Theory of Mind (ToM) in Artificial Intelligence

Theory of Mind (ToM) is defined as the cognitive ability to attribute mental states—such as beliefs, desires, intentions, and knowledge—to oneself and others, and to recognize that these states can diverge from reality and from one another. In artificial intelligence, ToM concerns the design and evaluation of systems capable of inferring, modeling, and reasoning about the mental states underlying the behavior of agents—human or artificial—in social and interactive contexts. ToM is central not only to social cognition in humans but also to the development of collaborative, interpretable, and trustworthy AI. Current research spans computational modeling, cognitive theory, machine learning architectures, benchmarking, and the implementation of ToM-driven behavior in multi-agent and human-AI systems.

1. Foundational Principles and Computational Paradigms

The foundational concept of ToM in computational settings derives from the psychological definition: the attribution of beliefs, desires, and intentions (often abbreviated as BDI) to explain and predict agents' behavior. In machine learning, ToM is generally operationalized as the ability to infer such latent variables from observable data (actions, language, sensor readings) using a variety of model classes, including:

• Bayesian Inverse Planning: Models infer agents’ goals and beliefs as probabilistic latent variables, updating beliefs stepwise as new actions or states are observed. For instance, the likelihood of an agent’s action given a hypothesized goal and belief is estimated, and hypotheses are compared using accumulated log-likelihoods (Pöppel et al., 2019 , Zhang et al., 2 Jun 2025 ).
• Neural Meta-Learning Architectures: Systems such as ToMnet (Rabinowitz et al., 2018 ) decompose the problem into components: a character network producing stable trait embeddings, a mental state network encoding current episodic context, and a prediction network that combines these to forecast future behavior or beliefs.
• Trait-based and Memory-Augmented Models: Extensions like Trait-ToM introduce fast-weights or hypernetworks to dynamically modulate predictions based on inferred traits (Nguyen et al., 2022 ), while models such as ToMMY leverage episodic memory and hierarchical attention to support multi-step, context-sensitive reasoning (Nguyen et al., 2023 ).

These approaches are further informed by comparisons to human cognitive architecture, which is not modular but composed of multiple interlocking regions and cognitive functions (Meulen et al., 28 Feb 2025 ).

2. Bayesian and Satisficing Reasoning

Bayesian Theory of Mind (BToM) formally models inference over mental states as updates within a belief distribution: $$P(\text{mental state} | \text{observed action}) \propto P(\text{action}\mid \text{mental state})P(\text{mental state})$$ This framework allows for full Bayesian reasoning—integrating over all possible combinations of goals and beliefs—though this can be intractable. To address computational constraints, "satisficing mentalizing" strategies have been developed, including:

• Specialized models that assume the agent's world or goals are fully known, drastically reducing inference complexity.
• Switching models that dynamically shift between specialized models based on the "surprise" incurred from discrepancies between predicted and observed actions, balancing efficiency and accuracy (Pöppel et al., 2019 ).

This resource-bounded perspective aligns with human bounded rationality and enables tractable, robust ToM inference in uncertain real-world tasks.

3. Neural Architectures and Representation Learning

Modern neural ToM models leverage innovations in representation learning to handle the diversity and temporal dependency in real-world social reasoning:

• Meta-learning and Disentanglement: ToMnet and descendants meta-learn prior distributions over agent types, enabling rapid inference about novel agents from few observations and incorporating variational information bottleneck terms to extract low-dimensional, interpretable trait embeddings (Rabinowitz et al., 2018 ).
• Dynamic Trait Attribution and Fast Weights: Trait-ToM introduces hypernetworks that generate per-actor fast weights for prediction modules, better capturing individual differences and supporting transfer learning across agent populations (Nguyen et al., 2022 ).
• Memory-augmented Mechanisms: ToMMY stores episodic event memories as key-value pairs and employs hierarchical attention to retrieve relevant past experiences, supporting multi-step and temporally distant inference (Nguyen et al., 2023 ).

Empirical assessments consistently show such architectures produce superior performance on tasks involving false-belief attribution and in contexts demanding long-range temporal inference.

4. Benchmarks and Evaluations

Assessment of ToM in AI has advanced from synthetic, short-form tasks to comprehensive and more ecologically valid benchmarks:

• Classic and Extended False-Belief Tests: Tools such as the Sally-Anne test and its gridworld variants require models to predict agent behavior under false or incomplete beliefs (Rabinowitz et al., 2018 , Nguyen et al., 2023 ).
• Multitask and Multimodal Benchmarks: MMToM-QA and related datasets integrate video, text, and symbolic reasoning, challenging models on both multimodal integration and multi-step update necessity (Zhang et al., 2 Jun 2025 ).
• Long-Context and Narrative-based Evaluations: CharToM-QA emphasizes context-rich, character-driven inference, demonstrating that human ToM leverages narrative history far more effectively than current LLMs (Zhou et al., 3 Jan 2025 ).
• Multilingual and Cross-cultural Testing: XToM assesses ToM capability across diverse languages, revealing performance inconsistencies and highlighting the importance of language and cultural context (Chan et al., 3 Jun 2025 ).
• Comprehensive Human-level Frameworks: ToMBench systematically spans 8 tasks and 31 ToM abilities, exposing persistent 10–16% performance gaps between leading LLMs and humans in social cognition tasks (Chen et al., 23 Feb 2024 ).

These benchmarks reveal that, despite progress, current AI systems remain fragile, particularly on higher-order, context-rich, and multilingual ToM reasoning.

5. Limitations and Open Challenges

Key limitations currently constraining progress in machine ToM include:

• Contextual Integration: LLMs and neural ToM architectures struggle to leverage global background information and long-term narrative history in inference, a domain where human reasoning excels (Zhou et al., 3 Jan 2025 ).
• Scalability to Multimodal and Complex Environments: Existing architectures either require heavy fine-tuning or encode ToM-specific priors that do not generalize well to complex, unseen scenarios. Bayesian stepwise planners that combine small ToM-specialist models with large world-knowledge models offer recent, scalable advances (Zhang et al., 2 Jun 2025 ).
• Mutual and Higher-Order ToM: Most systems do not support mutual, recursive, or higher-order (beyond second-order) inference robustly, as real-life collaboration and deception detection require. Benchmarks like HI-TOM underscore steep accuracy drops as ToM order increases (He et al., 2023 ).
• Lack of Grounding and Cultural Sensitivity: Many approaches insufficiently account for individual and cultural variety in ToM styles, leading to anthropomorphic over-attribution or inadequate generalization (Meulen et al., 28 Feb 2025 ).
• Distinction between Surface and Genuine ToM: LLMs can often pass surface-level ToM tasks, but analyses show their reasoning is frequently superficial, pattern-based, or reliant on memorized cues, with deep understanding unconfirmed (Holterman et al., 2023 , Jamali et al., 2023 ).

6. Future Directions

Promising avenues for advancing ToM in AI systems include:

• Integration of Cognitive Models and Large LMs: Fusing Bayesian/cognitive-science-inspired frameworks with massive pre-trained LLMs, using mechanisms such as weak-to-strong control, enables efficient and generalizable ToM inference in complex environments (Zhang et al., 2 Jun 2025 ).
• Hybrid, Multimodal, and Interactive Systems: Moving beyond text-based tests to multimodal, physically-grounded, and interactive environments will facilitate the emergence and assessment of more naturalistic ToM capabilities.
• Personalization and Cultural Awareness: Models must learn and adapt ToM reasoning strategies dynamically, reflecting the variability across users and cultures, and be transparent in their perspective-taking (Meulen et al., 28 Feb 2025 , Chan et al., 3 Jun 2025 ).
• Mutual and Team-based ToM: Enabling mutual modeling and true strategy adjustment in human-AI teams, as well as robust reasoning about other AIs (“silico-centric ToM”), is crucial for collaboration, trust, and safe deployment (Zhang et al., 13 Sep 2024 , Mukherjee et al., 14 Mar 2024 ).
• Benchmarks and Standardization: Continued development of broad, contamination-free, and culturally diverse benchmarks is needed for honest progress measurement and comparison.

7. Summary Table: Key Models and Capabilities

Theory of Mind in artificial intelligence is advancing through the confluence of cognitive theory, scalable modeling, and systematic, context-rich evaluation. While machine ToM capabilities have improved in specific domains and benchmarks, achieving robust, general, and human-interpretable social reasoning remains an open interdisciplinary challenge, requiring advances in modeling, learning, evaluation, and cross-cultural generalization.