Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis
The article presents a new framework called Agent-as-Peer-Debriefer designed to enhance qualitative data analysis using large language models. This framework incorporates peer debriefing practices to improve the credibility and depth of analysis. The study demonstrates that this multi-agent approach aligns more closely with human-annotated codes compared to traditional methods.
- ▪The Agent-as-Peer-Debriefer framework integrates peer debriefing into qualitative data analysis.
- ▪It employs a Hierarchical Coding Agent and three Peer-Debriefing Agents with distinct analytical perspectives.
- ▪Testing on multiple datasets shows that the framework produces results more aligned with human codes than a single-LLM baseline.
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Computer Science > Artificial Intelligence arXiv:2605.24600 (cs) [Submitted on 23 May 2026] Title:Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis Authors:Zhimin Lin, Kun Cheng, Fan Bai, Jie Gao View a PDF of the paper titled Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis, by Zhimin Lin and 3 other authors View PDF Abstract:Large language models (LLMs) are increasingly used for qualitative data analysis (QDA), yet their outputs often miss the depth and nuance of human analysis. We argue this gap reflects a missing credibility practice from human QDA: peer debriefing, in which an analyst seeks feedback from a disinterested peer and uses it to refine their coding.
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