{"as_of":"2026-08-10T19:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ea2ae645aae7af9c3d5b8503b0a29b796a535703767b02ed91a65fd7143e3ecd","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":"2026-08-10T06:31:04.303077+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":"2026-08-10T16:21:30.223204Z","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":"2026-06-28T18:42:29.107580Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2305.12090","last_updated":"2024-05-29T16:46:47Z","snapshot_observed_at":"2026-07-06T15:29:57.940337Z","submitted_at":"2023-05-20T04:32:59Z","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation","version":2},"cited_work":{"arxiv_id":"2305.12090","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.12090","snapshot_observed_at":"2026-06-28T18:42:29.107580Z","title":"Up5: Unbiased foun- dation model for fairness-aware recommendation","venue":null,"work_id":"802dfa65-2137-45a9-87cd-e55974d7b6f7","year":2023},"citing_paper":{"arxiv_id":"2411.10915","last_updated":"2026-05-01T02:07:46Z","snapshot_observed_at":"2026-07-06T19:51:28.494860Z","submitted_at":"2024-11-16T23:54:53Z","title":"Bias in Large Language Models: Origin, Evaluation, and Mitigation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-23T17:08:09.267577Z"},"links":{"cited_paper":"/paper/2305.12090","citing_paper":"/paper/2411.10915"},"observation_digest":"sha256:ad88972f2b9d80989d8c7c4ba285ba8ca8d8a4fcbf8c967b8d7a31e2079f8ed1","observation_id":"317e08f1-2663-423e-b77a-6148e249ead3","resolution":{"observed_at":"2026-05-23T17:08:12.434631Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12090","last_updated":"2024-05-29T16:46:47Z","snapshot_observed_at":"2026-07-06T15:29:57.940337Z","submitted_at":"2023-05-20T04:32:59Z","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12090","snapshot_observed_at":"2026-08-10T16:21:30.223204Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.13344","last_updated":"2025-01-23T03:05:13Z","snapshot_observed_at":"2026-08-10T16:38:17.127760Z","submitted_at":"2025-01-23T03:05:13Z","title":"Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T16:21:30.223204Z"},"links":{"cited_paper":"/paper/2305.12090","citing_paper":"/paper/2501.13344"},"observation_digest":"sha256:ad223091e18ab60051405b3cfcc0524bc67406479b527d376b4674eeffa419b9","observation_id":"512f701b-5513-4ed2-a6bd-5ad863a8f67e","resolution":{"observed_at":"2026-08-10T16:21:30.223204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12090","last_updated":"2024-05-29T16:46:47Z","snapshot_observed_at":"2026-07-06T15:29:57.940337Z","submitted_at":"2023-05-20T04:32:59Z","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12090","snapshot_observed_at":"2026-08-09T10:29:00.866543Z","title":"Up5: Unbiased foundation model for fairness-aware recommendation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02966","last_updated":"2025-02-05T08:07:04Z","snapshot_observed_at":"2026-08-09T10:21:28.975612Z","submitted_at":"2025-02-05T08:07:04Z","title":"FACTER: Fairness-Aware Conformal Thresholding and Prompt Engineering for Enabling Fair LLM-Based Recommender Systems","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-09T10:29:00.866543Z"},"links":{"cited_paper":"/paper/2305.12090","citing_paper":"/paper/2502.02966"},"observation_digest":"sha256:8b6dceebeb0a7188ce37935f9fda464ccfc931f1b529eb41786eba3359066616","observation_id":"f8c1b134-f22e-4e5c-8d00-9ca07672e497","resolution":{"observed_at":"2026-08-09T10:29:00.866543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12090","last_updated":"2024-05-29T16:46:47Z","snapshot_observed_at":"2026-07-06T15:29:57.940337Z","submitted_at":"2023-05-20T04:32:59Z","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation","version":2},"cited_work":{"arxiv_id":"2305.12090","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.12090","snapshot_observed_at":"2026-06-28T18:42:29.107580Z","title":"Up5: Unbiased foun- dation model for fairness-aware recommendation","venue":null,"work_id":"802dfa65-2137-45a9-87cd-e55974d7b6f7","year":2023},"citing_paper":{"arxiv_id":"2510.27157","last_updated":"2026-05-09T03:19:23Z","snapshot_observed_at":"2026-08-02T06:54:15.933170Z","submitted_at":"2025-10-31T04:02:58Z","title":"A Survey on Generative Recommendation: Data, Model, and Tasks","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-18T03:47:08.208082Z"},"links":{"cited_paper":"/paper/2305.12090","citing_paper":"/paper/2510.27157"},"observation_digest":"sha256:a81ad8db5ee0edf4713031ef02e62c4095c60b7be86ac0375caa92c56aacbfb1","observation_id":"e7cf5a1a-a546-4d04-a9da-7a2a0c50f126","resolution":{"observed_at":"2026-05-18T03:50:52.104697Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12090","last_updated":"2024-05-29T16:46:47Z","snapshot_observed_at":"2026-07-06T15:29:57.940337Z","submitted_at":"2023-05-20T04:32:59Z","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation","version":2},"cited_work":{"arxiv_id":"2305.12090","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.12090","snapshot_observed_at":"2026-06-28T18:42:29.107580Z","title":"Up5: Unbiased foun- dation model for fairness-aware recommendation","venue":null,"work_id":"802dfa65-2137-45a9-87cd-e55974d7b6f7","year":2023},"citing_paper":{"arxiv_id":"2605.16113","last_updated":"2026-05-15T15:58:10Z","snapshot_observed_at":"2026-08-05T03:54:45.109264Z","submitted_at":"2026-05-15T15:58:10Z","title":"DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-20T19:02:47.017761Z"},"links":{"cited_paper":"/paper/2305.12090","citing_paper":"/paper/2605.16113"},"observation_digest":"sha256:9bc5da2219b6202b1ee901d96c82e23e74a5111cf206b65435a3ad9c3f0784ea","observation_id":"a505b3c4-64cd-4e4b-a445-d8730af8d586","resolution":{"observed_at":"2026-05-20T19:03:39.478203Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12090","last_updated":"2024-05-29T16:46:47Z","snapshot_observed_at":"2026-07-06T15:29:57.940337Z","submitted_at":"2023-05-20T04:32:59Z","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation","version":2},"cited_work":{"arxiv_id":"2305.12090","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2305.12090","snapshot_observed_at":"2026-06-28T18:42:29.107580Z","title":"Up5: Unbiased foun- dation model for fairness-aware recommendation","venue":null,"work_id":"802dfa65-2137-45a9-87cd-e55974d7b6f7","year":2023},"citing_paper":{"arxiv_id":"2606.00540","last_updated":"2026-05-30T05:14:53Z","snapshot_observed_at":"2026-08-01T17:48:34.431096Z","submitted_at":"2026-05-30T05:14:53Z","title":"Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-06-28T18:41:06.636352Z"},"links":{"cited_paper":"/paper/2305.12090","citing_paper":"/paper/2606.00540"},"observation_digest":"sha256:3880f9fa266d03d98e8ccda4ecddd010212a3c0e188726cea7d7acbd9ce1ce21","observation_id":"a83e4d0b-5d22-4b35-9eee-5e28859af6ff","resolution":{"observed_at":"2026-06-28T18:42:29.109194Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2305.12090/citation-record","integrity":"/paper/2305.12090/integrity","json":"/paper/2305.12090/citation-record.json","paper":"/paper/2305.12090"},"outbound":[],"paper":{"arxiv_id":"2305.12090","last_updated":"2024-05-29T16:46:47Z","latest_version":2,"primary_category":"cs.IR","snapshot_observed_at":"2026-07-06T15:29:57.940337Z","submitted_at":"2023-05-20T04:32:59Z","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.12090."}