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Paper Citation Record · LEDGER

Newfluence: Boosting Model interpretability and Understanding in High Dimensions

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2507.11895.

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

pith.paper-citation-record.v1
2507.11895 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:05:27.565899Z

measured 37 of 37 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T17:47:21.976868Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T17:50:26.377614Z

Reference resolution

36 of 36 outbound references displayed

  • verified exact2
  • verified fuzzy20
  • unresolved14
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9839b0e5-13ef-4808-8efe-487590dc9555 · outbound

This paper cites write newline.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions write newline

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:05:25.072845Z digest=sha256:44b14b0f6da708fa1907ef3436d2e1a8ccdf23e2b63610dba06181aef537c1ad

Observation deb672e7-15a7-4531-9d8a-b92104164c8e · outbound

This paper cites Approximate leave-one-out cross validation for regression with l1 regularizers.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Approximate leave-one-out cross validation for regression with l1 regularizers

Reference 2

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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.

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Observation ab695f7f-e610-4a99-98f5-41eaf578838c · outbound

This paper cites an unresolved cited work.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Unresolved cited work

Reference 3

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Source-reported events for the cited work

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Observation 0f6c931e-69aa-46d8-8f61-5c8b3df774f2 · outbound

This paper cites Influence Functions in Deep Learning Are Fragile.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Influence Functions in Deep Learning Are Fragile

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:05:25.232575Z digest=sha256:c6cc91b73f943a5b0f72b128d6d9e2cc6865db5e48378bf7684906588c9828cc

Observation 75f96fc4-8231-4d94-b6a1-3d8029dd2be4 · outbound

This paper cites and Montanari, A.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Montanari, A

Reference 5

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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.

source=arxiv_source observed=2026-08-06T17:05:25.285006Z digest=sha256:925aa0c709f700fbf5ded61555895b900a56d2fd7eb639944b1d794f99c3b0cd

Observation 10e4aa38-6803-47bf-9212-1793c8db09f6 · outbound

This paper cites L., Maleki, A., and Montanari, A.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions L., Maleki, A., and Montanari, A

Reference 6

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verified fuzzy
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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.

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Observation 63a59537-0ea4-4cc9-b4a9-9e78f41b48dd · outbound

This paper cites J., Lim, C., and Yu, B.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions J., Lim, C., and Yu, B

Reference 7

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verified fuzzy
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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.

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Observation 71c41fa2-81bd-4300-9abf-d32511767b53 · outbound

This paper cites and Zou, J.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Zou, J

Reference 8

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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.

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Observation 109de9b3-f76f-4686-b154-20ab34936066 · outbound

This paper cites Studying Large Language Model Generalization with Influence Functions.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Studying Large Language Model Generalization with Influence Functions

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2856289d-7b7d-471a-8bc5-a2a375b0c339 · outbound

This paper cites Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8ed9bafc-b399-40fb-87e2-c1f11c4586c8 · outbound

This paper cites and Lowd, D.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Lowd, D

Reference 11

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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.

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Observation d5b6759c-98b7-405d-ae36-915dd790c4db · outbound

This paper cites an unresolved cited work.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation 7c19b59b-329b-43de-9e67-2e97852f6bfc · outbound

This paper cites Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions

Reference 13

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verified exact
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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.

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Observation 0695dd63-fab4-407d-b070-69230b1592b5 · outbound

This paper cites M., Engstrom, L., Leclerc, G., and Madry, A.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions M., Engstrom, L., Leclerc, G., and Madry, A

Reference 14

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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.

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Observation dc49b621-61bf-42c3-a521-350bc7493d8b · outbound

This paper cites and Maleki, A.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Maleki, A

Reference 15

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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.

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Observation fe021222-1271-45a2-815f-65e5ded8a168 · outbound

This paper cites A., Hynes, N., G \"u rel, N.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions A., Hynes, N., G \"u rel, N

Reference 16

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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.

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Observation b8d857d5-7919-498c-be77-6f2fa3dad7f5 · outbound

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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation c65d0de4-682d-4887-968a-9909d747e7b4 · outbound

This paper cites and Zou, J.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Zou, J

Reference 18

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Source-reported events for the cited work

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Observation 37559ed8-5782-44c3-bd40-7f1aac894895 · outbound

This paper cites DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

Reference 19

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Source-reported events for the cited work

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Observation a0eb37ea-059a-4ac1-8b7f-eef63b1462c2 · outbound

This paper cites Minimum $\ell_{1}$-norm interpolators: Precise asymptotics and multiple descent.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Minimum $\ell_{1}$-norm interpolators: Precise asymptotics and multiple descent

Reference 20

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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.

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Observation 732e271e-2e33-49c6-8f5e-8d0b251aec68 · outbound

This paper cites Understanding Impact of Human Feedback via Influence Functions.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Understanding Impact of Human Feedback via Influence Functions

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6eb0b4d3-2a14-4cd0-bc5b-25f3f68d3794 · outbound

This paper cites M., Georgiev, K., Ilyas, A., Leclerc, G., and Madry, A.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions M., Georgiev, K., Ilyas, A., Leclerc, G., and Madry, A

Reference 22

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Observation c8290ff3-604b-4baf-bc17-e81535a85f27 · outbound

This paper cites Estimating training data influence by tracing gradient descent.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Estimating training data influence by tracing gradient descent

Reference 23

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Observation e083e853-a034-42fa-a4e0-44b6f60d69f5 · outbound

This paper cites and Maleki, A.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Maleki, A

Reference 24

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Observation 173da1c4-4c6e-45c1-bfba-37fc1d4cf290 · outbound

This paper cites Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions

Reference 25

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Source-reported events for the cited work

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Observation 3fa7b1b5-8385-494f-8eb3-d784ed81c69b · outbound

This paper cites The shapley value in machine learning.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions The shapley value in machine learning

Reference 26

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Source-reported events for the cited work

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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Theoretical and practical perspectives on what influence functions do

Reference 27

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Newfluence: Boosting Model interpretability and Understanding in High Dimensions and Najmi, A

Reference 28

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Source-reported events for the cited work

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Observation f7397b09-3781-4119-94b6-c66f11a698e3 · outbound

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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Unresolved cited work

Reference 29

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Observation 09930eed-dc32-4281-8517-995258c11db8 · outbound

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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Data Shapley in One Training Run

Reference 30

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Observation 8b6cddc4-3d21-4dae-a9fa-9c7ded7fd880 · outbound

This paper cites Capturing the Temporal Dependence of Training Data Influence.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Capturing the Temporal Dependence of Training Data Influence

Reference 31

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Source-reported events for the cited work

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Newfluence: Boosting Model interpretability and Understanding in High Dimensions Approximate leave-one-out for fast parameter tuning in high dimensions

Reference 32

Resolution
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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.

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Newfluence: Boosting Model interpretability and Understanding in High Dimensions I., and Ravikumar, P

Reference 33

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Source-reported events for the cited work

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Observation 4b973cbc-4500-4848-b58b-edfe9da41961 · outbound

This paper cites Correcting large language model behavior via influence function.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Correcting large language model behavior via influence function

Reference 34

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Source-reported events for the cited work

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Observation 6f4f0f2d-edb4-4b26-8f06-60522d9fd144 · outbound

This paper cites Does _p -minimization outperform _1 -minimization? IEEE Transactions on Information Theory, 63 0 (11): 0 6896--6935, 2017.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Does _p -minimization outperform _1 -minimization? IEEE Transactions on Information Theory, 63 0 (11): 0 6896--6935, 2017

Reference 35

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Source-reported events for the cited work

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Observation 7e220514-e9fb-4268-8ab4-25e40dda9238 · outbound

This paper cites Certified Data Removal Under High-dimensional Settings.

Newfluence: Boosting Model interpretability and Understanding in High Dimensions Certified Data Removal Under High-dimensional Settings

Reference 36

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Pith citing papers

Observation b95c9581-e190-46f1-8c82-74f79a21966f · inbound

On the Accuracy of Newton Step and Influence Function Data Attributions cites this paper.

On the Accuracy of Newton Step and Influence Function Data Attributions Newfluence: Boosting Model interpretability and Understanding in High Dimensions

Reference 25

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