Paper Citation Record · LEDGER

Were RNNs All We Needed?

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2410.01201.

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

pith.paper-citation-record.v1
2410.01201 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:38:34.658374Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:29:51.728866Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6caa69af-69e0-4106-be57-7f82f8ad36b6 · inbound

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture cites this paper.

GraphMinNet: Learning Dependencies in Graphs with Light Complexity Minimal Architecture Were RNNs All We Needed?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T19:38:34.658374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c57ff4f0-05c6-416b-b8b7-b1fa73e5bc64 · inbound

Temporal horizons in forecasting: a performance-learnability trade-off cites this paper.

Temporal horizons in forecasting: a performance-learnability trade-off Were RNNs All We Needed?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:02:56.776435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cad1da19-64d5-40e3-9867-bd0534405420 · inbound

AuroraLong: Bringing RNNs Back to Efficient Open-Ended Video Understanding cites this paper.

AuroraLong: Bringing RNNs Back to Efficient Open-Ended Video Understanding Were RNNs All We Needed?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:29:50.131873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a823a39-1a77-4584-a4eb-64e8474d2a7d · inbound

EEvAct: Early Event-Based Action Recognition with High-Rate Two-Stream Spiking Neural Networks cites this paper.

EEvAct: Early Event-Based Action Recognition with High-Rate Two-Stream Spiking Neural Networks Were RNNs All We Needed?

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T18:38:18.816732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50ebb843-2ee8-4adc-81ed-b8bde9815ea8 · inbound

Scalable Memristive-Friendly Reservoir Computing for Time Series Classification cites this paper.

Scalable Memristive-Friendly Reservoir Computing for Time Series Classification Were RNNs All We Needed?

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:10.539225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e1c9762c-acd9-4e62-bf11-acb0bfc225b2 · inbound

The Impossibility Triangle of Long-Context Modeling cites this paper.

The Impossibility Triangle of Long-Context Modeling Were RNNs All We Needed?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:41:08.959580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5f65338-cb07-43fc-ae24-0be7c21aa226 · inbound

Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications cites this paper.

Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications Were RNNs All We Needed?

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:37:26.802986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d59f8b96-057d-4802-bc9c-bb0c98f0c249 · inbound

On the Importance of Multistability for Horizon Generalization in Reinforcement Learning cites this paper.

On the Importance of Multistability for Horizon Generalization in Reinforcement Learning Were RNNs All We Needed?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:27:24.345615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b2233fe-be13-4822-973c-4dbed1e3b1c9 · inbound

Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo cites this paper.

Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo Were RNNs All We Needed?

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:42:32.378733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8460de12-91b8-4c7d-b233-2dd6c09560ff · inbound

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations cites this paper.

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations Were RNNs All We Needed?

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:09:07.278539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation edef7844-89c2-4240-b99e-30fe384a7bb9 · inbound

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations cites this paper.

Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations Were RNNs All We Needed?

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:46.364707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 29275fe8-5aa3-43d8-b302-a1910f83b935 · inbound

Building Generalization Into Behavior Generation Via Adaptive Compositions of Regularities cites this paper.

Building Generalization Into Behavior Generation Via Adaptive Compositions of Regularities Were RNNs All We Needed?

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:36:09.225673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09e02fc7-86a6-4ebf-9589-594ea3ee8e7b · inbound

Trading Complexity for Expressivity Through Structured Generalized Linear Token Mixing cites this paper.

Trading Complexity for Expressivity Through Structured Generalized Linear Token Mixing Were RNNs All We Needed?

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:16:00.570853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80bbb2b8-9cec-474e-b418-61cc98927191 · inbound

Pretraining Recurrent Networks without Recurrence cites this paper.

Pretraining Recurrent Networks without Recurrence Were RNNs All We Needed?

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:26:56.642519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fc48db01-f484-477e-89c9-45f53dfe39be · inbound

Pretraining Recurrent Networks without Recurrence cites this paper.

Pretraining Recurrent Networks without Recurrence Were RNNs All We Needed?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T12:20:55.469333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b11b0929-031a-47a1-ae92-5bef3021ffc7 · inbound

Don't Listen to Me: A Lightweight, Low-Latency Model for Own-Voice Cancellation in Far-Field Speech Enhancement cites this paper.

Don't Listen to Me: A Lightweight, Low-Latency Model for Own-Voice Cancellation in Far-Field Speech Enhancement Were RNNs All We Needed?

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T12:29:51.730169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 46053489-1298-4052-958c-13c0d673912f · inbound

Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings cites this paper.

Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings Were RNNs All We Needed?

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:35:40.712808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.