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Paper Citation Record · LEDGER

Benchmarking the Personalization Capabilities of Large Language Models

As of 8 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2607.20471.

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

pith.paper-citation-record.v1
2607.20471 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:17:11.362573Z

measured 59 of 59 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

59 of 59 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved57
  • parse uncertain1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8cfa92e6-85bf-449c-9728-b90ed636a02d · outbound

This paper cites 2025 , eprint=.

Benchmarking the Personalization Capabilities of Large Language Models 2025 , eprint=

Reference 1

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Observation 00e3b28b-b348-4c2f-b205-bfe2c342000a · outbound

This paper cites 2011 , publisher=.

Benchmarking the Personalization Capabilities of Large Language Models 2011 , publisher=

Reference 2

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source=arxiv_source observed=2026-08-02T13:17:02.237343Z digest=sha256:b45f07882e8af27b341baab36fa6603d8aeb6372941be9cdb516d90d02eae0d8

Observation 3fbdf8fd-0202-4ff0-ba90-eb6821e8750d · outbound

This paper cites 1953 , publisher=.

Benchmarking the Personalization Capabilities of Large Language Models 1953 , publisher=

Reference 3

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Observation f7f2c513-4e80-42e5-858f-3368b55e0f0a · outbound

This paper cites 1990 , organization =.

Benchmarking the Personalization Capabilities of Large Language Models 1990 , organization =

Reference 4

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Observation 78126114-298e-46ce-832c-274004612e51 · outbound

This paper cites Toward a contextualized understanding of inside sales: the role of sales development in effective lead funnel management , volume =.

Benchmarking the Personalization Capabilities of Large Language Models Toward a contextualized understanding of inside sales: the role of sales development in effective lead funnel management , volume =

Reference 5

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Observation f3a5dbb2-5b8d-463e-af1c-65c3c8cf8e63 · outbound

This paper cites The communication of ideas , volume=.

Benchmarking the Personalization Capabilities of Large Language Models The communication of ideas , volume=

Reference 6

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Observation baa0bea5-3c77-4fd5-8b52-b44491427348 · outbound

This paper cites Annual Review of Psychology , volume=.

Benchmarking the Personalization Capabilities of Large Language Models Annual Review of Psychology , volume=

Reference 8

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Observation c20d6bb5-2f87-425d-ba93-e564dcc31f38 · outbound

This paper cites , address =.

Benchmarking the Personalization Capabilities of Large Language Models , address =

Reference 9

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Observation 748fb4f2-204c-43fd-b252-581792eda4cf · outbound

This paper cites Recommender Systems Handbook , volume =.

Benchmarking the Personalization Capabilities of Large Language Models Recommender Systems Handbook , volume =

Reference 10

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Observation 6ba7d2c2-43d1-48e7-a414-7fc1864180d7 · outbound

This paper cites Journal of marketing , volume=.

Benchmarking the Personalization Capabilities of Large Language Models Journal of marketing , volume=

Reference 11

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Observation 135e2439-4911-44d8-b745-795d3a3e652d · outbound

This paper cites Psychology & Marketing , volume=.

Benchmarking the Personalization Capabilities of Large Language Models Psychology & Marketing , volume=

Reference 12

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Observation bf9c634e-da6c-4a58-9589-8cdd3f2d36b4 · outbound

This paper cites Online Display Advertising Markets: A Literature Review and Future Directions , volume =.

Benchmarking the Personalization Capabilities of Large Language Models Online Display Advertising Markets: A Literature Review and Future Directions , volume =

Reference 13

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Observation 28945a68-5edb-4845-aa9e-d94003fbefc0 · outbound

This paper cites Personalized Dialogue Generation with Persona-Adaptive Attention , volume =.

Benchmarking the Personalization Capabilities of Large Language Models Personalized Dialogue Generation with Persona-Adaptive Attention , volume =

Reference 14

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Observation 3758077c-c000-4028-8b30-cbb20d4fc03c · outbound

This paper cites Recommender systems handbook , pages=.

Benchmarking the Personalization Capabilities of Large Language Models Recommender systems handbook , pages=

Reference 15

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Observation 0b68a94c-99c4-4166-8a13-e0c628ddb8de · outbound

This paper cites Artificial intelligence in marketing , pages=.

Benchmarking the Personalization Capabilities of Large Language Models Artificial intelligence in marketing , pages=

Reference 16

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Observation 03ce5ca4-86e2-4d55-93cf-ea9bfcdf03e6 · outbound

This paper cites Discover Sustainability , volume=.

Benchmarking the Personalization Capabilities of Large Language Models Discover Sustainability , volume=

Reference 17

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Observation 22c93428-b740-40a0-abdc-b5691baa1900 · outbound

This paper cites International Journal of Artificial Intelligence in Education , pages=.

Benchmarking the Personalization Capabilities of Large Language Models International Journal of Artificial Intelligence in Education , pages=

Reference 18

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Observation 4b1d2022-f174-4dbc-8235-5c03a84f4e8e · outbound

This paper cites arXiv preprint arXiv:2512.03373 , year=.

Benchmarking the Personalization Capabilities of Large Language Models arXiv preprint arXiv:2512.03373 , year=

Reference 19

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Observation 1db820b7-f34a-4d9a-adfe-58e26bfbdf45 · outbound

This paper cites World Wide Web , volume=.

Benchmarking the Personalization Capabilities of Large Language Models World Wide Web , volume=

Reference 20

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Observation f110fe67-b506-4ac2-baed-6711fa3d8cbe · outbound

This paper cites Nature Machine Intelligence , volume=.

Benchmarking the Personalization Capabilities of Large Language Models Nature Machine Intelligence , volume=

Reference 21

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Observation 0062bf09-cc83-41c2-ab75-8e6dd73b8e30 · outbound

This paper cites American Economic Review , volume=.

Benchmarking the Personalization Capabilities of Large Language Models American Economic Review , volume=

Reference 22

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Observation 60b4d486-c1a8-41a1-a365-03771f2f92ad · outbound

This paper cites GitHub Repository , howpublished =.

Benchmarking the Personalization Capabilities of Large Language Models GitHub Repository , howpublished =

Reference 23

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Observation aa770357-9125-4dbc-91ed-d5a00e194699 · outbound

This paper cites Qwen Technical Report.

Benchmarking the Personalization Capabilities of Large Language Models Qwen Technical Report

Reference 24

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Observation ebe98cbd-d7f6-4f65-b28b-d8b08cc1b545 · outbound

This paper cites Assisting in Writing W ikipedia-like Articles From Scratch with Large Language Models.

Benchmarking the Personalization Capabilities of Large Language Models Assisting in Writing W ikipedia-like Articles From Scratch with Large Language Models

Reference 25

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Observation 4b523a8e-cd1d-43f1-968a-55f3675e9fcc · outbound

This paper cites and Peters, Heinrich and Harari, Gabriella and Cerf, M.

Benchmarking the Personalization Capabilities of Large Language Models and Peters, Heinrich and Harari, Gabriella and Cerf, M

Reference 27

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Observation aac06ac3-d49a-4b66-8cdf-90d879ffbfc6 · outbound

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Benchmarking the Personalization Capabilities of Large Language Models Recuperado el , volume=

Reference 28

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Observation 16a6a9e3-b3dc-47e0-b289-e9d7b860775e · outbound

This paper cites Scientific Reports , volume=.

Benchmarking the Personalization Capabilities of Large Language Models Scientific Reports , volume=

Reference 29

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Observation c29d239d-7157-437f-a878-cae804432262 · outbound

This paper cites When large language models meet personalization: perspectives of challenges and opportunities , volume=.

Benchmarking the Personalization Capabilities of Large Language Models When large language models meet personalization: perspectives of challenges and opportunities , volume=

Reference 30

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Observation b9f18820-4b95-40cb-8af1-5958b2d16686 · outbound

This paper cites an unresolved cited work.

Benchmarking the Personalization Capabilities of Large Language Models Unresolved cited work

Reference 31

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Observation 42c6baa4-18fe-4290-b2b3-fd695c41879c · outbound

This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

Benchmarking the Personalization Capabilities of Large Language Models Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 32

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Observation 63ded312-2a7c-48b5-9049-a2926036a099 · outbound

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Benchmarking the Personalization Capabilities of Large Language Models Findings of the Association for Computational Linguistics: ACL 2025 , pages=

Reference 37

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Benchmarking the Personalization Capabilities of Large Language Models 2024 , date =

Reference 38

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Benchmarking the Personalization Capabilities of Large Language Models 2025 , howpublished =

Reference 39

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Observation c9abafcf-77bc-4b7b-aa4f-bed0765a8c78 · outbound

This paper cites an unresolved cited work.

Benchmarking the Personalization Capabilities of Large Language Models Unresolved cited work

Reference 42

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Observation 9e97dc73-7cb9-4ae1-bb7a-3ede2fbbb2ed · outbound

This paper cites an unresolved cited work.

Benchmarking the Personalization Capabilities of Large Language Models Unresolved cited work

Reference 43

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Observation 3718f2a2-0ae9-4ec4-a253-397f85d0e42a · outbound

This paper cites Persobench: Benchmarking personalized response generation in large language models.

Benchmarking the Personalization Capabilities of Large Language Models Persobench: Benchmarking personalized response generation in large language models

Reference 44

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Observation 6217d196-30ee-4c45-b7d8-b77bd6cf8419 · outbound

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Benchmarking the Personalization Capabilities of Large Language Models Claude sonnet 4.6

Reference 45

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Observation 94e28fb5-a812-44f9-9ccc-cf7fd6fd3c68 · outbound

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Benchmarking the Personalization Capabilities of Large Language Models Bright data serp api, 2026

Reference 46

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Observation a7305197-89ec-4fd4-843a-4fc5f8a7abcc · outbound

This paper cites When large language models meet personalization: Perspectives of challenges and opportunities.

Benchmarking the Personalization Capabilities of Large Language Models When large language models meet personalization: Perspectives of challenges and opportunities

Reference 47

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Observation 916d6e92-4e80-466f-8c3c-f083cfb7327b · outbound

This paper cites Online display advertising markets: A literature review and future directions.

Benchmarking the Personalization Capabilities of Large Language Models Online display advertising markets: A literature review and future directions

Reference 48

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source=arxiv_source observed=2026-08-02T13:17:08.788035Z digest=sha256:af7a38e996c18ee3741f6ead1c466cabb26a0ed71c79941ac046f120c833a8c9

Observation bb9693a7-0b5c-4794-b555-07da4e2721b7 · outbound

This paper cites Measuring the persuasiveness of language models, 2024.

Benchmarking the Personalization Capabilities of Large Language Models Measuring the persuasiveness of language models, 2024

Reference 49

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source=arxiv_source observed=2026-08-02T13:17:09.016203Z digest=sha256:068bcdaafcabdbb6aa59864698229bab859c89c1a06c2965635fb14f16a0f89f

Observation 0ffcb66b-eb61-437a-bf62-0f470b30810e · outbound

This paper cites Hovland, I.L.

Benchmarking the Personalization Capabilities of Large Language Models Hovland, I.L

Reference 50

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no resolver link, observed 2026-08-02T13:17:09.223699Z

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source=arxiv_source observed=2026-08-02T13:17:09.223699Z digest=sha256:c6cb49342d9c8d79738783fafe3efecc7479f605be413699a8ff660f350d1136

Observation 82e45193-30be-4000-9024-8b06bf31ea5d · outbound

This paper cites Personamem-v2: Towards personalized intelligence via learning implicit user personas and agentic memory.

Benchmarking the Personalization Capabilities of Large Language Models Personamem-v2: Towards personalized intelligence via learning implicit user personas and agentic memory

Reference 51

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no resolver link, observed 2026-08-02T13:17:09.421926Z

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source=arxiv_source observed=2026-08-02T13:17:09.421926Z digest=sha256:acbacbb069928bb50c45e5956f50bb4c783f8badf42c5953103ed89e961da437

Observation 463fbc9d-b102-471d-a329-48084d295654 · outbound

This paper cites Bayesian persuasion.

Benchmarking the Personalization Capabilities of Large Language Models Bayesian persuasion

Reference 52

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no resolver link, observed 2026-08-02T13:17:09.554358Z

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source=arxiv_source observed=2026-08-02T13:17:09.554358Z digest=sha256:b2ac0be53ad587e4dd71d8a581b85c899500c3e9945e921601570db889b7ba78

Observation a7a34b1f-25b7-482d-b02a-9927b4521423 · outbound

This paper cites Open deep research.

Benchmarking the Personalization Capabilities of Large Language Models Open deep research

Reference 53

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no resolver link, observed 2026-08-02T13:17:09.684044Z

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source=arxiv_source observed=2026-08-02T13:17:09.684044Z digest=sha256:2438f2e1f5b942a7554d2cd0a5efa0d2e7a72f1adb2ce37048555c2b2c763d55

Observation 39d35ae4-f23a-47b8-9d7d-f6522e4f77b4 · outbound

This paper cites The structure and function of communication in society.

Benchmarking the Personalization Capabilities of Large Language Models The structure and function of communication in society

Reference 54

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no resolver link, observed 2026-08-02T13:17:09.822133Z

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source=arxiv_source observed=2026-08-02T13:17:09.822133Z digest=sha256:e3784e9cc1984e1487671aef4e180b89b8fa5bb9e8cee04f816940f477a9f170

Observation b7a42867-3b8e-4f87-b3a1-afb6ee7d0ab8 · outbound

This paper cites A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations.

Benchmarking the Personalization Capabilities of Large Language Models A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations

Reference 55

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no resolver link, observed 2026-08-02T13:17:09.892437Z

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source=arxiv_source observed=2026-08-02T13:17:09.892437Z digest=sha256:993f30f842079b7f66526840d1e36d2d2b7450dd92130fbc8661179666fdad09

Observation 1d066889-8be4-4195-8c33-8a53a6c1be2a · outbound

This paper cites Vaid, Heinrich Peters, Gabriella Harari, and M.

Benchmarking the Personalization Capabilities of Large Language Models Vaid, Heinrich Peters, Gabriella Harari, and M

Reference 56

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no resolver link, observed 2026-08-02T13:17:09.982324Z

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source=arxiv_source observed=2026-08-02T13:17:09.982324Z digest=sha256:4afcc7e11c0511045f61529d38f1ae4b6f35b5a1398dd6bd0f52279ee586dd81

Observation 9642e141-34a1-4345-a8ae-5f435eec67dd · outbound

This paper cites GPT-4 Technical Report.

Benchmarking the Personalization Capabilities of Large Language Models GPT-4 Technical Report

Reference 57

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source=arxiv_source observed=2026-08-02T13:17:10.072952Z digest=sha256:bfdc6f189860ef316b6582b34ed769895710d3457b521527b7963417b81952ab

Observation c1479c7a-dbad-4b86-94fc-0513e1d4b6d1 · outbound

This paper cites Petty and John T.

Benchmarking the Personalization Capabilities of Large Language Models Petty and John T

Reference 58

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no resolver link, observed 2026-08-02T13:17:10.167186Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-02T13:17:10.167186Z digest=sha256:e33aa6ffda9eebce01dec5a2f0edcc5252c28241ec2cc700673b89b71939099a

Observation 8b248729-dfc4-43b7-a2c6-2bea768df217 · outbound

This paper cites Introduction to recommender systems handbook.

Benchmarking the Personalization Capabilities of Large Language Models Introduction to recommender systems handbook

Reference 59

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no resolver link, observed 2026-08-02T13:17:10.226954Z

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source=arxiv_source observed=2026-08-02T13:17:10.226954Z digest=sha256:6ff6238b2278ccbdf218f17d1aa498994d4c665a17a04231aca68b16398f11f3

Observation 68e24bc9-51f5-402b-a31f-fc9eb6150756 · outbound

This paper cites Lamp: When large language models meet personalization.

Benchmarking the Personalization Capabilities of Large Language Models Lamp: When large language models meet personalization

Reference 60

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no resolver link, observed 2026-08-02T13:17:10.281755Z

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source=arxiv_source observed=2026-08-02T13:17:10.281755Z digest=sha256:94c05b8315c4fd572e42088d2fb9bead0c62a3e18e146096d00164ad4eff8366

Observation 16d608cf-0df5-45a8-a134-9408620b3bc7 · outbound

This paper cites Assisting in writing W ikipedia-like articles from scratch with large language models.

Benchmarking the Personalization Capabilities of Large Language Models Assisting in writing W ikipedia-like articles from scratch with large language models

Reference 61

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no resolver link, observed 2026-08-02T13:17:10.408634Z

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source=arxiv_source observed=2026-08-02T13:17:10.408634Z digest=sha256:c33167c7e3d8f7c34251e967ba3362f59f92280e2e7d822251c4e526121e78ba

Observation b534f86a-9a2e-4b32-82e8-efd55aba5408 · outbound

This paper cites The role of large language models in personalized learning: a systematic review of educational impact.

Benchmarking the Personalization Capabilities of Large Language Models The role of large language models in personalized learning: a systematic review of educational impact

Reference 62

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no resolver link, observed 2026-08-02T13:17:10.495864Z

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source=arxiv_source observed=2026-08-02T13:17:10.495864Z digest=sha256:abc046d58d367c0aa81372681291fdae895cb4fbbf49746494ed413082e2ac20

Observation 94399c8b-7033-430f-923a-4bd487afa3b8 · outbound

This paper cites Generative ai in the classroom: effects of context-personalized learning material and tasks on motivation and performance.

Benchmarking the Personalization Capabilities of Large Language Models Generative ai in the classroom: effects of context-personalized learning material and tasks on motivation and performance

Reference 63

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no resolver link, observed 2026-08-02T13:17:10.634387Z

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source=arxiv_source observed=2026-08-02T13:17:10.634387Z digest=sha256:9286723dec4db57deed3b9d111d465f68c86aa29de960ff999f491f7cd340d0e

Observation 1d3fd1be-de71-4874-bc41-1a5cdc1b9e0b · outbound

This paper cites Toward a contextualized understanding of inside sales: the role of sales development in effective lead funnel management.

Benchmarking the Personalization Capabilities of Large Language Models Toward a contextualized understanding of inside sales: the role of sales development in effective lead funnel management

Reference 65

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verified exact
doi, observed 2026-08-02T13:18:22.206278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-02T13:17:10.920233Z digest=sha256:f718f0ec57183538475dab65d00e3e9ffb953cf1ba89f23c527485e66bdabbc0

Observation 7c7c37ad-bbf6-4557-8c67-a7cf229df309 · outbound

This paper cites Qwen2.5 Technical Report.

Benchmarking the Personalization Capabilities of Large Language Models Qwen2.5 Technical Report

Reference 66

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no resolver link, observed 2026-08-02T13:17:11.018880Z

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source=arxiv_source observed=2026-08-02T13:17:11.018880Z digest=sha256:7b0efaf9cc50bb101d6989ee0b072e8935685bed0a3d9c2cc155ccf816514976

Observation a5adc424-366a-4000-a3db-d5470bd59fa6 · outbound

This paper cites an unresolved cited work.

Benchmarking the Personalization Capabilities of Large Language Models Unresolved cited work

Reference 67

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no resolver link, observed 2026-08-02T13:17:11.186148Z

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source=arxiv_source observed=2026-08-02T13:17:11.186148Z digest=sha256:5505adeb6dee0db0953f6190be4bfecf21e1375eb05936c00bd66aeba523873e

Observation 5fbb63b1-36dc-47ee-a619-bf5fb577e908 · outbound

This paper cites Personalens: A benchmark for personalization evaluation in conversational ai assistants.

Benchmarking the Personalization Capabilities of Large Language Models Personalens: A benchmark for personalization evaluation in conversational ai assistants

Reference 68

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no resolver link, observed 2026-08-02T13:17:11.362573Z

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source=arxiv_source observed=2026-08-02T13:17:11.362573Z digest=sha256:e43b0315a6c91ea25a3f155aca3aefde9e12f9c0ef07db5f320352390864acc6

Pith citing papers

No inbound Pith citation observations are available.