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Paper Citation Record · LEDGER

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2606.11446.

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

pith.paper-citation-record.v1
2606.11446 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T13:10:42.454041Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

  • verified exact8
  • verified fuzzy0
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 99e2388d-7f95-4ddf-a954-7728459252ec · outbound

This paper cites Compositional 3d scene generation using locally conditioned diffusion.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Compositional 3d scene generation using locally conditioned diffusion

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2d610ea3-021b-43a8-b860-5b3ed7a959f9 · outbound

This paper cites State of the art on diffusion models for visual computing.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling State of the art on diffusion models for visual computing

Reference 2

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Observation ae4825cd-06ae-4b9a-8e00-0747874b03fa · outbound

This paper cites Post-hoc Concept Bottleneck Models.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Post-hoc Concept Bottleneck Models

Reference 3

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arxiv_id, observed 2026-07-03T05:27:40.521800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 7a9822d0-44ee-4c0c-8f0f-0a46f006b097 · outbound

This paper cites Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations

Reference 4

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arxiv_id, observed 2026-07-03T05:27:40.527110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 96068eae-7d30-42ae-a3e0-1594c342adee · outbound

This paper cites Label-Free Concept Bottleneck Models.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Label-Free Concept Bottleneck Models

Reference 5

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arxiv_id, observed 2026-07-03T05:27:40.519512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation fefee43d-732d-4f1f-82d5-0ea4d45cc4d2 · outbound

This paper cites Interpretable aneurysm classification via 3d concept bottleneck models: Integrating morphological and hemodynamic clinical features.arXiv preprint arXiv:2603.07399, 2026.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Interpretable aneurysm classification via 3d concept bottleneck models: Integrating morphological and hemodynamic clinical features.arXiv preprint arXiv:2603.07399, 2026

Reference 6

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arxiv_id, observed 2026-07-03T05:27:40.522000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 63e89b06-79f4-4ca3-be52-ac19dcd36e53 · outbound

This paper cites Language in a bottle: Language model guided concept bottlenecks for interpretable image classification.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Language in a bottle: Language model guided concept bottlenecks for interpretable image classification

Reference 7

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source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:d227a3c260248015bb76152ba71d0c82de5562a91e2533678586cdbfb2dab11d

Observation 15f7d97c-7a62-4810-a9c1-7923d25e4cb0 · outbound

This paper cites Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey

Reference 8

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arxiv_id, observed 2026-07-03T05:27:40.524932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:a6146c13ad010250e6cc19b96711f99aeb2a2acb58b3d1c5b8200ad2fc3d2739

Observation 4a5e6afb-e294-43ec-8488-6e707b5648cc · outbound

This paper cites Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era

Reference 9

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verified exact
arxiv_id, observed 2026-07-03T05:27:40.535381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d07fb96b-9ca2-44c0-85a8-a68a341a8d95 · outbound

This paper cites A Survey On Text-to-3D Contents Generation In The Wild.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling A Survey On Text-to-3D Contents Generation In The Wild

Reference 10

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arxiv_id, observed 2026-07-03T05:27:40.532551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:fa83eb984f3025795e51281510c38634f70b2f7a5a70d7ac19efb9aa27af888f

Observation 23185a32-bc26-4420-99b8-c5ed21d3c573 · outbound

This paper cites Diffusion probabilistic models for 3d point cloud generation.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Diffusion probabilistic models for 3d point cloud generation

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:d9d1ed6e67019b5cf7346384f977ab714022156ac97cf2ebebd96f76f5b7e4d6

Observation 693e9fc4-bf7d-4bec-80a5-619ec45b3a11 · outbound

This paper cites Sp-gan: Sphere-guided 3d shape generation and manipula- tion.ACM Transactions on Graphics (TOG), 40(4):1–12, 2021.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Sp-gan: Sphere-guided 3d shape generation and manipula- tion.ACM Transactions on Graphics (TOG), 40(4):1–12, 2021

Reference 12

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Observation 20a1cee8-d4c9-48bb-a1ea-4f9046ef173f · outbound

This paper cites Lion: Latent point diffusion models for 3d shape generation.Advances in Neural Information Processing Systems, 35:10021–10039, 2022.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Lion: Latent point diffusion models for 3d shape generation.Advances in Neural Information Processing Systems, 35:10021–10039, 2022

Reference 13

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Observation 1fdb5326-7499-4561-9a2d-fe12da252d4e · outbound

This paper cites Concept bottleneck models for explainable decision making: A survey of progress, taxonomy, and future directions.Preprint at ResearchGate, 2022.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Concept bottleneck models for explainable decision making: A survey of progress, taxonomy, and future directions.Preprint at ResearchGate, 2022

Reference 14

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source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:a3f26d0a71553da1c6158660e418ef60cdc9973c91ffd63a37f084f9dbed67ff

Observation 6992c588-8ad8-42ef-875d-428a741f648c · outbound

This paper cites Explainable generative ai: A two-stage review of existing techniques and future research directions.AI, 7(1):31, 2026.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Explainable generative ai: A two-stage review of existing techniques and future research directions.AI, 7(1):31, 2026

Reference 15

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:f3e6ce6b9dd89eab7bc5bc2862ae5cd525a35bd51dd30c478c64f3e0da6153ba

Observation fa7d5781-374f-442f-b210-0c9e64c12873 · outbound

This paper cites Interpretable 3d neural object volumes for robust conceptual reasoning.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Interpretable 3d neural object volumes for robust conceptual reasoning

Reference 16

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Observation 09443274-d180-4ea6-a25d-a6d5ff09edbd · outbound

This paper cites Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Human-in-the-Loop: Quantitative Evaluation of 3D Models Generation by Large Language Models

Reference 17

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arxiv_id, observed 2026-07-03T05:27:40.535493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:e78c564230bf2d833c7d5daaae11fafa8608267257fb4d436dc115dcfcf0ad93

Observation 22b8a1b8-db35-484b-9053-f3f8fbcfefe1 · outbound

This paper cites Simon and Schuster, 2021.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Simon and Schuster, 2021

Reference 18

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source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:309407b76836fb6ad526bdb917bcb76a3fa8a03cc9c1fb7ee8eda987b490d287

Observation f9b05d4e-cf82-4bb5-9a04-a60d07517876 · outbound

This paper cites Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding

Reference 19

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Observation 0c47db87-06a6-4851-b158-d8de65f0ffc1 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 20

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Observation 45517cca-ce9a-4651-bc4c-527f79cba47c · outbound

This paper cites Foldingnet: Point cloud auto-encoder via deep grid deformation.

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Foldingnet: Point cloud auto-encoder via deep grid deformation

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-27T13:10:42.454041Z digest=sha256:e2dd180da2b078943304bf25f585493cb2720049c94753198baa87b73d647d57

Observation 4b9f35e4-56cb-4426-ac08-ec6418d49c19 · outbound

This paper cites Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCA V).

3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCA V)

Reference 22

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Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.