# ScalableRAG authors propose exact corpus operations without costly ingestion [Canonical reading](https://escapemeridian.com/readings/scalablerag-authors-propose-exact-corpus-operations-without-b5340e0f) Authors Hilaf Hasson, Aditya Chakravarty, Jayant Thomas, and Krishna Gogineni introduce ScalableRAG, a system that lets an agent build and combine named document and value sets at query time. They argue this closes a gap in retrieval-augmented generation (RAG): lightweight retrievers can find evidence but do not expose the corpus-wide operations needed for counts, intersections, and aggregations, while systems that support such operations require expensive ingestion. The authors report that Zero-Ingestion ScalableRAG needs no preprocessing or vector index and matches the leading baseline on four conventional question-answering benchmarks. On the aggregative Hotels-50 benchmark, they report a score of 71.74, while removing the workspace lowers the score to 38.4. A second version, Limited-Ingestion ScalableRAG, adds a lightweight index; the authors report results above a judge score of 80 at five million documents, with 13 to 16 seconds per question and under 20 minutes of ingestion. The paper’s central argument is that a query-time workspace can combine exact corpus operations with low ingestion cost and scale to large collections. These are reported benchmark results, not evidence here of performance across other workloads. How well would the approach perform on real-world collections and questions beyond the reported benchmarks? 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 arXiv cs.AI · Article Original source date: Jul 27, 2026 (2026-07-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-14T00:01:34.152Z. First observed: 2026-10-28T05:56:36.597Z. Retrieved: 2026-10-30T20:17:17.249Z. - [ScalableRAG: High-Quality RAG with Exact Corpus Operations at Minimal Ingestion Cost](https://arxiv.org/abs/2607.25135) ## Limits The evidence is the paper’s abstract and reports benchmark results; it does not establish performance across real-world collections or workloads beyond those evaluated. ## Open question How well would ScalableRAG perform on real-world collections and questions beyond the reported benchmarks? ## Source-stated ScalableRAG uses a persistent typed-set workspace for query-time corpus operations. > a persistent typed-set workspace: an agent builds named document and value sets at query time and composes them through set algebra, typed extraction, aggregation, and diagnostics. - [Supporting source](https://arxiv.org/abs/2607.25135) ## Source-stated The authors report that Zero-Ingestion ScalableRAG needs no preprocessing or vector index and matches the leading baseline on four conventional QA benchmarks. > Zero-Ingestion ScalableRAG needs no preprocessing, not even a vector index. It matches the leading baseline on four conventional QA benchmarks - [Supporting source](https://arxiv.org/abs/2607.25135) ## Source-stated The authors report a Hotels-50 score of 71.74, falling to 38.4 when the workspace is removed. > on aggregative Hotels-50 beats every evaluated system, scoring 71.74: 36.23 above the leading baseline (SRAG), and 40.22 points above A-RAG. Removing the workspace drops Hotels-50 to 38.4. - [Supporting source](https://arxiv.org/abs/2607.25135) ## Source-stated The authors report that Limited-Ingestion ScalableRAG scores above 80 at five million documents, with 13 to 16 seconds per question and under 20 minutes of ingestion. > is the only evaluated system above 80 judge score at 5M documents, at 13 to 16 seconds per question and under 20 minutes ingestion - [Supporting source](https://arxiv.org/abs/2607.25135) ## Source and contributor context Hasson, Hilaf, Chakravarty, Aditya, Thomas, Jayant, Gogineni, Krishna · 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. - [Definition reference](https://developers.google.com/machine-learning/glossary#retrieval-augmented-generation-rag) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## TIME TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services. - [Explore this context](/entities/time) - [Reference](https://time.com/) ## Editorial provenance Version 1. Publication ID: bbbbae51-e1f7-4b10-8fa9-99d50e8d74f0. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-14T00:01:34.152Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 7470ca99ed14e029704f2664fb16af59dd38679b3a5098de92d55a434562f465. Source text hash: 4b9754796e035522f4ac97dd125d965559952c6f8f7f23aed0bb014975521763. ## Revision and source-check history ### Version 1 — 2026-11-14T00:01:34.152Z Authors Hilaf Hasson, Aditya Chakravarty, Jayant Thomas, and Krishna Gogineni introduce ScalableRAG, a system that lets an agent build and combine named document and value sets at query time. They argue this closes a gap in retrieval-augmented generation (RAG): lightweight retrievers can find evidence but do not expose the corpus-wide operations needed for counts, intersections, and aggregations, while systems that support such operations require expensive ingestion. The authors report that Zero-Ingestion ScalableRAG needs no preprocessing or vector index and matches the leading baseline on four conventional question-answering benchmarks. On the aggregative Hotels-50 benchmark, they report a score of 71.74, while removing the workspace lowers the score to 38.4. A second version, Limited-Ingestion ScalableRAG, adds a lightweight index; the authors report results above a judge score of 80 at five million documents, with 13 to 16 seconds per question and under 20 minutes of ingestion. The paper’s central argument is that a query-time workspace can combine exact corpus operations with low ingestion cost and scale to large collections. These are reported benchmark results, not evidence here of performance across other workloads. How well would the approach perform on real-world collections and questions beyond the reported benchmarks? ## Original source arXiv cs.AI · Article Original source date: Jul 27, 2026 (2026-07-27). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-14T00:01:34.152Z. First observed: 2026-10-28T05:56:36.597Z. Retrieved: 2026-10-30T20:17:17.249Z. - [ScalableRAG: High-Quality RAG with Exact Corpus Operations at Minimal Ingestion Cost](https://arxiv.org/abs/2607.25135) ## Limits The evidence is the paper’s abstract and reports benchmark results; it does not establish performance across real-world collections or workloads beyond those evaluated. ## Open question How well would ScalableRAG perform on real-world collections and questions beyond the reported benchmarks? ## Source-stated ScalableRAG uses a persistent typed-set workspace for query-time corpus operations. > a persistent typed-set workspace: an agent builds named document and value sets at query time and composes them through set algebra, typed extraction, aggregation, and diagnostics. - [Supporting source](https://arxiv.org/abs/2607.25135) ## Source-stated The authors report that Zero-Ingestion ScalableRAG needs no preprocessing or vector index and matches the leading baseline on four conventional QA benchmarks. > Zero-Ingestion ScalableRAG needs no preprocessing, not even a vector index. It matches the leading baseline on four conventional QA benchmarks - [Supporting source](https://arxiv.org/abs/2607.25135) ## Source-stated The authors report a Hotels-50 score of 71.74, falling to 38.4 when the workspace is removed. > on aggregative Hotels-50 beats every evaluated system, scoring 71.74: 36.23 above the leading baseline (SRAG), and 40.22 points above A-RAG. Removing the workspace drops Hotels-50 to 38.4. - [Supporting source](https://arxiv.org/abs/2607.25135) ## Source-stated The authors report that Limited-Ingestion ScalableRAG scores above 80 at five million documents, with 13 to 16 seconds per question and under 20 minutes of ingestion. > is the only evaluated system above 80 judge score at 5M documents, at 13 to 16 seconds per question and under 20 minutes ingestion - [Supporting source](https://arxiv.org/abs/2607.25135) ## Source and contributor context Hasson, Hilaf, Chakravarty, Aditya, Thomas, Jayant, Gogineni, Krishna · 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. - [Definition reference](https://developers.google.com/machine-learning/glossary#retrieval-augmented-generation-rag) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## TIME TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services. - [Explore this context](/entities/time) - [Reference](https://time.com/) ## Editorial provenance Version 1. Publication ID: bbbbae51-e1f7-4b10-8fa9-99d50e8d74f0. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-14T00:01:34.152Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 7470ca99ed14e029704f2664fb16af59dd38679b3a5098de92d55a434562f465. Source text hash: 4b9754796e035522f4ac97dd125d965559952c6f8f7f23aed0bb014975521763. ### passed — 2026-11-14T00:01:34.152Z — anthropic/claude-opus-5.5 Publication: bbbbae51-e1f7-4b10-8fa9-99d50e8d74f0 attribution — pass: The reading correctly names the four listed authors and consistently frames the performance figures as author-reported results, not established facts. > Authors:Hilaf Hasson, Aditya Chakravarty, Jayant Thomas, Krishna Gogineni summary — pass: The title, summary and question faithfully convey the abstract's capability-cost gap argument, the query-time workspace and both system variants. Terms such as ingestion, vector index and judge score are left to tooltips, but the core idea remains understandable to a general reader: the system is cheap to set up and can count or combine across documents. The question is a fair unresolved issue rather than a leading claim. > ScalableRAG closes this gap with a persistent typed-set workspace: an agent builds named document and value sets at query time date — pass: originalPublished 2026-07-27 matches the v1 submission date. The later v2 revision and the feed date are correctly not used. > \[Submitted on 27 Jul 2026 (v1), last revised 26 Oct 2026 (this version, v2)\] support — pass: Every numeric claim matches the abstract: 71.74 on Hotels-50, 38.4 without the workspace, above 80 at 5M documents, 13 to 16 seconds per question, and under 20 minutes of ingestion. The scaling statement reflects the authors' own million-document viability claim. The limitation correctly bounds benchmark results without inventing stakeholders or impacts. > ScalableRAG combines minimal ingestion, conventional-QA parity, exact corpus operations, and million-document viability; no evaluated alternative provides all four. media — pass: The selected media is the verified direct arXiv abstract page for this paper, which is the most direct access to the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/b5340e0f41d1be973355a0e05dcf5057f344cd7cb07d940002db4ebce402d07b/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/scalablerag-authors-propose-exact-corpus-operations-without-b5340e0f.json)