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Paper Citation Record · LEDGER

Exact Phase Transitions in Deep Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2205.12510.

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

pith.paper-citation-record.v1
2205.12510 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T07:13:47.557748Z

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

1
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a536cd7a-2011-4158-98e0-3a8690528c1b · 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 Exact Phase Transitions in Deep Learning

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-01T07:13:47.445005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T07:13:47.445005Z digest=sha256:5f95b6f6a84a32a1770a8202839714144506a46ac3d30a73f0e4b04e77364e46

Observation 873dcb82-c4b5-4ac5-9d91-cb2ffa41b94a · 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 Exact Phase Transitions in Deep Learning

Reference 2022

Resolution
verified exact
local_arxiv, observed 2026-08-01T07:18:33.780245Z

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=2026-08-01T07:13:47.557748Z digest=sha256:27fe9227820b807100d911d699b9fd9ac7f9091465c7462e71cfa6312d07a13e