Propositional interpretability in artificial intelligence

David Chalmers · 2025 · arXiv

Read the paper

Proposes interpreting AI systems through attitudes like belief and desire, sets ‘thought logging’ as a goal, and assesses probes, sparse autoencoders and chain of thought as routes to it.

Questions it bears on

• Do models represent goals and preferences