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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-10T06:31:04.303077+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:ad0e18a20f7a926d670db5364e4274f86f510edd65da344bdf8437ce6f76b8d0

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:f063a7cecdfe6812d746fe63aba6bff36cb53e9472a28bb9fa607398b595b1de

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=sha256:222650e6c091941789aab789615e54579b04529346bfa5f679c8f7b444e1912c

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=sha256:167960c5048db031694ad51e2d17f6fc48bb038409065fc2b608d623fb526b39

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:c31cfa8569d05dbb7f0f844312d970c657943f3a6b6d69acc9662966702e4673

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=sha256:38079c28f954cb8f07a85bfe409edd6802600dc2173df2de2b8d730b66951ec4

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=sha256:390b02a9e0be14a2313792e511d1e414fc1b2686c323927d4563eb091128de52

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=sha256:347ccac446060684871dea39cb65f98087721e0f5b819678e0c4b60bec9cadae

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:ce25573fd39efaa001c36566c06bf270aa5f5b78a6201067b63172dfc9339d05

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=sha256:579abfbe947050849048a8f72ca0bd7be48adfd6826d44654b22d7bffab6608d

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=sha256:015f0459281a3567a45b2fde68a7200bd45b7c76af71fbe4c71642fca0c351de

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:d81e692426e7bd31093e18dbc8c23d5b46fa14e441e5093eb0dbf163a501d0e7

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=sha256:665bc67741015fc69d25981193471012a57c7105998919ca72e67027ea24247f

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=sha256:32f5aea1fabd6cf993efc05808c4fac131a12c2e6e52fba14f4305c76328a068

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=sha256:31242fcb50f5ca184e7c6e7e2ddfd6c0e18bc2898228805f8a0e3f6743bdaea6

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=sha256:666cdee79ff645320e62e0e391b310710aaff84de42673f300cc5aee0ab68dd9

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:d9b33348b376f82a82bee2e1cc850d894db63624bac7131929489f59a3253d24

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=sha256:7574364d39ed53ffa00ca5f94a7c274bbd7797cf0fb5b25b9f50e870f5309e4f

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=sha256:3efe3a61d9ca95a899b3707cc81fd364099d878f2a7d002bff9551c70bd60050

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=sha256:8ec9b9adb6ac5136ed541b07ebfc6f16fb2b31fc2e3876a1a46e3320c46e3ada

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:a7026ba78e76029e0108133b266bb4f6582d5a641529db7cca658a88d85b381d

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=sha256:8422dd0d7af017dfdbd588376b216c1641036c49eebbce08e71c7a678f2bfc75

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=sha256:3f862c9ef40e53ef89b71fac2eb0890e83559654d2cd124f88538a8ffbefcf1b

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:a15980dc5afdfb19a967e4aaa6774a389229ea53bd9e95b3016e92cd7650c281

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=sha256:0b2598b60915615b85ec257243eeca38f0bdfa444bdf12237e17f946fac1d6cc

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=sha256:04853c1db3c2d0d57d780d69b684fb8f4b2882b13d3d609d648ab86abc20ab33

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=sha256:35d6f90cf5be7795d9fa0f18259668405fe5a95da9ed8d069892fe0958e337d3

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=sha256:0fdb3646c51e9e2c0ff0556e1bac26ea936e63b3de5a12c59a5350c3e9644f0c

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=sha256:422596ae1390445a0b2d782b5d455ebaadbb6e3a1c1fb0050a8e0968d52bc040

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=sha256:1db052b46d0bc4cb52cda240446bc4cbade5b83f9b18c760aa0cc0d279a0e121

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:eced949294237ea50b8888c632fff656ce7dc444986bfa1ad5ea9242c5c1816d

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=sha256:8a8f4630e8127d26fa59d952afa2a600e982ff711d1a20490e9abe791270b662

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=sha256:1b28d114e7294b560487911e5661acb2e0d37410452f135ce05d47544c61bf80

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:d29f3ffcb0b7e19a1e3751e96c4c52631b1200771009b2d3d5fac2e14b089100

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=sha256:1de5a5f3d722122f5387598ab982f41b8b54dafe6a15437bde2d058720e39fb2

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:a2b9696317bfcfde1bff83a326010c1dbc90dff00aaf340ccf50688d8dc8d470

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=arxiv_source observed=2026-08-03T01:45:02.965743Z digest=sha256:e0f44c8e74af2e303ff1eeaf137c7edbb15642f1ea132a100ec11993d58a4348

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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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T01:45:03.044996Z digest=sha256:0727ab0b5137fb7e3b0fba855da0fa9ab5389d29af16402b4247fe0fe5caf3d2

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T01:45:03.132271Z digest=sha256:8a6e6db3752355c4f17d046be4f2f4f71f25f254171e29cdcdf2b511be0baa12

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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source=arxiv_source observed=2026-08-03T01:45:03.211963Z digest=sha256:1662f45ae0a970df470090dc760d916fdae298c878dd74e43ae1916ee0bceaf0

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=sha256:20fceafa3f73883a3195b29b4caeda02f55282a6d661c1ec848185c30ac62104

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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source=arxiv_source observed=2026-08-03T01:45:03.338510Z digest=sha256:90dffb6fe53e51af258ba69e1a5687d970841f8640196244e274ac2bbcc88d93

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=sha256:0342431fc3fb33b04b1f93d6a0dc90faf11512c191049048e31b54eb2198fe58

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

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source=arxiv_source observed=2026-08-03T01:45:03.501837Z digest=sha256:db4a714be0aeba7f555978790a2ade3adb18e4614e79b85702f478181e11d3e6

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=sha256:4db9365e337a513593a76ffe3cde559b9537a0218541c9f895350605c8b69da5

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

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source=arxiv_source observed=2026-08-03T01:45:03.779093Z digest=sha256:5fc234f7e413d8a7bdee4be04588eb0697d7a467614ca1ada8e1f0c8c80a45d0

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

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source=arxiv_source observed=2026-08-03T01:45:03.879806Z digest=sha256:b0b25c1194f7869168e2480e11e04014325e38023919ca283b59c2065f0630fc

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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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T01:45:03.979438Z digest=sha256:4a2b6afc87f9363f91fd88e8951809ad9e984f9b6d55179412e701f989dfa4cd

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

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source=arxiv_source observed=2026-08-03T01:45:04.048262Z digest=sha256:ee0261e7fc4657c9a924e05ca0f38754bcf5879dde5fb41119ca53be70e512fd

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

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source=arxiv_source observed=2026-08-03T01:45:04.183044Z digest=sha256:5b815941d9fcbef4e2a576a48b7d2500ac6b703c9bf1c8d56d32c702d9cde06e

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=sha256:3f53354abc2c981e6cc16dd66583ea3d561108a2fb5d49649ff4a12ad2c3f1ef

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

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source=arxiv_source observed=2026-08-03T01:45:04.402744Z digest=sha256:1f38b543e7b75a27d9d94317d0fe293a39b7f7bbd31daf89cdff6a237bc4a11f

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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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T01:45:04.548071Z digest=sha256:b60f5580add89e16e5ef8e12625b463ed9d851b8630f353b202a40cad66e9c0d

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

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source=arxiv_source observed=2026-08-03T01:45:04.640986Z digest=sha256:beef022fa85535758efeddbc2d8e9ed4d7133693abec9417e1aa66a683498ef0

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

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source=arxiv_source observed=2026-08-03T01:45:04.739136Z digest=sha256:734cccbaa5809fe87f8ad279d8089778826ad3726b7801d2723291317bdfd622

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

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source=arxiv_source observed=2026-08-03T01:45:04.911409Z digest=sha256:b0cf3d1f5c8a6fb26ed4d6fe0a250021ce6505950672115034ac863782d1282c

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:d590adb8ede964d449e742039e708bd4931b05dd9f39cf87dc2751184df0b14a

Pith citing papers

No inbound Pith citation observations are available.