Agentic-ToM: Cognition-Inspired Agentic Processing For Enhancing Theory of Mind Reasoning

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 psychologicallygrounded 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. Our code is publicly available.