February 2026

Fast matrix multiplication can be described as searching for low-rank decompositions of the matrix–multiplication tensor. We design a neural architecture, textsc{StrassenNet}, which reproduces the Strassen algorithm for $2times 2$ multiplication. Across many independent runs the network always converges to a rank-$7$ tensor, thus numerically recovering Strassen’s optimal algorithm. We then train the same architecture on $3times 3$ multiplication with rank $rin{19,dots,23}$. Our experiments reveal a clear numerical threshold: models with $r=23$ attain significantly lower validation error than those […]

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Wireless communications face both opportunities and challenges due to the coexistence of 5G New Radio (NR) high-band, 5G mid-band, and 5G low-band technologies. Each technology uses both licensed and unlicensed spectrum to operate in separate frequency bands. For example, 5G NR uses the high-band of 24+ GHz, the mid-band of 2-6 GHz, or the low-band of less than 2 GHz, including the 5 GHz band via Licensed-Assisted Access (LAA). With the use of sophisticated coexistence mechanisms and optimization […]

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By Doug Needham, DataOps.live Technical debt is a challenge for any digital team. Suggestions to avoid it get overridden or ignored, and it’s a rare treat to get to fix the things we know need fixing. If only our PMs and data owners understood what we know—how terrifying technical debt really is—maybe they’d push back less when we fight to build things the right way.  I think I’ve found the way to tell them. Fairy tales, parables, and […]

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Hi everyone, We are currently expanding our team of experts to help train and evaluate the next generation of Large Language Models (LLMs). We are looking for high-quality contributors across various specialized domains and languages. Available Roles Include: Multimodal Eval & SFT/CoT: Creating high-quality reasoning and demonstration data. Expert RLHF: Ranking and grading model outputs based on preference. Red Teaming: Testing models for safety, bias, and edge cases. Specialized Annotation: Domain-specific labeling. We are seeking expertise in: STEM: […]

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Cardiovascular disease is the primary cause of death globally, necessitating early identification, precise risk classification, and dependable decision-support technologies. The advent of large language models (LLMs) provides new zero-shot and few-shot reasoning capabilities, even though machine learning (ML) algorithms, especially ensemble approaches like Random Forest, XGBoost, LightGBM, and CatBoost, are excellent at modeling complex, non-linear patient data and routinely beat logistic regression. This research predicts cardiovascular disease using a merged dataset of 1,190 patient records, comparing traditional machine […]

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Machine unlearning, which enables a model to forget specific data, is crucial for ensuring data privacy and model reliability. However, its effectiveness can be severely undermined in real-world scenarios where models learn unintended biases from spurious correlations within the data. This paper investigates the unique challenges of unlearning from such biased models. We identify a novel phenomenon we term “shortcut unlearning," where models exhibit an “easy to learn, yet hard to forget" tendency. Specifically, models struggle to forget […]

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