Paper Citation Record · LEDGER

Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.01992.

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

pith.paper-citation-record.v1
2406.01992 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T01:38:11.039361Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T07:25:28.379934Z

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 de8b0355-d873-4599-b997-d572114e887d · inbound

Alternating Gradient-Type Algorithm for Bilevel Optimization with Inexact Lower-Level Solutions via Moreau Envelope-based Reformulation cites this paper.

Alternating Gradient-Type Algorithm for Bilevel Optimization with Inexact Lower-Level Solutions via Moreau Envelope-based Reformulation Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-23T07:25:28.382184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c6d46870-8a6a-444a-9d1c-c3032cbdd9a3 · inbound

On the Stability and Generalization of First-order Bilevel Minimax Optimization cites this paper.

On the Stability and Generalization of First-order Bilevel Minimax Optimization Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:36:08.397108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 99094965-f619-413c-ac3f-08e8b7bbd924 · inbound

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets cites this paper.

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions

Reference 175

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T03:11:18.811128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 16a0883b-247d-4b8a-bc9e-6af45f97c09a · 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 Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions

Reference 286

Resolution: unresolved; no resolver link, observed 2026-08-01T00:57:28.693196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60dbdb57-9856-44df-9558-af99d04b24e2 · inbound

Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity cites this paper.

Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T01:38:11.039361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.