arXiv:2601.07985v1 Announce Type: new Abstract: The rapid proliferation of misinformation across online platforms underscores the urgent need for robust, up-to-date, explainable, and multilingual fact-checking resources. However, existing datasets are limited in scope, often lacking multimodal evidence, structured annotations, and detailed links between claims, evidence, and verdicts. This paper introduces a comprehensive data collection and processing pipeline that constructs multimodal fact-checking datasets in French and German languages by aggregating ClaimReview feeds, scraping full debunking articles, normalizing heterogeneous claim verdicts, […]

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This paper presents a physics-informed neural network approach for dynamic modeling of saturable synchronous machines, including cases with spatial harmonics. We introduce an architecture that incorporates gradient networks directly into the fundamental machine equations, enabling accurate modeling of the nonlinear and coupled electromagnetic constitutive relationship. By learning the gradient of the magnetic field energy, the model inherently satisfies energy balance (reciprocity conditions). The proposed architecture can universally approximate any physically feasible magnetic behavior and offers several advantages over […]

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Architectural Paradigms and Market Consolidation in Production AI Systems The AI tooling landscape of 2026 represents a fascinating inflection point in the maturation of large language model (LLM) applications. What began as a Cambrian explosion of specialized tools has rapidly consolidated into distinct architectural patterns, each optimizing for specific computational trade-offs and user interaction paradigms. The Foundation Model Triumvirate: Diverging Optimization Strategies The dominance of ChatGPT, Claude, and Gemini isn’t merely about brand recognition — it reflects fundamentally different approaches to […]

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arXiv:2601.09871v1 Announce Type: new Abstract: Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its introduction in the human-AI interaction literature, it has gained traction by generalizing the reliance paradigm and by offering a more practical alternative to the contested construct of ‘trust in AI.’ Yet complementarity faces key theoretical challenges: it lacks precise theoretical anchoring, it is formalized just as a post hoc indicator […]

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arXiv:2202.11393v3 Announce Type: replace-cross Abstract: Adding random noise to database query results is an important tool for achieving privacy. A challenge is to minimize this noise while still meeting privacy requirements. Recently, a sufficient and necessary condition for $(epsilon, delta)$-differential privacy for Gaussian noise was published. This condition allows the computation of the minimum privacy-preserving scale for this distribution. We extend this work and provide a sufficient and necessary condition for $(epsilon, delta)$-differential privacy for all symmetric and […]

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arXiv:2603.03453v1 Announce Type: new Abstract: High-definition (HD) maps are important for autonomous driving, but their manual generation and maintenance is very expensive. This motivates the usage of an automated map generation pipeline. Fleet vehicles provide sufficient sensors for map generation, but their measurements are less precise, introducing noise into the mapping pipeline. This work focuses on mitigating the localization noise component through aligning radar measurements in terms of raw radar point clouds of vehicle poses of different drives […]

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arXiv:2603.02673v1 Announce Type: new Abstract: Functional ANOVA offers a principled framework for interpretability by decomposing a model’s prediction into main effects and higher-order interactions. For independent features, this decomposition is well-defined, strongly linked with SHAP values, and serves as a cornerstone of additive explainability. However, the lack of an explicit closed-form expression for general dependent distributions has forced practitioners to rely on costly sampling-based approximations. We completely resolve this limitation for categorical inputs. By bridging functional analysis with […]

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The Internet of Things (IoT) systems increasingly depend on continual learning to adapt to non-stationary environments. These environments can include factors such as sensor drift, changing user behavior, device aging, and adversarial dynamics. Contrastive continual learning (CCL) combines contrastive representation learning with incremental adaptation, enabling robust feature reuse across tasks and domains. However, the geometric nature of contrastive objectives, when paired with replay-based rehearsal and stability-preserving regularization, introduces new security vulnerabilities. Notably, backdoor attacks can exploit embedding alignment […]

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In The Odyssey, after years of war, Ulysses offers his enemies, the Trojans, a supposed gift of peace: a large wooden horse. This would become known as the Trojan horse, and it wasn’t a harmless gift. In reality, inside the horse were several soldiers who would attack Troy from within, which was the downfall of that city. Now we have that in the malware version, and it can steal your cryptocurrency as well.  This type of malicious software […]

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