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Paper Citation Record · LEDGER

Low-rank bias, weight decay, and model merging in neural networks

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.17340.

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

pith.paper-citation-record.v1
2502.17340 v2

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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:45.753110Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:06:56.078942Z

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 378b8d95-8e07-44af-a729-c5aba2b9918a · inbound

Deciphering Two Training Clocks in Grokking via Deep Linear Network Theory with Conditional ReLU Reduction cites this paper.

Deciphering Two Training Clocks in Grokking via Deep Linear Network Theory with Conditional ReLU Reduction Low-rank bias, weight decay, and model merging in neural networks

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:06:56.080208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-28T02:26:16.631418Z digest=sha256:2c4b7fb7d94fbd75fd0b055c9074e8480724f292964110f21c6eb06023a564df

Observation 4ae5cb14-3c04-4f06-90d1-2cbfd84ed1c5 · inbound

Cascading Through the Hierarchy: Regularizer-Induced Feature Detection as Phase Transitions in Deep Linear Neural Networks cites this paper.

Cascading Through the Hierarchy: Regularizer-Induced Feature Detection as Phase Transitions in Deep Linear Neural Networks Low-rank bias, weight decay, and model merging in neural networks

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-15T14:39:45.753110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:39:45.753110Z digest=sha256:03cb9d53dcf5bf96a5fcb21deb8ccdd295cc5d23dce01114a7f7178b136a020c