arXiv:2606. 03471v1 Announce Type: new Abstract: This paper proposes, for the first time, a rigorous formal definition of the concept of Machine Theory of Mind, based on principles supported by evidence from cognitive psychology, neuroscience and artificial intelligence, and uses the above as a lens to examine state-of-the-art and current efforts in the field, driving a potential agenda for further research there able to "crack" the problem.
By Fabio Cuzzolin
DialToM is a Theory of Mind benchmark created from naturalistic human-human dialogues, using a multiple-choice format. It introduces a State-Driven Diagnostic Probe that requires models to predict dialogue trajectories based solely on isolated mental-state profiles, without dialogue context. The evaluation shows that large language models are good at inferring mental states (Literal ToM) but struggle to use them for social forecasting (Functional ToM), while a domain expert scores 100% accuracy, highlighting a clear human‑AI gap.
By Neemesh Yadav, Palakorn Achananuparp, Jing Jiang, Ee-Peng Lim
arXiv:2608. 09638v1 Announce Type: new Abstract: Theory of Mind (ToM) is essential for agent interactions, yet existing evaluations either rely on static scenarios that oversimplify mental-state reasoning or interactive settings that provide limited diagnostic insight.
By Yen-Shan Chen, Yu Chian Duan, Chih-En Kuo, Jian-Bin Wu, Yun-Nung Chen
CogGym is a scalable, unified framework that standardizes diverse cognitive experiments into a task‑agnostic Experiment Markup Language (EML) for systematic comparison of human and AI behavior. The initial release curates 258 experiments from 100 papers focused on human commonsense reasoning and evaluates 50 large language models, revealing a scaling trend where larger models better reproduce human judgments but still lag far behind human split‑half reliability. The framework aims to continually incorporate new cognitive science experiments to track where model behavior aligns with or diverges from human cognition as models evolve.
By Lance Ying, Jinzhou Wu, Yingshan Susan Wang, Shivam Aarya, Luca M. Schulze Buschoff, Harry Chen, Katherine M. Collins, Andrea de Varda, Shuhao Fu, Sean Dae Houlihan, Akshay K. Jagadish, Guangyuan Jiang, Samuel Kiegeland, Tetsu Kurumisawa, Rongzhi Liu, Ryan Liu, Ningshan Ma, Kathryn McGregor, Younes Strittmatter, Polina Tsvilodub, Jacob Hoover Vigly, Sarah Wu, Enjie Xu, Yiling Yun, Kelsey Allen, Tyler Brooke-Wilson, Brian Christian, Evelina Fedorenko, Michael C. Frank, Michael Franke, Tao Gao, Samuel J. Gershman, Robert D. Hawkins, Jennifer Hu, Julian Jara-Ettinger, Max Kleiman-Weiner, Sydney Levine, Tal Linzen, Hongjing Lu, Timothy O'Donnell, Desmond C. Ong, Steven T. Piantadosi, Rebecca Saxe, Eric Schulz, Tianmin Shu, Felix A. Sosa, Ilia Sucholutsky, Tan Zhi-Xuan, Tomer Ullman, Fei Xu, Ilker Yildirim, Jian-Qiao Zhu, Thomas L. Griffiths, Tobias Gerstenberg, Kevin Smith, Joshua B. Tenenbaum
arXiv:2502. 20502v2 Announce Type: replace Abstract: Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as approximate models of human cognition.
By Lance Ying, Katherine M. Collins, Lionel Wong, Ilia Sucholutsky, Ryan Liu, Adrian Weller, Tianmin Shu, Thomas L. Griffiths, Joshua B. Tenenbaum
arXiv:2501. 17629v2 Announce Type: replace-cross Abstract: Several studies claim that large language models have passed the Turing Test and hence can "think", yet none follow Turing's original instructions precisely.
By Sharon Temtsin, Diane Proudfoot, David Kaber, Christoph Bartneck
The paper argues that artificial agentic systems, which operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time, should be evaluated through systematic observation, perturbation, and interpretation of their actions rather than solely on performance outcomes. It draws on lessons from behavioral sciences to motivate this position and proposes a research agenda that includes methods for recovering decision strategies from action sequences, constructing environments that isolate behavioral differences, and probing emergent dynamics in multi‑agent systems. These directions aim to establish a rigorous science of AI behavior.
By Manuel Cherep, Nikhil Singh, Pattie Maes
The article discusses how artificial intelligence is beginning to automate scientific discovery, specifically in the realm of cognitive science. It outlines four key challenges for developing an automated science of the mind: representing experiments, generating synthetic behavior, synthesizing models, and closing the loop to discover psychological theories. The authors argue that addressing these challenges will enable AI to systematically advance our understanding of the mind.
By Akshay K. Jagadish, Milena Rmus, Kristin Witte, Marvin Mathony, Marcel Binz, Eric Schulz
arXiv:2604.27927v2 Announce Type: replace
Abstract: We introduce a framework called LAPITHS (Language model Analysis through Paradigm grounded Interpretations of Theses about Human likenesS) and use...
By Matteo Da Pelo, Alessio Donvito, Claudio Frongia, Pietro Salis, Antonio Lieto
The article proposes an updated Turing Test that incorporates energy consumption as a key metric, arguing that the original test is insufficient for distinguishing human from machine intelligence in the context of modern AI. It suggests that by adding an energy constraint, the test evaluates intelligence through the lens of efficiency, linking abstract thinking to tangible resource limits. The new test also provides a measurable, practical endpoint, encouraging society to balance AI time savings against total resource costs.
By Adam Winchell
arXiv:2603.18007v2 Announce Type: replace-cross
Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to inf...
By Anna Babarczy, Andras Lukacs, Peter Vedres, Zeteny Bujka
arXiv:2606. 26460v1 Announce Type: new Abstract: AI-based scientific automation is increasingly possible by using agents to generate hypotheses, design experiments, and analyze data.
By Ben Prystawski, Kushin Mukherjee, Daniel Wurgaft, Linas Nasvytis, Michael Y. Li, Noah D. Goodman, Michael C. Frank