mlsanityData to 7 Oct 2026

Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning

arXiv 2412.13631 ↗First posted 18 Dec 2024cs.AI cs.CLcited by 9

On the map: LLM reasoning → Reasoning benchmarks and evaluation

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.

Key facts. Cited by 9 papers in the map (data through 7 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.

Builds on

9 of 67 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Probing the Robustness of Theory of Mind in Large Language Models8 Oct
  2. 2024PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models4 Mar
  3. 2023Understanding Social Reasoning in Language Models with Language Models21 Jun

Other references · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 288
  3. 2023Building Cooperative Embodied Agents Modularly with Large Language Modelscited by 231
  4. 2023Evaluating Large Language Models in Theory of Mind Taskscited by 217
  5. 2023Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planningcited by 59
  6. 2024Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at Trivial Alterations to the False Belief Task?cited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 5.

9 papers.

  1. 2025Infusing Theory of Mind into Socially Intelligent LLM Agentscited by 7
  2. 2025A Review of Incorporating Psychological Theories in LLMscited by 5
  3. 2025MastermindEval: A Simple But Scalable Reasoning Benchmarkcited by 4
  4. 2026A formal definition and meta-model for a machine theory of mindcited by 1
  5. 2025Sensitivity Meets Sparsity: The Impact of Extremely Sparse Parameter Patterns on Theory-of-Mind of Large Language Modelscited by 0
  6. 2026CoMMET: A Psychologically Grounded Benchmark for Evaluating Theory of Mind in Multimodal LLMscited by 0
  7. 2026The Intelligent Disobedience Game: Formulating Disobedience in Stackelberg Games and Markov Decision Processescited by 0
  8. 2026Agent-ToM: Learning to Monitor Autonomous LLM Agents via Theory-of-Mind Reasoningcited by 0
  9. 2026Assessing mentalization in humans and large language modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models
  2. 2024ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models
  3. 2026Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models
  4. 2023MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic
  5. 2025Recognizing Artificial Minds: A Philosophical Defense of AI Cognition
  6. 2024Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models
  7. 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models
  8. 2024Position: Theory of Mind Benchmarks are Broken for Large Language Models