Have you ever wondered why, in an age where Artificial Intelligence can generate images from scratch and write poetry, we still struggle with a task as trivial as copying a table from a PDF file to Excel? This is the paradox of today’s technology: we have sent rovers to Mars, but a supplier’s invoice in PDF format is still a “black box” for our computers. For decades, we lived in an era that could be called the “digital dark ages” of document processing. Our tools – classic OCR (Optical Character Recognition) engines – were like medieval scribes: capable of transcribing letters, but understanding not a word of what they wrote, and certainly not grasping what a table, chart, or complex mathematical formula was. ...
Cost-Constrained LLM Cascades — Meet C3PO
Imagine you have an army of helpers — several different Large Language Models (LLMs), each capable of handling tasks from simple queries to complex reasoning. But each helper costs something: time, compute, or actual money if you’re using an API. So the question is: Can we orchestrate these models wisely — starting from the cheapest one that might do the job, escalating only when needed — without exceeding a cost budget? ...
Accurate Satellite Rain Forecasting with Physics-Conditioned Neural Networks
Imagine this: you’re driving, clouds are gathering, and your weather app says “heavy rain in 15 minutes” — but there are no local radars, and it gets it wrong. Sounds familiar? That’s exactly the kind of problem tackled by the new research paper Precipitation nowcasting of satellite data using physically conditioned neural networks (by Antônio Catão et al.). The authors present a model that can forecast precipitation using only satellite data, powered by a neural network that’s conditioned by physics. In short: less “black box” magic, more scientific reasoning — and better forecasts where radar coverage is weak or nonexistent. ...
A Universal Crime Predictor – How Hypernetworks and Knowledge Graphs Are Transforming Forecasting
Imagine this: you’re in a new city that’s just starting to collect crime data – but the types of crimes differ completely from those in your city. Is it possible to train one model that works across both cities? That’s the question tackled by the recent paper 📄 Learning A Universal Crime Predictor with Knowledge-guided Hypernetworks by Fidan Karimova et al., which introduces a framework called HYSTL (HYpernetwork-enhanced Spatial Temporal Learning). ...
SNOO – Old-School Nesterov Momentum in a New Jacket: Making Big Models Learn Faster
Imagine you’re training a massive language model — the kind that takes weeks to learn even the basics. Every training step costs time, electricity, and a small fortune. In such a world, even a tiny bump in efficiency feels like finding a way to get free coffee at work — small, but sweet. Enter SNOO – Step-K Nesterov Outer Optimizer, a clever idea that takes Nesterov momentum, a decades-old optimization trick, and applies it in a new place — outside the normal training loop. The result? Models that learn faster and more smoothly, without much extra computational cost. ...
“Who Said Neural Networks Aren’t Linear?” — explained like over coffee
Alright, let’s start simple. Everyone who’s dabbled a bit in machine learning knows one thing: neural networks are nonlinear. That’s what makes them powerful — they can model weird, curvy, complex relationships, not just straight lines. But the authors of the paper “Who Said Neural Networks Aren’t Linear?” (Nimrod Berman, Assaf Hallak, Assaf Shocher) asked a cheeky question: what if that’s not entirely true? What if nonlinearity is just… a matter of perspective? ...
CHORD — Smart On-Device Recommendations Without Killing Your Battery
In apps like online stores, streaming platforms, or social media, we want to show users things they might like — “Hey, maybe you’ll enjoy this too.” That’s what recommendation systems do. Usually, those models live in the cloud — big servers crunch data and send you suggestions. But lately, more and more of that work is moving onto the user’s device (phone, tablet). Why? Because: it’s faster (less waiting), it’s more private (fewer data uploads), it saves server resources. But here’s the catch: devices vary. Some phones are monsters, others barely keep up. So how do you fit a good AI model on both? ...
Attention as a Compass – Teaching Reasoning Models to Explore Smarter
Large Language Models (LLMs) are no longer just text generators — they are becoming reasoners, capable of solving mathematical problems, logical puzzles, or planning tasks step by step. One of the key challenges is how to improve the quality of this reasoning. Traditional Reinforcement Learning (RL) rewards only the final outcome, but in complex reasoning it makes more sense to evaluate each intermediate step. This is called process-supervised RL (PSRL). ...
No Prior, No Leakage – can we really reconstruct data from a neural network?
In the era of artificial intelligence, privacy protection is one of the hottest topics. Neural networks often “memorize” pieces of training data. In extreme cases, an attacker could try to reconstruct the original examples just from the trained model’s parameters (so-called reconstruction attacks). Imagine a medical model that could reveal fragments of sensitive patient images — alarming, right? The new paper “No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks” (arxiv.org) challenges this fear. It shows that without additional knowledge (priors), reconstruction is fundamentally undecidable. In other words: model parameters alone may not be enough to recover the training data. ...
How to Detect Credit Card Fraud?
Today, credit card transactions are everywhere — online shopping, bill payments, travel, etc. Unfortunately, the number of fraud cases is also growing. The challenge is that frauds are very rare compared to normal transactions. This means that simple models trained on raw data often “ignore” these rare cases — because statistically, it’s cheaper to be wrong on a few frauds than on thousands of normal payments. The paper “Credit Card Fraud Detection” (arXiv:2509.15044) analyzes how to improve fraud detection by applying data preprocessing techniques (class balancing) and comparing several models. This is crucial because the effectiveness of such systems has real-world consequences — for banks, payment platforms, and user security. ...