Paper Citation Record · LEDGER

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 12 inbound Pith citation observations for arXiv:2502.04320.

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

pith.paper-citation-record.v1
2502.04320 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:51:30.517584Z

measured 64 of 64 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:41:21.200157Z

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

52 of 52 outbound references displayed

verified exact5
verified fuzzy0
unresolved46
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 8c7919ed-079b-461e-8aa8-f182a905f229 · outbound

This paper cites Quantifying Attention Flow in Transformers.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Quantifying Attention Flow in Transformers

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.333740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a83e9e0a-cadd-41c2-881e-f837daf8404b · outbound

This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features SegDiff: Image Segmentation with Diffusion Probabilistic Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.338470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 770be66a-b762-4493-8a3d-cb35658fbf49 · outbound

This paper cites Label-Efficient Semantic Segmentation with Diffusion Models.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Label-Efficient Semantic Segmentation with Diffusion Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.342260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e2458e1-311e-4e71-bfce-0e29cbd9e825 · outbound

This paper cites Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.346022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 609d7847-06fe-45fd-81f0-198bdca9e3db · outbound

This paper cites InstructPix2Pix: Learning to Follow Image Editing Instructions.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features InstructPix2Pix: Learning to Follow Image Editing Instructions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.349907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c2b28d1-55b2-4abb-a2b3-0ba950d533b6 · outbound

This paper cites Extracting Training Data from Diffusion Models.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Extracting Training Data from Diffusion Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.353679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72881085-ab39-4e6d-88f9-d7d1d4a4d515 · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Emerging Properties in Self-Supervised Vision Transformers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.357789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2c4b612-51a8-4d5b-9205-10bc526d8363 · outbound

This paper cites Transformer Interpretability Beyond Attention Visualization.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Transformer Interpretability Beyond Attention Visualization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.361375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 738c633f-5567-49ac-84ac-e2c21dfce76b · outbound

This paper cites Attend-and- Excite : Attention - Based Semantic Guidance for Text -to- Image Diffusion Models.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Attend-and- Excite : Attention - Based Semantic Guidance for Text -to- Image Diffusion Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.365200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2a8940a-b9c6-40bf-b148-c1de63cc00c1 · outbound

This paper cites Training-Free Layout Control with Cross-Attention Guidance.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Training-Free Layout Control with Cross-Attention Guidance

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.368808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9aaf1493-47d9-4307-b7be-1fcfaa83e78e · outbound

This paper cites PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T22:51:31.022701Z

Source-reported events for the cited work

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Observation 4457faec-e223-4efd-b871-6533c22a2f1e · outbound

This paper cites FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.375916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd4542d8-af62-4912-92a8-5df459594db5 · outbound

This paper cites Vision Transformers Need Registers.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Vision Transformers Need Registers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.379390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7e74634-b778-4be1-b99f-f4391ec3d053 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.383170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a07c957-12c5-4a2d-9ef3-c1161b607ab5 · outbound

This paper cites Diffusion Self-Guidance for Controllable Image Generation.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Diffusion Self-Guidance for Controllable Image Generation

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-08T22:51:30.982597Z

Source-reported events for the cited work

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Observation 40f0e65d-fc3d-4050-bbba-995415c4e134 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.390325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e950b0dd-ccd9-4dbf-a0a4-cff94d7573cb · outbound

This paper cites an unresolved cited work.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Unresolved cited work

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation defb6842-ee33-495f-bcea-0fa5f2286cda · outbound

This paper cites Interpreting CLIP's Image Representation via Text-Based Decomposition.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Interpreting CLIP's Image Representation via Text-Based Decomposition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.398000Z

Source-reported events for the cited work

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Observation 13b9f209-b716-4b72-b900-0eef2cc62d94 · outbound

This paper cites ImageNet Auto - Annotation with Segmentation Propagation.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features ImageNet Auto - Annotation with Segmentation Propagation

Reference 19

Resolution: verified exact; doi, observed 2026-08-08T22:51:30.558819Z

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.

Observation 856f6a24-3d80-4fa8-a1ca-a9bdc88335d6 · outbound

This paper cites Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control

Reference 20

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Source-reported events for the cited work

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Observation ca5d20cf-7f76-4232-b4b3-32e816d04992 · outbound

This paper cites Unsupervised Semantic Segmentation by Distilling Feature Correspondences.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Reference 21

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Source-reported events for the cited work

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Observation 775f2ebe-a2fd-41ae-9611-4341a5bf3584 · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 22

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Source-reported events for the cited work

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Observation a29b1f14-7036-4d01-9d5f-ed42a0faf9e5 · outbound

This paper cites Generalization in diffusion models arises from geometry-adaptive harmonic representations.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Generalization in diffusion models arises from geometry-adaptive harmonic representations

Reference 23

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Source-reported events for the cited work

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Observation a5dc0093-a2ad-4a58-a1d8-449fc0adde92 · outbound

This paper cites Diffusion Models for Open-Vocabulary Segmentation.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Diffusion Models for Open-Vocabulary Segmentation

Reference 24

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Observation 8c4fd63f-497c-4c19-8ecf-7a7679d91aa7 · outbound

This paper cites Segment Anything.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Segment Anything

Reference 25

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Observation 645504f3-664f-4a1d-b8bb-024d27065370 · outbound

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.426778Z

Source-reported events for the cited work

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Observation 5a2c4880-61f3-4e11-96be-4b05bf4f886c · outbound

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Unresolved cited work

Reference 27

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Observation 0f6928a4-2f4a-452e-a257-6eebbbc4badb · outbound

This paper cites Your Diffusion Model is Secretly a Zero-Shot Classifier.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Your Diffusion Model is Secretly a Zero-Shot Classifier

Reference 28

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Observation 0c117fd9-4b46-4f8f-922a-4e389e6440d6 · outbound

This paper cites Open-vocabulary Object Segmentation with Diffusion Models.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Open-vocabulary Object Segmentation with Diffusion Models

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-08T22:51:30.776111Z

Source-reported events for the cited work

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Observation 3295af62-2c4f-4b70-a679-68a0e565a74b · outbound

This paper cites Flow Matching for Generative Modeling.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Flow Matching for Generative Modeling

Reference 30

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Source-reported events for the cited work

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Observation 78f599a0-771b-449e-bcc4-4f8fb1a48f46 · outbound

This paper cites Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T22:51:30.443951Z

Source-reported events for the cited work

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Observation 4b6825cf-7455-447f-b22b-a9eebb838bee · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 32

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Observation 5e4174c8-2611-4510-bd2f-5d3f579344b5 · outbound

This paper cites Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-08T22:51:30.736499Z

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Observation 6210da07-c566-4922-b569-c40f2a9bfddf · outbound

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Unresolved cited work

Reference 34

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Observation 9392d588-5623-4982-a831-ff44d36e8ef6 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features DINOv2: Learning Robust Visual Features without Supervision

Reference 35

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This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 36

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Observation a225cf19-2489-4d88-96a7-706d4a8b8889 · outbound

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Scalable Diffusion Models with Transformers

Reference 37

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Observation 1d7beaa9-8877-496a-9198-0f0a0de79938 · outbound

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 38

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Learning Transferable Visual Models From Natural Language Supervision

Reference 39

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 40

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features SAM 2: Segment Anything in Images and Videos

Reference 41

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features High-Resolution Image Synthesis with Latent Diffusion Models

Reference 42

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 43

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

Reference 45

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Observation d0b2afb8-8fe5-417b-9f19-cb62552bbb8a · outbound

This paper cites CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor

Reference 46

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Observation 7a90582c-664f-43de-ad20-1304c543abfa · outbound

This paper cites What the DAAM: Interpreting Stable Diffusion Using Cross Attention.

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features What the DAAM: Interpreting Stable Diffusion Using Cross Attention

Reference 47

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Observation 927927ff-6866-4d31-b25a-ca8ed11d3295 · outbound

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion

Reference 48

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions

Reference 49

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features Understanding and Improving Layer Normalization

Reference 50

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 51

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 52

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ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features write newline

Reference 53

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Pith citing papers

Observation 233f2121-ba18-4444-9534-6a742e222961 · inbound

LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers cites this paper.

LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 11

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From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models cites this paper.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 25

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Observation e373986a-7abc-4b74-a3b0-20d237a9dc01 · inbound

The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in Text-to-Image Models cites this paper.

The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in Text-to-Image Models ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-21T23:44:26.945529Z

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Observation 8e151f58-cd7c-4cf5-925e-a8d75f068994 · inbound

Attention of a Kiss: Exploring Attention Maps in Video Diffusion for XAIxArts cites this paper.

Attention of a Kiss: Exploring Attention Maps in Video Diffusion for XAIxArts ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T13:27:41.569139Z

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Observation 286c8497-c10b-40f7-8d9c-569fb466ffb7 · inbound

S3OD: Towards Generalizable Salient Object Detection with Synthetic Data cites this paper.

S3OD: Towards Generalizable Salient Object Detection with Synthetic Data ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T08:19:59.521627Z

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Observation 014dd85b-2817-4cc2-be1c-e2a8fccbdcff · inbound

TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation cites this paper.

TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:53:29.908134Z

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.

Observation fe15ad0f-f369-4514-aa9c-f95023a90daa · inbound

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment cites this paper.

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:16:12.253353Z

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.

Observation 26316507-ecd0-4158-ae13-55a10d372608 · inbound

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment cites this paper.

FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T15:32:58.066814Z

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Observation 1e0e87f6-6282-4654-a044-369e679d2a9a · inbound

Consistency Regularised Gradient Flows for Inverse Problems cites this paper.

Consistency Regularised Gradient Flows for Inverse Problems ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 66

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:25:55.059096Z

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.

Observation b53677f1-09e8-4e1f-b252-47ec5ad2bebb · inbound

What Concepts Lie Within? Detecting and Suppressing Risky Content in Diffusion Transformers cites this paper.

What Concepts Lie Within? Detecting and Suppressing Risky Content in Diffusion Transformers ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:06:26.433353Z

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.

Observation 794bcc62-c9f1-4579-80ab-8a00799ac7e8 · inbound

Unified Safe In-context Image Generation in Multimodal Diffusion Transformers via Restricting Unsafe Information Flows cites this paper.

Unified Safe In-context Image Generation in Multimodal Diffusion Transformers via Restricting Unsafe Information Flows ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T16:07:09.083615Z

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.

Observation 1c2eb649-ccb4-42b1-a613-5a0adcff069e · inbound

Text Template Tokens Are Implicit Semantic Registers in Diffusion Transformers cites this paper.

Text Template Tokens Are Implicit Semantic Registers in Diffusion Transformers ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T13:25:54.283706Z

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Unavailable: canonical work link unavailable.