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Paper Citation Record · LEDGER

Efficiently Escaping Saddle Points in Bilevel Optimization

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.03684.

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

pith.paper-citation-record.v1
2202.03684 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 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T00:57:26.828482Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T01:58:29.012902Z

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 e4a1d37b-97a0-462e-a244-126bed3f8979 · inbound

On the Nature of Regularity Assumptions in Bilevel Optimization with Constrained Lower-level Problem cites this paper.

On the Nature of Regularity Assumptions in Bilevel Optimization with Constrained Lower-level Problem Efficiently Escaping Saddle Points in Bilevel Optimization

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-15T01:58:29.014398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

source=arxiv_source observed=2026-05-15T01:57:37.847553Z digest=sha256:6d9ff8d09d2be0fbe62ba379f2e4b20e512e007478b05ac72c5410928fa6289a

Observation b042a0d4-6472-48ec-92d2-6a2712b7157d · inbound

Optimization under Persistent State-Dependent Bias: Gradient-based Method and Complexity Analysis cites this paper.

Optimization under Persistent State-Dependent Bias: Gradient-based Method and Complexity Analysis Efficiently Escaping Saddle Points in Bilevel Optimization

Reference 264

Resolution
unresolved
no resolver link, observed 2026-08-01T00:57:26.828482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T00:57:26.828482Z digest=sha256:02bdbce28478d96175eb2da49fc2c3339200672c0d09c89c12b5d273c98d45c2