Paper Citation Record · LEDGER

QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2203.05740.

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

pith.paper-citation-record.v1
2203.05740 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T16:10:57.543897Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:16:48.290504Z

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 481baac7-9b24-4986-a7a1-9e1e883658e2 · inbound

AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization cites this paper.

AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:52:23.058469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation fc23cf6f-f278-4dc4-89ac-1f56cd512da9 · inbound

When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization cites this paper.

When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:51:46.061390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 9647a2e2-8e7a-4499-bd62-7f6c08853bf1 · inbound

When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization cites this paper.

When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T16:10:57.543897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bceef409-cd65-4933-9119-2c0e151c3633 · inbound

Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization cites this paper.

Nonlinear Bipolar Compensation: Handling Outliers in Post-Training Quantization QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:59:01.892057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation dd8d07a8-aee8-4afa-87fd-d632407d86b8 · inbound

CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model cites this paper.

CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:32:47.896291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 391be1b4-f984-48d2-b99c-d145c673b518 · inbound

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization cites this paper.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:58:17.771436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation b296fcaa-424e-4950-81bd-7e4cf0dd5164 · inbound

GoQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization cites this paper.

GoQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:24:00.722123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 4f826c91-87c5-4aa7-bcbc-6da7e0d860a1 · inbound

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models cites this paper.

Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:16:48.292159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.

Observation 876428d7-64c8-44d9-85fd-0ac3d57af8f6 · inbound

MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers cites this paper.

MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-31T02:57:29.293074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.