Paper Citation Record · LEDGER

Norm Tweaking: High-performance Low-bit Quantization of Large Language Models

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2309.02784.

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

pith.paper-citation-record.v1
2309.02784 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:09:46.745046Z

measured 0 of 1 external citation measurements

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 2026-08-23T06:30:58.430688+00:00.

Observation 4e6f0f41-db65-4dc9-aceb-38cd5c643d26 · inbound

Deploying Foundation Model Powered Agent Services: A Survey cites this paper.

Deploying Foundation Model Powered Agent Services: A Survey Norm Tweaking: High-performance Low-bit Quantization of Large Language Models

Reference 211

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:46.745046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91dfbf79-da4b-491f-a9e8-db66a71a3b3d · inbound

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring cites this paper.

Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring Norm Tweaking: High-performance Low-bit Quantization of Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.478372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.