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Paper Citation Record · LEDGER

Coverage-centric Coreset Selection for High Pruning Rates

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2210.15809.

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

pith.paper-citation-record.v1
2210.15809 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 2026-08-17T06:30:58.91139+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:30:09.307285Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:28:52.591114Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 7d5cd2d2-1bc0-428b-bab4-29c560d59292 · inbound

Data Pruning in Generative Diffusion Models cites this paper.

Data Pruning in Generative Diffusion Models Coverage-centric Coreset Selection for High Pruning Rates

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-12T17:30:09.307285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:30:09.307285Z digest=sha256:012df2437819c219f113fde496e8f973cf023a83969339e9698d88195cfada1f

Observation 95c4caa9-3c92-4dd5-8844-67d366a46fb0 · inbound

Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information cites this paper.

Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information Coverage-centric Coreset Selection for High Pruning Rates

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T16:40:12.812916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:40:12.812916Z digest=sha256:f74b7a432b18e8d42b58263d61d263cb69811760a8373f12a8f28d461cbf417b

Observation 6725f29e-4eee-46d7-a72b-7a7c4b93082c · inbound

Integrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model cites this paper.

Integrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model Coverage-centric Coreset Selection for High Pruning Rates

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-10T17:49:15.559588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:49:15.559588Z digest=sha256:730009f7a16edd04d739fbc8b4fc251e6b93a47333a8b193c8531bde79de574f

Observation 29b8f116-d98c-4bf7-b498-a4294d758b08 · inbound

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty cites this paper.

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty Coverage-centric Coreset Selection for High Pruning Rates

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-08T16:55:17.642708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:55:17.642708Z digest=sha256:6ff783d21128e12ba695c2f1be6cfeab0699abf5a454233d40d37d5cfeab57ee

Observation da884035-96d9-469c-a374-9389e8013c92 · inbound

Extending Dataset Pruning to Object Detection: A Variance-based Approach cites this paper.

Extending Dataset Pruning to Object Detection: A Variance-based Approach Coverage-centric Coreset Selection for High Pruning Rates

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T14:53:13.532165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:53:13.532165Z digest=sha256:a2f5f1b823d7debb3a21b4ac37fb2db8ca1afacdf2a9ce1e050fe4876ea415a0

Observation 3990c533-352a-4c60-a394-81ed3c9993ea · inbound

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection cites this paper.

Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection Coverage-centric Coreset Selection for High Pruning Rates

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T00:23:29.686357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:23:29.686357Z digest=sha256:8fc48c4a3796184c0042070172b867dc20a385daf41750425b33b71cc21e933a

Observation 2bc69678-e40c-4db1-8a00-1c76da808f4d · 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 Coverage-centric Coreset Selection for High Pruning Rates

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-19T04:42:58.746986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-19T04:42:33.911146Z digest=sha256:3e1b2bebfdbd9d69b5c5891f62d75901081e74e880671fa921f614e942052405

Observation 674260a7-b5ff-42bc-9d39-00cdaec04f00 · inbound

Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning cites this paper.

Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning Coverage-centric Coreset Selection for High Pruning Rates

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T16:39:58.368301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:39:58.368301Z digest=sha256:0c816ece2febd98729fcb33c2d8941737d4a39caa8e5db3e1a5dd78c07ce6b65

Observation 17cb91fd-1eb1-4fd6-a441-1d73d27125ff · inbound

Omnimodal Dataset Distillation via High-order Proxy Alignment cites this paper.

Omnimodal Dataset Distillation via High-order Proxy Alignment Coverage-centric Coreset Selection for High Pruning Rates

Reference 76

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T09:05:59.676700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T16:15:46.835576Z digest=sha256:b95653e2dd994504bcee55d1186167ca71f53e11ebe7a396c1a7194229de6e6a

Observation 0307d814-f7d4-4026-8054-8b84c3519b3b · inbound

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization cites this paper.

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization Coverage-centric Coreset Selection for High Pruning Rates

Reference 87

Resolution
malformed identifier
arxiv_id, observed 2026-05-11T05:30:57.899850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-10T18:06:46.131725Z digest=sha256:dbc1ffc5f4628682b37ed60cfd2ac9b3dfd270f07feef98dbb89011842306380

Observation 5ec4baaf-6569-413e-bd8d-879b8259a37c · 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 Coverage-centric Coreset Selection for High Pruning Rates

Reference 36

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T22:05:05.658603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-30T22:02:30.217607Z digest=sha256:8a082d33415e517b89a7e6710ee8e13feed897a448563ad4d98b1f31370bb3e3

Observation a3f2c3d3-6680-45c4-a64f-0033c0221f77 · inbound

Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets? cites this paper.

Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets? Coverage-centric Coreset Selection for High Pruning Rates

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T19:28:52.594665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-06-27T01:46:20.177256Z digest=sha256:fc92c8d0241d243eac44c241c54767e1cc836ebb0c93a061e5e2d5c419bfd084

Observation 724f1912-2e1e-43c8-8302-600676d3df1e · inbound

A Coreset Selection Framework with Ensemble Aggregation for Image Classification cites this paper.

A Coreset Selection Framework with Ensemble Aggregation for Image Classification Coverage-centric Coreset Selection for High Pruning Rates

Reference 7

Resolution
unresolved
no resolver link, observed 2026-07-13T05:24:51.424634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-13T05:24:51.424634Z digest=sha256:c311381b3ee547a0ba82bc63109569fd8555743f8cfee1a509e753dbf43f8fab