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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-08T06:32:00.761636+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

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:b242f4f1d48edde75ae8fb04d9ffedef96b51f669ee0d9980e60c5bddb1467db

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=sha256:638ae64a75aaf4aa66eaf10e6706b3b0dd8b7929837f9f46581841a42360966c

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=sha256:1a71af098e0843cd2d8d759ec179f40f7b11eef59b6e46aeeb724f0d15bf1fbe

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=sha256:0a68136f7b712055bd98c98659c609142c12cdff0429b69dd8fc948cffffb243

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:ccb5eac4aa2410336e4527ed1bd276aef64041050362f27d44ea4269f540118e

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=sha256:964159e6fa944b28145ea750087bef85263ecba16ae2dd6e33f05d144a030440

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:e2c04d57c36e2010435f84bf7e8f7f3c07cd9c53cd7f6392dbdcfe33d1eeb881

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=sha256:0863c702b6f032abbc6b140b242f16634eb1e414cf6b652162f773c361fc0a3e

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=sha256:9ece7f2a2a1e16619c8b23b29d605e31e2e00b1f43a85228893a5dbd92c1e187

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=sha256:8feb6892240027e90fc15cda2486aa9ae3948e06ee5f7da659a473035b3d8c9f

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=sha256:7b70cb6abc06486c7e068dd8bad1be56600645863bece7e3f059729d26520e7b

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:fd55c62462ca507df38cb2f735fcca6d05e674a04f6c90cfb189d3b3ac592b30

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:ad5408b1e0fe09096c02e4b846bcd8b5def11e4c1ff34fca055b46aabbec8294

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:ebfc3a072b4ea09dc4b819aa426b5d31b42238900fa5d955a9ed2c4ee58e190f

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=sha256:73888f479b10efe224762bdd40723ffee1f1d61f66ad8b08d83d25a7c6228b19

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:caffabfa51d176bfc94e3cdfc67b76775e5900a2193e02b8fac6ca637b1231da

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=sha256:87e7230b8d39d5af049d6768781fb9f91607946c5d416d4218ab86c284ecdf2e

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:aabf82e01c7f32bc486700ac571bef17e947d8579ab63b4b9642d94005be4b9c

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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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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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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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:e9440ffe387665e126dccd923b79e133367c94aa4d04818086f84401af520535

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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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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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=sha256:72fd243fbe557cacc8f21213504466847371908305d8decc3ec5d6ce2b0807dc

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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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.