# Household Movement Detection in Mixed-Format Occupancy Data Using LLM-Based Entity Resolution

arXiv: https://arxiv.org/abs/2607.21614 · HTML: https://mlsanity.com/paper/2607.21614/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 25 May 2026.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [(Almost) All of Entity Resolution](https://mlsanity.com/paper/2008.04443/) (2020)
- [Policy-Aware Generative AI for Safe, Auditable Data Access Governance](https://mlsanity.com/paper/2510.23474/) (2025)
- [Case Count Metric for Comparative Analysis of Entity Resolution Results](https://mlsanity.com/paper/2601.02824/) (2026)
- [A System for Name and Address Parsing with Large Language Models](https://mlsanity.com/paper/2601.18014/) (2026)
- [Evaluating Semantic and Syntactic Understanding in Large Language Models for Payroll Systems](https://mlsanity.com/paper/2601.18012/) (2026)

## Abstract (arXiv)

Entity resolution (ER) typically relies on pairwise similarity comparisons between records, which limits its ability to capture indirect relationships present in demographic occupancy data. An important indirect pattern arises from household movement, where multiple individuals relocate together across addresses, but detecting such patterns is difficult due to mixed-format records, noise, duplication, and the absence of stable identifiers. This paper proposes an AI-enhanced framework for detecting indirect entity links associated with household movement in unstandardized name-address data. The approach integrates prompt-based large language model (LLM) named entity recognition for extracting personal names and addresses without extensive preprocessing, semantic text embeddings for robust similarity computation, and graph-based reasoning to infer group-level movement patterns. Experimental evaluation on SPX benchmark datasets (S8-S12) generated using the Synthetic Occupancy Generator demonstrates that incorporating indirect household movement evidence improves recall by 8-15% while maintaining high precision, yielding F1-score gains of 6-8% over a strong pairwise baseline.