---
title: "PUPPET: Neural-Symbolic Standardized Patients for Mental Health"
description: "The work offers a customizable, explainable alternative to black-box patient simulators for counselor education. 60 references."
authors: ["Chen Xu", "Yu Ji", "Zhenyu Lyu", "Yang Yi", "Yizhe Yang", "Luyao Ji", "Chaoyi Chen", "Xianyang Wang", "Tian Lan", "Zhihua Wang", "Juan Wang", "Xunde Dong", "Fuze Tian", "Qunxi Dong", "Bin Hu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.121/"
doi: "10.18653/v1/2026.acl-long.121"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-puppet-neural-symbolic-standardized-patients/"
---
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# PUPPET: Neural-Symbolic Standardized Patients for Mental Health

**Authors:** [Chen Xu](https://md.research.elamur.ai/free/en/authors/chen-xu/), Yu Ji, Zhenyu Lyu, Yang Yi, [Yizhe Yang](https://md.research.elamur.ai/free/en/authors/yizhe-yang/), Luyao Ji, Chaoyi Chen, Xianyang Wang, [Tian Lan](https://md.research.elamur.ai/free/en/authors/tian-lan/), Zhihua Wang, Juan Wang, Xunde Dong, Fuze Tian, Qunxi Dong and [Bin Hu](https://md.research.elamur.ai/free/en/authors/bin-hu/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.121/) · [PDF](https://aclanthology.org/2026.acl-long.121.pdf) · [DOI 10.18653/v1/2026.acl-long.121](https://doi.org/10.18653/v1/2026.acl-long.121) · [OpenAlex](https://openalex.org/W7166880621)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Therapist shortages call for scalable training. Human Standardized Patients (actors portraying clients) are the training gold standard, but they are expensive, scarce and emotionally draining. Existing LLM patient simulators sound realistic, yet they often comply with flawed interventions and cannot explain their reactions. PUPPET is a neural-symbolic Virtual Standardized Patient built on an OBSERVE-THINK-BEHAVE pipeline:

- **OBSERVE:** a neural module turns trainee utterances into truth values for atomic propositions.
- **THINK:** a symbolic layer applies trainer-written if-then rules through Probabilistic Logic, which drives transitions in a Probabilistic State Machine of mental states.
- **BEHAVE:** a neural generator produces a reply consistent with the sampled state.

On top of this, PUPPET-TRAINER shows learners the full chain from intervention to rule to state to response. Across three clinical scenarios, PUPPET beat baselines on clinical faithfulness and pedagogical value. The work offers a customizable, explainable alternative to black-box patient simulators for counselor education.

## Key points
- Experts encode causal rules linking therapist behaviors to patient mental states. For example, attempting childhood-feeling rewriting without first building rapport can trigger a suicidal-risk state.
- Probabilistic transitions keep natural variability, while the rules enforce consistent, error-sensitive reactions.
- In discriminant-validity tests over 15 rounds, Expert, Novice and Toxic user profiles produced clearly distinct trajectories:
  - Expert sessions moved mostly toward Positive states.
  - Toxic sessions drove the patient toward Negative states.
  - Novice sessions fluctuated.
- Novice counselors' CASES-R self-efficacy rose across five sessions. A Progressive-rule group kept improving where a Fixed group plateaued (p \< 0.01).
- The main limitation is that the system is text-only and lacks prosody and facial cues.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Beijing Institute of Technology](https://md.research.elamur.ai/free/en/universities/beijing-institute-of-technology/) — University, Beijing, China · 43 works · 3,652 citations
- [Lanzhou University](https://md.research.elamur.ai/free/en/universities/lanzhou-university/) — University, Lanzhou, China · 8 works · 24 citations

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## Cite

Cite as: Chen Xu, Yu Ji, Zhenyu Lyu, Yang Yi, Yizhe Yang, Luyao Ji, Chaoyi Chen, Xianyang Wang, Tian Lan, Zhihua Wang, Juan Wang, Xunde Dong, Fuze Tian, Qunxi Dong and Bin Hu. 2026. PUPPET: Neural-Symbolic Standardized Patients for Mental Health. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2026puppet,
  title = {{PUPPET: Neural-Symbolic Standardized Patients for Mental Health}},
  author = {Chen Xu, Yu Ji, Zhenyu Lyu, Yang Yi, Yizhe Yang, Luyao Ji, Chaoyi Chen, Xianyang Wang, Tian Lan, Zhihua Wang, Juan Wang, Xunde Dong, Fuze Tian, Qunxi Dong and Bin Hu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.121},
  url = {https://doi.org/10.18653/v1/2026.acl-long.121}
}
```

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- [next → PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding](https://md.research.elamur.ai/free/en/papers/park-2026-pure-post-hoc-unlocking-refinement/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/46/)

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*Updated 2 November 2026*