The commoditization of Optical Character Recognition (OCR) has historically been a race to the bottom on price, often at the expense of structural fidelity. However, the release of Mistral OCR 3 signals a distinct shift in the market. By claiming state-of-the-art accuracy on complex tables and handwriting—while undercutting AWS Textract and Google Document AI by significant margins—Mistral is positioning its proprietary model not just as a cheaper alternative, but as a technically superior parsing engine for RAG (Retrieval-Augmented […]

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Understanding how large-scale functional brain networks reorganize during cognitive decline remains a central challenge in neuroimaging. While recent self-supervised models have shown promise for learning representations from resting-state fMRI, their internal mechanisms are difficult to interpret, limiting mechanistic insight. We propose BrainInterNet, a network-aware self-supervised framework based on masked reconstruction with cross-attention that explicitly models inter-network dependencies in rs-fMRI. By selectively masking predefined functional networks and reconstructing them from remaining context, our approach enables direct quantification of network […]

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arXiv:2602.07086v1 Announce Type: new Abstract: Enterprise systems increasingly require natural language interfaces that can translate user requests into structured operations such as SQL queries and REST API calls. While large language models (LLMs) show promise for code generation [Chen et al., 2021; Huynh and Lin, 2025], their effectiveness in domain-specific enterprise contexts remains underexplored, particularly when both retrieval and modification tasks must be handled jointly. This paper presents a comprehensive evaluation of three retrieval-augmented generation (RAG) variants [Lewis […]

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Parallelism, kernels, and why memory matters most Being a PhD student working on AI and NLP, I’ve spent quite some time using PyTorch and other high-level frameworks that abstract away the GPU. But recent discussions about whether I should learn CUDA pushed me to step back and revisit the basics: where all of this started, and why it changed. This isn’t an official “learning guide” or a course roadmap. It’s going back to first principles and sharing the insights I’m learning […]

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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. ## AI Doesn’t Mean the End of Work for Us By @bernard [ 4 Min read ] I believe that AI’s impact and future pathways are overstated because human nature is ignored in such statements. Read More. Claude Code Launches Teleport Workflow: Start Anywhere, Continue […]

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arXiv:2409.20412v2 Announce Type: replace-cross Abstract: Understanding the dose-response relation between a continuous treatment and the outcome for an individual can greatly drive decision-making, particularly in areas like personalized drug dosing and personalized healthcare interventions. Point estimates are often insufficient in these high-risk environments, highlighting the need for uncertainty quantification to support informed decisions. Conformal prediction, a distribution-free and model-agnostic method for uncertainty quantification, has seen limited application in continuous treatments or dose-response models. To address this gap, we […]

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arXiv:2601.10719v1 Announce Type: new Abstract: Perceived trustworthiness underpins how users navigate online information, yet it remains unclear whether large language models (LLMs),increasingly embedded in search, recommendation, and conversational systems, represent this construct in psychologically coherent ways. We analyze how instruction-tuned LLMs (Llama 3.1 8B, Qwen 2.5 7B, Mistral 7B) encode perceived trustworthiness in web-like narratives using the PEACE-Reviews dataset annotated for cognitive appraisals, emotions, and behavioral intentions. Across models, systematic layer- and head-level activation differences distinguish high- from […]

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arXiv:2602.20163v1 Announce Type: new Abstract: Aphasia is an acquired language disorder caused by injury to the regions of the brain that are responsible for language. Aphasia may impair the use and comprehension of written and spoken language. The Western Aphasia Battery-Revised (WAB-R) is an assessment tool administered by speech-language pathologists (SLPs) to evaluate the aphasia type and severity. Because the WAB-R measures isolated linguistic skills, there has been growing interest in the assessment of discourse production as a […]

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arXiv:2601.03327v1 Announce Type: new Abstract: Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging. In this work, we introduce the first ordinal classification framework for forecasting wildfire severity levels directly aligned with operational decision-making in France. Our study investigates the influence of loss-function design on the ability of neural models to predict rare yet critical high-severity fire occurrences. We compare standard cross-entropy with several ordinal-aware objectives, including the […]

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arXiv:2601.18897v1 Announce Type: new Abstract: Wastewater treatment plants consume 1-3% of global electricity, making accurate energy forecasting critical for operational optimization and sustainability. While machine learning models provide point predictions, they lack explainable uncertainty quantification essential for risk-aware decision-making in safety-critical infrastructure. This study develops an Interval Type-2 Adaptive Neuro-Fuzzy Inference System (IT2-ANFIS) that generates interpretable prediction intervals through fuzzy rule structures. Unlike black-box probabilistic methods, the proposed framework decomposes uncertainty across three levels: feature-level, footprint of uncertainty […]

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