Beyond Words: Integrating Theory of Mind into Conversational Agents for Human-Like Belief, Desire, and Intention Alignment (2025.findings-acl)
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| Challenge: | Empirical evaluations of LLaMA-3 models demonstrate that ToM-informed alignment improves response quality, achieving win rates of 63% and 67%, respectively. |
| Approach: | They investigate whether open-source LLaMA models can represent and retain ToM-related constructs and whether they can be used to generate more aligned responses. |
| Outcome: | The proposed models can represent and retain ToM-related constructs and improve response quality. |
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Theory of Mind in Large Language Models: Assessment and Enhancement (2025.acl-long)
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| Challenge: | Theory of Mind (ToM) is a cornerstone of human social intelligence . Large Language Models (LLMs) are increasingly integrated into daily life . |
| Approach: | They analyze evaluation benchmarks and enhancement strategies to evaluate LLMs' ToM capabilities. |
| Outcome: | The proposed and widely used story-based benchmarks and enhancement strategies are used to evaluate LLMs' ToM capabilities. |
Agentic-ToM: Cognition-Inspired Agentic Processing For Enhancing Theory of Mind Reasoning (2025.findings-emnlp)
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| Challenge: | Current models struggle with reasoning about others’ perspectives, limiting their ability to attribute mental states to oneself and others. |
| Approach: | They propose to embed psychologically-grounded functions into LLMs to enable them to attribute mental states to oneself and others, known as Theory of Mind. |
| Outcome: | The proposed approach outperforms baselines on three ToM datasets without task-specific modifications. |
Infusing Theory of Mind into Socially Intelligent LLM Agents (2026.findings-acl)
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| Challenge: | Theory of Mind (ToM) is a key aspect of human social intelligence, yet chatbots and LLMs do not typically integrate it. |
| Approach: | They propose a method that integrates Theory of Mind (ToM) into chatbots and dialogue agents to generate mental states between dialogue turns. |
| Outcome: | The proposed method improves dialogue and social interaction by integrating ToM with dialogue lookahead. |
CoSToM: Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models (2026.acl-long)
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| Challenge: | Large language models lack intrinsic cognition and cannot generalize to complex task-specific scenarios. |
| Approach: | They propose a framework that transitions from mechanistic interpretation to active intervention to map internal distributions of ToM features and implement it via targeted activation steering within ToM-critical layers. |
| Outcome: | The proposed framework significantly enhances human-like social reasoning capabilities and dialogue quality. |
On Emergent Social World Models — Evidence for Functional Integration of Theory of Mind and Pragmatic Reasoning in Language Models (2026.acl-long)
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| Challenge: | Large language models (LMs) possess astonishing abilities and prove useful for a plethora of downstream tasks, but controversy persists regarding how to conceptualize their capacities. |
| Approach: | They analyze LMs’ performance across seven subcategories of ToM abilities using a large localizer dataset than used in prior work. |
| Outcome: | The proposed models recruit shared computational mechanisms for general Theory of Mind (ToM) and language-specific pragmatic reasoning on a substantially larger localizer dataset than used in prior work. |
MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic (2023.findings-emnlp)
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| Challenge: | Theory of Mind (ToM) is a critical component of intelligence but its assessment remains the subject of heated debates. |
| Approach: | They propose to use dynamic epistemic logic to isolate a particular component of ToM and generate controlled problems in English natural language. |
| Outcome: | The proposed language model scales from 70M to 6B and 350M to 174B do not consistently yield better results than random chance. |
Machine Theory of Mind Needs Machine Validation (2025.findings-acl)
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| Challenge: | In recent years there has been an explosion of interest in studying the extent to which language models (LMs) display a theory of mind (ToM) despite the growth of evaluation tools, the extent of evidence for ToM remains unclear. |
| Approach: | They conduct a survey of 16 recent studies aimed at measuring ToM in language models and found that only half do so for patterns only a machine might exploit. |
| Outcome: | The results show that the datasets that show high LM performance on ToM tasks are easier than their peers, likely due to the presence of spurious patterns in the data. |
Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations (2026.findings-acl)
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| Challenge: | Existing benchmarks measure ToM capability improvement through story-reading, multiple-choice questions from a third-person perspective, while ignoring the first-person, dynamic nature of human-AI interactions. |
| Approach: | They propose a new paradigm of interactive ToM evaluation with both perspective and metric shifts. |
| Outcome: | The proposed approach improves the performance of four representative LLM enhancement techniques using real-world datasets and a user study. |
Theory of Mind for Multi-Agent Collaboration via Large Language Models (2023.emnlp-main)
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| Challenge: | Recent large language models (LLMs) have demonstrated impressive accomplishments in reasoning and planning, but their abilities in multi-agent collaborations remain unexplored. |
| Approach: | They propose to use explicit belief state representations to enhance task performance and the accuracy of ToM inferences for LLM-based agents. |
| Outcome: | The proposed model improves performance and accuracy of ToM inferences for LLM-based agents. |
XToM: Exploring the Multilingual Theory of Mind for Large Language Models (2026.acl-long)
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Chunkit Chan, Yauwai Yim, Hongchuan Zeng, Zhiying Zou, Xinyuan Cheng, Zhifan Sun, Zheye Deng, Kawai Chung, Yuzhuo Ao, Fan Yixiang, Cheng Jiayang, Ercong Nie, Ginny Wong, Helmut Schmid, Hinrich Schuetze, Simon See, Yangqiu Song
| Challenge: | Existing evaluations of ToM in LLMs are limited to English, neglecting the linguistic diversity that shapes human cognition. |
| Approach: | They propose a multilingual benchmark that evaluates ToM across five languages . they find that models excel in multilingual language understanding, but their ToM performance varies across languages. |
| Outcome: | The proposed benchmark evaluates LLMs across five languages and incorporates diverse task scenarios. |