Paper Citation Record · LEDGER

FPTQ: Fine-grained Post-Training Quantization for Large Language Models

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2308.15987.

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

pith.paper-citation-record.v1
2308.15987 v1

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

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:11:39.154076Z

measured 0 of 1 external citation measurements

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 a9b0cfc4-8867-4b32-ba9a-943a58288921 · inbound

Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference cites this paper.

Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference FPTQ: Fine-grained Post-Training Quantization for Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T21:11:39.154076Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06a8be02-421a-4184-be4e-0ac9464da862 · inbound

Deploying Foundation Model Powered Agent Services: A Survey cites this paper.

Deploying Foundation Model Powered Agent Services: A Survey FPTQ: Fine-grained Post-Training Quantization for Large Language Models

Reference 201

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:46.638543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 845afe81-1327-4b44-ae0e-bd8772d76f91 · inbound

BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference cites this paper.

BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference FPTQ: Fine-grained Post-Training Quantization for Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T22:44:30.133415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bddd51e-f1fd-41ce-8db9-77f7de62512a · 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 FPTQ: Fine-grained Post-Training Quantization for Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T16:19:57.485389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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