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.

翻译：长文档的激增对信息检索（IR）提出了根本性挑战，其篇幅长度、分散的证据以及复杂的结构要求超越标准段落级技术的专门方法。本综述首次对长文档检索（LDR）进行了全面论述，整合了三个主要时代的方法、挑战与应用。我们系统梳理了从经典词法模型与早期神经模型，到现代预训练语言模型（PLM）及大语言模型（LLM）的演进历程，涵盖了段落聚合、分层编码、高效注意力机制以及最新的LLM驱动的重排序与检索技术等关键范式。除模型外，本文还回顾了特定领域的应用、专门的评估资源，并概述了效率权衡、多模态对齐与忠实性等关键开放挑战。本综述旨在为基座模型时代的长文档检索研究提供一个整合的参考框架和前瞻性的发展议程。