Paper Citation Record · LEDGER

Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2408.10920.

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

pith.paper-citation-record.v1
2408.10920 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:41:17.917288Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T19:22:49.069783Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
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 25edca5c-9dd1-435d-bbb7-afeee542aa90 · inbound

ICLR: In-Context Learning of Representations cites this paper.

ICLR: In-Context Learning of Representations Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T23:24:01.433607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1455651e-362a-4beb-a8c4-7d1c26ced448 · inbound

Low-Rank Adapting Models for Sparse Autoencoders cites this paper.

Low-Rank Adapting Models for Sparse Autoencoders Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.679852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d85f121-2957-482e-8de5-c751fb6d4f24 · inbound

Understanding sparse autoencoder scaling in the presence of feature manifolds cites this paper.

Understanding sparse autoencoder scaling in the presence of feature manifolds Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T16:41:17.917288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6e7bcdd-8e7a-40a9-b2d6-871bc31b8ff7 · inbound

AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting cites this paper.

AR-KAN: Autoregressive-Weight-Enhanced Kolmogorov-Arnold Network for Time Series Forecasting Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:22:49.072343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bc6bd27d-abe4-4d4e-8bd8-704f17d0bcb8 · inbound

Predicting Where Steering Vectors Succeed cites this paper.

Predicting Where Steering Vectors Succeed Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:00:03.819952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 338005e0-bfca-497c-b4bc-907e44ef3db1 · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 221

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:21:09.098220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3c095d9b-3a1d-4d16-8985-eb2f86ed1c14 · inbound

Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior cites this paper.

Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 207

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:16:07.210281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.