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All Topics CI Causal Induction CD Cognitive Development CEIL Cultural Evolution and Iterated Learning DMRL Decision Making and Reinforcement Learning E Education F Foundations IB Inductive Biases NBM Nonparametric Bayesian Models P Perception PR Probabilistic Reasoning RPM Rational Process Models S&C Similarity and Categorization SC Social Cognition SML Statistical Models of Language
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DMRL SML Arumugam, D. , & Griffiths, T. L. (2025). Toward efficient exploration by large language model agents. Proceedings of the 14th International Conference on Learning Representations (ICLR). (pdf)
DMRL Bastankhah, M. , Liu, G. , Arumugam, D. , Griffiths, T. L. , & Eysenbach, B. (2026) Demystifying the mechanisms behind emergent exploration in goal-conditioned RL. Proceedings of the 14th International Conference on Learning Representations (ICLR). (pdf)
RPM SC Ham, H. , Arumugam, D. , Correa, C. G. , Zhao, B. , Griffiths, T. L. , & Velez, N. (2026). Rational Teachers Should 'Lie' to Bounded Students. Proceedings of the 48th Annual Conference of the Cognitive Science Society . (pdf)
F SML Liu, R. , Arumugam, D. , Zhang, C. E. , Escola, S. , Pitkow, X. , & Griffiths, T. L. (2026). Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents. (preprint)
SML Mieczkowski, E. , Ku, A. , Eisape, T. , Arumugam, D. , Matters, J. , Collins, K. , Sucholutsky, I. , & Griffiths, T.L. (2026). Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs. (preprint)
DMRL SML Zhu, J.Q. , Xie, H. , Arumugam, D. , Wilson, R. C. , & Griffiths, T. L. (2026). Using reinforcement learning to train large language models to explain human decisions. Proceedings of the 14th International Conference on Learning Representations (ICLR) . (pdf)
DMRL F Arumugam, D. , & Griffiths, T. L. (2025). On Temporal Credit Assignment and Data-Efficient Reinforcement Learning. Finding the Frame Workshop at RLC (pdf)
PR SML Geng, J. , Chen, H. , Arumugam, D. , & Griffiths, T. L. (2025). Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems. (preprint)
DMRL F Turner, C. R. , Arumugam, D. , Nelson, L. , & Griffiths, T. L. (2025). Trade-offs between tasks induced by capacity constraints bound the scope of intelligence. 47th Annual Meeting of the Cognitive Science Society. (pdf)
DMRL SML Veselovsky, V. , Stroebl, B. , Bencomo, G. , Arumugam, D. , Schut, L. , Narayanan, A. , & Griffiths, T. L. (2025). Hindsight Merging: Diverse Data Generation with Language Models. Proceedings of the 41st Conference on Uncertainty in Artificial Intelligence. (pdf)
CEIL DMRL Zhao, B. , Mieczkowski, E. , Arumugam, D. , Velez, N. , & Griffiths, T. L. (2025). Discovering Hidden Laws in Innovation by Recombination. 47th Annual Meeting of the Cognitive Science Society. (pdf)