Neague, Petru, Stokkink, Quinten, de Vos, Martijn, Pires, Rafael, Randl, Mathis, Kermarrec, Anne-Marie, Pouwelse, Johan · Nov 5, 2026
Petru Neague and coauthors present ZeroRAG for decentralized retrieval
Markdown · JSON · Original source
Petru Neague and coauthors present ZeroRAG, a decentralized routing layer for (RAG), where documents are retrieved to provide context for a (LLM). They target a problem in centralized RAG: organizations and individuals may be unable or unwilling to give a central party custody of their documents because of privacy, copyright or regulatory compliance. In ZeroRAG, documents stay with the peers that own them. Each peer keeps a bounded set of neighbor advertisements and a cache of routed queries. The system uses these to route queries through a best-first walk and progressively rewire the overlay toward semantically relevant neighbors. The authors say this self-organization improves recall over without a central coordinator or global data structure. In evaluations with three real-world workloads and two decentralized baselines, they report better recall and fewer network messages than the baselines. They also report results approaching a centralized document store in recall and LLM answer accuracy, recovery after catastrophic peer failures, and scaling to 100 000 peers. Escape Meridian interpretation: the approach could matter where document owners want retrieval without handing over custody, though the abstract does not establish how its reported performance carries over to other settings.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 5, 2026 (2026-11-05).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-08T03:32:25.433Z. First observed: 2026-11-06T05:03:23.125Z. Retrieved: 2026-11-08T03:18:45.045Z.
Referenced by
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Limits
The evidence is the paper's abstract, which names three real-world workloads and two decentralized baselines but does not identify them or provide evaluation details. The abstract reports that ZeroRAG approaches a centralized document store in recall and answer accuracy, but does not quantify that comparison.
Open question
How do the reported results vary across the three real-world workloads and two decentralized baselines?
Source-stated
The authors propose ZeroRAG as a decentralized routing layer for RAG that routes queries across peers without central orchestration or global data structures.
We present ZeroRAG, a decentralized routing layer for document retrieval in RAG that performs semantic query routing across an overlay of peers with- out global data structures or centralized orchestration.
Source-stated
In ZeroRAG, documents remain with their owners, while peers use local neighbor advertisements and a cache of routed queries to guide routing and rewire the overlay toward semantically relevant neighbors.
Documents remain with the peers that own them, and each peer keeps only lightweight local state: a bounded set of neighbor advertisements and a cache of recently routed queries.
Source-stated
The authors report that ZeroRAG outperforms two decentralized baselines on recall and network messages, approaches a centralized store's recall and LLM answer accuracy, self-heals after catastrophic peer failures, and scales to 100 000 peers.
We find that ZeroRAG outperforms baselines in terms of recall and required network messages, approaches the recall and LLM answer accuracy of a centralized document store, self-heals after catastrophic peer failures, and scales to 100 000 peers.
Escape Meridian interpretation
The approach could be relevant to document owners who want retrieval without handing custody of their documents to a central party; the abstract does not show how the results carry over to other settings.
documents are owned by organizations, devices, and individuals who cannot or will not surrender custody of them to a central party
Source and contributor context
Neague, Petru, Stokkink, Quinten, de Vos, Martijn, Pires, Rafael, Randl, Mathis, Kermarrec, Anne-Marie, Pouwelse, Johan · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Retrieval-augmented generation
A setup in which a model first looks up relevant documents and then answers using them.
Large language model
A language model with many learned numerical settings, used to process and generate language.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Editorial provenance
Version 1. Publication ID: a68158c0-24ea-4124-8dd8-221d15bee6b7.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-08T03:32:25.433Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 286662709c1bfa96d67fcec5a36b99b290e644e9d086a616bbda22713da63226. Source text hash: 45df1f0a7df7535f08e22c50812f1d601a75c8a4dc797fda4fcb26d115f29847.
Revision and source-check history
Version 1 · 2026-11-08T03:32:25.433Z
Petru Neague and coauthors present ZeroRAG for decentralized retrieval
Petru Neague and coauthors present ZeroRAG, a decentralized routing layer for (RAG), where documents are retrieved to provide context for a (LLM). They target a problem in centralized RAG: organizations and individuals may be unable or unwilling to give a central party custody of their documents because of privacy, copyright or regulatory compliance. In ZeroRAG, documents stay with the peers that own them. Each peer keeps a bounded set of neighbor advertisements and a cache of routed queries. The system uses these to route queries through a best-first walk and progressively rewire the overlay toward semantically relevant neighbors. The authors say this self-organization improves recall over without a central coordinator or global data structure. In evaluations with three real-world workloads and two decentralized baselines, they report better recall and fewer network messages than the baselines. They also report results approaching a centralized document store in recall and LLM answer accuracy, recovery after catastrophic peer failures, and scaling to 100 000 peers. Escape Meridian interpretation: the approach could matter where document owners want retrieval without handing over custody, though the abstract does not establish how its reported performance carries over to other settings.
Original source
cs.AI · Article
Source details
Original source date: Nov 5, 2026 (2026-11-05).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-08T03:32:25.433Z. First observed: 2026-11-06T05:03:23.125Z. Retrieved: 2026-11-08T03:18:45.045Z.
Limits
The evidence is the paper's abstract, which names three real-world workloads and two decentralized baselines but does not identify them or provide evaluation details. The abstract reports that ZeroRAG approaches a centralized document store in recall and answer accuracy, but does not quantify that comparison.
Open question
How do the reported results vary across the three real-world workloads and two decentralized baselines?
Source-stated
The authors propose ZeroRAG as a decentralized routing layer for RAG that routes queries across peers without central orchestration or global data structures.
We present ZeroRAG, a decentralized routing layer for document retrieval in RAG that performs semantic query routing across an overlay of peers with- out global data structures or centralized orchestration.
Source-stated
In ZeroRAG, documents remain with their owners, while peers use local neighbor advertisements and a cache of routed queries to guide routing and rewire the overlay toward semantically relevant neighbors.
Documents remain with the peers that own them, and each peer keeps only lightweight local state: a bounded set of neighbor advertisements and a cache of recently routed queries.
Source-stated
The authors report that ZeroRAG outperforms two decentralized baselines on recall and network messages, approaches a centralized store's recall and LLM answer accuracy, self-heals after catastrophic peer failures, and scales to 100 000 peers.
We find that ZeroRAG outperforms baselines in terms of recall and required network messages, approaches the recall and LLM answer accuracy of a centralized document store, self-heals after catastrophic peer failures, and scales to 100 000 peers.
Escape Meridian interpretation
The approach could be relevant to document owners who want retrieval without handing custody of their documents to a central party; the abstract does not show how the results carry over to other settings.
documents are owned by organizations, devices, and individuals who cannot or will not surrender custody of them to a central party
Source and contributor context
Neague, Petru, Stokkink, Quinten, de Vos, Martijn, Pires, Rafael, Randl, Mathis, Kermarrec, Anne-Marie, Pouwelse, Johan · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Retrieval-augmented generation
A setup in which a model first looks up relevant documents and then answers using them.
Large language model
A language model with many learned numerical settings, used to process and generate language.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
TIME
TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Editorial provenance
Version 1. Publication ID: a68158c0-24ea-4124-8dd8-221d15bee6b7.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-08T03:32:25.433Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 286662709c1bfa96d67fcec5a36b99b290e644e9d086a616bbda22713da63226. Source text hash: 45df1f0a7df7535f08e22c50812f1d601a75c8a4dc797fda4fcb26d115f29847.
passed · 2026-11-08T03:32:25.433Z · anthropic/claude-opus-5.5
Publication: a68158c0-24ea-4124-8dd8-221d15bee6b7
attribution — pass: The draft correctly attributes ZeroRAG to Petru Neague and coauthors, who are listed as authors. It keeps the performance results framed as the authors' reported findings, not established facts.
Authors:Petru Neague, Quinten Stokkink, Martijn de Vos, Rafael Pires, Mathis Randl, Anne-Marie Kermarrec, Johan Pouwelse
summary — pass: The draft leads with the actor and the development. It explains RAG and LLM in plain terms and states the motivating custody problem: privacy, copyright and compliance. It also preserves the mechanism and the reported results. Terms such as overlay, best-first walk and recall are kept exactly and suit term-library tooltips. The plain gloss that documents stay with their owners keeps the core idea readable. The title is accurate, and the question is a genuine open issue about the evaluation details.
Documents remain with the peers that own them, and each peer keeps only lightweight local state
date — pass: The original publication date of 2026-11-05 matches the v1 submission date on arXiv. The feed date of 2026-11-06 is correctly not used.
[Submitted on 5 Nov 2026]
support — pass: Each claim matches the abstract. The interpretation is labeled as Escape Meridian's, is conditional, carries an excerpt and keeps the gap: it notes the abstract does not establish generalization. The limitations accurately say the workloads and baselines are unnamed and the centralized comparison is unquantified. No invented beneficiaries or costs appear.
approaches the recall and LLM answer accuracy of a centralized document store, self-heals after catastrophic peer failures, and scales to 100 000 peers
media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct access to the source.
Challenge or correct this reading
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A setup in which a model first looks up relevant documents and then answers using them.
Definition referenceA language model with many learned numerical settings, used to process and generate language.
Definition referenceTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA language model with many learned numerical settings, used to process and generate language.
Definition referenceA setup in which a model first looks up relevant documents and then answers using them.
Definition referenceA language model with many learned numerical settings, used to process and generate language.
Definition referenceTIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services.
Explore TIMEarXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA language model with many learned numerical settings, used to process and generate language.
Definition reference