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Paper Citation Record · LEDGER

Subspace Optimization for Large Language Models with Convergence Guarantees

As of 23 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 2026-08-23T06:30:58.430688+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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-23T05:57:09.276224Z digest=sha256:053b035105b4a8b7edfc09e137966addc88dc9e8c88e120ba3b37c4a707a1d23

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=sha256:5eb23c4de6dc8bc063265b37df9b9c9486c3a8c1c74d37d0acf4b433a1ec81b2

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:ea9083a6ba201e3ab678be10b090443830fee49e25daac2ee4d149809a4c06b3

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:a3e6500ea5351016dc40d3ec90c1099b8481a4edb407701ad6ad581aa1409c5a

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-21T23:46:54.620034Z digest=sha256:413d46ab52f49b8c80c2b8da7b40b43003ca048fa60badbb02bcada44ab56f4c

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=sha256:1e5f3a4ce02cdb65d0481b6fbf7d7d34f973e4a7fa4f1d77dec0767aae828d75

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-18T14:51:30.312509Z digest=sha256:a599397721577cc54ec5f1411ffe8136d0e7e4a5314e96eaac778a7ebc25f39d

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-08T13:06:05.234216Z digest=sha256:e90e596959d5d0259b5970a87e0b7c566ce95d21d9837a0437dfe21013dc31f1

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-12T04:47:04.868735Z digest=sha256:892bc47282f1399344a24de31e29164d7f81737eeaf484c95e1bf846c7e46a06

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-13T06:20:42.781613Z digest=sha256:8f8ab7998c730a0f46905a085bca1f5fb7064f45f8b1eae7ee71dd8d3a309f05

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-07-08T22:16:44.668076Z digest=sha256:9184adece7de1eb28b4a56965c55ba95c07da12e93661f66e654db9bfadfaab2

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=sha256:749d2d012b059ecedbbab68e5019375dcea1fecedfd4e7523de8e35ff5335285