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Paper Citation Record · LEDGER

1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.14267.

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

pith.paper-citation-record.v1
2408.14267 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 2026-08-16T04:52:46.696395Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T04:30:57.207000Z

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 cae948a6-4b5f-40e3-a968-5b5c06ff15a2 · inbound

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics cites this paper.

Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics 1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-16T04:52:46.696395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:52:46.696395Z digest=sha256:e4dedaefe3e77975a736dbcb9d41603bee7e2d1d293290ede99338f6ec50636f

Observation 37b01b98-088b-40bb-8fe7-0337f435d86e · inbound

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities cites this paper.

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities 1-Bit FQT: Pushing the Limit of Fully Quantized Training to 1-bit

Reference 83

Resolution
verified exact
local_arxiv, observed 2026-08-16T04:30:57.213034Z

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=pdf_text observed=2026-08-16T04:30:56.273257Z digest=sha256:757690fb83f670fced61dbd79fc117aa24a54310e241f2885d3584cacb1ab7eb