Selected work

Representative Papers

Personality, Role, and Expressive Style in Large Language Models: An Interactionist Analysis

May 27, 2026

This study addresses the limitations of current approaches that rely solely on prompting to specify Big Five personality traits, which often fail to ensure consistent personality expression in language model dialogues. Adopting an interactionist perspective, this work systematically demonstrates for the first time that personality expression emerges from the context-dependent interplay among personality settings, social roles, and expressive styles—challenging the conventional assumption that personality can be controlled through prompts alone. Through a factorial design generating English–Japanese dialogue data, combined with LLM-as-a-judge evaluation and cross-lingual comparative analysis, the study reveals that social roles significantly influence openness, expressive style predominantly shapes conscientiousness and agreeableness, and neuroticism is primarily driven by explicit personality settings. Notably, stable personality impressions can still be elicited through social roles and expressive styles even without explicit personality prompts, with highly consistent findings across both languages.

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BeliefNest: A Joint Action Simulator for Embodied Agents with Theory of Mind

May 18, 2025

Embodied agents lack explicit Theory of Mind (ToM) modeling capabilities for open-domain collaborative tasks. Method: We propose BeliefNest, an open-source joint-action simulator built in Minecraft, featuring the first dynamic hierarchical nested belief modeling framework. It structurally represents self- and other-centered multi-order belief states as parseable graph models, directly mapped to LLM prompts to enable interpretable, evaluable ToM-driven decision-making. The approach integrates embodied simulation, hierarchical belief graphs, LLM prompt engineering, and a novel false-belief task evaluation protocol. Contribution/Results: Experiments demonstrate that BeliefNest accurately infers others’ beliefs and predicts belief-guided behavior. Quantitative evaluation on standardized false-belief tasks confirms that nested belief modeling significantly enhances multi-agent coordination performance, establishing a new benchmark for interpretable, ToM-aware embodied AI.

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Recent publications

Latest Papers

Personality, Role, and Expressive Style in Large Language Models: An Interactionist Analysis

May 27, 2026

This study addresses the limitations of current approaches that rely solely on prompting to specify Big Five personality traits, which often fail to ensure consistent personality expression in language model dialogues. Adopting an interactionist perspective, this work systematically demonstrates for the first time that personality expression emerges from the context-dependent interplay among personality settings, social roles, and expressive styles—challenging the conventional assumption that personality can be controlled through prompts alone. Through a factorial design generating English–Japanese dialogue data, combined with LLM-as-a-judge evaluation and cross-lingual comparative analysis, the study reveals that social roles significantly influence openness, expressive style predominantly shapes conscientiousness and agreeableness, and neuroticism is primarily driven by explicit personality settings. Notably, stable personality impressions can still be elicited through social roles and expressive styles even without explicit personality prompts, with highly consistent findings across both languages.

0 citationsRead paper

BeliefNest: A Joint Action Simulator for Embodied Agents with Theory of Mind

May 18, 2025

Embodied agents lack explicit Theory of Mind (ToM) modeling capabilities for open-domain collaborative tasks. Method: We propose BeliefNest, an open-source joint-action simulator built in Minecraft, featuring the first dynamic hierarchical nested belief modeling framework. It structurally represents self- and other-centered multi-order belief states as parseable graph models, directly mapped to LLM prompts to enable interpretable, evaluable ToM-driven decision-making. The approach integrates embodied simulation, hierarchical belief graphs, LLM prompt engineering, and a novel false-belief task evaluation protocol. Contribution/Results: Experiments demonstrate that BeliefNest accurately infers others’ beliefs and predicts belief-guided behavior. Quantitative evaluation on standardized false-belief tasks confirms that nested belief modeling significantly enhances multi-agent coordination performance, establishing a new benchmark for interpretable, ToM-aware embodied AI.

0 citationsRead paper