# ODRA: Synthesizing Cognitive Behavioral Therapy Sessions with Structured Chain-Of-Thought and Dynamic Patient Resistance

arXiv: https://arxiv.org/abs/2608.04524 · HTML: https://mlsanity.com/paper/2608.04524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 5 Aug 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 9 of 9)

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- [Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness](https://mlsanity.com/paper/2510.04484/) (2025)
- [MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions](https://mlsanity.com/paper/2509.04183/) (2025)
- [Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires](https://mlsanity.com/paper/2510.25384/) (2025)
- [CALM-IT: Enhancing Synthetic Motivational Interviewing Dialogue Quality with Conversational Dynamics](https://mlsanity.com/paper/2601.10085/) (2026)
- [A Decade-Scale Benchmark Evaluating LLMs' Clinical Practice Guidelines Detection and Adherence in Multi-turn Conversations](https://mlsanity.com/paper/2603.25196/) (2026)
- [Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs](https://mlsanity.com/paper/2604.20382/) (2026)
- [Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning](https://mlsanity.com/paper/2509.00975/) (2025)

## Abstract (arXiv)

Synthetic generation of Cognitive Behavioral Therapy (CBT) sessions is challenged by two competing demands: adhering to strict therapeutic structure while modeling the resistant, unpredictable behavior of real patients. Existing script-based methods fail to capture dynamic therapeutic interactions, while multi-agent approaches struggle to adhere to CBT's sequential structure; both suffer from sycophancy, producing overly compliant patients that misrepresent real clinical settings. In this work we introduce ODRA, a novel framework for synthesizing therapy dialogues through a Chain-of-Thought (CoT) strategy grounded in foundational CBT guidelines (Beck, 2020). ODRA further incorporates a resistance orchestrator to solve patient sycophancy, which employs steering techniques to elicit behaviors aligned with their resistance level. Automated and expert evaluations show that ODRA significantly outperforms existing methods across therapeutic skills, CBT alignment, and patient behavioral fidelity, with licensed psychologists preferring ODRA sessions across 12 of 13 clinical metrics. Furthermore, models fine-tuned on our dataset demonstrate superior therapeutic performance against both cooperative and resistant patients, validating that explicit resistance modeling in synthetic training data directly translates to downstream clinical robustness.