Paper Citation Record · LEDGER

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters

As of 7 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 4 inbound Pith citation observations for arXiv:2506.14530.

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

pith.paper-citation-record.v1
2506.14530 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:28:44.499298Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T02:14:38.644041Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T12:16:57.389694Z

Reference resolution

53 of 53 outbound references displayed

verified exact1
verified fuzzy22
unresolved30
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4561add7-f94b-41d3-a25d-adc8865c0415 · outbound

This paper cites Qwen Technical Report.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Qwen Technical Report

Reference 1

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Source-reported events for the cited work

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Observation fe78cf12-c86e-46f4-8135-81a4c9ae5a9f · outbound

This paper cites Rademacher and gaussian complexities: Risk bounds and structural results.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Rademacher and gaussian complexities: Risk bounds and structural results

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 113461f1-a7e4-4c61-a371-caa50b6813bb · outbound

This paper cites DeepSeek-V3 Technical Report.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters DeepSeek-V3 Technical Report

Reference 3

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Source-reported events for the cited work

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Observation 50f9a5b7-9f8e-448c-8342-2abd49537da1 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters QLoRA: Efficient Finetuning of Quantized LLMs

Reference 4

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Observation abeddf7b-7e53-4746-9c12-1baa38dae98f · outbound

This paper cites Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Reference 5

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Source-reported events for the cited work

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Observation 66c46c12-ee65-414d-830f-b44320ccf9a0 · outbound

This paper cites Efficient adaptation of large vision transformer via adapter re-composing.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Efficient adaptation of large vision transformer via adapter re-composing

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:50.121561Z

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Observation b508df02-891a-4a8f-806b-cbf61f1320c4 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2018.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2018

Reference 7

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Observation 7213516c-a363-4a30-80bd-f55e54363e7b · outbound

This paper cites an unresolved cited work.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Unresolved cited work

Reference 8

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 56ffb907-59a7-4e1c-ac24-63047da71ab8 · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 9

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Observation fa65a0a5-7b93-4399-b84c-562da4ec4100 · outbound

This paper cites Mahoney, and Kurt Keutzer.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Mahoney, and Kurt Keutzer

Reference 10

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Source-reported events for the cited work

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Observation f4b6a9f7-2615-4955-9b43-427ca31be702 · outbound

This paper cites Majorization of gaussian processes and geometric applications.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Majorization of gaussian processes and geometric applications

Reference 11

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Observation b4f42db1-5663-4f7f-b12f-c8ba9c298889 · outbound

This paper cites Xing, and Yoon Kim.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Xing, and Yoon Kim

Reference 12

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Source-reported events for the cited work

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Observation f0203d22-dfab-4e6a-b124-4b0996eafdc0 · outbound

This paper cites SVDiff: Compact Parameter Space for Diffusion Fine-Tuning.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters SVDiff: Compact Parameter Space for Diffusion Fine-Tuning

Reference 13

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Source-reported events for the cited work

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Observation aa035ff7-027f-4206-b7bc-9f822aab70fe · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 14

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Observation 3c09a719-21bc-4eba-a884-caaa4848b48c · outbound

This paper cites Towards a unified view of parameter-efficient transfer learning.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Towards a unified view of parameter-efficient transfer learning

Reference 15

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Observation 34b0a033-e981-4074-abe3-524c78c567bf · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Lora: Low-rank adaptation of large language models

Reference 16

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Observation 11f973e9-f08f-4330-b2f3-acef725af31e · outbound

This paper cites Lee, and Ernest K.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Lee, and Ernest K

Reference 17

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Source-reported events for the cited work

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Observation ff01a909-205a-4df8-86a0-0d7d4944b6fc · outbound

This paper cites Nola: Networks as linear combination of low rank random basis, 2023.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Nola: Networks as linear combination of low rank random basis, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:49.048086Z

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Observation b8789460-dd77-4dc0-87aa-35fc713d09b3 · outbound

This paper cites Kopiczko, Tijmen Blankevoort, and Yuki M.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Kopiczko, Tijmen Blankevoort, and Yuki M

Reference 19

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Source-reported events for the cited work

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Observation 541f57db-7334-40aa-b1a0-1f3dd41ed6e1 · outbound

This paper cites Fast randomized low-rank adaptation of pre-trained language models with pac regularization.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Fast randomized low-rank adaptation of pre-trained language models with pac regularization

Reference 20

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ebd7a1ed-7a2d-4cd4-a6c6-e02091c66606 · outbound

This paper cites Graphadapter: Tuning vision-language models with dual knowledge graph.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Graphadapter: Tuning vision-language models with dual knowledge graph

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:48.649763Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f4cfc307-67d0-4dc2-8225-8cd778f0636e · outbound

This paper cites PAC -tuning: Fine-tuning pre-trained language models with PAC -driven perturbed gradient descent.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters PAC -tuning: Fine-tuning pre-trained language models with PAC -driven perturbed gradient descent

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:48.424173Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3867a646-87cf-4be4-b1ad-4d72927e85ac · outbound

This paper cites Black, Adrian Weller, and Bernhard Sch \"o lkopf.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Black, Adrian Weller, and Bernhard Sch \"o lkopf

Reference 23

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Observation 1ab69905-c495-48c1-9d7a-9c9541688302 · outbound

This paper cites Lorentz, Manfred v.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Lorentz, Manfred v

Reference 24

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Observation 1d55c6f5-8e4c-4a96-adb3-649177fe3e7d · outbound

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Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Unresolved cited work

Reference 25

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Observation 3690a7d8-03c9-414d-9bd2-8f8e7f58d448 · outbound

This paper cites Score distillation via reparametrized DDIM.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Score distillation via reparametrized DDIM

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:47.795480Z

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Observation d164aff2-ae6c-4575-902e-d1e73045401f · outbound

This paper cites A kernel-based view of language model fine-tuning.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters A kernel-based view of language model fine-tuning

Reference 27

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Observation 3e22fc2a-5cd9-402e-ad74-c4fbd38a9945 · outbound

This paper cites Gpt-4 technical report, 2023.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Gpt-4 technical report, 2023

Reference 28

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Observation 8da69855-40d0-48f4-a95f-bfd8367b4e5e · outbound

This paper cites Bronstein.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Bronstein

Reference 29

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Observation 89648b03-5004-4d30-8840-f6a7a9fad848 · outbound

This paper cites Limitations on approximation by deep and shallow neural networks.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Limitations on approximation by deep and shallow neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:47.190935Z

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Observation 854de3ad-eaf4-4f2b-839d-665e4b541dea · outbound

This paper cites Lipschitz widths.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Lipschitz widths

Reference 31

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Observation bf96fc9f-178d-4688-83cc-1bcf311cd52d · outbound

This paper cites A dapter H ub: A framework for adapting transformers.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters A dapter H ub: A framework for adapting transformers

Reference 32

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Observation a54e5703-7b73-43b2-b778-6be9c9f21062 · outbound

This paper cites Qwen2.5 Technical Report.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Qwen2.5 Technical Report

Reference 33

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Observation 79884fff-ecb1-4609-b8cf-15a7ccb36cd2 · outbound

This paper cites What’s hidden in a randomly weighted neural network? In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters What’s hidden in a randomly weighted neural network? In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 34

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Observation 357b2f47-0bff-470f-a5e8-013702536f36 · outbound

This paper cites Pivotal tuning for latent-based editing of real images.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Pivotal tuning for latent-based editing of real images

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:46.773403Z

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Observation cfd7f0de-117e-419b-b344-5744e303788e · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters High-Resolution Image Synthesis with Latent Diffusion Models

Reference 36

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Observation d32fa9d4-00a2-452b-b60e-b2fe0761d355 · outbound

This paper cites The littlewood--offord problem and invertibility of random matrices.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters The littlewood--offord problem and invertibility of random matrices

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:46.510104Z

Source-reported events for the cited work

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Observation 8f303483-a3d2-454a-be45-585c19ca4afc · outbound

This paper cites DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

Reference 38

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Observation 86cb33c1-c06b-43e5-a491-66111d0e1f18 · outbound

This paper cites A sharp inverse littlewood-offord theorem.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters A sharp inverse littlewood-offord theorem

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:46.304894Z

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Observation 41aaaa0b-122e-4693-825c-d4961c3e6188 · outbound

This paper cites Inverse littlewood-offord theorems and the condition number of random discrete matrices.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Inverse littlewood-offord theorems and the condition number of random discrete matrices

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:46.096793Z

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Observation e6bc0598-fe79-4096-9b02-a4710728345c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters LLaMA: Open and Efficient Foundation Language Models

Reference 41

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Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters van der Vaart and Jon A

Reference 42

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This paper cites Introduction to the non-asymptotic analysis of random matrices.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Introduction to the non-asymptotic analysis of random matrices

Reference 43

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Observation e4ad13c6-9212-487d-982f-f585b915301b · outbound

This paper cites Wainwright.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Wainwright

Reference 44

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Observation 69f5e61a-5f4d-4ef7-a578-3c7f63f99790 · outbound

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Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Tina: Tiny Reasoning Models via LoRA

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T00:28:43.564233Z

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Observation 56fa959c-e82f-4b0d-b2cb-6616d3ac55da · outbound

This paper cites Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T00:28:43.677951Z

Source-reported events for the cited work

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Observation 2f3462be-0449-490f-aa29-2eeb8c796be8 · outbound

This paper cites ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:28:44.797667Z

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Observation 997c86c3-a6f3-4bae-b9a2-49aff0c6511e · outbound

This paper cites Towards a Unified View on Visual Parameter-Efficient Transfer Learning.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Towards a Unified View on Visual Parameter-Efficient Transfer Learning

Reference 48

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Source-reported events for the cited work

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Observation 39768ebb-a3dd-486d-8d9d-1587326f74b4 · outbound

This paper cites Low-rank few-shot adaptation of vision-language models.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Low-rank few-shot adaptation of vision-language models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T00:28:44.012742Z

Source-reported events for the cited work

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Observation f353bca3-3ffa-49b3-841d-5afac10463fe · outbound

This paper cites The expressive power of low-rank adaptation.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters The expressive power of low-rank adaptation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:45.924218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 144cc3c5-2f78-4f30-9ee7-fd7e1f760a9c · outbound

This paper cites Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning, 2023 a.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning, 2023 a

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:45.725703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 18cf8f03-14b9-4192-82c3-f1f3b08c5b4b · outbound

This paper cites Adalora: Adaptive budget allocation for parameter-efficient fine-tuning, 2023 b.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Adalora: Adaptive budget allocation for parameter-efficient fine-tuning, 2023 b

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:45.488253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2abf90d7-637f-4c94-91a2-51c7d68c6690 · outbound

This paper cites Asymmetry in low-rank adapters of foundation models.

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters Asymmetry in low-rank adapters of foundation models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:28:45.238940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 7d1bb97a-56d0-4699-9c29-532e3494de1e · inbound

Training-Free Generative Sampling via Moment-Matched Score Smoothing cites this paper.

Training-Free Generative Sampling via Moment-Matched Score Smoothing Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:33:32.464066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3ffe8f13-7a4f-4a55-bbda-ede4caac1dc6 · inbound

LoRA vs. Full Fine-Tuning: A Theoretical Perspective cites this paper.

LoRA vs. Full Fine-Tuning: A Theoretical Perspective Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:18:16.814921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6a1e3411-87cc-4297-ad07-373987a4c8af · inbound

The Deterministic Horizon: Impossibility Results as Design Specifications for Trustworthy AI Systems cites this paper.

The Deterministic Horizon: Impossibility Results as Design Specifications for Trustworthy AI Systems Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters

Reference 142

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:30:22.698577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9c9d3486-dd12-4427-b999-c6dc16e62fd6 · inbound

High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model cites this paper.

High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:16:57.391161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.