Paper Citation Record · LEDGER

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale

As of 11 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 2 inbound Pith citation observations for arXiv:2501.19099.

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

pith.paper-citation-record.v1
2501.19099 v2

Coverage vector

measured 82 of 82 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:23:48.766033Z

measured 84 of 84 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T21:19:55.074853Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T21:23:44.552740Z

Reference resolution

82 of 82 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6009c94b-6174-4e48-93df-2b16938c9887 · outbound

This paper cites write newline.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale write newline

Reference 1

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Observation 044b5013-7c47-486d-910b-d33e67f09b13 · outbound

This paper cites and Cutkosky, A.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Cutkosky, A

Reference 2

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Observation 3559e6d6-b513-458f-a725-b5444ec1a0e8 · outbound

This paper cites The second pascal recognising textual entailment challenge.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale The second pascal recognising textual entailment challenge

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.984362Z

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Observation e65b326d-1862-47a2-8fc1-a919450409da · outbound

This paper cites The fifth pascal recognizing textual entailment challenge.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale The fifth pascal recognizing textual entailment challenge

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.972838Z

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Observation a170ac73-3ed4-40dd-97cd-dacc7e732953 · outbound

This paper cites S., Cao, L., Choromanski, K., and Scheinberg, K.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale S., Cao, L., Choromanski, K., and Scheinberg, K

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.961063Z

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Observation 3ea6992d-ccd7-4dfe-b7f7-bc64b257f1c5 · outbound

This paper cites and Elisseeff, A.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Elisseeff, A

Reference 6

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Observation 5e12838c-0b44-4afc-b900-2b0855f9d041 · outbound

This paper cites A zeroth-order block coordinate descent algorithm for huge-scale black-box optimization.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale A zeroth-order block coordinate descent algorithm for huge-scale black-box optimization

Reference 7

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Observation 52551dfc-fd93-490c-ac96-901c5a543e59 · outbound

This paper cites Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.931322Z

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Observation 2f4b4512-4ac8-4fc2-ba47-988b44e747d9 · outbound

This paper cites On the convergence of a class of adam-type algorithms for non-convex optimization.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale On the convergence of a class of adam-type algorithms for non-convex optimization

Reference 9

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Observation df6583af-2229-478b-af51-b34e54458b07 · outbound

This paper cites Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures

Reference 10

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Observation 98e59ae0-9e69-411d-9669-6d6e693c3619 · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.910952Z

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Observation fae34bbc-d09c-4796-ba20-dee696198e09 · outbound

This paper cites Large scale machine learning.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Large scale machine learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.897796Z

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Observation 56a8af1a-3530-4c1e-8596-1ead2a80dfbd · outbound

This paper cites The pascal recognising textual entailment challenge.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale The pascal recognising textual entailment challenge

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.886351Z

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Observation 613d31a6-225a-41b5-be01-2bba13717512 · outbound

This paper cites T., Padmanabhan, S., and Ye, G.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale T., Padmanabhan, S., and Ye, G

Reference 14

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Observation 376b28aa-9e2f-4dc3-8a28-0623133e2d48 · outbound

This paper cites The commitmentbank: Investigating projection in naturally occurring discourse.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale The commitmentbank: Investigating projection in naturally occurring discourse

Reference 15

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Observation 916651cc-3553-4f78-ad07-3b6d0add9f39 · outbound

This paper cites Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs

Reference 16

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Observation f1907d10-7f1c-485c-ad29-de115ed88ce9 · outbound

This paper cites C., Jordan, M.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale C., Jordan, M

Reference 17

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Observation 2bb10140-5f44-486e-8a32-6b20dcaa0481 · outbound

This paper cites Variance-reduced zeroth-order methods for fine-tuning language models.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Variance-reduced zeroth-order methods for fine-tuning language models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.831179Z

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Observation 0eb4cd7e-0dfe-464c-b974-f23becf8de20 · outbound

This paper cites and Lan, G.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Lan, G

Reference 19

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This paper cites W., and Keutzer, K.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale W., and Keutzer, K

Reference 20

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Observation edcfc91d-b2f4-4523-8b87-916329f53469 · outbound

This paper cites An investigation into neural net optimization via hessian eigenvalue density.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale An investigation into neural net optimization via hessian eigenvalue density

Reference 21

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Observation 7a3460df-a1f0-481d-8649-97f43425ee06 · outbound

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Unresolved cited work

Reference 22

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Observation 9b5e9bab-ed59-4b4e-9218-0e92cc4f7b00 · outbound

This paper cites Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity

Reference 23

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Unresolved cited work

Reference 24

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Observation 003f6808-f663-4c73-beb8-08cf59d11c16 · outbound

This paper cites Train faster, generalize better: Stability of stochastic gradient descent.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Train faster, generalize better: Stability of stochastic gradient descent

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.764580Z

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Observation 121944b0-874f-46a5-b5c1-51a3cb6eaccb · outbound

This paper cites Stable Rank and Intrinsic Dimension of Real and Complex Matrices.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Stable Rank and Intrinsic Dimension of Real and Complex Matrices

Reference 26

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This paper cites On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables

Reference 27

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This paper cites Improved zeroth-order variance reduced algorithms and analysis for nonconvex optimization.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Improved zeroth-order variance reduced algorithms and analysis for nonconvex optimization

Reference 28

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Observation fbe7db84-543d-4392-9d5b-4ad863a53b79 · outbound

This paper cites How large language models encode context knowledge? a layer-wise probing study.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale How large language models encode context knowledge? a layer-wise probing study

Reference 29

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Observation 62d7e469-e35b-4c28-96db-3aedf253707e · outbound

This paper cites Looking beyond the surface: A challenge set for reading comprehension over multiple sentences.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Looking beyond the surface: A challenge set for reading comprehension over multiple sentences

Reference 30

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This paper cites RA - L o RA : Rank-adaptive parameter-efficient fine-tuning for accurate 2-bit quantized large language models.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale RA - L o RA : Rank-adaptive parameter-efficient fine-tuning for accurate 2-bit quantized large language models

Reference 31

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Observation 8a3ad5eb-fb11-437b-a842-f2b5f1e3381a · outbound

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale SqueezeLLM: Dense-and-Sparse Quantization

Reference 32

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Ba, J

Reference 33

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Shamir, O

Reference 34

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This paper cites J., and Bengio, S.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale J., and Bengio, S

Reference 35

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale H., Gonzalez, J., Zhang, H., and Stoica, I

Reference 36

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Observation a6a31ec0-d573-4b74-ba95-d66db58516aa · outbound

This paper cites Decoderlens: Layerwise interpretation of encoder-decoder transformers.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Decoderlens: Layerwise interpretation of encoder-decoder transformers

Reference 37

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Observation 9f38011a-a54d-47f9-8df5-40a758219160 · outbound

This paper cites Stability and generalization of stochastic optimization with nonconvex and nonsmooth problems.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Stability and generalization of stochastic optimization with nonconvex and nonsmooth problems

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.655676Z

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This paper cites The winograd schema challenge.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale The winograd schema challenge

Reference 39

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This paper cites A zeroth-order adaptive learning rate method to reduce cost of hyperparameter tuning for deep learning.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale A zeroth-order adaptive learning rate method to reduce cost of hyperparameter tuning for deep learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.636772Z

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This paper cites Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Reference 41

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale General stability analysis for zeroth-order optimization algorithms

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.625034Z

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This paper cites Sparse mezo: Less parameters for better performance in zeroth-order llm fine-tuning.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Sparse mezo: Less parameters for better performance in zeroth-order llm fine-tuning

Reference 43

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Observation 6b3828db-1e00-4630-93fb-41d3dd7552b5 · outbound

This paper cites BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models

Reference 44

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Unresolved cited work

Reference 45

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Observation e8679e3c-8b0d-405f-aa34-cc9490cb6214 · outbound

This paper cites D., Chen, D., and Arora, S.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale D., Chen, D., and Arora, S

Reference 46

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Unresolved cited work

Reference 47

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Observation 246f6de1-1d8d-4bf4-b3fa-f729ecbbd0fc · outbound

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Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Spokoiny, V

Reference 48

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This paper cites B., and Swami, A.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale B., and Swami, A

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.576586Z

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Observation 7eb370a1-d90f-4167-aaa8-ea5481078aac · outbound

This paper cites The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size

Reference 50

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Observation ab919fa6-f7b1-4f1e-9503-03aaf5c00a80 · outbound

This paper cites Traces of class/cross-class structure pervade deep learning spectra.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Traces of class/cross-class structure pervade deep learning spectra

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.564588Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 219295c3-2f6b-430d-b75d-5b0777a4c7be · outbound

This paper cites Y., Jung, Y., Kundu, S., Kim, K., and Yang, E.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Y., Jung, Y., Kundu, S., Kim, K., and Yang, E

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.552588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 90482bd4-775a-497c-b91a-c10344aa14b0 · outbound

This paper cites WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.655854Z

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Observation 0fd7e9fd-8df3-4fef-a058-71fe1b3384be · outbound

This paper cites Know what you don’t know: Unanswerable questions for squad.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Know what you don’t know: Unanswerable questions for squad

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.541196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f08b81ad-0725-493c-b517-f01c96e5bc3c · outbound

This paper cites An optimal structured zeroth-order algorithm for non-smooth optimization.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale An optimal structured zeroth-order algorithm for non-smooth optimization

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.529622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6a7ac6ae-85f8-45fc-a828-96870196cf89 · outbound

This paper cites An optimal structured zeroth-order algorithm for non-smooth optimization.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale An optimal structured zeroth-order algorithm for non-smooth optimization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.517109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b8ccc40c-d24d-4449-8c75-81e6534b14cc · outbound

This paper cites J., Kale, S., and Kumar, S.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale J., Kale, S., and Kumar, S

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.670821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f45d1404-2c57-49bc-934e-a2699e59af97 · outbound

This paper cites and Monro, S.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Monro, S

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.674564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c9537a-87d3-4899-ae9a-cdf6e2f26fc3 · outbound

This paper cites A., and Gordon, A.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale A., and Gordon, A

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.497818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 25ec4cb4-aca3-47df-80bb-309ff61a588d · outbound

This paper cites Empirical Analysis of the Hessian of Over-Parametrized Neural Networks.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.682233Z

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Observation 92bb1adf-21d5-4bb2-86c8-40edae391b6b · outbound

This paper cites On the complexity of bandit and derivative-free stochastic convex optimization.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale On the complexity of bandit and derivative-free stochastic convex optimization

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.486294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a0447577-4b80-4fd5-9157-9650a6c037bf · outbound

This paper cites Layer by Layer: Uncovering Hidden Representations in Language Models.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Layer by Layer: Uncovering Hidden Representations in Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.689713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54f789c6-d7c6-407f-8e3e-2a9053796b4e · outbound

This paper cites D., Ng, A., and Potts, C.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale D., Ng, A., and Potts, C

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.474730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cbd5481c-c48d-4696-93e9-3719b4df4dd8 · outbound

This paper cites an unresolved cited work.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Unresolved cited work

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.697050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 843f1d90-441b-4c1e-b399-85a3bdd86158 · outbound

This paper cites and Ye, Y.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Ye, Y

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.456410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b097fdf-2667-48cd-9ee0-b8000306aa14 · outbound

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

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale LLaMA: Open and Efficient Foundation Language Models

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.704648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faf4b0a2-9769-4399-baa4-817e47f05677 · outbound

This paper cites Zarts: On zero-order optimization for neural architecture search.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Zarts: On zero-order optimization for neural architecture search

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.445069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d4f04e0b-18d4-449d-8f77-659617102e5d · outbound

This paper cites The evaluation of the collision matrix.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale The evaluation of the collision matrix

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.434082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f04ab3a9-6cce-4bfe-8d44-1174383f8105 · outbound

This paper cites an unresolved cited work.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-09T21:23:49.422859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6a4114ef-8ddd-438a-9aa6-7c35d4f0e59f · outbound

This paper cites Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.720771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91b2cdba-484b-4328-a139-350d351c36a6 · outbound

This paper cites Zeroth-Order Fine-Tuning of LLMs in Random Subspaces.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Zeroth-Order Fine-Tuning of LLMs in Random Subspaces

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.724626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ab8544c-0b8e-4ae9-87b6-ae2e4abf2a5f · outbound

This paper cites Adablock: Sgd with practical block diagonal matrix adaptation for deep learning.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Adablock: Sgd with practical block diagonal matrix adaptation for deep learning

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.411343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dbf6925b-5638-4ecd-8b1c-d6faab471024 · outbound

This paper cites and Pilanci, M.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale and Pilanci, M

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.398412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f4d49c16-e17a-440e-8922-501ba623b57f · outbound

This paper cites Complexity of finding stationary points of nonconvex nonsmooth functions.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Complexity of finding stationary points of nonconvex nonsmooth functions

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.386377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18300d77-4ca1-4bbb-9095-96f9c6bf85fe · outbound

This paper cites When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-08-09T21:23:48.854857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74826394-bc3a-4b32-a630-38949f87c857 · outbound

This paper cites ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.743281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 853b833e-882e-4cf8-a936-41335bea49e1 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale OPT: Open Pre-trained Transformer Language Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.746989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 717faab1-1ec2-4a7c-8b88-beecba3f411e · outbound

This paper cites How to robustify black-box ML models? a zeroth-order optimization perspective.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale How to robustify black-box ML models? a zeroth-order optimization perspective

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.373166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df81907c-0ac0-4d38-8de6-ee71584caa04 · outbound

This paper cites Why Transformers Need Adam: A Hessian Perspective.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Why Transformers Need Adam: A Hessian Perspective

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.754308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad696d68-c53b-4649-bb03-4fcf7da39b1d · outbound

This paper cites Adam-mini: Use Fewer Learning Rates To Gain More.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Adam-mini: Use Fewer Learning Rates To Gain More

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-09T21:23:48.758135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2ec3b0e-bea0-47f0-a078-b425effb4f78 · outbound

This paper cites Investigating layer importance in large language models.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale Investigating layer importance in large language models

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.360632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b9663b1-dbec-4c87-ba71-d283174123c9 · outbound

This paper cites D., Yin, W., Hong, M., Wang, Z., Liu, S., and Chen, T.

Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale D., Yin, W., Hong, M., Wang, Z., Liu, S., and Chen, T

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T21:23:49.348575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 2d1be0e0-10c5-4f75-9d8a-ed8871c0eeee · inbound

ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models cites this paper.

ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:10:34.217034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5eedb13a-8011-4acc-92cb-5bacb154b6f0 · inbound

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered cites this paper.

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T21:23:44.554054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.