How recasting propositional logic as sparse algebra leads to an elegant and efficient design

Universal Feature Upsampling

3D trajectory position error estimation autofocus, antenna pattern normalization, and polarimetric calibration for drone mounted SAR radar.

Data at the Centre vs. Pipeline-First Data Management, What Constitutes Resiliency in Data Systems Today, and Defensive Designs that are Built to Mistrust.

This paper presents Extract-0, a 7-billion parameter language model specifically optimized for document information extraction that achieves performance exceeding models with parameter counts several orders of magnitude larger. Through a novel combination of synthetic data generation, supervised fine-tuning with Low-Rank Adaptation (LoRA), and reinforcement learning via Group Relative Policy Optimization (GRPO), Extract-0 achieves a mean reward of 0.573 on a benchmark of 1,000 diverse document extraction tasks, outperforming GPT-4.1 (0.457), o3 (0.464), and GPT-4.1-2025 (0.459). The training methodology employs a memory-preserving synthetic data generation pipeline that produces 280,128 training examples from diverse document sources, followed by parameterefficient fine-tuning that modifies only 0.53% of model weights (40.4M out of 7.66B parameters). The reinforcement learning phase introduces a novel semantic similarity-based reward function that handles the inherent ambiguity in information extraction tasks. This research demonstrates that task-specific optimization can yield models that surpass general-purpose systems while requiring substantially fewer computational resource.

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Also, most data cleaning articles suck

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Qwen3-omni is a natively end-to-end, omni-modal LLM developed by the Qwen team at Alibaba Cloud, capable of understanding text, audio, images, and video, as well as generating speech in real time. ...

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You’re trying to build something useful with AI, and then you hit the wall of “how the heck do I get all these random documents into my…

a python cli tool to diff arxiv papers. Contribute to apoorvalal/arxivdiff development by creating an account on GitHub.

Press shortcut → speak → get text. Free and open source. More local-first apps soon ❤️ - epicenter-so/epicenter