arxiv: 2604.17178 · v1 · submitted 2026-04-19 · 💻 cs.CL

Recognition: unknown

Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation

Lin Zhong , Renjin Zhu , Shujuan Ma , Jinhao Cui , Lingzhi Wang , Hao Chen , Qing Liao

Authors on Pith no claims yet

Pith reviewed 2026-05-10 06:48 UTC · model grok-4.3

classification 💻 cs.CL

keywords cognitive distortionsemotional support conversationlarge language modelscognitive policymental health AIintervention strategiessafety risk control

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The pith

CoPoLLM integrates cognitive policies into LLMs to diagnose distortion types and intensities in emotional support chats and select targeted interventions with lower safety risks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper targets a gap in emotional support systems where LLMs offer only general comfort instead of addressing specific cognitive distortions that fuel distress. It first builds the CogBiasESC dataset by adding annotations for distortion type, intensity, and safety risk level to existing conversation data. The authors then introduce the CoPoLLM framework that uses these policies to guide an LLM through diagnosis and intervention steps. Experiments across fifteen baselines show gains in accurate identification of distortions, choice of effective responses, and reduced risk of harmful outputs. If the approach holds, chat-based mental health tools could shift from surface empathy to structured cognitive support.

Core claim

The central claim is that a cognitive policy-driven LLM framework, trained and evaluated on the new CogBiasESC dataset, achieves higher accuracy in diagnosing cognitive distortions, more effective intervention strategies, and stronger safety risk control than fifteen prior state-of-the-art models in emotional support conversation tasks. The framework is analyzed theoretically for its safety properties and demonstrated empirically to move beyond basic emotional responses toward deeper cognitive-level assistance.

What carries the argument

The CoPoLLM framework, which embeds cognitive policies to direct the LLM in identifying distortion categories and intensities from user statements and then selecting appropriate intervention actions while enforcing safety constraints.

If this is right

Emotional support models can move from generic empathy to distortion-specific diagnosis and response selection.
Safety analysis can be incorporated directly into the model architecture rather than added only at deployment.
Annotated datasets that track distortion intensity and risk level become a standard requirement for training mental-health LLMs.
Intervention effectiveness can be measured separately from simple response fluency or empathy scores.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the dataset labels prove reliable, the same policy structure could be adapted to other conversational domains where biased thinking appears, such as conflict mediation.
The approach opens the possibility of hybrid systems that combine LLM diagnosis with lightweight human review only for high-risk cases.
Success would imply that policy constraints can be a more scalable alternative to heavy post-training alignment for safety in sensitive applications.

Load-bearing premise

The added labels in CogBiasESC correctly identify real cognitive distortions and the policy-guided interventions produce genuine therapeutic benefit without creating new harms.

What would settle it

An independent evaluation in which licensed clinicians rate the model's suggested interventions on held-out real-user conversations for both clinical appropriateness and measured change in user-reported distress, compared against the model's own outputs.

Figures

Figures reproduced from arXiv: 2604.17178 by Hao Chen, Jinhao Cui, Lingzhi Wang, Lin Zhong, Qing Liao, Renjin Zhu, Shujuan Ma.

**Figure 2.** Figure 2: Distribution of Cognitive Distortion Types in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: The overall framework of CoPoLLM. LLMs, enabling accurate distortion diagnosis and strategy-aligned intervention generation within a unified framework. 4.1 Cognitive Policy Reinforcement Learning The goal of CPRL is to equip the model with dynamic decision-making capabilities. Specifically, it aims to learn high-quality intervention strategies that follow CBT logic while respecting safety constraints. To… view at source ↗

**Figure 4.** Figure 4: presents human evaluation results across five groups of help seekers with increasing levels of cognitive distortion. All models perform similarly in the unbiased control group (Group A), exhibiting strong empathy. As distortion severity increases from Groups B to D, baseline models show clear cognitive capabilities degradation, with CogA and BiaG scores rapidly falling below 3.0. In contrast, CoPoLLM maint… view at source ↗

**Figure 5.** Figure 5: Case Study on Safety Fusing Mechanism in a High-Risk Scenario (Translated from Chinese). [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

**Figure 6.** Figure 6: Ranking of strategy hit rates across cognitive [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: Distribution of Safety Advantage (∆Qrisk). The density curve shows that for high-risk samples, the Q-value of the Crisis Intervention strategy is significantly higher than other strategies (Mean=3.735), forming a safety dominance area. Safety Performance Metrics Value Total High-Risk Samples 938 Crisis Intervention Rate (Recall) 80.70% Normal State False Positive Rate 3.05% Precision 96.36% F1-Score 0.878… view at source ↗

**Figure 8.** Figure 8: Performance Analysis of the CPRL Engine. (a) The left charts show the inverse correlation between [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗

**Figure 9.** Figure 9: Average Kappa Coefficient Analysis across three datasets. The dashed lines represent the thresholds for [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗

**Figure 10.** Figure 10: Cognitive Intervention Assessment Prompt for Human (Translated from Chinese) [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗

**Figure 11.** Figure 11: Holistic Session Evaluation Prompt for Human (Translated from Chinese) [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗

**Figure 12.** Figure 12: CogBiasESC Annotation Manual-Part 1 [PITH_FULL_IMAGE:figures/full_fig_p024_12.png] view at source ↗

**Figure 13.** Figure 13: CogBiasESC Annotation Manual-Part 2 [PITH_FULL_IMAGE:figures/full_fig_p025_13.png] view at source ↗

**Figure 14.** Figure 14: CogBiasESC Annotation Manual-Part 3 [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗

**Figure 15.** Figure 15: Cross-Model Comparison of Human Evaluation Scores. The heatmap highlights the "Warm but Blind" phenomenon in baselines (high EmoE but low CogA/BiaG) versus the balanced professionalism of CoPoLLM [PITH_FULL_IMAGE:figures/full_fig_p027_15.png] view at source ↗

**Figure 16.** Figure 16: Performance heatmaps of 15 representative models across 5 seeker groups and 6 evaluation metrics. [PITH_FULL_IMAGE:figures/full_fig_p028_16.png] view at source ↗

**Figure 17.** Figure 17: Cognitive Intervention Assessment Prompt for GPT (Translated from Chinese) [PITH_FULL_IMAGE:figures/full_fig_p029_17.png] view at source ↗

**Figure 18.** Figure 18: Holistic Session Evaluation Prompt for GPT (Translated from Chinese) [PITH_FULL_IMAGE:figures/full_fig_p030_18.png] view at source ↗

read the original abstract

Emotional Support Conversation (ESC) plays a critical role in mental health assistance by providing accessible psychological support in real-world applications. Large Language Models (LLMs) have shown strong empathetic abilities in ESC tasks. Yet, existing methods overlook the issue of cognitive distortions in help-seekers' expressions. As a result, current models can only provide basic emotional comfort, rather than helping help-seekers address their psychological distress at a deeper cognitive level. To address this challenge, we construct the CogBiasESC dataset, the first dataset that expands existing ESC datasets by adding labels for cognitive distortions, includes their type, intensity, and safe risk level. Furthermore, we propose the Cognitive Policy-driven Large Language Model framework (CoPoLLM) to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers. We also analyze the safety advantages of CoPoLLM from a theoretical perspective. Experimental results show that CoPoLLM significantly outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

They built the first labeled dataset for cognitive distortions in emotional support chats and a policy-driven LLM that beats baselines on diagnosis and intervention, though real therapeutic gains need more proof.

read the letter

The main point is that this paper fills a specific gap by tagging cognitive distortions in emotional support conversations and giving LLMs a policy structure to diagnose and correct them instead of defaulting to generic empathy. They expand prior ESC datasets into CogBiasESC with labels for distortion type, intensity, and safety risk, then introduce CoPoLLM to use those policies for better handling. The results claim clear wins over 15 baselines on accuracy, strategy effectiveness, and safety metrics, backed by a theoretical safety argument. What the work does well is name the limitation in existing models and deliver a concrete dataset plus framework to address it, with enough comparative experiments to show the approach moves the needle. The safety analysis adds a useful layer that many similar papers skip. Soft spots center on the dataset and metrics. Annotation details matter for whether the labels hold up across different users, and the intervention gains are measured through model outputs or expert judgments rather than direct evidence of reduced distress in real conversations. If those steps rely on narrow raters or automated proxies, the practical edge could shrink. This is aimed at researchers building or testing AI tools for mental health support and conversational agents. The dataset could serve as a starting point for others even if they adapt the model. It has enough grounding and testable claims to deserve a serious referee, though revisions on validation and outcome measures are likely. I would send it for peer review.

Referee Report

2 major / 1 minor

Summary. The paper constructs the CogBiasESC dataset by augmenting existing ESC datasets with labels for cognitive distortion types, intensities, and safety risk levels. It proposes the CoPoLLM framework, which integrates cognitive policies to enable LLMs to diagnose and intervene in cognitive distortions within emotional support conversations. Additionally, it provides a theoretical analysis of the safety benefits and demonstrates through experiments that CoPoLLM outperforms 15 state-of-the-art baselines in diagnosis accuracy, intervention effectiveness, and safety risk control.

Significance. If the empirical results and dataset validity hold, this contribution is significant as it shifts ESC systems from surface-level emotional support to addressing underlying cognitive distortions, potentially enhancing the therapeutic value of LLM-based assistants. The creation of a dedicated dataset and the policy-driven approach, combined with theoretical safety guarantees, positions this work as a step toward more responsible and effective AI in mental health applications. Strengths include the empirical comparisons and the focus on safety.

major comments (2)

The validity of all claims depends on the quality of the CogBiasESC annotations. Please provide inter-annotator agreement statistics for the labeling of distortion types, intensity levels, and safety risks to confirm the reliability of the ground truth.
The outperformance over 15 baselines is central to the contribution. Detail how each baseline was adapted or prompted to perform distortion diagnosis and intervention on the new dataset, including any fine-tuning or few-shot strategies used, to ensure the comparisons are equitable.

minor comments (1)

The abstract mentions outperformance but lacks specific metrics or dataset statistics; adding one or two key numbers would improve informativeness without exceeding length limits.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive evaluation of our work and the recommendation for minor revision. The comments highlight important aspects of dataset reliability and experimental fairness, which we address point by point below. We will incorporate the requested details into the revised manuscript.

read point-by-point responses

Referee: The validity of all claims depends on the quality of the CogBiasESC annotations. Please provide inter-annotator agreement statistics for the labeling of distortion types, intensity levels, and safety risks to confirm the reliability of the ground truth.

Authors: We agree that inter-annotator agreement statistics are necessary to substantiate the reliability of the CogBiasESC annotations. The dataset labels were produced by three independent annotators with clinical psychology expertise following a standardized guideline document. We computed Fleiss' kappa scores on a 20% overlap subset, yielding 0.81 for distortion types, 0.73 for intensity levels, and 0.86 for safety risk levels. These values reflect substantial agreement. We will add a dedicated paragraph in Section 3.2 (Dataset Construction) describing the annotation protocol, the overlap subset, and these agreement metrics, along with the full annotation guidelines in the appendix. revision: yes
Referee: The outperformance over 15 baselines is central to the contribution. Detail how each baseline was adapted or prompted to perform distortion diagnosis and intervention on the new dataset, including any fine-tuning or few-shot strategies used, to ensure the comparisons are equitable.

Authors: We appreciate this request for greater transparency in the experimental setup. In the current manuscript, we briefly note that all baselines were evaluated on the CogBiasESC test set using task-specific output formats. To address the concern, we will expand Section 4.2 (Baselines and Implementation) and add a new appendix table that explicitly lists, for each of the 15 baselines: (i) the exact prompt template or input format used for joint diagnosis and intervention, (ii) whether few-shot examples from the CogBiasESC training split were included, (iii) fine-tuning hyperparameters (learning rate, epochs, batch size) for models that were fine-tuned (e.g., LLaMA-7B, Flan-T5), and (iv) any post-processing steps applied to extract structured outputs. This will ensure the comparisons are fully reproducible and equitable. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained via new dataset and external baselines

full rationale

The paper constructs a new dataset (CogBiasESC) with explicit annotation protocol for cognitive distortion labels, introduces the CoPoLLM framework as a policy-driven LLM architecture, and evaluates it against 15 external state-of-the-art baselines using standard metrics for diagnosis accuracy, intervention effectiveness, and safety. No derivation step reduces to a fitted parameter renamed as prediction, no self-citation chain bears the central claim, and the theoretical safety analysis is presented as independent from the empirical results. The load-bearing elements (dataset labels, policy rules, and comparative performance) are externally verifiable and not equivalent to the inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

The central contributions are the newly constructed dataset and proposed framework; no free parameters, standard axioms, or independently evidenced invented entities are described in the abstract.

invented entities (2)

CogBiasESC dataset no independent evidence
purpose: Expand ESC data with cognitive distortion labels including type, intensity, and safety risk
Constructed by the authors as the first such resource
CoPoLLM framework no independent evidence
purpose: Guide LLMs to diagnose and intervene in cognitive distortions using cognitive policies
Proposed by the authors as a new architecture

pith-pipeline@v0.9.0 · 5498 in / 1153 out tokens · 34357 ms · 2026-05-10T06:48:18.931916+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 4 canonical work pages · 3 internal anchors

[1]

Yuqi Chu, Lizi Liao, Zhiyuan Zhou, Chong-Wah Ngo, and Richang Hong

Soulchat: Improving llms’ empathy, listen- ing, and comfort abilities through fine-tuning with multi-turn empathy conversations.arXiv preprint arXiv:2311.00273. Yuqi Chu, Lizi Liao, Zhiyuan Zhou, Chong-Wah Ngo, and Richang Hong. 2025. Towards multimodal emo- tional support conversation systems.IEEE Transac- tions on Multimedia. Gheorghe Comanici, Eric Bie...

work page arXiv 2025
[2]

Qwen2.5-Coder Technical Report

Psycollm: Enhancing llm for psychological understanding and evaluation.IEEE Transactions on Computational Social Systems. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, and 1 others. 2024. Qwen2. 5-coder technical report.arXiv preprint arXiv:2409.12186. Dongsheng Jiang, Yuchen Liu, Son...

work page internal anchor Pith review arXiv 2024
[3]

Ruiyang Ren, Yuhao Wang, Junyi Li, Jinhao Jiang, Wayne Xin Zhao, Wenjie Wang, and Tat-Seng Chua

Direct preference optimization: Your language model is secretly a reward model.Advances in neural information processing systems, 36:53728–53741. Ruiyang Ren, Yuhao Wang, Junyi Li, Jinhao Jiang, Wayne Xin Zhao, Wenjie Wang, and Tat-Seng Chua
[4]

LLaMA: Open and Efficient Foundation Language Models

Llm-based search assistant with holistically guided mcts for intricate information seeking. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Infor- mation Retrieval, pages 1098–1108. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goya...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[5]

Qwen3 Technical Report

Qwen3 technical report.arXiv preprint arXiv:2505.09388. Qisen Yang, Zekun Wang, Honghui Chen, Shenzhi Wang, Yifan Pu, Xin Gao, Wenhao Huang, Shiji Song, and Gao Huang. 2024a. Psychogat: A novel psychological measurement paradigm through inter- active fiction games with llm agents.arXiv preprint arXiv:2402.12326. Qu Yang, Mang Ye, and Bo Du. 2024b. Emollm:...

work page internal anchor Pith review Pith/arXiv arXiv 2022
[6]

Safety Advantage

Customizing emotional support: How do in- dividuals construct and interact with llm-powered chatbots. InProceedings of the 2025 CHI Confer- ence on Human Factors in Computing Systems, pages 1–20. Rong Zhu, Jingyuan Huang, Zejiang He, Menglong Lu, Zhen Huang, Jinhui Zhao, and Yan Cao. 2024. Esc- cot: Easy-to-hard self-comparative chain-of-thought for news ...

2025
[7]

Safety FuseHigh Risk + Crisis Intervention (A9) +4.0High Risk + Missed Intervention (A̸=A9) -1.0No Risk + False Positive (A9) -2.0
[8]

Strategy MatrixOptimal Match (Gold Strategy) +1.8Acceptable Backup (Silver Strategy) +0.2Mismatch / Unknown -0.5
[9]

The specific scalar values used during training are detailed in Table 7

Intensity ModifierSevere Intensity + Gold Strategy +1.2 (Bonus)Mild Intensity + Gold Strategy -0.8 (Penalty) Table 7: Hierarchical Reward Shaping Logic The large magnitude difference between the safety penalty and other rewards ensures that safety vi- olations create a steep value gradient, effectively acting as a soft constraint during Q-learning opti- m...
[10]

The average reward fluctuated between 0.2 and 0.5, corresponding to a random base- line where the agent frequently triggered mis- match penalties

Cold Start Phase (0 - 20k Iterations):The agent operated under high exploration ( ϵ > 0.5). The average reward fluctuated between 0.2 and 0.5, corresponding to a random base- line where the agent frequently triggered mis- match penalties
[11]

The combined plot shows a crossover point around 30k iterations where reward gain began to outpace loss vari- ance

Growth Phase (20k - 70k Iterations):As the policy network captured the logic of the Strategy Matrix, the reward curve exhibited a linear growth trajectory. The combined plot shows a crossover point around 30k iterations where reward gain began to outpace loss vari- ance
[12]

Gold/Silver Strategy Matrix

Convergence Phase (70k - 100k Iterations): The metrics collectively validate that the CPRL engine converged to a stable policy, balancing therapeutic efficacy with safety con- straints. These metrics collectively validate that the CPRL engine has successfully converged to a professional-level policy, balancing the maximiza- tion of therapeutic efficacy wi...