Paper Citation Record · LEDGER

On the Impact of Calibration Data in Post-training Quantization and Pruning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2311.09755.

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

pith.paper-citation-record.v1
2311.09755 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 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:34:13.962578Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T22:57:26.718837Z

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 d6e59e3c-955e-45d6-9b20-4671e59d86f2 · inbound

$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts cites this paper.

$\mu$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts On the Impact of Calibration Data in Post-training Quantization and Pruning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:13.962578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 331c618e-2ccc-4c6e-bb5e-71621d0386a4 · inbound

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness cites this paper.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness On the Impact of Calibration Data in Post-training Quantization and Pruning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T17:55:14.215638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59665ac1-6a4d-4c99-8044-87687c225c44 · inbound

Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels cites this paper.

Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels On the Impact of Calibration Data in Post-training Quantization and Pruning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:41:16.285739Z

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 466a9892-4fec-4c28-9ee8-b7668b1a24f9 · inbound

EinSort: Sorting is All We Need for Tensorizing LLM cites this paper.

EinSort: Sorting is All We Need for Tensorizing LLM On the Impact of Calibration Data in Post-training Quantization and Pruning

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:57:26.720144Z

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.