I’d like to share some slides and code for a “Memory Model 101 workshop” I gave recently, which has some minimal examples to illustrate the Rumelhart network & catastrophic interference :) slides: shorturl.at/q2iKq code: github.com/qihongl/demo-r…

Fantastic to see Anthropic, in collaboration with neuronpedia, creating open source tools for studying circuits with transcoders. There's a lot of interesting work to be done I'm also very glad someone finally found a use for our Gemma Scope transcoders! Credit to Arthur Conmy

It was great to be part of this statement. I wholeheartedly agree. It is a wild lucky coincidence that models often express dangerous intentions aloud, and it would be foolish to waste this opportunity. It is crucial to keep chain of thought monitorable as long as possible

1/N I’m excited to share that our latest OpenAI experimental reasoning LLM has achieved a longstanding grand challenge in AI: gold medal-level performance on the world’s most prestigious math competition—the International Math Olympiad (IMO).

Nice - we could have prompted our way to winning IMO gold on an already available model!

New Anthropic Research: “Inverse Scaling in Test-Time Compute” We found cases where longer reasoning leads to lower accuracy. Our findings suggest that naïve scaling of test-time compute may inadvertently reinforce problematic reasoning patterns. 🧵

Alex Dimakis But teacher and student share same init