Paper Citation Record · LEDGER

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2502.05003.

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

pith.paper-citation-record.v1
2502.05003 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:03:44.648169Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:47:26.479505Z

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 dd72ea8d-4238-480b-89eb-25cb911e6a7b · inbound

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models cites this paper.

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T23:03:44.648169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d03675b-b4af-43a8-9b9a-58862f3451e3 · inbound

Scaling Law for Quantization-Aware Training cites this paper.

Scaling Law for Quantization-Aware Training QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:41:08.146988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d5d4d8a-3600-43c7-8af9-e07044b03515 · inbound

FP4 All the Way: Fully Quantized Training of LLMs cites this paper.

FP4 All the Way: Fully Quantized Training of LLMs QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:38.408806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c0ccf2a-2b84-4fdc-995f-53ff96de54c8 · inbound

Unified Scaling Laws for Compressed Representations cites this paper.

Unified Scaling Laws for Compressed Representations QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:40:09.909242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd28a4c3-804b-470b-960f-b73fc20931a0 · inbound

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models cites this paper.

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models QuEST: Stable Training of LLMs with 1-Bit Weights and Activations

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:47:26.483109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.