Paper Citation Record · LEDGER

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations

As of 10 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.21723.

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

pith.paper-citation-record.v1
2507.21723 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:30:20.460682Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

46 of 46 outbound references displayed

verified exact4
verified fuzzy29
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f411207d-015c-4d2a-867b-ca50c036df60 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.217346Z

Source-reported events for the cited work

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Observation 9ee3ee81-0045-4867-b66b-fbb2d77a5e0b · outbound

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

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.221245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1d16561-632b-463d-a64c-bb651523cd3f · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Swin transformer: Hierarchical vision transformer using shifted windows

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.225260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3af6e54-3f46-413b-9be5-8b8e7d7a71f2 · outbound

This paper cites Focal self-attention for local-global interactions in vision transformers, 2022.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Focal self-attention for local-global interactions in vision transformers, 2022

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.897356Z

Source-reported events for the cited work

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Observation 1e152e4f-30f5-4a89-9b51-a630880839f0 · outbound

This paper cites Transformers in Vision: A Survey.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Transformers in Vision: A Survey

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:30:20.593300Z

Source-reported events for the cited work

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Observation 44a350a3-eebf-4233-b740-00f973675c99 · outbound

This paper cites Deep learning for automated visual inspection in manufacturing and maintenance: A survey of open- access papers.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Deep learning for automated visual inspection in manufacturing and maintenance: A survey of open- access papers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.887187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 057d7133-d7d8-4b23-ad44-4f3b8247b129 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.239537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aab5e02c-c11e-4fc9-885b-cf2801072245 · outbound

This paper cites Language Models are Few-Shot Learners.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Language Models are Few-Shot Learners

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.243107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07d94a53-70b4-40d3-a1c6-060e04d61907 · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Swin transformer v2: Scaling up capacity and resolution

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.878057Z

Source-reported events for the cited work

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Observation 81584b99-7cff-4fe1-8659-22fa63a5e56a · outbound

This paper cites Florence: A new foundation model for computer vision, 2021.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Florence: A new foundation model for computer vision, 2021

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.249301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85b2b37d-3f94-42ea-9343-20c988f9cbd3 · outbound

This paper cites Focal modulation networks, 2022.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Focal modulation networks, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.863184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9ef92adb-bf2b-4ee0-a74a-3ba3fffa6ecb · outbound

This paper cites Internimage: Exploring large-scale vision foundation models with deformable convolutions, 2022.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Internimage: Exploring large-scale vision foundation models with deformable convolutions, 2022

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.853952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation def594cd-a5f3-4dbc-8a0d-7b8b59173f9a · outbound

This paper cites Explainability and evaluation of vision transformers: An in-depth experimental study.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Explainability and evaluation of vision transformers: An in-depth experimental study

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.843705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 964f34e6-aa8c-4b90-91e9-7702be00b946 · outbound

This paper cites Holistically Explainable Vision Transformers.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Holistically Explainable Vision Transformers

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:30:20.560664Z

Source-reported events for the cited work

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Observation e54bd905-7441-4c3d-aa2d-2a18491db48b · outbound

This paper cites Explainability of Vision Transformers: A Comprehensive Review and New Perspectives.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Explainability of Vision Transformers: A Comprehensive Review and New Perspectives

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:30:20.547731Z

Source-reported events for the cited work

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Observation 9dc51fbe-ee60-411f-a99f-caf2978b567d · outbound

This paper cites Scoville and Brenda Milner.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Scoville and Brenda Milner

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.834771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d910c9a-282a-451b-886b-b350fe6e22f1 · outbound

This paper cites an unresolved cited work.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:30:20.825925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ffe7a150-4e4b-4256-a01a-51d1322de7ab · outbound

This paper cites Ungerleider and Mortimer Mishkin.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Ungerleider and Mortimer Mishkin

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.816992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a786605-63d8-402a-a8ef-6c1440d15f02 · outbound

This paper cites The emotional brain: The mysterious underpinnings of emotional life.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations The emotional brain: The mysterious underpinnings of emotional life

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.806663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95751be9-5847-41b4-ac84-f8bd04245184 · outbound

This paper cites Ablation Studies in Artificial Neural Networks.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Ablation Studies in Artificial Neural Networks

Reference 20

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Source-reported events for the cited work

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Observation 2a3ccc11-5530-49bf-a2a2-924585219c66 · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.283892Z

Source-reported events for the cited work

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Observation ca47ba86-a87c-4d62-89eb-70dd56b0bcb4 · outbound

This paper cites Microsoft coco: Common objects in context, 2014.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Microsoft coco: Common objects in context, 2014

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.797643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 88965b03-d901-43ac-a347-ea5776ebc5fd · outbound

This paper cites A tutorial on speech understanding systems.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations A tutorial on speech understanding systems

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.788838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 988db4ca-a666-478d-86c1-78c4da465236 · outbound

This paper cites Aggregated residual transformations for deep neural networks, 2017.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Aggregated residual transformations for deep neural networks, 2017

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.779967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e70ddedf-5527-48f1-9131-12b44ebdc4cd · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks, 2015.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Faster r-cnn: Towards real-time object detection with region proposal networks, 2015

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.771144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f9faca06-e1c5-442c-8830-74ca7f7931a9 · outbound

This paper cites Feature pyramid networks for object detection, 2017.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Feature pyramid networks for object detection, 2017

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.300334Z

Source-reported events for the cited work

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Observation a3279186-faf1-4cb2-aaab-93f9d3c26669 · outbound

This paper cites End-to-end object detection with transformers, 2020.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations End-to-end object detection with transformers, 2020

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.303538Z

Source-reported events for the cited work

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Observation 0783a53a-9917-4076-894b-a899a4d70f10 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation, 2013.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Rich feature hierarchies for accurate object detection and semantic segmentation, 2013

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.751403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7ba0499e-4a6b-4a79-b96b-0607b3cfff15 · outbound

This paper cites Bayan Bruss.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Bayan Bruss

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.743183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d723381d-e897-4494-a99f-634fcdddf685 · outbound

This paper cites Vishnusai, Tejas R.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Vishnusai, Tejas R

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.735248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e0fe3a45-9629-44f4-82cf-ae657c842714 · outbound

This paper cites Lillian, Richard Meyes, and Tobias Meisen.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Lillian, Richard Meyes, and Tobias Meisen

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.727036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9091d591-8dcb-4f1b-8313-3e7402f58fb3 · outbound

This paper cites Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.718761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86459300-bb07-45fa-bb6e-f2589f0c1187 · outbound

This paper cites Transparent and interpretable failure prediction of sensor time series data with convolutional neural networks.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Transparent and interpretable failure prediction of sensor time series data with convolutional neural networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.709663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0f350166-50ff-40f3-90dd-39848a15871a · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.422735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df5d87b9-076a-407e-bb5d-cc3dd7ff6162 · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T12:30:20.426052Z

Source-reported events for the cited work

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Observation 793535c1-96dd-488b-b9fa-93a51c34e8db · outbound

This paper cites Zico Kolter.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Zico Kolter

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.700694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74c6f64b-6402-47e7-b1c3-92869421eee3 · outbound

This paper cites Upop: Unified and progressive pruning for compressing vision-language transformers.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Upop: Unified and progressive pruning for compressing vision-language transformers

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.690994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ccb172c8-3599-4845-979c-ee2c87e0d72f · outbound

This paper cites UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:30:20.495967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c560df5d-ad48-4d97-a4e1-cb3fe3bc043c · outbound

This paper cites Width & depth pruning for vision transformers.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Width & depth pruning for vision transformers

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.681963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d620ba91-0114-4c83-9966-e9817b23c226 · outbound

This paper cites X-pruner: explainable pruning for vision transformers.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations X-pruner: explainable pruning for vision transformers

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.671384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80c37a3c-d37e-4ba0-96fa-d4648fc808ab · outbound

This paper cites Revisiting Token Pruning for Object Detection and Instance Segmentation.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Revisiting Token Pruning for Object Detection and Instance Segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.662006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0473867e-d3f2-430c-a636-b6ce2cb6080f · outbound

This paper cites Efficient pruning of detection transformer in remote sensing using ant colony evolutionary pruning.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Efficient pruning of detection transformer in remote sensing using ant colony evolutionary pruning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.652657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f9855da8-1c3c-44d7-a949-0e7616701e49 · outbound

This paper cites Pruning detr: efficient end-to-end object detection with sparse structured pruning.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Pruning detr: efficient end-to-end object detection with sparse structured pruning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.643705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5918456-fd38-4222-97d3-985cb948e74d · outbound

This paper cites Deformable detr: Deformable transformers for end-to-end object detection, 2020.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Deformable detr: Deformable transformers for end-to-end object detection, 2020

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.633699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b55e1bf1-300e-46c8-8a61-f2363cf1a5c4 · outbound

This paper cites DINO: DETR with improved denoising anchor boxes for end-to-end object detection.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations DINO: DETR with improved denoising anchor boxes for end-to-end object detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.622935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 96c0c158-f380-4493-b005-4c42444f824d · outbound

This paper cites Rezatofighi, N.

Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations Rezatofighi, N

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:30:20.612897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.