arXiv:2601.20026v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong generative capabilities but remain vulnerable to confabulations, fluent yet unreliable outputs that vary arbitrarily even under identical prompts. Leveraging a quantum tensor network based pipeline, we propose a quantum physics inspired uncertainty quantification framework that accounts for aleatoric uncertainty in token sequence probability for semantic equivalence based clustering of LLM generations. This offers a principled and interpretable scheme for hallucination detection. We further introduce an entropy maximization […]

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Claude Code started as a terminal coding assistant. It now runs as a layered agentic system. Underneath, Claude Code separates memory, hooks, skills, subagents, plugins, and MCP into distinct layers. Each layer changes what the model can see or do. This article covers 25 features and strategies for scaling Claude Code. It is written for AI engineers, software engineers, and data scientists. Every code example follows a documented format and runs as written. Each item is labeled by […]

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Every week, IPinfo actively measures the entire routable internet. Not a sample. Not a subset. The full address space. This information is the foundation of everything we do. While most IP intelligence providers rely on self-reported registry data from WHOIS and geofeeds, we built something different: ProbeNet, a self-owned and operated internet measurement platform that generates the ground truth behind our geolocation, network, and privacy intelligence data. Today, we’re making that infrastructure directly accessible. With the launch of ProbeNet […]

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arXiv:2604.18797v1 Announce Type: new Abstract: Automatic pancreas segmentation is fundamental to abdominal MRI analysis, yet deep learning models trained on one MRI sequence often fail catastrophically when applied to another-a challenge that has received little systematic investigation. We introduce CrossPan, a multi-institutional benchmark comprising 1,386 3D scans across three routinely acquired sequences (T1-weighted, T2-weighted, and Out-of-Phase) from eight centers. Our experiments reveal three key findings. First, cross-sequence domain shifts are far more severe than cross-center variability: models achieving […]

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arXiv:2601.18966v1 Announce Type: new Abstract: Users trust algorithms more when they can predict the algorithms’ behavior. Simple algorithms trivially yield predictively accurate mental models, but modern AI algorithms have often been assumed too complex for people to build predictive mental models, especially in the social media domain. In this paper, we describe conditions under which even complex algorithms can yield predictive mental models, opening up opportunities for a broader set of human-centered algorithms. We theorize that users will […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Your AI Model Isn’t Broken. Your Data Is By @melissaindia [ 7 Min read ] Your AI model isn’t failing; your data is. Learn how clean, verified data improves model accuracy and how easy it is to fix with APIs. Read More. The Hidden […]

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arXiv:2602.18530v1 Announce Type: new Abstract: This study compares multiple deep learning architectures for the automated, image-based classification of five locally cultivated black table olive varieties in Turkey: Gemlik, Ayvalik, Uslu, Erkence, and Celebi. Using a dataset of 2500 images, ten architectures – MobileNetV2, EfficientNetB0, EfficientNetV2-S, ResNet50, ResNet101, DenseNet121, InceptionV3, ConvNeXt-Tiny, ViT-B16, and Swin-T – were trained using transfer learning. Model performance was evaluated using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen’s Kappa, ROC-AUC, number of parameters, […]

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Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems has thus far been limited by existing task suites. They suffer from many issues, such as: poor evaluation methodologies; data contamination; and containing saturated or very similar problems. Here, we introduce DiscoGen, a procedural generator of algorithm discovery tasks for machine learning, such as developing optimisers for reinforcement learning or loss functions for image […]

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arXiv:2601.18658v1 Announce Type: cross Abstract: When developing clinical prediction models, it can be challenging to balance between global models that are valid for all patients and personalized models tailored to individuals or potentially unknown subgroups. To aid such decisions, we propose a diagnostic tool for contrasting global regression models and patient-specific (local) regression models. The core utility of this tool is to identify where and for whom a global model may be inadequate. We focus on regression models […]

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