Language
You Don’t Need to Share Data to Train a Language Model Anymore—FlexOlmo Demonstrates How
The development of large-scale language models (LLMs) has historically required centralized access to extensive datasets, many of which are sensitive, copyrighted, or governed by usage restrictions. This constraint severely limits the participation of data-rich organizations operating in regulated or proprietary environments. FlexOlmo—introduced by researchers at the Allen Institute for AI and collaborators—proposes a modular training…
Mistral AI Releases Devstral 2507 for Code-Centric Language Modeling
Mistral AI, in collaboration with All Hands AI, has released updated versions of its developer-focused large language models under the Devstral 2507 label. The release includes two models—Devstral Small 1.1 and Devstral Medium 2507—designed to support agent-based code reasoning, program synthesis, and structured task execution across large software repositories. These models are optimized for performance…
CSS Intelligence: Speculating On The Future Of A Smarter Language — Smashing Magazine
CSS has evolved from a purely presentational language into one with growing logical powers — thanks to features like container queries, relational pseudo-classes, and the if() function. Is it still just for styling, or is it becoming something more? Gabriel Shoyombo explores how smart CSS has become over the years, where it is heading, the…
Thought Anchors: A Machine Learning Framework for Identifying and Measuring Key Reasoning Steps in Large Language Models with Precision
Understanding the Limits of Current Interpretability Tools in LLMs AI models, such as DeepSeek and GPT variants, rely on billions of parameters working together to handle complex reasoning tasks. Despite their capabilities, one major challenge is understanding which parts of their reasoning have the greatest influence on the final output. This is especially crucial for…
Unpacking the bias of large language models
Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document or conversation, while neglecting the middle. This “position bias” means that, if a lawyer is using an LLM-powered virtual assistant to retrieve a certain phrase in a 30-page affidavit, the LLM is more likely to…
The Art of Duolingo Notifications: The Subtle Manipulation of Language Learners
Let’s get something straight: Duolingo is a brilliant app. It’s become a household name in the realm of language learning, and for good reason. The bite-sized lessons, the daily streaks, the gamified points, the whole dopamine-driven experience—it works. It works really well. And it’s not just the design that makes it addictive—it’s the notifications. Duolingo…
This AI paper from DeepSeek-AI Explores How DeepSeek-V3 Delivers High-Performance Language Modeling by Minimizing Hardware Overhead and Maximizing Computational Efficiency
The growth in developing and deploying large language models (LLMs) is closely tied to architectural innovations, large-scale datasets, and hardware improvements. Models like DeepSeek-V3, GPT-4o, Claude 3.5 Sonnet, and LLaMA-3 have demonstrated how scaling enhances reasoning and dialogue capabilities. However, as their performance increases, so do computing, memory, and communication bandwidth demands, placing substantial strain…
Making AI-generated code more accurate in any language
Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers’ lives easier if that code follows the rules of the programming language and doesn’t cause a computer to crash. Some methods exist for ensuring LLMs conform to the rules of whatever language they are generating text…
RT-2: New model translates vision and language into action
Research Published 28 July 2023 Authors Yevgen Chebotar, Tianhe Yu Robotic Transformer 2 (RT-2) is a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalised instructions for robotic control High-capacity vision-language models (VLMs) are trained on web-scale datasets, making these systems remarkably good at recognising visual…
NVIDIA AI Researchers Introduce FFN Fusion: A Novel Optimization Technique that Demonstrates How Sequential Computation in Large Language Models LLMs can be Effectively Parallelized
Large language models (LLMs) have become vital across domains, enabling high-performance applications such as natural language generation, scientific research, and conversational agents. Underneath these advancements lies the transformer architecture, where alternating layers of attention mechanisms and feed-forward networks (FFNs) sequentially process tokenized input. However, with an increase in size and complexity, the computational burden required…