OpenRaiser/Intern-Atlas huggingface dataset:
Security-as-a-Function for IDS/IPS in Softwarized Network and Applications to 5G Network Systems
Venue: arXiv cs.NI 2025
Year: 2025

The service-based architecture of 5G network allows network operators to place virtualized network functions on commodity hardware, unlike the traditional vendor-specific hardware-based functionalities. However, it expands the security vulnerabilities and threats to the 5G network. While there exist several theoretical studies on network function placement and service routing, a few focused on the security aspects of the 5G network systems. This paper focuses on safeguarding the 5G core network systems from DoS and DDoS attacks by placing intrusion detection and prevention systems (IDS-IPS) as virtualized network functions following the 5G standalone architecture. To ensure the virtualized placement of IDS-IPS, first, we provide thorough virtual machine (VM)-based and containerized implementation details and evaluate the network performance with two scenarios, IDS and IPS, in the presence of TCP and UDP applications. Second, we apply the VM-based implementation of IDS-IPS on a softwarized 5G core network and study the network performances. The experiment results on network throughput, latency, and packet drop reveal that the softwarized IDS-IPS can meet the QoS requirements of 5G applications, while safeguarding the network from DoS and DDoS attacks.
Temporal-Conditional Referring Video Object Segmentation with Noise-Free Text-to-Video Diffusion Model
Venue: arXiv cs.CV 2025
Year: 2025

Referring Video Object Segmentation (RVOS) aims to segment specific objects in a video according to textual descriptions. We observe that recent RVOS approaches often place excessive emphasis on feature extraction and temporal modeling, while relatively neglecting the design of the segmentation head. In fact, there remains considerable room for improvement in segmentation head design. To address this, we propose a Temporal-Conditional Referring Video Object Segmentation model, which innovatively integrates existing segmentation methods to effectively enhance boundary segmentation capability. Furthermore, our model leverages a text-to-video diffusion model for feature extraction. On top of this, we remove the traditional noise prediction module to avoid the randomness of noise from degrading segmentation accuracy, thereby simplifying the model while improving performance. Finally, to overcome the limited feature extraction capability of the VAE, we design a Temporal Context Mask Refinement (TCMR) module, which significantly improves segmentation quality without introducing complex designs. We evaluate our method on four public RVOS benchmarks, where it consistently achieves state-of-the-art performance.
A Generalized Learning Framework for Self-Supervised Contrastive Learning
Venue: arXiv cs.LG 2025
Year: 2025

Self-supervised contrastive learning (SSCL) has recently demonstrated superiority in multiple downstream tasks. In this paper, we generalize the standard SSCL methods to a Generalized Learning Framework (GLF) consisting of two parts: the aligning part and the constraining part. We analyze three existing SSCL methods: BYOL, Barlow Twins, and SwAV, and show that they can be unified under GLF with different choices of the constraining part. We further propose empirical and theoretical analyses providing two insights into designing the constraining part of GLF: intra-class compactness and inter-class separability, which measure how well the feature space preserves the class information of the inputs. However, since SSCL can not use labels, it is challenging to design a constraining part that satisfies these properties. To address this issue, we consider inducing intra-class compactness and inter-class separability by iteratively capturing the dynamic relationship between anchor and other samples and propose a plug-and-play method called Adaptive Distribution Calibration (ADC) to ensure that samples that are near or far from the anchor point in the original input space are closer or further away from the anchor point in the feature space. Both the theoretical analysis and the empirical evaluation demonstrate the superiority of ADC.
Reactive Semantics for User Interface Description Languages
Venue: arXiv cs.PL 2025
Year: 2025

User Interface Description Languages (UIDLs) are high-level languages that facilitate the development of Human-Machine Interfaces, such as Graphical User Interface (GUI) applications. They usually provide first-class primitives to specify how the program reacts to an external event (user input, network message), and how data flows through the program. Although these domain-specific languages are now widely used to implement safety-critical GUIs, little work has been invested in their formalization and verification. In this paper, we propose a denotational semantic model for a core reactive UIDL, Smalite, which we argue is expressive enough to encode constructs from more realistic languages. This preliminary work may be used as a stepping stone to produce a formally verified compiler for UIDLs.
Two-Factor Authentication Smart Entryway Using Modified LBPH Algorithm
Venue: arXiv cs.CV 2025
Year: 2025

Face mask detection has become increasingly important recently, particularly during the COVID-19 pandemic. Many face detection models have been developed in smart entryways using IoT. However, there is a lack of IoT development on face mask detection. This paper proposes a two-factor authentication system for smart entryway access control using facial recognition and passcode verification and an automation process to alert the owner and activate the surveillance system when a stranger is detected and controls the system remotely via Telegram on a Raspberry Pi platform. The system employs the Local Binary Patterns Histograms for the full face recognition algorithm and modified LBPH algorithm for occluded face detection. On average, the system achieved an Accuracy of approximately 70%, a Precision of approximately 80%, and a Recall of approximately 83.26% across all tested users. The results indicate that the system is capable of conducting face recognition and mask detection, automating the operation of the remote control to register users, locking or unlocking the door, and notifying the owner. The sample participants highly accept it for future use in the user acceptance test.
Conflicting Scores, Confusing Signals: An Empirical Study of Vulnerability Scoring Systems
Venue: arXiv cs.CR 2025
Year: 2025

Accurately assessing software vulnerabilities is essential for effective prioritization and remediation. While various scoring systems exist to support this task, their differing goals, methodologies and outputs often lead to inconsistent prioritization decisions. This work provides the first large-scale, outcome-linked empirical comparison of four publicly available vulnerability scoring systems: the Common Vulnerability Scoring System (CVSS), the Stakeholder-Specific Vulnerability Categorization (SSVC), the Exploit Prediction Scoring System (EPSS), and the Exploitability Index. We use a dataset of 600 real-world vulnerabilities derived from four months of Microsoft's Patch Tuesday disclosures to investigate the relationships between these scores, evaluate how they support vulnerability management task, how these scores categorize vulnerabilities across triage tiers, and assess their ability to capture the real-world exploitation risk. Our findings reveal significant disparities in how scoring systems rank the same vulnerabilities, with implications for organizations relying on these metrics to make data-driven, risk-based decisions. We provide insights into the alignment and divergence of these systems, highlighting the need for more transparent and consistent exploitability, risk, and severity assessments.
CRISP: Persistent Concept Unlearning via Sparse Autoencoders
Venue: arXiv cs.CL 2025
Year: 2025

As large language models (LLMs) are increasingly deployed in real-world applications, the need to selectively remove unwanted knowledge while preserving model utility has become paramount. Recent work has explored sparse autoencoders (SAEs) to perform precise interventions on monosemantic features. However, most SAE-based methods operate at inference time, which does not create persistent changes in the model's parameters. Such interventions can be bypassed or reversed by malicious actors with parameter access. We introduce CRISP, a parameter-efficient method for persistent concept unlearning using SAEs. CRISP automatically identifies salient SAE features across multiple layers and suppresses their activations. We experiment with two LLMs and show that our method outperforms prior approaches on safety-critical unlearning tasks from the WMDP benchmark, successfully removing harmful knowledge while preserving general and in-domain capabilities. Feature-level analysis reveals that CRISP achieves semantically coherent separation between target and benign concepts, allowing precise suppression of the target features.
DeH4R: A Decoupled and Hybrid Method for Road Network Graph Extraction
Venue: arXiv cs.CV 2025
Year: 2025

The automated extraction of complete and precise road network graphs from remote sensing imagery remains a critical challenge in geospatial computer vision. Segmentation-based approaches, while effective in pixel-level recognition, struggle to maintain topology fidelity after vectorization postprocessing. Graph-growing methods build more topologically faithful graphs but suffer from computationally prohibitive iterative ROI cropping. Graph-generating methods first predict global static candidate road network vertices, and then infer possible edges between vertices. They achieve fast topology-aware inference, but limits the dynamic insertion of vertices. To address these challenges, we propose DeH4R, a novel hybrid model that combines graph-generating efficiency and graph-growing dynamics. This is achieved by decoupling the task into candidate vertex detection, adjacent vertex prediction, initial graph contruction, and graph expansion. This architectural innovation enables dynamic vertex (edge) insertions while retaining fast inference speed and enhancing both topology fidelity and spatial consistency. Comprehensive evaluations on CityScale and SpaceNet benchmarks demonstrate state-of-the-art (SOTA) performance. DeH4R outperforms the prior SOTA graph-growing method RNGDet++ by 4.62 APLS and 10.18 IoU on CityScale, while being approximately 10 $\times$ faster. The code will be made publicly available at https://github.com/7777777FAN/DeH4R.
Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled data to generate pseudo labels. Recently, advanced state space models, represented by Mamba, have shown efficient handling of long-range dependencies. This drives us to explore their potential in semi-supervised medical image segmentation. In this paper, we propose a novel Diversity-enhanced Collaborative Mamba framework (namely DCMamba) for semi-supervised medical image segmentation, which explores and utilizes the diversity from data, network, and feature perspectives. Firstly, from the data perspective, we develop patch-level weak-strong mixing augmentation with Mamba's scanning modeling characteristics. Moreover, from the network perspective, we introduce a diverse-scan collaboration module, which could benefit from the prediction discrepancies arising from different scanning directions. Furthermore, from the feature perspective, we adopt an uncertainty-weighted contrastive learning mechanism to enhance the diversity of feature representation. Experiments demonstrate that our DCMamba significantly outperforms other semi-supervised medical image segmentation methods, e.g., yielding the latest SSM-based method by 6.69% on the Synapse dataset with 20% labeled data.
CausalPlan: Empowering Efficient LLM Multi-Agent Collaboration Through Causality-Driven Planning
Venue: arXiv cs.AI 2025
Year: 2025

Large language model (LLM) agents-especially smaller, open-source models-often produce causally invalid or incoherent actions in collaborative tasks due to their reliance on surface-level correlations rather than grounded causal reasoning. This limitation undermines their performance in terms of coordination and planning in dynamic environments. We address this challenge with CausalPlan, a two-phase framework that integrates explicit structural causal reasoning into the LLM planning process. At the core of CausalPlan is the Structural Causal Action (SCA) model, which learns a causal graph from agent trajectories to capture how prior actions and current environment states influence future decisions. This structure is then used to guide action selection by assigning causal scores to LLM-generated proposals, reweighting them accordingly, or falling back to causally grounded alternatives when needed. By embedding this causal knowledge directly into the decision loop, CausalPlan constrains planning to intervention-consistent behaviours without requiring fine-tuning of the LLM itself. We evaluate CausalPlan on the Overcooked-AI benchmark across five multi-agent coordination tasks and four LLMs of varying sizes: Gemma-7B, Llama-8B, Qwen-14B, and Llama-70B. Experimental results show that CausalPlan consistently reduces invalid actions and improves collaboration in both AI-AI and human-AI settings, outperforming strong reinforcement learning baselines. Our findings highlight the value of causality-driven planning for deploying efficient, interpretable, and generalisable multi-agent LLM systems.
NodeShield: Runtime Enforcement of Security-Enhanced SBOMs for Node.js
Venue: arXiv cs.CR 2025
Year: 2025

The software supply chain is an increasingly common attack vector for malicious actors. The Node.js ecosystem has been subject to a wide array of attacks, likely due to its size and prevalence. To counter such attacks, the research community and practitioners have proposed a range of static and dynamic mechanisms, including process- and language-level sandboxing, permission systems, and taint tracking. Drawing on valuable insight from these works, this paper studies a runtime protection mechanism for (the supply chain of) Node.js applications with the ambitious goals of compatibility, automation, minimal overhead, and policy conciseness. Specifically, we design, implement and evaluate NodeShield, a protection mechanism for Node.js that enforces an application's dependency hierarchy and controls access to system resources at runtime. We leverage the up-and-coming SBOM standard as the source of truth for the dependency hierarchy of the application, thus preventing components from stealthily abusing undeclared components. We propose to enhance the SBOM with a notion of capabilities that represents a set of related system resources a component may access. Our proposed SBOM extension, the Capability Bill of Materials or CBOM, records the required capabilities of each component, providing valuable insight into the potential privileged behavior. NodeShield enforces the SBOM and CBOM at runtime via code outlining (as opposed to inlining) with no modifications to the original code or Node.js runtime, thus preventing unexpected, potentially malicious behavior. Our evaluation shows that NodeShield can prevent over 98% out of 67 known supply chain attacks while incurring minimal overhead on servers at less than 1ms per request. We achieve this while maintaining broad compatibility with vanilla Node.js and a concise policy language that consists of at most 7 entries per dependency.
Data-Driven Discovery of Multi-Dimensional Breakage Population Balance Equations
Venue: arXiv cs.CE 2025
Year: 2025

Multi-dimensional breakage is a ubiquitous phenomenon in natural systems, yet the systematic discovery of underlying governing equations remains a long-standing challenge. Current inverse solution techniques are restricted to one-dimensional cases and typically depend on the availability of a priori system knowledge, thus limiting their applicability. By leveraging advances in data-driven sparse regression techniques, we develop the Multi-Dimensional Breakage Population Balance Equation Identification (mPBE ID) algorithm for discovering multi-dimensional breakage population balance equations (mPBEs) directly from data. Our mPBE-ID enables tractable identification of mPBEs by incorporating several key strategies, namely, a breakage-informed constrained sparse regression, targeted candidate library functions construction via insights from Dynamic Mode Decomposition (DMD), and robust handling of noisy/limited data through ensembling (bagging/bragging). Notably, we demonstrate how the DMD is indispensable for distilling dominant breakage dynamics which can then be used to facilitate the systematic inclusion of candidate library terms. We showcase the ability of the mPBE-ID to discover different forms of mPBE (including those with discontinuous stoichiometric kernels) even when tested against noisy and limited data. We anticipate that the mPBE-ID will serve as a foundational framework for future extensions to generalize the discovery of multi-dimensional PBEs for various high-dimensional particulate phenomena.
Is-NeRF: In-scattering Neural Radiance Field for Blurred Images
Venue: arXiv cs.GR 2025
Year: 2025

Neural Radiance Fields (NeRF) has gained significant attention for its prominent implicit 3D representation and realistic novel view synthesis capabilities. Available works unexceptionally employ straight-line volume rendering, which struggles to handle sophisticated lightpath scenarios and introduces geometric ambiguities during training, particularly evident when processing motion-blurred images. To address these challenges, this work proposes a novel deblur neural radiance field, Is-NeRF, featuring explicit lightpath modeling in real-world environments. By unifying six common light propagation phenomena through an in-scattering representation, we establish a new scattering-aware volume rendering pipeline adaptable to complex lightpaths. Additionally, we introduce an adaptive learning strategy that enables autonomous determining of scattering directions and sampling intervals to capture finer object details. The proposed network jointly optimizes NeRF parameters, scattering parameters, and camera motions to recover fine-grained scene representations from blurry images. Comprehensive evaluations demonstrate that it effectively handles complex real-world scenarios, outperforming state-of-the-art approaches in generating high-fidelity images with accurate geometric details.
Revisiting RAG Ensemble: A Theoretical and Mechanistic Analysis of Multi-RAG System Collaboration
Venue: arXiv cs.AI 2025
Year: 2025

Retrieval-Augmented Generation (RAG) technology has been widely applied in recent years. However, despite the emergence of various RAG frameworks, a single RAG framework still cannot adapt well to a broad range of downstream tasks. Therefore, how to leverage the advantages of multiple RAG systems has become an area worth exploring. To address this issue, we have conducted a comprehensive and systematic investigation into ensemble methods based on RAG systems. Specifically, we have analyzed the RAG ensemble framework from both theoretical and mechanistic analysis perspectives. From the theoretical analysis, we provide the first explanation of the RAG ensemble framework from the perspective of information entropy. In terms of mechanism analysis, we have explored the RAG ensemble framework from both the pipeline and module levels. We carefully select four different pipelines (Branching, Iterative, Loop, and Agentic) and three different modules (Generator, Retriever, and Reranker) to solve seven different research questions. The experiments show that aggregating multiple RAG systems is both generalizable and robust, whether at the pipeline level or the module level. Our work lays the foundation for similar research on the multi-RAG system ensemble.
Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models
Venue: arXiv cs.RO 2025
Year: 2025

Existing driving style recognition systems largely depend on low-level sensor-derived features for training, neglecting the rich semantic reasoning capability inherent to human experts. This discrepancy results in a fundamental misalignment between algorithmic classifications and expert judgments. To bridge this gap, we propose a novel framework that integrates Semantic Privileged Information (SPI) derived from large language models (LLMs) to align recognition outcomes with human-interpretable reasoning. First, we introduce DriBehavGPT, an interactive LLM-based module that generates natural-language descriptions of driving behaviors. These descriptions are then encoded into machine learning-compatible representations via text embedding and dimensionality reduction. Finally, we incorporate them as privileged information into Support Vector Machine Plus (SVM+) for training, enabling the model to approximate human-like interpretation patterns. Experiments across diverse real-world driving scenarios demonstrate that our SPI-enhanced framework outperforms conventional methods, achieving F1-score improvements of 7.6% (car-following) and 7.9% (lane-changing). Importantly, SPI is exclusively used during training, while inference relies solely on sensor data, ensuring computational efficiency without sacrificing performance. These results highlight the pivotal role of semantic behavioral representations in improving recognition accuracy while advancing interpretable, human-centric driving systems.
Forecasting Smog Events Using ConvLSTM: A Spatio-Temporal Approach for Aerosol Index Prediction in South Asia
Venue: arXiv cs.CV 2025
Year: 2025

The South Asian Smog refers to the recurring annual air pollution events marked by high contaminant levels, reduced visibility, and significant socio-economic impacts, primarily affecting the Indo-Gangetic Plains (IGP) from November to February. Over the past decade, increased air pollution sources such as crop residue burning, motor vehicles, and changing weather patterns have intensified these smog events. However, real-time forecasting systems for increased particulate matter concentrations are still not established at regional scale. The Aerosol Index, closely tied to smog formation and a key component in calculating the Air Quality Index (AQI), reflects particulate matter concentrations. This study forecasts aerosol events using Sentinel-5P air constituent data (2019-2023) and a Convolutional Long-Short Term Memory (ConvLSTM) neural network, which captures spatial and temporal correlations more effectively than previous models. Using the Ultraviolet (UV) Aerosol Index at 340-380 nm as the predictor, results show the Aerosol Index can be forecasted at five-day intervals with a Mean Squared Error of ~0.0018, loss of ~0.3995, and Structural Similarity Index of ~0.74. While effective, the model can be improved by integrating additional data and refining its architecture.