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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:45:02.200177Z digest=sha256:6ad6ea1295492085777be278032dc894f7c63cf8705554482bf90bfc4f5a7dfb

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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no resolver link, observed 2026-08-06T16:45:02.372296Z

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source=pdf_text observed=2026-08-06T16:45:02.372296Z digest=sha256:3c927a4a92b9af8b78ca6e29268bfe934581d9acca623f5fc572997afb46f7eb

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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no resolver link, observed 2026-08-06T16:45:02.589586Z

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source=pdf_text observed=2026-08-06T16:45:02.589586Z digest=sha256:8e1da32e50e29365441ba9afb077fd3efdae2c630516d252c1dd7b94397c01ee

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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no resolver link, observed 2026-08-06T16:45:02.762139Z

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source=pdf_text observed=2026-08-06T16:45:02.762139Z digest=sha256:db2e2f0d6f2c49eadd700ea0735e39aeec5da42ff8eb2326f51b21cd471713c6

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.092115Z digest=sha256:d8709c71af87a06af93b87ba5f3717f94db9f60f4630dc5003f9650fd4263529

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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no resolver link, observed 2026-08-06T16:45:03.241959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.241959Z digest=sha256:54e1c697026c7ae5e4b6c4d27c09aeb62bfe1a69d44b268d31132ac633914007

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.396010Z digest=sha256:525f0bff7c0d30b17f47e988346006f36e51b33f5beb7fe8de9914108e8d73ce

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=sha256:24a2cf91f99f9c4f857f054b5ffac0d5bfe0a7477dac113533bbfba8fd105a9e

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=sha256:7fbb549776e0fb05e0321d9c2ab917af32e7ce16b821fc3f89c722cfffa0cb71

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:45:03.690544Z digest=sha256:d97c9328b65c4b91bd8b9a1b45c49847731dc667340137a90c57366ceb809b18

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:45:03.904157Z digest=sha256:f470ff54736c9babcd070e6648865bc1050b18eed6e9c3c52ca5a13cba3983f7

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=sha256:0e94d6082ba7212049f4908b4a76ecc8cc7b9804e3289cc3c2429908ba763ed0

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:04.439133Z digest=sha256:0d5ffad564370f21ae63130ae8fe361f01f9ac8e7d853808f2bbac483c82e9b4

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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no resolver link, observed 2026-08-06T16:45:03.760497Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.760497Z digest=sha256:c980623e939f0048b7ea8af295e91ffa52cb916d915b51aeced2f0a0e0711ef5

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:45:02.666040Z digest=sha256:abe12a4e3a72e5675e9cc9e8527d5705c4c5c82b67defdeced9373634f6a2c56

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:ee23fd376e9d8b1440b9e817441946960a1d10368e36dc07814852042acb2af6

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:a705c4dd810548db723fe57eeafbec7b0c064f381c71da84cf1fa5cce759cd44

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:01.805403Z digest=sha256:b3f5c02b325866d3ae84c2bb1bfea0bf8450a5caf29a62af4c8bd2b2ecf40e68

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:c6fcd45060925a11e032be6554bf0faaaf9f19adac3aac7cc511875ea4e011f7

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T16:45:02.057317Z digest=sha256:002bb2479fde61d2122dd8246f34e8d8d8ce3db46d2368dbe3a59137dc069c58

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:ae1abf4c7c8cd17acdc9ecb4701b5fa54bce2003980de4dad52479aa16e149fe

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T15:23:23.950152Z digest=sha256:0f7b53db6a4218f61ac3aeeb3bd53487392c5d01876d0e70a40834cdcdceda5e

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T06:19:08.362022Z digest=sha256:04da8e983a1c07b43ab9533acd2591fdaa97a9fa146431f8834d3fcf57fdfd73