Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FPTQ: Fine-grained Post-Training Quantization for Large Language Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2308.15987.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T19:12:56.120257Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-20T05:23:03.679674Z
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 2b2b9adc-cc39-43be-adae-207ac90fcf55 · inbound
MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization cites this paper.
MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization FPTQ: Fine-grained Post-Training Quantization for Large Language Models
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:12:56.120257Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea2e2da1-6c11-417a-ab5c-00f035b25fcf · inbound
SnapMLA: Efficient Long-Context MLA Decoding via Hardware-Aware FP8 Quantized Pipelining cites this paper.
SnapMLA: Efficient Long-Context MLA Decoding via Hardware-Aware FP8 Quantized Pipelining FPTQ: Fine-grained Post-Training Quantization for Large Language Models
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T06:00:40.790664Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 252f550d-653e-4562-b7dd-97540c52e123 · inbound
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models cites this paper.
Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models FPTQ: Fine-grained Post-Training Quantization for Large Language Models
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T05:23:03.681828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.