Spontaneous Theory of Mind for Artificial Intelligence
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Abstract (arXiv)
Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM -- reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts textitand spontaneously engage in social reasoning.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 69 references that are in the map. First posted on arXiv 16 Feb 2024.
Builds on
6 of 69 references.
References · 6
- 2017Attention Is All You Needcited by 59,932
- 2023Large Language Models Fail on Trivial Alterations to Theory-of-Mind Taskscited by 288
- 2018Machine Theory of Mindcited by 222
- 2022Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMscited by 220
- 2022Do Large Language Models know what humans know?cited by 60
- 2023Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoningcited by 11
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.