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Paper Citation Record · LEDGER

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs

As of 22 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 2 inbound Pith citation observations for arXiv:2509.10377.

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

pith.paper-citation-record.v1
2509.10377 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:57:25.255094Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:14:02.870202Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T14:35:55.658485Z

Reference resolution

52 of 52 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved52
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf48e78e-0b69-4ae9-a2d0-b527a833f4ea · outbound

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

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Git Re-Basin: Merging Models modulo Permutation Symmetries

Reference 1

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source=arxiv_source observed=2026-08-04T17:57:24.594815Z digest=sha256:c90fd05dd7950f9d24b7eb0e1928e892ddf6caff3326c329f74154f85d6274cb

Observation 466e96f0-cd6b-4af6-ba4f-f0e8352ac675 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 2

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source=arxiv_source observed=2026-08-04T17:57:24.668959Z digest=sha256:4e62531233c6c276cc2405e5c035aaed5f8427e65dda5bafb4ca0d38c340cfeb

Observation 2bccc623-be7b-453a-9f6c-9029ea27ee30 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-08-04T17:57:24.731000Z digest=sha256:2beb8e3e5986ecbce699166638cac2c0b891257e35075e34412a42f97428c2ec

Observation 18dc9542-dbe7-4ce0-8934-929aa1388199 · outbound

This paper cites Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning

Reference 4

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source=arxiv_source observed=2026-08-04T17:57:24.835167Z digest=sha256:fb8c93444cc3d3be0fb80c58d8665ffcc607d0cafc36ade495ca1e3d2e94fb8d

Observation 627f668c-cf03-4e56-bf81-3ac7bb0a284d · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-08-04T17:57:24.895457Z digest=sha256:99fe1c870f39e02701873e37cf5f3ca52ea270a91d8e9dfda51c64b300f6ef36

Observation 908cd80c-7c1c-40df-8dda-7a05ec1c1449 · outbound

This paper cites Task-Specific Expert Pruning for Sparse Mixture-of-Experts.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Task-Specific Expert Pruning for Sparse Mixture-of-Experts

Reference 6

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source=arxiv_source observed=2026-08-04T17:57:25.004216Z digest=sha256:8835e590e257f4ab1ee6ff8ad5febf254b6095b00ba3542b5fdb291de205b5f0

Observation 1d23c995-f4f9-4fcc-8664-25a4f20c7489 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-04T17:57:25.019805Z digest=sha256:b1dad9fb275f7402ea0fe4cf3e98268c6e9c2560e0e07b4d6860b3dcb216ecec

Observation 937ebf75-9781-49e1-bfa1-9f6ce591e767 · outbound

This paper cites A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 8

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source=arxiv_source observed=2026-08-04T17:57:25.024965Z digest=sha256:17aef01ee09b73f13e373fe2144cf85d97492aaef3ef2afe6901efc535d0a2dd

Observation 6fa22ee5-0922-44fa-9345-b5d104b3b7e4 · outbound

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

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 9

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source=arxiv_source observed=2026-08-04T17:57:25.030025Z digest=sha256:0077971177b66670f4017ec00c419d2c5dc9893986cced2947d750c10cc38573

Observation be2f1e30-cece-42c6-8f40-40d8a7ecbf03 · outbound

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

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 10

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source=arxiv_source observed=2026-08-04T17:57:25.034771Z digest=sha256:1e897f27b280e6c80f8b87dca8f56fb4f0dfc9c9bf570fe1889cfb5dfe729080

Observation 042beabb-d61b-4254-8cc3-6117c1bb2b2b · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-08-04T17:57:25.047632Z digest=sha256:35d0ed996d33f047ae84248c8e92928f4f3dcbbef0f1723bbc3aaf6dbd547c08

Observation f09e663e-d60c-44cd-af20-4a65f8f92a0c · outbound

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

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 12

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source=arxiv_source observed=2026-08-04T17:57:25.053312Z digest=sha256:c0dba91068b5be7ca8e647975a65e1155b8f0d856fdc27b7f7f9f306c281d8a1

Observation 360a7001-d0d3-4daf-b180-b4ba478c2cb4 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 13

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no resolver link, observed 2026-08-04T17:57:25.064191Z

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source=arxiv_source observed=2026-08-04T17:57:25.064191Z digest=sha256:9e1767dc9e0867b461cf849bc4b02356433f841696a9f74a3ca30d1a5f2e27b6

Observation d94c7e28-c8ed-4985-8239-4986049b9868 · outbound

This paper cites Enhancing Large Language Models through Structured Reasoning.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Enhancing Large Language Models through Structured Reasoning

Reference 14

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source=arxiv_source observed=2026-08-04T17:57:25.068676Z digest=sha256:9408f31417de6bfe1081a4630041518edd7dd96ce1be975f19244127f2567b55

Observation f5038bc5-5295-49e7-adc2-2e9f36c9abf0 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 15

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no resolver link, observed 2026-08-04T17:57:25.073361Z

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source=arxiv_source observed=2026-08-04T17:57:25.073361Z digest=sha256:255a2d0081c099d331da0eb21d8f74d7906cc58c7d01b40df7e3ea5f8e700df9

Observation 7bdbe17f-3b3a-4a46-a047-249925abe0b4 · outbound

This paper cites Delta Decompression for MoE-based LLMs Compression.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Delta Decompression for MoE-based LLMs Compression

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:57:25.077844Z digest=sha256:6ff6caf1d20ebebbcc0fd90560fd257b4477f96a3557e6d6ed3704c4e0ab6027

Observation 6aba06ae-3b10-48c2-b60a-d84d17d1e76b · outbound

This paper cites Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques

Reference 17

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source=arxiv_source observed=2026-08-04T17:57:25.082947Z digest=sha256:2f2194b1dd3460c815ea535b4f550e2bf3fbd54ee108ff1b8e175568849ea92e

Observation a83d21a5-8cbe-40cb-b0c2-8cc503c00db2 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Measuring Massive Multitask Language Understanding

Reference 18

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source=arxiv_source observed=2026-08-04T17:57:25.087985Z digest=sha256:91a2ecf9678ab5ffd679ef89ead2ba14fd28a2ed4af6e158fa1a0aad6cb3a005

Observation a898aaab-472e-45b6-a8bd-490ba004b6a3 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 19

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no resolver link, observed 2026-08-04T17:57:25.093266Z

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source=arxiv_source observed=2026-08-04T17:57:25.093266Z digest=sha256:9b75ceed51e58405503971d7fdd6282a863c8db44c1b5ee9df49b7a699b3d829

Observation a31b5f8a-3e13-44aa-86c8-70f30982774c · outbound

This paper cites Mixture Compressor for Mixture-of-Experts LLMs Gains More.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Mixture Compressor for Mixture-of-Experts LLMs Gains More

Reference 20

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source=arxiv_source observed=2026-08-04T17:57:25.097951Z digest=sha256:6aff5842fa43b797ba372d53ecee819cee6e593cdabe5942f6f73ac3888581bb

Observation 579d5f38-9307-4dc3-9105-4db8d017c367 · outbound

This paper cites Mixtral of Experts.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Mixtral of Experts

Reference 21

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source=arxiv_source observed=2026-08-04T17:57:25.103790Z digest=sha256:0800c4856be616a0667336d2ce2ad5abfe8007de6b7b8e4a1c4fd507a129e649

Observation ed0a51ce-2bb7-47f3-a67a-2f571bdd768d · outbound

This paper cites Gonzalez, Hao Zhang, and Ion Stoica.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Gonzalez, Hao Zhang, and Ion Stoica

Reference 22

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source=arxiv_source observed=2026-08-04T17:57:25.108836Z digest=sha256:540cd2b768e990f18f0eaf5ae3d6ccca288a9674666c68216b2b86f477f19d19

Observation 1d054ccb-b942-499f-a29d-6e18b595ffc3 · outbound

This paper cites Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy

Reference 23

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source=arxiv_source observed=2026-08-04T17:57:25.113322Z digest=sha256:839242f2288f4ae7e4df0f54d06dc54375b779fe520247d012eeb06bdb1eb84f

Observation dd9e411a-dd3e-46f0-be74-774c7a56a097 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 24

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source=arxiv_source observed=2026-08-04T17:57:25.118540Z digest=sha256:0ae1bb0551bfc331ed334ac04105ebdc6c8528df40070eb94f7aaabb9406f518

Observation efafb95a-c198-4414-bad4-e54edd43d164 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 25

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source=arxiv_source observed=2026-08-04T17:57:25.122791Z digest=sha256:59e67c03fd50cd1b8f2d6e519130174d53f004a6d87f538df0ea90d9a7df5e80

Observation 65736905-6bd2-43c5-9f58-6fb9e04ed6bc · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 26

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source=arxiv_source observed=2026-08-04T17:57:25.127723Z digest=sha256:9ae798474f0871e5430684cf50012a9f33156e11f8297ec873bf2305a2d8333f

Observation eb438bf1-619e-47cf-a254-589dc0b72d80 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 27

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:57:25.133608Z digest=sha256:d25619d7f8bb53f15fa94e0c040e2a98b476c959b1e037c2adaa1b7fe65ef190

Observation d2ed7b9a-afbb-42ac-a504-ba2da156393d · outbound

This paper cites SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models

Reference 28

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source=arxiv_source observed=2026-08-04T17:57:25.137742Z digest=sha256:04014d57289b4f231c1d3da8495539e7612d641c68d5f5ee582ed0a0f22de787

Observation 85bede88-5042-444a-81ed-6c89262a02bb · outbound

This paper cites DeepSeek-V3 Technical Report.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs DeepSeek-V3 Technical Report

Reference 29

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Observation b966d609-c9b1-400b-9d19-4d782f931642 · outbound

This paper cites Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 30

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source=arxiv_source observed=2026-08-04T17:57:25.147648Z digest=sha256:18377bf71de6c95f878fb2491e95b7895afdfa03e17f180f04422ff9090ecb42

Observation 339fbb8d-4a5f-44cb-8f36-47fdd420d8e1 · outbound

This paper cites A Closer Look into Mixture-of-Experts in Large Language Models.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs A Closer Look into Mixture-of-Experts in Large Language Models

Reference 31

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source=arxiv_source observed=2026-08-04T17:57:25.153185Z digest=sha256:b5d307cc2008951cb6fc5508ff975aa27dd8081134b2d87d0e21207590bee7ea

Observation 0283ff76-3549-4e48-8be1-a5c9d2fb79ea · outbound

This paper cites Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 32

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source=arxiv_source observed=2026-08-04T17:57:25.160350Z digest=sha256:b0c4b89d5a7e836b6b96ed848c5c0b4b4e9d6d26600851a5e29530d67885226f

Observation f0dfaeab-3c9a-43e5-82a7-4e052e5f32a5 · outbound

This paper cites Reassessing Layer Pruning in LLMs: New Insights and Methods.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 33

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source=arxiv_source observed=2026-08-04T17:57:25.164991Z digest=sha256:1acd711b5a5ef8d5c3105a3e98e12dab4ffbb64c2e98195e2f023332769dfcbd

Observation d71b0ce6-75fd-4f3f-8df1-ed15f3101898 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 34

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source=arxiv_source observed=2026-08-04T17:57:25.170241Z digest=sha256:9e0c5329b0414368c8a7e51fb35d6882c0c9f21d73efe5a95ca479b733c6a8ba

Observation 66f656e5-96d6-49d0-8ef0-1146817ec954 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 35

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source=arxiv_source observed=2026-08-04T17:57:25.175846Z digest=sha256:0de822891f219e4e4190b81d254014cf4397915ca65e8104f3be0936fe47593e

Observation f0b20185-3194-4438-bf50-8eb0afdb05c2 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 36

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no resolver link, observed 2026-08-04T17:57:25.180650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:57:25.180650Z digest=sha256:c2b02e07c3e8a84d4ef162fda5d144e6d6110ee83cb264dc92745e8189ed7b66

Observation 73e63b12-686f-4254-9ef1-67a4c922ae3b · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 37

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no resolver link, observed 2026-08-04T17:57:25.185203Z

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source=arxiv_source observed=2026-08-04T17:57:25.185203Z digest=sha256:1cd98e47c1978bad7ff9815dbeeb025429ab9a4a4b3953ad08bd562ffb07b7f2

Observation 0ccbf1cf-3a24-4622-b68b-147a3b87ef4f · outbound

This paper cites From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches

Reference 38

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no resolver link, observed 2026-08-04T17:57:25.190738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation dd8df2a2-b520-4b9c-beef-e1ee2c50ed48 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 39

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no resolver link, observed 2026-08-04T17:57:25.195584Z

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source=arxiv_source observed=2026-08-04T17:57:25.195584Z digest=sha256:1896edd34c6a24664931a272d5a79f033c690ce1300cd3f4dadfc794425f1a54

Observation 7abf9dbe-ece2-4d9d-b8d4-98ebfb8a0ddf · outbound

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

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 40

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no resolver link, observed 2026-08-04T17:57:25.200825Z

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source=arxiv_source observed=2026-08-04T17:57:25.200825Z digest=sha256:fdbd8038d6674e9a866bcb3ac8c468ec181524075bd5514eb2ff02c3645c4040

Observation ebb09801-678f-47d2-841b-1f097f2258ae · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs A Simple and Effective Pruning Approach for Large Language Models

Reference 41

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no resolver link, observed 2026-08-04T17:57:25.205681Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-04T17:57:25.205681Z digest=sha256:533f0b3932df8a3265034facc69bbd25cc45c2ab11b8e7d2b74ef1c33dfe0d84

Observation 37f397d3-582a-44e3-8af6-adc426ac2799 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 42

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no resolver link, observed 2026-08-04T17:57:25.210288Z

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source=arxiv_source observed=2026-08-04T17:57:25.210288Z digest=sha256:8c3c6b20b9aa76d61dc225f5f66d725f31e5ae6f4cf53d6c571080443c893682

Observation 08c9e74e-3e7c-44c8-9e29-d72f963ee022 · outbound

This paper cites MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router

Reference 43

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no resolver link, observed 2026-08-04T17:57:25.214956Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-04T17:57:25.214956Z digest=sha256:da42ae7990b3e970c9dc13823ef2cb9d2e7dfbe7e16e4c38817655cb6520f8a7

Observation 3d273610-bb58-42c6-9c14-550ee648fdfe · outbound

This paper cites Qwen2 Technical Report.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Qwen2 Technical Report

Reference 44

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no resolver link, observed 2026-08-04T17:57:25.219776Z

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source=arxiv_source observed=2026-08-04T17:57:25.219776Z digest=sha256:3a34aa07203f8ed4531e0a30959a0518382be4d73ed630d3e24b890a282c8c0b

Observation c3df6d3c-33f2-43a4-89fc-75718c3ca28c · outbound

This paper cites MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition

Reference 45

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no resolver link, observed 2026-08-04T17:57:25.224160Z

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source=arxiv_source observed=2026-08-04T17:57:25.224160Z digest=sha256:fdc6bed5a455e63ba9968a811fc22b924ccb55522728981a2a46b69c5de950ab

Observation 1d0156df-6286-4c81-8de3-9e49c397941f · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 46

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no resolver link, observed 2026-08-04T17:57:25.228652Z

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source=arxiv_source observed=2026-08-04T17:57:25.228652Z digest=sha256:c04e3019d2625bd99e155edf04eb5ec6249cf31f5b614db186f3886e42f719b1

Observation 9bc4d272-ba4c-4662-8227-240cfce9a586 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 47

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unresolved
no resolver link, observed 2026-08-04T17:57:25.232821Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-04T17:57:25.232821Z digest=sha256:ceecd36bad3095103aeba27c756fec5251340a884469f5db9b5925ba53bedf06

Observation f5d242e3-0bbb-487b-ae5c-e2bf044ea548 · outbound

This paper cites an unresolved cited work.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Unresolved cited work

Reference 48

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no resolver link, observed 2026-08-04T17:57:25.237108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:57:25.237108Z digest=sha256:3f6ccd476903b1aca7a78d3bffe5e4788d6a5fe44a08615362e4af40fd38b6eb

Observation 082a5ad0-e22f-4980-99a6-b3ab8d3e15f9 · outbound

This paper cites Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering

Reference 49

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no resolver link, observed 2026-08-04T17:57:25.241225Z

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source=arxiv_source observed=2026-08-04T17:57:25.241225Z digest=sha256:609699e5ad1bc14de8b1a61833c22b6d503d7995ce90078181d0a40157f37f9d

Observation cf65be58-0a90-4f4b-a38d-d32fbfe1f93c · outbound

This paper cites Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning

Reference 50

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no resolver link, observed 2026-08-04T17:57:25.245487Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:57:25.245487Z digest=sha256:1c799d7cce47cc40b9c8791e813c226e6362c232f255b26a2d44982fb977ffbd

Observation 52775d87-3003-44ef-999a-08a84882c0a1 · outbound

This paper cites online" 'onlinestring :=.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs online" 'onlinestring :=

Reference 51

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no resolver link, observed 2026-08-04T17:57:25.250322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:57:25.250322Z digest=sha256:0d2a3dc6f680fbf14e36347cc692ad70411890a28b941ed32ab3c67b989bf416

Observation 0a1667ec-1b23-4a51-990d-e3107a455d2a · outbound

This paper cites write newline.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs write newline

Reference 52

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unresolved
no resolver link, observed 2026-08-04T17:57:25.255094Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:57:25.255094Z digest=sha256:b60e7d3540a4f4189e3253418d4485c416048d02fb81e889c3e07432145aec2e

Pith citing papers

Observation 11382ae6-745f-4052-9da7-9f2f864d48f5 · inbound

OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale cites this paper.

OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs

Reference 49

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no resolver link, observed 2026-08-03T04:14:02.870202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T04:14:02.870202Z digest=sha256:f2560c3121c0a6a23e73d38ec5e679796fc510346b8ba22498fec0f56041d939

Observation 55a17f90-94f9-499e-97eb-d6f5a6bc7478 · inbound

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE cites this paper.

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs

Reference 67

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verified exact
arxiv_id, observed 2026-05-15T14:35:55.661187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T14:34:48.524592Z digest=sha256:1f22927c34657871067c3a31ea9797deb52c90f7ecf3a9355c437efd0d8585b6