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Paper Citation Record · LEDGER

PTQD: Accurate Post-Training Quantization for Diffusion Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2305.10657.

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

pith.paper-citation-record.v1
2305.10657 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T01:04:45.740471Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:17:58.028502Z

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 9c78b94d-0259-4719-8dc9-e21634321c64 · inbound

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices cites this paper.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T05:44:06.969085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:44:06.969085Z digest=sha256:b81bf6b20dafe977ec7f94f6396a4bded65fac6116f78908df590a00f3d1d4db

Observation 60ad4437-0c07-40e9-9090-5c3a281053dd · inbound

DiTReducio: A Training-Free Acceleration for DiT-Based TTS via Progressive Calibration cites this paper.

DiTReducio: A Training-Free Acceleration for DiT-Based TTS via Progressive Calibration PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-04T19:11:59.515491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T19:11:59.515491Z digest=sha256:85288ef8f9f9ccf5b60d528c222566bce065f83cec411b9e399bb61e0f2ae9b3

Observation 44511747-15b7-47f0-b8e8-cebfb5981830 · inbound

FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling cites this paper.

FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 59

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:21:08.461744Z

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=2026-05-10T18:10:06.994557Z digest=sha256:0f10f2569fc3a9bb4e3d8dff6bdfe993910c08eb25b3e99616faf1797ebb5a51

Observation 6dc307b1-cc43-4d5b-8300-5df0da0fa5c5 · inbound

OFA-Diffusion Compression: Compressing Diffusion Model in One-Shot Manner cites this paper.

OFA-Diffusion Compression: Compressing Diffusion Model in One-Shot Manner PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:11:02.101361Z

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=2026-05-10T15:37:25.939058Z digest=sha256:b866c4217ac95ab3711c85e228666142a717244fbbe78930a15748635d3bc0cb

Observation 3ba97499-6cd2-48b3-922a-bb25b555c152 · inbound

TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload cites this paper.

TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-20T05:13:03.701766Z

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=2026-05-20T05:08:07.318040Z digest=sha256:3c1cae4d1831af437e455e3f82e3a50f9b4e2fd8cac5bd794d798e6d908c1663

Observation b6042bcc-01ff-4fc9-a603-dcc6c5036c85 · inbound

Q-ARVD: Quantizing Autoregressive Video Diffusion Models cites this paper.

Q-ARVD: Quantizing Autoregressive Video Diffusion Models PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T05:29:39.448589Z

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=2026-05-21T05:29:04.061943Z digest=sha256:627ac0633f33e93851c9afb185ab3ac38763b3ee0a54b17784b5361e1d8f595c

Observation fe3e0cad-3cf9-42b2-b1e0-9dfd66e189ba · inbound

VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation cites this paper.

VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-25T05:00:22.567733Z

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=2026-05-25T04:57:03.913813Z digest=sha256:dada4291cb61cd2153e78523f2392a345314931d2ac85292685136deb8a14fba

Observation 79f5eb79-e22d-4ec8-8204-f529678188a5 · inbound

Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs cites this paper.

Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-03T10:17:58.030081Z

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=2026-06-27T10:05:40.235610Z digest=sha256:3e76504d69d5e0c6c931f80f56037b72de394281df7aa11ba60a957a813152ee

Observation 7ac9b83b-64cb-40bc-893c-200112f04bcc · inbound

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

TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models PTQD: Accurate Post-Training Quantization for Diffusion Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-08T01:04:45.740471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T01:04:45.740471Z digest=sha256:ec253a30aa7e4e7135e90eb2c714a7767a0dc85e868332d72fa0bdb6c0626526