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Paper Citation Record · LEDGER

Latent State Models of Training Dynamics

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2308.09543.

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

pith.paper-citation-record.v1
2308.09543 v3

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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T07:13:46.092398Z

measured 1 of 1 external citation measurements

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

Source: pith, 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

0
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 329a0a04-96e0-474b-967e-cfb95e7dd1f7 · inbound

Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights cites this paper.

Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights Latent State Models of Training Dynamics

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-01T07:13:45.857689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T07:13:45.857689Z digest=sha256:e13bbed998cb3dde358b7fd4530691333ebc17637603d72197ffe04638a84a4a

Observation 1d5df555-8adc-40e5-bcdd-460e2eed66d5 · inbound

Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights cites this paper.

Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights Latent State Models of Training Dynamics

Reference 2023

Resolution
metadata mismatch
local_arxiv, observed 2026-08-01T07:18:34.558183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-01T07:13:46.092398Z digest=sha256:459b66335a6bfefa2aa8d1f457a5685082ccd6c1b54d1a57330bc03ee689cb84