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Paper Citation Record · LEDGER

Dynamic Sparse Training with Structured Sparsity

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2305.02299.

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

pith.paper-citation-record.v1
2305.02299 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:55:16.221690Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:29:16.384426Z

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 93ea14e3-4ca7-488c-9965-413f3bca0e8c · inbound

Symmetric Pruning of Large Language Models cites this paper.

Symmetric Pruning of Large Language Models Dynamic Sparse Training with Structured Sparsity

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-09T21:55:16.221690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:55:16.221690Z digest=sha256:b3cbc1a75d37659b7e1a7158600a0467949103396605a8371431d2366be55a68

Observation f3813647-2d1c-423c-a7f8-020091578e14 · inbound

Advancing Weight and Channel Sparsification with Enhanced Saliency cites this paper.

Advancing Weight and Channel Sparsification with Enhanced Saliency Dynamic Sparse Training with Structured Sparsity

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-09T04:15:29.137105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T04:15:29.137105Z digest=sha256:af6384e5e074d34213f65d4f75b41f71eb861df97bbeb2ca390bec8ceb859907

Observation 4c9d4b81-0a21-4600-a0c2-d683edb36dc6 · inbound

Dynamic Sparse Training of Diagonally Sparse Networks cites this paper.

Dynamic Sparse Training of Diagonally Sparse Networks Dynamic Sparse Training with Structured Sparsity

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-07T04:15:14.519561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:15:14.519561Z digest=sha256:12b009513f54e56dff000b4bb1c17f7e1ec59aee2eca682dd9da952cc120f5c9

Observation d7b40c06-87dc-442a-bb6b-4b288eb7b9b5 · inbound

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Dynamic Sparse Training with Structured Sparsity

Reference 124

Resolution
unresolved
no resolver link, observed 2026-08-04T21:06:26.280149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T21:06:26.280149Z digest=sha256:507d55ea95cfbff1abc0035077ec17158f14e33e5559a3f9e0e18602ae007190

Observation 793d3538-7bc2-47a3-9ea2-345dc87693cc · inbound

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks cites this paper.

SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks Dynamic Sparse Training with Structured Sparsity

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-04T09:39:37.022959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T09:39:37.022959Z digest=sha256:778de60b590e76739a5b26a21fd0ffea30de1a4ae8a2ecb0ee76bca9f7e384e5

Observation 332e0b4b-4f00-447a-9c33-16a0d7616acd · inbound

On the Stability of Growth in Structural Plasticity cites this paper.

On the Stability of Growth in Structural Plasticity Dynamic Sparse Training with Structured Sparsity

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-19T15:42:38.747256Z

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=2026-05-19T15:38:00.503752Z digest=sha256:e985d96e6584f8c515585a9a103a2bb0d9e399a3cda3c80bea52a0a8431a5a90

Observation 58b31f89-bc9b-4323-b66d-b62abb2d35b5 · inbound

On the Stability of Growth in Structural Plasticity cites this paper.

On the Stability of Growth in Structural Plasticity Dynamic Sparse Training with Structured Sparsity

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-30T20:55:03.841324Z

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=2026-06-30T20:53:15.045006Z digest=sha256:acf398a03b9f67545d2640d22f221f0aa020fbc68360e16b2ee2a7c4ed397714

Observation e30b54d3-278f-4fe5-b3e1-d54f1913410f · inbound

HORST: Composing Optimizer Geometries for Sparse Transformer Training cites this paper.

HORST: Composing Optimizer Geometries for Sparse Transformer Training Dynamic Sparse Training with Structured Sparsity

Reference 148

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:29:42.144734Z

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=arxiv_source observed=2026-05-21T06:28:22.117741Z digest=sha256:270365bee36adc541e4be2a9db4c9daa20873290b4a5f4038611edf7240b3091

Observation d39193be-3ec9-4edd-8988-1c3bfac3b7a4 · inbound

GrapNet: A Programmable Dynamic-Architecture Neural Graph Substrate cites this paper.

GrapNet: A Programmable Dynamic-Architecture Neural Graph Substrate Dynamic Sparse Training with Structured Sparsity

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:29:16.386598Z

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=arxiv_source observed=2026-06-26T21:09:32.333816Z digest=sha256:6268d91be3da48414c6aa6e6ea04d99ad86344797b964893966df87b90798052

Observation ebd3c216-b495-4a92-bb44-6cd0934186f0 · inbound

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference cites this paper.

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference Dynamic Sparse Training with Structured Sparsity

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-01T17:12:35.883139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:12:35.883139Z digest=sha256:d38ef3cd696b5ed5f7a5c17b75e2e1c8cb09299fa65cca661a77c8f5b34267a9