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PyTorch NumPy Implementation Code Repository Released
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here's where the code lives: https://
github.com/pytorch/pytorc
h/tree/main/torch/_numpy
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PyTorch torch.compile Advanced Indexing Bug Report Filed
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the numpy compatible torch.compile behavior is definitely a bug. the expected behavior is the numpy output should be the same as torch.compile output.
torch's advanced indexing differing from numpy's advanced indexing is a bit of an aside.
I've just filed a bug report, -
Polygenic Risk Scores Prove Critical for Heart Disease Prediction
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Hardly anyone knows their polygenic risk score for heart disease and here's yet another study to show how important it is for predicting a person's risk.
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Semantic-Guided Diverse Decoding for LLM
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Semantic-guided Diverse Decoding for LLM This paper present SemDiD, a decoding trick that steers each beam into a different part of the model’s semantic space and keeps them apart while checking basic probability thresholds, so you can end up with good and diverse answers.
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LLM Pretraining Evolution: From Crisis to Solved Problem
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two or three years ago, this was a prevailing sentiment “training the largest LLMs is very hard. only a few people know how. everyone else is failing” it was hard to avoid huge loss spikes. now pretraining is a solved problem. what changed? do we just clean our data better?
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Nanonets-OCR-s: Advanced Document Understanding Beyond Text Extraction
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Nanonets-OCR-s: Enabling Rich, Structured Markdown for Document Understanding Most OCR tools stop at text.
But real-world documents are more than words, they’re tables, logos, watermarks, and structure. Nanonets-OCR-s doesn’t just extract, it understands.
A next-gen -
AI News April 2025: In-Depth and Concise Overview
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AI News April 2025: In-Depth and Concise
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @Scobleizer @AndrewYNg @drfeifei @KirkDBorne @fchollet @rowancheung @antgrasso -
V-JEPA 2: Meta’s AI Breakthrough for Physical World Understanding
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V-JEPA 2: Meta’s Breakthrough in AI for the Physical World Meta AI’s V-JEPA 2 is here, understanding video, predicting outcomes, and planning actions without a single label.
This is zero-shot learning for robotics, and it's groundbreaking. Read the blog to see how AI just -
Sakana AI AB-MCTS: Multiple AI Models Collaborating to Solve Complex Problems
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Sakana AI debuted AB-MCTS, an algorithm that lets different AI models work together, using their strengths and mistakes, to solve complex problems It used ChatGPT, Gemini, and DeepSeek to solve 30% of ARC-AGI-2 puzzles vs just 23% for top solo models