Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2504.14152.
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 2026-08-03T11:10:52.675955Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T12:59:52.342198Z
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 21f023f3-e810-45d9-8b5c-71c8d81cc12d · inbound
ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs cites this paper.
ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T11:10:52.675955Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b67e4756-23bd-44ba-a7d9-efcd2a5e17aa · inbound
SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models cites this paper.
SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
Reference 1997
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T05:51:30.119869Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bc863dbc-6cf7-4162-8af6-298115b03b33 · inbound
MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM cites this paper.
MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T21:53:19.986284Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9c0770d4-f862-4ce7-acda-9e751952125e · inbound
SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference cites this paper.
SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T12:59:52.343675Z
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.