# Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki

arXiv: https://arxiv.org/abs/2605.25480 · HTML: https://mlsanity.com/paper/2605.25480/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 25 May 2026.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)
- Synthetic data and self-improvement for LLMs / [Self-evolving search agents](https://mlsanity.com/synth/self-evolving-search-agents/)

## Builds on (most cited first, 4 of 4)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [GLM-5: from Vibe Coding to Agentic Engineering](https://mlsanity.com/paper/2602.15763/) (2026)
- [Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading](https://mlsanity.com/paper/2310.05029/) (2023)

## Built on by (most cited first, 9 of 9)

- [From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents](https://mlsanity.com/paper/2607.08028/) (2026)
- [Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence](https://mlsanity.com/paper/2608.21156/) (2026)
- [Hybrid Adversarial Defence for Natural Language Understanding Tasks](https://mlsanity.com/paper/2606.04612/) (2026)
- [WikiKV: Schema-Evolving Path-Indexed Storage for Hierarchical Knowledge Navigation](https://mlsanity.com/paper/2606.14275/) (2026)
- [Why Git Is the Memory Solution for the Agentic Development Lifecycle](https://mlsanity.com/paper/2607.14390/) (2026)
- [WikiLoop: Jointly Learning to Build and Navigate Agent-Native Wikis with Downstream Feedback](https://mlsanity.com/paper/2607.26604/) (2026)
- [Training a Knowledge Base: Supervised Structure Learning for Agent-Curated Document Stores](https://mlsanity.com/paper/2608.21829/) (2026)
- [SearchWiki: Learning to Build and Navigate Knowledge Wikis for Active Information Seeking](https://mlsanity.com/paper/2608.29953/) (2026)
- [Follow the Entities: A Corpus Map for Agentic Search](https://mlsanity.com/paper/2609.37226/) (2026)

## Abstract (arXiv)

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.