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Paper Citation Record · LEDGER

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

As of 22 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2501.03432.

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

pith.paper-citation-record.v1
2501.03432 v3

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:24.838480Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:54:10.084387Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T21:54:10.202784Z

Reference resolution

43 of 43 outbound references displayed

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  • verified fuzzy2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 368984b9-b249-430c-82b5-b39c854bbc62 · outbound

This paper cites The atlas experiment at the cern large hadron collider.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection The atlas experiment at the cern large hadron collider

Reference 1

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Observation 9436ca10-4f9e-4ef6-8110-11fc2d2314a9 · outbound

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Unresolved cited work

Reference 2

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Observation 2b776b3e-9082-407c-90b3-290e7eef2920 · outbound

This paper cites Software performance of the atlas track reconstruction for lhc run 3.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Software performance of the atlas track reconstruction for lhc run 3

Reference 3

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Observation 96ab31a7-e5fb-4b09-b372-c0f753de28a9 · outbound

This paper cites Guest, K.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Guest, K

Reference 4

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Observation 542c6581-31e4-4add-b7b9-3717105da93a · outbound

This paper cites The LHCb Detector at the LHC.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection The LHCb Detector at the LHC

Reference 5

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Observation ece14c3c-c90d-4d1b-9897-57d37642b56d · outbound

This paper cites Staszewski and J.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Staszewski and J

Reference 6

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Observation 10777ae0-e84e-4194-98f0-14c0f4578f79 · outbound

This paper cites Crochet and P.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Crochet and P

Reference 7

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Observation 7b129644-a98b-4643-b52d-6ab01dcb7b70 · outbound

This paper cites Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

Reference 8

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Unresolved cited work

Reference 9

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Observation 30b0f1ea-ef57-47bd-811f-4c3922dfc0a9 · outbound

This paper cites Parameterized Explainer for Graph Neural Network.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Parameterized Explainer for Graph Neural Network

Reference 10

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This paper cites GNNExplainer: Generating Explanations for Graph Neural Networks.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection GNNExplainer: Generating Explanations for Graph Neural Networks

Reference 11

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Observation 7022f36b-8602-49dc-be49-eddcb5932eb9 · outbound

This paper cites Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques

Reference 12

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Observation ea8c76dc-3c28-410f-88af-233244ef2724 · outbound

This paper cites Ghorbani, A.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Ghorbani, A

Reference 13

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This paper cites Adebayo, J.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Adebayo, J

Reference 14

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Observation 7fa738f4-f982-4dc6-9d0a-7df0ea2fcd27 · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection A Generalization of Transformer Networks to Graphs

Reference 15

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Observation c1a6b444-05a4-4277-8c28-78147e37b116 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 16

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This paper cites Search for direct production of electroweakinos in final states with one lepton, jets and missing transverse momentum in pp collisions at √s = 13 tev with the atlas detector.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Search for direct production of electroweakinos in final states with one lepton, jets and missing transverse momentum in pp collisions at √s = 13 tev with the atlas detector

Reference 17

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Observation c28227d9-5831-4858-b8d9-52e562840f52 · outbound

This paper cites Shlomi, P.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Shlomi, P

Reference 18

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Duarte and J.-R

Reference 19

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Reference 20

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection DeZoort, P

Reference 21

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Attention Is All You Need

Reference 22

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

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 23

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Unresolved cited work

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection On the Relationship between Self-Attention and Convolutional Layers

Reference 25

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Learning Factored Representations in a Deep Mixture of Experts

Reference 26

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This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 27

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts

Reference 28

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Chen and C

Reference 29

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection A Unified Approach to Interpreting Model Predictions

Reference 30

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Pezoa, L

Reference 31

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Lapuschkin, A

Reference 32

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This paper cites Explainable ai for ml jet taggers using expert variables and layerwise relevance propagation.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Explainable ai for ml jet taggers using expert variables and layerwise relevance propagation

Reference 33

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Explaining machine-learned particle-flow reconstruction

Reference 34

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Estimating Training Data Influence by Tracing Gradient Descent

Reference 35

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Do Feature Attribution Methods Correctly Attribute Features?

Reference 36

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Unresolved cited work

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This paper cites On the Equivalence between Positional Node Embeddings and Structural Graph Representations.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection On the Equivalence between Positional Node Embeddings and Structural Graph Representations

Reference 38

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Unresolved cited work

Reference 2008

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Unresolved cited work

Reference 2011

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Sanity Checks for Saliency Maps

Reference 2018

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Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Unresolved cited work

Reference 2019

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This paper cites Sign and Basis Invariant Networks for Spectral Graph Representation Learning.

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection Sign and Basis Invariant Networks for Spectral Graph Representation Learning

Reference 2023

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Pith citing papers

Observation 32fd192c-14b5-4006-a92b-2ae7c2751bd4 · inbound

Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data cites this paper.

Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

Reference 27

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