Deep learning classifiers deployed in scientific and industrial settings face a fundamental yet unrecognized problem: they cannot distinguish between clean in- puts and corrupted data that violates physical laws. When a medical CT scanner produces images with motion artifacts, or a reservoir sensor transmits pressure readings that violate Darcy’s law, standard neural networks process these physi- cally impossible inputs with unwarranted confidence—a silent failure mode with potentially catastrophic consequences. Existing approaches address robustness in isolation: normalization methods adapt […]

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arXiv:2603.15643v1 Announce Type: new Abstract: Green Stormwater Infrastructure (GSI) systems, such as permeable pavement, rain gardens, and bioretention facilities, require continuous inspection and maintenance to ensure long-term performance. However, domain knowledge about GSI is often scattered across municipal manuals, regulatory documents, and inspection forms. As a result, non-expert users and maintenance staff may struggle to obtain reliable and actionable guidance from field observations. Although Large Language Models (LLMs) have demonstrated strong general reasoning and language generation capabilities, they […]

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Hate speech on social media reproduces norms of inequality and gender stereotypes, disproportionately affecting women. This study proposes a hybrid approach that integrates emotional tone classification with explicit hostility detection to strengthen preventive moderation. We constructed a corpus from three open datasets (1,236,371 records; 1,003,991 after ETL) and represented the text using TF-IDF and contextual RoBERTa embeddings. We trained individual models (RoBERTa fine-tuned, Random Forest, and XGBoost) and a stacking metamodel (Gradient Boosting) that combines their probabilities. On […]

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arXiv:2603.17048v1 Announce Type: new Abstract: Modern neural networks achieve strong performance but remain difficult to interpret in high-dimensional visual domains. Counterfactual explanations (CFEs) provide a principled approach to interpreting black-box predictions by identifying minimal input changes that alter model outputs. However, existing CFE methods often rely on dataset-specific generative models and incur substantial computational cost, limiting their scalability to high-resolution data. We propose SCE-LITE-HQ, a scalable framework for counterfactual generation that leverages pretrained generative foundation models without task-specific […]

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Contrastive learning has emerged as a powerful framework for learning generalizable representations, yet its theoretical understanding remains limited, particularly under imbalanced data distributions that are prevalent in real-world applications. Such an imbalance can degrade representation quality and induce biased model behavior, yet a rigorous characterization of these effects is lacking. In this work, we develop a theoretical framework to analyze the training dynamics of contrastive learning with Transformer-based encoders under imbalanced data. Our results reveal that neuron weights […]

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arXiv:2602.02546v1 Announce Type: new Abstract: Large language models (LLMs) deliver strong performance, but their high compute and memory costs make deployment difficult in resource-constrained scenarios. Weight-only post-training quantization (PTQ) is appealing, as it reduces memory usage and enables practical speedup without low-bit operators or specialized hardware. However, accuracy often degrades significantly in weight-only PTQ at sub-4-bit precision, and our analysis identifies two main causes: (1) down-projection matrices are a well-known quantization bottleneck, but maintaining their fidelity often requires […]

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PANAMA CITY, January 30, 2026 – BingX, a leading cryptocurrency exchange and Web3-AI company, today announced the integration of its TradFi suite into BingX AI Bingo, giving users direct access to the global market data and AI-powered trading-signal interpretation within the BingX trading experience. Following strong user demand for AI-enhanced crypto trading, BingX is extending its industry-leading AI capabilities to support multi-asset decision-making across both digital and traditional markets. With the new integration, BingX is pioneering TradFi opportunities […]

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arXiv:2603.03378v2 Announce Type: new Abstract: Large language model (LLM) agents offer a promising data-driven approach to automating Site Reliability Engineering (SRE), yet their enterprise deployment is constrained by three challenges: restricted access to proprietary data, unsafe action execution under permission-governed environments, and the inability of closed systems to improve from failures. We present AOI (Autonomous Operations Intelligence), a trainable multi-agent framework formulating automated operations as a structured trajectory learning problem under security constraints. Our approach integrates three key […]

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I spent many years working with founders and now I work with researchers. Although there are always individual exceptions, on average it’s surprising to me how different the best people in these groups are (including in some qualities that I had assumed were present in great people everywhere, like very high levels of self-belief). So I’ve been thinking about the ways they’re the same, because maybe there is something to learn about qualities of really effective people in […]

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