DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search

• URL: http://arxiv.org/abs/2602.05014v3
• Date: Thu, 12 Feb 2026 02:45:40 GMT
• Title: DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search
• Authors: Zhanli Li, Huiwen Tian, Lvzhou Luo, Yixuan Cao, Ping Luo,
• Abstract summary: DeepRead is a structure-aware document reasoning agent designed to operationalize document-native structural priors into actionable reasoning capabilities.<n>DeepRead elicits a human-like locate-then-read'' reasoning paradigm, effectively mitigating the context fragmentation inherent in traditional retrieval methods.
• Score: 23.447631421934847
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the rapid advancement of tool-use capabilities in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) is shifting from static, one-shot retrieval toward autonomous, multi-turn evidence acquisition. However, existing agentic search frameworks typically treat long documents as flat collections of unstructured chunks, disregarding the native hierarchical organization and sequential logic essential for human comprehension. To bridge this gap, we introduce \textbf{DeepRead}, a structure-aware document reasoning agent designed to operationalize document-native structural priors into actionable reasoning capabilities. Leveraging the structural fidelity of modern OCR, DeepRead constructs a paragraph-level, coordinate-based navigation system and equips the LLM with two synergistic tools: \textsf{Retrieve} for scanning-aware localization, and \textsf{ReadSection} for contiguous, order-preserving reading within specific hierarchical scopes. This design elicits a human-like ``locate-then-read'' reasoning paradigm, effectively mitigating the context fragmentation inherent in traditional retrieval methods. Extensive evaluations across four benchmarks spanning diverse document types demonstrate that DeepRead outperforms Search-o1-style agentic search baselines by an average of 10.3\%. Fine-grained behavioral analysis further confirms that DeepRead autonomously adopts human-aligned reading strategies, validating the critical role of structural awareness in achieving precise document reasoning. Our code is available at https://github.com/Zhanli-Li/DeepRead.

Related papers

• MoDora: Tree-Based Semi-Structured Document Analysis System [62.01015188258797]
  Semi-structured documents integrate diverse interleaved data elements arranged in various and often irregular layouts.<n>MoDora is an LLM-powered system for semi-structured document analysis.<n> Experiments show MoDora outperforms baselines by 5.97%-61.07% in accuracy.
  arXiv  Detail & Related papers  (2026-02-26T14:48:49Z)
• FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents [53.03492387564392]
  We introduce FS-Researcher, a file-system-based framework that scales deep research beyond the context window via a persistent workspace.<n>A Context Builder agent browses the internet, writes structured notes, and archives raw sources into a hierarchical knowledge base that can grow far beyond context length.<n>A Report Writer agent then composes the final report section by section, treating the knowledge base as the source of facts.
  arXiv  Detail & Related papers  (2026-02-02T03:00:19Z)
• BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents [11.158307125677375]
  Retrieval-Augmented Generation (RAG) queries highly relevant information from external complex documents.<n>We introduce BookRAG, a novel RAG approach targeted for documents with a hierarchical structure.<n>BookRAG achieves state-of-the-art performance, significantly outperforming baselines in both retrieval recall and QA accuracy.
  arXiv  Detail & Related papers  (2025-12-03T03:40:49Z)
• Resolving Evidence Sparsity: Agentic Context Engineering for Long-Document Understanding [49.26132236798123]
  Vision Language Models (VLMs) have gradually become a primary approach in document understanding.<n>We propose SLEUTH, a multi agent framework that orchestrates a retriever and four collaborative agents in a coarse to fine process.<n>The framework identifies key textual and visual clues within the retrieved pages, filters for salient visual evidence such as tables and charts, and analyzes the query to devise a reasoning strategy.
  arXiv  Detail & Related papers  (2025-11-28T03:09:40Z)
• ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents [17.497004687630742]
  Vision-language models (VLMs) excel at interpreting text-rich images but struggle with long, visually complex documents.<n>We present Active Long-DocumEnt Navigation (ALDEN), a multi-turn reinforcement learning framework that fine-tunes VLMs as interactive agents.
  arXiv  Detail & Related papers  (2025-10-29T16:32:26Z)
• Model-Document Protocol for AI Search [11.377241012645994]
  We introduce the Model-Document Protocol (MDP), a general framework that formalizes how raw text is bridged to large language models (LLMs)<n>Rather than treating retrieval as passage fetching, MDP defines multiple pathways that transform unstructured documents into task-specific, LLM-ready inputs.<n>As an instantiation, we present MDP-Agent, which realizes the protocol through an agentic process.
  arXiv  Detail & Related papers  (2025-10-29T04:29:17Z)
• Struc-EMB: The Potential of Structure-Aware Encoding in Language Embeddings [16.728984584960738]
  This paper introduces and systematically evaluates a new paradigm for generating structure-aware text embeddings.<n>We investigate two primary in-process methods: sequential concatenation and parallel caching.<n>Our analysis reveals critical trade-offs: sequential concatenation excels with noisy, moderate-length contexts, while parallel caching scales more effectively to long, high-signal contexts but is more susceptible to distractors.
  arXiv  Detail & Related papers  (2025-10-09T19:45:54Z)
• Equipping Retrieval-Augmented Large Language Models with Document Structure Awareness [15.810758425275322]
  We propose Retrieve-DocumentRoute-Read (RDR2), a novel framework that explicitly incorporates structural information throughout the RAG process.<n>RDR2 employs an LLM-based router to dynamically navigate document structure trees, jointly evaluating content relevance and hierarchical relationships to assemble optimal evidence.<n>Our key innovation lies in formulating document routing as a trainable task, with automatic action curation and structure-aware passage selection inspired by human reading strategies.
  arXiv  Detail & Related papers  (2025-10-05T17:04:24Z)
• WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research [73.58638285105971]
  This paper tackles textbfopen-ended deep research (OEDR), a complex challenge where AI agents must synthesize vast web-scale information into insightful reports.<n>We introduce textbfWebWeaver, a novel dual-agent framework that emulates the human research process.<n>Our framework establishes a new state-of-the-art across major OEDR benchmarks, including DeepResearch Bench, DeepConsult, and DeepResearchGym.
  arXiv  Detail & Related papers  (2025-09-16T17:57:21Z)
• Structured Attention Matters to Multimodal LLMs in Document Understanding [52.37530640460363]
  We investigate how input format influences document comprehension performance.<n>We discover that raw OCR text often impairs rather than improves MLLMs' performance.<n>We propose a novel structure-preserving approach that encodes document elements using the LaTex paradigm.
  arXiv  Detail & Related papers  (2025-06-19T07:16:18Z)
• Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering [51.7493726399073]
  We present a discourse-aware hierarchical framework to enhance long document question answering.<n>The framework involves three key innovations: specialized discourse parsing for lengthy documents, LLM-based enhancement of discourse relation nodes, and structure-guided hierarchical retrieval.
  arXiv  Detail & Related papers  (2025-05-26T14:45:12Z)
• Learning Refined Document Representations for Dense Retrieval via Deliberate Thinking [58.69615583599489]
  Deliberate Thinking based Retriever (Debater) is a novel approach that enhances document representations by incorporating a step-by-step thinking process.<n>Debater significantly outperforms existing methods across several retrieval benchmarks.
  arXiv  Detail & Related papers  (2025-02-18T15:56:34Z)
• DAPR: A Benchmark on Document-Aware Passage Retrieval [57.45793782107218]
  We propose and name this task emphDocument-Aware Passage Retrieval (DAPR) While analyzing the errors of the State-of-The-Art (SoTA) passage retrievers, we find the major errors (53.5%) are due to missing document context. Our created benchmark enables future research on developing and comparing retrieval systems for the new task.
  arXiv  Detail & Related papers  (2023-05-23T10:39:57Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.