Pith. sign in

Paper Citation Record · LEDGER

From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.11239.

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

pith.paper-citation-record.v1
2407.11239 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:36:04.436881Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T12:45:37.368172Z

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 56d9ee7b-bcd1-435f-99cd-ea56ddb9ad75 · inbound

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models cites this paper.

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-09T15:36:04.436881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T15:36:04.436881Z digest=sha256:ca43c080d700e61c764e6f30240d95745b38ca9a9ddc56c119396b96d41bc201

Observation 2b676826-9b17-4b04-b090-538dc0d1f1d4 · inbound

Accelerating Attention with Basis Decomposition cites this paper.

Accelerating Attention with Basis Decomposition From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-04T12:54:31.091978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T12:54:31.091978Z digest=sha256:6811f4c99b8008dc44b8c128493598313b24d32ee66f7d9fddf379fd685ac4ba

Observation c380c0bc-7622-4b41-93ec-11ecb2949cb3 · inbound

Geometrically Principled Randomized Optimization for Efficient LLM Training cites this paper.

Geometrically Principled Randomized Optimization for Efficient LLM Training From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-04T12:51:25.155303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T12:51:25.155303Z digest=sha256:9eff9821987e3c0ad83b8911f673b1785ba77283a95ee3a63e7ece0e619721cb

Observation b234c5ff-8a7a-46b3-84b3-70822a73f37d · inbound

SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression cites this paper.

SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-15T12:45:37.370437Z

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=2026-05-15T12:43:57.572912Z digest=sha256:4270e1143bcb1a567ae172776a5ac9be6c3e44503f80f54b21379ee3961e35d3

Observation 9e1f4ea6-7da9-41b6-a7a3-91fedb994ee1 · inbound

TIDE: Every Layer Knows the Token Beneath the Context cites this paper.

TIDE: Every Layer Knows the Token Beneath the Context From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 90

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:56:10.061257Z

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=2026-05-08T10:35:46.447739Z digest=sha256:42f2f3a5b4ee3c1f2adfd9e318e650e7d05cd85c1dd87ab04d4f0d0cba330b5c

Observation e35cd850-9a20-4f6d-a820-8fd300780d6a · inbound

Pro-KLShampoo: Projected KL-Shampoo with Whitening Recovered by Orthogonalization cites this paper.

Pro-KLShampoo: Projected KL-Shampoo with Whitening Recovered by Orthogonalization From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T19:01:12.792211Z

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=2026-05-08T13:06:05.234216Z digest=sha256:6a70c24e611a2ed3894f2b80e092800fe1731fa553b21f2a7fa447b252e6ed2f