considering realistic trajectories, which is not suited for real-world deployments. In this work, we further investigate the problem by formulating the Multi-Robot Exploration with Communication Constraints and Intermittent Connectivity (MRE-CCIC) problem. We propose a Mixed-Integer Linear Program (MILP) formulation to generate rendezvous plans and a policy to follow them based on the Rendezvous Tracking for Unknown Scenarios (RTUS) mechanism. The RTUS is a simple rule to allow robots to follow the assigned plan, considering unknown conditions. Finally, we evaluated our method in a large-scale environment configured in Gazebo simulations. The results suggest that our method can follow the plan promptly and accomplish the task efficiently. We provide an open-source implementation of both the MILP plan generator and the large-scale MRE-CCIC.
SCI: A Metacognitive Control for Signal Dynamics
Venue: arXiv cs.LG 2025
Year: 2025

Modern deep learning systems are typically deployed as open-loop function approximators: they map inputs to outputs in a single pass, without regulating how much computation or explanatory effort is spent on a given case. In safety-critical settings, this is brittle: easy and ambiguous inputs receive identical processing, and uncertainty is only read off retrospectively from raw probabilities. We introduce the Surgical Cognitive Interpreter (SCI), a lightweight closed-loop metacognitive control layer that wraps an existing stochastic model and turns prediction into an iterative process. SCI monitors a scalar interpretive state SP(t), here instantiated as a normalized entropy-based confidence signal, and adaptively decides whether to stop, continue sampling, or abstain. The goal is not to improve accuracy per se, but to regulate interpretive error ΔSP and expose a safety signal that tracks when the underlying model is likely to fail. We instantiate SCI around Monte Carlo dropout classifiers in three domains: vision (MNIST digits), medical time series (MIT-BIH arrhythmia), and industrial condition monitoring (rolling-element bearings). In all cases, the controller allocates more inference steps to misclassified inputs than to correct ones (up to about 3-4x on MNIST and bearings, and 1.4x on MIT-BIH). The resulting ΔSP acts as a usable safety signal for detecting misclassifications (AUROC 0.63 on MNIST, 0.70 on MIT-BIH, 0.86 on bearings). Code and reproducibility: https://github.com/vishal-1344/sci
Cross-view Joint Learning for Mixed-Missing Multi-view Unsupervised Feature Selection
Venue: arXiv cs.LG 2025
Year: 2025

Incomplete multi-view unsupervised feature selection (IMUFS), which aims to identify representative features from unlabeled multi-view data containing missing values, has received growing attention in recent years. Despite their promising performance, existing methods face three key challenges: 1) by focusing solely on the view-missing problem, they are not well-suited to the more prevalent mixed-missing scenario in practice, where some samples lack entire views or only partial features within views; 2) insufficient utilization of consistency and diversity across views limits the effectiveness of feature selection; and 3) the lack of theoretical analysis makes it unclear how feature selection and data imputation interact during the joint learning process. Being aware of these, we propose CLIM-FS, a novel IMUFS method designed to address the mixed-missing problem. Specifically, we integrate the imputation of both missing views and variables into a feature selection model based on nonnegative orthogonal matrix factorization, enabling the joint learning of feature selection and adaptive data imputation. Furthermore, we fully leverage consensus cluster structure and cross-view local geometrical structure to enhance the synergistic learning process. We also provide a theoretical analysis to clarify the underlying collaborative mechanism of CLIM-FS. Experimental results on eight real-world multi-view datasets demonstrate that CLIM-FS outperforms state-of-the-art methods.
Reducing Hallucinations in LLM-Generated Code via Semantic Triangulation
Venue: arXiv cs.SE 2025
Year: 2025

When generating code from natural language prompts, an LLM samples programs from a probability distribution, many of which might be incorrect. Sample consensus techniques - such as majority voting or validation against generated tests or specifications - aim to identify a correct program in the sample or abstain if none is valid. However, existing methods often fail to select a correct solution when its sampling probability is low, or when the problem permits multiple valid but non-equivalent solutions. Additionally, they often fail to abstain when no correct solution is present in the sample. To overcome these limitations, we introduce semantic triangulation, which transforms a programming problem in a way that non-trivially alters its semantics while preserving an exact, verifiable mapping between solutions before and after transformation. We theoretically establish that verifying consistency across such problem transformations increases confidence that generated programs reflect accurate generalization rather than spurious statistical correlations, enabling more reliable sample consensus and abstention. On the LiveCodeBench and CodeElo benchmarks, using GPT-4o and DeepSeek-V3 models, semantic triangulation increases reliability of generated code by 21% compared to the method that selects only high-confidence solutions with the probability threshold 0.5, while being able to pinpoint correct solutions at sampling probabilities as low as 0.14. Apart from that, it is also the only approach to consistently form true consensus on tasks with multiple valid but non-equivalent solutions.
ProofWright: Towards Agentic Formal Verification of CUDA
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) are increasingly used to automatically generate optimized CUDA kernels, substantially improving developer productivity. However, despite rapid generation, these kernels often contain subtle correctness bugs and lack formal safety guarantees. Runtime testing is inherently unreliable - limited input coverage and reward hacking can mask incorrect behavior - while manual formal verification is reliable but cannot scale to match LLM output rates, creating a critical validation bottleneck. We present ProofWright, an agentic verification framework that bridges this gap by integrating automated formal verification with LLM-based code generation. ProofWright provides end-to-end guarantees of memory safety, thread safety, and semantic correctness for LLM-generated CUDA kernels. On KernelBench L1, ProofWright verifies safety properties for 74% of generated kernels, uncovers subtle correctness errors missed by conventional testing, and establishes semantic equivalence for a class of element-wise kernels. With a modest overhead of 3 minutes per kernel, ProofWright demonstrates that scalable, automated formal verification of LLM-generated GPU code is feasible - offering a path toward trustworthy high-performance code generation without sacrificing developer productivity.
Optimal Self-Consistency for Efficient Reasoning with Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Self-consistency (SC) is a widely used test-time inference technique for improving performance in chain-of-thought reasoning. It involves generating multiple responses, or samples from a large language model (LLM) and selecting the most frequent answer. This procedure can naturally be viewed as a majority vote or empirical mode estimation. Despite its effectiveness, SC is prohibitively expensive at scale when naively applied to datasets, and it lacks a unified theoretical treatment of sample efficiency and scaling behavior. In this paper, we provide the first comprehensive analysis of SC's scaling behavior and its variants, drawing on mode estimation and voting theory. We derive and empirically validate power law scaling for self-consistency across datasets, and analyze the sample efficiency for fixed-allocation and dynamic-allocation sampling schemes. From these insights, we introduce Blend-ASC, a novel variant of self-consistency that dynamically allocates samples to questions during inference, achieving state-of-the-art sample efficiency. Our approach uses 6.8x fewer samples than vanilla SC on average, outperforming both fixed- and dynamic-allocation SC baselines, thereby demonstrating the superiority of our approach in terms of efficiency. In contrast to existing variants, Blend-ASC is hyperparameter-free and can fit an arbitrary sample budget, ensuring it can be easily applied to any self-consistency application.
Active Learning of Symbolic Automata Over Rational Numbers
Venue: arXiv cs.LG 2025
Year: 2025

Automata learning has many applications in artificial intelligence and software engineering. Central to these applications is the $L^*$ algorithm, introduced by Angluin. The $L^*$ algorithm learns deterministic finite-state automata (DFAs) in polynomial time when provided with a minimally adequate teacher. Unfortunately, the $L^*$ algorithm can only learn DFAs over finite alphabets, which limits its applicability. In this paper, we extend $L^*$ to learn symbolic automata whose transitions use predicates over rational numbers, i.e., over infinite and dense alphabets. Our result makes the $L^*$ algorithm applicable to new settings like (real) RGX, and time series. Furthermore, our proposed algorithm is optimal in the sense that it asks a number of queries to the teacher that is at most linear with respect to the number of transitions, and to the representation size of the predicates.
Impact of UK Postgraduate Student Experiences on Academic Performance in Blended Learning: A Data Analytics Approach
Venue: arXiv cs.CY 2025
Year: 2025

Blended learning has become a dominant educational model in higher education in the UK and worldwide, particularly after the COVID-19 pandemic. This is further enriched with accompanying pedagogical changes, such as strengthened asynchronous learning, and the use of AI (from ChatGPT and all other similar tools that followed) and other technologies to aid learning. While these educational transformations have enabled flexibility in learning and resource access, they have also exposed new challenges on how students can construct successful learning in hybrid learning environments. In this paper, we investigate the interaction between different dimensions of student learning experiences (ranging from perceived acceptance of teaching methods and staff support/feedback to learning pressure and student motivation) and academic achievement within the context of postgraduate blended learning in UK universities. To achieve this, we employed a combination of several data analytics techniques including visualization, statistical tests, regression analysis, and latent profile analysis. Our empirical results (based on a survey of 255 postgraduate students and holistically interpreted via the Community of Inquiry (CoI) framework) demonstrated that learning activities combining teaching and social presences, and tailored academic support through effective feedback are critical elements for successful postgraduate experience in blended learning contexts. Regarding contributions, this research advances the understanding of student success by identifying the various ways demographic, experiential, and psychological factors impact academic outcomes. And in theoretical terms, it contributes to the extension of the CoI framework by integrating the concept of learner heterogeneity and identifying four distinct student profiles based on how they engage in the different CoI presences.
Ground Plane Projection for Improved Traffic Analytics at Intersections
Venue: arXiv cs.CV 2025
Year: 2025

Accurate turning movement counts at intersections are important for signal control, traffic management and urban planning. Computer vision systems for automatic turning movement counts typically rely on visual analysis in the image plane of an infrastructure camera. Here we explore potential advantages of back-projecting vehicles detected in one or more infrastructure cameras to the ground plane for analysis in real-world 3D coordinates. For single-camera systems we find that back-projection yields more accurate trajectory classification and turning movement counts. We further show that even higher accuracy can be achieved through weak fusion of back-projected detections from multiple cameras. These results suggeest that traffic should be analyzed on the ground plane, not the image plane
CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

Hyperspectral image (HSI) classification faces critical challenges, including high spectral dimensionality, complex spectral-spatial correlations, and limited training samples with severe class imbalance. While CNNs excel at local feature extraction and transformers capture long-range dependencies, their isolated application yields suboptimal results due to quadratic complexity and insufficient inductive biases. We propose CLAReSNet (Convolutional Latent Attention Residual Spectral Network), a hybrid architecture that integrates multi-scale convolutional extraction with transformer-style attention via an adaptive latent bottleneck. The model employs a multi-scale convolutional stem with deep residual blocks and an enhanced Convolutional Block Attention Module for hierarchical spatial features, followed by spectral encoder layers combining bidirectional RNNs (LSTM/GRU) with Multi-Scale Spectral Latent Attention (MSLA). MSLA reduces complexity from $\mathcal{O}(T^2D)$ to $\mathcal{O}(T\log(T)D)$ by adaptive latent token allocation (8-64 tokens) that scales logarithmically with the sequence length. Hierarchical cross-attention fusion dynamically aggregates multi-level representations for robust classification. Experiments conducted on the Indian Pines and Salinas datasets show state-of-the-art performance, achieving overall accuracies of 99.71% and 99.96%, significantly surpassing HybridSN, SSRN, and SpectralFormer. The learned embeddings exhibit superior inter-class separability and compact intra-class clustering, validating CLAReSNet's effectiveness under severe class imbalance.
Multilaminate piezoelectric PVDF actuators to enhance performance of soft micro robots
Venue: arXiv cs.RO 2025
Year: 2025

Multilayer piezoelectric polyvinylidene fluoride (PVDF) actuators are a promising approach to enhance performance of soft microrobotic systems. In this work, we develop and characterize multilayer PVDF actuators with parallel voltage distribution across each layer, bridging a unique design space between brittle high-force PZT stacks and compliant but lower-bandwidth soft polymer actuators. We show the effects of layer thickness and number of layers in actuator performance and their agreement with a first principles model. By varying these parameters, we demonstrate actuators capable of >3 mm of free deflection, >20 mN of blocked force, and >=500 Hz, while operating at voltages as low as 150 volts. To illustrate their potential for robotic integration, we integrate our actuators into a planar, translating microrobot that leverages resonance to achieve locomotion with robustness to large perturbations.
Leveraging Quantum-Based Architectures for Robust Diagnostics
Venue: arXiv cs.CV 2025
Year: 2025

The objective of this study is to diagnose and differentiate kidney stones, cysts, and tumors using Computed Tomography (CT) images of the kidney. This study leverages a hybrid quantum-classical framework in this regard. We combine a pretrained ResNet50 encoder, with a Quantum Convolutional Neural Network (QCNN) to explore quantum-assisted diagnosis. We pre-process the kidney images using denoising and contrast limited adaptive histogram equalization to enhance feature extraction. We address class imbalance through data augmentation and weighted sampling. Latent features extracted by the encoder are transformed into qubits via angle encoding and processed by a QCNN. The model is evaluated on both 8-qubit and 12-qubit configurations. Both architectures achieved rapid convergence with stable learning curves and high consistency between training and validation performance. The models reached a test accuracy of 0.99, with the 12-qubit configuration providing improvements in overall recall and precision, particularly for Cyst and Tumor detection, where it achieved perfect recall for Cysts and a tumor F1-score of 0.9956. Confusion matrix analysis further confirmed reliable classification behavior across all classes, with very few misclassifications. Results demonstrate that integrating classical pre-processing and deep feature extraction with quantum circuits enhances medical diagnostic performance.
Multi-Domain EEG Representation Learning with Orthogonal Mapping and Attention-based Fusion for Cognitive Load Classification
Venue: arXiv cs.HC 2025
Year: 2025

We propose a new representation learning solution for the classification of cognitive load based on Electroencephalogram (EEG). Our method integrates both time and frequency domains by first passing the raw EEG signals through the convolutional encoder to obtain the time domain representations. Next, we measure the Power Spectral Density (PSD) for all five EEG frequency bands and generate the channel power values as 2D images referred to as multi-spectral topography maps. These multi-spectral topography maps are then fed to a separate encoder to obtain the representations in frequency domain. Our solution employs a multi-domain attention module that maps these domain-specific embeddings onto a shared embedding space to emphasize more on important inter-domain relationships to enhance the representations for cognitive load classification. Additionally, we incorporate an orthogonal projection constraint during the training of our method to effectively increase the inter-class distances while improving intra-class clustering. This enhancement allows efficient discrimination between different cognitive states and aids in better grouping of similar states within the feature space. We validate the effectiveness of our model through extensive experiments on two public EEG datasets, CL-Drive and CLARE for cognitive load classification. Our results demonstrate the superiority of our multi-domain approach over the traditional single-domain techniques. Moreover, we conduct ablation and sensitivity analyses to assess the impact of various components of our method. Finally, robustness experiments on different amounts of added noise demonstrate the stability of our method compared to other state-of-the-art solutions.