# Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer

arXiv: https://arxiv.org/abs/2208.09257 · HTML: https://mlsanity.com/paper/2208.09257/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2022.

## Built on by (most cited first, 30 of 31)

- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
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- [How Does Generative Retrieval Scale to Millions of Passages?](https://mlsanity.com/paper/2305.11841/) (2023)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation](https://mlsanity.com/paper/2412.11919/) (2024)
- [Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding](https://mlsanity.com/paper/2404.14600/) (2024)
- [GLEN: Generative Retrieval via Lexical Index Learning](https://mlsanity.com/paper/2311.03057/) (2023)
- [A Survey of Generative Search and Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2404.16924/) (2024)
- [UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models](https://mlsanity.com/paper/2312.11036/) (2023)
- [Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval](https://mlsanity.com/paper/2504.01403/) (2025)
- [IncDSI: Incrementally Updatable Document Retrieval](https://mlsanity.com/paper/2307.10323/) (2023)
- [Generative Dense Retrieval: Memory Can Be a Burden](https://mlsanity.com/paper/2401.10487/) (2024)
- [Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval](https://mlsanity.com/paper/2504.05181/) (2025)
- [1-PAGER: One Pass Answer Generation and Evidence Retrieval](https://mlsanity.com/paper/2310.16568/) (2023)
- [On Synthetic Data Strategies for Domain-Specific Generative Retrieval](https://mlsanity.com/paper/2502.17957/) (2025)
- [Self-Retrieval: End-to-End Information Retrieval with One Large Language Model](https://mlsanity.com/paper/2403.00801/) (2024)
- [A Survey of Generative Information Retrieval](https://mlsanity.com/paper/2406.01197/) (2024)
- [MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora](https://mlsanity.com/paper/2507.09924/) (2025)
- [Bottleneck-Minimal Indexing for Generative Document Retrieval](https://mlsanity.com/paper/2405.10974/) (2024)
- [Purely Semantic Indexing for LLM-based Generative Recommendation and Retrieval](https://mlsanity.com/paper/2509.16446/) (2025)
- [DiffuGR: Generative Document Retrieval with Diffusion Language Models](https://mlsanity.com/paper/2511.08150/) (2025)
- [Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval](https://mlsanity.com/paper/2407.11504/) (2024)
- [QueStER: Query Specification for Generative keyword-based Retrieval](https://mlsanity.com/paper/2511.05301/) (2025)
- [From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines](https://mlsanity.com/paper/2604.13468/) (2026)
- [Breaking Token Into Concepts: Exploring Extreme Compression in Token Representation Via Compositional Shared Semantics](https://mlsanity.com/paper/2509.17737/) (2025)
- [A Simple and Effective Framework for Symmetric Consistent Indexing in Large-Scale Dense Retrieval](https://mlsanity.com/paper/2512.13074/) (2025)
- [Integrating Chain-of-Thought into Generative Retrieval: A Preliminary Study](https://mlsanity.com/paper/2605.22358/) (2026)
- [Generative Chinese Statute Retrieval](https://mlsanity.com/paper/2607.11109/) (2026)
- [UniGD: A Unified Generative-Discriminative Framework for Industrial Retrieval](https://mlsanity.com/paper/2608.03150/) (2026)
- [GRAFT: Graph-Distilled Generative Retrieval for Facet-Aware Scientific Literature Exploration](https://mlsanity.com/paper/2608.22381/) (2026)

## Abstract (arXiv)

Document retrieval has been extensively studied within the index-retrieve framework for decades, which has withstood the test of time. Unfortunately, such a pipelined framework limits the optimization of the final retrieval quality, because indexing and retrieving are separated stages that can not be jointly optimized in an end-to-end manner. In order to unify these two stages, we explore a model-based indexer for document retrieval. Concretely, we propose Ultron, which encodes the knowledge of all documents into the model and aims to directly retrieve relevant documents end-to-end. For the model-based indexer, how to represent docids and how to train the model are two main issues to be explored. Existing solutions suffer from semantically deficient docids and limited supervised data. To tackle these two problems, first, we devise two types of docids that are richer in semantics and easier for model inference. In addition, we propose a three-stage training workflow to capture more knowledge contained in the corpus and associations between queries and docids. Experiments on two public datasets demonstrate the superiority of Ultron over advanced baselines for document retrieval.