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Paper Citation Record · LEDGER

On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.07125.

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

pith.paper-citation-record.v1
2306.07125 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:26:47.032432Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T06:32:38.824996Z

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 314cbbbc-bfa3-41d4-88ae-0cbd085a4484 · inbound

A ghost mechanism: An analytical model of abrupt learning in recurrent networks cites this paper.

A ghost mechanism: An analytical model of abrupt learning in recurrent networks On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-23T06:32:38.827414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-23T06:31:21.006691Z digest=sha256:9dc1abca1019362d32992e260a75e6ca84bf1cfb609d431ce662f9fdf06ccdd8

Observation 7cd0cf58-a310-4330-be56-1d591ea47c32 · inbound

Understanding and controlling the geometry of memory organization in RNNs cites this paper.

Understanding and controlling the geometry of memory organization in RNNs On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-08T13:26:47.032432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:26:47.032432Z digest=sha256:d7790e736ffc16ab08e4dea9572069f41b2e74c10287c05e359a4b5927383b64