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Linear attention replaces the unbounded KV cache of softmax attention with a fixed-size recurrent state. This cuts sequence mixing to linear time and decoding to constant memory. The hard part is not what to forget. It is how to edit a compressed memory without scrambling existing associations. NVIDIA has released Gated DeltaNet-2, a linear attention […]
Microsoft Research’s AI Frontiers lab released Fara1.5. It is a family of computer-use agent (CUA) models for the browser. The release ships three sizes: Fara1.5-4B, Fara1.5-9B, and Fara1.5-27B. The models are integrated with MagenticLite, Microsoft’s sandboxed browser interface for these agents. Computer-use agents are pixel-to-action models that drive a real browser. They read screenshots and
Most AI models today are not designed for sustained, multi-step autonomous execution. Tasks like running hundreds of iterative code modifications, or chaining tool calls across hours without human intervention, require a different kind of model architecture and training focus. Alibaba’s Qwen team formally announced Qwen3.7-Max at the 2026 Alibaba Cloud Summit on May 20. Although,
Qwen Introduces Qwen3.7-Max: A Reasoning Agent Model With a 1M-Token Context Window Read More »
Cohere just released Command A+, as an open-source model targeting enterprise agentic workflows. Available under an Apache 2.0 license, Command A+ is a mixture-of-experts (MoE) model built for high-performance agentic tasks with minimal compute overhead. The model is optimized for reasoning, agentic workflows, RAG, multilingual, and multimodal document processing. It unifies capabilities from four prior
NVIDIA researchers have released Nemotron-Labs-Diffusion, a language model family that unifies three decoding modes in one architecture. The model supports autoregressive (AR) decoding, diffusion-based parallel decoding, and self-speculation decoding. It is available in 3B, 8B, and 14B parameter sizes. The family includes base, instruct, and vision-language variants. Sequential Decoding Limits Throughput Standard autoregressive (AR) language
Simultaneous interpretation is one of the harder problems in applied AI. You’re asking a model to translate speech before the speaker has finished a sentence. Every extra second of delay breaks the illusion of real-time communication. Alibaba’s Qwen team has been chipping away at this with each release. Their latest model, Qwen3.5-LiveTranslate-Flash, brings that latency
As LLM-powered agents move from research to production, one design tension is becoming harder to ignore: the more useful cloud-hosted memory becomes, the more private user data it exposes. Researchers from MemTensor (Shanghai), HONOR Device and Tongji University have introduced MemPrivacy, a framework that attempts to resolve this tension without sacrificing the utility that makes
Modern language models are trained on data with extremely uneven token distributions. A small number of words appear in almost every sentence, while many rare but meaningful tokens occur only occasionally. This creates a hidden optimization challenge: parameters associated with common tokens receive constant gradient updates, while parameters tied to rare tokens may go hundreds
Stochastic Gradient Descent (SGD’s) Frequency Bias and How Adam Fixes It Read More »
Pretraining frontier-scale LLMs in FP8 is now standard practice, but moving to 4-bit floating point has remained an open research problem because narrower formats compress dynamic range and amplify quantization error at long token horizons. A new research from NVIDIA describes a pretraining methodology built around NVFP4, a 4-bit microscaling format supported natively by Blackwell
In this tutorial, we explore how to apply post-training quantization to an instruction-tuned language model using llmcompressor. We start with an FP16 baseline and then compare multiple compression strategies, including FP8 dynamic quantization, GPTQ W4A16, and SmoothQuant with GPTQ W8A8. Along the way, we benchmark each model variant for disk size, generation latency, throughput, perplexity,
