Paper Citation Record · LEDGER

Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2309.05516.

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

pith.paper-citation-record.v1
2309.05516 v5

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 2026-08-13T06:32:02.005865+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:19:57.378916Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:39:45.486717Z

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 1c748489-ba7c-4797-a677-e32498911cd4 · 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 Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.378916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c50c2699-05fa-4df3-9c25-4c403dac3da7 · inbound

AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results cites this paper.

AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:22:28.147545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6c4e8709-e7a7-4e5d-9709-47d47966f795 · inbound

Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference cites this paper.

Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:05.672891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13897263-99c9-41a4-b548-2c9e10523d40 · inbound

FP4 All the Way: Fully Quantized Training of LLMs cites this paper.

FP4 All the Way: Fully Quantized Training of LLMs Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:25:37.677654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea7a7258-9f9b-4821-b58d-3b2a190050b8 · inbound

Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs cites this paper.

Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:03.353964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac8665c1-2352-42d6-bc81-66718da17911 · inbound

The Banach-Butterfly Invariant: Influence-Adaptive Walsh Geometry for Ternary Polynomial Threshold Functions cites this paper.

The Banach-Butterfly Invariant: Influence-Adaptive Walsh Geometry for Ternary Polynomial Threshold Functions Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:01:06.034768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9655b8fb-b687-4b26-9637-5895bce65ffa · inbound

XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference cites this paper.

XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:35:04.696590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 51cbc768-980b-4c8e-84df-767742d27da3 · inbound

Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization cites this paper.

Influence-Inspired Spectral Rotations for Extreme Low-Bit LLM Quantization Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:14:39.035548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b51e980-e882-4b1a-a20d-f5696eb83eb8 · inbound

QAM-W: Joint 2D Codebook Quantization for LLM Weights via Hadamard Rotation and Activation-Aware Scaling cites this paper.

QAM-W: Joint 2D Codebook Quantization for LLM Weights via Hadamard Rotation and Activation-Aware Scaling Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T22:34:02.848788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4fd0ac99-272f-4b02-9f98-05b82fae10df · inbound

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation cites this paper.

GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:39:45.488116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.