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Paper Citation Record · LEDGER

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations

As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2411.14489.

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

pith.paper-citation-record.v1
2411.14489 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:47:26.023182Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:47:25.902060Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T16:47:26.160280Z

Reference resolution

34 of 34 outbound references displayed

  • verified exact1
  • verified fuzzy14
  • unresolved17
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e3568320-910f-43a4-bf06-b473f4b010af · outbound

This paper cites an unresolved cited work.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Unresolved cited work

Reference 1

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unresolved
raw_fallback, observed 2026-08-12T16:47:26.418061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 94679594-71d8-4573-adf8-85260f7e2eba · outbound

This paper cites Without loss of generality, we use GRU to illustrate the definition of GhostRNN.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Without loss of generality, we use GRU to illustrate the definition of GhostRNN

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 5f9e2e86-a684-492f-8d9e-87743a5b4042 · outbound

This paper cites an unresolved cited work.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Unresolved cited work

Reference 3

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.910806Z digest=sha256:6d770270056ddcb49d63e21b135d13c8a32e15af0ca5a2963b981d8a37cd0e5c

Observation e4b5ec7e-4738-44dd-a443-51ca857308a6 · outbound

This paper cites an unresolved cited work.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Unresolved cited work

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.919197Z digest=sha256:c1cdb85243361d8a0326ad97ce566dcb65c460725836d25b55b74ef8232b0bd3

Observation 363878b9-a5e7-4303-920e-da0cd473cf93 · outbound

This paper cites • GRU-TasNet.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations • GRU-TasNet

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.915157Z digest=sha256:bcbebd28db5c1cd549ff466d02d6097f3f396a999059fe65b4d494eec81019ec

Observation 2b8225d9-a6e8-4415-aa01-54e22c97feed · outbound

This paper cites Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech sepa- ration,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Dual-path rnn: efficient long sequence modeling for time-domain single-channel speech sepa- ration,

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.947634Z digest=sha256:109ca387cfd0d147972ade176d0bd92e357db70d960b94a8e534ee8dd2669569

Observation 988edbb8-8b94-4afb-974f-42a62a44c403 · outbound

This paper cites 2022D01D43).

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations 2022D01D43)

Reference 7

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raw_fallback, observed 2026-08-12T16:47:26.360545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.923108Z digest=sha256:abe28ae75589634364fba869d8ec6b27706102f60021775a328e42045eee2f4a

Observation a6c04cf5-8fec-4507-a5a0-49e8a34c8bef · outbound

This paper cites Long short-term memory,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Long short-term memory,

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.926924Z digest=sha256:eb125c7d2401c5608ee9800968e1b8d84c27ef1d038779481e2c4fe881ad244b

Observation 3589998f-ccce-4c3f-81c7-969cf5214c27 · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.931065Z digest=sha256:c9c2534b025265d844ca430c753fbb313fd2ef12d15a447b4e4d5c29b8dbe80e

Observation 402039aa-da20-4d56-b821-93727f00fd4d · outbound

This paper cites Hello Edge: Keyword Spotting on Microcontrollers.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Hello Edge: Keyword Spotting on Microcontrollers

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.935118Z digest=sha256:7f78d7e91dde14a7e5333b67ea44b8ac8158829438420dfe6b0fc798601cd8d9

Observation 6a70889e-6b8d-4898-8371-8a4f93ecfb1f · outbound

This paper cites Streaming keyword spotting on mobile devices.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Streaming keyword spotting on mobile devices

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.938967Z digest=sha256:f0d7ccea889d4252dfe78ded129708cfb3fdf149bda7e748ec9a93093016169b

Observation f1ac0804-7ed9-407f-95e7-35c4cadf0e5b · outbound

This paper cites DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.942936Z digest=sha256:8849b44d41801b4927a4d57f898be4fe6b769ec33d6a2a2745ddc51218d54a9a

Observation 5374ae97-75af-47ac-9644-2c5eecd9220a · outbound

This paper cites GhostRNN: Reducing State Redundancy in RNN with Cheap Operations.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations GhostRNN: Reducing State Redundancy in RNN with Cheap Operations

Reference 13

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local_arxiv, observed 2026-08-12T16:47:26.164550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.902060Z digest=sha256:df9a6ee97a922e6b79ca46cd358719f1033d290e88bca3ae2164f3394cb62acd

Observation 9244c02e-7823-42e2-ae91-34d505058800 · outbound

This paper cites Exploring architectures, data and units for streaming end-to-end speech recognition with rnn-transducer,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Exploring architectures, data and units for streaming end-to-end speech recognition with rnn-transducer,

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.951185Z digest=sha256:afee5f35c69c970297a6d186548f5a1be65ab91b28fa78fc03c1be1dffcfcc2f

Observation c3180c84-01bc-4dcd-ba19-e0170fd3c921 · outbound

This paper cites Nonlinear residual echo sup- pression using a recurrent neural network.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Nonlinear residual echo sup- pression using a recurrent neural network

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-12T16:47:26.327222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.954576Z digest=sha256:53542112dfdaf3231b771c83afe204f51bba5f1d0059d9b7a98b4d6da68d6841

Observation 1f7f7795-488c-4347-81c8-8c89efbedb99 · outbound

This paper cites Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.958214Z digest=sha256:7a876ba415eacd55b234dbad9fed78628dbf89f5cbcfc4afa9f7b15ab327552f

Observation e668f69f-ed9a-49cd-948b-20a1b58c69b3 · outbound

This paper cites Attention is all you need,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Attention is all you need,

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.961800Z digest=sha256:30a2c8f4e1d684a75d364c956732c18d14d515503d2142c3d3ebf5aab940e815

Observation 4fe06d07-22ba-4577-ac27-a4a1ce20afd0 · outbound

This paper cites Gate-variants of gated recurrent unit (gru) neural networks,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Gate-variants of gated recurrent unit (gru) neural networks,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:47:26.307340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.965311Z digest=sha256:136fc7f0114f76aa61f1789e45d5abd7cee9aa337c7ec220706a605d4c19eff3

Observation 075d4fcf-6ea0-4876-aaec-c133df461daf · outbound

This paper cites Light gated recurrent units for speech recognition,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Light gated recurrent units for speech recognition,

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-12T16:47:26.295638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.968616Z digest=sha256:cf1a95719f3642423755ccd0701702511ff04571b2430b877d3352f69fb0f4ac

Observation 17923d87-e2d7-4c91-8750-11d14ff961e2 · outbound

This paper cites An optimized recurrent unit for ultra- low-power keyword spotting,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations An optimized recurrent unit for ultra- low-power keyword spotting,

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-12T16:47:26.281785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.972172Z digest=sha256:ebf1bb73b3d818cf74c23c6d7256dea898727968112ee9860a4e773922a18915

Observation deff5774-f97a-4b5d-9cf7-f366d794e7cb · outbound

This paper cites Sitgru: single-tunnelled gated re- current unit for abnormality detection,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Sitgru: single-tunnelled gated re- current unit for abnormality detection,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:47:26.269867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.975655Z digest=sha256:42f69cc2e3a19473bf926966698728c5ceef127b8b484d55c36295c17cafb939

Observation 2b53ce4a-d6cb-4ea4-a403-040f112acd88 · outbound

This paper cites Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks

Reference 22

Resolution
verified exact
local_arxiv, observed 2026-08-12T16:47:26.092088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.979428Z digest=sha256:fa5d5798716a8353c306cf79cfd6ce3646a96372eeac88dd116287cb37467086

Observation a4a77769-d9b4-4746-87b6-bdf97643cf0b · outbound

This paper cites Ghost- net: More features from cheap operations,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Ghost- net: More features from cheap operations,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.983076Z digest=sha256:9b63dc4d8a70ed21b2a9a3a8f328caaff378759782ff7b68cb61d2be0f175340

Observation dc5f7d78-60bc-4768-aea4-c7d5b7d45bff · outbound

This paper cites Learning long-term de- pendencies with gradient descent is difficult,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Learning long-term de- pendencies with gradient descent is difficult,

Reference 24

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raw_fallback, observed 2026-08-12T16:47:26.246616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.986652Z digest=sha256:b7a07a4144ff43fae91fba59695f674f1740f1b3871a0586c568086de542b1e6

Observation 845bdd0f-c5af-43f9-b3ea-f1b5eccd1fe9 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.990379Z digest=sha256:86434c0dd8b2d705d4222b6874c3521aa38354194702b60ef6cd68ae2b52304b

Observation 6f62c89c-bb19-4c9d-a1a8-176ef37e330e · outbound

This paper cites End-to-end low resource keyword spotting through character recognition and beam-search re-scoring,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations End-to-end low resource keyword spotting through character recognition and beam-search re-scoring,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T16:47:26.235390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.993990Z digest=sha256:b0b2c6337aa0cff4dfb36fb91e38e3edb6c118232b1ee44be6a04fe606364472

Observation 7619ca74-a52c-444c-9310-ace1ec8b0838 · outbound

This paper cites LibriMix: An Open-Source Dataset for Generalizable Speech Separation.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations LibriMix: An Open-Source Dataset for Generalizable Speech Separation

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:25.997599Z digest=sha256:9fd2d2f637defad3f923d45e2d3344bbde95feeed01f11eaae9fe456eeb2f62c

Observation efa4e11b-432b-47a1-a513-18bff2d8ace4 · outbound

This paper cites Lib- rispeech: an asr corpus based on public domain audio books,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Lib- rispeech: an asr corpus based on public domain audio books,

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:26.001398Z digest=sha256:3987332cacf7820395a5e46b42eea9d5708dbe5aaf7619808c5d43b463a0b1a7

Observation 77ce84e3-9121-4d41-ab6d-f94e2fb956b9 · outbound

This paper cites WHAM!: Extending Speech Separation to Noisy Environments.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations WHAM!: Extending Speech Separation to Noisy Environments

Reference 29

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no resolver link, observed 2026-08-12T16:47:26.005454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:26.005454Z digest=sha256:24b1b8d6204f6b28c362390f80eb9f8deed8489f75b7b517de89432e1da6708b

Observation 2a1d9669-c51f-4158-a5ba-0c344b9106e3 · outbound

This paper cites As- teroid: the PyTorch-based audio source separation toolkit for re- searchers,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations As- teroid: the PyTorch-based audio source separation toolkit for re- searchers,

Reference 30

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raw_fallback, observed 2026-08-12T16:47:26.217298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:26.009193Z digest=sha256:b4eae2a686efd51ab58677a0bf954f1dcf352123d151664d1d461fce1b7f13d0

Observation d2d9582c-95e1-4159-91a0-4f73c16de4da · outbound

This paper cites Real-time single-channel dereverbera- tion and separation with time-domain audio separation network.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Real-time single-channel dereverbera- tion and separation with time-domain audio separation network

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-12T16:47:26.204677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:26.012509Z digest=sha256:a8ac07b6ab22d5142a46eb0c7fe88e97f58e694b78429a9465c97d80c9bac750

Observation 5b794d19-376e-43b9-90b7-9baef6cb5a66 · outbound

This paper cites Sdr– half-baked or well done?.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Sdr– half-baked or well done?

Reference 32

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no resolver link, observed 2026-08-12T16:47:26.016503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:26.016503Z digest=sha256:841b5089eabde0765093259d43b26f124f8b5dbeec83e362386074cd27b76c76

Observation 11185d92-5f08-4e1a-8684-697908774185 · outbound

This paper cites An al- gorithm for intelligibility prediction of time–frequency weighted noisy speech,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations An al- gorithm for intelligibility prediction of time–frequency weighted noisy speech,

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T16:47:26.019934Z digest=sha256:84255f2c5fd4fd8df6062f57192df03d5be11c6115c02187066609e3df210496

Observation e28ef1ee-d7a3-463d-a36a-f2485f9c79b0 · outbound

This paper cites Mindspore,.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations Mindspore,

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-12T16:47:26.177286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:26.023182Z digest=sha256:b230e8ea26defd63478db895ef29f84e9df23e992fed40be8989717c1625af09

Pith citing papers

Observation 5374ae97-75af-47ac-9644-2c5eecd9220a · inbound

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations cites this paper.

GhostRNN: Reducing State Redundancy in RNN with Cheap Operations GhostRNN: Reducing State Redundancy in RNN with Cheap Operations

Reference 13

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metadata mismatch
local_arxiv, observed 2026-08-12T16:47:26.164550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T16:47:25.902060Z digest=sha256:df9a6ee97a922e6b79ca46cd358719f1033d290e88bca3ae2164f3394cb62acd