Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States

Zixuan Wang1* Yufan Zhou2* Jinzhou Tang1* Xinle Yu1 Chengjun Wu1 Lyumanshan Ye1 Zhaoxiang Feng1 Letian Peng1 Adyasha Patra1 Fan Bai5 Enze Ma3 Zhengding Hu1 Jianyang Gu4 Zhao Wang1 Yufei Ding1 Jingbo Shang1 Tianmin Shu5 Zhiting Hu1 Zhen Wang1‡
1 UC San Diego2 KU Leuven3 University of Illinois Chicago4 The Ohio State University5 Johns Hopkins University

* Equal contribution   ‡ Corresponding author

Method

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M2D-Sim shares an evolving user state with the Oracle to generate dialogue and QA supervision for a student.
M2D-Sim shares an evolving user state with the Oracle. Dialogue students learn from the persona and history, with the state withheld.

Dataset taxonomy

Distributions in the paper’s analysis subset. Behavior modes are adapted from TUNA; fallback modes are excluded. Select a chart for the full figure and labels.

Results

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Qwen2.5-7B-Instruct, the strongest same-backbone baseline per task, and M2D-Chat on personalization and theory of mind.
Qwen2.5-7B-Instruct comparisons against the strongest evaluated same-backbone baseline per task (paper Tables 1–2). AutoToM scores higher on ToMi. BigToM requires both true- and false-belief variants to be correct.
Citation
@misc{wang2026mind2dialoguetraininghumanawarelanguage,
      title={Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States},
      author={Zixuan Wang and Yufan Zhou and Jinzhou Tang and Xinle Yu and Chengjun Wu and Lyumanshan Ye and Zhaoxiang Feng and Letian Peng and Adyasha Patra and Fan Bai and Enze Ma and Zhengding Hu and Jianyang Gu and Zhao Wang and Yufei Ding and Jingbo Shang and Tianmin Shu and Zhiting Hu and Zhen Wang},
      year={2026},
      eprint={2609.15972},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2609.15972},
}