Paper Citation Record · LEDGER

Resurrecting Recurrent Neural Networks for Long Sequences

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2303.06349.

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

pith.paper-citation-record.v1
2303.06349 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 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 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:49:09.446301Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

43
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cac50261-77a3-497f-8254-d135f042bfe4 · inbound

Retentive Network: A Successor to Transformer for Large Language Models cites this paper.

Retentive Network: A Successor to Transformer for Large Language Models Resurrecting Recurrent Neural Networks for Long Sequences

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:29:59.886181Z

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 ce98f525-b304-4c09-adf3-bb09c72f19be · inbound

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding cites this paper.

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding Resurrecting Recurrent Neural Networks for Long Sequences

Reference 109

Resolution: verified exact; arxiv_id, observed 2026-05-12T20:22:10.673251Z

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 ab224967-1657-4ec9-95cb-60effefa0f98 · inbound

Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory cites this paper.

Decision Trees That Remember: Gradient-Based Learning of Recurrent Decision Trees with Memory Resurrecting Recurrent Neural Networks for Long Sequences

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T23:49:09.446301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dc5cc42-3b6b-4639-8509-fbab59e8cf3b · inbound

L2RU: a Structured State Space Model with prescribed L2-bound cites this paper.

L2RU: a Structured State Space Model with prescribed L2-bound Resurrecting Recurrent Neural Networks for Long Sequences

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-22T22:42:13.726748Z

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 caab8c05-60db-45b6-96f1-b1f50c6e58b0 · inbound

MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models cites this paper.

MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models Resurrecting Recurrent Neural Networks for Long Sequences

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:08:11.477758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66db41c9-10aa-4ee0-b226-8083178a6391 · inbound

SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks cites this paper.

SiLIF: Structured State Space Model Dynamics and Parametrization for Spiking Neural Networks Resurrecting Recurrent Neural Networks for Long Sequences

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:07:15.254503Z

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 46de0cee-fe64-45a8-a0c2-e61b5445fbee · inbound

mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling cites this paper.

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

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T05:52:06.818812Z

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 e9aeb0b7-13da-4852-a0d2-5725dd2e16d2 · inbound

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

Scalable Memristive-Friendly Reservoir Computing for Time Series Classification Resurrecting Recurrent Neural Networks for Long Sequences

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:10.526571Z

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 5ae67641-f2a3-4b72-aae5-c7466aa54696 · 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 Resurrecting Recurrent Neural Networks for Long Sequences

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:42:32.349333Z

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 eb4c1dc6-b1b3-4c29-945d-0ebcb5911f0f · 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 Resurrecting Recurrent Neural Networks for Long Sequences

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-20T22:09:07.283213Z

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 ddfce801-466d-41a2-b8d3-3d518e7f532a · 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 Resurrecting Recurrent Neural Networks for Long Sequences

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:46.354431Z

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 f7f4d150-1dbf-47d4-8cc6-d656cd16af1f · inbound

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning cites this paper.

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning Resurrecting Recurrent Neural Networks for Long Sequences

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T14:44:45.414595Z

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 6ed5995c-3a39-4779-a151-65b108406d57 · inbound

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning cites this paper.

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning Resurrecting Recurrent Neural Networks for Long Sequences

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T16:06:03.674959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1273b7c3-6014-471a-9078-1ab59d6ef817 · inbound

Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees cites this paper.

Free Parametrization of L_2-Bounded Structured State-Space Controllers for Nonlinear Control with Stability Guarantees Resurrecting Recurrent Neural Networks for Long Sequences

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T12:10:53.844589Z

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 908aaf08-db1a-4296-ad6a-1b56f4da9b4b · 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 Resurrecting Recurrent Neural Networks for Long Sequences

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T12:29:51.718429Z

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 e20879da-abe8-4a52-9875-4630e7364d98 · inbound

Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns cites this paper.

Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns Resurrecting Recurrent Neural Networks for Long Sequences

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:29:59.831983Z

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.