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Paper Citation Record · LEDGER

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2607.26497.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.26497 v3

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T01:45:05.061275Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved56
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa50c9f7-5ddd-4446-a84a-ad747ea36fef · outbound

This paper cites SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking

Reference 4

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source=arxiv_source observed=2026-08-03T01:44:57.055335Z digest=sha256:e53733985747466ee384c9bdffad194178bc81c7ebf3b6900bc180cb77e59f09

Observation b6dc27a3-1b80-4c25-8a47-1cac4c9bf30f · outbound

This paper cites HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models

Reference 6

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source=arxiv_source observed=2026-08-03T01:44:57.236629Z digest=sha256:f503e4f2b5504e407479daff50843ea382e646d9039cedc2d96fc735d1683721

Observation bc391039-99d9-415d-851d-38910bc46193 · outbound

This paper cites From RAG to Memory: Non-Parametric Continual Learning for Large Language Models.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

Reference 7

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source=arxiv_source observed=2026-08-03T01:44:57.399850Z digest=sha256:be634e2cfaa36a0b133a4e142194766dd877973d927b8ea290867a175735b65e

Observation 75444b3e-a8e0-46be-b336-0389f7ab595d · outbound

This paper cites Active Retrieval Augmented Generation.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Active Retrieval Augmented Generation

Reference 9

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source=arxiv_source observed=2026-08-03T01:44:57.583524Z digest=sha256:21f7156a285cf130a3025cd56323b9743474d071ebf8ec1e1d7dffb9dc14f1b8

Observation cdd72a37-1028-4d0d-8e40-d066d6a1c882 · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Dense Passage Retrieval for Open-Domain Question Answering

Reference 12

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source=arxiv_source observed=2026-08-03T01:44:57.924742Z digest=sha256:4d4d7ba03344ca802bc30c4cf367c9d1737cf75487286c0dd6057a9a3bda94f3

Observation 8055cc13-1163-4341-883f-8de523128c72 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 15

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source=arxiv_source observed=2026-08-03T01:44:58.299686Z digest=sha256:7fac8c34d7842cb026b5897a55bc51882f82b9bc50e1a328b7d3b3315039d6ef

Observation 5422ced9-9c42-4d97-8417-55d02e819f60 · outbound

This paper cites Graph Retrieval-Augmented Generation: A Survey.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Graph Retrieval-Augmented Generation: A Survey

Reference 19

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source=arxiv_source observed=2026-08-03T01:44:58.735632Z digest=sha256:0553d3be1d8c2231244c23181186350a05290aa0d8b0e05864936d9a73b1a81a

Observation a87c4ac4-3cb9-4e53-afd5-7af0294cc0ca · outbound

This paper cites Measuring and Narrowing the Compositionality Gap in Language Models.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Measuring and Narrowing the Compositionality Gap in Language Models

Reference 20

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source=arxiv_source observed=2026-08-03T01:44:58.815823Z digest=sha256:4e5624142cdd97d00600f4e14324d5be9c1cfde70903bc885073dfc6eea4f7eb

Observation 5f24706e-36a2-4882-8d39-2f83379eacb3 · outbound

This paper cites In-Context Retrieval-Augmented Language Models.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms In-Context Retrieval-Augmented Language Models

Reference 22

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source=arxiv_source observed=2026-08-03T01:44:58.960285Z digest=sha256:96457c7c98cb101baa80fde2684b3cc042cbb62f515f288c923fc57e5335bd07

Observation 7f1672a7-1603-44d3-8838-8abb4414cae7 · outbound

This paper cites The probabilistic relevance framework: BM25 and beyond.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms The probabilistic relevance framework: BM25 and beyond

Reference 23

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source=arxiv_source observed=2026-08-03T01:44:59.027812Z digest=sha256:8f3b725df0544157f90cd6e42776616c5563f843425a0bc620c6737edb0e0e46

Observation 9a345307-a561-4ae9-9104-651a17563e33 · outbound

This paper cites Toolformer: Language Models Can Teach Themselves to Use Tools.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Toolformer: Language Models Can Teach Themselves to Use Tools

Reference 25

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source=arxiv_source observed=2026-08-03T01:44:59.235529Z digest=sha256:e9c9422c6662bb1bd1abdbbcd06b18ab79f36dc176c32fb453f55fe4b668148f

Observation 97b60704-d5b3-440b-a3b2-e0e86951be44 · outbound

This paper cites Reflexion: Language Agents with Verbal Reinforcement Learning.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Reflexion: Language Agents with Verbal Reinforcement Learning

Reference 26

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source=arxiv_source observed=2026-08-03T01:44:59.360226Z digest=sha256:6826ac7e26c4fe1e0ab59665f4987449cc9a9f2b1bdc931751c06b958afdff9c

Observation 23045ae3-592d-4f39-acc6-3f0ef45ba58d · outbound

This paper cites Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Reference 28

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source=arxiv_source observed=2026-08-03T01:44:59.572875Z digest=sha256:b0f0bf4e726e4c5689d06ddf0d8ff761f7aecca358bfef67b2aeac8657b8e0bc

Observation 8a865612-80d0-4506-b571-b2e1daeba2da · outbound

This paper cites Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Reference 35

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source=arxiv_source observed=2026-08-03T01:45:00.224933Z digest=sha256:28af4c917a50a0bf3a948e2e8e14bc2d85707b1fc5352053335a6332e1a9b8f7

Observation f25734fa-a0da-4ce0-8be8-28ce00010e9d · outbound

This paper cites From Local to Global: A Graph RAG Approach to Query-Focused Summarization.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms From Local to Global: A Graph RAG Approach to Query-Focused Summarization

Reference 36

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source=arxiv_source observed=2026-08-03T01:45:00.296470Z digest=sha256:57126373e72b16f5c3c2a04549409425a50e42e13b862b52b6a78b05ed81623f

Observation 0462491d-b396-4161-bfe5-f6c066595c0c · outbound

This paper cites 2021 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2021 , eprint=

Reference 37

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source=arxiv_source observed=2026-08-03T01:45:00.353553Z digest=sha256:f87af066ce0f5445f54b3aa968d381c7f5616ef9bcd7e830674d0221ad6ab3fd

Observation 64f872e4-8d79-4312-841d-0609d959ac06 · outbound

This paper cites 2023 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2023 , eprint=

Reference 38

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source=arxiv_source observed=2026-08-03T01:45:00.462482Z digest=sha256:d1de2fc8811b2a42f3be592709343ce138abf320bf2f11346906340eb5e2912a

Observation d4a6f8e4-3516-4d13-a4b7-6902ce15d3dc · outbound

This paper cites LightRAG: Simple and Fast Retrieval-Augmented Generation.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms LightRAG: Simple and Fast Retrieval-Augmented Generation

Reference 39

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source=arxiv_source observed=2026-08-03T01:45:00.547491Z digest=sha256:ba8a97f77a08ab8e743d3f3d6ed026cf893dfd8d1a140cb87a578d89f60f5f24

Observation 60ca2644-b7a2-4242-9dbd-da81a22ae3b0 · outbound

This paper cites an unresolved cited work.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Unresolved cited work

Reference 40

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source=arxiv_source observed=2026-08-03T01:45:00.634175Z digest=sha256:5e9de2d6b5e35a57d8e1a0293cee3ee82cc3868547e008424b543bd598181dba

Observation fedc0324-271f-42e6-9043-0b25cff9a776 · outbound

This paper cites 2025 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2025 , eprint=

Reference 41

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source=arxiv_source observed=2026-08-03T01:45:00.757175Z digest=sha256:c0f4968398b1309dd4f4a8b4a928194d6daa2f8ac13643d586e8ab5b675f6931

Observation c3bd064a-a7ed-47fa-8559-51725bbec41b · outbound

This paper cites REALM: Retrieval-Augmented Language Model Pre-Training.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms REALM: Retrieval-Augmented Language Model Pre-Training

Reference 42

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source=arxiv_source observed=2026-08-03T01:45:00.834695Z digest=sha256:1c1304b68f77e758585169073aae72a6c539aedd36e22bab81e6a0ee7a00dc5e

Observation c5a91392-fd3e-4608-9a11-622a5746bd73 · outbound

This paper cites Proceedings of the 11th international conference on World Wide Web , pages=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Proceedings of the 11th international conference on World Wide Web , pages=

Reference 43

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source=arxiv_source observed=2026-08-03T01:45:00.899334Z digest=sha256:eb4925ac1e0c56dd21377f5e9fbf50481fb9152eda210922978ab678622d151a

Observation e6a652e1-d244-44a2-8780-6c734adf3436 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Advances in Neural Information Processing Systems , volume=

Reference 44

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source=arxiv_source observed=2026-08-03T01:45:00.946347Z digest=sha256:678821dd781d765cf36f0c7637e91d4d090cf5e3c576b20156ab2ef3ae33459c

Observation 4fd60e34-d2ae-4980-9058-988d41caf478 · outbound

This paper cites 2021 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2021 , eprint=

Reference 45

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source=arxiv_source observed=2026-08-03T01:45:00.996447Z digest=sha256:2af7a2f68d2ef6267cc23b38219a10a2645a561cefd8697f2d9cccb1dfa451f2

Observation dfc98c2c-4912-4c15-a1ad-efeb526e4ab1 · outbound

This paper cites Proceedings of the 16th conference of the european chapter of the association for computational linguistics: main volume , pages=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Proceedings of the 16th conference of the european chapter of the association for computational linguistics: main volume , pages=

Reference 46

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source=arxiv_source observed=2026-08-03T01:45:01.055018Z digest=sha256:795b9333015f40c5d408ae7102575604bb5f1f2da699178f26a4fd644ff2bae0

Observation 26e17e99-5b3f-4bd8-853f-b95ac6e0ab14 · outbound

This paper cites 2023 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2023 , eprint=

Reference 47

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source=arxiv_source observed=2026-08-03T01:45:01.136395Z digest=sha256:cd2eabe3296423df7a26ddfb95c0554ac0f5245f00921b0b8bf68350613ad4d6

Observation 1431b8c6-3064-4867-81d3-f88c40893d84 · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 48

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source=arxiv_source observed=2026-08-03T01:45:01.285774Z digest=sha256:7c615a34594d7c85f9325c414a75233b9409ec63f4de55f7949a8334aab3107c

Observation 94270887-7118-46f7-89a8-40ce505439aa · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2024 , pages=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Findings of the Association for Computational Linguistics: ACL 2024 , pages=

Reference 49

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source=arxiv_source observed=2026-08-03T01:45:01.517397Z digest=sha256:11933c4d39243999712957c40ec3786f460fc020116b22ebc99186434e7b1091

Observation 2aa30d10-5887-4892-b0ed-766b81c6d820 · outbound

This paper cites Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Reference 50

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source=arxiv_source observed=2026-08-03T01:45:01.606462Z digest=sha256:ce931afdae0e39ad159a06d4714d17cb28fd3dcff18c9d3a115ee33f77a269a8

Observation a336ca96-77ab-4413-9980-f84929e71f38 · outbound

This paper cites 2020 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2020 , eprint=

Reference 51

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source=arxiv_source observed=2026-08-03T01:45:01.792709Z digest=sha256:629b7f4a5544c27e96eec2b4a27d5e4fc731bf14fecd38ce7d447bd4d87c5fcc

Observation 9a263b7b-f2c5-40e4-a629-ac3363b195e0 · outbound

This paper cites ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Reference 52

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source=arxiv_source observed=2026-08-03T01:45:01.968047Z digest=sha256:2ac453afe4d85b6fa4e2a28b173f652cc9580de9977acb771e25edf701fe11af

Observation 27e16ef4-100d-4faf-8070-a86d3af21a23 · outbound

This paper cites Efficient Memory Management for Large Language Model Serving with PagedAttention.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Efficient Memory Management for Large Language Model Serving with PagedAttention

Reference 53

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source=arxiv_source observed=2026-08-03T01:45:02.124758Z digest=sha256:2348cad66d15b6301644170a8e832d56ebbf553e771fff26d59d2bb1cc4b8d21

Observation beba015d-9b8a-4253-bc96-0d916d3414bb · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Retrieval-Augmented Generation for Knowledge-Intensive

Reference 54

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source=arxiv_source observed=2026-08-03T01:45:02.287135Z digest=sha256:e180607c9c359495944a512ffbd35e100657529f5299d8362a77ba508ae6f66e

Observation 9fa6afcf-4d27-4cd7-9e0e-9b80dce61699 · outbound

This paper cites Companion Proceedings of the ACM on Web Conference 2025 , pages=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Companion Proceedings of the ACM on Web Conference 2025 , pages=

Reference 55

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source=arxiv_source observed=2026-08-03T01:45:02.477217Z digest=sha256:344a421cd3dfb9133e65f491dc5d367e7506a73ca30efdbf7a57a1e97076e177

Observation 2e3c249d-a813-4bdd-b487-570d15b7e6af · outbound

This paper cites Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 56

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source=arxiv_source observed=2026-08-03T01:45:02.618087Z digest=sha256:073744cd7afdce8f0c33fc6de528253bfb0b246b564fcac64ee2df53bf884a3e

Observation c5a7733e-31d4-4289-b5fe-c4a62d582b69 · outbound

This paper cites GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning

Reference 57

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source=arxiv_source observed=2026-08-03T01:45:02.796376Z digest=sha256:8de3b1f111f1c59cfb18ba9863ca1e5a5941a7fb8af95916f3eeea935f271d9b

Observation 9c6988b3-0ca1-46c3-9423-c83deadd8fa7 · outbound

This paper cites WebGPT: Browser-assisted question-answering with human feedback.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms WebGPT: Browser-assisted question-answering with human feedback

Reference 58

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:02.965743Z digest=sha256:51dd3df42ec0d7ea83be82bcfb2832c61946c77cef5703efa6a8418e9afd10b6

Observation 04f6cc06-34db-4704-bec2-133f68ae655a · outbound

This paper cites 2024 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2024 , eprint=

Reference 59

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no resolver link, observed 2026-08-03T01:45:03.044996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.044996Z digest=sha256:ca1e2eba8a733acb0676e980d1deb5aaf974f0d5a5e70d6bf9572203d8754886

Observation 4ed48455-bc06-4cd9-8adf-e35d424cf386 · outbound

This paper cites 2023 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2023 , eprint=

Reference 60

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no resolver link, observed 2026-08-03T01:45:03.132271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.132271Z digest=sha256:d3ea577442570cde7047fd43b308e1f0d3ea74533e73b670deb73157ec46f8b7

Observation bd3b0046-b4ce-47eb-8b0f-199a41ea5fbe · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 61

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no resolver link, observed 2026-08-03T01:45:03.211963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.211963Z digest=sha256:dab5526327893af4c2cbc26b8a24a1c166ecd5f99d457d00cec06425a20b0d7f

Observation 99577342-0b48-4896-a2f4-8029dd3f7f11 · outbound

This paper cites 2023 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2023 , eprint=

Reference 62

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no resolver link, observed 2026-08-03T01:45:03.270422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.270422Z digest=sha256:eae82e28cf49161c69415eda41ae87e765ff0d0171fcbc671e1bf8c1c251422e

Observation 930dd302-9755-4884-9aff-380472d72cf3 · outbound

This paper cites The Probabilistic Relevance Framework:.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms The Probabilistic Relevance Framework:

Reference 63

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no resolver link, observed 2026-08-03T01:45:03.338510Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T01:45:03.338510Z digest=sha256:40011e630e9a4179e6bbf6df00b41ef4272f5d9b1c0fde7e9650de20fdce29c6

Observation 18f0cfd1-18fc-42e2-8172-ba852a30b32e · outbound

This paper cites 2023 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2023 , eprint=

Reference 64

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no resolver link, observed 2026-08-03T01:45:03.422183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.422183Z digest=sha256:62de0046036ff559b1a2093e3f8f7e06b2d6cffd960f951948f4fbecc30faca9

Observation a14cdc19-793a-4ea7-8172-b9e28814a5bf · outbound

This paper cites 2023 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2023 , eprint=

Reference 65

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no resolver link, observed 2026-08-03T01:45:03.501837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.501837Z digest=sha256:ab4e0b7a60aee306f6a98f81978539139054d7e8f3173e53c2c1ee65f30dbc33

Observation 913ca23f-5184-4540-ba84-7f7b6e2d6829 · outbound

This paper cites Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs

Reference 66

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no resolver link, observed 2026-08-03T01:45:03.669016Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T01:45:03.669016Z digest=sha256:8550d0903d332d52305916d7e96af80fc93d7ee8584f88e89be0cee369f99919

Observation f2454a17-6ebd-4c52-9218-03ca9647f2e3 · outbound

This paper cites EnterpriseRAG-Bench: A RAG Benchmark for Company Internal Knowledge.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms EnterpriseRAG-Bench: A RAG Benchmark for Company Internal Knowledge

Reference 67

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no resolver link, observed 2026-08-03T01:45:03.779093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.779093Z digest=sha256:585f2d12db29283f09a127d16897d6bcfcd828aaddc7b5ae06dd7773fd54c206

Observation ad7cb938-b9ea-4072-b573-87e6f250ee70 · outbound

This paper cites 2510.10114 , archivePrefix=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2510.10114 , archivePrefix=

Reference 68

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no resolver link, observed 2026-08-03T01:45:03.879806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.879806Z digest=sha256:fbd0552b28dd4a8368b0ad70a29983be71acfe436b855a7fccd93c59376f4dfe

Observation 16b3510f-8900-4410-99ce-d357c2035280 · outbound

This paper cites an unresolved cited work.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Unresolved cited work

Reference 69

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no resolver link, observed 2026-08-03T01:45:03.979438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.979438Z digest=sha256:f621181caca94ab49b86c3fe3d3e12ac9b800281406c130b0268cdb1e8319ce1

Observation a6a03da5-f3c1-457f-94a4-4991a02f9bed · outbound

This paper cites OpenHands: An Open Platform for AI Software Developers as Generalist Agents.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms OpenHands: An Open Platform for AI Software Developers as Generalist Agents

Reference 70

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no resolver link, observed 2026-08-03T01:45:04.048262Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T01:45:04.048262Z digest=sha256:7dca685cf706401222c03a0c0569a521f00774e0b8bc44e2512e6d44e18f8e2f

Observation 20290a4a-7acd-4bfd-9520-f6c4330ef833 · outbound

This paper cites CRAG -- Comprehensive RAG Benchmark.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms CRAG -- Comprehensive RAG Benchmark

Reference 71

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no resolver link, observed 2026-08-03T01:45:04.183044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:04.183044Z digest=sha256:346d4de11509a053c76d555eb207455b76e34df2c66013ae0b0dc54e8a6921ef

Observation 9ac065fc-483d-479b-8f56-a64f64a5c9fe · outbound

This paper cites 2024 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2024 , eprint=

Reference 72

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no resolver link, observed 2026-08-03T01:45:04.291943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:04.291943Z digest=sha256:1ece507bff8c7819819f90db2e4c9de0a4820866ebe233a6c2a16f5c8dc8e6c7

Observation b63f34b2-3ba9-485b-ac27-e76125e508ac · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 73

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no resolver link, observed 2026-08-03T01:45:04.402744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:04.402744Z digest=sha256:ea2ed84023d7cd6331fa86c8c97832cb5a209945d8188794a6262665d58cb18b

Observation 88a76be0-ff02-44ff-8a08-cfe084ad1a8f · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms ReAct: Synergizing Reasoning and Acting in Language Models

Reference 74

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no resolver link, observed 2026-08-03T01:45:04.548071Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T01:45:04.548071Z digest=sha256:c6f13986dafabf962dfe9a2bb78fca0ba46d609bc523869c6b21ac6a9d8810ff

Observation f3f974fb-43be-4edb-8934-23dd9c8fe21b · outbound

This paper cites RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Reference 75

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no resolver link, observed 2026-08-03T01:45:04.640986Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:04.640986Z digest=sha256:04d6149dbeb9a77977682ce4513982e426569fe9421a4de6e6349d4fa36d489d

Observation 31ca9ceb-c218-47f3-aac7-708a48b10c97 · outbound

This paper cites 2024 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2024 , eprint=

Reference 76

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no resolver link, observed 2026-08-03T01:45:04.739136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:04.739136Z digest=sha256:80874af0d27fbfea6b187d44443b5fc76e7b4197f1f953c3a81f725584b6ffe5

Observation 174b3f13-d37e-418c-b3d8-8d3a7858c345 · outbound

This paper cites SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

Reference 77

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no resolver link, observed 2026-08-03T01:45:04.911409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:04.911409Z digest=sha256:a7cd0cb78ea5629309113498efc947eae47597cc3c24d0878d248e3f2be0cb71

Observation f6cca988-228a-49aa-afd7-c0e7ad6145ca · outbound

This paper cites 2023 , eprint=.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms 2023 , eprint=

Reference 78

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no resolver link, observed 2026-08-03T01:45:05.061275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:05.061275Z digest=sha256:87ca08ef7346b47d8bead8201371daf34c5d3520dc1a3a5928b4940dd3df1f58

Pith citing papers

No inbound Pith citation observations are available.