Paper Citation Record · LEDGER

QuantEase: Optimization-based Quantization for Language Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2309.01885.

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

pith.paper-citation-record.v1
2309.01885 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:25:56.043979Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:56:27.571291Z

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 81eaa5ab-a916-4479-9896-aee906f07dfa · inbound

A Survey on Efficient Inference for Large Language Models cites this paper.

A Survey on Efficient Inference for Large Language Models QuantEase: Optimization-based Quantization for Language Models

Reference 200

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:39:33.223207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3be87f72-e4e0-4525-9676-670a62fce13b · 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 QuantEase: Optimization-based Quantization for Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.368395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 885e6a86-93df-4c1f-bdfb-3e81b0923770 · inbound

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization cites this paper.

CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization QuantEase: Optimization-based Quantization for Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T23:25:54.600693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab94960-63d4-4e32-a623-9dce8cb7122e · inbound

Semantic Retention and Extreme Compression in LLMs: Can We Have Both? cites this paper.

Semantic Retention and Extreme Compression in LLMs: Can We Have Both? QuantEase: Optimization-based Quantization for Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:56.043979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6637be0c-4ca1-4c3d-8ddc-3212534fc42b · inbound

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization cites this paper.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization QuantEase: Optimization-based Quantization for Language Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:30:44.076691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f028cbb6-8e60-4fb9-9a6e-ce851316d0af · inbound

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization cites this paper.

OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization QuantEase: Optimization-based Quantization for Language Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:18.223978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 39e6369f-0991-448e-8259-471e9fdd7b3a · inbound

Averaged Evaluation Masks Capability Trade-Offs: Multi-Source Calibration for High-Sparsity LLM Pruning cites this paper.

Averaged Evaluation Masks Capability Trade-Offs: Multi-Source Calibration for High-Sparsity LLM Pruning QuantEase: Optimization-based Quantization for Language Models

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T01:56:27.573652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.