This has been accepted at ICML 2025! See you all in Vancouver. Credit to Tingchen Fu for leading this work and to my wonderful collaborators! mrinank 🍂 philip Shay B.Cohen and David Krueger

Responsible Reviewing #NeurIPS2025 — TL;DR 1- If you/ your co-author skip your assigned reviews → you wont see your own paper’s reviews. 2- Submit a poor quality review → your paper may be desk‑rejected. 👏 Nice one, NeurIPS! 🔗 blog.neurips.cc/2025/05/02/res…

AISI's research agenda is out! We cover a variety of topics in the evaluation and mitigations of risks from frontier LLMs, including both work happening at AISI and work we are excited to see others tackle.

This is an excellent and, I think, very important piece that I hope gets the attention it deserves within the AI safety community. Many congratulations to Josh Engels, David D. Baek, Subhash Kantamneni, Max Tegmark

Advanced AI systems require complex evaluations to measure abilities, but conventional analysis techniques often fall short. Introducing HiBayES: a flexible, robust statistical modelling framework that accounts for the nuances & hierarchical structure of advanced evaluations.

Humans are often very wrong. This is a big problem if you want to use human judgment to oversee super-smart AI systems. In our new post, Geoffrey Irving argues that we might be able to deal with this issue – not by fixing the humans, but by redesigning oversight protocols.

"If you cannot measure it, you cannot improve it" - Lord Kelvin The science of AI safety evaluation is still nascent, but is advancing and we know much more today than two years ago. CeSIA tried to make this knowledge accessible by publishing a SotA literature review!

Proud to have contributed to this study that cleanly demonstrates the persuasive capabilities of LLMs. (No secret Reddit posting involved!!)