# LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research

arXiv: https://arxiv.org/abs/2509.24294 · HTML: https://mlsanity.com/paper/2509.24294/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 26 of 26)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [Break It Down: A Question Understanding Benchmark](https://mlsanity.com/paper/2001.11770/) (2020)
- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models](https://mlsanity.com/paper/2304.07366/) (2023)
- [Vibe coding: programming through conversation with artificial intelligence](https://mlsanity.com/paper/2506.23253/) (2025)
- [Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation](https://mlsanity.com/paper/2212.09994/) (2022)
- [CoAIcoder: Examining the Effectiveness of AI-assisted Human-to-Human Collaboration in Qualitative Analysis](https://mlsanity.com/paper/2304.05560/) (2023)
- [Large Language Models in Qualitative Research: Uses, Tensions, and Intentions](https://mlsanity.com/paper/2410.07362/) (2024)
- [Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification](https://mlsanity.com/paper/2307.01972/) (2023)
- [SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions](https://mlsanity.com/paper/2408.05357/) (2024)
- [HICode: Hierarchical Inductive Coding with LLMs](https://mlsanity.com/paper/2509.17946/) (2025)
- [LogiGAN: Learning Logical Reasoning via Adversarial Pre-training](https://mlsanity.com/paper/2205.08794/) (2022)
- [Automating Thematic Analysis: How LLMs Analyse Controversial Topics](https://mlsanity.com/paper/2405.06919/) (2024)
- [Zero-Shot On-the-Fly Event Schema Induction](https://mlsanity.com/paper/2210.06254/) (2022)
- [TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews](https://mlsanity.com/paper/2503.20666/) (2025)
- [Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning](https://mlsanity.com/paper/2506.23998/) (2025)
- [QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums](https://mlsanity.com/paper/2405.05345/) (2024)
- [Applications and Implications of Large Language Models in Qualitative Analysis: A New Frontier for Empirical Software Engineering](https://mlsanity.com/paper/2412.06564/) (2024)
- [Intent-Aware Schema Generation And Refinement For Literature Review Tables](https://mlsanity.com/paper/2507.19521/) (2025)
- [Large Language Model for Qualitative Research -- A Systematic Mapping Study](https://mlsanity.com/paper/2411.14473/) (2024)
- [PolicyPulse: LLM-Synthesis Tool for Policy Researchers](https://mlsanity.com/paper/2505.23994/) (2025)
- [Efficiency with Rigor! A Trustworthy LLM-powered Workflow for Qualitative Data Analysis](https://mlsanity.com/paper/2501.00775/) (2025)

## Built on by (most cited first, 6 of 6)

- [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)
- [Co-Refine: AI-Powered Tool Supporting Qualitative Analysis](https://mlsanity.com/paper/2604.19309/) (2026)
- [Computational KJ-Ho: An Analyst-Bias-Free Insight Extraction Framework from Large-Scale Qualitative Data Using Domain-Specialized LLMs](https://mlsanity.com/paper/2608.16467/) (2026)
- [Using Grounded Theory for Agent Behavior Analysis at Scale](https://mlsanity.com/paper/2608.30391/) (2026)
- [Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research](https://mlsanity.com/paper/2609.22209/) (2026)

## Abstract (arXiv)

Grounded theory offers deep insights from qualitative data, but its reliance on expert-intensive manual coding presents a major scalability bottleneck. Existing computational tools either fail on full automation or lack flexible schema construction. We introduce LOGOS, a novel, end-to-end framework that fully automates the grounded theory workflow, transforming raw text into a structured, hierarchical theory. LOGOS integrates LLM-driven coding, semantic clustering, graph reasoning, and a novel iterative refinement process to build highly reusable codebooks. To ensure fair comparison, we also introduce a principled 5-dimensional metric and a train-test split protocol for standardized, unbiased evaluation. Across five diverse corpora, LOGOS consistently outperforms strong baselines and achieves a remarkable average 80.4% alignment with an expert-developed schema on complex datasets. LOGOS demonstrates a potential to democratize and scale qualitative research without sacrificing theoretical nuance.