Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2402.14866.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:52:21.249448Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-16T12:16:17.039197Z
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 b972c15e-35e2-4a06-877e-6224ca043421 · inbound
FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs cites this paper.
FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:52:21.249448Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87caf8c7-1907-426f-a2b8-11d9402ac3e1 · inbound
ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models cites this paper.
ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T20:06:29.213641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.