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Paper Citation Record · LEDGER

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

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 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 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:18:30.679852Z

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 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.

source=pdf_text observed=2026-08-09T20:18:30.679852Z digest=sha256:8b3cbcd54764ecf33aea3ad653ae5ee0fb1b2812a169aaa1bf209ef6470a28ca

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T19:22:44.376784Z digest=sha256:4c8b233cef6d310e4ecd0c53926c15ce300c224242a3f7ebae4cd45e9c06c0ec

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 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T10:59:30.755424Z digest=sha256:f723cedd86ff98499b8b4cd9b170e5b07f4b2e4e222c01fb46bb2ffdb53c94be

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-09T20:11:17.616190Z digest=sha256:ddf074666b25673370a37ff081faab603333eb731aee4104cc061b2a6f17b774

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 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-08T17:47:09.591001Z digest=sha256:0f646258526440f10219e513cf4029cb6ca3ff9062b136609922d2e9556a22ab