{"as_of":"2026-08-07T23:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f4181165e5d6a80d541a8993de9e2e13c71cbc47ee91160c9c91c8f5320b0a41","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:04:26.384046Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T22:43:42.349733Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2312.06820","last_updated":"2023-12-11T20:12:46Z","snapshot_observed_at":"2026-08-07T17:10:54.639386Z","submitted_at":"2023-12-11T20:12:46Z","title":"Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.06820","snapshot_observed_at":"2026-08-07T15:04:26.384046Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16532","last_updated":"2026-06-30T09:30:42Z","snapshot_observed_at":"2026-08-07T17:11:24.309969Z","submitted_at":"2025-05-22T11:21:51Z","title":"Causal-Invariant Cross-Domain Out-of-Distribution Recommendation","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:04:26.384046Z"},"links":{"cited_paper":"/paper/2312.06820","citing_paper":"/paper/2505.16532"},"observation_digest":"sha256:9275a43031059c13f4030ad7588b435a5bdaf959d59c89b357b23322e7a2ef96","observation_id":"fdfcd495-7f76-4cb4-97d1-0e333fe46cb2","resolution":{"observed_at":"2026-08-07T15:04:26.384046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.06820","last_updated":"2023-12-11T20:12:46Z","snapshot_observed_at":"2026-08-07T17:10:54.639386Z","submitted_at":"2023-12-11T20:12:46Z","title":"Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning","version":1},"cited_work":{"arxiv_id":"2312.06820","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.06820","snapshot_observed_at":"2026-08-06T22:43:42.349733Z","title":"Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning","venue":"cs.AI","work_id":"1ba4985d-ff23-42c0-a427-ec59c3e84cf9","year":2023},"citing_paper":{"arxiv_id":"2507.02928","last_updated":"2025-06-26T03:49:13Z","snapshot_observed_at":"2026-08-06T22:34:19.035341Z","submitted_at":"2025-06-26T03:49:13Z","title":"Mitigating Hidden Confounding by Progressive Confounder Imputation via Large Language Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:43:37.005080Z"},"links":{"cited_paper":"/paper/2312.06820","citing_paper":"/paper/2507.02928"},"observation_digest":"sha256:0f1469cd99f2d8d583eb9a2518cb578cd38767c6d649efb487ee1ad8653fee2d","observation_id":"a03bcf97-2075-4062-a7cb-fdf48d4263fb","resolution":{"observed_at":"2026-08-06T22:43:42.355253Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2312.06820/citation-record","integrity":"/paper/2312.06820/integrity","json":"/paper/2312.06820/citation-record.json","paper":"/paper/2312.06820"},"outbound":[],"paper":{"arxiv_id":"2312.06820","last_updated":"2023-12-11T20:12:46Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T17:10:54.639386Z","submitted_at":"2023-12-11T20:12:46Z","title":"Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2312.06820."}