Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.02763.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T12:07:14.107234Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T12:07:14.470257Z
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 c94e5b85-c580-4df9-a17e-51e11091f156 · inbound
Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers cites this paper.
Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T12:07:14.477448Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation e6d42115-9932-4ba7-8750-1761b9a812e0 · inbound
I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation cites this paper.
I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation ADFQ-ViT: Activation-Distribution-Friendly Post-Training Quantization for Vision Transformers
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T17:57:09.614245Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.