While these potential applications are showing where the tangible value will be in using reasoning models, the reality is that they are still nascent, and we have not seen widespread adoption for a ...

Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller steps. These powerful models are particularly good at challenging tasks ...

Inception, the company behind the first commercial diffusion large language models (dLLMs), today announced the launch of Mercury 2, the fastest reasoning LLM and first reasoning dLLM. Mercury 2 ...

Nvidia researchers developed dynamic memory sparsification (DMS), a technique that compresses the KV cache in large language models by up to 8x while maintaining reasoning accuracy — and it can be ...

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...

The new Mercury 2 AI model uses diffusion reasoning to generate 1,000 tokens per second; it runs about 5x faster than Haiku, speed limits are ...

Local models work best when you meet them halfway ...

Yann LeCun’s argues that there are limitations of chain-of-thought (CoT) prompting and large language model (LLM) reasoning. LeCun argues that these fundamental limitations will require an entirely ...

Researchers compare two solutions for approximating LLM rankings of Claude 4, GPT-4o, Gemini 2.5, and Grok-3. Researchers published the results of a study showing how AI search rankings can be ...

When your AI assistant calculates revenue, bonuses, VAT or financial summaries, it isn’t doing math. It’s telling a convincing story about numbers.

A 9-language interface and LLM Selector expand global accessibility while giving enterprises greater control over AI ...