arXiv:2601.00021v1 Announce Type: new Abstract: We present a physical theory of intelligence grounded in irreversible information processing in systems constrained by conservation laws. An intelligent system is modelled as a coupled agent-environment process whose evolution transforms information into goal-directed work. To connect information to physical state, we introduce the Conservation-Congruent Encoding (CCE) framework, in which encodings correspond to metastable basins of attraction whose separability is enforced by conservation laws. Within this framework, intelligence is defined as the amount […]

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Artificial intelligence and legal technology are reshaping the landscape of personal injury law in Philadelphia, introducing significant changes. The advancements offer new capabilities for legal professionals, enhancing the strategic approach lawyers take in managing cases. The integration of AI and legal tech into personal injury law is changing how legal practices operate in Philadelphia. By using advanced technologies, like predictive analytics, law firms can gain valuable insights that were previously unattainable. The innovation aids in case management and […]

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arXiv:2603.06599v1 Announce Type: new Abstract: Artificial intelligence pipelines — spanning data collection, model training, deployment, and post-deployment monitoring — concentrate ethical risks that intensify with multimodal and agentic systems. Existing governance instruments, including the EU AI Act, the IEEE 7000 series, and the NIST AI Risk Management Framework, provide high-level guidance but often lack enforceable, end-to-end operational controls. This paper presents an ethics-by-design control architecture that embeds consequentialist, deontological, and virtue-ethical reasoning into stage-specific enforcement mechanisms across the […]

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arXiv:2601.10926v1 Announce Type: new Abstract: Currently, there are thousands of large pretrained language models (LLMs) available to social scientists. How do we select among them? Using validity, reliability, reproducibility, and replicability as guides, we explore the significance of: (1) model openness, (2) model footprint, (3) training data, and (4) model architectures and fine-tuning. While ex-ante tests of validity (i.e., benchmarks) are often privileged in these discussions, we argue that social scientists cannot altogether avoid validating computational measures (ex-post). […]

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arXiv:2601.12284v1 Announce Type: new Abstract: The rapid evolution of the automobile sector, driven by advancements in connected and autonomous vehicles (CAVs), has transformed how vehicles communicate, operate, and interact with their surroundings. Technologies such as Vehicle-to-Everything (V2X) communication enable autonomous cars to generate and exchange substantial amounts of data with real-world entities, enhancing safety, improving performance, and delivering personalized user experiences. However, this data-driven ecosystem introduces significant challenges, particularly concerning data privacy, security, and governance. The absence of […]

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arXiv:2601.05586v1 Announce Type: cross Abstract: Neural networks have shown state-of-the-art performances in various classification and regression tasks. Rectified linear units (ReLU) are often used as activation functions for the hidden layers in a neural network model. In this article, we establish the connection between the Poisson hyperplane processes (PHP) and two-layer ReLU neural networks. We show that the PHP with a Gaussian prior is an alternative probabilistic representation to a two-layer ReLU neural network. In addition, we show […]

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This paper presents a serverless MLOps framework orchestrating the complete ML lifecycle from data ingestion, training, deployment, monitoring, and retraining to using event-driven pipelines and managed services. The architecture is model-agnostic, supporting diverse inference patterns through standardized interfaces, enabling rapid adaptation without infrastructure overhead. We demonstrate practical applicability through an industrial implementation for Harmonized System (HS) code prediction, a compliance-critical task where short, unstructured product descriptions are mapped to standardized codes used by customs authorities in global trade. […]

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Deep learning has driven remarkable progress in visual recognition, yet state-of-the-art models remain heavily reliant on large-scale labeled datasets and high-performance GPU infrastructure, assumptions that rarely hold in real-world industrial settings where data is scarce and deployment occurs on CPU-based systems. This working paper introduces the Ensemble Spatial-Agnostic Basis Adaptation Network (Ensemble SABA-Net), a lightweight classification framework explicitly designed for low-data and resource-constrained environments. The proposed architecture departs from conventional convolutional and attention-based designs by operating directly on […]

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