Paper Citation Record · LEDGER

MUFASA: A Multi-Layer Framework for Slot Attention

As of 9 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2602.07544.

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

pith.paper-citation-record.v1
2602.07544 v2

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:36:29.720331Z

measured 55 of 55 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

55 of 55 outbound references displayed

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verified fuzzy0
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Outbound references

Observation d237e5a2-c2b6-4e77-adc9-d14e8e39ca9b · outbound

This paper cites On the effectiveness of ViT features as local semantic descrip- tors.

MUFASA: A Multi-Layer Framework for Slot Attention On the effectiveness of ViT features as local semantic descrip- tors

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.478436Z

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Observation 05ac3a7b-0a74-4eab-b736-d4573292f149 · outbound

This paper cites MONet: Unsupervised Scene Decomposition and Representation.

MUFASA: A Multi-Layer Framework for Slot Attention MONet: Unsupervised Scene Decomposition and Representation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.483883Z

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Observation 00fdf60d-60db-47f4-903e-d5d4a14d023b · outbound

This paper cites MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding.

MUFASA: A Multi-Layer Framework for Slot Attention MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.489126Z

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Observation d09ef0cf-36ce-4b03-adfd-741dc4a4e588 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.NeurIPS, pages 9912–9924, 2020.

MUFASA: A Multi-Layer Framework for Slot Attention Unsupervised learning of visual features by contrasting cluster assignments.NeurIPS, pages 9912–9924, 2020

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.494340Z

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Observation 25225bf7-016e-4bbd-a9c7-567f37ac0508 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

MUFASA: A Multi-Layer Framework for Slot Attention Emerg- ing properties in self-supervised vision transformers

Reference 5

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Observation 97fdf7e7-d4f6-480c-b014-6f93ffba95ce · outbound

This paper cites Learning phrase representations using RNN encoder–decoder for statistical machine translation.

MUFASA: A Multi-Layer Framework for Slot Attention Learning phrase representations using RNN encoder–decoder for statistical machine translation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.503207Z

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Observation 96dd4671-e4e5-4e5c-8401-2cf03b91ad53 · outbound

This paper cites Slot Structured World Models.

MUFASA: A Multi-Layer Framework for Slot Attention Slot Structured World Models

Reference 7

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Observation 6e134d80-8702-4a1e-bde5-9255c1e27474 · outbound

This paper cites Zero-shot object-centric representation learning.

MUFASA: A Multi-Layer Framework for Slot Attention Zero-shot object-centric representation learning

Reference 8

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Observation ca580e3b-67cb-4703-b8e8-90ad2638398c · outbound

This paper cites General- ization and robustness implications in object-centric learning.

MUFASA: A Multi-Layer Framework for Slot Attention General- ization and robustness implications in object-centric learning

Reference 9

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Observation 433913a5-2bae-4a6f-b9ca-1d2e17e79d15 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

MUFASA: A Multi-Layer Framework for Slot Attention An image is worth 16x16 words: Transformers for image recognition at scale

Reference 10

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Observation ed7242d6-c7cd-4f23-abca-2251b671d85a · outbound

This paper cites Kosiorek, Oiwi Parker Jones, and Ingmar Posner.

MUFASA: A Multi-Layer Framework for Slot Attention Kosiorek, Oiwi Parker Jones, and Ingmar Posner

Reference 11

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Observation 98c6798d-ed60-4a41-abaa-c21bcca685f9 · outbound

This paper cites an unresolved cited work.

MUFASA: A Multi-Layer Framework for Slot Attention Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.531389Z

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Observation 5606e3d4-aa7e-46e2-b993-f726363d8b54 · outbound

This paper cites Williams, John Winn, and Andrew Zisserman.

MUFASA: A Multi-Layer Framework for Slot Attention Williams, John Winn, and Andrew Zisserman

Reference 13

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Observation d43dd27d-0187-4700-87d6-f4f00a8b76d1 · outbound

This paper cites Vision meets robotics: The KITTI dataset.IJRR,.

MUFASA: A Multi-Layer Framework for Slot Attention Vision meets robotics: The KITTI dataset.IJRR,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.540019Z

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Observation dab43100-a02e-44c2-8bd9-d5f06fb1fe0e · outbound

This paper cites Multi-object representation learning with iterative variational inference.

MUFASA: A Multi-Layer Framework for Slot Attention Multi-object representation learning with iterative variational inference

Reference 15

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Observation 16d0b308-6dc0-4c5e-99cc-3a8eb3b3c70c · outbound

This paper cites Kubric: A scalable dataset generator.

MUFASA: A Multi-Layer Framework for Slot Attention Kubric: A scalable dataset generator

Reference 16

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Observation fe37d488-5106-495c-a56f-7358dd9b37f9 · outbound

This paper cites Fuchs, Ingmar Posner, and Andrea Vedaldi.

MUFASA: A Multi-Layer Framework for Slot Attention Fuchs, Ingmar Posner, and Andrea Vedaldi

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.553100Z

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Observation 7d709c26-d2ec-4bd4-91f7-ace85abbbdba · outbound

This paper cites Masked autoencoders are scalable vision learners.

MUFASA: A Multi-Layer Framework for Slot Attention Masked autoencoders are scalable vision learners

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.557413Z

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Observation 6b00d2fc-5484-4e45-bc88-3f95c478b556 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

MUFASA: A Multi-Layer Framework for Slot Attention Gaussian Error Linear Units (GELUs)

Reference 19

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Observation 205903df-d635-4ca2-8a70-301f03f9234c · outbound

This paper cites Visuomotor control in multi-object scenes using object-aware representations.

MUFASA: A Multi-Layer Framework for Slot Attention Visuomotor control in multi-object scenes using object-aware representations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.566548Z

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Observation 8c96dd62-a8ac-476e-a352-865f8c3662db · outbound

This paper cites Improving object- centric learning with query optimization.

MUFASA: A Multi-Layer Framework for Slot Attention Improving object- centric learning with query optimization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.570580Z

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Observation 016493b4-becb-4f66-89be-e4302fb16408 · outbound

This paper cites Scalor: Generative world models with scalable object representations.

MUFASA: A Multi-Layer Framework for Slot Attention Scalor: Generative world models with scalable object representations

Reference 22

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Observation 0703ee58-4b4d-4764-83ec-aaf9d0a6bd16 · outbound

This paper cites Object-centric slot diffusion.

MUFASA: A Multi-Layer Framework for Slot Attention Object-centric slot diffusion

Reference 23

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Observation 2493fb3d-80ea-43c8-aec9-f29923163d63 · outbound

This paper cites Denoising criterion for variational auto- encoding framework.

MUFASA: A Multi-Layer Framework for Slot Attention Denoising criterion for variational auto- encoding framework

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.583993Z

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Unavailable: canonical work link unavailable.

Observation 48b1c2bd-ecf6-4b93-a0b2-8e014e7d50ce · outbound

This paper cites Lawrence Zitnick, and Ross Girshick.

MUFASA: A Multi-Layer Framework for Slot Attention Lawrence Zitnick, and Ross Girshick

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.588643Z

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Observation d59ca423-642a-41e6-8200-34b9b3a83564 · outbound

This paper cites SPOT: Self-training with patch-order permutation for object-centric learning with autoregressive transformers.

MUFASA: A Multi-Layer Framework for Slot Attention SPOT: Self-training with patch-order permutation for object-centric learning with autoregressive transformers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.593132Z

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Observation 4e21eef9-c25c-4c81-b781-13b83e637d0c · outbound

This paper cites Clevr- Tex: A texture-rich benchmark for unsupervised multi-object segmentation.

MUFASA: A Multi-Layer Framework for Slot Attention Clevr- Tex: A texture-rich benchmark for unsupervised multi-object segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.598786Z

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Observation 5035bd08-cb61-493a-bfee-1d39018fed93 · outbound

This paper cites Bootstrapping top-down information for self-modulating slot attention.

MUFASA: A Multi-Layer Framework for Slot Attention Bootstrapping top-down information for self-modulating slot attention

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.603921Z

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Observation bf66e2d0-d1d0-4105-8e37-fffdf283ac03 · outbound

This paper cites The perception of hierarchical structure.

MUFASA: A Multi-Layer Framework for Slot Attention The perception of hierarchical structure

Reference 29

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Observation 1b8b10e4-024e-45ab-aea9-43e453886bb3 · outbound

This paper cites Kingma and Jimmy Ba.

MUFASA: A Multi-Layer Framework for Slot Attention Kingma and Jimmy Ba

Reference 30

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Observation 0a286c3f-ce50-4f85-b4a5-b9b899bdc238 · outbound

This paper cites Elsayed, Aravindh Mahen- dran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jon- schkowski, Alexey Dosovitskiy, and Klaus Greff.

MUFASA: A Multi-Layer Framework for Slot Attention Elsayed, Aravindh Mahen- dran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jon- schkowski, Alexey Dosovitskiy, and Klaus Greff

Reference 31

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Observation 476c0b41-3e07-468d-be45-12e3cd0c4386 · outbound

This paper cites Sequential attend, infer, repeat: Generative modelling of moving objects.NeurIPS, 31, 2018.

MUFASA: A Multi-Layer Framework for Slot Attention Sequential attend, infer, repeat: Generative modelling of moving objects.NeurIPS, 31, 2018

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.620976Z

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Observation 039cff75-4b4b-42d4-8dd6-dc250d6b7314 · outbound

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MUFASA: A Multi-Layer Framework for Slot Attention Unresolved cited work

Reference 33

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Observation 7e2a513b-ef41-4415-8ac5-b47b55956acd · outbound

This paper cites Scouter: Slot attention- based classifier for explainable image recognition.

MUFASA: A Multi-Layer Framework for Slot Attention Scouter: Slot attention- based classifier for explainable image recognition

Reference 34

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Observation 8a43763a-38c6-4aa1-a5fd-fc499f0ce37b · outbound

This paper cites Learning object- centric representations of multi-object scenes from multiple views.

MUFASA: A Multi-Layer Framework for Slot Attention Learning object- centric representations of multi-object scenes from multiple views

Reference 35

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Observation 0862e2d0-aa7d-40a5-924e-dc822c242a07 · outbound

This paper cites Lawrence Zitnick.

MUFASA: A Multi-Layer Framework for Slot Attention Lawrence Zitnick

Reference 36

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Source-reported events for the cited work

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Observation 33cc9c76-d519-4dff-8c7e-ee4409c26de2 · outbound

This paper cites Improving generative imagination in object-centric world models.

MUFASA: A Multi-Layer Framework for Slot Attention Improving generative imagination in object-centric world models

Reference 37

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Observation 94d51749-d6c4-4af1-9760-af33b48a0154 · outbound

This paper cites Space: Unsupervised object-oriented scene representation via spatial attention and decomposition.

MUFASA: A Multi-Layer Framework for Slot Attention Space: Unsupervised object-oriented scene representation via spatial attention and decomposition

Reference 38

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Observation 23b5db1c-1e54-4657-8c01-5a045f4e8e0a · outbound

This paper cites StructDiffusion: Language-guided creation of physically-valid structures using unseen objects.

MUFASA: A Multi-Layer Framework for Slot Attention StructDiffusion: Language-guided creation of physically-valid structures using unseen objects

Reference 39

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Observation ff9b2701-6954-41db-ac6f-fbbe88edea0d · outbound

This paper cites Object-centric learn- ing with slot attention.

MUFASA: A Multi-Layer Framework for Slot Attention Object-centric learn- ing with slot attention

Reference 40

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Observation d4e4c104-6d54-4934-a5ee-21fddd4f7749 · outbound

This paper cites Diffusion hyperfeatures: Searching through time and space for semantic correspondence.

MUFASA: A Multi-Layer Framework for Slot Attention Diffusion hyperfeatures: Searching through time and space for semantic correspondence

Reference 41

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Observation 7c1609e1-7114-42f2-906d-142d05c00a4f · outbound

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

MUFASA: A Multi-Layer Framework for Slot Attention DINOv2: Learning Robust Visual Features without Supervision

Reference 42

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Observation eb9dcc3d-d739-4e6d-9578-7421b06a8558 · outbound

This paper cites Barron, Ferran Marques, and Jitendra Malik.

MUFASA: A Multi-Layer Framework for Slot Attention Barron, Ferran Marques, and Jitendra Malik

Reference 43

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Observation 135d78db-a92d-4bc1-8c49-f88112e1d008 · outbound

This paper cites Do vision trans- formers see like convolutional neural networks? InNeurIPS, pages 12116–12128, 2021.

MUFASA: A Multi-Layer Framework for Slot Attention Do vision trans- formers see like convolutional neural networks? InNeurIPS, pages 12116–12128, 2021

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.673888Z

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Observation 38f227af-4e3f-487c-bae9-9c73bb638a2f · outbound

This paper cites Bridging the gap to real-world object- centric learning.

MUFASA: A Multi-Layer Framework for Slot Attention Bridging the gap to real-world object- centric learning

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.678373Z

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Observation 2e6d0bf2-7a86-4c9b-96bf-7e7a135c3bfc · outbound

This paper cites Illiterate DALL-E learns to compose.

MUFASA: A Multi-Layer Framework for Slot Attention Illiterate DALL-E learns to compose

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.682553Z

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Observation 20c93127-9a83-44fe-b365-c241e852d92d · outbound

This paper cites Simple unsu- pervised object-centric learning for complex and naturalistic videos.NeurIPS, pages 18181–18196, 2022.

MUFASA: A Multi-Layer Framework for Slot Attention Simple unsu- pervised object-centric learning for complex and naturalistic videos.NeurIPS, pages 18181–18196, 2022

Reference 47

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Observation a48d8021-e74b-4205-b54f-005201879c35 · outbound

This paper cites Analyzing Local Representations of Self-supervised Vision Transformers.

MUFASA: A Multi-Layer Framework for Slot Attention Analyzing Local Representations of Self-supervised Vision Transformers

Reference 48

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Observation bb2ad601-4426-4d9b-89b3-7cb915f53d27 · outbound

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

MUFASA: A Multi-Layer Framework for Slot Attention Gomez, Łukasz Kaiser, and Illia Polosukhin

Reference 49

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Observation c391a2bd-e935-4e42-b384-60093389dc42 · outbound

This paper cites Burgess, and Alexander Lerchner.

MUFASA: A Multi-Layer Framework for Slot Attention Burgess, and Alexander Lerchner

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.699247Z

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Observation 98549afe-02ef-41b4-9c47-64f7b8263f38 · outbound

This paper cites SlotFormer: Unsupervised visual dynamics simulation with object-centric models.

MUFASA: A Multi-Layer Framework for Slot Attention SlotFormer: Unsupervised visual dynamics simulation with object-centric models

Reference 51

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Observation a925545c-7097-4904-8627-4c7561ed2588 · outbound

This paper cites SlotDiffusion: Object-centric generative model- ing with diffusion models.

MUFASA: A Multi-Layer Framework for Slot Attention SlotDiffusion: Object-centric generative model- ing with diffusion models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T03:36:29.707807Z

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Observation b65d57fc-b766-4919-8e09-6834f43d3635 · outbound

This paper cites Dense connector for MLLMs.

MUFASA: A Multi-Layer Framework for Slot Attention Dense connector for MLLMs

Reference 53

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Observation 531528f7-cb1c-42bd-8e6e-41a56708087a · outbound

This paper cites Zeiler and Rob Fergus.

MUFASA: A Multi-Layer Framework for Slot Attention Zeiler and Rob Fergus

Reference 54

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Observation 0a93b191-ac5f-4473-8ec4-49bd7a3c5b1f · outbound

This paper cites Implementation Details In this section, we provide a more detailed overview of the training and implementation details for DINOSAUR-M and SPOT-M.

MUFASA: A Multi-Layer Framework for Slot Attention Implementation Details In this section, we provide a more detailed overview of the training and implementation details for DINOSAUR-M and SPOT-M

Reference 2014

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Pith citing papers

No inbound Pith citation observations are available.