Paper Citation Record · LEDGER

Quantifying Attention Flow in Transformers

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 46 inbound Pith citation observations for arXiv:2005.00928.

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

pith.paper-citation-record.v1
2005.00928 v2

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measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 46 of 46 standing notices

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

measured 46 of 46 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:51:30.333740Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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Outbound references

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Pith citing papers

Observation 8cdf6adb-3f23-499d-9665-5035d9dbfeae · inbound

A Generalist Agent cites this paper.

A Generalist Agent Quantifying Attention Flow in Transformers

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:24:49.863701Z

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Observation e0e50dfa-f366-4655-8246-e8603b0d36e2 · inbound

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey cites this paper.

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey Quantifying Attention Flow in Transformers

Reference 122

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

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features cites this paper.

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

Reference 1

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Observation 0ebbbca7-d98b-4828-a18f-a5f9d9f62c76 · inbound

AutoOcc: Automatic Open-Ended Semantic Occupancy Annotation via Vision-Language Guided Gaussian Splatting cites this paper.

AutoOcc: Automatic Open-Ended Semantic Occupancy Annotation via Vision-Language Guided Gaussian Splatting Quantifying Attention Flow in Transformers

Reference 1

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Observation 17bfe896-a768-47fe-8d9f-8ac878f9159e · inbound

GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction cites this paper.

GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction Quantifying Attention Flow in Transformers

Reference 1

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Source-reported events for the cited work

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Observation 8674c74c-2692-43b9-9e95-f0908281cb3b · inbound

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization cites this paper.

TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization Quantifying Attention Flow in Transformers

Reference 1

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Observation f734de23-a2c2-4c48-93d0-27f5f9171264 · inbound

PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis cites this paper.

PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis Quantifying Attention Flow in Transformers

Reference 1

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Observation 5a93fd9e-8533-45a2-a271-1af7751eec61 · inbound

Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising cites this paper.

Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising Quantifying Attention Flow in Transformers

Reference 60

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Observation c726c330-bfe5-4691-807a-92c4078ef2cf · inbound

VIP: Visual Information Protection through Adversarial Attacks on Vision-Language Models cites this paper.

VIP: Visual Information Protection through Adversarial Attacks on Vision-Language Models Quantifying Attention Flow in Transformers

Reference 48

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Observation 742c8be5-1163-49cd-8f1a-77687eb14bb4 · inbound

CytoSAE: Interpretable Cell Embeddings for Hematology cites this paper.

CytoSAE: Interpretable Cell Embeddings for Hematology Quantifying Attention Flow in Transformers

Reference 2

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Observation ce5e59b4-c102-4b92-a90c-313177188c0b · inbound

Self-Guided Masked Autoencoder cites this paper.

Self-Guided Masked Autoencoder Quantifying Attention Flow in Transformers

Reference 1

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Observation 83f2a644-b095-4893-a360-d15ca788a86b · inbound

Learning from Limited and Imperfect Data cites this paper.

Learning from Limited and Imperfect Data Quantifying Attention Flow in Transformers

Reference 3

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Observation ce68e053-2c55-46b5-8982-b395b474b0df · inbound

Towards White-Box Deep Wireless Sensing cites this paper.

Towards White-Box Deep Wireless Sensing Quantifying Attention Flow in Transformers

Reference 1

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Observation 1850da1b-af9b-4c34-abde-0d40da4c2f75 · inbound

User Experience Estimation in Human-Robot Interaction Via Multi-Instance Learning of Multimodal Social Signals cites this paper.

User Experience Estimation in Human-Robot Interaction Via Multi-Instance Learning of Multimodal Social Signals Quantifying Attention Flow in Transformers

Reference 35

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Observation b12bd536-3b2d-4159-8daa-fa518836909a · inbound

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models cites this paper.

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models Quantifying Attention Flow in Transformers

Reference 22

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Observation 049a9238-c45e-43bc-a367-b3bee50d3a95 · inbound

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models cites this paper.

Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models Quantifying Attention Flow in Transformers

Reference 22

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Observation 2854709d-b58b-4bc0-a3b7-b692fc08a9f4 · inbound

DSS-Prompt: Dynamic-Static Synergistic Prompting for Few-Shot Class-Incremental Learning cites this paper.

DSS-Prompt: Dynamic-Static Synergistic Prompting for Few-Shot Class-Incremental Learning Quantifying Attention Flow in Transformers

Reference 1

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Observation 7eb3a107-96c1-4951-881f-f8b38204efee · inbound

Generalizable Federated Learning using Client Adaptive Focal Modulation cites this paper.

Generalizable Federated Learning using Client Adaptive Focal Modulation Quantifying Attention Flow in Transformers

Reference 1

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Observation 45f6f04e-d8da-4e59-845b-883a9289e622 · inbound

Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML cites this paper.

Safer Skin Lesion Classification with Global Class Activation Probability Map Evaluation and SafeML Quantifying Attention Flow in Transformers

Reference 1

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Observation 4a7308f7-d8fd-408b-a6cf-d3385c780575 · inbound

Full-Frequency Temporal Patching and Structured Masking for Enhanced Audio Classification cites this paper.

Full-Frequency Temporal Patching and Structured Masking for Enhanced Audio Classification Quantifying Attention Flow in Transformers

Reference 16

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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 Quantifying Attention Flow in Transformers

Reference 1

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Attention Maps in 3D Shape Classification for Dental Stage Estimation with Class Node Graph Attention Networks cites this paper.

Attention Maps in 3D Shape Classification for Dental Stage Estimation with Class Node Graph Attention Networks Quantifying Attention Flow in Transformers

Reference 1

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Observation 9bc1ead4-5206-4132-b382-47ce173412d1 · inbound

An Autoencoder and Vision Transformer-based Interpretability Analysis of the Differences in Automated Staging of Second and Third Molars cites this paper.

An Autoencoder and Vision Transformer-based Interpretability Analysis of the Differences in Automated Staging of Second and Third Molars Quantifying Attention Flow in Transformers

Reference 41

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Observation 851662fb-b912-41b3-9720-4eaed3a07fb1 · inbound

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification cites this paper.

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification Quantifying Attention Flow in Transformers

Reference 1

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Robust Representation Learning in Masked Autoencoders cites this paper.

Robust Representation Learning in Masked Autoencoders Quantifying Attention Flow in Transformers

Reference 24

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From Features to Actions: Explainability in Traditional and Agentic AI Systems cites this paper.

From Features to Actions: Explainability in Traditional and Agentic AI Systems Quantifying Attention Flow in Transformers

Reference 1

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Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models cites this paper.

Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models Quantifying Attention Flow in Transformers

Reference 14

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Observation f8672565-f836-4027-b530-b4d0209bf1fd · inbound

Feature-level Interaction Explanations in Multimodal Transformers cites this paper.

Feature-level Interaction Explanations in Multimodal Transformers Quantifying Attention Flow in Transformers

Reference 7

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Observation 78fa4814-e139-4a81-ab65-cfbff1572fd2 · inbound

Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs cites this paper.

Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs Quantifying Attention Flow in Transformers

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:01.986433Z

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

Observation db9aa949-4c6c-4fd9-988a-530f4094b6db · inbound

Saccade Attention Networks: Using Transfer Learning of Attention to Reduce Network Sizes cites this paper.

Saccade Attention Networks: Using Transfer Learning of Attention to Reduce Network Sizes Quantifying Attention Flow in Transformers

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T15:50:34.205622Z

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

Observation 0c312f64-b47f-42d0-87d0-982cca4e9c10 · inbound

SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT cites this paper.

SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT Quantifying Attention Flow in Transformers

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:35:41.068620Z

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 7b9bd0db-0a56-41b1-8a1a-8e09cf1f666b · inbound

Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding cites this paper.

Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding Quantifying Attention Flow in Transformers

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:41:27.368672Z

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Observation 4ca1d0af-a91e-4aba-910a-c28bbc1be9ce · inbound

From Clever Hans to Scientific Discovery: Interpreting EEG Foundational Transformers with LRP cites this paper.

From Clever Hans to Scientific Discovery: Interpreting EEG Foundational Transformers with LRP Quantifying Attention Flow in Transformers

Reference 39

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Observation fa9c52a8-f794-45d1-8f28-d6e9b00743dd · inbound

Architecture-Aware Explanation Auditing for Industrial Visual Inspection cites this paper.

Architecture-Aware Explanation Auditing for Industrial Visual Inspection Quantifying Attention Flow in Transformers

Reference 6

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Observation 55432e1d-3f55-437e-a5a5-6b50e38beb58 · inbound

Architecture-Aware Explanation Auditing for Industrial Visual Inspection cites this paper.

Architecture-Aware Explanation Auditing for Industrial Visual Inspection Quantifying Attention Flow in Transformers

Reference 6

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Observation b35bd84e-d748-43a1-921b-a6e618189432 · inbound

Architecture-Aware Explanation Auditing for Industrial Visual Inspection cites this paper.

Architecture-Aware Explanation Auditing for Industrial Visual Inspection Quantifying Attention Flow in Transformers

Reference 6

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Learning Quantifiable Visual Explanations Without Ground-Truth cites this paper.

Learning Quantifiable Visual Explanations Without Ground-Truth Quantifying Attention Flow in Transformers

Reference 71

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Observation dbd9e45b-da10-487e-8eb7-5750d8121450 · inbound

HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals cites this paper.

HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals Quantifying Attention Flow in Transformers

Reference 64

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Observation ce54d5ba-5864-42a2-a73e-22d7cb7e8f00 · inbound

AnchorDiff: Training-Free Concept Grounding for MM-DiTs via Anchor-Based Graph Propagation cites this paper.

AnchorDiff: Training-Free Concept Grounding for MM-DiTs via Anchor-Based Graph Propagation Quantifying Attention Flow in Transformers

Reference 25

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Observation 8efe30e9-e9f5-4da4-a91e-1c8fc90e6a51 · inbound

When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics cites this paper.

When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics Quantifying Attention Flow in Transformers

Reference 1

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Observation 1d87175d-9e5c-4bf4-97ec-09d4afc110f4 · inbound

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cites this paper.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers Quantifying Attention Flow in Transformers

Reference 70

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:32:46.521352Z

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Observation 9029c408-7c15-4afa-9d75-a55fabd7e1f3 · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Quantifying Attention Flow in Transformers

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-07-04T11:09:46.361747Z

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Observation d2ef224f-3a4d-47bd-9368-4cf8b00182ec · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Quantifying Attention Flow in Transformers

Reference 70

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Transient Reserves, Sink Dampers, and the Failure of Eigenvalue Reasoning in the Attention Propagator cites this paper.

Transient Reserves, Sink Dampers, and the Failure of Eigenvalue Reasoning in the Attention Propagator Quantifying Attention Flow in Transformers

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Observation 7cf9b3bf-10ff-4972-aa37-fa6ecbdb7da7 · inbound

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers cites this paper.

Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers Quantifying Attention Flow in Transformers

Reference 36

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Observation f9338660-a8c5-447a-83f7-40168d8b0e1e · inbound

AGNFormer I: Reconstruction of AGN spectra using a probabilistic transformer model cites this paper.

AGNFormer I: Reconstruction of AGN spectra using a probabilistic transformer model Quantifying Attention Flow in Transformers

Reference 163

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