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Most AI memory remembers the user. It stores your preferences, your tastes, and your role. Perplexity is taking a different path. Today, Perplexity launched Brain, a self-improving memory system for its agent product, Computer. Brain does not focus on remembering you. It remembers what the agent did. That reframes what memory in AI is for. […]
Long-context large language models (LLMs) face a memory bottleneck that has nothing to do with model weights. During decoding, transformers cache the key and value (KV) vectors for every token at every layer so they don’t have to recompute attention. This cache grows linearly with sequence length and batch size, and at long context with
The KV Cache Compression Race: TurboQuant vs OSCAR vs EpiCache Read More »
Most biology benchmarks ask narrow, fact-based questions with clean answers. Scientists weigh imperfect evidence and make decisions. OpenAI released LifeSciBench and it targets that gap directly. Even the strongest model passes roughly one task in three. The benchmark is far from saturated. What is LifeSciBench LifeSciBench contains 750 expert-authored tasks. They span seven workflows and
In this tutorial, we explore how NVIDIA SkillSpector helps us evaluate AI skills for security risks before they are used in real-world workflows. We build a controlled corpus containing both benign and deliberately vulnerable skills, scan them through SkillSpector’s programmatic LangGraph workflow, and organize the resulting risk scores and findings with pandas. We then visualize
Vercel has released eve, an open-source framework for building, running, and scaling agents. The project is published as the npm package eve, licensed under Apache-2.0. Building an agent should mean defining what it does. It should not mean assembling all the plumbing that an agent needs to run in production. eve is the framework Vercel
MiniMax released MSA (MiniMax Sparse Attention), a sparse attention method built directly on Grouped Query Attention (GQA). It targets one bottleneck: the quadratic cost of softmax attention at long context. The MiniMax research team tested it inside a 109B-parameter Mixture-of-Experts model trained with native multimodal data. They also open-sourced an inference kernel and shipped a
OpenAI published a new pre-deployment safety method called Deployment Simulation. The idea is direct. Before a model ships, simulate its deployment first. Replay past conversations through the new candidate model. Then study how it behaves in realistic contexts. OpenAI already uses insights from the method during model development. It has informed mitigations and deployment decisions,
In this tutorial, we implement xFormers: a practical toolkit for building fast, memory-efficient Transformer models on GPUs. We begin by validating memory-efficient attention against a standard attention implementation, then compare their speed and memory consumption across different sequence lengths. We then examine causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi positional biases. Finally,
The Qwen team has released three embodied AI models, grouped as Qwen-Robot-Suite. The three are Qwen-RobotManip, Qwen-RobotWorld, and Qwen-RobotNav. Each is built on a Qwen vision-language backbone and targets a different robotics problem. Qwen-RobotManip is a Vision-Language-Action model for manipulation, built on Qwen3.5-4B. Qwen-RobotWorld is a language-conditioned video world model with a 60-layer MMDiT and
Nous Research has shipped a change to Hermes Agent. Its delegate tool can now run subagents asynchronously. Per the announcement, delegated work no longer blocks the parent chat. Hermes Agent is an open-source personal agent from Nous Research. A parent agent can spawn child agents, called subagents, to fan out work. Until now, that delegation
