Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2301.12017.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:43:42.054479Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T15:45:48.753174Z
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 a5f95e1d-1a70-40d1-b2b4-357946b8c537 · inbound
Yi: Open Foundation Models by 01.AI cites this paper.
Yi: Open Foundation Models by 01.AI Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
Reference 83
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T05:47:27.923565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3a8be816-0f47-4b37-ac35-168f117bef20 · inbound
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis cites this paper.
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:43:42.054479Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ed3bb80-a40d-4716-947d-0d3fa925855f · inbound
JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators cites this paper.
JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T15:45:48.754779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 144f4061-7c12-437e-9372-b2fe10766e23 · inbound
JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators cites this paper.
JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T11:46:50.520083Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.