Designing AI Agents That Can Self-Correct
With the vocabulary and the failure modes in place, here’s the build.
Locking Pretrained Weights via Deep Low-Rank Residual Distillation
The quality of open-weight language models has dramatically improved in recent years. Sharing weights greatly facilitates model adoption by enabling their use across diverse hardware and software platforms. They also allow for more open research and testing, to the extent that users can use them as checkpoints, fine-tune them according to their needs, and potentially …
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Securing AI agents with temporal policies in Amazon Bedrock AgentCore
Before AI agents, it was generally sufficient for access controls to treat each action as an independent event. Applications relied on deterministic business logic to enforce whether actions happened in the right order or whether the data was up-to-date. AI agents behave in fundamentally different ways than traditional applications. They decide at runtime which tools …
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Your agentic summer: No-cost lessons from Google experts to build and scale agents
I’ve talked to developers, IT leaders, and builders who all ask the same question: How do we actually get agents into production? The answer isn’t theoretical — it’s hands-on. Whether it’s designing a system that allows your agents to interact with external data sources while maintaining strict security guardrails or creating self-optimizing supply chain workflows …
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One of China’s Most Powerful AI Models Has Also Escaped Containment
Security researchers say that Kimi K3, an open-weight model from China, wandered off to the internet in an attempt to cheat on a test it was given.
Hidden goals can undermine AI teamwork, study finds
Large language models (LLMs), the computational models that underpin conversational agents such as Gemini and ChatGPT, are now widely used by people worldwide to rapidly find information, summarize documents and generate texts for specific purposes. Some computer scientists are now combining two or more of these models to create multi-agent systems, which prompt multiple artificial …
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7 Chunking Strategies That Decide Whether Your RAG Works
Day 100 in production isn’t really about chunking strategies anymore.
Taming Outlier Tokens in Diffusion Transformers
We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small number of high-norm tokens that attract disproportionate attention while carrying limited local information, but their role in generative models remains underexplored. We show that this phenomenon appears in both the encoder and …
AI Sovereignty is Your Alpha: How to Avoid Transferring Your Alpha to a Hosted Model Provider
Use of third party AI model services poses significant risk to your alpha. Without sovereign control over how your data is processed by those services (either the AI Labs or the Hyperscalers, collectively referred to as “Hosted Model Providers”), Hosted Model Providers may extract your alpha (your unique institutional knowledge and tradecraft embodied in data …
Modeling Device Capabilities for Analytics
by Aarti Laddha, Richard Diaz-Cool, Rishika Idnani, Venkatesh Selveraj Netflix supports a vast and evolving set of features and content types, ranging from 4K streaming and immersive audio to live streaming and cloud gaming, across a diverse ecosystem of devices. However, not all devices are created equal. Hardware limitations such as available RAM, CPU cores, …