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Paper Citation Record · LEDGER

Subspace Optimization for Large Language Models with Convergence Guarantees

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2410.11289.

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

pith.paper-citation-record.v1
2410.11289 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:36:04.589680Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T22:25:39.548166Z

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 d9141f1c-e2fc-42e2-a2ca-79107ea60e5d · inbound

GWT: Scalable Optimizer State Compression for Large Language Model Training cites this paper.

GWT: Scalable Optimizer State Compression for Large Language Model Training Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-23T05:57:36.612097Z

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=2026-05-23T05:57:09.276224Z digest=sha256:9b212cd16542aa9ee7caf1e0cee4e5447697842d08f92dbbaed3f9f38d97d61b

Observation dfac57be-e5e6-4ba9-8383-5e67cd2b3517 · inbound

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

CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-09T15:36:04.589680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T15:36:04.589680Z digest=sha256:ddd35bee9efc4162574aa3fbce4728ad1a1e0a3766c1289dd771dfce914bd4b5

Observation 723f37ca-d918-4659-b148-8b7389db98c5 · inbound

A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models cites this paper.

A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-08T13:31:42.048006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:31:42.048006Z digest=sha256:33a7c68473104b2c1382716cb02ca11d41ce95ceb3bb3faf91abc612ee821eb6

Observation ebb7ee23-15e3-4f34-9b01-749953309ab2 · inbound

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking cites this paper.

Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T13:06:24.246688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:06:24.246688Z digest=sha256:b0482ec0969d71dc96269d152664ab600d714d2375be15d6f3738ae00384bc01

Observation 38f9b6a3-ba15-47bc-9cdb-220ec0cb7a97 · inbound

Memory-Efficient Differentially Private Training with Gradient Random Projection cites this paper.

Memory-Efficient Differentially Private Training with Gradient Random Projection Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-21T23:50:47.514836Z

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=2026-05-21T23:46:54.620034Z digest=sha256:e1ef7c3a0236da25a299dd94614494ffaefd9676b5d54e2668f63adbbea429ea

Observation ccd0a2ae-c1ed-4255-8051-ae80ee6c9889 · inbound

From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees cites this paper.

From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-04T17:01:42.075076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:01:42.075076Z digest=sha256:5f5aa3a862850069cf2cece96cc168493811cb8fd6a9d1bcc2c64d00c511aedf

Observation 1dd91a1a-c8d2-43cf-bea9-6cf9a2778342 · inbound

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure cites this paper.

CR-Net: Scaling Parameter-Efficient Training with Cross-Layer Low-Rank Structure Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-18T14:52:41.177711Z

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=2026-05-18T14:51:30.312509Z digest=sha256:e7e9d937d52715c68da327d4c14116fd21f56933076ff03d27ec377d918543b9

Observation 76f83585-c9ce-427b-9269-1557acd2287a · inbound

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

Pro-KLShampoo: Projected KL-Shampoo with Whitening Recovered by Orthogonalization Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:01:12.885801Z

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=sha256:32ef6178bdc2eebe21db29acfb99eadb13202a8d18b3af17f4acce796ba8e05f

Observation a9cb9c52-fe7c-4318-945c-3696a6d1c235 · inbound

BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization cites this paper.

BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:56:26.503281Z

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=2026-05-12T04:47:04.868735Z digest=sha256:c1f90fe2854ef35e94c5a4fd1862a4197754503c8c7df967142ac519143049ea

Observation eb0a1a1a-e5e2-4bc7-99b0-1f2950ec9d72 · inbound

BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization cites this paper.

BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-13T06:22:23.282182Z

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=2026-05-13T06:20:42.781613Z digest=sha256:741fbc72ccdb2548e8f957d6f75f6136a602684e1618c0bc41d2ee5471bff910

Observation 9143cfc6-cddb-42fe-81f2-ec2ad5ef5bcc · inbound

No Subspace to Track: Non-Identifiability and Optimizer State in Low-Rank Training cites this paper.

No Subspace to Track: Non-Identifiability and Optimizer State in Low-Rank Training Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-07-08T22:25:39.549901Z

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=2026-07-08T22:16:44.668076Z digest=sha256:13c510d42ea5799eabcdd7fad089818140b3f3c78346b3948f8a0c6cbc39a4ab

Observation 1bc5d689-3c66-4b8c-8c83-a11f84e130fc · inbound

No Subspace to Track: Non-Identifiability and Optimizer State in Low-Rank Training cites this paper.

No Subspace to Track: Non-Identifiability and Optimizer State in Low-Rank Training Subspace Optimization for Large Language Models with Convergence Guarantees

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-02T08:27:40.379240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T08:27:40.379240Z digest=sha256:4e1d8a3e3a694d4d95e14a912916dab0ede6c5fba322c08370e7faef1aa076d1