Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
From Low Rank Gradient Subspace Stabilization to Low-Rank Weights: Observations, Theories, and Applications
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.11239.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T12:54:31.091978Z
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 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.
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.
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 2026-08-08T06:32:00.761636+00:00.
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 2026-08-08T06:32:00.761636+00:00.
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 2026-08-08T06:32:00.761636+00:00.