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Paper Citation Record · LEDGER

Large language models for generating rules, yay or nay?

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.06835.

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

pith.paper-citation-record.v1
2406.06835 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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:06:47.717726Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:38:58.249224Z

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 8bf0105a-6bcd-4dd2-a54f-624019fdf8ad · inbound

Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate cites this paper.

Improve Rule Retrieval and Reasoning with Self-Induction and Relevance ReEstimate Large language models for generating rules, yay or nay?

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-15T21:06:47.717726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:06:47.717726Z digest=sha256:7ac9029f0f312d17ea2c040b44bdf7b10c3e74e788670066eba1929998ea6b83

Observation c0336909-15c3-40f6-8f10-3a2620dd1a60 · inbound

RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules cites this paper.

RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules Large language models for generating rules, yay or nay?

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-03T21:38:58.250559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-03T21:29:48.169524Z digest=sha256:81f1775a58eabb95cc7d5a7f44acaa4a96e970786848c84dcd78c1ca40fbba0d