# CogToM: A Comprehensive Theory of Mind Benchmark inspired by Human Cognition for Large Language Models

arXiv: https://arxiv.org/abs/2601.15628 · HTML: https://mlsanity.com/paper/2601.15628/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 66 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks](https://mlsanity.com/paper/2302.08399/) (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)
- [FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions](https://mlsanity.com/paper/2310.15421/) (2023)
- [Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models](https://mlsanity.com/paper/2305.14763/) (2023)
- [SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs](https://mlsanity.com/paper/2410.13648/) (2024)
- [NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding](https://mlsanity.com/paper/2404.13627/) (2024)
- [ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind](https://mlsanity.com/paper/2305.15068/) (2023)
- [MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic](https://mlsanity.com/paper/2305.03353/) (2023)

## Built on by (most cited first, 4 of 4)

- [EmoLLM: Appraisal-Grounded Cognitive-Emotional Co-Reasoning in Large Language Models](https://mlsanity.com/paper/2603.16553/) (2026)
- [Can Large Language Models Simulate Human Cognition Beyond Behavioral Imitation?](https://mlsanity.com/paper/2603.27694/) (2026)
- [ZenGen: Social Mind for LLMs](https://mlsanity.com/paper/2607.23740/) (2026)
- [Beyond Successful Play: Diagnosing Opponent Modeling in Large Language Models](https://mlsanity.com/paper/2610.17310/) (2026)

## Abstract (arXiv)

Whether Large Language Models (LLMs) truly possess human-like Theory of Mind (ToM) capabilities has garnered increasing attention. However, existing benchmarks remain largely restricted to narrow paradigms like false belief tasks, failing to capture the full spectrum of human cognitive mechanisms. We introduce CogToM, a comprehensive, theoretically grounded benchmark comprising over 8000 bilingual instances across 46 paradigms, validated by 49 human annotator.A systematic evaluation of 22 representative models, including frontier models like GPT-5.1 and Qwen3-Max, reveals significant performance heterogeneities and highlights persistent bottlenecks in specific dimensions. Further analysis based on human cognitive patterns suggests potential divergences between LLM and human cognitive structures. CogToM offers a robust instrument and perspective for investigating the evolving cognitive boundaries of LLMs.