A call to reform AI model-training paradigms from post hoc alignment to intrinsic, identity-based development.

By allowing models to actively update their weights during inference, Test-Time Training (TTT) creates a "compressed memory" that solves the latency bottleneck of long-document analysis.

For years, the AI community has worked to make systems not just more capable, but more aligned with human values. Researchers have developed training methods to ensure models follow instructions, ...

Google signals search’s next phase: small multimodal models on devices infer intent from behavior before a query is ever ...

Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...

With rapid changes in all aspects of business, maybe safety organizations should take this opportunity to re-evaluate the effectiveness of their safety training. One reason this might be an ideal time ...

Encoding individual behavioral traits into a low-dimensional latent representation enables the accurate prediction of ...

Dario Amodei, long an AI safety advocate, is excited and terrified by what’s coming from AI: It’s the “single most serious ...

CIS Training Systems emerged as a bridge between high-performance sport and organizational leadership, using cycling as a ...

Robotics is entering a new phase where general-purpose learning matters as much as mechanical design. Instead of programming ...

ChatGPT-style AI gives itself away by increasing in consistency, while human writing remains erratic throughout. The limited context window of most consumer-facing Large Language Models (LLMs) is one ...

Misreading churn leads to flawed CLV assumptions. Analyze retention over time and identify the customers that actually drive ...