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Paper Citation Record · LEDGER

FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.08035.

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

pith.paper-citation-record.v1
2407.08035 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:47:06.575966Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:56:47.631735Z

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 7d57bc16-1b97-4a00-8ca7-b46b2ade876b · inbound

Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset cites this paper.

Named-Entity Recognition in the Crime Domain (CrimeNER): Case Study and Dataset FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-02T19:28:59.953565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:28:59.953565Z digest=sha256:f78142a0b3a335d7a84f2b6c1d22591b14545c23fd9fca5d7a7436758a794dee

Observation 102aeb49-0f0c-41e6-81b7-01bd4721896f · inbound

Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition cites this paper.

Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-06-11T02:08:30.857564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T06:53:38.384323Z digest=sha256:393d01592b5975fa2bb703373e166ff3f846815b96965e42e1c8c2c18ecd397d

Observation c952776e-10c2-463f-a0c0-d7baf806f1ce · inbound

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach cites this paper.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-04T01:47:06.575966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:47:06.575966Z digest=sha256:6f6ec89492bf7a717084b63f5dd0ff2a09488fe5a5202ddffce526383651522f