Agreed that likely humans would outperform more! At the moment we don’t have a human baseline for AmongUs vs. language models yet, so we wouldn’t be able to tell if it improved, but it’s a good follow-up.
Adrià Garriga-alonso
Karma: 1,230
Sparsity is the enemy of feature extraction (ft. absorption)
Among Us: A Sandbox for Agentic Deception
A Bunch of Matryoshka SAEs
Feature Hedging: Another way correlated features break SAEs
Illusory Safety: Redteaming DeepSeek R1 and the Strongest Fine-Tunable Models of OpenAI, Anthropic, and Google
Crafting Polysemantic Transformer Benchmarks with Known Circuits
I’m curious what you mean, but I don’t entirely understand. If you give me a text representation of the level I’ll run it! :) Or you can do so yourself
Here’s the text representation for level 53
########## ########## ########## ####### # ######## # # ###.@# # $ $$ # #. #.$ # # . ## ##########
Maybe in this case it’s a “confusion” shard? While it seems to be planning and produce optimizing behavior, it’s not clear that it will behave as a utility maximizer.
Thank you!! I agree it’s a really good mesa-optimizer candidate, it remains to see now exactly how good. It’s a shame that I only found out about it about a year ago :)
Pacing Outside the Box: RNNs Learn to Plan in Sokoban
Asking for an acquaintance. If I know some graduate-level machine learning, and have read ~most of the recent mechanistic interpretability literature, and have made good progress understanding a small-ish neural network in the last few months.
Is ARENA for me, or will it teach things I mostly already know?
(I advised this person that they already have ARENA-graduate level, but I want to check in case I’m wrong.)
Compact Proofs of Model Performance via Mechanistic Interpretability
How did you feed the data into the model and get predictions? Was there a prompt and then you got the model’s answer? Then you got the logits from the API? What was the prompt?
Catastrophic Goodhart in RL with KL penalty
Thank you for working on this Joseph!
Thank you! Could you please provide more context? I don’t know what ‘E’ you’re referring to.
I think it’s still true that a lot of individual AI researchers are motivated by something approximating play (they call it “solving interesting problems”), even if the entity we call Anthropic is not. These researchers are in Anthropic and outside of it. This applies to all companies of course.