Paper Citation Record · LEDGER

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers

As of 5 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.25346.

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

pith.paper-citation-record.v1
2607.25346 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:33:20.857613Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

50 of 50 outbound references displayed

verified exact0
verified fuzzy0
unresolved50
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d364b808-47ab-4e61-bb34-15891fd1d12a · outbound

This paper cites World Wide Web , volume =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers World Wide Web , volume =

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:16.668231Z

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Observation 0c415365-ec13-43fb-9075-cc4c02800c11 · outbound

This paper cites ACM Transactions on Information Systems , volume =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers ACM Transactions on Information Systems , volume =

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:16.736423Z

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Observation 436f06c6-882b-43b3-96c4-18b65606c340 · outbound

This paper cites Proceedings of the 16th ACM Conference on Recommender Systems , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 16th ACM Conference on Recommender Systems , series =

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:16.816581Z

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Observation f6e013f9-28cf-405d-b98c-d91fc5e4c635 · outbound

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

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Advances in Neural Information Processing Systems , volume =

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:16.908624Z

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Observation eebc892a-5907-433d-8ad5-92753123730f · outbound

This paper cites Advances in Information Retrieval , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Advances in Information Retrieval , series =

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:16.980350Z

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Observation af00fbcf-e495-4231-86e2-682be7b72cfa · outbound

This paper cites Proceedings of the 17th ACM Conference on Recommender Systems , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 17th ACM Conference on Recommender Systems , series =

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.051551Z

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Observation 00643b51-80f3-490d-bc43-71fea2171530 · outbound

This paper cites Proceedings of the 22nd ACM International Conference on Information & Knowledge Management , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 22nd ACM International Conference on Information & Knowledge Management , series =

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.087125Z

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Observation 3fa37efb-19f9-45d5-8e6f-c1b505638bb5 · outbound

This paper cites Proceedings of the 10th ACM Conference on Recommender Systems , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 10th ACM Conference on Recommender Systems , series =

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.141934Z

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Observation 6cbc7919-90bf-4a67-9c25-f0627790d4de · outbound

This paper cites Sentence-.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Sentence-

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.252112Z

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Observation 9402ea63-1451-4c35-baeb-1dcea91175ac · outbound

This paper cites Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing , series =

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.341819Z

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Observation 73451494-d094-4b2d-a780-0800f3d4737c · outbound

This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics , series =

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.385120Z

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Observation adeffcb3-9ce0-40f1-8f9b-f1635dc1f566 · outbound

This paper cites 2025 , url =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2025 , url =

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.449307Z

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Observation bc135b12-51c5-425c-a1c4-e40536df1f46 · outbound

This paper cites Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , series =

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.527664Z

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Observation 004f36c2-e673-4c97-90ff-4674410b9ce1 · outbound

This paper cites Proceedings of the ACM Web Conference 2023 , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the ACM Web Conference 2023 , series =

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.642652Z

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Observation bcfb89a4-40db-4476-87dc-4d0e0b655ea1 · outbound

This paper cites 2018 IEEE International Conference on Data Mining , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2018 IEEE International Conference on Data Mining , series =

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.717691Z

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Observation 632cafba-1fbe-4ef5-95a8-a75e422528ed · outbound

This paper cites Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , series =

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.720343Z

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Observation f4985f41-87f6-44f5-928a-2ad8e5a977ae · outbound

This paper cites 2019 , publisher =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2019 , publisher =

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.725724Z

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Observation deb69fb6-6cba-4ee7-9db7-f67ec19cc828 · outbound

This paper cites 4th International Conference on Learning Representations , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 4th International Conference on Learning Representations , series =

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.816002Z

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Observation 5130586f-91e6-43d8-8afe-4c5ebe39402b · outbound

This paper cites Passage Re-ranking with.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Passage Re-ranking with

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:17.932319Z

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Observation 5adbec5f-8fea-4b61-bff3-fe185e34dba8 · outbound

This paper cites 2020 , publisher =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2020 , publisher =

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.100669Z

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Observation 7271b93a-4c31-40a3-8db5-2cf6ba30958b · outbound

This paper cites Proceedings of the 39th International Conference on Machine Learning , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 39th International Conference on Machine Learning , series =

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.251823Z

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Observation f77da433-617d-44dd-b8f4-a88e3e4ae52b · outbound

This paper cites 2022 , doi =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2022 , doi =

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.408652Z

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Observation 9c204b19-bf29-4f96-89aa-742eb2d63eae · outbound

This paper cites 2024 , publisher =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2024 , publisher =

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.536094Z

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Observation 4cffb5d4-30c3-415a-a398-f477f0bcbb34 · outbound

This paper cites Uncovering.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Uncovering

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.572507Z

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Observation 0b05e943-0c1b-4bb1-bd46-11460e8f7673 · outbound

This paper cites 2024 , publisher =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2024 , publisher =

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.603794Z

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Observation 0a2b3c60-cf54-4f52-8ec6-19a3d45154c1 · outbound

This paper cites Computer , volume =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Computer , volume =

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.744598Z

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Observation 490e67b7-3971-4eb6-a1fb-8a4593aca35a · outbound

This paper cites 2009 , publisher =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2009 , publisher =

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.851537Z

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Observation 59e0cc04-d633-4263-9a41-d6d411ac8fd4 · outbound

This paper cites Proceedings of the 26th International Conference on World Wide Web , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 26th International Conference on World Wide Web , series =

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:18.956403Z

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Observation fb2fa104-0596-4c58-ae87-7a91cd864a05 · outbound

This paper cites Proceedings of the 13th ACM Conference on Recommender Systems , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 13th ACM Conference on Recommender Systems , series =

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.069097Z

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Observation efff8e56-45e9-4a0b-821c-8dfb673814a5 · outbound

This paper cites Companion Proceedings of the Web Conference 2020 , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Companion Proceedings of the Web Conference 2020 , series =

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.157251Z

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Observation 872e1824-d229-4dad-882b-f6d4a09aa627 · outbound

This paper cites Cross-Batch Negative Sampling for Training Two-Tower Recommenders.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Cross-Batch Negative Sampling for Training Two-Tower Recommenders

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.237845Z

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Observation da5b8cbc-5f6e-4d04-9bf3-25fcd592a668 · outbound

This paper cites 2022 , publisher =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2022 , publisher =

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.278355Z

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Observation f1f3f325-6358-4a5b-8e3a-4adee1f1c3dd · outbound

This paper cites NIPS Deep Learning and Representation Learning Workshop , year =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers NIPS Deep Learning and Representation Learning Workshop , year =

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.390481Z

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Observation cd843cdb-6afa-4a69-a323-de1c8c3b4229 · outbound

This paper cites 2025 , doi =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2025 , doi =

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.500763Z

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Observation 0c410654-93fe-473a-bfa1-30e30a2576cb · outbound

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

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Advances in Neural Information Processing Systems , volume =

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.617919Z

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Observation 1cea7808-d557-40a9-b267-18c14530c031 · outbound

This paper cites 2019 , publisher =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2019 , publisher =

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.695234Z

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Observation fccdd987-1713-4483-9616-04f96c408410 · outbound

This paper cites 2024 , url =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2024 , url =

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.767831Z

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Observation 6f6f73b3-0b70-4ae9-a97e-a4f71b64240c · outbound

This paper cites Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.872233Z

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Observation 550ea79d-b404-46d6-8c64-dd5b713d1573 · outbound

This paper cites Proceedings of the 25th International Conference on World Wide Web , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 25th International Conference on World Wide Web , series =

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:19.966369Z

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Observation 166ddfdb-0b07-46bd-a21c-f108c2a73401 · outbound

This paper cites Qwen3 Technical Report.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Qwen3 Technical Report

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.064819Z

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Observation 42d7f60e-d413-4c44-99a2-1e17aee79b42 · outbound

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The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Training Large Language Models to Reason in a Continuous Latent Space

Reference 41

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Observation f18ce1f2-db21-4f19-b78f-2cb09cb6aaa3 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , series =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Proceedings of the 41st International Conference on Machine Learning , series =

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.213927Z

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Observation c62eeab5-1295-46fc-b238-afdca2d3c7be · outbound

This paper cites Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.279167Z

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Observation 78cd0b21-3653-4389-a371-54253d7aba74 · outbound

This paper cites 2026 , address =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2026 , address =

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.363166Z

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Observation a190c546-22a3-41c3-8df1-11b70aaf1bec · outbound

This paper cites 2025 , doi =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2025 , doi =

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.425975Z

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Observation 0ffc99b0-d663-498c-a341-f78958f5ddee · outbound

This paper cites 2025 , doi =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2025 , doi =

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.534716Z

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Observation cf035122-bff1-4a39-84df-034899906598 · outbound

This paper cites 2025 , doi =.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers 2025 , doi =

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.636897Z

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Observation 636e30c4-1433-4db5-a29c-f8e4021c1a12 · outbound

This paper cites Finite Scalar Quantization:.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Finite Scalar Quantization:

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.669641Z

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Observation 0008cafd-78f8-4d09-9177-c790772c3c6b · outbound

This paper cites an unresolved cited work.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Unresolved cited work

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.770266Z

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Observation 9d3ca4f6-11e1-4e44-a566-18ba819a335c · outbound

This paper cites Deep Learning Recommendation Model for Personalization and Recommendation Systems.

The Case Against Generation for Retrieval: Discriminative Language Models as Effective Retrievers Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T01:33:20.857613Z

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Pith citing papers

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