{"as_of":"2026-08-18T03:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:76cbf234e46e47efbc8aac9ea974a4b4dbd25a58205972b1369ca23f092fe05b","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":"2026-08-17T06:30:58.91139+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":"2026-08-06T16:27:57.297526Z","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":"2026-05-11T03:50:54.240382Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.04181","last_updated":"2024-07-04T22:55:02Z","snapshot_observed_at":"2026-08-16T13:36:56.026592Z","submitted_at":"2024-07-04T22:55:02Z","title":"Orchestrating LLMs with Different Personalizations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04181","snapshot_observed_at":"2026-08-06T16:27:57.297526Z","title":"Interpretable preferences via multi-objective reward modeling and mixture-of-experts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.13541","last_updated":"2025-07-17T21:21:54Z","snapshot_observed_at":"2026-08-14T12:31:59.520847Z","submitted_at":"2025-07-17T21:21:54Z","title":"PrefPalette: Personalized Preference Modeling with Latent Attributes","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:27:57.297526Z"},"links":{"cited_paper":"/paper/2407.04181","citing_paper":"/paper/2507.13541"},"observation_digest":"sha256:6abe7d02c7eed71d3f61d6f595eabdfd5af0e98ce048e0b096baaee8f05fc91e","observation_id":"e309b76b-c133-4107-ad35-377004c6953a","resolution":{"observed_at":"2026-08-06T16:27:57.297526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04181","last_updated":"2024-07-04T22:55:02Z","snapshot_observed_at":"2026-08-16T13:36:56.026592Z","submitted_at":"2024-07-04T22:55:02Z","title":"Orchestrating LLMs with Different Personalizations","version":1},"cited_work":{"arxiv_id":"2407.04181","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.04181","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2407.04181 , year=","venue":null,"work_id":"918151de-29fb-4f6f-a288-8c567bc762da","year":null},"citing_paper":{"arxiv_id":"2605.07162","last_updated":"2026-05-08T02:47:30Z","snapshot_observed_at":"2026-08-16T23:49:42.481692Z","submitted_at":"2026-05-08T02:47:30Z","title":"CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-11T02:16:28.593349Z"},"links":{"cited_paper":"/paper/2407.04181","citing_paper":"/paper/2605.07162"},"observation_digest":"sha256:6b67a018699f144bfa7e2380f1a2d8c7f908f192ef4c61eaadb1b574dfca9174","observation_id":"f633b238-b73a-47a4-9b2b-bd746d90cbb5","resolution":{"observed_at":"2026-05-11T03:50:54.253185Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2407.04181/citation-record","integrity":"/paper/2407.04181/integrity","json":"/paper/2407.04181/citation-record.json","paper":"/paper/2407.04181"},"outbound":[],"paper":{"arxiv_id":"2407.04181","last_updated":"2024-07-04T22:55:02Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-16T13:36:56.026592Z","submitted_at":"2024-07-04T22:55:02Z","title":"Orchestrating LLMs with Different Personalizations"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.04181."}