Announcing new capabilities in Vertex AI Training for large-scale training

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Building and scaling generative AI models demands enormous resources, but this process can get tedious. Developers wrestle with managing job queues, provisioning clusters, and resolving dependencies just to ensure consistent results. This infrastructure overhead, along with the difficulty of discovering the optimal training recipe and navigating the endless maze of hyperparameter and model architecture choices, …

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MiniMax-M2 is the new king of open source LLMs (especially for agentic tool calling)

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Watch out, DeepSeek and Qwen! There’s a new king of open source large language models (LLMs), especially when it comes to something enterprises are increasingly valuing: agentic tool use — that is, the ability to go off and use other software capabilities like web search or bespoke applications — without much human guidance. That model …

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Beyond electronics: Optical system performs feature extraction with unprecedented low latency

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Many modern artificial intelligence (AI) applications, such as surgical robotics and real-time financial trading, depend on the ability to quickly extract key features from streams of raw data. This process is currently bottlenecked by traditional digital processors. The physical limits of conventional electronics prevent the reduction in latency and the gains in throughput required in …

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From human clicks to machine intent: Preparing the web for agentic AI

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For three decades, the web has been designed with one audience in mind: People. Pages are optimized for human eyes, clicks and intuition. But as AI-driven agents begin to browse on our behalf, the human-first assumptions built into the internet are being exposed as fragile. The rise of agentic browsing — where a browser doesn’t …

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Bias after Prompting: Persistent Discrimination in Large Language Models

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A dangerous assumption that can be made from prior work on the bias transfer hypothesis (BTH) is that biases do not transfer from pre-trained large language models (LLMs) to adapted models. We invalidate this assumption by studying the BTH in causal models under prompt adaptations, as prompting is an extremely popular and accessible adaptation strategy …

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Post-Training Generative Recommenders with Advantage-Weighted Supervised Finetuning

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Author: Keertana Chidambaram, Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya (*The work was done when Keertana interned at Netflix.) Introduction This blog focuses on post-training generative recommender systems. Generative recommenders (GRs) represent a new paradigm in the field of recommendation systems (e.g. HSTU, OneRec). These models draw inspiration from recent advancements in transformer architectures used for …

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