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Paper Citation Record · LEDGER

Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2305.15541.

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

pith.paper-citation-record.v1
2305.15541 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:33:04.978077Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:18:43.459078Z

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 346e63d9-9325-4271-ad06-06abd9fb1153 · inbound

Do Large Language Models Excel in Complex Logical Reasoning with Formal Language? cites this paper.

Do Large Language Models Excel in Complex Logical Reasoning with Formal Language? Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:17.891901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:55:17.891901Z digest=sha256:bd63f70499cce66910a2afb9abba79604ea950782c65ea7cb6164e2e57bcf98c

Observation 385b9e34-d4a9-4db1-94f0-13e0653191e1 · inbound

Breaking the Myth: Can Small Models Infer Postconditions Too? cites this paper.

Breaking the Myth: Can Small Models Infer Postconditions Too? Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-06T17:41:43.597810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:41:43.597810Z digest=sha256:395326e23518b5e8e6d12b937e897db1cda648d1ebfbdf95bea8a6a737dc63c2

Observation 73a5f6b2-5341-4cc7-844e-612d58fc6265 · inbound

Semantic-Aware Logical Reasoning via a Semiotic Framework cites this paper.

Semantic-Aware Logical Reasoning via a Semiotic Framework Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:01:23.908007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-18T12:57:45.584017Z digest=sha256:7f9a435fea09d08798717fa12500d7a2b8e1d2bdd44cb9ae0c3e9b5121569529

Observation b6024eda-90c6-4a28-91fc-de5ba76ec7b9 · inbound

From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS cites this paper.

From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:06:04.999357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T04:08:38.327402Z digest=sha256:689c7b7f13eb2d470fb52e39c006867aeccf4d5e9a56a5af9020dcec40a6977c

Observation 76e2e688-96e7-4e20-8ff7-dc89ff4a3155 · inbound

ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards cites this paper.

ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:27:22.730404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-27T20:22:27.538227Z digest=sha256:38c8c203a773ad21e003a34155cabde05785b863ae3b3c92d4f5ca1008c7bd4f

Observation 643cf3db-b54f-413a-aaa5-5b185e095a13 · inbound

Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning cites this paper.

Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T17:18:43.460636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-27T04:24:11.949884Z digest=sha256:f3895068eb09c36ff22449eeff9c95f7b29a1c89f138b0f640bfd321ef935a33

Observation ae7b1701-c4cc-4393-a214-0c9d8fb73715 · inbound

Semiotic logical hexagon theory for LLM logical reasoning cites this paper.

Semiotic logical hexagon theory for LLM logical reasoning Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T15:33:04.978077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T15:33:04.978077Z digest=sha256:1e4bb23dd09c6d76eb16761436cec4e6311ca2d049fea91fc7e963645a9ac691

Observation e11008e5-5b81-47c7-b8f4-e4a5470ac164 · inbound

Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse cites this paper.

Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-12T16:59:19.276960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T16:59:19.276960Z digest=sha256:858c5cece29599282e73f905e7cb616eda7fe02046a99b2824d5d821ae4b89ad

Observation 3e955c01-0843-4ec5-add9-988d898f3aed · inbound

Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse cites this paper.

Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation

Reference 125

Resolution
unresolved
no resolver link, observed 2026-08-15T14:19:34.728438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T14:19:34.728438Z digest=sha256:35f767334db159ccd3dbca9691dcaa30aaeb6284c35b4d19faca48828ab08f84