Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2309.02784.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T03:55:40.548166Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T02:39:33.183848Z
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 3241a9a5-03a8-4c67-b29b-c81b78e9b47d · inbound
A Survey on Efficient Inference for Large Language Models cites this paper.
A Survey on Efficient Inference for Large Language Models Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Reference 188
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T02:39:33.185483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation db20af93-eb7e-4b9b-8bb2-9d879a52b75d · inbound
Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems cites this paper.
Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T03:55:40.548166Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.