Tenenbaum, Joshua B.

Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning

Nye, M., Tessler, M. H., Tenenbaum, J. B., & Lake, B. M. (2021) · arXiv

Topic ModelingLogic, Reasoning, and Knowledge

Cited by 40

Improved Contrastive Divergence Training of Energy Based Models

Du, Y., Li, S., Tenenbaum, J., & Mordatch, I. (2020) · arXiv

Generative Adversarial Networks and Image SynthesisAdversarial Robustness in Machine Learning

Cited by 36

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

Wong, L., Grand, G., Lew, A. K., Goodman, N. D., Mansinghka, V. K., Andreas, J., & Tenenbaum, J. B. (2023) · arXiv

Logic, Reasoning, and KnowledgeNatural Language Processing Techniques

Cited by 36

Getting aligned on representational alignment

Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Cueva, C. J., Grant, E., Groen, I., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningExplainable Artificial Intelligence (XAI)

Cited by 36

Inferring the intentional states of autonomous virtual agents

Pantelis, P. C., Baker, C. L., Cholewiak, S. A., Sanik, K., Weinstein, A., Wu, C.-C., Tenenbaum, J. B., & Feldman, J. (2014) · Cognition

Child and Animal Learning DevelopmentSocial Robot Interaction and HRI

Cited by 34

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

Empowerment contributes to exploration behaviour in a creative video game

Brändle, F., Stocks, L. J., Tenenbaum, J. B., Gershman, S. J., & Schulz, E. (2023) · Nature Human Behaviour

Digital Games and MediaEducational Games and Gamification

Cited by 34

Finding Friend and Foe in Multi-Agent Games

Serrino, J., Kleiman-Weiner, M., Parkes, D. C., & Tenenbaum, J. B. (2019) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 33

Top-Down Synthesis for Library Learning

Bowers, M., Olausson, T. X., Wong, L., Grand, G., Tenenbaum, J. B., Ellis, K., & Solar-Lezama, A. (2023) · Proceedings of the ACM on Programming Languages

Logic, programming, and type systemsEvolutionary Algorithms and Applications

Cited by 33

3DP3: 3D Scene Perception via Probabilistic Programming

Gothoskar, N., Cusumano-Towner, M., Zinberg, B., Ghavamizadeh, M., Pollok, F., Garrett, A., Tenenbaum, J. B., Gutfreund, D., & Mansinghka, V. K. (2021) · arXiv

3D Shape Modeling and AnalysisRobot Manipulation and Learning

Cited by 18

The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology

Jara-Ettinger, J., Gweon, H., Schulz, L. E., & Tenenbaum, J. B. (2016) · Trends in Cognitive Sciences

Child and Animal Learning DevelopmentPsychology of Moral and Emotional Judgment

Cited by 15

Theory of Minds: Understanding Behavior in Groups through Inverse Planning

Shum, M., Kleiman-Weiner, M., Littman, M. L., & Tenenbaum, J. B. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence

Child and Animal Learning DevelopmentReinforcement Learning in Robotics

Cited by 14

Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks

Collins, K. M., Wong, C., Feng, J., Wei, M., & Tenenbaum, J. B. (2022) · arXiv

Topic ModelingAI-based Problem Solving and Planning

Cited by 13

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI

Gan, C., Zhou, S., Schwartz, J., Alter, S., Bhandwaldar, A., Gutfreund, D., Yamins, D. L. K., DiCarlo, J. J., McDermott, J., Torralba, A., et al. (2021) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 13

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems

Dalrymple, D. "., Skalse, J., Bengio, Y., Russell, S., Tegmark, M., Seshia, S., Omohundro, S., Szegedy, C., Goldhaber, B., Ammann, N., et al. (2024) · arXiv

Safety Systems Engineering in AutonomyFormal Methods in Verification

Cited by 12

Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

Tung, H.-Y., Ding, M., Chen, Z., Bear, D., Gan, C., Tenenbaum, J. B., Yamins, D. L., Fan, J. E., & Smith, K. A. (2023) · arXiv

Advanced Vision and ImagingRobotics and Sensor-Based Localization

Cited by 8

Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes

Gothoskar, N., Ghavami, M., Li, E., Curtis, A., Noseworthy, M., Chung, K., Patton, B., Freeman, W. T., Tenenbaum, J. B., Klukas, M., et al. (2023) · arXiv

3D Shape Modeling and AnalysisRobot Manipulation and Learning

Cited by 5

Learning to Act from Actionless Videos through Dense Correspondences

Ko, P.-C., Mao, J., Du, Y., Sun, S.-H., & Tenenbaum, J. B. (2023) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 3

Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians

Chandra, K., Kleiman-Weiner, M., Ragan-Kelley, J., & Tenenbaum, J. B. (2026) · arXiv

Ethics and Social Impacts of AIMisinformation and Its Impacts

Cited by 3

Evidence from formal logical reasoning reveals that the language of thought is not natural language

Kean, H., Fung, A., Jaggers, P., Chen, J., Rule, J. S., Benn, Y., Tenenbaum, J. B., Piantadosi, S. T., Varley, R. A., & Fedorenko, E. (2026) · Proceedings of the National Academy of Sciences

Neurobiology of Language and BilingualismLogic, Reasoning, and Knowledge

Cited by 2

LILO: Learning Interpretable Libraries by Compressing and Documenting Code

Grand, G., Wong, L., Bowers, M., Olausson, T. X., Liu, M., Tenenbaum, J. B., & Andreas, J. (2023) · arXiv

Software Engineering ResearchEvolutionary Algorithms and Applications

Cited by 2

Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains

Subramaniam, V., Du, Y., Tenenbaum, J. B., Torralba, A., Li, S., & Mordatch, I. (2025) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 2

Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models

Ivanova, A. A., Sathe, A., Lipkin, B., Kumar, U., Radkani, S., Clark, T. H., Kauf, C., Hu, J., Pramod, R. T., Grand, G., et al. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 2

Code-enabled language models can outperform reasoning models on diverse tasks

Zhang, C. E., Colas, C., Poesia, G., Tenenbaum, J. B., & Andreas, J. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0