Pith. sign in

Paper Citation Record · LEDGER

Feature emergence via margin maximization: case studies in algebraic tasks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.07568.

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

pith.paper-citation-record.v1
2311.07568 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:31:21.714527Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T15:21:09.329425Z

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 b25d254f-e221-4b1f-9701-47174cb8b4a0 · inbound

The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations cites this paper.

The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations Feature emergence via margin maximization: case studies in algebraic tasks

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T19:31:21.714527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:31:21.714527Z digest=sha256:75e06910f9ea357ec33ff4c9b37bdca051bc95cde4f241fad5a0db67dad255da

Observation 6d53c3a0-b920-4b86-ab6c-d8e7707d828e · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Feature emergence via margin maximization: case studies in algebraic tasks

Reference 203

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:21:09.335176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-09T20:11:17.616190Z digest=sha256:6dfe7f9c591fb988e3b0f00d819949c3bbb3cbbfb2d4faec5136c6ffbfbabfd4