# Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning

arXiv: https://arxiv.org/abs/2412.13631 · HTML: https://mlsanity.com/paper/2412.13631/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Building Cooperative Embodied Agents Modularly with Large Language Models](https://mlsanity.com/paper/2307.02485/) (2023)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [Understanding Social Reasoning in Language Models with Language Models](https://mlsanity.com/paper/2306.15448/) (2023)
- [Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning](https://mlsanity.com/paper/2312.05230/) (2023)
- [PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models](https://mlsanity.com/paper/2403.02246/) (2024)
- [Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at Trivial Alterations to the False Belief Task?](https://mlsanity.com/paper/2406.14737/) (2024)
- [Probing the Robustness of Theory of Mind in Large Language Models](https://mlsanity.com/paper/2410.06271/) (2024)

## Built on by (most cited first, 9 of 9)

- [Infusing Theory of Mind into Socially Intelligent LLM Agents](https://mlsanity.com/paper/2509.22887/) (2025)
- [A Review of Incorporating Psychological Theories in LLMs](https://mlsanity.com/paper/2505.00003/) (2025)
- [MastermindEval: A Simple But Scalable Reasoning Benchmark](https://mlsanity.com/paper/2503.05891/) (2025)
- [A formal definition and meta-model for a machine theory of mind](https://mlsanity.com/paper/2606.03471/) (2026)
- [Assessing mentalization in humans and large language models](https://mlsanity.com/paper/2608.26291/) (2026)
- [Sensitivity Meets Sparsity: The Impact of Extremely Sparse Parameter Patterns on Theory-of-Mind of Large Language Models](https://mlsanity.com/paper/2504.04238/) (2025)
- [CoMMET: A Psychologically Grounded Benchmark for Evaluating Theory of Mind in Multimodal LLMs](https://mlsanity.com/paper/2603.11915/) (2026)
- [The Intelligent Disobedience Game: Formulating Disobedience in Stackelberg Games and Markov Decision Processes](https://mlsanity.com/paper/2603.20994/) (2026)
- [Agent-ToM: Learning to Monitor Autonomous LLM Agents via Theory-of-Mind Reasoning](https://mlsanity.com/paper/2605.24216/) (2026)

## Abstract (arXiv)

Theory of Mind (ToM) capabilities in LLMs have recently become a central object of investigation. Cognitive science distinguishes between two steps required for ToM tasks: 1) determine whether to invoke ToM, which includes the appropriate Depth of Mentalizing (DoM), or level of recursion required to complete a task; and 2) applying the correct inference given the DoM. In this position paper, we first identify several lines of work in different communities in AI, including LLM benchmarking, ToM add-ons, ToM probing, and formal models for ToM. We argue that recent work in AI tends to focus exclusively on the second step which are typically framed as static logic problems. We conclude with suggestions for improved evaluation of ToM capabilities inspired by dynamic environments used in cognitive tasks.