Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2502.15799.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T09:59:50.242872Z
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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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.