Paper Citation Record · LEDGER

Rethinking Dataset Distillation: Hard Truths about Soft Labels

As of 16 July 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2604.18811.

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

pith.paper-citation-record.v1
2604.18811 v1

Coverage vector

measured 45 of 45 reference resolution

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measured 45 of 45 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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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

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f1a24e38-00cd-49b3-ae74-1dddd122f8e7 · outbound

This paper cites Knowledge distilla- tion: A good teacher is patient and consistent.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Knowledge distilla- tion: A good teacher is patient and consistent

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.224694Z

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No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation e514c05a-40a6-4460-8c11-ddd965239743 · outbound

This paper cites Dataset distillation by matching training trajectories.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Dataset distillation by matching training trajectories

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.226861Z

Source-reported events for the cited work

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Observation 6b8142ee-dc14-4270-afab-1a95d02b0c83 · outbound

This paper cites Generalizing dataset distillation via deep generative prior.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Generalizing dataset distillation via deep generative prior

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.228675Z

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No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 34019411-3a4a-4302-9e0b-b62ca3d27ddf · outbound

This paper cites Lightweight dataset pruning without full training via example difficulty and prediction uncertainty.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Lightweight dataset pruning without full training via example difficulty and prediction uncertainty

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.216647Z

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Observation 3f1e619e-e619-4419-baf0-9ce12d098cad · outbound

This paper cites Dc- bench: Dataset condensation benchmark.Advances in Neu- ral Information Processing Systems, 35:810–822.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Dc- bench: Dataset condensation benchmark.Advances in Neu- ral Information Processing Systems, 35:810–822

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.208823Z

Source-reported events for the cited work

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Observation 09359595-8353-49e3-9f84-70bf71705d53 · outbound

This paper cites Scaling up dataset distillation to imagenet-1k with constant memory.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Scaling up dataset distillation to imagenet-1k with constant memory

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.206833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation c46c65bd-821f-4207-a3d1-a2d88755a1ca · outbound

This paper cites Fast and accurate data resid- ual matching for dataset distillation.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Fast and accurate data resid- ual matching for dataset distillation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.210810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation a6131b61-6414-4323-9f9c-45f11c934ce2 · outbound

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

Rethinking Dataset Distillation: Hard Truths about Soft Labels Imagenet: A large-scale hierarchical image database

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.212666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 0cc2fc4b-137e-4f8d-abcf-121378444ef5 · outbound

This paper cites Diversity-driven synthesis: Enhancing dataset distilla- tion through directed weight adjustment.Advances in neural information processing systems, 37:119443–119465.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Diversity-driven synthesis: Enhancing dataset distilla- tion through directed weight adjustment.Advances in neural information processing systems, 37:119443–119465

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.214716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation decb5ea6-eac4-4604-b53e-2298bd47ec48 · outbound

This paper cites Knowledge distillation: A survey.Interna- tional Journal of Computer Vision, 129(6):1789–1819.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Knowledge distillation: A survey.Interna- tional Journal of Computer Vision, 129(6):1789–1819

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.220856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation c57ba8ca-d7f6-4490-9bce-09ea57859e14 · outbound

This paper cites Scaling laws for data filtering–data curation cannot be compute agnostic.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Scaling laws for data filtering–data curation cannot be compute agnostic

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.201162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 9b24eb47-2e9b-447e-8184-26198e497d70 · outbound

This paper cites Efficient dataset distillation via minimax diffusion.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Efficient dataset distillation via minimax diffusion

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.204874Z

Source-reported events for the cited work

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Observation 990a0872-f8a6-44be-9584-fcfe2966e394 · outbound

This paper cites Deepcore: A comprehensive library for coreset selection in deep learn- ing.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Deepcore: A comprehensive library for coreset selection in deep learn- ing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.222644Z

Source-reported events for the cited work

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Observation 32243cdc-992e-459e-92f3-2e1255ab4e55 · outbound

This paper cites Towards lossless dataset dis- tillation via difficulty-aligned trajectory matching.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Towards lossless dataset dis- tillation via difficulty-aligned trajectory matching

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.218616Z

Source-reported events for the cited work

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Observation b180a92b-d41d-4401-8e24-3ebd455f5c8a · outbound

This paper cites Large- scale dataset pruning with dynamic uncertainty.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Large- scale dataset pruning with dynamic uncertainty

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.183240Z

Source-reported events for the cited work

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Observation f2dbb5c6-ce72-4c06-a39c-b4a0ba77b285 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Distilling the Knowledge in a Neural Network

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-05-10T09:38:42.750014Z

Source-reported events for the cited work

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Observation 38fd723b-4a7e-4a5a-b7bc-b0d681cea220 · outbound

This paper cites Submodular combinatorial information mea- sures with applications in machine learning.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Submodular combinatorial information mea- sures with applications in machine learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.185481Z

Source-reported events for the cited work

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Observation 23afdace-7e7d-45b2-b39d-6d9a30791298 · outbound

This paper cites Scaling Laws for Neural Language Models.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Scaling Laws for Neural Language Models

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-10T09:38:42.753700Z

Source-reported events for the cited work

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Observation 9e5c5dcb-3f13-4c4e-9f3c-c95e6d145cd0 · outbound

This paper cites Glister: Generalization based data subset selection for efficient and robust learning.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Glister: Generalization based data subset selection for efficient and robust learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.187629Z

Source-reported events for the cited work

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Observation 767dc84a-8bad-4264-8512-9c1af977150c · outbound

This paper cites an unresolved cited work.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Unresolved cited work

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.189805Z

Source-reported events for the cited work

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Observation 7f6499d6-f2e2-45c5-acb3-80c610b6d44b · outbound

This paper cites Selmatch: Effectively scaling up dataset distillation via selection-based initializa- tion and partial updates by trajectory matching.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Selmatch: Effectively scaling up dataset distillation via selection-based initializa- tion and partial updates by trajectory matching

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.181490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 33cb5f12-4b74-4a38-ba5a-75af4e7f579f · outbound

This paper cites Awesome dataset distillation.https : / / github.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Awesome dataset distillation.https : / / github

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.191686Z

Source-reported events for the cited work

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Observation 8214c534-0981-4622-8c94-a4821bf8455d · outbound

This paper cites Active learning by acquiring contrastive examples.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Active learning by acquiring contrastive examples

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.197593Z

Source-reported events for the cited work

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Observation dd7f5f5f-0418-4d0c-94b0-f4b26b1e5464 · outbound

This paper cites Coresets for data-efficient training of machine learning mod- els.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Coresets for data-efficient training of machine learning mod- els

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.177663Z

Source-reported events for the cited work

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Observation ffbecb07-717c-4d06-8f6c-a05fecd0a0e5 · outbound

This paper cites Repeated random sampling for minimizing the time-to-accuracy of learning.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Repeated random sampling for minimizing the time-to-accuracy of learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.193672Z

Source-reported events for the cited work

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Observation 45c6879f-8fa6-4d75-97e3-326578c753db · outbound

This paper cites Deep learning on a data diet: Finding important ex- amples early in training.Advances in neural information processing systems, 34:20596–20607.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Deep learning on a data diet: Finding important ex- amples early in training.Advances in neural information processing systems, 34:20596–20607

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.168377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 7a5d9fef-db7b-4169-92f2-c4c95e313790 · outbound

This paper cites A la- bel is worth a thousand images in dataset distillation.

Rethinking Dataset Distillation: Hard Truths about Soft Labels A la- bel is worth a thousand images in dataset distillation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.170213Z

Source-reported events for the cited work

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Observation 536328b5-13fd-425a-b5bd-8a5a30ea31ab · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Rethinking Dataset Distillation: Hard Truths about Soft Labels High-resolution image synthesis with latent diffusion models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.172175Z

Source-reported events for the cited work

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Observation 7ed6f358-9f9c-4064-9c8c-9745a97186db · outbound

This paper cites Data distillation: A survey.Transactions on Machine Learning Research.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Data distillation: A survey.Transactions on Machine Learning Research

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.175865Z

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Observation a7815067-3def-4966-a713-f8ee71ee8b6c · outbound

This paper cites Generalized large-scale data condensa- tion via various backbone and statistical matching.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Generalized large-scale data condensa- tion via various backbone and statistical matching

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.161124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation b9144f7c-3033-4c18-80c8-c5e789881567 · outbound

This paper cites Elucidating the design space of dataset condensation.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Elucidating the design space of dataset condensation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.199398Z

Source-reported events for the cited work

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Observation 5732bbf0-c770-48fa-82e9-6943c7760fc5 · outbound

This paper cites Beyond neural scaling laws: beat- ing power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Beyond neural scaling laws: beat- ing power law scaling via data pruning.Advances in Neural Information Processing Systems, 35:19523–19536

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.163014Z

Source-reported events for the cited work

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Observation 6541caf9-5fb7-4924-aa36-891eefedc0cc · outbound

This paper cites Dˆ 4: Dataset distillation via disentangled diffu- sion model.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Dˆ 4: Dataset distillation via disentangled diffu- sion model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.166441Z

Source-reported events for the cited work

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Observation 180fd5b6-cea2-4fb2-a811-dd920b6671c8 · outbound

This paper cites On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm.

Rethinking Dataset Distillation: Hard Truths about Soft Labels On the diversity and realism of distilled dataset: An efficient dataset distilla- tion paradigm

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.202951Z

Source-reported events for the cited work

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Observation 83f74ba7-603e-4fb4-a67a-1c07f43e8f18 · outbound

This paper cites an unresolved cited work.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Unresolved cited work

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.155713Z

Source-reported events for the cited work

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Observation 876c352b-0c70-4025-a459-73a13fce1375 · outbound

This paper cites Cafe: Learning to condense dataset by align- ing features.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Cafe: Learning to condense dataset by align- ing features

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.157452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 56854c23-886a-484e-b56f-5320b2c10f97 · outbound

This paper cites an unresolved cited work.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Unresolved cited work

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.164674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 295a1d03-7aee-4b0f-a946-16c7340975f9 · outbound

This paper cites Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.Advances in Neural Information Process- ing Systems, 36:73582–73603.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective.Advances in Neural Information Process- ing Systems, 36:73582–73603

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.152174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 70e0a51c-fe7d-4bda-bae7-59f2ce4b7b72 · outbound

This paper cites Dataset condensation with dis- tribution matching.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Dataset condensation with dis- tribution matching

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.153991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation aeb69cc0-1167-4029-98bb-50859fa2fa25 · outbound

This paper cites Dataset condensation with gradient matching.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Dataset condensation with gradient matching

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.150192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation f798e9b7-d9ce-482a-bfa6-7570ad1fb359 · outbound

This paper cites Dataset distillation using neural feature regression.Advances in Neu- ral Information Processing Systems, 35:9813–9827.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Dataset distillation using neural feature regression.Advances in Neu- ral Information Processing Systems, 35:9813–9827

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.159324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation d865bd71-eec8-4e83-9627-895122e7ff00 · outbound

This paper cites Many subsequent works, like EDC [31], DW A [9], G-VBSM [30], etc.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Many subsequent works, like EDC [31], DW A [9], G-VBSM [30], etc

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.173971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation cc03fe2e-bcf8-4a5b-b2a6-39c6cf563607 · outbound

This paper cites an unresolved cited work.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Unresolved cited work

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.179583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 90a0c1bb-d808-4979-aa33-13c17cb4b26a · outbound

This paper cites The model architecture is ConvNet- D3, and we compare performance for both IPC 10 and IPC.

Rethinking Dataset Distillation: Hard Truths about Soft Labels The model architecture is ConvNet- D3, and we compare performance for both IPC 10 and IPC

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-21T22:25:43.195743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Observation 23f0b5bf-acbd-4357-9bdd-92e54a2dc0b1 · outbound

This paper cites Avg. Transfer.

Rethinking Dataset Distillation: Hard Truths about Soft Labels Avg. Transfer

Reference 45

Resolution: malformed identifier; arxiv_id, observed 2026-05-10T09:38:42.760422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-15T06:30:58.975436+00:00.

Pith citing papers

No inbound Pith citation observations are available.