UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models | Paper Citation Record

Observation 0be6d88a-4465-4de0-b242-5b55d4e730d7 · outbound

This paper cites International Conference on Learning Representations , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International Conference on Learning Representations , year=

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.319297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 788ebb1c-10ae-458a-8de8-fc2f7d334469 · outbound

This paper cites International Conference on Machine Learning , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International Conference on Machine Learning , pages=

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.321025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f7f25e1-fdec-45e3-b41e-3f66c231c415 · outbound

This paper cites Nature Machine Intelligence , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Nature Machine Intelligence , volume=

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.315803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d7c9e38-0687-43ba-8790-276d0a283d53 · outbound

This paper cites The Twelfth International Conference on Learning Representations , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models The Twelfth International Conference on Learning Representations , year=

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.310423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd70e0c6-056d-490d-a86e-0b8daa13a7c7 · outbound

This paper cites A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.372572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c41694d9-374f-472e-9386-ff1792d9a228 · outbound

This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.312233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4bbea2e-6bd4-497b-8ca5-f0bd630085e9 · outbound

This paper cites Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T19:08:54.369245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9fbee1ad-623b-460f-91a1-52ab536e977f · outbound

This paper cites arXiv preprint arXiv:2502.15436 , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models arXiv preprint arXiv:2502.15436 , year=

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.380874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad37c185-6726-407e-b744-7e685a17b710 · outbound

This paper cites The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.314107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2c659bcc-a2c5-49a5-94de-b0b897be20b8 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.293601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation be448eba-8363-4a24-848a-077687a8e927 · outbound

This paper cites FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-26T02:02:26.506599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e108583a-fbf2-4f5e-b581-e4c8c711e19c · outbound

This paper cites ICLR 2025 Workshop on Modularity for Collaborative, Decentralized, and Continual Deep Learning , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models ICLR 2025 Workshop on Modularity for Collaborative, Decentralized, and Continual Deep Learning , year=

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.295448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f2d7e63e-f49d-46f7-ae85-5e13564ac428 · outbound

This paper cites International Conference on Learning Representations , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International Conference on Learning Representations , year=

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.299046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 530a5beb-1318-4b1b-8093-bc719ab2f754 · outbound

This paper cites International Conference on Artificial Intelligence and Statistics , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International Conference on Artificial Intelligence and Statistics , pages=

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.301201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30a1f700-48dc-442b-8706-5c3d466a7a87 · outbound

This paper cites ST-MoE: Designing Stable and Transferable Sparse Expert Models.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.316621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 099bf210-82e4-4240-8e4b-af340a8dc30a · outbound

This paper cites Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T19:08:54.313335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9b97d7af-2852-4897-996f-29b670c48b0e · outbound

This paper cites Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ NeurIPS 2023) , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ NeurIPS 2023) , year=

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.285845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 92de40b9-6631-48f6-be98-1b9f52cba233 · outbound

This paper cites Workshop on Machine Learning and Compression, NeurIPS 2024 , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Workshop on Machine Learning and Compression, NeurIPS 2024 , year=

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.287936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8fbec5a0-811c-4030-b8d0-b948662fbd06 · outbound

This paper cites 1998 , publisher=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models 1998 , publisher=

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.289896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation af9f68f8-ca57-441c-90de-15ad16826636 · outbound

This paper cites Illinois Journal of Mathematics , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Illinois Journal of Mathematics , volume=

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.291710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bcd3b76a-22f8-4d89-9c08-34c1be5a9750 · outbound

This paper cites Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.299411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3271561-71fa-490d-9e06-bc31441ac24c · outbound

This paper cites Pilot: Building the Federated Multimodal Instruction Tuning Framework.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Pilot: Building the Federated Multimodal Instruction Tuning Framework

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.359877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0a59f653-660d-43f7-8446-5eb8ed33d7b0 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.324321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f6e53f5-26f6-49f3-b140-0d92d66131aa · outbound

This paper cites Proceedings of the ACM Web Conference 2023 , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the ACM Web Conference 2023 , pages=

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.317593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dfa39df2-e3ea-40b6-b036-9364c5357d44 · outbound

This paper cites Advances in Mathematics , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Mathematics , volume=

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.322651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f6210934-c273-49d0-8616-872b169a8d5a · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.293094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a85c2485-7406-47ef-b9a4-f79638724629 · outbound

This paper cites Towards Building the.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Towards Building the

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.296921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d3ae7dd-673c-4f3c-a5f0-d5f1ecbf6ed8 · outbound

This paper cites FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.378054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5a75d7d1-3f63-4f74-b1ab-9a95655a6a22 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.300793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 828f8b86-3c6d-47b3-9ebe-0990ccff23f6 · outbound

This paper cites Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities , year=

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.304449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 33b81e0b-7f1c-4e4a-bdc8-857e11736968 · outbound

This paper cites International conference on machine learning , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International conference on machine learning , pages=

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.308471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f7d5529-7ff4-4f99-8c24-cc6089dadc78 · outbound

This paper cites International Conference on Learning Representations , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International Conference on Learning Representations , year=

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.287448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4916eed1-e3dc-4a6d-8146-fc63476409fb · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.310810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d0377284-899e-47cd-8bd4-72e63dece601 · outbound

This paper cites International conference on machine learning , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International conference on machine learning , pages=

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.285396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a58217d0-0695-421a-92ab-0f69610fc47d · outbound

This paper cites On the Convergence of SGD with Biased Gradients.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models On the Convergence of SGD with Biased Gradients

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T19:08:54.375386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8476326-96b0-4777-a95e-d237aab6d682 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.283382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 302a0591-2bb1-40ad-a057-010812a728aa · outbound

This paper cites Conference on Learning Theory , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Conference on Learning Theory , pages=

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.291335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9f3dfacc-dfb2-4ba2-a4f1-0c97b3f32217 · outbound

This paper cites SIAM review , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models SIAM review , volume=

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.283703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2e59f68-96aa-4215-8901-65774eeb30b7 · outbound

This paper cites International conference on machine learning , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International conference on machine learning , pages=

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.305178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5548305d-a693-4bfa-ad65-8c180dcb9a2d · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.281534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9160cf71-bd22-40c3-9a55-5f9cba5a5fe5 · outbound

This paper cites 2013 , publisher=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models 2013 , publisher=

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.269891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0be94494-e079-4c7c-8d9b-74462a5a564f · outbound

This paper cites Linear convergence of gradient and proximal-gradient methods under the polyak-.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Linear convergence of gradient and proximal-gradient methods under the polyak-

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.271820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21e117f8-261e-4a5b-a9d6-48851d79bd44 · outbound

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

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 43

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.362960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d0dbdd0-c458-4c2d-a4db-f8c3041bf1f0 · outbound

This paper cites Journal of Machine Learning Research , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Journal of Machine Learning Research , volume=

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.263634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1121e0b5-a651-4086-a7c2-167c314be330 · outbound

This paper cites International conference on machine learning , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International conference on machine learning , pages=

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.265878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 739253a8-c944-4e04-8402-5144d038db50 · outbound

This paper cites Mixtral of Experts.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Mixtral of Experts

Reference 46

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.357101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5e337c8c-6438-40c5-8d15-5318006157c3 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 47

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.366268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2333b3ba-f8cf-4b12-b2a4-739b24f2b67b · outbound

This paper cites 2021 , url=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models 2021 , url=

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.267938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3c7e1a1c-a805-4b5a-b2d6-6eaecace31aa · outbound

This paper cites Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts

Reference 49

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.353999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd1c89e0-d197-48c2-adca-e800a072b1d9 · outbound

This paper cites Han and Yuan Zhong , booktitle=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Han and Yuan Zhong , booktitle=

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.277755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cb635fac-eafd-454c-b463-e89018c06de9 · outbound

This paper cites GRIN: GRadient-INformed MoE.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models GRIN: GRadient-INformed MoE

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.351169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b2755e0-4ebf-4c6e-8c89-10c19ee862b4 · outbound

This paper cites ReMoE: Fully Differentiable Mixture-of-Experts with Re.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models ReMoE: Fully Differentiable Mixture-of-Experts with Re

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.279711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ed57a94-6d29-4ded-8a1a-5f906232a695 · outbound

This paper cites arXiv preprint arXiv:2504.12463 , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models arXiv preprint arXiv:2504.12463 , year=

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.348182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 111dde8f-5a31-469f-bb4f-b4296464b8cb · outbound

This paper cites A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning , volume =.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning , volume =

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.306633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7cdb1bc1-abb4-4d6d-8369-09f465eac2c7 · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.254649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2501a5c9-34e5-48bd-8306-ed22f80dea79 · outbound

This paper cites Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.259223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d8ed8ed-6606-492a-8a0a-72bd075d2f2f · outbound

This paper cites Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.261483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7968a1d-84ec-4d34-802e-e6d59a4d4f84 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models SocialIQA: Commonsense Reasoning about Social Interactions

Reference 58

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.342333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2af03fca-4c38-4dc9-84b8-a5a948dc16fc · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 59

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.336488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d4f69285-3bd1-4cc3-ac40-f3d5d55461a1 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 60

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.339517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1cd6b953-8e08-43ae-99e6-91b289700005 · outbound

This paper cites Winogrande: an adversarial winograd schema challenge at scale.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Winogrande: an adversarial winograd schema challenge at scale

Reference 61

Resolution: verified exact; doi, observed 2026-05-20T19:08:53.846994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 198924b5-c47e-489e-81d1-1a045eaa8bc5 · outbound

This paper cites First Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models First Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models , year=

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.257098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6586d771-f44c-4354-a064-aca2d4411dc4 · outbound

This paper cites arXiv preprint arXiv:2411.19557 , year=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models arXiv preprint arXiv:2411.19557 , year=

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.345328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4c0d49ba-77c1-48c3-9222-de35874c6805 · outbound

This paper cites OLMoE: Open Mixture-of-Experts Language Models.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models OLMoE: Open Mixture-of-Experts Language Models

Reference 64

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.332348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 283f820d-70d6-4f9e-bd6c-3c53a298cff9 · outbound

This paper cites Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.302668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d1bdd1b0-fc42-408b-a9c5-3f9127dc773a · outbound

This paper cites an unresolved cited work.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-05-20T19:13:41.270817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ae4e41d6-ac5a-4966-8aad-918f8b8cda39 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.307054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0421c08b-3207-488b-85e5-f861cc712ee4 · outbound

This paper cites Neurocomputing , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Neurocomputing , volume=

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.227211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4c0111a5-7e11-4a7c-aef3-cc7bc8327b59 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.219487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cc8777d2-e39a-49d4-b87f-1df536db6b88 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.221530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8efab690-f784-4eaf-b7ea-d64432b9697f · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Advances in Neural Information Processing Systems , volume=

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.226572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9cdfcf5a-5b13-41ff-8ccc-2fc865e40bc0 · outbound

This paper cites Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:08:54.326466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1906faf3-83ab-4ae4-8141-2052b90945f7 · outbound

This paper cites ACM Transactions on Knowledge Discovery from Data , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models ACM Transactions on Knowledge Discovery from Data , volume=

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.225534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75802fc7-d566-412f-af6b-43f848ab3f94 · outbound

This paper cites Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.217500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21e8f002-1eb0-4eb5-8f5e-b627ceb3e16f · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 75

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.319388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e69fd90e-c282-47b3-8c1f-93881b39da85 · outbound

This paper cites International conference on machine learning , pages=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models International conference on machine learning , pages=

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.233065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 55d6af90-e920-4a3b-aace-95697d2e65bb · outbound

This paper cites ACM Computing Surveys , volume=.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models ACM Computing Surveys , volume=

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T19:13:41.244519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d3aca156-f4b6-4144-baca-829511edd8aa · outbound

This paper cites Scaling Laws for Neural Language Models.

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models Scaling Laws for Neural Language Models

Reference 78

Resolution: metadata mismatch; local_arxiv, observed 2026-05-20T19:08:54.322622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers