Paper Citation Record · LEDGER

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization

As of 9 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2607.25451.

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

pith.paper-citation-record.v1
2607.25451 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:28:36.762990Z

measured 18 of 18 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

18 of 18 outbound references displayed

verified exact0
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e3f5fecc-d6ed-44b8-b5ee-aeed06212a53 · outbound

This paper cites Zhang, T.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Zhang, T

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.712811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7269da0-e556-4774-a7ce-e707226b0c3e · outbound

This paper cites Emergent and Predictable Memorization in Large Language Models.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Emergent and Predictable Memorization in Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.723197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2df8b138-86f4-42a5-bf37-5a1e913ef9e6 · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Quantifying Memorization Across Neural Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.729415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 085b05b4-f378-4c53-b80b-e941b0999c33 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization QLoRA: Efficient Finetuning of Quantized LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.735702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b59d21eb-9eb5-43ed-a63c-79a568d2f17c · outbound

This paper cites The case for 4-bit precision: k-bit Inference Scaling Laws.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization The case for 4-bit precision: k-bit Inference Scaling Laws

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.738468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e6d474f-7e79-4c0b-bb68-074a9202963c · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.741304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3f4cbaa-3822-4071-b36b-51b4accf3ae0 · outbound

This paper cites Membership Inference Attacks against Machine Learning Models.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Membership Inference Attacks against Machine Learning Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.755151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2cd9ca4-0413-4f7c-aea5-8d6e9181b755 · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.757755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54b9eacb-6ae6-4566-b8d2-9f66742b6eaa · outbound

This paper cites Catastrophic Failure of LLM Unlearning via Quantization.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Catastrophic Failure of LLM Unlearning via Quantization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.760419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9aaec1cc-968f-45b3-ace2-4d7672637229 · outbound

This paper cites Quantifying and Analyzing Entity-level Memorization in Large Language Models.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Quantifying and Analyzing Entity-level Memorization in Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.762990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54cbab7-193e-4ca6-ba91-6b1e437596ed · outbound

This paper cites Pointer Sentinel Mixture Models.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Pointer Sentinel Mixture Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.752498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 095ae816-1f2f-4559-af5d-28f19fa92466 · outbound

This paper cites Measuring memorization in language models via probabilistic extraction.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Measuring memorization in language models via probabilistic extraction

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.744125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c08c5f00-e512-4bec-91e8-c1bbee4adaf8 · outbound

This paper cites Extracting Training Data from Large Language Models.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Extracting Training Data from Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.726398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c26642-dfff-45da-ace2-1de055239726 · outbound

This paper cites LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.732330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efd23a73-1290-47cb-ac87-2a7c340265f7 · outbound

This paper cites Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.719776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19fdb6d2-73f5-4fee-bf57-d9ae4e481d44 · outbound

This paper cites How Quantization Impacts Privacy Risk on LLMs for Code?.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization How Quantization Impacts Privacy Risk on LLMs for Code?

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.747000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed7c5182-a069-4a4f-a6ae-659dec6c2b29 · outbound

This paper cites Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.716504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc02a45e-ab91-4dc6-a180-a48b3ea83203 · outbound

This paper cites CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage.

Bits and Memories: Measuring Verbatim Extraction Across LLM Quantization CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-01T02:28:36.749900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.