Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others. Although this capability is crucial for human interaction, testing on Large Language Models (LLMs) reveals that they possess only a rudimentary understanding of it. Although the most capable closed-source LLMs have come close to human performance on some ToM tasks, they still perform poorly on complex variations of the task that involve more structured reasoning. In this work, we utilize the concept of “pretend-play”, or “Simulation Theory” from cognitive psychology to propose “Decompose-ToM”: an LLM-based inference algorithm that improves model performance on complex ToM tasks. We recursively simulate user perspectives and decompose the ToM task into a simpler set of tasks: subject identification, question-reframing, world model updation, and knowledge availability. We test the algorithm on higher-order ToM tasks and a task testing for ToM capabilities in a conversational setting, demonstrating that our approach shows significant improvement across models compared to baseline methods while requiring minimal prompt tuning across tasks and no additional model training. Our code is publicly available.

The capacity to attribute mental states like beliefs, desires, and intentions to oneself and others, known as Theory of Mind (ToM), is fundamental to human social intelligence. As Large Language Models (LLMs) are increasingly integrated into complex interactive systems, developing their ToM capabilities is crucial. Such capabilities enable LLMs to understand and predict human behavior, leading to more intuitive and productive interactions. However, current models often struggle with sophisticated reasoning about others’ perspectives. In this work, we propose “Agentic-ToM”, showing that guiding LLMs by embedding psychologically-grounded functions for capabilities such as ‘perspective taking’ and mental state tracking markedly improves their proficiency in ToM tasks. We evaluate the approach on three diverse ToM datasets and show that this method significantly outperforms baselines across all tasks without requiring task-specific modifications.