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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.16826 |
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| _version_ | 1866908840400781312 |
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| author | Doering, Nigel Malladi, Rahath Sangwan, Arshia Danks, David Rahman, Tauhidur |
| author_facet | Doering, Nigel Malladi, Rahath Sangwan, Arshia Danks, David Rahman, Tauhidur |
| contents | Theory of mind (ToM) enables AI systems to infer agents' hidden goals and mental states, but existing approaches focus mainly on small human understandable gridworld spaces. We introduce HiVAE, a hierarchical variational architecture that scales ToM reasoning to realistic spatiotemporal domains. Inspired by the belief-desire-intention structure of human cognition, our three-level VAE hierarchy achieves substantial performance improvements on a 3,185-node campus navigation task. However, we identify a critical limitation: while our hierarchical structure improves prediction, learned latent representations lack explicit grounding to actual mental states. We propose self-supervised alignment strategies and present this work to solicit community feedback on grounding approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16826 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind Doering, Nigel Malladi, Rahath Sangwan, Arshia Danks, David Rahman, Tauhidur Machine Learning Artificial Intelligence Theory of mind (ToM) enables AI systems to infer agents' hidden goals and mental states, but existing approaches focus mainly on small human understandable gridworld spaces. We introduce HiVAE, a hierarchical variational architecture that scales ToM reasoning to realistic spatiotemporal domains. Inspired by the belief-desire-intention structure of human cognition, our three-level VAE hierarchy achieves substantial performance improvements on a 3,185-node campus navigation task. However, we identify a critical limitation: while our hierarchical structure improves prediction, learned latent representations lack explicit grounding to actual mental states. We propose self-supervised alignment strategies and present this work to solicit community feedback on grounding approaches. |
| title | HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.16826 |