Microsoft Research Develops Novel Approaches to Enforce Privacy in AI Models
A team of AI researchers at Microsoft introduces two novel approaches for enforcing contextual integrity in large language models: PrivacyChecker, an open-source lightweight module that acts as a privacy shield during inference, and CI-CoT + CI-RL, an advanced training method designed to teach models to reason about privacy.
Google’s Eight Essential Multi-Agent Design Patterns
Google recently published a guide outlining eight essential design patterns for multi-agent systems, ranging from sequential pipelines to human-in-the-loop architecture. The guide provides concrete explanations of each pattern along with sample code for Google’s Agent Development Kit.
Intel DeepMath Introduces a Smart Architecture to Make LLMs Better at Math
Intel has announced DeepMath, a lightweight agent built on Qwen3-Thinking that specializes in solving mathematical problems. To address common limitations of LLMs in math reasoning, DeepMath generates small Python scripts that support and enhance its problem-solving process.
Google Releases Gemma Scope 2 to Deepen Understanding of LLM Behavior
Gemma Scope 2 is a suite of tools designed to interpret the behavior of Gemini 3 models, enabling researchers to analyze emergent model behaviors, audit and debug AI agents, and devise mitigation strategies against security issues like jailbreaks, hallucinations and sycophancy.