# A Survey of Long-Document Retrieval in the PLM and LLM Era

arXiv: https://arxiv.org/abs/2509.07759 · HTML: https://mlsanity.com/paper/2509.07759/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search](https://mlsanity.com/paper/2602.05014/) (2026)
- [Knowledge-Grounded Agentic Large Language Models for Multi-Hazard Understanding from Reconnaissance Reports](https://mlsanity.com/paper/2511.14010/) (2025)
- [Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research](https://mlsanity.com/paper/2604.17843/) (2026)
- [Supporting software engineering tasks with agentic AI: Demonstration on document retrieval and test scenario generation](https://mlsanity.com/paper/2602.04726/) (2026)
- [Grounding Memory Summarization in Utility Intent](https://mlsanity.com/paper/2609.33417/) (2026)

## Abstract (arXiv)

The proliferation of long-form documents presents a fundamental challenge to information retrieval (IR), as their length, dispersed evidence, and complex structures demand specialized methods beyond standard passage-level techniques. This survey provides the first comprehensive treatment of long-document retrieval (LDR), consolidating methods, challenges, and applications across three major eras. We systematize the evolution from classical lexical and early neural models to modern pre-trained (PLM) and large language models (LLMs), covering key paradigms like passage aggregation, hierarchical encoding, efficient attention, and the latest LLM-driven re-ranking and retrieval techniques. Beyond the models, we review domain-specific applications, specialized evaluation resources, and outline critical open challenges such as efficiency trade-offs, multimodal alignment, and faithfulness. This survey aims to provide both a consolidated reference and a forward-looking agenda for advancing long-document retrieval in the era of foundation models.