Paper Citation Record · LEDGER

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization

As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2507.01841.

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

pith.paper-citation-record.v1
2507.01841 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:50:32.617056Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

45 of 45 outbound references displayed

verified exact1
verified fuzzy30
unresolved14
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 48d19968-0436-418b-917e-ca6d00924ed6 · outbound

This paper cites Foundation mod- els defining a new era in vision: a survey and outlook.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Foundation mod- els defining a new era in vision: a survey and outlook

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.465180Z

Source-reported events for the cited work

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Observation 42c31b59-5cf9-4822-b4c5-495f14fa136d · outbound

This paper cites LoTR: Low Tensor Rank Weight Adaptation.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization LoTR: Low Tensor Rank Weight Adaptation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T20:50:32.372815Z

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Observation fa9d0279-e1e0-4e4e-b66a-15636e7cb77d · outbound

This paper cites The challenges of the nonlinear regime for physics-informed neural networks.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization The challenges of the nonlinear regime for physics-informed neural networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.448647Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2455da63-6a2a-46c9-9a7e-b78838e8f940 · outbound

This paper cites AdaptFormer: Adapting vision Transformers for scalable visual recognition.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization AdaptFormer: Adapting vision Transformers for scalable visual recognition

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.432380Z

Source-reported events for the cited work

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Observation 4c0513e4-8f50-4f45-a432-be7bee979f4c · outbound

This paper cites QLoRA: Efficient fine- tuning of quantized LLMs.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization QLoRA: Efficient fine- tuning of quantized LLMs

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.416236Z

Source-reported events for the cited work

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Observation bcfeb679-0937-4aef-b123-19928436c505 · outbound

This paper cites LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices

Reference 6

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Observation 80465611-7247-43da-b165-90d3d71c138a · outbound

This paper cites Parameter-efficient fine-tuning of large-scale pre-trained language models.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Parameter-efficient fine-tuning of large-scale pre-trained language models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:50:32.401899Z

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Observation f87a2c81-1f99-4e29-b0c3-378801538e89 · outbound

This paper cites KronA: Parameter Efficient Tuning with Kronecker Adapter.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization KronA: Parameter Efficient Tuning with Kronecker Adapter

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T20:50:32.406929Z

Source-reported events for the cited work

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Observation 35381c0a-5f57-4769-a91b-bb493b6c7800 · outbound

This paper cites Implicit style-content sepa- ration using B-LoRA.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Implicit style-content sepa- ration using B-LoRA

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.388589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1aba0e5e-60b4-487b-a29a-3f7cb04fb6dc · outbound

This paper cites Submodular functions and optimization, volume 58.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Submodular functions and optimization, volume 58

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:50:32.418771Z

Source-reported events for the cited work

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Observation 2fa23cd2-d967-41cd-919d-c03bc4818702 · outbound

This paper cites Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:50:32.783182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8052b575-2606-493d-b7a7-816b65e2122d · outbound

This paper cites Escaping from saddle points—online stochastic gradient for tensor decomposition.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Escaping from saddle points—online stochastic gradient for tensor decomposition

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.359558Z

Source-reported events for the cited work

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Observation 5afb3fba-b986-4eaf-b755-9352f90f397a · outbound

This paper cites Matrix completion has no spurious local minimum.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Matrix completion has no spurious local minimum

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.341921Z

Source-reported events for the cited work

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Observation 1bdfbe2b-24d6-4b0a-a151-340a866bb133 · outbound

This paper cites Parameter-efficient fine- tuning for large models: A comprehensive survey.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Parameter-efficient fine- tuning for large models: A comprehensive survey

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.324311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9057990-4c27-4e0e-9d0f-79d0ffe9e9eb · outbound

This paper cites LoRA+: Efficient low rank adaptation of large models.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization LoRA+: Efficient low rank adaptation of large models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.306120Z

Source-reported events for the cited work

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Observation faa1fbd5-7488-4214-ba54-6db42511624b · outbound

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

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization LoRA: Low-rank adaptation of large language models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.289183Z

Source-reported events for the cited work

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Observation 09374d3d-fe54-49b9-9cce-7c8e213921fe · outbound

This paper cites Lee, and Ernest K.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Lee, and Ernest K

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.272660Z

Source-reported events for the cited work

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Observation 2fc523b2-0ef3-4eef-8efc-b2b62b57cc56 · outbound

This paper cites Visual prompt tuning.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Visual prompt tuning

Reference 18

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Observation f48cf59a-1f84-4a0a-b8a5-f1a2c8fd97ff · outbound

This paper cites Physics-informed machine learning.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Physics-informed machine learning

Reference 19

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Observation 7c2da706-7662-4bfa-8fb6-dd1a5cbf5889 · outbound

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Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Unresolved cited work

Reference 20

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Observation 296cc1fd-aedd-495e-a8e5-f5afe873b2cc · outbound

This paper cites Submodular function maximization.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Submodular function maximization

Reference 21

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Observation ba85fff2-448b-4834-9afe-f7ce0c8c9d8d · outbound

This paper cites Lorasculpt: Sculpting lora for harmonizing general and specialized knowledge in multimodal large language models.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Lorasculpt: Sculpting lora for harmonizing general and specialized knowledge in multimodal large language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.206277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cec1c613-c655-4b4d-b6da-cbbffbfa6f5f · outbound

This paper cites ALoRA: Allocating low- rank adaptation for fine-tuning large language models.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization ALoRA: Allocating low- rank adaptation for fine-tuning large language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.188831Z

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Observation ac5be458-b0cb-41a5-94d6-41b138c60841 · outbound

This paper cites HyperLoRA for PDEs.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization HyperLoRA for PDEs

Reference 24

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Source-reported events for the cited work

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Observation 7c9c1975-1587-42f8-8e7b-be8a95075984 · outbound

This paper cites PIHLoRA: Physics-informed hypernetworks for low-ranked adapta- tion.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization PIHLoRA: Physics-informed hypernetworks for low-ranked adapta- tion

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.160012Z

Source-reported events for the cited work

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Observation d3dcf278-c143-4e6e-845b-9758b238c86e · outbound

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

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization A kernel- based view of language model fine-tuning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.143627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 26763338-ee1d-440d-b34e-36696670768f · outbound

This paper cites A survey on LoRA of large language models.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization A survey on LoRA of large language models

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.126645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6468e8a2-9a56-47d4-848c-7d1ffafeb2e4 · outbound

This paper cites Near-optimal sketchy natu- ral gradients for physics-informed neural networks.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Near-optimal sketchy natu- ral gradients for physics-informed neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.109375Z

Source-reported events for the cited work

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Observation b7413970-6d79-4c7f-aa2a-aea8c8a23382 · outbound

This paper cites Achieving high accuracy with PINNs via energy nat- ural gradient descent.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Achieving high accuracy with PINNs via energy nat- ural gradient descent

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.091830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 775f186c-ebc9-40ff-97d1-9ba9920b607e · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear par- tial differential equations.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear par- tial differential equations

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.075366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d59c02bc-c115-43bd-92d5-6a2eb17b11ce · outbound

This paper cites Fine-tuning protein language mod- els boosts predictions across diverse tasks.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Fine-tuning protein language mod- els boosts predictions across diverse tasks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.058245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c4e2b39-5859-4e48-9d8e-dbad0e756118 · outbound

This paper cites LoRA vs full fine-tuning: An illusion of equivalence.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization LoRA vs full fine-tuning: An illusion of equivalence

Reference 32

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Observation f2787a93-acdf-4008-a83e-1d751b3b5d7b · outbound

This paper cites Tensor decomposition for compressing recurrent neural network.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Tensor decomposition for compressing recurrent neural network

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.038124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a7e28d8d-1f8e-4433-8080-401f9d3e7fb0 · outbound

This paper cites DyLoRA: Parameter-efficient tuning of pre-trained models using dynamic search-free low-rank adap- tation.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization DyLoRA: Parameter-efficient tuning of pre-trained models using dynamic search-free low-rank adap- tation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.019870Z

Source-reported events for the cited work

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Observation b688931c-2d24-491a-819b-c82bc61ba177 · outbound

This paper cites Parameter-efficient fine-tuning in large language models: A survey of methodologies.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Parameter-efficient fine-tuning in large language models: A survey of methodologies

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:33.001093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ef3f0cfe-6b25-4e72-bdab-cc1b7ca77fe6 · outbound

This paper cites Metaxas, and Hao Wang.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Metaxas, and Hao Wang

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:32.980782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f5ec345c-d9f3-40c2-8505-d6ceaed764f9 · outbound

This paper cites Transfer learning in physics-informed neurals networks: Full fine-tuning, lightweight fine-tuning, and low-rank adaptation.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Transfer learning in physics-informed neurals networks: Full fine-tuning, lightweight fine-tuning, and low-rank adaptation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:32.963991Z

Source-reported events for the cited work

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Observation 492f002b-2cf1-442e-b712-d24f5a6d7377 · outbound

This paper cites Yang, Maxime Robeyns, Xi Wang, and Laurence Aitchison.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Yang, Maxime Robeyns, Xi Wang, and Laurence Aitchison

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:32.947119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2496fe8d-d940-45f5-bb5d-a20511df51dc · outbound

This paper cites LoRETTA: Low-rank economic tensor-train adaptation for ultra-low-parameter fine-tuning of large language models.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization LoRETTA: Low-rank economic tensor-train adaptation for ultra-low-parameter fine-tuning of large language models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:32.930047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f0b5d45b-2372-4d07-b2c9-5ef411a33e0e · outbound

This paper cites Ranking and tuning pre-trained models: A new paradigm for exploiting model hubs.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Ranking and tuning pre-trained models: A new paradigm for exploiting model hubs

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:32.913160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ba0554b-df99-4dba-8ac0-cbb9c1a251ac · outbound

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

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization The expressive power of low-rank adaptation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T20:50:32.600394Z

Source-reported events for the cited work

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Observation c451a1ec-7944-42b0-9f82-8fffb1af7df7 · outbound

This paper cites Adaptive budget allocation for parameter-efficient fine-tuning.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Adaptive budget allocation for parameter-efficient fine-tuning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T20:50:32.605767Z

Source-reported events for the cited work

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Observation 3cdf865b-e39d-465f-b662-610cad7fc237 · outbound

This paper cites Personalized LoRA for human-centered text understanding.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Personalized LoRA for human-centered text understanding

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:32.872246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c25ef6ab-fc64-4970-954d-e552e4e7bf09 · outbound

This paper cites Neural prompt search.

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Neural prompt search

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:50:32.854743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d69c62c5-5b4b-48f3-97f6-e9be8a0c6ee1 · outbound

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Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization Unresolved cited work

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T20:50:32.498696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.