Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How well can a large language model explain business processes as perceived by users?
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.12846.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T05:59:50.397433Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T08:21:00.783847Z
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 2528f702-388b-483d-a9b0-154bc095b839 · inbound
Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization cites this paper.
Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization How well can a large language model explain business processes as perceived by users?
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:59:50.397433Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a3447152-c87e-4038-9168-331592fc6099 · inbound
SAGE: A Service Agent Graph-guided Evaluation Benchmark cites this paper.
SAGE: A Service Agent Graph-guided Evaluation Benchmark How well can a large language model explain business processes as perceived by users?
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:21:00.788347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.