Choosing the Right AI Agent Memory Strategy: A Decision-Tree Approach
In this article, you will learn how to choose the right memory strategy for an AI agent by working through a simple decision tree, one…
Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies
This paper was accepted at the AI4TCI (Workshop on AI for Secure and Trustworthy Critical Infrastructure Systems) Workshop at the International Conference on Availability, Reliability and Security (ARES) 2026. Autonomous negotiation agents are increasingly deployed in high-stakes settings such as insurance and procurement. While cryptographic techniques protect explicitly disclosed constraint values, they fail to address …
Fine-tune NVIDIA Nemotron 3 models with Amazon SageMaker AI serverless model customization
Model customization transforms general-purpose AI models into specialized enterprise assets. By fine-tuning foundation models (FMs) on domain-specific data, businesses teach AI their unique workflows, terminology, and deep domain specialization, along with strict adherence to brand voice and fewer hallucinations. For enterprises, this is more than an optimization. It’s the creation of proprietary intellectual property. A …
Frontier and Center: Who evaluates the evaluations?
Editor’s note: Some of the most interesting questions in AI are being asked by information theoreticians, around how to provide context to an emerging class of AI agents. A few weeks ago, we waded into those waters with a blog about the Open Knowledge Format, a specification that formalizes the LLM-wiki pattern into a portable, …
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OpenAI’s Head of Safety Is Leaving the Company
Johannes Heidecke’s departure comes as OpenAI tries to further integrate its research and safety teams.
Brain-inspired hardware brings faster, lower-power anomaly detection to AI systems
The brain’s cerebellum doesn’t waste energy analyzing every moment. Instead, it constantly monitors the world for the unexpected—and springs into action only when something suddenly changes.
LLM Orchestration Frameworks Compared: LangChain vs. LlamaIndex vs. Raw API Calls
The default assumption in most LLM developer communities is that you start with raw API calls and graduate to a framework as your project grows.
Incentivizing Temporal-Awareness in Egocentric Video Understanding Models
Multimodal large language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in egocentric settings where reasoning depends on the correct ordering and evolution of events. This deficiency stems in part from training objectives that fail to explicitly reward temporal reasoning and instead rely on frame-level spatial …
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MCP tool design: Practical approaches and tradeoffs
When Model Context Protocol (MCP) tools underperform, the cause is rarely the protocol itself but the tool design. Many teams start by exposing an existing API as-is and trusting the agent to figure out the rest. It is a natural way to extend APIs to agentic systems and generative AI coding tools. For straightforward use …
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Solve harder problems with AlphaEvolve, now available to everyone on Google Cloud
Many of the most challenging and valuable problems in the world are related to optimization. Now, AI is now making these problems tractable. If you’ve ever tried to design a microchip, plan a delivery network, or optimize a training architecture for a large AI model, you know how hard it is to find the most …
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