Paper Citation Record · LEDGER

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts

As of 17 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2507.18464.

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

pith.paper-citation-record.v1
2507.18464 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:36:34.741919Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

28 of 28 outbound references displayed

verified exact3
verified fuzzy14
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation df9746b6-5d62-4162-a941-e7b3ad809ce5 · outbound

This paper cites In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.610237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 50388fdd-5edc-4329-9f30-abebcf4468fc · outbound

This paper cites In: Proceedings of the 2007 SIAM international conference on data mining.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: Proceedings of the 2007 SIAM international conference on data mining

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.580798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1454a823-305e-4a70-8023-40f601c08c38 · outbound

This paper cites In: Joint European conference on machine learning and knowledge discovery in databases.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: Joint European conference on machine learning and knowledge discovery in databases

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.562195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e826187e-e074-4a80-a0bc-55364d089b2a · outbound

This paper cites Journal of Machine Learning Research - Proceedings Track11, 44–50 (2010).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Journal of Machine Learning Research - Proceedings Track11, 44–50 (2010)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.540730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5ba28425-b420-463b-80de-26a2c0e0d29a · outbound

This paper cites Routledge (1984).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Routledge (1984)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.598182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 355170c0-cb55-489a-b436-5f153da3b295 · outbound

This paper cites Information Sciences265, 50–67 (May 2014).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Information Sciences265, 50–67 (May 2014)

Reference 6

Resolution: verified exact; doi, observed 2026-08-06T14:36:34.912505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 05e816b0-1266-4c36-b5de-42c79646f17f · outbound

This paper cites In: Piangerelli, M., Prenkaj, B., Rotalinti, Y., Joshi, A., Stilo, G.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: Piangerelli, M., Prenkaj, B., Rotalinti, Y., Joshi, A., Stilo, G

Reference 7

Resolution: verified exact; doi, observed 2026-08-06T14:36:34.889985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 741395c3-66e4-4f78-87ac-5a203c3be3fe · outbound

This paper cites An Online Boosting Algorithm with Theoretical Justifications.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts An Online Boosting Algorithm with Theoretical Justifications

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T14:36:34.867405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f2ae1637-b718-472f-802a-bc8690b79545 · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.622349Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d0a5573-2607-454f-b072-4c4763552132 · outbound

This paper cites In: Proceedings ofthesixthACMSIGKDDinternationalconferenceonKnowledgediscovery and data mining.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: Proceedings ofthesixthACMSIGKDDinternationalconferenceonKnowledgediscovery and data mining

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.521121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e38cce36-410b-481a-b335-4b31a18805c9 · outbound

This paper cites Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.638105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d367107-89e5-414e-be8a-f50fbd8ba548 · outbound

This paper cites ACM computing surveys (CSUR)46(4), 1–37 (2014).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts ACM computing surveys (CSUR)46(4), 1–37 (2014)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.504385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4605fec0-1726-44b4-9bed-d1feb7919075 · outbound

This paper cites In: Proceedings of the 2nd International Workshop on MetaOS for the Cloud-Edge-IoT Continuum.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: Proceedings of the 2nd International Workshop on MetaOS for the Cloud-Edge-IoT Continuum

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.484629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d43ff74-9a31-4cbb-aa77-5ada6e61af88 · outbound

This paper cites Machine Learning pp.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Machine Learning pp

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.461730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7e724d0b-4108-43dc-b63d-be2a9a761652 · outbound

This paper cites CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.663437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6dca97e-fd1e-45f0-8310-a00119af61b0 · outbound

This paper cites In: 2019 IEEE International Conference on Data Mining (ICDM).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: 2019 IEEE International Conference on Data Mining (ICDM)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.436161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9bd0cef6-bf77-422a-b76c-fab380d06e86 · outbound

This paper cites Neural computation3(1), 79–87 (1991).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Neural computation3(1), 79–87 (1991)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.673240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c823260-410f-4a47-bffd-6a1ef00f8768 · outbound

This paper cites Theory on Mixture-of-Experts in Continual Learning.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Theory on Mixture-of-Experts in Continual Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.678116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 621dc4d0-93ac-4c44-ae06-5368ef565309 · outbound

This paper cites Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.682826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c647a0c1-a642-4119-be9c-d80661e827c8 · outbound

This paper cites Frontiers in psychology4, 504 (2013).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Frontiers in psychology4, 504 (2013)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.397365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa9507b7-f493-4e68-a489-1b6842062fab · outbound

This paper cites In: International Workshop on Artificial Intelligence and Statistics.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: International Workshop on Artificial Intelligence and Statistics

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.378267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8583c28d-68bb-464e-af3e-93ee09998035 · outbound

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

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.702152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f837ade-c075-4d73-9c22-6de2ba7fa406 · outbound

This paper cites In: Proceedings of the seventh ACM SIGKDD interna- tional conference on Knowledge discovery and data mining.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: Proceedings of the seventh ACM SIGKDD interna- tional conference on Knowledge discovery and data mining

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.359889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7c04d65-063a-4871-affc-6b0ec71c15fa · outbound

This paper cites Expert Systems with Applications213, 118934 (2023).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Expert Systems with Applications213, 118934 (2023)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.315490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b791407-3ec7-400d-aa06-cdbd143a1498 · outbound

This paper cites Technical Report: TCD-CS-2004-15, Department of Computer Science Trin- ity College, Dublin (2004) 16 Aspis, Cajas Ordoñez, et al.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Technical Report: TCD-CS-2004-15, Department of Computer Science Trin- ity College, Dublin (2004) 16 Aspis, Cajas Ordoñez, et al

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:36:35.296050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2b0bf067-c69d-4ec8-b383-927122089b02 · outbound

This paper cites OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models.

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.730135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4cea0da-54a2-420c-9d44-170bb7a9cc08 · outbound

This paper cites In: 2024 IEEE 24th International Conference on Communication Technology (ICCT).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts In: 2024 IEEE 24th International Conference on Communication Technology (ICCT)

Reference 27

Resolution: verified exact; raw_fallback, observed 2026-08-06T14:36:35.139935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a417a22-cafb-4b70-9732-65f2a3ff1a17 · outbound

This paper cites Machine Learning98(3), 455–482 (Apr 2014).

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts Machine Learning98(3), 455–482 (Apr 2014)

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T14:36:34.741919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.