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Paper Citation Record · LEDGER

Efficient Quantization Strategies for Latent Diffusion Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2312.05431.

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

pith.paper-citation-record.v1
2312.05431 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T01:04:45.880234Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T14:25:29.243982Z

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 c176a620-7503-433f-badc-c93311f27b86 · inbound

A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models cites this paper.

A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models Efficient Quantization Strategies for Latent Diffusion Models

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-10T14:25:29.246084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T14:24:53.745784Z digest=sha256:ddf6c46a34a2d43f75f49ff2dff60112c59b80141f859ed6fa584747e4e31073

Observation 7208adda-274a-49a0-a012-6117c5974203 · inbound

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts cites this paper.

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts Efficient Quantization Strategies for Latent Diffusion Models

Reference 256

Resolution
unresolved
no resolver link, observed 2026-08-01T16:42:32.486210Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T16:42:32.486210Z digest=sha256:84da86dd54763a1356e341cdeea33375f686f7238798c867a0792f7e0fe17a31

Observation cd350135-7752-4c02-986d-8013a40ea7db · inbound

TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models cites this paper.

TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models Efficient Quantization Strategies for Latent Diffusion Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-08T01:04:45.880234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T01:04:45.880234Z digest=sha256:54c8b50fe4b598ba07d6a68c4e88d1a2452ed8dce48bd3ce8844cb7628c71ec3