OpenRaiser/Intern-Atlas huggingface dataset:
Anubuddhi: A Multi-Agent AI System for Designing and Simulating Quantum Optics Experiments
Venue: arXiv cs.AI 2025
Year: 2025

We present Anubuddhi, a multi-agent AI system that designs and simulates quantum optics experiments from natural language prompts without requiring specialized programming knowledge. The system composes optical layouts by arranging components from a three-tier toolbox via semantic retrieval, then validates designs through physics simulation with convergent refinement. The architecture combines intent routing, knowledge-augmented generation, and dual-mode validation (QuTiP and FreeSim). We evaluated 13 experiments spanning fundamental optics (Hong-Ou-Mandel interference, Michelson/Mach-Zehnder interferometry, Bell states, delayed-choice quantum eraser), quantum information protocols (BB84 QKD, Franson interferometry, GHZ states, quantum teleportation, hyperentanglement), and advanced technologies (boson sampling, electromagnetically induced transparency, frequency conversion). The system achieves design-simulation alignment scores of 8--9/10, with simulations faithfully modeling intended physics. A critical finding distinguishes structural correctness from quantitative accuracy: high alignment confirms correct physics architecture, while numerical predictions require expert review. Free-form simulation outperformed constrained frameworks for 11/13 experiments, revealing that quantum optics diversity demands flexible mathematical representations. The system democratizes computational experiment design for research and pedagogy, producing strong initial designs users can iteratively refine through conversation.
Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Financial market prediction is a challenging application of machine learning, where even small improvements in directional accuracy can yield substantial value. Most models struggle to exceed 55--57\% accuracy due to high noise, non-stationarity, and market efficiency. We introduce a hybrid ensemble framework combining quantum sentiment analysis, Decision Transformer architecture, and strategic model selection, achieving 60.14\% directional accuracy on S\&P 500 prediction, a 3.10\% improvement over individual models. Our framework addresses three limitations of prior approaches. First, architecture diversity dominates dataset diversity: combining different learning algorithms (LSTM, Decision Transformer, XGBoost, Random Forest, Logistic Regression) on the same data outperforms training identical architectures on multiple datasets (60.14\% vs.\ 52.80\%), confirmed by correlation analysis ($r>0.6$ among same-architecture models). Second, a 4-qubit variational quantum circuit enhances sentiment analysis, providing +0.8\% to +1.5\% gains per model. Third, smart filtering excludes weak predictors (accuracy $<52\%$), improving ensemble performance (Top-7 models: 60.14\% vs.\ all 35 models: 51.2\%). We evaluate on 2020--2023 market data across seven instruments, covering diverse regimes including the COVID-19 crash and inflation-driven correction. McNemar's test confirms statistical significance ($p<0.05$). Preliminary backtesting with confidence-based filtering (6+ model consensus) yields a Sharpe ratio of 1.2 versus buy-and-hold's 0.8, demonstrating practical trading potential.
How Do Graph Signals Affect Recommendation: Unveiling the Mystery of Low and High-Frequency Graph Signals
Venue: arXiv cs.LG 2025
Year: 2025

Spectral graph neural networks (GNNs) are highly effective in modeling graph signals, with their success in recommendation often attributed to low-pass filtering. However, recent studies highlight the importance of high-frequency signals. The role of low-frequency and high-frequency graph signals in recommendation remains unclear. This paper aims to bridge this gap by investigating the influence of graph signals on recommendation performance. We theoretically prove that the effects of low-frequency and high-frequency graph signals are equivalent in recommendation tasks, as both contribute by smoothing the similarities between user-item pairs. To leverage this insight, we propose a frequency signal scaler, a plug-and-play module that adjusts the graph signal filter function to fine-tune the smoothness between user-item pairs, making it compatible with any GNN model. Additionally, we identify and prove that graph embedding-based methods cannot fully capture the characteristics of graph signals. To address this limitation, a space flip method is introduced to restore the expressive power of graph embeddings. Remarkably, we demonstrate that either low-frequency or high-frequency graph signals alone are sufficient for effective recommendations. Extensive experiments on four public datasets validate the effectiveness of our proposed methods. Code is avaliable at https://github.com/mojosey/SimGCF.
A Special Case of Quadratic Extrapolation Under the Neural Tangent Kernel
Venue: arXiv cs.LG 2025
Year: 2025

It has been demonstrated both theoretically and empirically that the ReLU MLP tends to extrapolate linearly for an out-of-distribution evaluation point. The machine learning literature provides ample analysis with respect to the mechanisms to which linearity is induced. However, the analysis of extrapolation at the origin under the NTK regime remains a more unexplored special case. In particular, the infinite-dimensional feature map induced by the neural tangent kernel is not translationally invariant. This means that the study of an out-of-distribution evaluation point very far from the origin is not equivalent to the evaluation of a point very near the origin. And since the feature map is rotation invariant, these two special cases may represent the most canonically extreme bounds of ReLU NTK extrapolation. Ultimately, it is this loose recognition of the two special cases of extrapolation that motivate the discovery of quadratic extrapolation for an evaluation close to the origin.
TAO-Net: Two-stage Adaptive OOD Classification Network for Fine-grained Encrypted Traffic Classification
Venue: arXiv cs.LG 2025
Year: 2025

Encrypted traffic classification aims to identify applications or services by analyzing network traffic data. One of the critical challenges is the continuous emergence of new applications, which generates Out-of-Distribution (OOD) traffic patterns that deviate from known categories and are not well represented by predefined models. Current approaches rely on predefined categories, which limits their effectiveness in handling unknown traffic types. Although some methods mitigate this limitation by simply classifying unknown traffic into a single "Other" category, they fail to make a fine-grained classification. In this paper, we propose a Two-stage Adaptive OOD classification Network (TAO-Net) that achieves accurate classification for both In-Distribution (ID) and OOD encrypted traffic. The method incorporates an innovative two-stage design: the first stage employs a hybrid OOD detection mechanism that integrates transformer-based inter-layer transformation smoothness and feature analysis to effectively distinguish between ID and OOD traffic, while the second stage leverages large language models with a novel semantic-enhanced prompt strategy to transform OOD traffic classification into a generation task, enabling flexible fine-grained classification without relying on predefined labels. Experiments on three datasets demonstrate that TAO-Net achieves 96.81-97.70% macro-precision and 96.77-97.68% macro-F1, outperforming previous methods that only reach 44.73-86.30% macro-precision, particularly in identifying emerging network applications.
A Survey on Reconfigurable Intelligent Surfaces in Practical Systems: Security and Privacy Perspectives
Venue: arXiv cs.CR 2025
Year: 2025

Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative technology capable of reshaping wireless environments through dynamic manipulation of electromagnetic waves. While extensive research has explored their theoretical benefits for communication and sensing, practical deployments in smart environments such as homes, vehicles, and industrial settings remain limited and under-examined, particularly from security and privacy perspectives. This survey provides a comprehensive examination of RIS applications in real-world systems, with a focus on the security and privacy threats, vulnerabilities, and defensive strategies relevant to practical use. We analyze scenarios with two types of systems (with and without legitimate RIS) and two types of attackers (with and without malicious RIS), and demonstrate how RIS may introduce new attacks to practical systems, including eavesdropping, jamming, and spoofing attacks. In response, we review defenses against RIS-related attacks in these systems, such as applying additional security algorithms, disrupting attackers, and early detection of unauthorized RIS. We also discuss scenarios in which the legitimate user applies an additional RIS to defend against attacks. To support future research, we also provide a collection of open-source tools, datasets, demos, and papers at: https://awesome-ris-security.github.io/. By highlighting RIS's functionality and its security/privacy challenges and opportunities, this survey aims to guide researchers and engineers toward the development of secure, resilient, and privacy-preserving RIS-enabled practical wireless systems and environments.
ReactorFold: Generative discovery of nuclear reactor cores via emergent physical reasoning
Venue: arXiv cs.LG 2025
Year: 2025

Designing nuclear reactor cores requires navigating large discrete design spaces governed by complex neutronic interactions. Traditional deterministic, metaheuristic, and machine-learning-assisted methods search within fixed, human-defined configuration spaces, limiting their ability to discover fundamentally new design topologies. Here we introduce ReactorFold, a generative framework that reformulates fuel-assembly design as a sequence modeling problem for language models. Using Monte Carlo data, parameter-efficient fine-tuning, and Direct Preference Optimization (DPO), the model learns the latent structure of a pressurized-water-reactor assembly and generates candidate layouts in a single forward pass. Notably, the DPO-aligned model exhibits emergent design-space expansion: despite being trained exclusively on configurations with a fixed number of gadolinium burnable absorber (Gd) rods, it autonomously adjusts Gd inventory to satisfy strict power-peaking constraints. The model also discovers high-performing asymmetric configurations that challenge conventional symmetric loading heuristics, accessing design regimes inaccessible to conventional search methods and demonstrating that language models can internalize causal physical relationships and transcend human-imposed design constraints.
Adversarial Robustness in Financial Machine Learning: Defenses, Economic Impact, and Governance Evidence
Venue: arXiv cs.LG 2025
Year: 2025

We evaluate adversarial robustness in tabular machine learning models used in financial decision making. Using credit scoring and fraud detection data, we apply gradient based attacks and measure impacts on discrimination, calibration, and financial risk metrics. Results show notable performance degradation under small perturbations and partial recovery through adversarial training.
Automated Formalization of Probabilistic Requirements from Structured Natural Language
Venue: arXiv cs.PL 2025
Year: 2025

Integrating autonomous and adaptive behavior into software-intensive systems presents significant challenges for software development, as uncertainties in the environment or decision-making processes must be explicitly captured. These challenges are amplified in safety- and mission-critical systems, which must undergo rigorous scrutiny during design and development. Key among these challenges is the difficulty of specifying requirements that use probabilistic constructs to capture the uncertainty affecting these systems. To enable formal analysis, such requirements must be expressed in precise mathematical notations such as probabilistic logics. However, expecting developers to write requirements directly in complex formalisms is unrealistic and highly error-prone. We extend the structured natural language used by NASA's Formal Requirement Elicitation Tool (FRET) with support for the specification of unambiguous and correct probabilistic requirements, and develop an automated approach for translating these requirements into logical formulas. We propose and develop a formal, compositional, and automated approach for translating structured natural-language requirements into formulas in probabilistic temporal logic. To increase trust in our formalizations, we provide assurance that the generated formulas are well-formed and conform to the intended semantics through an automated validation framework and a formal proof. The extended FRET tool enables developers to specify probabilistic requirements in structured natural language, and to automatically translate them into probabilistic temporal logic, making the formal analysis of autonomous and adaptive systems more practical and less error-prone.
An empirical analysis of zero-day vulnerabilities disclosed by the zero day initiative
Venue: arXiv cs.CR 2025
Year: 2025

Zero-day vulnerabilities represent some of the most critical threats in cybersecurity, as they correspond to previously unknown flaws in software or hardware that are actively exploited before vendors can develop and deploy patches. During this exposure window, affected systems remain defenseless, making zero-day attacks particularly damaging and difficult to mitigate. This study analyzes the Zero Day Initiative (ZDI) vulnerability disclosures reported between January and April 2024, Cole [2025] comprising a total of 415 vulnerabilities. The dataset includes vulnerability identifiers, Common Vulnerability Scoring System (CVSS) v3.0 scores, publication dates, and short textual descriptions. The primary objectives of this work are to identify trends in zero-day vulnerability disclosures, examine severity distributions across vendors, and investigate which vulnerability characteristics are most indicative of high severity. In addition, this study explores predictive modeling approaches for severity classification, comparing classical machine learning techniques with deep learning models using both structured metadata and unstructured textual descriptions. The findings aim to support improved patch prioritization strategies, more effective vulnerability management, and enhanced organizational preparedness against emerging zero-day threats.
XBIDetective: Leveraging Vision Language Models for Identifying Cross-Browser Visual Inconsistencies
Venue: arXiv cs.SE 2025
Year: 2025

Browser rendering bugs can be challenging to detect for browser developers, as they may be triggered by very specific conditions that are exhibited on only a very small subset of websites. Cross-browser inconsistencies (XBIs), variations in how a website is interpreted and displayed on different browsers, can be helpful guides to detect such rendering bugs. Although visual and Document Object Model (DOM)-based analysis techniques exist for detecting XBIs, they often struggle with dynamic and interactive elements. In this study, we discuss our industry experience with using vision language models (VLMs) to identify XBIs. We present the XBIDetective tool which automatically captures screenshots of a website in Mozilla Firefox and Google Chrome, and analyzes them with a VLM for XBIs. We evaluate XBIDetective's performance with an off-the-shelf and a fine-tuned VLM on 1,052 websites. We show that XBIDetective can identify cross-browser discrepancies with 79% accuracy and detect dynamic elements and advertisements with 84% and 85% accuracy, respectively, when using the fine-tuned VLM. We discuss important lessons learned, and we present several potential practical use cases for XBIDetective, including automated regression testing, large-scale monitoring of websites, and rapid triaging of XBI bug reports.
Implementing a Scalable, Redeployable and Multitiered Repository for FAIR and Secure Scientific Data Sharing: The BIG-MAP Archive
Venue: arXiv cs.DB 2025
Year: 2025

Data sharing in large consortia, such as research collaborations or industry partnerships, requires addressing both organizational and technical challenges. A common platform is essential to promote collaboration, facilitate exchange of findings, and ensure secure access to sensitive data. Key technical challenges include creating a scalable architecture, a user-friendly interface, and robust security and access control. The BIG-MAP Archive is a cloud-based, disciplinary, private repository designed to address these challenges. Built on InvenioRDM, it leverages platform functionalities to meet consortium-specific needs, providing a tailored solution compared to general repositories. Access can be restricted to members of specific communities or open to the entire consortium, such as the BATTERY 2030+, a consortium accelerating advanced battery technologies. Uploaded data and metadata are controlled via fine grained permissions, allowing access to individual project members or the full initiative. The formalized upload process ensures data are formatted and ready for publication in open repositories when needed. This paper reviews the repository's key features, showing how the BIG-MAP Archive enables secure, controlled data sharing within large consortia. It ensures data confidentiality while supporting flexible, permissions-based access and can be easily redeployed for other consortia, including MaterialsCommons4.eu and RAISE (Resource for AI Science in Europe).
Workload Characterization for Branch Predictability
Venue: arXiv cs.AR 2025
Year: 2025

Conditional branch prediction predicts the likely direction of a conditional branch instruction to support ILP extraction. Branch prediction is a pattern recognition problem that learns mappings between a context to the branch outcome. An accurate predictor reduces the number of instructions executed on the wrong path resulting in an improvement of performance and energy consumption. In this paper, we present a workload characterization methodology for branch prediction. We propose two new workload-driven branch prediction accuracy identifiers -- branch working set size and branch predictability. These parameters are highly correlated with misprediction rates of modern branch prediction schemes (e.g. TAGE and perceptron). We define the branch working set of a trace as a group of most frequently occurring branch contexts, i.e. the 3-part tuple of branch address, and associated global and local history. We analyze the branch working set's size and predictability on a per-trace basis to study its relationship with a modern branch predictor's accuracy. We have characterized 2,451 workload traces into seven branch working set size and nine predictability categories after analyzing their branch behavior. We present further insights into the source of prediction accuracy and favored workload categories for modern branch predictors.
Seeing Beyond Words: Self-Supervised Visual Learning for Multimodal Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in connecting vision and language, yet their proficiency in fundamental visual reasoning tasks remains limited. This limitation can be attributed to the fact that MLLMs learn visual understanding primarily from textual descriptions, which constitute a subjective and inherently incomplete supervisory signal. Furthermore, the modest scale of multimodal instruction tuning compared to massive text-only pre-training leads MLLMs to overfit language priors while overlooking visual details. To address these issues, we introduce JARVIS, a JEPA-inspired framework for self-supervised visual enhancement in MLLMs. Specifically, we integrate the I-JEPA learning paradigm into the standard vision-language alignment pipeline of MLLMs training. Our approach leverages frozen vision foundation models as context and target encoders, while training the predictor, implemented as the early layers of an LLM, to learn structural and semantic regularities from images without relying exclusively on language supervision. Extensive experiments on standard MLLM benchmarks show that JARVIS consistently improves performance on vision-centric benchmarks across different LLM families, without degrading multimodal reasoning abilities. Our source code is publicly available at: https://github.com/aimagelab/JARVIS.
Boosting t-SNE Efficiency for Sequencing Data: Insights from Kernel Selection
Venue: arXiv cs.LG 2025
Year: 2025

Dimensionality reduction techniques are essential for visualizing and analyzing high-dimensional biological sequencing data. t-distributed Stochastic Neighbor Embedding (t-SNE) is widely used for this purpose, traditionally employing the Gaussian kernel to compute pairwise similarities. However, the Gaussian kernel's lack of data-dependence and computational overhead limit its scalability and effectiveness for categorical biological sequences. Recent work proposed the isolation kernel as an alternative, yet it may not optimally capture sequence similarities. In this study, we comprehensively evaluate nine different kernel functions for t-SNE applied to molecular sequences, using three embedding methods: One-Hot Encoding, Spike2Vec, and minimizers. Through both subjective visualization and objective metrics (including neighborhood preservation scores), we demonstrate that the cosine similarity kernel in general outperforms other kernels, including Gaussian and isolation kernels, achieving superior runtime efficiency and better preservation of pairwise distances in low-dimensional space. We further validate our findings through extensive classification and clustering experiments across six diverse biological datasets (Spike7k, Host, ShortRead, Rabies, Genome, and Breast Cancer), employing multiple machine learning algorithms and evaluation metrics. Our results show that kernel selection significantly impacts not only visualization quality but also downstream analytical tasks, with the cosine similarity kernel providing the most robust performance across different data types and embedding strategies, making it particularly suitable for large-scale biological sequence analysis.
Management von Sensordaten im Smarthome: Besonderheiten und Ansätze
Venue: arXiv cs.HC 2025
Year: 2025

A wide variety of simple sensors, e.g. for temperature, light, or humidity, is finding its way into smart homes. There are special features to consider with regard to the data collected by these sensors: a) the nature of the measured data as "thin but big data" that needs to be contextualized and interpreted, b) which both algorithms and humans are capable of doing (resulting in comprehensive information in the context of the home, including the recognition of activities, behavior, and health of the residents), and c) uses that lead to interesting positive applications, but also to misuse and implications for privacy. When managing such data, it is necessary to take these special features into account, for which the principles of user experience, human-data interaction, and data protection should be considered together. We present our research tool "Sensorkit" and the participatory research approach used with it to collect sensor data in real homes. In our findings, we present identified challenges and explain how we address them through a) meaningful default settings, b) opportunities for users to interact and intervene, and c) life-cycle management of the data. Important aspects include phases before, during, and after the collection, processing, and use of the sensor data, as well as the provision of user-friendly tools and user involvement. Our findings inform beyond the scope of a research project also the development and use of commercial smart home devices and services.