# Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning

arXiv: https://arxiv.org/abs/2506.23998 · HTML: https://mlsanity.com/paper/2506.23998/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents](https://mlsanity.com/paper/2503.01935/) (2025)
- [LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis](https://mlsanity.com/paper/2310.15100/) (2023)
- [Automating Thematic Analysis: How LLMs Analyse Controversial Topics](https://mlsanity.com/paper/2405.06919/) (2024)
- [TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews](https://mlsanity.com/paper/2503.20666/) (2025)
- [Harnessing ChatGPT for thematic analysis: Are we ready?](https://mlsanity.com/paper/2310.14545/) (2023)

## Built on by (most cited first, 9 of 9)

- [How to Interpret Agent Behavior](https://mlsanity.com/paper/2605.13625/) (2026)
- [LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research](https://mlsanity.com/paper/2509.24294/) (2025)
- [SFT-TA: Supervised Fine-Tuned Agents in Multi-Agent LLMs for Automated Inductive Thematic Analysis](https://mlsanity.com/paper/2509.17167/) (2025)
- [Position: Thematic Analysis of Unstructured Clinical Transcripts with Large Language Models](https://mlsanity.com/paper/2509.14597/) (2025)
- [Automated Healthcare Thematic Analysis using Multi-Agent Large Language Model: Algorithm Development and Evaluation](https://mlsanity.com/paper/2512.16063/) (2025)
- [Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era](https://mlsanity.com/paper/2601.11739/) (2026)
- [Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance](https://mlsanity.com/paper/2603.08989/) (2026)
- [CentaurTA Studio: A Self-Improving Human-Agent Collaboration System for Thematic Analysis](https://mlsanity.com/paper/2604.18589/) (2026)
- [Using Grounded Theory for Agent Behavior Analysis at Scale](https://mlsanity.com/paper/2608.30391/) (2026)

## Abstract (arXiv)

Congenital heart disease (CHD) presents complex, lifelong challenges often underrepresented in traditional clinical metrics. While unstructured narratives offer rich insights into patient and caregiver experiences, manual thematic analysis (TA) remains labor-intensive and unscalable. We propose a fully automated large language model (LLM) pipeline that performs end-to-end TA on clinical narratives, which eliminates the need for manual coding or full transcript review. Our system employs a novel multi-agent framework, where specialized LLM agents assume roles to enhance theme quality and alignment with human analysis. To further improve thematic relevance, we optionally integrate reinforcement learning from human feedback (RLHF). This supports scalable, patient-centered analysis of large qualitative datasets and allows LLMs to be fine-tuned for specific clinical contexts.