Paper Citation Record · LEDGER

Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2306.11648.

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

pith.paper-citation-record.v1
2306.11648 v1

Coverage vector

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Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T16:00:22.991619Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T23:19:03.548128Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5353a3c8-3bbb-49d0-81af-79f0b3d31938 · inbound

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry cites this paper.

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T16:00:22.991619Z

Source-reported events for the cited work

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Observation b968f917-a696-46f1-ae1f-d11dcaa7c6e1 · inbound

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models cites this paper.

Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T15:35:44.109864Z

Source-reported events for the cited work

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Observation 42aa587d-8dc7-47bd-9e09-884a6319f07c · inbound

Smotrom tvoja pa ander drogoj verden! Resurrecting Dead Pidgin with Generative Models: Russenorsk Case Study cites this paper.

Smotrom tvoja pa ander drogoj verden! Resurrecting Dead Pidgin with Generative Models: Russenorsk Case Study Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:08:31.796562Z

Source-reported events for the cited work

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Observation 94911d3b-afb7-4106-97f8-76f5207560e9 · inbound

A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities cites this paper.

A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T18:24:26.555669Z

Source-reported events for the cited work

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Observation f1287663-edfe-4e76-b3bd-4c639ed8dc28 · inbound

DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations cites this paper.

DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:17:26.438684Z

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.

Observation e8c9ecbe-f483-4dfe-96b9-9f1e591cbada · inbound

DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations cites this paper.

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T12:08:58.262405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e526658-8be8-4bb6-a89f-12f7b723910d · inbound

Querying an astronomical database using large language models: the ALeRCE text-to-SQL system cites this paper.

Querying an astronomical database using large language models: the ALeRCE text-to-SQL system Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-03T23:19:03.550463Z

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.