Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2309.05516.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:36:05.672891Z
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 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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.