TOPIC

Theory of Mind

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Can AI predict social norms better than humans?

Explores whether language models can achieve superhuman accuracy at predicting what communities find socially appropriate, and what that capability reveals about the difference between prediction and genuine participation.

Can AI systems learn social norms without embodied experience?

Large language models exceed individual human accuracy at predicting collective social appropriateness judgments. Does this reveal that embodied experience is unnecessary for cultural competence, or do systematic AI failures point to limits of statistical learning?

Can language models solve ToM benchmarks without real reasoning?

Do current theory-of-mind benchmarks actually measure mental state reasoning, or can models exploit surface patterns and distribution biases to achieve high scores? This matters because it determines whether benchmark performance indicates genuine understanding.

Can models recognize how individuals reason differently?

Do language models capture the distinct reasoning paths and strategic styles that individual humans use when reaching the same conclusion? Current evaluations ignore this dimension entirely.

Can language models actually introspect about their own states?

Do LLM self-reports reveal genuine access to their internal processes, or do they merely echo patterns from training data? Understanding when self-reports reflect actual causal linkage to internal states matters for trusting model explanations.

Do large language models genuinely simulate mental states?

This explores whether LLMs perform authentic theory of mind reasoning or rely on surface-level pattern matching. The distinction matters because evaluation format—multiple-choice versus open-ended—reveals very different capability levels.

Can language models track how minds change during persuasion?

Do LLMs understand evolving mental states in persuasive dialogue, or do they only capture fixed attitudes? This explores whether models can update their reasoning as a person's beliefs shift across conversation turns.

What breaks when humans and AI models misunderstand each other?

Explores whether misalignment in mutual theory of mind between humans and AI creates only communication problems or produces material consequences in autonomous action and collaboration.

Why do reasoning models fail at theory of mind tasks?

Recent LLMs optimized for formal reasoning dramatically underperform at social reasoning tasks like false belief and recursive belief modeling. This explores whether reasoning optimization actively degrades the ability to track other agents' mental states.

Does reinforcement learning on theory of mind collapse with model scale?

When RL improves social reasoning, does the quality of reasoning depend on model size? The question matters because accuracy alone may hide whether models are actually thinking or just pattern-matching.

Do LLMs predict persuasion based on actual dialogue or training bias?

Why do large language models consistently predict concession-based persuasion intentions even when dialogue context suggests otherwise? Understanding this gap reveals how alignment training shapes not just model behavior but also how models perceive others' intentions.

Source papers 25

The Arxiv papers behind this sub-topic. Links may take you off-site to arxiv.org.

A Systematic Review on the Evaluation of Large Language Models in Theory of Mind Tasks
This systematic review synthesizes current efforts to assess LLMs’ ability to perform ToM tasks—an essential aspect of human cognition involving the attribution of mental states to oneself and others.…
AI Models Exceed Individual Human Accuracy in Predicting Everyday Social Norms
A fundamental question in cognitive science concerns how social norms are acquired and represented. While humans typically learn norms through embodied social experience, we investigated whether large…
Can Large Language Models Understand Argument Schemes?
Argument schemes represent stereotypical patterns of reasoning that occur in everyday arguments. However, despite their usefulness, argument scheme classification — that is, classifying natural langua…
Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?
Can Large Language Models (LLMs) simulate humans in making important decisions? Recent research has unveiled the potential of using LLMs to develop role-playing language agents (RPLAs), mimicking main…
DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration
Real-time human-artificial intelligence (AI) collaboration is crucial yet challenging, especially when AI agents must adapt to diverse and unseen human behaviors in dynamic scenarios. Existing large l…
Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses
Despite advancements, the extent to which LLMs truly understand ToM reasoning and how closely it aligns with human ToM reasoning remains inadequately explored in open-ended scenarios. Motivated by thi…
Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?
Recent advancements in Large Language Models (LLMs) have shown promising performance on ToM benchmarks, raising the question: Do these benchmarks necessitate explicit human-like reasoning processes, o…
Does It Make Sense to Speak of Introspection in Large Language Models?
Large language models (LLMs) exhibit compelling linguistic behaviour, and sometimes offer self-reports, that is to say statements about their own nature, inner workings, or behaviour. In humans, such …
Evaluating Large Language Models in Theory of Mind Tasks
Many animals excel at using cues such as vocalization, body posture, gaze, or facial expression to predict other animals’ behavior and mental states. Dogs, for example, can easily distinguish between …
Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems
Abstract. LLM-based MAS are gaining popularity due to their potential for collaborative problem-solving enhanced by advances in natural language comprehension, reasoning, and planning. Research in The…
Expedient Assistance and Consequential Misunderstanding: Envisioning an Operationalized Mutual Theory of Mind
Design fictions allow us to prototype the future. They enable us to interrogate emerging or non-existent technologies and examine their implications. We present three design fictions that probe the po…
Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
Existing LLM reasoning methods have shown impressive capabilities across various tasks, such as solving math and coding problems. However, applying these methods to scenarios without ground-truth answ…
Improving Dialog Systems for Negotiation with Personality Modeling
In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent’s high-level strategy in negotiation …
InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individuali…
MOMENTS: A Comprehensive Multimodal Benchmark for Theory of Mind
Understanding Theory of Mind is essential for building socially intelligent multimodal agents capable of perceiving and interpreting human behavior. We introduce MOMENTS (Multimodal Mental States), a …
Machine Psychology
we highlight and summarize theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" f…
MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
Human social interactions depend on the ability to infer others’ unspoken intentions, emotions, and beliefs—a cognitive skill grounded in the psychological concept of Theory of Mind (ToM). While large…
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patie…
PersuasiveToM: A Benchmark for Evaluating Machine Theory of Mind in Persuasive Dialogues
The ability to understand and predict the mental states of oneself and others, known as the Theory of Mind (ToM), is crucial for effective social scenarios. Although recent studies have evaluated ToM …
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
As Large Language Models (LLMs) gain agentic abilities, they will have to navigate complex multiagent scenarios, interacting with human users and other agents in cooperative and competitive settings. …
Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?
we study a special application of ToM abilities that has higher stakes and possibly irreversible consequences : Human Robot Interaction. In this work, we explore the task of Perceived Behavior Recogni…
Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models
Large Language Models (LLMs) have generated considerable interest and debate regarding their potential emergence of Theory of Mind (ToM). Several recent inquiries reveal a lack of robust ToM in these …
Towards Machine Theory of Mind with Large Language Model-Augmented Inverse Planning
We propose a hybrid approach to machine Theory of Mind (ToM) that uses large language models (LLMs) as a mechanism for generating hypotheses and likelihood functions with a Bayesian inverse planning m…
Towards Safe and Honest AI Agents with Neural Self-Other Overlap
As AI systems increasingly make critical decisions, deceptive AI poses a significant challenge to trust and safety. We present Self-Other Overlap (SOO) fine-tuning, a promising approach in AI Safety t…
Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
Communicating in natural language is a powerful tool in multiagent settings, as it enables independent agents to share information in partially observable settings and allows zero-shot coordination wi…