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Paper Citation Record · LEDGER

Adapprox: Adaptive Approximation in Adam Optimization via Randomized Low-Rank Matrices

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.14958.

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

pith.paper-citation-record.v1
2403.14958 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:35:40.247371Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T18:35:40.304676Z

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 0b28fa6a-0895-48f6-8cb4-0bdc270963a8 · inbound

Low-rank Momentum Factorization for Memory Efficient Training cites this paper.

Low-rank Momentum Factorization for Memory Efficient Training Adapprox: Adaptive Approximation in Adam Optimization via Randomized Low-Rank Matrices

Reference 65

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:35:40.307682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-06T18:35:40.247371Z digest=sha256:183af71fc2fa83856ae13367c5fc1803edbc4b16efdb35f4c50f642e4656f135

Observation b1c3a37b-2c5e-43af-a6be-8bf5131f3b4a · inbound

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

Geometrically Principled Randomized Optimization for Efficient LLM Training Adapprox: Adaptive Approximation in Adam Optimization via Randomized Low-Rank Matrices

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-04T12:51:27.244782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T12:51:27.244782Z digest=sha256:2efb3209ec64b9fbf2b8a07c277d8ed802abc94a4a12e38e9f7677814ea32516