# A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks

arXiv: https://arxiv.org/abs/2502.06470 · HTML: https://mlsanity.com/paper/2502.06470/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 24 of 24)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://mlsanity.com/paper/2310.01405/) (2023)
- [Steering Language Models With Activation Engineering](https://mlsanity.com/paper/2308.10248/) (2023)
- [AI safety via debate](https://mlsanity.com/paper/1805.00899/) (2018)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (2023)
- [Risks from Learned Optimization in Advanced Machine Learning Systems](https://mlsanity.com/paper/1906.01820/) (2019)
- [A Careful Examination of Large Language Model Performance on Grade School Arithmetic](https://mlsanity.com/paper/2405.00332/) (2024)
- [Understanding Social Reasoning in Language Models with Language Models](https://mlsanity.com/paper/2306.15448/) (2023)
- [FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions](https://mlsanity.com/paper/2310.15421/) (2023)
- [Large Language Models can Strategically Deceive their Users when Put Under Pressure](https://mlsanity.com/paper/2311.07590/) (2023)
- [AI Sandbagging: Language Models can Strategically Underperform on Evaluations](https://mlsanity.com/paper/2406.07358/) (2024)
- [Secret Collusion among AI Agents: Multi-Agent Deception via Steganography](https://mlsanity.com/paper/2402.07510/) (2024)
- [On scalable oversight with weak LLMs judging strong LLMs](https://mlsanity.com/paper/2407.04622/) (2024)
- [Coercing LLMs to do and reveal (almost) anything](https://mlsanity.com/paper/2402.14020/) (2024)
- [Defending Against Unforeseen Failure Modes with Latent Adversarial Training](https://mlsanity.com/paper/2403.05030/) (2024)
- [Transformers represent belief state geometry in their residual stream](https://mlsanity.com/paper/2405.15943/) (2024)
- [Escalation Risks from Language Models in Military and Diplomatic Decision-Making](https://mlsanity.com/paper/2401.03408/) (2024)
- [LLMs achieve adult human performance on higher-order theory of mind tasks](https://mlsanity.com/paper/2405.18870/) (2024)
- [Uncovering Deceptive Tendencies in Language Models: A Simulated Company AI Assistant](https://mlsanity.com/paper/2405.01576/) (2024)
- [Intelligent Virtual Assistants with LLM-based Process Automation](https://mlsanity.com/paper/2312.06677/) (2023)
- [Unveiling Theory of Mind in Large Language Models: A Parallel to Single Neurons in the Human Brain](https://mlsanity.com/paper/2309.01660/) (2023)
- [Brittle Minds, Fixable Activations: Understanding Belief Representations in Language Models](https://mlsanity.com/paper/2406.17513/) (2024)

## Built on by (most cited first, 8 of 8)

- [Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets](https://mlsanity.com/paper/2505.20120/) (2025)
- [To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks](https://mlsanity.com/paper/2602.10625/) (2026)
- [Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations](https://mlsanity.com/paper/2605.15205/) (2026)
- [OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning](https://mlsanity.com/paper/2509.14803/) (2025)
- [Are LLMs Smarter Than Chimpanzees? An Evaluation on Perspective Taking and Knowledge State Estimation](https://mlsanity.com/paper/2601.12410/) (2026)
- [Do Large Language Models Get Caught in Hofstadter-Mobius Loops?](https://mlsanity.com/paper/2603.13378/) (2026)
- [Toward Human-Centered Multi-Agent Systems: Integrating Cognition, Culture, Values, and Cooperation in AI Agents](https://mlsanity.com/paper/2606.08274/) (2026)
- [ARGUS: Theory-of-Mind Guided Argument Generation with Strategy-Aware Planning and Knowledge Grounding](https://mlsanity.com/paper/2608.20405/) (2026)

## Abstract (arXiv)

Theory of Mind (ToM), the ability to attribute mental states to others and predict their behaviour, is fundamental to social intelligence. In this paper, we survey studies evaluating behavioural and representational ToM in Large Language Models (LLMs), identify important safety risks from advanced LLM ToM capabilities, and suggest several research directions for effective evaluation and mitigation of these risks.