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Paper Citation Record · LEDGER

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2506.05678.

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

pith.paper-citation-record.v1
2506.05678 v3

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:22:22.376764Z

measured 35 of 35 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.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

  • verified exact0
  • verified fuzzy23
  • unresolved12
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b68a4bac-e5c6-4993-a954-9231b7224328 · outbound

This paper cites @esa (Ref.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions @esa (Ref

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0a94128c-0481-4b26-b503-6b162425bc99 · outbound

This paper cites an unresolved cited work.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Unresolved cited work

Reference 2

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Observation 099e65f9-189c-4db7-a11b-8d7c8a644053 · outbound

This paper cites an unresolved cited work.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Unresolved cited work

Reference 3

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Observation 18370100-2b0e-42a6-8170-59c36882e105 · outbound

This paper cites Quality over Quantity in Attention Layers : When Adding More Heads Hurts.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Quality over Quantity in Attention Layers : When Adding More Heads Hurts

Reference 4

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.124101Z digest=sha256:f45297f3dd1235e8bf57a8a0ddfece750150f00500a094e2fdcfbb67aa68ef5b

Observation d3cfa963-6456-4ae0-aab8-d41b03c875f8 · outbound

This paper cites Zico Kolter, and Vladlen Koltun.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Zico Kolter, and Vladlen Koltun

Reference 5

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raw_fallback, observed 2026-08-07T10:22:23.010873Z

Source-reported events for the cited work

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

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Observation 8b3ba32a-9d4d-4f5c-8ed3-05d1a534cc1d · outbound

This paper cites LongBench : A Bilingual , Multitask Benchmark for Long Context Understanding , June 2024.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions LongBench : A Bilingual , Multitask Benchmark for Long Context Understanding , June 2024

Reference 6

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Source-reported events for the cited work

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

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Observation 2b890374-2905-468d-806f-5dfb364dd052 · outbound

This paper cites Bengio, P.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Bengio, P

Reference 7

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T10:22:22.151437Z digest=sha256:701af26a2fd9bff422d72d2e80072ac26e0a0ec468ea03c0a290f26471cfb392

Observation b859d881-c1fe-4521-a9c6-b078f1ef9c00 · outbound

This paper cites On the Relationship between Self-Attention and Convolutional Layers.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions On the Relationship between Self-Attention and Convolutional Layers

Reference 8

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no resolver link, observed 2026-08-07T10:22:22.158470Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-07T10:22:22.158470Z digest=sha256:e830a8b9b3062f3be61133ceff479b83a9dce4fcefa0a5f872ccfc34aa116c99

Observation d2d84b5b-ea8e-4466-9cba-5acb083895f6 · outbound

This paper cites BAMBOO : A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models , March 2024.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions BAMBOO : A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models , March 2024

Reference 9

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.165465Z digest=sha256:93935542374bd6a2bc5c5db1bbd5487434ff4c158fbf9121595d39a4fd9b26c8

Observation a565ce8e-c803-4517-9f5e-6067f3174fda · outbound

This paper cites The Pile : An 800GB Dataset of Diverse Text for Language Modeling , December 2020.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions The Pile : An 800GB Dataset of Diverse Text for Language Modeling , December 2020

Reference 10

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:22:22.172765Z digest=sha256:e1f48003873fda9deb854c57ad703ee834f253efcd5ade90eb25daa73baef70d

Observation 4901f98f-d98b-48c8-9db4-c16c35eca769 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces , August 2022 a.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Efficiently Modeling Long Sequences with Structured State Spaces , August 2022 a

Reference 11

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.180753Z digest=sha256:4094a250e8908e5ac987929a8d23aec0ae8ebfd4e1768e165ec72a9c468dbc00

Observation ebb642d5-47db-4d64-84dc-7d0689815d52 · outbound

This paper cites On the Parameterization and Initialization of Diagonal State Space Models , August 2022 b.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions On the Parameterization and Initialization of Diagonal State Space Models , August 2022 b

Reference 12

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.188569Z digest=sha256:e7d2dc7cc519961ee3d1691b6a8281152e403d17a3fd019417eb58a3fc51dd9b

Observation ccedb0eb-9bc9-416f-9a62-e86810bdc093 · outbound

This paper cites Long Short-Term Memory.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Long Short-Term Memory

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:22:22.205180Z digest=sha256:ea610fbd5fb37bc4b428a3aba621697ec69a8c000a11e1ee9404ff34a227332f

Observation a0918d4d-4153-4f72-98cc-6ed91902827d · outbound

This paper cites RULER : What 's the Real Context Size of Your Long-Context Language Models ?, August 2024.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions RULER : What 's the Real Context Size of Your Long-Context Language Models ?, August 2024

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.884647Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.214308Z digest=sha256:a6cd8912127cad17af6327f6ddf78668e41ab1418348809d2ee8b1f58f48ca06

Observation ec2cb9a1-bc6e-49cf-9853-79c41154b8b2 · outbound

This paper cites Approximation Rate of the Transformer Architecture for Sequence Modeling.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Approximation Rate of the Transformer Architecture for Sequence Modeling

Reference 15

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raw_fallback, observed 2026-08-07T10:22:22.866874Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.220388Z digest=sha256:da920078cb354311ce14acf77331cd1a069576c21d0a5bb0f620b90bb69209fa

Observation 544a491d-0124-429d-a43a-ed1ee378e8fc · outbound

This paper cites Approximation Theory of Convolutional Architectures for Time Series Modelling.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Approximation Theory of Convolutional Architectures for Time Series Modelling

Reference 16

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.232497Z digest=sha256:83a18ba07db5ea7fdb3a5a8af604729719f6f3ab731219e9526c5c0a54ebf8c5

Observation dfbc51c0-b322-4842-a608-98e09e7a30f4 · outbound

This paper cites Krizhevsky.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Krizhevsky

Reference 17

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.238045Z digest=sha256:e8e8b40f927a52b75df71c938133db4c8ffc36fde0ce0ea15f5028265a1d45fd

Observation 8ed72dbd-923e-4dc4-afd4-6851a78a759d · outbound

This paper cites Can Vision Transformers Perform Convolution ?, November 2021.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Can Vision Transformers Perform Convolution ?, November 2021

Reference 18

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raw_fallback, observed 2026-08-07T10:22:22.814336Z

Source-reported events for the cited work

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

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Observation d830c431-f00c-453c-a2a7-40f5c427307c · outbound

This paper cites Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks

Reference 19

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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.251380Z digest=sha256:d8ff4cc214e623fe16c3ac945f6d90d02956618ba6b729de488032fe480d9e0e

Observation 52bfaa91-4217-4c10-a409-f19f982c9431 · outbound

This paper cites Maas, Raymond E.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Maas, Raymond E

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.777521Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.259406Z digest=sha256:df42b2f42c76b0d6a63a25508e4b2c246b82f0918bb052e6f0ad0abc3e69ab91

Observation f31d887c-d201-4a3e-bcec-481c501b5db9 · outbound

This paper cites Pointer Sentinel Mixture Models.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Pointer Sentinel Mixture Models

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.760736Z

Source-reported events for the cited work

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

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Observation 980e9167-313f-408b-856c-b4076e38be63 · outbound

This paper cites Stable Recurrent Models , March 2019.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Stable Recurrent Models , March 2019

Reference 22

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

source=arxiv_source observed=2026-08-07T10:22:22.275003Z digest=sha256:90adc7ef7c4b2fdf4a1c507e3356aa346d241dc6915a32263d053d55fbe34810

Observation 70eb2665-2bf8-4cc4-bc75-12a12b763b33 · outbound

This paper cites an unresolved cited work.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Unresolved cited work

Reference 23

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

source=arxiv_source observed=2026-08-07T10:22:22.282018Z digest=sha256:a97763f7b4a98b11e2a4653423ae20968049cf9c7bb8d900b7d40a48d0f1ce52

Observation afe50a40-1c05-4bcf-8c25-c65013be4d34 · outbound

This paper cites Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De

Reference 24

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verified fuzzy
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Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.291335Z digest=sha256:5ea1434fc2b7947e227626863b8624f27e9de2330ecf8f2f8a70eb2006891886

Observation 751dee40-3a91-44dc-8183-1bb5c36d180d · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context, June 2016.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions The LAMBADA dataset: Word prediction requiring a broad discourse context, June 2016

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.685906Z

Source-reported events for the cited work

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

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Observation 3ddbf31a-18b9-4253-8590-5067a7b30339 · outbound

This paper cites Rumelhart, Geoffrey E.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Rumelhart, Geoffrey E

Reference 26

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no resolver link, observed 2026-08-07T10:22:22.305146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:22:22.305146Z digest=sha256:bf157dff9ee880a57271e465cb4d14c1ef0028e90666810fcc04d1d3f59b032a

Observation c9353c12-f0e1-4a15-9366-a7d419d93038 · outbound

This paper cites Self-Attention with Relative Position Representations.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Self-Attention with Relative Position Representations

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T10:22:22.311167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:22:22.311167Z digest=sha256:b5fb7e6c37bceaf9e778415b3f4db1b62294010b4626ec2c22e0e9a2b98d7d74

Observation e37230f2-b154-4957-8e3c-b994dce8b051 · outbound

This paper cites RoFormer : Enhanced Transformer with Rotary Position Embedding , November 2023.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions RoFormer : Enhanced Transformer with Rotary Position Embedding , November 2023

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.666508Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.319484Z digest=sha256:0d8974651e85085ea49bce1b44763fd03b1bf0286a6d76c7b5a438ed8cc202bb

Observation 9202fbb5-e5a9-477f-86a7-d46ef51f8678 · outbound

This paper cites Long Range Arena : A Benchmark for Efficient Transformers.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Long Range Arena : A Benchmark for Efficient Transformers

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.648082Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.328247Z digest=sha256:b8a1d90c83c0652a990c716011b0f95b785f1f032ce3f92cc9d4fd015c4aa0b2

Observation e51cc932-c061-4a8d-94fa-97c38f9e96ab · outbound

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

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions WaveNet: A Generative Model for Raw Audio

Reference 30

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unresolved
no resolver link, observed 2026-08-07T10:22:22.337568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:22:22.337568Z digest=sha256:b72a53b9010defd47629de294a6d4bef3d48fe5f46252598851869b280494d58

Observation 4646a706-03fa-4309-bf79-960faac30586 · outbound

This paper cites Attention is All you Need.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Attention is All you Need

Reference 31

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unresolved
no resolver link, observed 2026-08-07T10:22:22.344874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:22:22.344874Z digest=sha256:6f6d150dff4260944626f16e124e11433cc171f24373309863add221f37091c9

Observation e2587cf0-da76-4096-b171-88fb3e5bf9a9 · outbound

This paper cites StableSSM : Alleviating the Curse of Memory in State-space Models through Stable Reparameterization.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions StableSSM : Alleviating the Curse of Memory in State-space Models through Stable Reparameterization

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.600924Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.352197Z digest=sha256:2ce0fceee3e25483b334d806217ea7ce1997512b663804c5ff699d4cb5d8e035

Observation 12027641-4e77-422c-aa6d-5e5438d2749d · outbound

This paper cites State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.581320Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.361037Z digest=sha256:fd9f757a9d3dce46f2628212e3491107547ae7901aaf311598ada999c98d3ead

Observation 7b0140f6-122d-4e64-8ff1-a21658262c68 · outbound

This paper cites Inverse Approximation Theory for Nonlinear Recurrent Neural Networks.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Inverse Approximation Theory for Nonlinear Recurrent Neural Networks

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.561663Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.370277Z digest=sha256:414df8309605a0ab70020775b4e4dd2ad66258882d00fe7927566b3486cef9da

Observation e75872e5-e021-4b51-b5f9-1150409fc43d · outbound

This paper cites Do RNN and LSTM have Long Memory ? In Proceedings of the 37th International Conference on Machine Learning , pp.\ 11365--11375.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions Do RNN and LSTM have Long Memory ? In Proceedings of the 37th International Conference on Machine Learning , pp.\ 11365--11375

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-07T10:22:22.544755Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-07T10:22:22.376764Z digest=sha256:1d5fb0937851509a6f3382be556d5079a253366358d22d3378f368f068841ad0

Pith citing papers

No inbound Pith citation observations are available.