Paper Citation Record · LEDGER

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2507.01829.

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

pith.paper-citation-record.v1
2507.01829 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T05:51:07.108143Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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verified fuzzy25
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External citation measurements

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Outbound references

Observation dcba34d7-d485-4cca-8ef6-33f3a2a8ff92 · outbound

This paper cites o ppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael K Kopp, G \.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling o ppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael K Kopp, G \

Reference 1

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Observation 98f0b756-1c2a-48b1-8393-cb78f223a4ae · outbound

This paper cites Learning long-term dependencies with gradient descent is difficult.IEEE Transactions on Neural Networks, 5(2):157–166.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Learning long-term dependencies with gradient descent is difficult.IEEE Transactions on Neural Networks, 5(2):157–166

Reference 2

Resolution: verified exact; doi, observed 2026-05-19T05:52:06.796293Z

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Observation 5f12dfc6-ac3c-4d5f-91f1-ededa3317b68 · outbound

This paper cites On the ability and limitations of transformers to recognize formal languages.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling On the ability and limitations of transformers to recognize formal languages

Reference 3

Resolution: verified exact; doi, observed 2026-05-19T05:52:06.748757Z

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Observation 3e30d907-5426-4092-af73-00fdffe7a44f · outbound

This paper cites MINIMALIST: switched-capacitor circuits for efficient in-memory computation of gated recurrent units.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling MINIMALIST: switched-capacitor circuits for efficient in-memory computation of gated recurrent units

Reference 4

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Observation 8669b8d1-8893-4a22-8e06-b4ea329e4840 · outbound

This paper cites Blelloch.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Blelloch

Reference 5

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Observation d84b3a0f-9931-4432-b696-50e57533b4e4 · outbound

This paper cites Quasi-recurrent neural networks.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Quasi-recurrent neural networks

Reference 6

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Observation 2e762815-c70d-41bb-bcba-5f9b2d742655 · outbound

This paper cites JAX : composable transformations of P ython+ N um P y programs.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling JAX : composable transformations of P ython+ N um P y programs

Reference 7

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Observation c468210e-71a7-4bc2-b776-78fa009aae5e · outbound

This paper cites In: Moschitti, A., Pang, B., Daelemans, W.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling In: Moschitti, A., Pang, B., Daelemans, W

Reference 8

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Observation 823d93be-4c3a-4640-bc6e-ebd5c92ff976 · outbound

This paper cites Empirical evaluation of gated recurrent neural networks on sequence modeling.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Empirical evaluation of gated recurrent neural networks on sequence modeling

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.424931Z

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Observation ac910411-98cb-4bac-9c6c-b2ca48c7e157 · outbound

This paper cites Hungry Hungry Hippos: Towards Language Modeling with State Space Models.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 10

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Observation ecb18205-c5f1-49d3-9784-e943601ff895 · outbound

This paper cites Were RNN s all we needed?.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Were RNN s all we needed?

Reference 11

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Observation a93d4cf5-44a8-4ef4-a93b-87845bbe5df4 · outbound

This paper cites Learning to forget: Continual prediction with lstm.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Learning to forget: Continual prediction with lstm

Reference 12

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Observation 2a425499-fb28-4b5a-bd4b-0378049bbc44 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Efficiently modeling long sequences with structured state spaces

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.403251Z

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Observation 2a6c84c6-b8a7-465b-bab3-2fd4ac517934 · outbound

This paper cites Learning delays in spiking neural networks using dilated convolutions with learnable spacings.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Learning delays in spiking neural networks using dilated convolutions with learnable spacings

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.410352Z

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Observation be0e9824-bb8e-4a86-af62-00d5b12fea23 · outbound

This paper cites Dilated convolution with learnable spacings.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Dilated convolution with learnable spacings

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.414043Z

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Observation d29e07d9-666e-457c-a107-a434290ac719 · outbound

This paper cites F lax: A neural network library and ecosystem for JAX.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling F lax: A neural network library and ecosystem for JAX

Reference 16

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Observation a3471660-6381-4765-87fc-6fef56b2fb1e · outbound

This paper cites Long short-term memory.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Long short-term memory

Reference 17

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Observation 41b906eb-ed8b-4245-8835-886540a51f4d · outbound

This paper cites Generalizations across filler-gap dependencies in neural language models.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Generalizations across filler-gap dependencies in neural language models

Reference 18

Resolution: verified exact; doi, observed 2026-05-19T05:52:06.770569Z

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Observation eff2ef8b-1ba9-4f83-b6e2-aab6d731cfd9 · outbound

This paper cites Hyndman and Anne B.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Hyndman and Anne B

Reference 19

Resolution: verified exact; doi, observed 2026-05-19T05:52:06.806212Z

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Observation 9d11741e-b04f-4759-830b-5073c02761cb · outbound

This paper cites Dilated Convolution with Learnable Spacings: beyond bilinear interpolation.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Dilated Convolution with Learnable Spacings: beyond bilinear interpolation

Reference 20

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Observation ace08d2b-ea5a-476e-b1dd-4c3840eb309d · outbound

This paper cites Algebraic theory of machines.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Algebraic theory of machines

Reference 21

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Observation 5052e428-62fc-4560-8867-d545372b0f00 · outbound

This paper cites MNIST handwritten digit database.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling MNIST handwritten digit database

Reference 22

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Observation 04dba6b2-2404-4e0e-8203-eb9aedc756cd · outbound

This paper cites What makes convolutional models great on long sequence modeling? In The Eleventh International Conference on Learning Representations.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling What makes convolutional models great on long sequence modeling? In The Eleventh International Conference on Learning Representations

Reference 23

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Observation 1cd5fb81-1b4c-47f9-ba89-db2eab27490b · outbound

This paper cites Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Ash, Surbhi Goel, Akshay Krishnamurthy, and Cyril Zhang

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.399622Z

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Observation 403a3775-11eb-4529-8240-60903c13199c · outbound

This paper cites Deterministic nonperiodic flow.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Deterministic nonperiodic flow

Reference 25

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Observation 0b7f1afd-9ff7-4839-ad0f-466bf14d0177 · outbound

This paper cites Parallelizing linear recurrent neural nets over sequence length.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Parallelizing linear recurrent neural nets over sequence length

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.443633Z

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Observation de8bb94a-1b48-4c8c-8258-46a533fbf9ff · outbound

This paper cites Context dependent recurrent neural network language model.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Context dependent recurrent neural network language model

Reference 27

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Observation 87aedb9f-05d0-40e6-8116-5fd7463ce0b5 · outbound

This paper cites Neural net architectures for temporal sequence processing.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Neural net architectures for temporal sequence processing

Reference 28

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Observation 46de0cee-fe64-45a8-a0c2-e61b5445fbee · outbound

This paper cites Resurrecting Recurrent Neural Networks for Long Sequences.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Resurrecting Recurrent Neural Networks for Long Sequences

Reference 29

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Observation 6b8aa356-6175-4826-ad6a-1698b44759c5 · outbound

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mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Unresolved cited work

Reference 30

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Observation 2965a690-a07b-4543-bc33-a9b587acdc24 · outbound

This paper cites Delay Embedding Theory of Neural Sequence Models.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Delay Embedding Theory of Neural Sequence Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:52:07.131689Z

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Observation f84c3cdf-e192-40d7-a7fd-948fc4175fc1 · outbound

This paper cites Regularization and nonlinearities for neural language models: when are they needed?.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Regularization and nonlinearities for neural language models: when are they needed?

Reference 32

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Observation fd8febd1-8993-44f1-96b5-ffa2354b03b1 · outbound

This paper cites Hierarchically gated recurrent neural network for sequence modeling.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Hierarchically gated recurrent neural network for sequence modeling

Reference 33

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Observation 87d1342e-6789-4210-9677-e15154faf3c0 · outbound

This paper cites The expressive capacity of state space models: A formal language perspective.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling The expressive capacity of state space models: A formal language perspective

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.362645Z

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This paper cites GLU Variants Improve Transformer.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling GLU Variants Improve Transformer

Reference 35

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This paper cites Large language models can be easily distracted by irrelevant context.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Large language models can be easily distracted by irrelevant context

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.380249Z

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This paper cites 12.4 Chemical chaos and attractor reconstruction.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling 12.4 Chemical chaos and attractor reconstruction

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.446610Z

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Observation 28c2112d-29e3-42d1-ba59-927d61637fca · outbound

This paper cites Detecting strange attractors in turbulence.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Detecting strange attractors in turbulence

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.435162Z

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This paper cites Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology

Reference 39

Resolution: verified exact; doi, observed 2026-05-19T05:52:06.775462Z

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correction dated 2024-09-19. Source: crossref record 10.1063/5.0233347->10.1063/5.0137223:correction, observed 2026-07-11T03:01:28.990358+00:00. This notice travels one citation hop only.

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This paper cites Long range arena : A benchmark for efficient transformers.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Long range arena : A benchmark for efficient transformers

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.431821Z

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Observation cadb1ba5-7425-47f3-bf22-8eac3cf54805 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling WaveNet: A Generative Model for Raw Audio

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-19T05:52:07.116959Z

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Observation 1773e1df-971f-45e8-a94e-0fedce4bbbc6 · outbound

This paper cites Attention is all you need.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Attention is all you need

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T05:52:08.440518Z

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Observation 60d6f975-308f-43af-9f31-14a2cda9b7a2 · outbound

This paper cites Waibel, T.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling Waibel, T

Reference 43

Resolution: verified exact; doi, observed 2026-05-19T05:52:06.737849Z

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Observation b78f11ac-2c5c-4906-9af6-cff42fa5355b · outbound

This paper cites doi: 10.18653/v1/W18-5423.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling doi: 10.18653/v1/W18-5423

Reference 44

Resolution: verified exact; doi, observed 2026-05-19T05:52:06.801347Z

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Observation 6071f62e-37c3-49e8-8371-e463ead3828d · outbound

This paper cites A ConvNet for the 2020s.

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling A ConvNet for the 2020s

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T05:52:06.826029Z

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

Pith citing papers

No inbound Pith citation observations are available.