Paper Citation Record · LEDGER

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2507.20369.

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

pith.paper-citation-record.v1
2507.20369 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:39:44.824894Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

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

21 of 21 outbound references displayed

verified exact0
verified fuzzy11
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 624beb39-a21f-4ffb-8a62-7a6ba5275d20 · outbound

This paper cites Clusters models, factors and characteristics.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Clusters models, factors and characteristics

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:48.716144Z

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 ebd62e6f-67c4-4ac1-8697-45c567b4c870 · outbound

This paper cites Data mining: concepts, models, methods, and algorithms.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Data mining: concepts, models, methods, and algorithms

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:48.434748Z

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 e7f032c1-26e3-4c12-8147-3466266b6205 · outbound

This paper cites A novel clustering approach: Artificial bee colony (abc) algorithm.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning A novel clustering approach: Artificial bee colony (abc) algorithm

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:48.114991Z

Source-reported events for the cited work

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Observation f57b3f2b-d751-4e8f-a717-92b320a4d7fa · outbound

This paper cites Data clustering: a review.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Data clustering: a review

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:47.741517Z

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 231ffbb1-23d6-4143-ade5-5ba16bca18af · outbound

This paper cites Data mining and knowledge discovery handbook.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Data mining and knowledge discovery handbook

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:47.431864Z

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 f5de3ba1-04b6-4a15-a2ba-ddf70c3cba66 · outbound

This paper cites Mathematical classification and clustering, volume 11.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Mathematical classification and clustering, volume 11

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:47.152747Z

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 1ee9454d-decd-4133-91cd-6e56438073f7 · outbound

This paper cites Some methods for classification and analysis of multivariate observations.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Some methods for classification and analysis of multivariate observations

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:46.810385Z

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 3d7bc5db-4254-4839-892e-a166c680c87d · outbound

This paper cites Attention is all you need.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Attention is all you need

Reference 8

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Source-reported events for the cited work

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Observation 57b28469-b0ee-4162-9e42-04a59c6559df · outbound

This paper cites Transformers Can Do Bayesian Inference.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Transformers Can Do Bayesian Inference

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:43.205319Z

Source-reported events for the cited work

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Observation fa4d1807-ae3e-4c96-ab52-74b2803d825d · outbound

This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:43.344754Z

Source-reported events for the cited work

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Observation ab45d90e-3418-4f41-a911-8348974500bd · outbound

This paper cites u ller, Lennart Purucker, Arjun Krishnakumar, Max K \.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning u ller, Lennart Purucker, Arjun Krishnakumar, Max K \

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:43.434764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f98b30ca-9531-4995-bcac-28156b5a31ac · outbound

This paper cites Efficient bayesian learning curve extrapolation using prior-data fitted networks.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Efficient bayesian learning curve extrapolation using prior-data fitted networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:46.494900Z

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 c0a74c63-03b7-420e-8ca3-7773b915f269 · outbound

This paper cites Forecastpfn: Universal forecasting for healthcare.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Forecastpfn: Universal forecasting for healthcare

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:46.206527Z

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 c7b8b196-460e-42c4-8e7d-e6028c0a66b0 · outbound

This paper cites Statistical Foundations of Prior-Data Fitted Networks.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Statistical Foundations of Prior-Data Fitted Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:43.874825Z

Source-reported events for the cited work

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Observation 8ac28109-46c2-42cd-bffa-a6d07e52ba0a · outbound

This paper cites On the method of bounded differences.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning On the method of bounded differences

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:44.053838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85346a02-3eb2-414c-bb54-dfc73cf21cd7 · outbound

This paper cites Pedregosa, G.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Pedregosa, G

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:44.204760Z

Source-reported events for the cited work

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Observation 402e3a24-ea65-4dc6-aa3e-474810627230 · outbound

This paper cites V-measure: A conditional entropy-based external cluster evaluation measure.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning V-measure: A conditional entropy-based external cluster evaluation measure

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:45.926891Z

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 128ee1d8-e2c7-4a70-a5e0-5da71b37bbc9 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning The mnist database of handwritten digit images for machine learning research

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:44.482194Z

Source-reported events for the cited work

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Observation 92783d63-bab6-4f23-be67-e8dd252d2c11 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:44.614993Z

Source-reported events for the cited work

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Observation 55c6f77e-c517-4ec0-ada9-391492931caf · outbound

This paper cites Longformer: The Long-Document Transformer.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Longformer: The Long-Document Transformer

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T13:39:44.729315Z

Source-reported events for the cited work

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Observation 878e9435-62fd-4c8b-839f-bc645583b020 · outbound

This paper cites Big bird: Transformers for longer sequences.

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning Big bird: Transformers for longer sequences

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:39:45.444893Z

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.

Pith citing papers

No inbound Pith citation observations are available.