OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources

Heejun Lee; Jinheon Baek; Minki Kang; Patara Trirat; Sangwoo Park; Soyeong Jeong; Sung Ju Hwang; Woongyeong Yeo

arxiv: 2605.29250 · v1 · pith:IQPAQF6Dnew · submitted 2026-05-28 · 💻 cs.CL · cs.AI· cs.IR· cs.LG

OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources

Jinheon Baek , Soyeong Jeong , Sangwoo Park , Woongyeong Yeo , Minki Kang , Patara Trirat , Heejun Lee , Sung Ju Hwang This is my paper

Pith reviewed 2026-06-29 08:13 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.IRcs.LG

keywords unified retrievalheterogeneous knowledge sourcesknowledge graphsrelational tablestext retrievalquery dispatchingsource identificationnatural language queries

0 comments

The pith

OmniRetrieval unifies retrieval over text, tables and graphs by dispatching native queries to each source's own engine instead of merging the sources.

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

The paper introduces OmniRetrieval to address queries that need information from structurally different knowledge sources at the same time. It works by taking a natural-language question, deciding which sources are relevant, and sending appropriately formatted queries to the native systems for text, relational tables, or graphs. This is evaluated on a benchmark of 13 datasets covering 309 separate knowledge bases, where the unified approach beats systems restricted to a single source type. A reader would care because everyday information needs often cross these boundaries, yet existing tools require choosing one source and losing the others' strengths. The method keeps each source's schemas, ontologies and operators intact so that expressive power is not traded away for compatibility.

Core claim

OmniRetrieval takes any natural-language query, identifies appropriate knowledge sources, and dispatches source-native queries to their native execution engines. Across an extensive benchmark spanning 13 datasets and 309 distinct knowledge bases over text, relational, and graph-structured sources, OmniRetrieval exceeds single-source baselines, demonstrating that it can serve as a general-purpose interface to the heterogeneous sources while preserving the structural distinctions that make each source valuable.

What carries the argument

An overarching dispatch layer that identifies suitable sources for a given query and routes native queries to each source's execution engine.

If this is right

A single interface becomes sufficient for queries that require facts from multiple source types at once.
Each source continues to deliver its specialized operators and schemas instead of being reduced to a common format.
Additional knowledge bases can be incorporated by adding only the corresponding dispatch logic.
Retrieval performance improves by exploiting the native strengths of text search, table joins, or graph traversals as needed.

Where Pith is reading between the lines

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

The approach could support follow-on reasoning steps that combine outputs from different source types without prior data conversion.
Organizations might avoid maintaining duplicate copies of the same facts in multiple formats.
Extending the dispatch layer to new source types would require only source-specific identification rules rather than full system retraining.

Load-bearing premise

An overarching layer can reliably identify the right sources and produce effective native queries without erasing structural affordances or adding prohibitive overhead.

What would settle it

If OmniRetrieval is run on the 13-dataset benchmark and its accuracy fails to exceed the strongest single-source baseline on most datasets, the claim of effective unified retrieval would be falsified.

read the original abstract

Real-world information needs require access to structurally diverse knowledge sources, from unstructured text and relational tables to knowledge graphs and property graphs. Existing retrievers, however, operate over one source at a time under a fixed query language, leaving the broader landscape of available knowledge fragmented behind incompatible interfaces. A natural attempt at unification would collapse these sources into a shared space, but this erases the structural affordances (such as schemas, ontologies, compositional operators) that give each source its expressive power. Effective retrieval over diverse knowledge, therefore, requires not homogenization but an overarching layer that meets each source on its own terms. To achieve this, we present OmniRetrieval, a framework that takes any natural-language query, identifies appropriate knowledge sources, and dispatches source-native queries to their native execution engines. Across an extensive benchmark spanning 13 datasets and 309 distinct knowledge bases over text, relational, and graph-structured sources, OmniRetrieval exceeds single-source baselines, demonstrating that it can serve as a general-purpose interface to the heterogeneous sources while preserving the structural distinctions that make each source valuable.

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.

Desk Editor's Note private letter to a colleague

OmniRetrieval's dispatching layer for routing queries to native engines across text, tables, and graphs is a practical engineering move, but the abstract leaves the methods and analysis too thin to judge the results.

read the letter

OmniRetrieval takes a natural-language query, picks the right sources among text, relational, and graph stores, and sends each one its own native query instead of flattening everything into a shared space. That dispatching idea is the main new piece; it directly tries to keep the schemas and operators that make each source useful.

The paper does a decent job laying out the problem and running a big evaluation across 13 datasets and 309 knowledge bases, claiming it beats single-source baselines. The scale of the test is useful for seeing whether the approach holds up in mixed settings.

The soft spot is the missing detail. The abstract gives no description of how source identification works, no ablations on the routing step, no error bars, and no breakdown of overhead or failure cases. Without those, the performance numbers are hard to trust or build on. If the full paper has clean code, clear pseudocode for the dispatcher, and proper statistical checks, that would fix most of it.

This is aimed at retrieval people who already deal with multiple backends in production systems. A reader who needs a general interface layer would get concrete ideas from the benchmark setup and the framing. It is worth sending to peer review so referees can check the implementation and see whether the gains are real or depend on particular dataset quirks.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces OmniRetrieval, a framework that accepts natural-language queries, identifies relevant knowledge sources among text, relational tables, and graph-structured KBs, and dispatches native queries to the corresponding execution engines. The central claim is that this dispatching layer outperforms single-source baselines across an extensive benchmark of 13 datasets and 309 distinct knowledge bases while preserving the structural affordances (schemas, ontologies, compositional operators) of each source type.

Significance. If the empirical results are robustly validated, the work would offer a practical general-purpose interface to heterogeneous knowledge without forcing homogenization, addressing a real fragmentation problem in retrieval systems. The scale of the benchmark (309 KBs) is a notable strength if the evaluation protocol is sound.

major comments (2)

[Evaluation] Evaluation section: the abstract and framing assert consistent outperformance over single-source baselines, yet no error bars, number of runs, ablation studies on the dispatching component, or statistical significance tests are referenced; without these the load-bearing claim that the overarching layer succeeds cannot be assessed.
[Method] Method section (source identification and dispatch): the paper states that the layer meets each source on its own terms without erasing structural affordances or incurring prohibitive overhead, but supplies no concrete algorithm, complexity analysis, or overhead measurements to substantiate this assumption that is central to the weakest-assumption critique.

minor comments (1)

[Benchmark description] Notation for the 309 knowledge bases and 13 datasets should be tabulated with explicit source-type breakdown for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of evaluation robustness and methodological detail. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Evaluation] Evaluation section: the abstract and framing assert consistent outperformance over single-source baselines, yet no error bars, number of runs, ablation studies on the dispatching component, or statistical significance tests are referenced; without these the load-bearing claim that the overarching layer succeeds cannot be assessed.

Authors: We agree that the current manuscript lacks error bars, multiple-run statistics, ablations on the dispatching layer, and significance tests, which weakens the ability to assess the central claim. In the revision we will rerun the full benchmark with at least five independent seeds, report mean and standard deviation, add an ablation isolating the source-identification and dispatch components, and include paired statistical significance tests against the single-source baselines. revision: yes
Referee: [Method] Method section (source identification and dispatch): the paper states that the layer meets each source on its own terms without erasing structural affordances or incurring prohibitive overhead, but supplies no concrete algorithm, complexity analysis, or overhead measurements to substantiate this assumption that is central to the weakest-assumption critique.

Authors: The manuscript currently presents the dispatch logic at a conceptual level. We acknowledge that a concrete algorithm, complexity analysis, and overhead measurements are missing. The revised version will include pseudocode for the identification and dispatch procedure, asymptotic complexity discussion, and empirical latency and memory overhead figures measured on the 309-KB benchmark. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical engineering framework with external benchmarks

full rationale

The paper describes an engineering framework (OmniRetrieval) for dispatching natural-language queries to native engines across heterogeneous sources, with performance claims resting solely on empirical results across 13 external datasets and 309 knowledge bases. No equations, fitted parameters, derivations, or self-citations appear in the provided text. The central claim is a direct empirical demonstration that the dispatching layer outperforms single-source baselines while preserving structural distinctions; this is not reduced to any self-referential input by construction. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an engineering framework description rather than a mathematical derivation; no free parameters, axioms or invented entities are stated in the abstract.

pith-pipeline@v0.9.1-grok · 5742 in / 1051 out tokens · 26062 ms · 2026-06-29T08:13:42.808289+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 1 canonical work pages · 1 internal anchor

[1]

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

URLhttps://www.vldb.org/pvldb/vol17/p1132-gao.pdf. Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. Precise zero-shot dense retrieval without relevance labels. In Anna Rogers, Jordan L. Boyd-Graber, and Naoaki Okazaki, editors,Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toro...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2023.acl-long.99 2023
[2]

<route_type> | <kb_id> query: <query> <context and result>
[3]

selected

<route_type> | <kb_id> query: <query> <context and result> ... Respond with JSON: {"selected": <integer index>} Figure12: Cross-source evidence selection prompt. 20 OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources [System] You are a strict but fair evaluator. You will see a user question and two sides: a PREDICTED side (the model’s ...

[1] [1]

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

URLhttps://www.vldb.org/pvldb/vol17/p1132-gao.pdf. Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. Precise zero-shot dense retrieval without relevance labels. In Anna Rogers, Jordan L. Boyd-Graber, and Naoaki Okazaki, editors,Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toro...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/2023.acl-long.99 2023

[2] [2]

<route_type> | <kb_id> query: <query> <context and result>

[3] [3]

selected

<route_type> | <kb_id> query: <query> <context and result> ... Respond with JSON: {"selected": <integer index>} Figure12: Cross-source evidence selection prompt. 20 OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources [System] You are a strict but fair evaluator. You will see a user question and two sides: a PREDICTED side (the model’s ...