causal-learning

This paper presents a method for learning implicit causal world models from multi-agent demonstrations, enabling agents to infer causal structures from observed behavior.

This paper develops a formal account of what generalist agents must store in memory to act near-optimally across multiple environments and goals, presenting a separation theorem that memory is necessary for domain disambiguation and transition-model reconstruction.

This study examines whether active exploration helps adults overcome the 'conjunctive handicap' in causal reasoning, comparing human performance to LLMs in a blicket detector task. Results show that active exploration improves conjunctive reasoning in adults, though some gaps remain, and LLMs approach human accuracy but explore less efficiently.