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Paper Citation Record · LEDGER

Dataset Pruning: Reducing Training Data by Examining Generalization Influence

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2205.09329.

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

pith.paper-citation-record.v1
2205.09329 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T23:02:23.453494Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:40:07.609687Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5af602d4-11a0-4e8e-99fb-e88dfa57fc1e · inbound

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation cites this paper.

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 168

Resolution
verified exact
arxiv_id, observed 2026-05-16T17:56:23.605564Z

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=arxiv_source observed=2026-05-16T17:56:23.281678Z digest=sha256:e79bfb58d2ce93af009cc35dd0d2951d923286a10ecac8c67ab9845c89ca2431

Observation a233c40c-5416-4fbe-ace5-5e205afcd03e · inbound

R.I.P.: Better Models by Survival of the Fittest Prompts cites this paper.

R.I.P.: Better Models by Survival of the Fittest Prompts Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T23:02:23.453494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T23:02:23.453494Z digest=sha256:74a7eada498fc4c4e6ffd6cd2776769a2fce07d4c96a854cfbb471a3a2559602

Observation 073918db-e43d-4bb2-b256-1c3e67c3a9f0 · inbound

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation cites this paper.

Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-09T14:15:39.371692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:15:39.371692Z digest=sha256:bda7915babc462ce128537999267de8ce659c614e8b7ba135698440f827b21b4

Observation df5cb2ad-f2cc-43f7-9a4b-a007ba840dea · inbound

Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality cites this paper.

Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-05-19T04:42:58.708905Z

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=2026-05-19T04:42:33.911146Z digest=sha256:878a3b2c988ff512bfad3ad1d937cd0778dbdce77576ec22062f413d13c716d3

Observation c2a8d3a5-f29c-4e6c-a723-76737d06d775 · inbound

Influence Functions for Preference Dataset Pruning cites this paper.

Influence Functions for Preference Dataset Pruning Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.472912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.472912Z digest=sha256:7bdd5bb97a581694a49e2207d1fe62baef246c0aac9ac4cf566af7e6a3036327

Observation 6bad427f-3841-44f5-adf0-b9a80d7127f9 · inbound

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models cites this paper.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-04T22:32:17.849316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:32:17.849316Z digest=sha256:7cb12d87bdf63b2dbf185d771be95182ead4e39e619c4811d03ad269e1b90c7f

Observation d068dc37-bb0b-4770-84c9-ff61f9175b1c · inbound

Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data cites this paper.

Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:21:23.867469Z

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=2026-05-18T13:20:56.418690Z digest=sha256:036a60b92dd97e8f3fe421e6cb9080e57773342d6dd522f14107647b6d2c5503

Observation d859c6a1-2d00-492d-8768-0045a8ede3f6 · inbound

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation cites this paper.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-03T02:34:21.345501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:34:21.345501Z digest=sha256:675d1d478a4283728bbaf171fb1a054b8b4770ae49114bb88650537f7100d14d

Observation 88deb570-5f9d-4490-8764-cfefe56d70e9 · inbound

Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks cites this paper.

Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:06:03.408797Z

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=2026-05-10T15:38:50.333310Z digest=sha256:fe21b7cdb3194e019412acbfcbd6d8b60f89f75e3b2262048675ed99dfe9d317

Observation 84a19e91-de4a-4ec0-8b46-c99377ef5dcd · inbound

Representation-Guided Parameter-Efficient LLM Unlearning cites this paper.

Representation-Guided Parameter-Efficient LLM Unlearning Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 140

Resolution
verified exact
arxiv_id, observed 2026-05-10T06:06:19.167225Z

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=arxiv_source observed=2026-05-10T06:01:46.885030Z digest=sha256:b995590279c5b4c2f7db306989fce453cfa1d6a333c7c2191c592262622cf8e9

Observation 91b0046e-0b63-4bcf-ba0c-75be0c970af0 · inbound

SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning cites this paper.

SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-06-30T22:05:05.675799Z

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=2026-06-30T22:02:30.217607Z digest=sha256:89e4831f84c64e3269232f89dd0693ce83864c577bff3caf629aed07e4da63b5

Observation 471f9fed-a8b5-4b85-bafc-938fed04291b · inbound

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework cites this paper.

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 127

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:07:27.179150Z

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=arxiv_source observed=2026-06-27T18:26:32.883834Z digest=sha256:dc827878a736ae6dc6a04ff9d8f335272c854f1bb9048785dc83ddbe94ad1d89

Observation 56b5d1f3-6972-48fb-b2fb-3f289b6ef74b · inbound

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation cites this paper.

Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-04T20:40:07.611636Z

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=2026-06-25T19:58:21.335371Z digest=sha256:06cb35f10ad8be2a0cc6dbed5a73fa9b23a2d44fa5ccada50bffbacba624c8e6