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Paper Citation Record · LEDGER

Scalify: scale propagation for efficient low-precision LLM training

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.17353.

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

pith.paper-citation-record.v1
2407.17353 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T10:01:56.131253Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T10:02:31.675438Z

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 84ef2b0d-6d00-4360-8707-47ea35c43375 · inbound

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention cites this paper.

Why Low-Precision Transformer Training Fails: An Analysis on Flash Attention Scalify: scale propagation for efficient low-precision LLM training

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-18T10:02:31.677451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-18T10:01:56.131253Z digest=sha256:deddfbe9127e981ff1d843e1dc6cea145733b7ace98c28980f65dbe80c93a2f3

Observation d9292303-2a7c-4659-afeb-e631cb0292ac · inbound

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models cites this paper.

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models Scalify: scale propagation for efficient low-precision LLM training

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-10T12:15:22.298309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T12:10:44.802059Z digest=sha256:394234ff7984b2ff7dd0a569ef515b0846d3fa685cbf1a7828bf4aba9ba9a40a