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Paper Citation Record · LEDGER

Q-ViT: Fully Differentiable Quantization for Vision Transformer

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2201.07703.

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

pith.paper-citation-record.v1
2201.07703 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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:34:37.600121Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T02:06:26.134967Z

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 f45b2a24-d1fa-46fa-80e0-470b2df1be80 · inbound

UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model cites this paper.

UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer Model Q-ViT: Fully Differentiable Quantization for Vision Transformer

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-10T17:34:37.600121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T17:34:37.600121Z digest=sha256:eb092277de7d03f96d6be7256bde69004a7268c370d08b93ccae40d3625600ee

Observation e6ac58b4-c9b3-4bf2-9538-e6cc66c8f84b · inbound

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats cites this paper.

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats Q-ViT: Fully Differentiable Quantization for Vision Transformer

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-07-02T02:06:26.137111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-28T11:20:06.292977Z digest=sha256:f9ae1bfa66c3915f3266178b6874b28d4e737b8e95dc37876581bbbc6b9e2187

Observation a7f2ba69-2e4f-4f6a-9051-69c02e0e1e90 · inbound

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats cites this paper.

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats Q-ViT: Fully Differentiable Quantization for Vision Transformer

Reference 37

Resolution
unresolved
no resolver link, observed 2026-07-15T10:56:54.155632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T10:56:54.155632Z digest=sha256:43481e0a1d18c7f242706c591e4716b0bf4f4cf0235c46be4ff68531b9c08f91

Observation 3f3ecb18-5068-452a-a7cc-e4c59bbbeed0 · inbound

qZACH-ViT: Quantization-Aware Intrinsic Explanations with Recursive Attribution-Stabilized Optimization cites this paper.

qZACH-ViT: Quantization-Aware Intrinsic Explanations with Recursive Attribution-Stabilized Optimization Q-ViT: Fully Differentiable Quantization for Vision Transformer

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-01T23:32:35.385258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T23:32:35.385258Z digest=sha256:73b2273a6dd60620cc8ec31612b25f26492c02b8ab5955156e8bf3521a1028d7

Observation 481aa083-e58a-491b-a42e-3bab8fdd011d · inbound

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation cites this paper.

DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation Q-ViT: Fully Differentiable Quantization for Vision Transformer

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T00:24:54.156270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:24:54.156270Z digest=sha256:ac35440a124a04b8e9c44d7d21a72eb1804b48a28a6276bec79a26bbdabcdd37