{"as_of":"2026-08-13T11:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ffd7bb2c0ef2f373a4a39123c77883500ddbd1ab85dc5998092768488258873c","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T05:13:50.115582Z","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":"2026-05-22T12:41:33.456655Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.02115","last_updated":"2024-01-04T07:50:24Z","snapshot_observed_at":"2026-08-13T04:49:11.555432Z","submitted_at":"2024-01-04T07:50:24Z","title":"Using LLM to select the right SQL Query from candidates","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02115","snapshot_observed_at":"2026-08-12T05:13:50.115582Z","title":"Using llm to select the right sql query from candidates.arXiv preprint arXiv:2401.02115, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00639","last_updated":"2025-06-02T15:22:19Z","snapshot_observed_at":"2026-08-12T12:26:41.250733Z","submitted_at":"2024-12-01T01:36:41Z","title":"Needle: A Generative AI-Powered Multi-modal Database for Answering Complex Natural Language Queries","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T05:13:50.115582Z"},"links":{"cited_paper":"/paper/2401.02115","citing_paper":"/paper/2412.00639"},"observation_digest":"sha256:78d03e07bfd6a492a96194983938d4bb7e49d2de841eddeab7e44b0e8a1e3e46","observation_id":"d148382c-1d54-40ea-a187-a6f4d78989a5","resolution":{"observed_at":"2026-08-12T05:13:50.115582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02115","last_updated":"2024-01-04T07:50:24Z","snapshot_observed_at":"2026-08-13T04:49:11.555432Z","submitted_at":"2024-01-04T07:50:24Z","title":"Using LLM to select the right SQL Query from candidates","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02115","snapshot_observed_at":"2026-08-07T13:12:38.877153Z","title":"Using llm to select the right sql query from candidates","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.23838","last_updated":"2025-05-28T13:23:38Z","snapshot_observed_at":"2026-08-09T21:29:24.735992Z","submitted_at":"2025-05-28T13:23:38Z","title":"Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T13:12:38.877153Z"},"links":{"cited_paper":"/paper/2401.02115","citing_paper":"/paper/2505.23838"},"observation_digest":"sha256:d512cb138f214e8a451a97ea4c9f7c507191fb05a84eff97a22c001d3012ad52","observation_id":"e63fac82-c76c-488b-8b28-780e46ac0cc5","resolution":{"observed_at":"2026-08-07T13:12:38.877153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02115","last_updated":"2024-01-04T07:50:24Z","snapshot_observed_at":"2026-08-13T04:49:11.555432Z","submitted_at":"2024-01-04T07:50:24Z","title":"Using LLM to select the right SQL Query from candidates","version":1},"cited_work":{"arxiv_id":"2401.02115","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.02115","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2401.02115 , year=","venue":null,"work_id":"014c685a-a600-43c3-b809-3c594c22f2b0","year":2024},"citing_paper":{"arxiv_id":"2509.12610","last_updated":"2026-03-03T06:03:52Z","snapshot_observed_at":"2026-08-12T15:57:37.637615Z","submitted_at":"2025-09-16T03:18:06Z","title":"ScaleDoc: Scaling LLM-based Predicates over Large Document Collections","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-22T12:39:39.823436Z"},"links":{"cited_paper":"/paper/2401.02115","citing_paper":"/paper/2509.12610"},"observation_digest":"sha256:ea2621b2ef0393a6467fce59555d1f33c0e7216c76dc4e18566cc78248caa7d6","observation_id":"862c3349-58d6-4dc8-a0f8-6049481873be","resolution":{"observed_at":"2026-05-22T12:41:33.461537Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02115","last_updated":"2024-01-04T07:50:24Z","snapshot_observed_at":"2026-08-13T04:49:11.555432Z","submitted_at":"2024-01-04T07:50:24Z","title":"Using LLM to select the right SQL Query from candidates","version":1},"cited_work":{"arxiv_id":"2401.02115","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.02115","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2401.02115 , year=","venue":null,"work_id":"014c685a-a600-43c3-b809-3c594c22f2b0","year":2024},"citing_paper":{"arxiv_id":"2605.04065","last_updated":"2026-05-07T04:49:30Z","snapshot_observed_at":"2026-08-11T02:56:20.139486Z","submitted_at":"2026-04-11T07:26:04Z","title":"Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs","version":2},"reference_index":214,"source":"arxiv_source","source_observed_at":"2026-05-10T16:58:10.013475Z"},"links":{"cited_paper":"/paper/2401.02115","citing_paper":"/paper/2605.04065"},"observation_digest":"sha256:fd4c138e18fadcf6340264d4b524be667b85d872555a1707fa29f49f8a7f43d3","observation_id":"a63a1541-30a7-411f-a72f-34f725c4a4f1","resolution":{"observed_at":"2026-05-11T07:45:59.467796Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02115","last_updated":"2024-01-04T07:50:24Z","snapshot_observed_at":"2026-08-13T04:49:11.555432Z","submitted_at":"2024-01-04T07:50:24Z","title":"Using LLM to select the right SQL Query from candidates","version":1},"cited_work":{"arxiv_id":"2401.02115","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.02115","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2401.02115 , year=","venue":null,"work_id":"014c685a-a600-43c3-b809-3c594c22f2b0","year":2024},"citing_paper":{"arxiv_id":"2605.04066","last_updated":"2026-05-07T04:57:40Z","snapshot_observed_at":"2026-08-13T05:10:20.778134Z","submitted_at":"2026-04-11T07:34:59Z","title":"Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning","version":2},"reference_index":199,"source":"arxiv_source","source_observed_at":"2026-05-10T16:51:19.555272Z"},"links":{"cited_paper":"/paper/2401.02115","citing_paper":"/paper/2605.04066"},"observation_digest":"sha256:bfc229318dec3cb0c93064f2cf2de2c832b3ee7c7ee43fd319cb063dc3058ae2","observation_id":"5f124b19-da56-404e-916e-0e1a0aadc8f6","resolution":{"observed_at":"2026-05-11T08:01:00.644346Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2401.02115/citation-record","integrity":"/paper/2401.02115/integrity","json":"/paper/2401.02115/citation-record.json","paper":"/paper/2401.02115"},"outbound":[],"paper":{"arxiv_id":"2401.02115","last_updated":"2024-01-04T07:50:24Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T04:49:11.555432Z","submitted_at":"2024-01-04T07:50:24Z","title":"Using LLM to select the right SQL Query from candidates"},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.02115."}