Browsing: Machine Intelligence

To be useful, AI agents must understand and be constrained by contextual behavioral norms to ensure they act appropriately. Inspired by the theory of Contextual Integrity, our new workshop report outlines key open research directions across system, model, and user levels to build AI agents users can trust.

The rapid advancement of text-to-image AI models, such as Nano Banana, Stable Diffusion and Flux, has fundamentally transformed creative design, allowing anyone to synthesize photorealistic, high-fidelity images from textual descriptions. However, steering these massive models to meet precise user intent, downstream goals, or strict visual constraints remains a delicate and unpredictable balancing act. For example,…

AI agents have shown great potential in automating real-world tasks, such as conducting a Google Search, reading local computer files, or executing generated Python scripts. To achieve such agentic workflows, LLMs need to learn how to use tools correctly and efficiently. To teach large language models tool uses, we need datasets of tool-use chains and…