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Paper Citation Record · LEDGER

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning

As of 10 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 2 inbound Pith citation observations for arXiv:2507.12750.

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

pith.paper-citation-record.v1
2507.12750 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:45:04.439133Z

measured 23 of 23 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 2026-05-15T15:23:23.950152Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T15:26:10.861102Z

Reference resolution

21 of 21 outbound references displayed

  • verified exact1
  • verified fuzzy2
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38e2bee1-51a8-4c3b-b766-40daccb9c408 · outbound

This paper cites DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning

Reference 4

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verified exact
local_arxiv, observed 2026-08-06T16:45:05.504927Z

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.

source=pdf_text observed=2026-08-06T16:45:02.200177Z digest=sha256:4819e9634e74fe8e78f1c73fbb7c88d15520bb144592f7f57ee8e6a490d63216

Observation 081f4996-1241-4aec-be32-d014a82b6324 · outbound

This paper cites A Comprehensive Survey of Dataset Distillation.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning A Comprehensive Survey of Dataset Distillation

Reference 5

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source=pdf_text observed=2026-08-06T16:45:02.372296Z digest=sha256:137efb4d0b09eb86a1d3391e7a33d4f821f38b5c26ade358543f3b1c5b3d1337

Observation 735d93c6-57d2-4076-b1c0-99856465a8ed · outbound

This paper cites Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

Reference 7

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source=pdf_text observed=2026-08-06T16:45:02.589586Z digest=sha256:d021f8730a76b2d8349f011d40134c460e42f1c773dbfc6a11184fb6856a30db

Observation 4367702a-6c57-44c3-859e-41be010f687c · outbound

This paper cites InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning

Reference 9

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source=pdf_text observed=2026-08-06T16:45:02.762139Z digest=sha256:d737cb6aec28a21f5e7ca140004f4031b3c025362246de66f3f5a95633fc1432

Observation 232bb2ab-8948-4894-b30e-f64679b049cb · outbound

This paper cites A Weighted K-Center Algorithm for Data Subset Selection.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning A Weighted K-Center Algorithm for Data Subset Selection

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.092115Z digest=sha256:c91c4fd96b5a785f059435e84398de02eca2d21ddaae3e17e52417b38fa22533

Observation 6110d497-23de-454e-bdc6-320e722586c5 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.241959Z digest=sha256:b78a5cd89212aa6e90a0fcfc8545b3125fece896cf63ccf5cabf54a53ac7f706

Observation f1ecba3f-b934-4c42-8545-cff1d5b567c2 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 13

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source=pdf_text observed=2026-08-06T16:45:03.396010Z digest=sha256:8263d556c4471c58567dd3d42b25e7062b239acdc55bc852e0801d8f446b548c

Observation 8f70c22b-e9e8-491a-afbb-5487152b62fe · outbound

This paper cites Dataset Distillation with Neural Characteristic Function: A Minmax Perspective.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Dataset Distillation with Neural Characteristic Function: A Minmax Perspective

Reference 14

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source=pdf_text observed=2026-08-06T16:45:03.542800Z digest=sha256:e7e0c66773f8b3d0be0f9dd8558959856e3eaf09a5f06ddb118aff7e4092314c

Observation 3fd587d9-4a72-46ff-a99d-ffee3889018d · outbound

This paper cites Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 15

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no resolver link, observed 2026-08-06T16:45:03.656920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.656920Z digest=sha256:fc9428b2ac0948a220917cb2a5cdd366dca99c90747049596ac526f7b2abc352

Observation 5c577850-a2ca-4bb7-9535-6cc947ab2f45 · outbound

This paper cites Submodularity in data subset selection and active learning.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Submodularity in data subset selection and active learning

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.118232Z

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.

source=pdf_text observed=2026-08-06T16:45:03.690544Z digest=sha256:fbf327676dfba1bff7ddee6bec5e7d8da5d9584e9d4d9d8a512420c525358885

Observation b12a6b9b-c675-4cd0-8011-8276fc08becb · outbound

This paper cites Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency

Reference 18

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local_arxiv, observed 2026-08-06T16:45:04.891872Z

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.

source=pdf_text observed=2026-08-06T16:45:03.904157Z digest=sha256:2778788db1a5656daff105974aa421cd64dd24d1761c2b621e6f8f7d6c671d15

Observation 78b01f67-e6c6-4b9c-aaa3-dbbb37594592 · outbound

This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 20

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source=pdf_text observed=2026-08-06T16:45:04.275829Z digest=sha256:b0123edb0f8bdb901bf1fb0dc35e966561670e152a5c9f3c439f8a5f1a669cae

Observation bef9d63e-db54-41a4-b6ca-ab05d391592f · outbound

This paper cites Dataset Distillation using Neural Feature Regression.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Dataset Distillation using Neural Feature Regression

Reference 21

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no resolver link, observed 2026-08-06T16:45:04.439133Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:04.439133Z digest=sha256:9371ab53c2dc87d06c411a112cd17916dd8180c42d68137364f3e90943380b0a

Observation 70d100c1-5d19-4578-9060-fe9eb31840ca · outbound

This paper cites CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Reference 2009

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.760497Z digest=sha256:77bbee41069bc46611ea0b0de4f177aec8073d79db4913a2ad6d54c753eb5fcb

Observation 8a4a339b-32bd-4a6e-aa6d-c3f0b882d9ec · outbound

This paper cites Infonce loss provably learns cluster- preserving representations.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Infonce loss provably learns cluster- preserving representations

Reference 2018

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verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.150060Z

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.

source=pdf_text observed=2026-08-06T16:45:02.666040Z digest=sha256:0b9c22082c62b6808306ad6558de09d9aa0adf41b332e63bff8dbd9db1b031d6

Observation 0041a258-e634-4362-89d4-a2981ace20f3 · outbound

This paper cites CLIP-Adapter: Better Vision-Language Models with Feature Adapters.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning CLIP-Adapter: Better Vision-Language Models with Feature Adapters

Reference 2020

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:01.932831Z digest=sha256:809fcfb1763b6febef5d66e1982f25c675f010cf285c1677c150cffec77a3111

Observation 10a05ded-31f2-4f93-8fff-11b81de16b7f · outbound

This paper cites Accelerating Deep Learning with Dynamic Data Pruning.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Accelerating Deep Learning with Dynamic Data Pruning

Reference 2021

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source=pdf_text observed=2026-08-06T16:45:02.924585Z digest=sha256:8acbf5c22277d0a5e075a57ceb95d13f50a58a63f26266bb791620047ecc549d

Observation 79714d65-d35d-4f60-a47b-71ad56dc09c7 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning Imagenet: A large-scale hierarchical image database

Reference 2022

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source=pdf_text observed=2026-08-06T16:45:01.805403Z digest=sha256:75084d9caf73d93b096c27c24899a2e07bc46e85e7b508323bea232835dbff57

Observation 37245830-2e1d-4202-8051-c8f5a7115bf8 · outbound

This paper cites D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data Pruning

Reference 2023

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source=pdf_text observed=2026-08-06T16:45:02.481008Z digest=sha256:31b9b0fe3a7de8a4c5d7bc2ae7ee9f8c969b7c9aaa84ec2079879cc618f2720b

Observation 28fdef32-5d6c-44d5-aeb6-e361646c266d · outbound

This paper cites RWKV-CLIP: A Robust Vision-Language Representation Learner.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning RWKV-CLIP: A Robust Vision-Language Representation Learner

Reference 2024

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metadata mismatch
local_arxiv, observed 2026-08-06T16:45:05.858038Z

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.

source=pdf_text observed=2026-08-06T16:45:02.057317Z digest=sha256:6b70d54292020d4b8c39d2a5a190deafe4b51bc92c1c69c291f67de7bb2af04c

Observation 78b885fd-e83d-4015-8427-d81d74dd68e8 · outbound

This paper cites When Dynamic Data Selection Meets Data Augmentation.

Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning When Dynamic Data Selection Meets Data Augmentation

Reference 2025

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source=pdf_text observed=2026-08-06T16:45:04.059871Z digest=sha256:b02417ec82565ad7e0131812fd19f38af612eb2e23a26a03d54928e1a39b3c10

Pith citing papers

Observation db446510-29a6-4439-80ef-12c4dbd0b8bb · inbound

Data Agent: Learning to Select Data via End-to-End Dynamic Optimization cites this paper.

Data Agent: Learning to Select Data via End-to-End Dynamic Optimization Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning

Reference 17

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arxiv_id, observed 2026-05-15T15:26:10.866034Z

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.

source=pdf_text observed=2026-05-15T15:23:23.950152Z digest=sha256:282228b1a5b726f0f992f36f8b57bd159e4a407adc8732cf36cd561f29668208

Observation f9f19c73-0b25-44ae-8065-dd17e72c28a0 · inbound

CAST: Collapse-Aware multi-Scale Topology Fusion for Multimodal Coreset Selection cites this paper.

CAST: Collapse-Aware multi-Scale Topology Fusion for Multimodal Coreset Selection Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning

Reference 38

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arxiv_id, observed 2026-05-13T06:22:23.413305Z

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

source=pdf_text observed=2026-05-13T06:19:08.362022Z digest=sha256:54dd1b9aac8bc26bb18e0f2fc96c5539017de6ef42a05a39d23b8f74bc2141ac