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Paper Citation Record · LEDGER

Model Fusion via Retrofitting

As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2507.00037.

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

pith.paper-citation-record.v1
2507.00037 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:52:02.267181Z

measured 29 of 29 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T06:59:09.799156Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T07:01:11.774460Z

Reference resolution

28 of 28 outbound references displayed

  • verified exact0
  • verified fuzzy10
  • unresolved18
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9928d30a-3a15-4e51-857c-41a8492baa8f · outbound

This paper cites Git Re-Basin: Merging Models modulo Permutation Symmetries.

Model Fusion via Retrofitting Git Re-Basin: Merging Models modulo Permutation Symmetries

Reference 1

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source=arxiv_source observed=2026-08-06T23:52:02.195128Z digest=sha256:0a2dbb4fcb2cf01fdcffc687e8743925c729399b5a3c826d9c79ebd5140589f6

Observation d67a1f82-d9e2-47ba-884c-2c25a7fde431 · outbound

This paper cites Np-hardness of euclidean sum-of-squares clustering.

Model Fusion via Retrofitting Np-hardness of euclidean sum-of-squares clustering

Reference 2

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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.

source=arxiv_source observed=2026-08-06T23:52:02.199076Z digest=sha256:6da5225dd6efc496b1d244a5d6a3bc425a8408fd1e31d6d8ffdf01d3d2fa88e4

Observation c21aa2aa-3f66-46ff-9d26-b889d57b3fae · outbound

This paper cites On Clustering Time Series Using Euclidean Distance and Pearson Correlation.

Model Fusion via Retrofitting On Clustering Time Series Using Euclidean Distance and Pearson Correlation

Reference 3

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source=arxiv_source observed=2026-08-06T23:52:02.202119Z digest=sha256:bd0b4db21d7a9c3e8c2593dc3e0cf8c2089c2ac8b8a81c28ae7ed6a18bb7f712

Observation 851f7885-7267-42a1-90a9-835d7a9ca96b · outbound

This paper cites Convergence properties of the k-means algorithms.

Model Fusion via Retrofitting Convergence properties of the k-means algorithms

Reference 4

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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.

source=arxiv_source observed=2026-08-06T23:52:02.205556Z digest=sha256:1ec1e88d4d8bdf830a2b7ba92179439598ff0f3927803946de1a1700068f2911

Observation 45b95519-748e-4b66-a0fd-412eff94ec48 · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Model Fusion via Retrofitting AutoAugment: Learning Augmentation Policies from Data

Reference 5

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source=arxiv_source observed=2026-08-06T23:52:02.208252Z digest=sha256:4140b6cb4b2ed391f8bac8733e31eb5a183af51c973986f3736c6e42cc14b07d

Observation 5dcd9057-b677-4a74-bb3d-b8689d7c50d0 · outbound

This paper cites How Important Is a Neuron?.

Model Fusion via Retrofitting How Important Is a Neuron?

Reference 6

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source=arxiv_source observed=2026-08-06T23:52:02.211249Z digest=sha256:955e4c2622912ca49eadec182c429b3fd4fba35b656adb3b2c3b1131d2f273cb

Observation 93cba7ec-f535-4601-b250-8d5f053bb511 · outbound

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

Model Fusion via Retrofitting An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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source=arxiv_source observed=2026-08-06T23:52:02.214356Z digest=sha256:be824938895a1946b0b39ab07f606aa52826029875eb8f8778faaaef2cb58825

Observation 3300390e-f534-4e88-a601-a1a723ffd00a · outbound

This paper cites Correlation-based pruning algorithm with weight compensation for feedforward neural networks.

Model Fusion via Retrofitting Correlation-based pruning algorithm with weight compensation for feedforward neural networks

Reference 8

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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.

source=arxiv_source observed=2026-08-06T23:52:02.216919Z digest=sha256:7f45709d7febe2290ebd502ec5578fef2f4c2add251dee8db8403b0bb5b12024

Observation 23763d3c-f1aa-4a73-bb0d-c4863e9db0fd · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Model Fusion via Retrofitting The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 9

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source=arxiv_source observed=2026-08-06T23:52:02.219293Z digest=sha256:1f99c04811049e53507586553bb8bb0843d57fc2f8d6024440b5136660afc565

Observation f6497ef3-866d-42bb-92b9-948ec5f88012 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Model Fusion via Retrofitting Distilling the Knowledge in a Neural Network

Reference 10

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source=arxiv_source observed=2026-08-06T23:52:02.221937Z digest=sha256:e47eef8c630c282e5c552ea8cffc3142750397fc0b7a913da905dadd1b8b4ecc

Observation e9302120-1ee4-42b8-88fb-4131836fe75a · outbound

This paper cites Flat minima.

Model Fusion via Retrofitting Flat minima

Reference 11

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source=arxiv_source observed=2026-08-06T23:52:02.224532Z digest=sha256:e14ef47152683cf2b6d5fadbfcd23de8294e7eb8e1bd48e90f8b78ed16551b9e

Observation d5ff9790-a921-4e59-89f6-ee3670a8e630 · outbound

This paper cites Transformer Fusion with Optimal Transport.

Model Fusion via Retrofitting Transformer Fusion with Optimal Transport

Reference 12

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source=arxiv_source observed=2026-08-06T23:52:02.227375Z digest=sha256:39d41d51ada74f1e86d0a5c6ca54b5af736da6189ecc1ecd4035844f712f338e

Observation 8976a05d-8c1e-4085-a5e4-483c521f19b0 · outbound

This paper cites A local search approximation algorithm for k-means clustering.

Model Fusion via Retrofitting A local search approximation algorithm for k-means clustering

Reference 13

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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.

source=arxiv_source observed=2026-08-06T23:52:02.229862Z digest=sha256:f6380bce4920d98130d59407f1070e2a3d720bc3539c3d2fcba79edfea65564b

Observation 671ba1be-2c1d-4e9c-9dbc-0f04224a3808 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Model Fusion via Retrofitting Adam: A Method for Stochastic Optimization

Reference 14

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source=arxiv_source observed=2026-08-06T23:52:02.232278Z digest=sha256:c5704c2217fb26e0b74c06547dc99a912cfbdc097fe9732437854ce9954659cd

Observation 4ee5c366-5f06-4853-a83e-5912c687fb29 · outbound

This paper cites Captum: A unified and generic model interpretability library for PyTorch.

Model Fusion via Retrofitting Captum: A unified and generic model interpretability library for PyTorch

Reference 15

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source=arxiv_source observed=2026-08-06T23:52:02.234785Z digest=sha256:d3eff4251c1f513658ccb0e2feb691bf8ea4d236c8e4d23fcf5e0c7b3a7692fd

Observation 78271b0e-9daf-4060-9dc6-0e71caf04510 · outbound

This paper cites The hungarian method for the assignment problem.

Model Fusion via Retrofitting The hungarian method for the assignment problem

Reference 16

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source=arxiv_source observed=2026-08-06T23:52:02.237460Z digest=sha256:12014acfceaa763cf260b5d9702a4949a1f34b472a6aee571e14985e2e8e854b

Observation 0cf9e106-2701-4756-ab2f-4f01c3f4eb3d · outbound

This paper cites On the surprising effectiveness of attention transfer for vision transformers.

Model Fusion via Retrofitting On the surprising effectiveness of attention transfer for vision transformers

Reference 17

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T23:52:02.239831Z digest=sha256:b942211faa4b5f8843a1924349193d695cf59c8e5a3bc7faf6e492b9336a678b

Observation c2ec5dd5-6b4c-4e3f-ab27-c86b6c85a55a · outbound

This paper cites Least squares quantization in pcm.

Model Fusion via Retrofitting Least squares quantization in pcm

Reference 18

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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.

source=arxiv_source observed=2026-08-06T23:52:02.242119Z digest=sha256:167f212035f3f912fcabf82a50c21133189638ed3941d4b7be07990564ba55ac

Observation fd6e6731-d08c-45ab-93e5-2413ac34008c · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

Model Fusion via Retrofitting Communication-efficient learning of deep networks from decentralized data

Reference 19

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source=arxiv_source observed=2026-08-06T23:52:02.244439Z digest=sha256:4c41a5988102d1342b50718f4d1ec8991cf6982b1916b4a45d7dd23bbb7dd359

Observation 802c77f8-cca9-4ab5-8c9b-027e9d0ee234 · outbound

This paper cites Vit-cifar.

Model Fusion via Retrofitting Vit-cifar

Reference 20

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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.

source=arxiv_source observed=2026-08-06T23:52:02.246951Z digest=sha256:0982fae2eaa07e9acaea3f2f7d587db217046757c0b41fb8eb373cf9f81ca405

Observation f7df4e7b-8cb1-48c0-b5f4-1f4ffff6b069 · outbound

This paper cites Compute trends across three eras of machine learning.

Model Fusion via Retrofitting Compute trends across three eras of machine learning

Reference 21

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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.

source=arxiv_source observed=2026-08-06T23:52:02.249231Z digest=sha256:fb7dd0e9e08c71cd96c32c28bc29293cfd95b3dce3708045811b82c7a8de9b2a

Observation 0205c9d7-36a6-4e3d-aa9e-b0c50bd0cb9c · outbound

This paper cites Learning important features through propagating activation differences.

Model Fusion via Retrofitting Learning important features through propagating activation differences

Reference 22

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source=arxiv_source observed=2026-08-06T23:52:02.251696Z digest=sha256:287e9e11b7bb4b99f8a0a775916a1f3597817512afceaf843914f7da1f9409eb

Observation 5459476a-5ff6-47b8-a424-b7e1edae9ee2 · outbound

This paper cites Model fusion via optimal transport.

Model Fusion via Retrofitting Model fusion via optimal transport

Reference 23

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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.

source=arxiv_source observed=2026-08-06T23:52:02.254050Z digest=sha256:1bd3b5268d6e4b3d2f4c85cb8846b7c7a83af4c15dc5c0155bd19d2e46146f30

Observation c7625bcf-85a5-41f0-86d2-8cd18dd50912 · outbound

This paper cites Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions.

Model Fusion via Retrofitting Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 24

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source=arxiv_source observed=2026-08-06T23:52:02.256519Z digest=sha256:375269fae592ef4d7751b65ce019392382e7846de535cfca54216248c402e389

Observation 1fc5af03-e447-411c-a47e-55edb92e2ebe · outbound

This paper cites Axiomatic attribution for deep networks.

Model Fusion via Retrofitting Axiomatic attribution for deep networks

Reference 25

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source=arxiv_source observed=2026-08-06T23:52:02.259054Z digest=sha256:95b01b4dc38116698f39770dd738d6da77edacc2be2dffeae2f82ae7c4e02665

Observation 6f466638-3841-4c9e-be82-a45e356d1bdc · outbound

This paper cites Attention is all you need.

Model Fusion via Retrofitting Attention is all you need

Reference 26

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source=arxiv_source observed=2026-08-06T23:52:02.261390Z digest=sha256:75504c9a432a528dce01ab3d953d1b71d01fbde4bf5a547dfc954b16252a6f42

Observation 92138dfc-2505-44b2-b6b2-16163a9ad9b4 · outbound

This paper cites Federated Learning with Matched Averaging.

Model Fusion via Retrofitting Federated Learning with Matched Averaging

Reference 27

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source=arxiv_source observed=2026-08-06T23:52:02.264225Z digest=sha256:ac456e8f75ff13b8563f854aba8dab16db8bf844321dd1612e92f3aac8c341c7

Observation 5b58d4ac-f87e-4405-9c16-76c5ed1ec4aa · outbound

This paper cites Source prompt: Coordinated pre-training of language models on diverse corpora from multiple sources.

Model Fusion via Retrofitting Source prompt: Coordinated pre-training of language models on diverse corpora from multiple sources

Reference 28

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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.

source=arxiv_source observed=2026-08-06T23:52:02.267181Z digest=sha256:d6bc23907f7b19a535bfcfea808a9c7b5216d4e2083111239a7b9fbe8d4bc9a4

Pith citing papers

Observation 648f74fb-4a33-4e7d-af4c-762d78ff9d44 · inbound

Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation cites this paper.

Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation Model Fusion via Retrofitting

Reference 31

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arxiv_id, observed 2026-05-29T03:04:34.358098Z

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.

source=arxiv_source observed=2026-05-22T06:59:09.799156Z digest=sha256:a9474f58bf8454c10f9b26864e6548d7b83f2b13dffffb70e631ccddbc3bfedc