Categories: FAANG

What Do Your Logits Know? (The Answer May Surprise You!)

Recent work has shown that probing model internals can reveal a wealth of information not apparent from the model generations. This poses the risk of unintentional or malicious information leakage, where model users are able to learn information that the model owner assumed was inaccessible. Using vision-language models as a testbed, we present the first systematic comparison of information retained at different “representational levels” as it is compressed from the rich information encoded in the residual stream through two natural bottlenecks: low-dimensional projections of the residual…

On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization

The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of density-induced metrics on parameter spaces, to analyze existing methods within the space of model compression, primarily focusing on operator factorization. Adopting this…

July 22, 2025

In "FAANG"

Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling

Standard discrete diffusion models treat all unobserved states identically by mapping them to an absorbing [MASK] token. This creates an ‘information void’ where semantic information that could be inferred from unmasked tokens is lost between denoising steps. We introduce Continuously Augmented Discrete Diffusion (CADD), a framework that augments the discrete…

December 11, 2025

In "FAANG"

Frontier Model Forum

We’re forming a new industry body to promote the safe and responsible development of frontier AI systems: advancing AI safety research, identifying best practices and standards, and facilitating information sharing among policymakers and industry.

July 27, 2023

In "FAANG"

AI Generated Robotic Content