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Paper Citation Record · LEDGER

Near-Optimal Solutions of Constrained Learning Problems

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.11844.

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

pith.paper-citation-record.v1
2403.11844 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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T05:26:06.829382Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:16:16.358373Z

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 6c2c36fa-b4b7-426d-a5b3-cf369e2460d0 · inbound

Everywhere Learning: Artificial Intelligence with Pointwise Constraints cites this paper.

Everywhere Learning: Artificial Intelligence with Pointwise Constraints Near-Optimal Solutions of Constrained Learning Problems

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-01T22:16:16.359721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-28T15:34:51.581338Z digest=sha256:3af818240c563c6b21bbf3e8429ceef3a4f9beb8520c20bce0b732515613eb36

Observation 3cf03e89-576e-494c-b7aa-fca5776f860a · inbound

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints cites this paper.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Near-Optimal Solutions of Constrained Learning Problems

Reference 22

Resolution
unresolved
no resolver link, observed 2026-07-13T05:26:06.829382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T05:26:06.829382Z digest=sha256:bf6c6f05afb2bf895083116d8947f3adf0391e70e19a1efcb37e7758411b1dc6