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Paper Citation Record · LEDGER

SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2405.16325.

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

pith.paper-citation-record.v1
2405.16325 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:30:31.064305Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:43:54.742148Z

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 7657d52f-6d28-451b-b329-5051d37c9f0c · 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 SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-09T17:26:45.465953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T17:26:45.465953Z digest=sha256:cbacb0022aefec060194deae28ffb98ae95f18d71529ff23e1f78f0e8553d664

Observation 1ce5f9e2-2257-4df7-a0f2-52512707ef44 · inbound

SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity cites this paper.

SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-15T19:30:31.064305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T19:30:31.064305Z digest=sha256:63bd6f079c4d1c26a21a620bf20ed352e77e82c94bd77b534e121c8409e5a680

Observation dd92140d-b171-44ce-8c9d-0eb824125b5c · 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 SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T05:11:11.646741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:11:11.646741Z digest=sha256:242198d6229afaf393c84256bdc72df12d848e1def6019bfc9aa87622542f901

Observation 78eec323-f019-409f-bcdc-798b2dbb9949 · 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 SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:43:54.743986Z

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=2026-06-29T19:40:42.033793Z digest=sha256:12b3005ff62f8160c2cce0e7af7fdb2f4d99e69d734e20f04002f3246c68f1ab

Observation c1eb0afa-b30c-49bd-b6dd-754ccc0131fa · inbound

ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers cites this paper.

ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-01T10:32:05.695707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T10:32:05.695707Z digest=sha256:b5c0fc70f76d0eb632d0976b51acd60f484e7cf01b41fd5f6ed69cb145937287