{"as_of":"2026-08-09T12:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f653ea136b321ebcff8ef6307efaac9c86e338b07f84c56d630a51b03b29fdef","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:35:44.109864Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T23:19:03.548128Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.11648","last_updated":"2023-06-20T16:16:56Z","snapshot_observed_at":"2026-08-07T22:44:57.786072Z","submitted_at":"2023-06-20T16:16:56Z","title":"Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11648","snapshot_observed_at":"2026-08-07T15:35:44.109864Z","title":"Harnessing the power of adversarial prompting and large language models for robust hypothesis generation in astronomy","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14599","last_updated":"2025-06-08T22:42:31Z","snapshot_observed_at":"2026-08-07T15:29:26.474390Z","submitted_at":"2025-05-20T16:49:40Z","title":"Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T15:35:44.109864Z"},"links":{"cited_paper":"/paper/2306.11648","citing_paper":"/paper/2505.14599"},"observation_digest":"sha256:0ce80f8d0a51887838efb5dfb8cc2f180737bb2407a035240610e4bd398bd472","observation_id":"b968f917-a696-46f1-ae1f-d11dcaa7c6e1","resolution":{"observed_at":"2026-08-07T15:35:44.109864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11648","last_updated":"2023-06-20T16:16:56Z","snapshot_observed_at":"2026-08-07T22:44:57.786072Z","submitted_at":"2023-06-20T16:16:56Z","title":"Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11648","snapshot_observed_at":"2026-08-07T12:08:31.796562Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.11065","last_updated":"2025-05-31T11:26:00Z","snapshot_observed_at":"2026-08-07T12:01:32.872313Z","submitted_at":"2025-05-31T11:26:00Z","title":"Smotrom tvoja pa ander drogoj verden! Resurrecting Dead Pidgin with Generative Models: Russenorsk Case Study","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T12:08:31.796562Z"},"links":{"cited_paper":"/paper/2306.11648","citing_paper":"/paper/2506.11065"},"observation_digest":"sha256:f186337c38fceaa264464ab2cc1ac8edaa8737a2a219de43c8b42e7a492aeb3d","observation_id":"42aa587d-8dc7-47bd-9e09-884a6319f07c","resolution":{"observed_at":"2026-08-07T12:08:31.796562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11648","last_updated":"2023-06-20T16:16:56Z","snapshot_observed_at":"2026-08-07T22:44:57.786072Z","submitted_at":"2023-06-20T16:16:56Z","title":"Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11648","snapshot_observed_at":"2026-08-06T18:24:26.555669Z","title":"Journal of Machine Learning Research, 25(70):1–53","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08425","last_updated":"2025-07-11T09:11:18Z","snapshot_observed_at":"2026-08-09T00:24:37.245521Z","submitted_at":"2025-07-11T09:11:18Z","title":"A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T18:24:26.555669Z"},"links":{"cited_paper":"/paper/2306.11648","citing_paper":"/paper/2507.08425"},"observation_digest":"sha256:bf9f8961a36a084a95e7a8dd029e73943e5b440c91a939d3f6bcc0bb3e0b2e5b","observation_id":"94911d3b-afb7-4106-97f8-76f5207560e9","resolution":{"observed_at":"2026-08-06T18:24:26.555669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11648","last_updated":"2023-06-20T16:16:56Z","snapshot_observed_at":"2026-08-07T22:44:57.786072Z","submitted_at":"2023-06-20T16:16:56Z","title":"Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy","version":1},"cited_work":{"arxiv_id":"2306.11648","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11648","snapshot_observed_at":"2026-07-03T23:19:03.548128Z","title":"2023 , journal =","venue":null,"work_id":"42307d8c-87c3-468d-8a84-2b17c1a0c176","year":2023},"citing_paper":{"arxiv_id":"2606.08532","last_updated":"2026-07-23T07:35:48Z","snapshot_observed_at":"2026-08-06T17:36:00.143791Z","submitted_at":"2026-06-07T09:26:03Z","title":"DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T19:00:15.920341Z"},"links":{"cited_paper":"/paper/2306.11648","citing_paper":"/paper/2606.08532"},"observation_digest":"sha256:a84176a1874b9237687fd6de14f1325d033bba7224de66b72c4127a1c7082a04","observation_id":"f1287663-edfe-4e76-b3bd-4c639ed8dc28","resolution":{"observed_at":"2026-07-02T22:17:26.438684Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11648","last_updated":"2023-06-20T16:16:56Z","snapshot_observed_at":"2026-08-07T22:44:57.786072Z","submitted_at":"2023-06-20T16:16:56Z","title":"Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11648","snapshot_observed_at":"2026-08-02T12:08:58.262405Z","title":"Ciucă, Y.-S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.08532","last_updated":"2026-07-23T07:35:48Z","snapshot_observed_at":"2026-08-06T17:36:00.143791Z","submitted_at":"2026-06-07T09:26:03Z","title":"DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations","version":5},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T12:08:58.262405Z"},"links":{"cited_paper":"/paper/2306.11648","citing_paper":"/paper/2606.08532"},"observation_digest":"sha256:5c41ba6897c7f04d8b1273a532f9d83aa187bc8c65094e2dd11c0e39bfb9e9f3","observation_id":"e8c9ecbe-f483-4dfe-96b9-9f1e591cbada","resolution":{"observed_at":"2026-08-02T12:08:58.262405Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11648","last_updated":"2023-06-20T16:16:56Z","snapshot_observed_at":"2026-08-07T22:44:57.786072Z","submitted_at":"2023-06-20T16:16:56Z","title":"Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy","version":1},"cited_work":{"arxiv_id":"2306.11648","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.11648","snapshot_observed_at":"2026-07-03T23:19:03.548128Z","title":"2023 , journal =","venue":null,"work_id":"42307d8c-87c3-468d-8a84-2b17c1a0c176","year":2023},"citing_paper":{"arxiv_id":"2606.18108","last_updated":"2026-06-16T16:12:16Z","snapshot_observed_at":"2026-08-03T18:33:14.007769Z","submitted_at":"2026-06-16T16:12:16Z","title":"Querying an astronomical database using large language models: the ALeRCE text-to-SQL system","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-06-26T22:35:44.328740Z"},"links":{"cited_paper":"/paper/2306.11648","citing_paper":"/paper/2606.18108"},"observation_digest":"sha256:a44f3be31077cf9dfa8db35133a92087169848a9b9a55d3eb733ea41d319363b","observation_id":"3e526658-8be8-4bb6-a89f-12f7b723910d","resolution":{"observed_at":"2026-07-03T23:19:03.550463Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.11648/citation-record","integrity":"/paper/2306.11648/integrity","json":"/paper/2306.11648/citation-record.json","paper":"/paper/2306.11648"},"outbound":[],"paper":{"arxiv_id":"2306.11648","last_updated":"2023-06-20T16:16:56Z","latest_version":1,"primary_category":"astro-ph.IM","snapshot_observed_at":"2026-08-07T22:44:57.786072Z","submitted_at":"2023-06-20T16:16:56Z","title":"Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2306.11648."}