Paper Citation Record · LEDGER

Assessing Sample Quality in Conditional Generation under Compositional Shift

As of 5 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2606.09601.

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

pith.paper-citation-record.v1
2606.09601 v2

Coverage vector

measured 38 of 38 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-06-27T17:21:37.570778Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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Outbound references

Observation 7dee812c-6600-41b5-a851-aa8f2c0a078e · outbound

This paper cites How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models.

Assessing Sample Quality in Conditional Generation under Compositional Shift How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models

Reference 1

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Observation 979fa771-15d8-4bef-8a90-5f3834f0be7d · outbound

This paper cites Synthetic data from diffusion models improves ImageNet classification.Transactions on Machine Learning Research, 2023.

Assessing Sample Quality in Conditional Generation under Compositional Shift Synthetic data from diffusion models improves ImageNet classification.Transactions on Machine Learning Research, 2023

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation c0e7e7e5-ba65-49d5-96f2-63a09e0ec93e · outbound

This paper cites Demystifying MMD GANs.

Assessing Sample Quality in Conditional Generation under Compositional Shift Demystifying MMD GANs

Reference 3

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Observation 3d39793c-a8ae-4653-87b2-2d63bc7f03f5 · outbound

This paper cites Cellprofiler: image analysis software for identifying and quantifying cell phenotypes.Genome biology, 7:R100, 2006.

Assessing Sample Quality in Conditional Generation under Compositional Shift Cellprofiler: image analysis software for identifying and quantifying cell phenotypes.Genome biology, 7:R100, 2006

Reference 4

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Observation 8678d9c0-ece9-45c0-a645-33cd496bcd8c · outbound

This paper cites Mor- phgen: Controllable and morphologically plausible generative cell-imaging.arXiv preprint arXiv:2510.01298, 2025.

Assessing Sample Quality in Conditional Generation under Compositional Shift Mor- phgen: Controllable and morphologically plausible generative cell-imaging.arXiv preprint arXiv:2510.01298, 2025

Reference 5

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Observation 165a992e-67b9-4066-b41b-d2d77a9b64e5 · outbound

This paper cites Out-of-distribution detection with relative angles.

Assessing Sample Quality in Conditional Generation under Compositional Shift Out-of-distribution detection with relative angles

Reference 6

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Observation 8cb61fed-072b-4d3b-a805-e764312bae06 · outbound

This paper cites Diffusion models beat GANs on image synthesis.

Assessing Sample Quality in Conditional Generation under Compositional Shift Diffusion models beat GANs on image synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation 3fd2f1b8-821d-485f-b6a4-cfb113c333c8 · outbound

This paper cites How Compositional Generalization and Creativity Improve as Diffusion Models are Trained.

Assessing Sample Quality in Conditional Generation under Compositional Shift How Compositional Generalization and Creativity Improve as Diffusion Models are Trained

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:29.061158Z

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Observation f5fc83b9-b714-4b49-864c-2eaed8afda54 · outbound

This paper cites Coind: Enabling logical compositions in diffusion models.

Assessing Sample Quality in Conditional Generation under Compositional Shift Coind: Enabling logical compositions in diffusion models

Reference 9

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Observation edc5ec98-8c19-4212-98f6-0e9fae658187 · outbound

This paper cites Is synthetic data from generative models ready for image recognition? In International Conference on Learning Representations, 2023.

Assessing Sample Quality in Conditional Generation under Compositional Shift Is synthetic data from generative models ready for image recognition? In International Conference on Learning Representations, 2023

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation ce5bf075-b586-4734-8c0f-633b90aa1787 · outbound

This paper cites GANs trained by a two time-scale update rule converge to a local nash equilibrium.

Assessing Sample Quality in Conditional Generation under Compositional Shift GANs trained by a two time-scale update rule converge to a local nash equilibrium

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation 87874c98-24d9-482d-96e4-fc2aaaec5936 · outbound

This paper cites Masked autoencoders for microscopy are scalable learners of cellular biology.

Assessing Sample Quality in Conditional Generation under Compositional Shift Masked autoencoders for microscopy are scalable learners of cellular biology

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation 1b8af986-90dc-481a-8893-020eda76a8d0 · outbound

This paper cites Improved precision and recall metric for assessing generative models.Advances in neural information processing systems, 32, 2019.

Assessing Sample Quality in Conditional Generation under Compositional Shift Improved precision and recall metric for assessing generative models.Advances in neural information processing systems, 32, 2019

Reference 13

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Observation f75c4f87-3c0c-4d19-a041-1533705b2ce4 · outbound

This paper cites A well-conditioned estimator for large-dimensional covariance matrices.Journal of Multivariate Analysis, 88(2):365–411, 2004.

Assessing Sample Quality in Conditional Generation under Compositional Shift A well-conditioned estimator for large-dimensional covariance matrices.Journal of Multivariate Analysis, 88(2):365–411, 2004

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation f8d8de66-af1c-41a9-b41c-08705f058abb · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks.Advances in neural information processing systems, 31, 2018.

Assessing Sample Quality in Conditional Generation under Compositional Shift A simple unified framework for detecting out-of-distribution samples and adversarial attacks.Advances in neural information processing systems, 31, 2018

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation f2f8d467-0058-41ff-bc10-e1fdf320a0dd · outbound

This paper cites Fast decision boundary based out-of-distribution detector.

Assessing Sample Quality in Conditional Generation under Compositional Shift Fast decision boundary based out-of-distribution detector

Reference 16

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Observation dfe18018-977e-4d9d-bb3a-4c9d87c82998 · outbound

This paper cites Energy-based out-of-distribution detection.Advances in neural information processing systems, 33:21464–21475, 2020.

Assessing Sample Quality in Conditional Generation under Compositional Shift Energy-based out-of-distribution detection.Advances in neural information processing systems, 33:21464–21475, 2020

Reference 17

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Observation 4566d148-0e12-4b7e-9de0-65bbe0c56bb3 · outbound

This paper cites Deep learning face attributes in the wild.

Assessing Sample Quality in Conditional Generation under Compositional Shift Deep learning face attributes in the wild

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation 07d3d919-bd26-46fa-a675-ed4827845667 · outbound

This paper cites Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers.

Assessing Sample Quality in Conditional Generation under Compositional Shift Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers

Reference 19

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Observation e9a8833d-80b0-412e-ab23-74cac3634beb · outbound

This paper cites Mahalanobis++: Improving OOD detection via feature normalization.

Assessing Sample Quality in Conditional Generation under Compositional Shift Mahalanobis++: Improving OOD detection via feature normalization

Reference 20

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Observation 4654eeb9-3b69-4c71-b28f-f8004ed99b8d · outbound

This paper cites Reliable fidelity and diversity metrics for generative models.

Assessing Sample Quality in Conditional Generation under Compositional Shift Reliable fidelity and diversity metrics for generative models

Reference 21

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Observation 8f724dee-96fd-4927-ae98-a3c9ebf39322 · outbound

This paper cites Morphodiff: Cellular morphology painting with diffusion models.

Assessing Sample Quality in Conditional Generation under Compositional Shift Morphodiff: Cellular morphology painting with diffusion models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation b648302f-dd50-4e7c-9c62-b07c4edd2f92 · outbound

This paper cites Dick, and Hidenori Tanaka.

Assessing Sample Quality in Conditional Generation under Compositional Shift Dick, and Hidenori Tanaka

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation 3de7ca79-c9f1-4ef6-aad8-d086b4f9b59b · outbound

This paper cites Emergence of hidden capabilities: Exploring learning dynamics in concept space.Advances in Neural Information Processing Systems, 37:84698–84729, 2024.

Assessing Sample Quality in Conditional Generation under Compositional Shift Emergence of hidden capabilities: Exploring learning dynamics in concept space.Advances in Neural Information Processing Systems, 37:84698–84729, 2024

Reference 24

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Observation 8033e9b8-27f3-483f-ad47-ce875435188c · outbound

This paper cites Probabilistic precision and recall towards reliable evaluation of generative models.

Assessing Sample Quality in Conditional Generation under Compositional Shift Probabilistic precision and recall towards reliable evaluation of generative models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation ad35acec-1c2b-4ed4-9798-93ff697ff9be · outbound

This paper cites Nearest neighbor guidance for out-of-distribution detection.

Assessing Sample Quality in Conditional Generation under Compositional Shift Nearest neighbor guidance for out-of-distribution detection

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation 2a6d5fb0-c158-4713-81a5-0baf1e9c690f · outbound

This paper cites Early Estimation of Language to Latent Alignment in Diffusion Models.

Assessing Sample Quality in Conditional Generation under Compositional Shift Early Estimation of Language to Latent Alignment in Diffusion Models

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-03T00:17:29.056144Z

Source-reported events for the cited work

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Observation 0020fe9d-986b-4a86-91cb-2309abc285a2 · outbound

This paper cites A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.

Assessing Sample Quality in Conditional Generation under Compositional Shift A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:29.063690Z

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Observation 207d0fd8-d915-4a77-b117-59fce6a3def7 · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

Assessing Sample Quality in Conditional Generation under Compositional Shift High- resolution image synthesis with latent diffusion models

Reference 29

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This paper cites As- sessing generative models via precision and recall.Advances in neural information processing systems, 31, 2018.

Assessing Sample Quality in Conditional Generation under Compositional Shift As- sessing generative models via precision and recall.Advances in neural information processing systems, 31, 2018

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Observation a42f3e59-469c-4173-95bb-0ae4d7acbe88 · outbound

This paper cites DINOv3.

Assessing Sample Quality in Conditional Generation under Compositional Shift DINOv3

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-03T00:17:29.058484Z

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Observation c285512c-e758-4863-a73e-d2bb63a4e337 · outbound

This paper cites Out-of-distribution detection with deep nearest neighbors.

Assessing Sample Quality in Conditional Generation under Compositional Shift Out-of-distribution detection with deep nearest neighbors

Reference 32

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This paper cites Rxrx1: A dataset for evaluating experimental batch correction methods.

Assessing Sample Quality in Conditional Generation under Compositional Shift Rxrx1: A dataset for evaluating experimental batch correction methods

Reference 33

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This paper cites Representation alignment for generation: Training diffusion transformers is easier than you think.

Assessing Sample Quality in Conditional Generation under Compositional Shift Representation alignment for generation: Training diffusion transformers is easier than you think

Reference 34

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Observation 1652ef7c-925b-4088-9001-3017b300a883 · outbound

This paper cites min gap” and “median gap.

Assessing Sample Quality in Conditional Generation under Compositional Shift min gap” and “median gap

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:29.066115Z

Source-reported events for the cited work

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Observation c0ae9fe3-6586-4734-a318-b8b28c731a39 · outbound

This paper cites This is below any stable per-condition KID bootstrap.

Assessing Sample Quality in Conditional Generation under Compositional Shift This is below any stable per-condition KID bootstrap

Reference 36

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Observation cd37391d-6f7d-4b3b-ab3d-142cd3d3dbed · outbound

This paper cites The support-shift test therefore covered a single class of perturbations rather than the diversity the held-out set was designed to provide.

Assessing Sample Quality in Conditional Generation under Compositional Shift The support-shift test therefore covered a single class of perturbations rather than the diversity the held-out set was designed to provide

Reference 37

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Observation f1e762e0-776f-4a8f-86bd-6faddbaaabf8 · outbound

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Assessing Sample Quality in Conditional Generation under Compositional Shift Trust spread

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-27T17:21:37.570778Z

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Pith citing papers

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