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Paper Citation Record · LEDGER

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.16427.

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

pith.paper-citation-record.v1
2607.16427 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T21:01:45.680930Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

36 of 36 outbound references displayed

  • verified exact5
  • verified fuzzy0
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 306b6af1-6f07-4216-9c07-8ab01eaf57c6 · outbound

This paper cites Scaling Laws Across Model Architectures:.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Scaling Laws Across Model Architectures:

Reference 1

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source=arxiv_source observed=2026-08-01T21:01:42.196314Z digest=sha256:dd01a0166c11ce76367bdfb54c60de49e418e666dd650b041d80185b310cb4bc

Observation 43ba4ce7-7cf0-4719-b9f4-3e1febc181cc · outbound

This paper cites Optimal Scaling Laws for Efficiency Gains in a Theoretical Transformer-Augmented Sectional MoE Framework.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Optimal Scaling Laws for Efficiency Gains in a Theoretical Transformer-Augmented Sectional MoE Framework

Reference 2

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source=arxiv_source observed=2026-08-01T21:01:42.273281Z digest=sha256:c899f6e86a141b57434bf276a4c272e16712efaa23c2946ff622100509d9cebd

Observation f2ce6276-b812-4b4d-bc03-309cb78e18f2 · outbound

This paper cites 2025 , url =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts 2025 , url =

Reference 3

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source=arxiv_source observed=2026-08-01T21:01:42.386683Z digest=sha256:4c2ff9d5958877e66a8a64421aa29d9d3193aaafa033bb5cc72e82793da0538e

Observation 197ee4ba-7bfe-485d-a4be-9c9c9bb4beae · outbound

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

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Advances in Neural Information Processing Systems , volume=

Reference 4

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source=arxiv_source observed=2026-08-01T21:01:42.533172Z digest=sha256:03385a036cff95881cc77f15aef29fd29244b1410c68013c34ac6211f701296e

Observation b92379b3-cc49-4aa3-949b-1610faaf542a · outbound

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

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Advances in Neural Information Processing Systems , volume=

Reference 5

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source=arxiv_source observed=2026-08-01T21:01:42.609177Z digest=sha256:189c69cccb15a452447d0e016a6b83a45e78239df9661875c572e6f50ae779b0

Observation f81a6464-fbf1-4859-a300-a6cedfab4507 · outbound

This paper cites IEEE Transactions on Parallel and Distributed Systems , volume=.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts IEEE Transactions on Parallel and Distributed Systems , volume=

Reference 6

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Observation 25f58908-ae22-4a92-8732-5ca872a8a9ed · outbound

This paper cites JOURNAL OF IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE , year=.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts JOURNAL OF IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE , year=

Reference 7

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source=arxiv_source observed=2026-08-01T21:01:42.769157Z digest=sha256:726622e541fe66506bac79b9b91fe03beeee282282ac0fa03133da6511de4b35

Observation e9430e14-a9f1-4186-b8ea-18e2ccd84f87 · outbound

This paper cites Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training

Reference 8

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source=arxiv_source observed=2026-08-01T21:01:42.897579Z digest=sha256:0f179e91848800ccee8a1c89868ceb772bb2df4f4dce2c0ddaf6592242f7bf6e

Observation 710d82a9-5937-4fb3-a030-56d45b9e30c7 · outbound

This paper cites Scaling Vision with Sparse Mixture of Experts , booktitle =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Scaling Vision with Sparse Mixture of Experts , booktitle =

Reference 9

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Observation 6590902d-e0fb-488f-8a8a-f6ca13a33984 · outbound

This paper cites The Thirteenth International Conference on Learning Representations,.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts The Thirteenth International Conference on Learning Representations,

Reference 10

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Observation eb8a1944-7517-4be3-918c-c050228da509 · outbound

This paper cites an unresolved cited work.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-08-01T21:01:43.249370Z digest=sha256:18927d66001170b8f0bf5f9b3f1c57f9dd565acf17fbfd554bebe6364057828d

Observation 03692010-1474-4107-9c89-1d44a2e84ec4 · outbound

This paper cites Le and Geoffrey E.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Le and Geoffrey E

Reference 12

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source=arxiv_source observed=2026-08-01T21:01:43.346274Z digest=sha256:b9c02c56a28f58f3e8c1b69d7946c0d3d8732740b31a6a55df6c8872e6ba738a

Observation 42c3c63b-86a5-4b3e-af61-38fed0f3c557 · outbound

This paper cites an unresolved cited work.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Unresolved cited work

Reference 13

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source=arxiv_source observed=2026-08-01T21:01:43.477025Z digest=sha256:09a76100763f9de7a08a5ada83b7ca1cb6b63a3b2f37b56b4b4f004b62f21ca7

Observation dad253c0-edef-468f-972b-5f79ca131f91 · outbound

This paper cites The Eleventh International Conference on Learning Representations,.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts The Eleventh International Conference on Learning Representations,

Reference 14

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source=arxiv_source observed=2026-08-01T21:01:43.586604Z digest=sha256:fe7577468fff310ae71d10775aeaa775d87e853314854e47df38dc61ba0e116e

Observation 7bf08ba7-f390-4ffd-962b-d20095dcc995 · outbound

This paper cites On the Representation Collapse of Sparse Mixture of Experts , booktitle =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts On the Representation Collapse of Sparse Mixture of Experts , booktitle =

Reference 15

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Observation 6df381eb-d2bd-438f-8ce7-9b20336d7aa4 · outbound

This paper cites SimSMoE: Toward Efficient Training Mixture of Experts via Solving Representational Collapse , booktitle =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts SimSMoE: Toward Efficient Training Mixture of Experts via Solving Representational Collapse , booktitle =

Reference 16

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

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Observation 815987c6-2914-495d-947f-39bee5a5a231 · outbound

This paper cites HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts , booktitle =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts , booktitle =

Reference 17

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doi, observed 2026-08-01T21:04:37.781518Z

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.

source=arxiv_source observed=2026-08-01T21:01:43.917892Z digest=sha256:81450a6d373af436559c45eb563f66b7e2e31c4f689a0327d3cfd7779ae5fa71

Observation e91fc752-bf5b-41e2-a028-f5d4337d1bb7 · outbound

This paper cites The Thirteenth International Conference on Learning Representations,.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts The Thirteenth International Conference on Learning Representations,

Reference 18

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Observation 2966cd7d-3d22-4f3f-8d4f-0a23bdc13775 · outbound

This paper cites On the Spatial Structure of Mixture-of-Experts in Transformers.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts On the Spatial Structure of Mixture-of-Experts in Transformers

Reference 19

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Observation 5dbd08e9-89c2-4412-af7c-5729dd35c667 · outbound

This paper cites CoRR , volume =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts CoRR , volume =

Reference 20

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Observation 1a894290-b6f6-4992-b6f1-f0f802bc0f88 · outbound

This paper cites Routing in Sparsely-gated Language Models responds to Context.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Routing in Sparsely-gated Language Models responds to Context

Reference 21

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

source=arxiv_source observed=2026-08-01T21:01:44.446222Z digest=sha256:76ac4dad78955890499ca88110d238549b293571a8c5d83d77f3ad67b9584771

Observation b3eb1676-d9b5-45fa-b5dc-933c48aaa32b · outbound

This paper cites arXiv preprint arXiv:2406.00023 , year=.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts arXiv preprint arXiv:2406.00023 , year=

Reference 22

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Observation 1ba42d76-4540-4001-9202-3e99a26a5a37 · outbound

This paper cites StableMoE: Stable Routing Strategy for Mixture of Experts , booktitle =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts StableMoE: Stable Routing Strategy for Mixture of Experts , booktitle =

Reference 23

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Observation 4aca609d-a384-416a-bc03-bf869e737e4c · outbound

This paper cites THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation , booktitle =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation , booktitle =

Reference 24

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Observation 6521630b-9f55-44bd-8e4e-519af4ce3809 · outbound

This paper cites CoRR , volume =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts CoRR , volume =

Reference 25

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Observation a3716d93-af71-445c-bde2-3fde07e00687 · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 26

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Observation a178d931-d408-4599-a0bb-1c4d2d7920bd · outbound

This paper cites Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation , booktitle =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation , booktitle =

Reference 27

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Observation 16e9ff11-e312-4341-891e-8a9754355d2e · outbound

This paper cites Advances in neural information processing systems , volume=.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Advances in neural information processing systems , volume=

Reference 28

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Observation b2a9e037-6f41-454d-9618-60224c408e70 · outbound

This paper cites Carbonell and Quoc Viet Le and Ruslan Salakhutdinov , editor =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Carbonell and Quoc Viet Le and Ruslan Salakhutdinov , editor =

Reference 29

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Observation c9db6a07-d923-4031-8676-1cbc4775a6bb · outbound

This paper cites an unresolved cited work.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Unresolved cited work

Reference 30

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Observation c979e64f-b1a2-4efc-85eb-a46977936354 · outbound

This paper cites Pointer Sentinel Mixture Models.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Pointer Sentinel Mixture Models

Reference 31

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Observation 7b22b3fc-ce7d-43b3-b6be-fb1b726ce9ca · outbound

This paper cites Manning and Andrew Y.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Manning and Andrew Y

Reference 32

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source=arxiv_source observed=2026-08-01T21:01:45.449827Z digest=sha256:d391622e0afc75895cb514d7e7b40acb3d41cacff3050c3136b3527b6b372b0f

Observation 94f57983-b277-423d-8748-5255bc16dd7d · outbound

This paper cites GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Reference 33

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source=arxiv_source observed=2026-08-01T21:01:45.502744Z digest=sha256:a5a5da0eb430ef152d7ea56447eb4eeeed0f615a43b39280ec8b012a0658658f

Observation 825cfe30-6d4b-4711-ae38-d260cbb63a50 · outbound

This paper cites Gini Coefficient as a Unified Metric for Evaluating Many-versus-Many Similarity in Vector Spaces.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Gini Coefficient as a Unified Metric for Evaluating Many-versus-Many Similarity in Vector Spaces

Reference 34

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local_arxiv, observed 2026-08-01T21:04:37.386942Z

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.

source=arxiv_source observed=2026-08-01T21:01:45.560163Z digest=sha256:5f02913ff4f99f2b26fff5df554bc107e41d9d3c5f932abe119fc86c1fbeaba0

Observation 361cc15f-a4b3-4256-8aba-1799c31ad435 · outbound

This paper cites MoEC: Mixture of Expert Clusters , booktitle =.

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts MoEC: Mixture of Expert Clusters , booktitle =

Reference 35

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doi, observed 2026-08-01T21:04:37.303952Z

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

source=arxiv_source observed=2026-08-01T21:01:45.624588Z digest=sha256:1a1be1c8cf3f98395e83c6a780c50bd1fe5d9fa194f1795d64bf43299bb2ec5c

Observation a8bf9005-b0ac-45c4-a20a-e491d257c94f · outbound

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

Multi-level context Modeling for consistent expert selection in Mixture-of-Experts Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 36

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Pith citing papers

No inbound Pith citation observations are available.