Paper Citation Record · LEDGER

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

As of 19 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 12 inbound Pith citation observations for arXiv:2412.14711.

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

pith.paper-citation-record.v1
2412.14711 v2

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:03:43.511527Z

measured 80 of 80 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:20:00.424283Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

Reference resolution

68 of 68 outbound references displayed

verified exact0
verified fuzzy9
unresolved59
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9d6e2ef-e017-4e61-9bed-ab9523faaf64 · outbound

This paper cites write newline.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing write newline

Reference 1

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Observation ff44d4df-1d09-4a74-8d6c-b42e5bb14496 · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 2

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Observation 39c51723-84f8-40d0-bca4-1eb0e7cce4da · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 3

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Observation 09a8d0a3-aef7-470e-8556-69f5a416f31b · outbound

This paper cites Memory Layers at Scale.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Memory Layers at Scale

Reference 4

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Source-reported events for the cited work

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Observation 90348c14-635e-49d2-a8de-3c49a9a534d0 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Piqa: Reasoning about physical commonsense in natural language

Reference 5

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Source-reported events for the cited work

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Observation 8079fb2b-9f22-4599-b0fa-d501f9827574 · outbound

This paper cites Unified scaling laws for routed language models.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Unified scaling laws for routed language models

Reference 6

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Source-reported events for the cited work

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Observation d48c7ae4-e3d1-4f71-a4b0-f5c74caf67ec · outbound

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

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 7

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Source-reported events for the cited work

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Observation 18bc3771-b8c0-4558-a35f-c63a2ca71455 · outbound

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

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 8

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Source-reported events for the cited work

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Observation 4d4f5389-99f9-4c0d-8778-75fa0adc1015 · outbound

This paper cites Switchhead: Accelerating transformers with mixture-of-experts attention.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Switchhead: Accelerating transformers with mixture-of-experts attention

Reference 9

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Observation 0723b4c2-b02c-4d4c-b19e-bef839dd4b32 · outbound

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

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 10

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Observation eecbff12-0150-4923-97d2-b6504e72eaf8 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 11

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Source-reported events for the cited work

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Observation efb8e80f-2500-4d04-a759-2d6031513a98 · outbound

This paper cites Megablocks: Efficient sparse training with mixture-of-experts.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Megablocks: Efficient sparse training with mixture-of-experts

Reference 12

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Observation adad460e-4385-4325-9a44-e73f4198378b · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 13

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Observation 77c3a6b3-e5c0-47b8-ac44-7f4b2087ee98 · outbound

This paper cites SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs

Reference 14

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Observation 56d34398-c134-4fb2-ac14-e2e07e4889a1 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 15

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Observation 936eafc2-7f2c-47e5-8154-07a025065691 · outbound

This paper cites Mixture of A Million Experts.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Mixture of A Million Experts

Reference 16

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Observation 2b074278-78de-4f66-a8ea-d8d8537c3e3c · outbound

This paper cites Ultra-Sparse Memory Network.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Ultra-Sparse Memory Network

Reference 17

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Observation 09675ec0-72d0-4bb2-9cf4-ede9a8f69495 · outbound

This paper cites A method for the construction of minimum-redundancy codes.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing A method for the construction of minimum-redundancy codes

Reference 18

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Observation d3511d93-80c8-4725-ac0d-d863203005c3 · outbound

This paper cites Adaptive mixtures of local experts.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Adaptive mixtures of local experts

Reference 19

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Source-reported events for the cited work

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Observation ee3157ef-1230-407b-bd8a-88780d1f929b · outbound

This paper cites Mixtral of Experts.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Mixtral of Experts

Reference 20

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Observation a5c2481d-b75f-4416-90d5-733af4514269 · outbound

This paper cites Minference 1.0: Accelerating pre-filling for long-context llms via dynamic sparse attention.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Minference 1.0: Accelerating pre-filling for long-context llms via dynamic sparse attention

Reference 21

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Observation 3bc32bfa-723c-4d27-b486-97a6c4057e07 · outbound

This paper cites RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models

Reference 22

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Observation 42634a3c-0215-4f9e-bcd9-8eb2313d6182 · outbound

This paper cites Hierarchical mixtures of experts and the em algorithm.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Hierarchical mixtures of experts and the em algorithm

Reference 23

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Observation 215704ec-8387-480f-bfc2-a53316fd4015 · outbound

This paper cites Scaling Laws for Neural Language Models.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Scaling Laws for Neural Language Models

Reference 24

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This paper cites Reducing activation recomputation in large transformer models.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Reducing activation recomputation in large transformer models

Reference 25

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This paper cites Scaling Laws for Fine-Grained Mixture of Experts.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Scaling Laws for Fine-Grained Mixture of Experts

Reference 26

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Observation 8a557761-dd5c-4c8e-8a22-390952c5c146 · outbound

This paper cites Race: Large-scale reading comprehension dataset from examinations.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Race: Large-scale reading comprehension dataset from examinations

Reference 27

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Observation 1f046903-1980-49ad-9a58-d0f1ec92dee9 · outbound

This paper cites Large memory layers with product keys.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Large memory layers with product keys

Reference 28

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Observation 977645d4-9a7c-4130-a1b3-776bc0995acd · outbound

This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 29

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This paper cites Base layers: Simplifying training of large, sparse models.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Base layers: Simplifying training of large, sparse models

Reference 30

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Observation 915e3ea1-e149-4e1a-b46c-622dfb12659e · outbound

This paper cites The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers

Reference 31

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

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs

Reference 32

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing GRIN: GRadient-INformed MoE

Reference 33

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Observation 91f93b93-dfa6-4a1b-941f-867a1da8d897 · outbound

This paper cites Decoupled Weight Decay Regularization.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Decoupled Weight Decay Regularization

Reference 34

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Observation 18b9bd51-1a3c-4b17-8def-39ac652d051d · outbound

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

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 35

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

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing OLMoE: Open Mixture-of-Experts Language Models

Reference 36

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Observation c9e523aa-becd-4f1a-bc78-6a68f50060ba · outbound

This paper cites Soft Merging of Experts with Adaptive Routing.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Soft Merging of Experts with Adaptive Routing

Reference 37

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This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Efficient large-scale language model training on gpu clusters using megatron-lm

Reference 38

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Observation d2b00b07-2775-4523-a215-e26a9d362983 · outbound

This paper cites The lambada dataset: Word prediction requiring a broad discourse context.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing The lambada dataset: Word prediction requiring a broad discourse context

Reference 39

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing From Sparse to Soft Mixtures of Experts

Reference 40

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Zero: Memory optimizations toward training trillion parameter models

Reference 41

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Hash layers for large sparse models

Reference 42

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Gradient estimation using stochastic computation graphs

Reference 43

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Neural Machine Translation of Rare Words with Subword Units

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing GLU Variants Improve Transformer

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 47

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Arctic open: Efficient foundation language models at snowflake, April 2024

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:03:45.474545Z

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This paper cites ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models

Reference 49

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Roformer: Enhanced transformer with rotary position embedding

Reference 50

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Retentive Network: A Successor to Transformer for Large Language Models

Reference 51

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Scattered Mixture-of-Experts Implementation

Reference 52

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing LLaMA: Open and Efficient Foundation Language Models

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ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Attention is all you need

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Observation 5bb93bef-ba84-417a-ba17-5dfcbd89549b · outbound

This paper cites Mixture of LoRA Experts.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Mixture of LoRA Experts

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.290889Z

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Observation 577dc926-44e0-4672-8999-421e403dc421 · outbound

This paper cites Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.299064Z

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Observation fabd7970-82eb-45d9-ad62-b3052b5a8ad7 · 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, pp.\ 4791--4800, 2019.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp.\ 4791--4800, 2019

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.309882Z

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Observation 60fd7a77-ea15-41d1-8efd-7048b1757bc2 · outbound

This paper cites Root mean square layer normalization.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Root mean square layer normalization

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.318063Z

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Observation 43ef4a49-216d-4709-966c-8ea8fe4322f5 · outbound

This paper cites Sageattention2 technical report: Accurate 4 bit attention for plug-and-play inference acceleration.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Sageattention2 technical report: Accurate 4 bit attention for plug-and-play inference acceleration

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.326704Z

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Observation 02282cb0-97a7-4632-b77a-b0a5cadea3e0 · outbound

This paper cites Sageattention: Accurate 8-bit attention for plug-and-play inference acceleration.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Sageattention: Accurate 8-bit attention for plug-and-play inference acceleration

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.337256Z

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Observation 0e425c24-7128-4368-af42-11676672fdf5 · outbound

This paper cites Mixture of Attention Heads: Selecting Attention Heads Per Token.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Mixture of Attention Heads: Selecting Attention Heads Per Token

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.349614Z

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Observation d53a36c7-de4b-443d-8a7c-00f66cd6fc92 · outbound

This paper cites Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.360501Z

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Observation f6f0ee2b-06d1-4404-9e12-157865775ea6 · outbound

This paper cites Mixture-of-experts with expert choice routing.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Mixture-of-experts with expert choice routing

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.369152Z

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Observation d834814b-5ff3-4579-96a8-9826b4e41ac8 · outbound

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

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing ST-MoE: Designing Stable and Transferable Sparse Expert Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.380149Z

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Observation f3528265-2cb6-43d4-8e75-46fc1d5bf4d8 · outbound

This paper cites Taming Sparsely Activated Transformer with Stochastic Experts.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Taming Sparsely Activated Transformer with Stochastic Experts

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.387092Z

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Observation b3f0fa02-d55d-4e85-a839-4c6cab105bc0 · outbound

This paper cites @esa (Ref.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing @esa (Ref

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.430478Z

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Observation f9e4df74-9e45-4e2d-b93f-7afd40656956 · outbound

This paper cites an unresolved cited work.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.460295Z

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Observation 8680a8d3-b962-485c-8af0-6fe366b931f0 · outbound

This paper cites and ``flip count.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing and ``flip count

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T12:03:43.511527Z

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Pith citing papers

Observation ad462045-dcd7-40d1-8b27-cfd91c2ebd0c · inbound

BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity cites this paper.

BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T18:20:00.424283Z

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Observation ab8686e6-3eb5-4afc-8b44-d1baf4d3f49f · inbound

RankMixer: Scaling Up Ranking Models in Industrial Recommenders cites this paper.

RankMixer: Scaling Up Ranking Models in Industrial Recommenders ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T15:35:48.428158Z

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Observation 878d5138-3799-4e4c-8c82-ff0eabd13369 · inbound

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts cites this paper.

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T21:55:12.177334Z

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Observation 9610377f-ff36-42ae-af6f-0c424901b020 · inbound

Maximum Score Routing For Mixture-of-Experts cites this paper.

Maximum Score Routing For Mixture-of-Experts ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-05T19:23:17.695452Z

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Observation b36adb7c-a25d-4662-a606-32fe8b5dd342 · inbound

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers cites this paper.

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:26.258436Z

Source-reported events for the cited work

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Observation 335144a3-b82c-458e-b6f5-d2b889d740f3 · inbound

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training cites this paper.

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T16:21:33.601078Z

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Observation ec3edc26-7909-472f-8f8f-56b8aa2b1b18 · inbound

When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models cites this paper.

When Are Experts Misrouted? Counterfactual Routing Analysis in Mixture-of-Experts Language Models ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:50:51.067116Z

Source-reported events for the cited work

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Observation a7ef6be2-6e0f-4616-bd14-b3983c3c9582 · inbound

When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing cites this paper.

When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:22:39.085705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a86e7b4-6f4f-41b7-97c6-1868611fd5d0 · inbound

STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning cites this paper.

STAR: Rethinking MoE Routing as Structure-Aware Subspace Learning ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:26.944882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d127188-e08e-422f-9983-9aa95ddf114d · inbound

Toward Calibrated Mixture-of-Experts Under Distribution Shift cites this paper.

Toward Calibrated Mixture-of-Experts Under Distribution Shift ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:59:33.772048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cb775f0d-b9ca-408b-832c-7376d6cc708d · inbound

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models cites this paper.

GeMoE: Gating Entropy is All You Need for Uncertainty-aware Adaptive Routing in MoE-based Large Vision-Language Models ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-04T15:39:56.483106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 40ad7794-9a79-451e-9e84-d535ede2d500 · inbound

Focusing on What Matters: Saliency-Harnessing Accurate Routing for Diffusion MoE cites this paper.

Focusing on What Matters: Saliency-Harnessing Accurate Routing for Diffusion MoE ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:29:51.505226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.