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Paper Citation Record · LEDGER

Poly-time universality and limitations of deep learning

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2001.02992.

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

pith.paper-citation-record.v1
2001.02992 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 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:49:49.716245Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T20:40:36.189460Z

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 961ea479-e4f8-4721-b7c4-e147025c7266 · inbound

Hardness of Learning Fixed Parities with Neural Networks cites this paper.

Hardness of Learning Fixed Parities with Neural Networks Poly-time universality and limitations of deep learning

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-10T22:49:49.716245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:49:49.716245Z digest=sha256:fa8614b1c3d4f8c8d6f94a149e0d7261e83d331a5168ada184b90108f6692b92

Observation 6f7ae1fc-bcf6-46cc-9f20-2ea125d7d72d · inbound

Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why Poly-time universality and limitations of deep learning

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-21T20:40:36.191105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-21T20:38:18.005002Z digest=sha256:99245da9a0c7804ee379ab67b12c5e4675508532db99f2ae75c96912968ac54d