Paper Citation Record · LEDGER

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

As of 10 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 6 inbound Pith citation observations for arXiv:2506.23266.

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

pith.paper-citation-record.v1
2506.23266 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:52:09.670315Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:50:27.875803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T12:43:25.630106Z

Reference resolution

48 of 48 outbound references displayed

verified exact2
verified fuzzy31
unresolved15
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4cbeb805-5288-4ccc-bc38-8fb592587ddb · outbound

This paper cites The fifth pascal recognizing textual entailment challenge.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging The fifth pascal recognizing textual entailment challenge

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:14.528001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 685b225d-df9a-4871-b6b0-8023773023be · outbound

This paper cites Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts

Reference 2

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Source-reported events for the cited work

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Observation 82ad7fa7-bad8-4170-a5be-b601728e321e · outbound

This paper cites Retraining-free merging of sparse mixture-of-experts via hierarchical clustering.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Retraining-free merging of sparse mixture-of-experts via hierarchical clustering

Reference 3

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Source-reported events for the cited work

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Observation 9cf00e1a-b7fa-4242-be09-4bf9782e5fe7 · outbound

This paper cites BoolQ: Exploring the surprising difficulty of natural yes/no questions.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging BoolQ: Exploring the surprising difficulty of natural yes/no questions

Reference 4

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Observation 6dd41cc4-0bb6-4dfb-82f6-f8cd02968f1c · outbound

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

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e22f0a7-798c-4b7f-8e1b-2d8ec1d18c51 · outbound

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

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cda31413-22d0-4219-9dba-e66f06ab1750 · outbound

This paper cites Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:13.806576Z

Source-reported events for the cited work

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Observation bc50fd45-23e2-49c1-a6a5-0d60b35fe4a5 · outbound

This paper cites Pruner-zero: Evolving symbolic pruning metric from scratch for large language models.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Pruner-zero: Evolving symbolic pruning metric from scratch for large language models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:13.649148Z

Source-reported events for the cited work

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Observation ea242f90-6ca7-403e-a5e9-57c4b830ca95 · outbound

This paper cites Stbllm: Breaking the 1-bit barrier with structured binary llms.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Stbllm: Breaking the 1-bit barrier with structured binary llms

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:13.498247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 605e28bf-de5c-4909-baa3-d6fa1ab91d18 · outbound

This paper cites A framework for few-shot language model evaluation, 2024.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging A framework for few-shot language model evaluation, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:13.394017Z

Source-reported events for the cited work

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Observation 99a03fbc-7fe5-43ad-b49c-a756344c84a2 · outbound

This paper cites Delta decompression for moe-based LLMs compression.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Delta decompression for moe-based LLMs compression

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:13.286720Z

Source-reported events for the cited work

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Observation 12f8360b-5c3d-45fa-adf9-feed04322a9a · outbound

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

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques

Reference 12

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Source-reported events for the cited work

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Observation 8bd3cfc2-66f3-4783-8686-5fd3e7403f31 · outbound

This paper cites Measuring massive multitask language understanding, 2021.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Measuring massive multitask language understanding, 2021

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:13.156053Z

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Observation 469665b2-edce-444e-8cfd-0da6fc610560 · outbound

This paper cites Manifold learning for parameter reduction.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Manifold learning for parameter reduction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:13.029198Z

Source-reported events for the cited work

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Observation ccabf47d-9a88-4769-a8a5-5fe36342d666 · outbound

This paper cites Mixture compressor for mixture-of-experts LLMs gains more.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Mixture compressor for mixture-of-experts LLMs gains more

Reference 15

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Source-reported events for the cited work

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Observation 5a37e8ef-ace2-4d26-a204-3b1b635d6802 · outbound

This paper cites Ikotun, Absalom E.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Ikotun, Absalom E

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:12.791601Z

Source-reported events for the cited work

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Observation 989650be-4358-4394-a7a5-2ba16045a54d · outbound

This paper cites Averaging weights leads to wider optima and better generalization.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Averaging weights leads to wider optima and better generalization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:12.673648Z

Source-reported events for the cited work

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Observation de1f4d1f-1936-473a-9873-7c891012f5ba · outbound

This paper cites Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, et al.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, et al

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:12.516835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 532c7770-3639-4377-87d1-163dafee60e5 · outbound

This paper cites Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping

Reference 19

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Observation 060cca54-192b-4a22-a3ef-037cea81d0d4 · outbound

This paper cites STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging STUN: Structured-Then-Unstructured Pruning for Scalable MoE Pruning

Reference 20

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Observation 335a3b12-a8c6-4b12-858b-0f4adde417ec · outbound

This paper cites Discovering sparsity allocation for layer-wise pruning of large language models.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Discovering sparsity allocation for layer-wise pruning of large language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:12.379512Z

Source-reported events for the cited work

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Observation 5864562e-7c48-4130-817c-ba5df65e76c6 · outbound

This paper cites Branch-train-merge: Embarrassingly parallel training of expert language models.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Branch-train-merge: Embarrassingly parallel training of expert language models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:12.309242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 75196950-bf2a-460b-aea7-a0eb6af839e5 · outbound

This paper cites Merge, then compress: Demystify efficient SMoe with hints from its routing policy.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Merge, then compress: Demystify efficient SMoe with hints from its routing policy

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:12.278644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1232bffa-8705-4ea2-8244-5898cac5653e · outbound

This paper cites Lee, Shengjie Sun, Wei Xue, and Yike Guo.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Lee, Shengjie Sun, Wei Xue, and Yike Guo

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:12.193812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8bcdba04-2eb9-40f5-8850-00eba842dc72 · outbound

This paper cites Als: Adaptive layer sparsity for large language models via activation correlation assessment.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Als: Adaptive layer sparsity for large language models via activation correlation assessment

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:12.083104Z

Source-reported events for the cited work

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Observation 805f692e-1b38-41e8-8255-d813c251de55 · outbound

This paper cites Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging

Reference 26

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Source-reported events for the cited work

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Observation 7660d774-09ea-415c-9f92-5a4c5e66991e · outbound

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

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 27

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Observation 977bd942-0be7-4686-87be-32a1fc2a61cb · outbound

This paper cites Not all experts are equal: Efficient expert pruning and skipping for mixture-of- experts large language models.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Not all experts are equal: Efficient expert pruning and skipping for mixture-of- experts large language models

Reference 28

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Observation d47c490c-7550-4743-839a-d7353713cd21 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 29

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Source-reported events for the cited work

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Observation e04a2f89-af66-46dc-9663-7d3abf7f2d28 · outbound

This paper cites Seer-moe: Sparse expert efficiency through regularization for mixture-of-experts, 2024.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Seer-moe: Sparse expert efficiency through regularization for mixture-of-experts, 2024

Reference 30

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Observation 251c96d4-4a0f-4673-b87a-39a89bf6e3bd · outbound

This paper cites LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging LIBMoE: A Library for comprehensive benchmarking Mixture of Experts in Large Language Models

Reference 31

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Observation 1b87261e-35a5-451d-9d22-cd73f26d36ed · outbound

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

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Winogrande: An adversarial winograd schema challenge at scale

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:11.749991Z

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Observation 1a310a93-f10e-49ba-9a9e-eae1d95de0b7 · outbound

This paper cites Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning

Reference 33

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Observation 9598610f-07a4-4393-9bf6-397d0a7b4a0d · outbound

This paper cites MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging MoESys: A Distributed and Efficient Mixture-of-Experts Training and Inference System for Internet Services

Reference 34

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Observation d69d7939-d2ed-48ca-aaac-6870caef5b32 · outbound

This paper cites Model fusion via optimal transport.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Model fusion via optimal transport

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:11.625157Z

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Observation 8c5ebc5b-1e73-40fb-9cc8-085a1bf9339f · outbound

This paper cites Powerinfer: Fast large language model serving with a consumer-grade gpu, 2023.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Powerinfer: Fast large language model serving with a consumer-grade gpu, 2023

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:11.503690Z

Source-reported events for the cited work

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Observation d209a50e-0fc9-4fac-bd72-4afea493e781 · outbound

This paper cites Optimizing mode connectivity via neuron alignment.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Optimizing mode connectivity via neuron alignment

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:11.395508Z

Source-reported events for the cited work

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Observation 0a3c2a72-6a81-4faf-bb84-1449b67e2d80 · outbound

This paper cites Qwen1.5-moe: Matching 7b model performance with 1/3 activated parameters", February 2024.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Qwen1.5-moe: Matching 7b model performance with 1/3 activated parameters", February 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:11.284321Z

Source-reported events for the cited work

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Observation 7b2e7a2c-5630-4a6d-9701-0b023011084d · outbound

This paper cites A global geometric framework for nonlinear dimensionality reduction.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging A global geometric framework for nonlinear dimensionality reduction

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:11.169066Z

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Observation 25d94ba9-1b3f-481b-b2f5-46273b36c9bb · outbound

This paper cites Parameter efficient neural networks with singular value decomposed kernels.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Parameter efficient neural networks with singular value decomposed kernels

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:11.035586Z

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Observation f9d01e2e-d6f0-440a-9805-27ff984b3b5c · outbound

This paper cites SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

Reference 41

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Observation b8c5a085-80f0-4a44-8a31-38ea07511d2b · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:10.829276Z

Source-reported events for the cited work

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Observation 8a9c4c16-8373-497e-ab30-db5d5a85d8f2 · outbound

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

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router

Reference 43

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Source-reported events for the cited work

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Observation 4a382819-d242-4b61-a129-5b03a08f677a · outbound

This paper cites Moe-infinity: Activation-aware expert offloading for efficient moe serving, 2024.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Moe-infinity: Activation-aware expert offloading for efficient moe serving, 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:10.652432Z

Source-reported events for the cited work

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Observation 7f1675b5-920c-4afd-b93d-fdf397cf57a1 · outbound

This paper cites Qwen2.5 Technical Report.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Qwen2.5 Technical Report

Reference 45

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Source-reported events for the cited work

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Observation 0272af74-5132-471d-816a-2bfd2ee463fa · outbound

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

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T21:52:09.523179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20950d3a-8daf-4794-85bf-442e0f6280c8 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:10.464408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 252b6627-c9de-4db3-874a-0f97c2f97b0d · outbound

This paper cites HyperMoE: Towards better mixture of experts via transferring among experts.

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging HyperMoE: Towards better mixture of experts via transferring among experts

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:10.328961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 433a73b8-7e44-4514-a606-9d264a6161c9 · inbound

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities cites this paper.

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

Reference 119

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:16:04.898004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6c0a9910-cf7e-472d-8cbc-5efe986b71f5 · inbound

Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression cites this paper.

Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T14:50:27.875803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa53dd31-ba35-43bd-a312-d9718e0365cf · inbound

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference cites this paper.

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T23:41:52.889236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f021178-66ab-4983-b88d-908922941285 · inbound

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

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:35:55.624299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5e930ae5-9ce4-44a2-a61a-c1a5805809b7 · inbound

Geometric Asymmetry in MoE Specialization: Functional Decorrelation and Representational Overlap cites this paper.

Geometric Asymmetry in MoE Specialization: Functional Decorrelation and Representational Overlap Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:49:15.477982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ec686f9-e5f0-4339-ad42-40ebbde49f3f · inbound

Pruning and Distilling Mixture-of-Experts into Dense Language Models cites this paper.

Pruning and Distilling Mixture-of-Experts into Dense Language Models Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:43:25.631665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.