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arxiv: 2605.25480 · v2 · pith:NF3ODQSGnew · submitted 2026-05-25 · 💻 cs.CL

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

Haoliang Ming , Feifei Li , Xiaoqing Wu , Wenhui Que This is my paper

Pith reviewed 2026-06-29 21:56 UTC · model grok-4.3

classification 💻 cs.CL
keywords retrieval-augmented generationLLM agentsmulti-hop question answeringself-evolving retrievalagent-native systemsWiki structurebidirectional links
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The pith

LLM-Wiki compiles documents into bidirectional-linked Wiki pages that agents navigate via tools to perform retrieval as iterative reasoning.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims current RAG systems limit agents to one-shot embedding lookups on flat chunks, which fails to support the searching, reading, traversing, and sufficiency decisions agents need. LLM-Wiki instead compiles source documents into structured Wiki pages with bidirectional links, exposes search/read/link-following as standard tool calls, and adds an Error Book for ongoing structural and semantic fixes. This turns retrieval into a reasoning process rather than a lookup step. On multi-hop QA benchmarks the approach delivers 2.0-8.1 F1 gains over HippoRAG 2, LightRAG, and GraphRAG while also leading on the AuthTrace structured-query set.

Core claim

By compiling documents into Wiki pages with bidirectional links, exposing search/read/link-following through tool-calling, and maintaining an Error Book for self-correction, LLM-Wiki operationalizes retrieval as reasoning; agents thereby achieve state-of-the-art F1 scores on HotpotQA, MuSiQue, and 2WikiMultiHopQA and highest accuracy on AuthTrace, with largest gains on multi-document structured queries.

What carries the argument

LLM-Wiki, the compilation of documents into bidirectional-linked Wiki pages plus tool-calling interface and Error Book that together let agents treat retrieval as navigable reasoning steps.

If this is right

  • Agents can follow links iteratively to gather evidence instead of receiving all context in one retrieval step.
  • The Error Book enables persistent correction of both link structure and content semantics across sessions.
  • The same compilation method improves performance on multi-document structured queries, not only chain-style multi-hop reasoning.
  • Retrieval performance becomes tied to the quality of the compiled structure rather than solely to embedding similarity.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The bidirectional links could surface cross-document connections that embedding similarity alone would miss, allowing agents to discover implicit relations during traversal.
  • A natural extension would be to let the agent itself trigger partial recompilation of affected Wiki pages when new documents arrive, rather than full re-indexing.
  • The tool-calling interface could be standardized across agent frameworks so any LLM agent can use the same Wiki navigation primitives without custom integration.

Load-bearing premise

That giving agents tool access to a compiled Wiki with bidirectional links and an Error Book will produce meaningfully better iterative reasoning than flat embedding retrieval on the same tasks.

What would settle it

A controlled test in which agents equipped with the LLM-Wiki tools show no accuracy advantage over identical agents using standard embedding-based chunk retrieval on the same multi-hop and structured-query benchmarks.

Figures

Figures reproduced from arXiv: 2605.25480 by Feifei Li, Haoliang Ming, Wenhui Que, Xiaoqing Wu.

Figure 1
Figure 1. Figure 1: Index-time construction and structured Wiki knowledge base in LLM-Wiki. Raw documents are compiled [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Retrieval-as-lookup versus retrieval-as-reasoning. Traditional flat-chunk RAG performs one-shot similarity [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Hop-wise F1 on 2WikiMHQA, grouped by reasoning depth (2-/4-hop). [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript proposes LLM-Wiki, an agent-native retrieval system that compiles documents into structured Wiki pages with bidirectional links, exposes search/read/link-following via tool-calling interfaces, and introduces an Error Book for persistent self-correction. It claims state-of-the-art results on HotpotQA, MuSiQue, and 2WikiMultiHopQA (outperforming HippoRAG 2, LightRAG, and GraphRAG by 2.0-8.1 F1 points) and best overall accuracy on AuthTrace, with particular gains on multi-document structured queries.

Significance. If substantiated, the shift from flat embedding-based RAG to a compilable, traversable, self-evolving Wiki structure could support more iterative reasoning in LLM agents. The Error Book mechanism for structural and semantic correction is a distinctive element that, if shown to drive the gains, would strengthen the case for retrieval-as-reasoning paradigms.

major comments (1)
  1. [Abstract] Abstract: the claim of specific F1 gains and SOTA status is presented without any experimental details, baseline implementations, error analysis, statistical significance tests, or ablation studies; the full text absence prevents verification of whether the results support the central claim that the Wiki structure and Error Book produce meaningfully better iterative reasoning than flat retrieval.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of specific F1 gains and SOTA status is presented without any experimental details, baseline implementations, error analysis, statistical significance tests, or ablation studies; the full text absence prevents verification of whether the results support the central claim that the Wiki structure and Error Book produce meaningfully better iterative reasoning than flat retrieval.

    Authors: The abstract is a concise summary following standard conventions; detailed experimental information is provided in the full manuscript. Section 4 describes baseline implementations and experimental setup, Section 5.3 contains error analysis, Tables 2-4 and Appendix B report statistical significance tests, and Section 4.3 plus Table 3 present ablation studies. These sections substantiate that the Wiki structure and Error Book drive gains in iterative reasoning over flat retrieval. The full text was submitted with the manuscript; if access was unavailable during review, we can resupply it. revision: no

Circularity Check

0 steps flagged

No significant circularity; empirical system proposal with no derivations

full rationale

The provided text consists solely of the abstract describing a proposed retrieval system (LLM-Wiki) and its empirical benchmark results. No equations, first-principles derivations, fitted parameters presented as predictions, or load-bearing self-citations appear. Performance claims are framed as experimental outcomes on HotpotQA, MuSiQue, 2WikiMultiHopQA, and AuthTrace without any reduction to inputs by construction. The full manuscript is referenced but not supplied here, and the abstract contains no technical steps amenable to circularity analysis. This is the expected non-finding for a system-description paper lacking mathematical claims.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities; ledger entries are therefore empty.

pith-pipeline@v0.9.1-grok · 5760 in / 1218 out tokens · 41630 ms · 2026-06-29T21:56:57.406724+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

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    AuthTrace is a diagnostic benchmark that annotates fan-in gradients in single-author corpora to measure evidence recall, precision, and answer correctness across eight systems in retrieval, memory, graph, and structur...

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