Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
- URL: http://arxiv.org/abs/2605.25480v2
- Date: Tue, 26 May 2026 10:31:56 GMT
- Title: Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
- Abstract summary: LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning.<n>Current Retrieval-Augmented Generation (RAG) systems organize external knowledge as flat chunks retrieved by embedding similarity.<n>We propose LLM-Wiki, an agent-native retrieval system that operationalizes the Retrieval-as-Reasoning paradigm by treating external knowledge as a compilable, composable, and self-evolving structure.
- Score: 6.956097396264084
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning: searching, reading, traversing, and deciding when evidence is sufficient. Yet current Retrieval-Augmented Generation (RAG) systems organize external knowledge as flat chunks retrieved by embedding similarity, exposing a retrieval-as-lookup interface ill-suited to iterative reasoning agents. We propose LLM-Wiki, an agent-native retrieval system that operationalizes the Retrieval-as-Reasoning paradigm by treating external knowledge as a compilable, composable, and self-evolving structure rather than a static retrieval index. LLM-Wiki compiles documents into structured Wiki pages with bidirectional links, exposes search, read, and link-following operations through standard tool-calling interfaces, and introduces an Error Book for persistent structural and semantic self-correction. LLM-Wiki achieves state-of-the-art results on HotpotQA, MuSiQue, and 2WikiMultiHopQA, outperforming HippoRAG 2, LightRAG, and GraphRAG by 2.0-8.1 F1 points. On AuthTrace, LLM-Wiki achieves the best overall accuracy, with especially strong gains on multi-document structured queries, confirming that compilation-based retrieval generalizes beyond chain-style multi-hop reasoning.
Related papers
- GrepSeek: Training Search Agents for Direct Corpus Interaction [66.69568141699311]
GrepSeek is an optimized direct corpus interaction (DCI) search agent that trains a compact search agent to find, filter, and compose evidence from large text corpora.<n>To make DCI practical at scale, we further use a semantics-preserving sharded-parallel execution engine that accelerates shell-based retrieval by up to $7.6times$ while preserving byte-exact equivalence with sequential execution of the shell command.
arXiv Detail & Related papers (2026-05-28T03:37:33Z) - Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction [127.64173950476702]
We study direct corpus interaction (DCI), where an agent searches the raw corpus directly with general-purpose terminal tools.<n>This approach requires no offline indexing and adapts naturally to evolving local corpora.<n>Across IR benchmarks and end-to-end agentic search tasks, this simple setup substantially outperforms strong sparse, dense, and reranking baselines.
arXiv Detail & Related papers (2026-05-03T19:13:11Z) - Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG [7.558933130117637]
Corpus2Skill distills a document corpus into a hierarchical skill directory offline.<n>The compilation pipeline iteratively clusters documents, generates LLM-written summaries at each level, and materializes the result as a tree of navigable skill files.<n>At serve time, the agent receives a bird's-eye view of the corpus, drills into topic branches via progressively finer summaries, and retrieves full documents by ID.
arXiv Detail & Related papers (2026-04-16T03:05:37Z) - Align then Train: Efficient Retrieval Adapter Learning [16.855103318831073]
We propose a label-efficient framework that trains retrieval adapters in two stages: self-supervised alignment and supervised adaptation.<n>Experiments on the MAIR benchmark, spanning 126 retrieval tasks across 6 domains, show that ERA improves retrieval in low-label settings.
arXiv Detail & Related papers (2026-04-03T19:04:42Z) - A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search [0.6299766708197881]
Ad hoc dataset search requires matching underspecified natural-language queries against sparse, heterogeneous metadata records.<n>We propose a bounded, auditable reference architecture for agentic hybrid retrieval that combines BM25 lexical search with dense-embedding retrieval.<n>An offline metadata augmentation step is introduced to reduce the vocabulary mismatch between user intent and provider-authored metadata.
arXiv Detail & Related papers (2026-03-28T22:56:57Z) - DeepRead: Document Structure-Aware Reasoning to Enhance Agentic Search [23.447631421934847]
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.
arXiv Detail & Related papers (2026-02-04T20:03:28Z) - 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) - 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) - Rethinking On-policy Optimization for Query Augmentation [49.87723664806526]
We present the first systematic comparison of prompting-based and RL-based query augmentation across diverse benchmarks.<n>We introduce a novel hybrid method, On-policy Pseudo-document Query Expansion (OPQE), which learns to generate a pseudo-document that maximizes retrieval performance.
arXiv Detail & Related papers (2025-10-20T04:16:28Z) - GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models [59.72897499248909]
We propose a novel graph retriever trained end-to-end with Large Language Models (LLMs)<n>Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together.<n>Our approach consistently achieves state-of-the-art performance, validating the strength of joint graph-LLM optimization for complex reasoning tasks.
arXiv Detail & Related papers (2025-09-20T02:38:00Z) - Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction [80.88654868264645]
Arranged and Organized Extraction Benchmark designed to evaluate ability of large language models to comprehend fragmented documents.<n>AOE includes 11 carefully crafted tasks across three diverse domains, requiring models to generate context-specific schema tailored to varied input queries.<n>Results show that even the most advanced models struggled significantly.
arXiv Detail & Related papers (2025-07-22T06:37:51Z) - Large Language Models are Good Relational Learners [55.40941576497973]
We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
arXiv Detail & Related papers (2025-06-06T04:07:55Z) - KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing [64.38243807002878]
We present KnowTrace, an elegant RAG framework to mitigate the context overload in large language models.<n>KnowTrace autonomously traces out desired knowledge triplets to organize a specific knowledge graph relevant to the input question.<n>It consistently surpasses existing methods across three multi-hop question answering benchmarks.
arXiv Detail & Related papers (2025-05-26T17:22:20Z) - Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
arXiv Detail & Related papers (2025-05-20T06:44:34Z) - Generative Retrieval for Book search [106.67655212825025]
We propose an effective Generative retrieval framework for Book Search.<n>It features two main components: data augmentation and outline-oriented book encoding.<n>Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines.
arXiv Detail & Related papers (2025-01-19T12:57:13Z) - DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain
Question Answering over Knowledge Base and Text [73.68051228972024]
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge.
Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
arXiv Detail & Related papers (2023-10-31T04:37:57Z)
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.