When the transformer architecture was introduced in 2017 in the now seminal Google paper "Attention Is All You Need," it became an instant cornerstone of modern artificial intelligence. Every major large language model (LLM) — from OpenAI's GPT series to Anthropic's Claude, Google's Gemini, and Meta's Llama — has been built on some variation of its central mechanism: attention, the mathematical operation that allows a model to look back across its entire input and decide what information matters most.Eight years later, the same mechanism that defined AI’s golden age is now showing its limits. Attention is powerful, but it is also expensive — its computational and memory costs scale quadratically with context length, creating an increasingly unsustainab [...]
Nous Research, the open-source artificial intelligence startup backed by crypto venture firm Paradigm, released a new competitive programming model on Monday that it says matches or exceeds several la [...]
Alibaba dropped Qwen3.5 earlier this week, timed to coincide with the Lunar New Year, and the headline numbers alone are enough to make enterprise AI buyers stop and pay attention.The new flagship ope [...]
Despite political turmoil in the U.S. AI sector, in China, the AI advances are continuing apace without a hitch.Earlier today, e-commerce giant Alibaba's Qwen Team of AI researchers, focused prim [...]
Alibaba's now famed Qwen AI development team has done it again: a little more than a day ago, they released the Qwen3.5 Medium Model series consisting of four new large language models (LLMs) wit [...]
Chinese AI and tech firms continue to impress with their development of cutting-edge, state-of-the-art AI language models.Today, the one drawing eyeballs is Alibaba Cloud's Qwen Team of AI resear [...]
Chinese e-commerce giant Alibaba's Qwen team of AI researchers has emerged in the last year as one of the global leaders of open source AI development, releasing a host of powerful large language [...]
Enterprise AI applications that handle large documents or long-horizon tasks face a severe memory bottleneck. As the context grows longer, so does the KV cache, the area where the model’s working me [...]
On a recent work trip, I had plenty of things to worry about — but being able to recharge my two smartphones, laptop and iPad were not among my concerns. In my carry-on luggage, I had two medium-cap [...]
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]