Paper Citation Record · LEDGER

Compositional Visual Generation with Composable Diffusion Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2206.01714.

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

pith.paper-citation-record.v1
2206.01714 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:31:07.277041Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 76748ce7-52d9-4aab-92fb-23365911c1ce · inbound

Aligning Text-to-Image Models using Human Feedback cites this paper.

Aligning Text-to-Image Models using Human Feedback Compositional Visual Generation with Composable Diffusion Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:39:15.492635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 20285424-595b-4a5a-8b1c-c6be35c7bfea · inbound

Training Diffusion Models with Reinforcement Learning cites this paper.

Training Diffusion Models with Reinforcement Learning Compositional Visual Generation with Composable Diffusion Models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:31.271091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6c7e9be2-9fce-43c6-a02c-590f692fb86a · inbound

Improving Factuality and Reasoning in Language Models through Multiagent Debate cites this paper.

Improving Factuality and Reasoning in Language Models through Multiagent Debate Compositional Visual Generation with Composable Diffusion Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:01:45.190561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 988ee760-8aa7-465c-aff5-a9cec1cfe7bf · inbound

Learning Interactive Real-World Simulators cites this paper.

Learning Interactive Real-World Simulators Compositional Visual Generation with Composable Diffusion Models

Reference 187

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T02:15:18.677830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3b9d7ced-726c-4517-a0eb-5e95ce7da35f · inbound

Compositional Scene Understanding through Inverse Generative Modeling cites this paper.

Compositional Scene Understanding through Inverse Generative Modeling Compositional Visual Generation with Composable Diffusion Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T13:31:07.277041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c9d4880-0781-4eaa-a364-e680ac9a6c13 · inbound

Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models cites this paper.

Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models Compositional Visual Generation with Composable Diffusion Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T21:48:54.880447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb28e6c0-6c50-4872-90f9-ec3166c33fd8 · inbound

Beyond Sliders: Mastering the Art of Diffusion-based Image Manipulation cites this paper.

Beyond Sliders: Mastering the Art of Diffusion-based Image Manipulation Compositional Visual Generation with Composable Diffusion Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T16:57:08.848047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0340d81-1283-41fc-8355-30a6d8d7f256 · inbound

Multi-Modal Manipulation via Multi-Modal Policy Consensus cites this paper.

Multi-Modal Manipulation via Multi-Modal Policy Consensus Compositional Visual Generation with Composable Diffusion Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-18T11:51:20.231334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b09319ea-3d88-4b3c-bae5-dc9deeba2415 · inbound

A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios cites this paper.

A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios Compositional Visual Generation with Composable Diffusion Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:58:18.268127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ac160add-e9ac-48df-a32e-94df98375934 · inbound

$Z^2$-Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models cites this paper.

$Z^2$-Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models Compositional Visual Generation with Composable Diffusion Models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:06:15.260949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c8172ef5-784f-410d-8c6b-d6f2046383bd · inbound

Stylistic Attribute Control in Latent Diffusion Models cites this paper.

Stylistic Attribute Control in Latent Diffusion Models Compositional Visual Generation with Composable Diffusion Models

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-08T18:44:00.014822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9db1815e-c8e7-4663-b52e-bdc067770ac4 · inbound

Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation cites this paper.

Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation Compositional Visual Generation with Composable Diffusion Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:39:50.143248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.