Interpretable AI agents in GOHR game revealed by sparse autoencoders

Researchers have developed interpretable experiments for a Transformer agent operating in the Game of Hidden Rules (GOHR). By training sparse autoencoders on the agent's decision-token embeddings, they were able to recover the underlying structure of the game. These autoencoder dimensions proved selective for specific concepts like chosen shapes or buckets, and also corresponded to interpretable strategies such as hypothesis probing and switching after negative feedback. AI

IMPACT Provides new methods for understanding the internal decision-making processes of AI agents.

RANK_REASON The cluster contains a research paper detailing interpretability experiments for an AI agent. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Hugging Face
• Shiwei Tang
• Sparse Autoencoders
• Transformer

• paper
• other

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