arXiv:2601.14440v1 Announce Type: new Abstract: Vision-language models (VLMs) lag behind text-only language models on mathematical reasoning when the same problems are presented as images rather than text. We empirically characterize this as a modality gap: the same question in text form yields markedly higher accuracy than its visually typeset counterpart, due to compounded failures in reading dense formulas, layout, and mixed symbolic-diagrammatic context. First, we introduce VisTIRA (Vision and Tool-Integrated Reasoning Agent), a tool-integrated reasoning framework that enables […]

Ver mais

arXiv:2601.07892v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) on resource-constrained edge devices is increasingly hindered by prohibitive memory and computational requirements. While ternary quantization offers a compelling solution by reducing weights to {-1, 0, +1}, current implementations suffer from a fundamental misalignment with commodity hardware. Most existing methods must choose between 2-bit aligned packing, which incurs significant bit wastage, or 1.67-bit irregular packing, which degrades inference speed. To resolve this tension, we propose Sherry, […]

Ver mais

arXiv:2602.04548v1 Announce Type: cross Abstract: We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learning problems. Our key innovation is a formal power series expansion of the loss evolution, with coefficients encoded by diagrams akin to Feynman diagrams. We show that this expansion has a well-defined large-size limit that can be used to reveal different learning phases and, in some cases, to obtain explicit solutions of […]

Ver mais

Discussions and questions about methods, approaches, and tools for estimating (relative) binding free energies of protein-ligand complexes are quite popular, and even the simplest tools can be quite tricky to use. Here, I want to briefly summarize the idea of molecular docking, and give a short overview about how we can use AutoDock 4.2’s hybrid approach for evaluating binding affinities.

Ver mais

There’s a moment in every Compose developer’s journey – a quiet realization that arrives somewhere between your third nested Box and your fifth Modifier.background(). Or maybe you just bumped into excellent article about Glitch Effect from here and decided to extend your knowledge. In these moments, you don’t need another layout node; you need the Canvas. In this series, we aren’t just looking at how to draw shapes. We are going under the hood of the Compose Drawing Pipeline to understand […]

Ver mais

arXiv:2602.11203v1 Announce Type: new Abstract: In the late 1970s, C.A. Petri introduced partially ordered event occurrences (runs), then called emph{processes}, as the appropriate model to describe the individual evolutions of distributed systems. Here, we present a unified framework for handling Petri nets and their runs, specifically to compose and decompose them. It is shown that, for nets $M$ and $N$, the set of runs of the composed net $M bullet N$ equals the composition of the runs of […]

Ver mais

When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of pattern classification in order to put my previous topics into context and to provide and introduction for the future topics that are going to follow.

Ver mais

Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

Ver mais

Mastering unsupervised learning opens up many avenues for a data scientist. There is so much scope in the vast expanse of unsupervised learning, yet many beginners in machine learning tend to shy away from it. I’m sure most newcomers will stick to basic clustering algorithms like K-Means clustering and hierarchical clustering. Clustering is an essential technique in machine learning and is used widely across domains and industries (think about Uber’s route optimization, Amazon’s recommendation system, Netflix’s customer segmentation, […]

Ver mais