---
title: "TRACE — paper by Tang et al. (2026) — summary and ideas"
description: "Current mental-health LLMs such as CRISP, SoulChat 2.0, SoulChat R1 and CACTUS tend to offer generic… 0.73. Spearman. 44 references."
authors: ["Shilin Tang", "Zunyi Yin", "Xuefeng Liang", "Guanghui Shi", "Song Tong", "Chen Guangyu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1711/"
doi: "10.18653/v1/2026.findings-acl.1711"
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/tang-2026-trace-two-phase-rl-causal/"
---
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# TRACE: Two-Phase RL for Causal Graph Exploration and Deeper Psychological Intervention in Dynamic Counseling Scenarios

**Authors:** Shilin Tang, Zunyi Yin, Xuefeng Liang, Guanghui Shi, Song Tong and Chen Guangyu · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-acl.1711/) · [PDF](https://aclanthology.org/2026.findings-acl.1711.pdf) · [DOI 10.18653/v1/2026.findings-acl.1711](https://doi.org/10.18653/v1/2026.findings-acl.1711) · [OpenAlex](https://openalex.org/W7166815645)

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

## Summary

Current mental-health LLMs such as CRISP, SoulChat 2.0, SoulChat R1 and CACTUS tend to offer generic empathy or follow fixed CBT scripts. They rarely work out why a client is distressed. TRACE draws on clinical case conceptualization and treats counseling as a task of rebuilding, turn by turn, a hidden causal graph of the client's psychology. The graph runs from observable reactions down to core beliefs, while information comes out only gradually. The authors built a dataset of 2,000 PsyQA-derived client profiles in Chinese and English, each paired with an ideal four-level causal graph generated by DeepSeek-v3.1. They then trained a counselor model with two-phase reinforcement learning. In the first phase, rewards favor uncovering graph nodes and links in a surface-to-deep order. In the second, rewards favor restructuring the irrational beliefs that were found. According to the authors, TRACE beats prior models on graph reconstruction, intervention effectiveness and overall dialogue quality. The work matters because it moves LLM counseling toward interventions grounded in the mechanisms behind distress, instead of reacting only to the emotions a client states.

## Key points

- **Causal graph design:** The ideal causal graph is a DAG with four node levels: surface (A), intermediate (B), deep (C) and core (D). Edges encode causal or maintenance relations.
- **Dataset:**
  - It contains 2,000 cases sampled from PsyQA.
  - A manual check of 500 graphs found that 90.4% met the clinical conceptualization criteria.
  - The data is split into D_cold (900, cold-start training), D_RL (1,000, RL environment simulation) and D_test (100, evaluation). D_RL and D_test were manually verified.
- **Evaluation reliability:**
  - DeepSeek-R1-0528 served as the automatic judge.
  - Five psychology graduate students rated 150 dialogues, with inter-annotator Cohen's κ = 0.73.
  - Spearman correlations between human and LLM scores ranged from 0.63 to 0.76 across dimensions, with p \< 0.05.
- **Phase-switch threshold:** The causal graph filling ratio φ_trans controls when the model moves from exploration to intervention. A value of 0.8 worked best (E_int = 1.37), compared with 1.05, 1.19 and 1.14 for 0.6, 0.7 and 0.9.
  - Switching too early leads to speculative interventions.
  - Switching too late leaves too little room for intervention.
- **Ablation and limitations:**
  - Removing intervention-phase RL hurts both intervention efficacy and general dialogue quality.
  - All training and testing used simulated AI clients, so clinical validation is still needed.

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

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- [Beijing Normal University](https://md.research.elamur.ai/free/en/universities/beijing-normal-university/) — University, Beijing, China · 15 works · 3,177 citations
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- [Xidian University](https://md.research.elamur.ai/free/en/universities/xidian-university/) — University, Xi'an, China · 14 works · 1,357 citations
- [Guangzhou Institute of Technology](https://md.research.elamur.ai/free/en/universities/guangzhou-institute-of-technology/) — University, Guangzhou, China · 2 works · 5 citations
- [Beijing Normal University, Zhuhai](https://md.research.elamur.ai/free/en/universities/beijing-normal-university-zhuhai/) — University, Zhuhai, China · 3 works

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## Cite

Cite as: Shilin Tang, Zunyi Yin, Xuefeng Liang, Guanghui Shi, Song Tong and Chen Guangyu. 2026. TRACE: Two-Phase RL for Causal Graph Exploration and Deeper Psychological Intervention in Dynamic Counseling Scenarios. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{tang2026trace,
  title = {{TRACE: Two-Phase RL for Causal Graph Exploration and Deeper Psychological Intervention in Dynamic Counseling Scenarios}},
  author = {Shilin Tang, Zunyi Yin, Xuefeng Liang, Guanghui Shi, Song Tong and Chen Guangyu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1711},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1711}
}
```

- [← previous TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering](https://md.research.elamur.ai/free/en/papers/wang-2026-trace-experiential-framework-coherent-multi/)
- [next → TRACER: Early Failure Detection for Task-Oriented Dialogue](https://md.research.elamur.ai/free/en/papers/nourbakhsh-2026-tracer-early-failure-detection-task/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/46/)

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*Updated 1 November 2026*