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Paper Citation Record · LEDGER

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.27680.

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

pith.paper-citation-record.v1
2607.27680 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T03:33:55.002375Z

measured 34 of 34 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

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation 3c867719-47df-4e69-881e-fc7c5ee64c29 · outbound

This paper cites Annals of Statistics 33(4):1497--1537.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Annals of Statistics 33(4):1497--1537

Reference 1

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source=arxiv_source observed=2026-08-01T03:33:52.785671Z digest=sha256:cbfb0a57e2abefc0bd945d61995c921f1da6170ce645e1f8186d054debed8e37

Observation e394ea98-282d-4536-9ce1-1d620055ece2 · outbound

This paper cites Machine Learning 79(1--2):151--175.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Machine Learning 79(1--2):151--175

Reference 2

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source=arxiv_source observed=2026-08-01T03:33:52.828806Z digest=sha256:3a7b2985c58ee65cf22b5f6e710615850d29a445bd08d93c6afd5285a2ae1d1d

Observation 3306739a-2a5e-49a2-8861-991ce95aad55 · outbound

This paper cites Transactions on Machine Learning Research.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Transactions on Machine Learning Research

Reference 3

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source=arxiv_source observed=2026-08-01T03:33:52.885577Z digest=sha256:0aea392eb4d296e91d814b8545306e5f0b12d8ff255beddf9cb9cb589b1c6053

Observation 832ca43c-13f1-4621-90e8-bdccbdc215fc · outbound

This paper cites IEEE Transactions on Information Theory 56(5):2053--2080.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection IEEE Transactions on Information Theory 56(5):2053--2080

Reference 4

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source=arxiv_source observed=2026-08-01T03:33:52.927261Z digest=sha256:1afbc70f761c742e7ce6e288776224c834630e5ea169b2e73f404e1110dea00e

Observation c9c00217-1068-424c-90a8-37f48542d5b9 · outbound

This paper cites Wiley-Interscience.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Wiley-Interscience

Reference 5

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source=arxiv_source observed=2026-08-01T03:33:52.990799Z digest=sha256:a222c82845d440d7d76c72334116887a34de92b5f3cab633ffc89ffe409cd165

Observation 3cfe07da-f526-4a4a-8de9-a8d01001520d · outbound

This paper cites Advances in Neural Information Processing Systems.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Advances in Neural Information Processing Systems

Reference 6

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source=arxiv_source observed=2026-08-01T03:33:53.048974Z digest=sha256:493d0966e9f4efaa0da518e3e2a1dbbdc8223ee6f0107fb14faef572153ee5a3

Observation 40ac8e6d-34d5-4eff-b7bd-44214ad99133 · outbound

This paper cites Johns Hopkins University Press.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Johns Hopkins University Press

Reference 7

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source=arxiv_source observed=2026-08-01T03:33:53.122863Z digest=sha256:2c1329d1849b58b2b4600e197daa87cf4b225e72a68e175235b7dd9b6b0c1d93

Observation 8156f7d0-4e5a-4009-9016-2f4875558be2 · outbound

This paper cites In: International Conference on Machine Learning (ICML).

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection In: International Conference on Machine Learning (ICML)

Reference 8

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source=arxiv_source observed=2026-08-01T03:33:53.169759Z digest=sha256:4dda80a23a48782b46d465a39b6f348d1a43d732d70b02fd92ffbcf636735da7

Observation d6961243-a1d4-4751-99a9-5207d96ae1bb · outbound

This paper cites arXiv preprint arXiv:240212354.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection arXiv preprint arXiv:240212354

Reference 9

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source=arxiv_source observed=2026-08-01T03:33:53.217121Z digest=sha256:8f10c8344bba56258c801614a33d928cbe259d35d21bbf4a1846abf58c9d2eeb

Observation 76249672-9a14-441d-a381-00fb4fdfde3a · outbound

This paper cites In: International Conference on Machine Learning (ICML).

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection In: International Conference on Machine Learning (ICML)

Reference 10

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source=arxiv_source observed=2026-08-01T03:33:53.284378Z digest=sha256:28638a2fed1f3f49614218aa24abab3352e4108b6c77507ec23e3fafc5d84037

Observation 7308cd61-3e9f-4110-9341-308e34e151e1 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

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source=arxiv_source observed=2026-08-01T03:33:53.354479Z digest=sha256:85c14514b4ff4d75255d26994239f1cea1f63c1a8f284e6e0984cc850ea31665

Observation 1031c9ba-c64b-42fd-941c-19597e794a24 · outbound

This paper cites arXiv preprint arXiv:240211867.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection arXiv preprint arXiv:240211867

Reference 12

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source=arxiv_source observed=2026-08-01T03:33:53.417093Z digest=sha256:930c0b38344d578108141bd20507bf31299dfad33a3c3e7302954b20640cea64

Observation 61008c5c-8c04-4ee5-b0d2-00063748b55e · outbound

This paper cites arXiv preprint arXiv:250614530.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection arXiv preprint arXiv:250614530

Reference 13

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source=arxiv_source observed=2026-08-01T03:33:53.481398Z digest=sha256:096d2f761cc7de4f4c29e01be161cbfbaa7f7146309ea80c6729aeeb5bec963e

Observation 38f5caac-727f-48a7-bfc8-b6412c615ce4 · outbound

This paper cites \'E cole d' \'E t \'e de Probabilit \'e s de Saint-Flour.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection \'E cole d' \'E t \'e de Probabilit \'e s de Saint-Flour

Reference 14

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source=arxiv_source observed=2026-08-01T03:33:53.557961Z digest=sha256:fef26b77454759efe647ee03cf759408adb86d80a3c4b1213e3ab0d024221371

Observation a9afcf30-147d-4eff-84ca-6cba31263b8d · outbound

This paper cites Annals of Statistics 39(5):2302--2329.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Annals of Statistics 39(5):2302--2329

Reference 15

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source=arxiv_source observed=2026-08-01T03:33:53.639843Z digest=sha256:f43723eb2c69db86503e8f1f370b8c409890fb8f2bf8e7a78a5859bcff49b9f3

Observation 924f36fb-7c42-44e9-93d9-dbe4fc392724 · outbound

This paper cites arXiv preprint arXiv:240603136.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection arXiv preprint arXiv:240603136

Reference 16

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source=arxiv_source observed=2026-08-01T03:33:53.723279Z digest=sha256:afd353e28e1140c3a119e996f562893d351c17abbb86f1947313420c9dc54230

Observation e835673b-a758-4d66-becf-7ec0c0970424 · outbound

This paper cites In: International Conference on Learning Representations (ICLR).

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection In: International Conference on Learning Representations (ICLR)

Reference 17

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source=arxiv_source observed=2026-08-01T03:33:53.796349Z digest=sha256:37abdccbc72eb38c9ded5f41c57bebda18a80c3740e49bef5443996fd25bb4a6

Observation 9d497480-b3ad-48af-922b-b37263316ec5 · outbound

This paper cites Empirical Methods in Natural Language Processing (EMNLP).

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Empirical Methods in Natural Language Processing (EMNLP)

Reference 18

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source=arxiv_source observed=2026-08-01T03:33:53.889745Z digest=sha256:9e4ec5c8518ec0379243635f668e70a55c9948e1a5b4ca82f5f9fc85f78857c8

Observation 19d9059e-d86f-4244-adb1-25d5082c3c7c · outbound

This paper cites In: Annual Meeting of the Association for Computational Linguistics (ACL).

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection In: Annual Meeting of the Association for Computational Linguistics (ACL)

Reference 19

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source=arxiv_source observed=2026-08-01T03:33:53.911305Z digest=sha256:163a1f0dee2a85d797b57eaa7851831cf3e49d002bcd549c252f334592c62360

Observation a7eee352-72e1-4321-ab30-54c08ad43923 · outbound

This paper cites Advances in Neural Information Processing Systems.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Advances in Neural Information Processing Systems

Reference 20

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Observation 2e31ad8b-582d-40fe-b045-d7e1b146a928 · outbound

This paper cites In: International Conference on Machine Learning (ICML).

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection In: International Conference on Machine Learning (ICML)

Reference 21

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Observation 11226325-c399-45e1-bfce-84c962a6bf08 · outbound

This paper cites In: International Conference on Learning Representations (ICLR).

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection In: International Conference on Learning Representations (ICLR)

Reference 22

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source=arxiv_source observed=2026-08-01T03:33:54.102470Z digest=sha256:9bfafd9dafcee2a15e1c12df8bd3f21a1409e4c2cab1b81e0195af4b561a7132

Observation 8a3c7497-79a5-4537-9096-ab793efe859c · outbound

This paper cites arXiv preprint arXiv:241008305.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection arXiv preprint arXiv:241008305

Reference 23

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Observation de314c19-cc73-42b4-a0b8-03c95117aa53 · outbound

This paper cites In: Conference on Learning Theory (COLT).

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection In: Conference on Learning Theory (COLT)

Reference 24

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source=arxiv_source observed=2026-08-01T03:33:54.266488Z digest=sha256:8969258800184a9c0f0a1810eacbacf828b6c1096c44661977797445225f896c

Observation 6bab990d-1591-4e85-a646-3256b34b3022 · outbound

This paper cites In: Journal of Machine Learning Research.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection In: Journal of Machine Learning Research

Reference 25

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source=arxiv_source observed=2026-08-01T03:33:54.357000Z digest=sha256:6f63dbce5b4981b5766b00fa58bed75cb582dbd3b36b8a0de82f61214a216c5b

Observation 281c9b19-4b72-44f3-9786-4e66ad7e2ca1 · outbound

This paper cites Annals of Statistics 39(2):1069--1097.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Annals of Statistics 39(2):1069--1097

Reference 26

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source=arxiv_source observed=2026-08-01T03:33:54.425763Z digest=sha256:6803978721b0b0c29825c021e9ac5f24696a23fada50b057c20e89f96a2f0908

Observation b044aec0-4494-4e38-8a73-84b7e64185a1 · outbound

This paper cites Statistical Science 27(4):538--557.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Statistical Science 27(4):538--557

Reference 27

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Observation e38d550e-300e-441a-9844-ccb01245066c · outbound

This paper cites Journal of Machine Learning Research 12:3413--3430.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Journal of Machine Learning Research 12:3413--3430

Reference 28

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source=arxiv_source observed=2026-08-01T03:33:54.535374Z digest=sha256:fc7d51f35305f4e1678cde9402595b72a67ba079d52e5302f56d0a07e6d94ec9

Observation a183458a-4bb7-498d-9c41-58125ed6e15f · outbound

This paper cites Cambridge University Press.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Cambridge University Press

Reference 29

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source=arxiv_source observed=2026-08-01T03:33:54.623324Z digest=sha256:1ebf3a90a62d967da7eeb83dc69d841922fe8eaafbcfaa49987623d5073621e9

Observation 6fd21371-300a-400d-87f4-9fd2ac122a0f · outbound

This paper cites Journal of Machine Learning Research 19(1):2822--2878.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Journal of Machine Learning Research 19(1):2822--2878

Reference 30

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source=arxiv_source observed=2026-08-01T03:33:54.692052Z digest=sha256:977b7c8d755f07072c16f7c39633b29c5b99a10a8eb348f71ed7a18bdabf07b8

Observation 47868dfa-639d-459a-b149-8c18c0696453 · outbound

This paper cites Proceedings of Research Workshop on Banach Space Theory (Iowa City) pp 169--185.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Proceedings of Research Workshop on Banach Space Theory (Iowa City) pp 169--185

Reference 31

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Observation 6e31476f-3a7f-43a5-a8c2-efcb923aa6c4 · outbound

This paper cites Cambridge University Press.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Cambridge University Press

Reference 32

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Observation 51ef4712-d38a-4040-b133-33a1363d540e · outbound

This paper cites Cambridge University Press.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection Cambridge University Press

Reference 33

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Observation d573aafb-caf5-4965-8dea-b34e57d121f6 · outbound

This paper cites The Expressive Power of Low-Rank Adaptation.

Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection The Expressive Power of Low-Rank Adaptation

Reference 34

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Pith citing papers

No inbound Pith citation observations are available.