Ullman, Tomer D.

Building machines that learn and think like people

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences

Psychiatry, Mental Health, NeuroscienceAI-based Problem Solving and Planning

Cited by 2,550

Building Machines That Learn and Think Like People

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2016) · arXiv

Psychiatry, Mental Health, NeuroscienceAI-based Problem Solving and Planning

Cited by 2,550

Mind games: Game engines as an architecture for intuitive physics

Ullman, T. D., Spelke, E., Battaglia, P., & Tenenbaum, J. B. (2017) · Trends in Cognitive Sciences

Child and Animal Learning DevelopmentSpatial Cognition and Navigation

Cited by 234

Ten-month-old infants infer the value of goals from the costs of actions

Liu, S., Ullman, T. D., Tenenbaum, J. B., & Spelke, E. S. (2017) · Science

Child and Animal Learning DevelopmentSocial Robot Interaction and HRI

Cited by 230

Rethink reporting of evaluation results in AI

Burnell, R., Schellaert, W., Burden, J., Ullman, T. D., Martinez-Plumed, F., Tenenbaum, J. B., Rutar, D., Cheke, L. G., Sohl-Dickstein, J., Mitchell, M., et al. (2023) · Science

Ethics and Social Impacts of AIScientific Computing and Data Management

Cited by 104

Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks

Ullman, T. (2023) · arXiv

Child and Animal Learning DevelopmentLogic, Reasoning, and Knowledge

Cited by 81

Testing Relational Understanding in Text-Guided Image Generation

Conwell, C., & Ullman, T. (2022) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 36

AGENT: A Benchmark for Core Psychological Reasoning

Shu, T., Bhandwaldar, A., Gan, C., Smith, K. A., Liu, S., Gutfreund, D., Spelke, E., Tenenbaum, J. B., & Ullman, T. D. (2021) · arXiv

Child and Animal Learning DevelopmentAI-based Problem Solving and Planning

Cited by 34

Causal implicatures from correlational statements

Gershman, S. J., & Ullman, T. D. (2023) · PLOS ONE

Bayesian Modeling and Causal InferenceCategorization, perception, and language

Cited by 21

Re-evaluating Theory of Mind evaluation in large language models

Hu, J., Sosa, F., & Ullman, T. (2025) · Philosophical Transactions of the Royal Society B: Biological Sciences

Child and Animal Learning DevelopmentPhilosophy and Theoretical Science

Cited by 10

One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversity

Murthy, S. K., Ullman, T., & Hu, J. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 8

Agents of Chaos

Shapira, N., Wendler, C., Yen, A., Sarti, G., Pal, K., Floody, O., Belfki, A., Loftus, A., Jannali, A. R., Prakash, N., et al. (2026) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 6