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Paper Citation Record · LEDGER

Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.01884.

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

pith.paper-citation-record.v1
2405.01884 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:02:13.126308Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T18:57:54.621963Z

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 29068ded-50b8-4657-b820-5acdf6651c52 · inbound

What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation cites this paper.

What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-12T05:02:13.126308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:02:13.126308Z digest=sha256:3f9e58dacc6d1da53b6ffc8b098ddf16dbf986f0a1138f06b1f92ca0b6c53d01

Observation 987b072c-decf-4373-8a15-ba66e27e13c2 · inbound

Enhancing User Intent for Recommendation Systems via Large Language Models cites this paper.

Enhancing User Intent for Recommendation Systems via Large Language Models Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-10T18:57:54.628530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-10T18:57:54.505496Z digest=sha256:b468eac20ed9d46145b0a3da1485254abf236b08d044a088de8811658074c61d