Paper Citation Record · LEDGER

Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2310.08915.

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

pith.paper-citation-record.v1
2310.08915 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:55:16.344876Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:06:16.827177Z

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 ac2fd5aa-031b-4acd-b816-861d161083d1 · inbound

Symmetric Pruning of Large Language Models cites this paper.

Symmetric Pruning of Large Language Models Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-09T21:55:16.344876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bb0768b-f1d0-41c6-ae2b-0377f58ecf50 · inbound

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs cites this paper.

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T17:26:45.392303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ca45ddc-6dd6-4a4b-aa4d-3f385f7fd442 · inbound

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity cites this paper.

Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:16:23.075668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 839236d8-5228-4f8f-9019-a9802689158d · inbound

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage cites this paper.

Toward Efficient SpMV in Sparse LLMs via Block Extraction and Compressed Storage Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T16:57:33.377605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a5aad82-a7e2-47bc-8c2b-4632188e5680 · inbound

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models cites this paper.

Amber Pruner: Leveraging N:M Activation Sparsity for Efficient Prefill in Large Language Models Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T05:11:11.693310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eb01700-c5d4-43e4-88fc-1862242d86c3 · 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 No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 250

Resolution: unresolved; no resolver link, observed 2026-08-04T21:06:26.915311Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f98ba68-47d3-4d4b-96cd-99064ff82678 · inbound

SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models cites this paper.

SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:05:49.223989Z

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.

Observation 4ac09952-2a4c-499c-a8a6-9c8e1944693a · inbound

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models cites this paper.

RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.707509Z

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.

Observation e2aa05a2-d2e1-48bd-80b2-d71257d21897 · inbound

CRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search cites this paper.

CRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:06:16.828733Z

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.