Paper Citation Record · LEDGER

Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2502.15799.

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

pith.paper-citation-record.v1
2502.15799 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:39:49.441328Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:17:28.791413Z

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 30cb9255-5283-4735-a203-d1790ba8f7e6 · inbound

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction cites this paper.

From 2:4 to 8:16 sparsity patterns in LLMs for Outliers and Weights with Variance Correction Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:47:07.699863Z

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 3820d1fe-f91f-428f-920e-d2f4b98dc854 · inbound

Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection cites this paper.

Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T09:59:50.242872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6f81f73-c6e2-4b9d-87ad-42573c42b59e · inbound

The Defense Trilemma: Why Prompt Injection Defense Wrappers Fail? cites this paper.

The Defense Trilemma: Why Prompt Injection Defense Wrappers Fail? Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:25:50.234083Z

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 0079afb4-c7d6-4d23-87df-0c626d1c38bb · inbound

Are Large Language Models Economically Viable for Industry Deployment? cites this paper.

Are Large Language Models Economically Viable for Industry Deployment? Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 65

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:01:19.026071Z

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 55c3f815-572d-4834-977f-6eab7e30dd8a · inbound

Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI cites this paper.

Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:51:17.632975Z

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 eb2e1ace-dc33-4d63-8522-6058e770afea · inbound

Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels cites this paper.

Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:57:42.507878Z

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 ded79d37-7556-4546-8afe-7acde5293003 · inbound

FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences cites this paper.

FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 58

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:56:27.989377Z

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 9fdfb61b-40b5-48a9-b570-9c5463794218 · inbound

Quality Is Not a Safety Proxy Under Quantization cites this paper.

Quality Is Not a Safety Proxy Under Quantization Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:28.793089Z

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 11bcb141-c635-4ecf-a75b-c2e430c0af86 · inbound

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs cites this paper.

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T08:38:51.187917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59678630-0d91-4eb9-aa43-b38c773a051e · inbound

Item Response Theory for AI Safety cites this paper.

Item Response Theory for AI Safety Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models

Reference 1965

Resolution: unresolved; no resolver link, observed 2026-08-06T05:39:49.441328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.