Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection | Paper Citation Record

Observation 43287aa0-aa24-4114-8f03-90e3dd1c037d · outbound

This paper cites BlackMamba: Mixture of Experts for State-Space Models.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection BlackMamba: Mixture of Experts for State-Space Models

Reference 1

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Observation 83cea58f-8948-4109-a043-0e7ffc771652 · outbound

This paper cites Piqa: Reasoning about phys- ical commonsense in natural language.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Piqa: Reasoning about phys- ical commonsense in natural language

Reference 2

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Observation 905298a4-452f-4a8b-abc2-30eac0cb9f5d · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Xception: Deep learning with depthwise separable convolutions

Reference 3

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Observation c46e0e3e-e359-4387-a097-f2eb2b377377 · outbound

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

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 4

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Observation 836bbf3b-d613-4e08-a16c-1ca30db11d10 · outbound

This paper cites SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention

Reference 5

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Observation 6def8f38-b877-4575-b2c4-07c3daf96f13 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 6

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Observation d0faa61e-050d-48ce-887e-8be57a85cf0f · outbound

This paper cites Language modeling with gated convolutional networks.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Language modeling with gated convolutional networks

Reference 7

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Observation e10fe4ab-9031-49e5-a69c-d6f1a50e3800 · outbound

This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 8

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Observation b2623c6a-600b-4baa-996b-eb17d8615318 · outbound

This paper cites DeepSeek-V3 Technical Report.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection DeepSeek-V3 Technical Report

Reference 9

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Observation b8137f2f-5bd4-4a54-b85c-6ec7b1077466 · outbound

This paper cites Hymba: A Hybrid-head Architecture for Small Language Models.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Hymba: A Hybrid-head Architecture for Small Language Models

Reference 10

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Observation dc7a3b5f-ddcd-4426-851f-209ecbd3d6c0 · outbound

This paper cites Glam: Efficient scaling of language models with mixture-of-experts.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Glam: Efficient scaling of language models with mixture-of-experts

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:59.374873Z

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Observation a7df6d69-ed71-45b9-a18d-71ada9c8e15f · outbound

This paper cites Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural networks, 107:3–11, 2018.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Sigmoid-weighted linear units for neural network function approximation in reinforcement learning.Neural networks, 107:3–11, 2018

Reference 12

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Observation 6d6e9960-19c2-40a0-afc9-f3fa0c8f72ab · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022

Reference 13

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Observation bb44b742-94fc-481c-a7a0-17291622754d · outbound

This paper cites MegaBlocks: Efficient Sparse Training with Mixture-of-Experts.Proceedings of Machine Learning and Systems, 5, 2023.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection MegaBlocks: Efficient Sparse Training with Mixture-of-Experts.Proceedings of Machine Learning and Systems, 5, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:58.791464Z

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Observation 94081f34-4cba-4873-aeee-707bd58cb256 · outbound

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

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 16

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Observation e12d6c0b-211c-4a93-bdb0-c7cf4d2c7704 · outbound

This paper cites On the parameterization and initialization of diagonal state space models.Advances in Neural Information Processing Systems, 35:35971–35983, 2022.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection On the parameterization and initialization of diagonal state space models.Advances in Neural Information Processing Systems, 35:35971–35983, 2022

Reference 17

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Observation 67c50fef-9c16-4056-8e91-38278f1381bf · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Efficiently Modeling Long Sequences with Structured State Spaces

Reference 18

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Observation c413497a-fa2a-441e-ba27-0545387fcba7 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 19

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Observation 09768454-b360-48a0-8baf-485b82b2c5f8 · outbound

This paper cites Diagonal state spaces are as effective as structured state spaces.Advances in Neural Information Processing Systems, 35:22982–22994, 2022.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Diagonal state spaces are as effective as structured state spaces.Advances in Neural Information Processing Systems, 35:22982–22994, 2022

Reference 20

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Observation 6da4b044-d2e7-4dbf-8c3f-371556bb434d · outbound

This paper cites Deep residual learning for image recognition.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Deep residual learning for image recognition

Reference 21

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Observation dec6715b-72a4-4251-a876-fc1c35c4a298 · outbound

This paper cites Adaptive mixtures of local experts.Neural computation, 3(1):79–87, 1991.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Adaptive mixtures of local experts.Neural computation, 3(1):79–87, 1991

Reference 22

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Observation 4f70fb5d-8f5c-4c11-a193-7ba223988bb9 · outbound

This paper cites Mixtral of Experts.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Mixtral of Experts

Reference 23

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Observation 0e642c8c-3db6-47e8-ae35-161c68f8d323 · outbound

This paper cites A new approach to linear filtering and prediction problems.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection A new approach to linear filtering and prediction problems

Reference 24

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Observation e16b7d02-cb39-45d6-8d00-b62b76a6db75 · outbound

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

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 25

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Observation ee647e48-a092-4177-a0d0-ec782870658a · outbound

This paper cites Zettlemoyer, and Lili Yu.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Zettlemoyer, and Lili Yu

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:57.725981Z

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Observation e9984a85-245c-4353-a756-9348a54c54bf · outbound

This paper cites Jamba: A Hybrid Transformer-Mamba Language Model.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Jamba: A Hybrid Transformer-Mamba Language Model

Reference 27

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Observation d45837fc-9079-4fa4-ba32-e5467ec42d13 · outbound

This paper cites Bridging Discrete and Backpropagation: Straight-Through and Beyond.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Bridging Discrete and Backpropagation: Straight-Through and Beyond

Reference 28

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Observation c1e42e00-6894-4642-9e8d-f855d9d5d89c · outbound

This paper cites Sparse Backpropagation for MoE Training.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Sparse Backpropagation for MoE Training

Reference 29

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Observation 6606f6ea-2c7c-4f94-901b-872060968c50 · outbound

This paper cites Megalodon: Efficient llm pretraining and inference with unlimited context length.Advances in Neural Information Processing Systems, 37:71831–71854, 2024.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Megalodon: Efficient llm pretraining and inference with unlimited context length.Advances in Neural Information Processing Systems, 37:71831–71854, 2024

Reference 30

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Observation cae67504-a2d9-484c-b850-292e263f985b · outbound

This paper cites Long Range Language Modeling via Gated State Spaces.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Long Range Language Modeling via Gated State Spaces

Reference 31

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Observation 75b0fa63-25e0-4298-919c-f44cf83c6db1 · outbound

This paper cites MiniMax-01: Scaling Foundation Models with Lightning Attention.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection MiniMax-01: Scaling Foundation Models with Lightning Attention

Reference 32

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Observation d6599066-3923-4656-89b0-2d1bf0bb8266 · outbound

This paper cites Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Reference 33

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Observation 54c02f4e-3540-46df-8266-6405fd93c791 · outbound

This paper cites Gpt-4 technical report.PREPRINT, 2023.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Gpt-4 technical report.PREPRINT, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:57.234742Z

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Observation 9b0522f5-e80f-4e78-b3ed-040980ecea0a · outbound

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

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 35

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Observation 4f650784-fa38-4c4c-a27f-d99778d3d4de · outbound

This paper cites Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:48.103876Z

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Observation bd727df7-b8f5-44f9-902a-6bcf81b59c89 · outbound

This paper cites MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Reference 37

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Observation fb089896-4dd5-4405-8b45-bad5ce2d1b4a · outbound

This paper cites Hyena hierarchy: Towards larger convolutional language models.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Hyena hierarchy: Towards larger convolutional language models

Reference 38

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Observation 30f7755b-eb4d-44ce-b16b-f869de9c78ce · outbound

This paper cites Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

Reference 39

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Observation 1c796a33-950a-43c5-acdb-f00f3e1f381c · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:48.627523Z

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Observation 24412b46-7042-4eee-8a49-9b6c0a21a618 · outbound

This paper cites GLU Variants Improve Transformer.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection GLU Variants Improve Transformer

Reference 41

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Observation c44c032e-0940-4f05-a05d-79c3be739b1c · outbound

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

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:49.087728Z

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Observation 792221a1-f14e-465a-96db-a1573809227b · outbound

This paper cites SlimPajama: A 627B token cleaned and deduplicated version of RedPajama, 2023.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection SlimPajama: A 627B token cleaned and deduplicated version of RedPajama, 2023

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:56.485189Z

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Observation 864a5499-7074-4f6b-b132-448e424ed5e4 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:49.370138Z

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Observation d70f3bf8-a48c-48fc-9180-6ceeea3f8d1c · outbound

This paper cites Selective structured state-spaces for long-form video understanding.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Selective structured state-spaces for long-form video understanding

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:55.944852Z

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Observation 3de527c2-e87a-4fd2-b81b-cbfde9ae1558 · outbound

This paper cites On layer normalization in the transformer architecture.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection On layer normalization in the transformer architecture

Reference 46

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Observation d84efa12-7a6e-4cad-9b5f-1d0037426333 · outbound

This paper cites Gated Delta Networks: Improving Mamba2 with Delta Rule.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Gated Delta Networks: Improving Mamba2 with Delta Rule

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:49.824833Z

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Observation 5d383351-55f5-4721-b6e5-e56ccdb39648 · outbound

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

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:50.055659Z

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Observation 2e27d699-5201-495b-a7e8-785a0d232894 · outbound

This paper cites Root mean square layer normalization.Advances in Neural Information Processing Systems, 32, 2019.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Root mean square layer normalization.Advances in Neural Information Processing Systems, 32, 2019

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:50.224917Z

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Observation dcb95f6e-96bd-4b32-9146-075f88b1ac5a · outbound

This paper cites Mixture of attention heads: Selecting attention heads per token.

Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection Mixture of attention heads: Selecting attention heads per token

Reference 50

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