{"as_of":"2026-08-08T12:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9406813f9df271881e41d60297b1f93aed89e8dab1a09a7294889eccb0653e8c","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:07:58.191210Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.06334/citation-record","integrity":"/paper/2506.06334/integrity","json":"/paper/2506.06334/citation-record.json","paper":"/paper/2506.06334"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:08:01.381953Z","title":"What kind of news gatekeepers do we want machines to be? filter bubbles, fragmentation, and the normative dimensions of algorithmic recommendations","venue":null,"work_id":"b1d18846-17b3-4acd-81e7-1f70fa15cbc2","year":2019},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.148680Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:5740ff9017ab3393e9e988043168b284b0e79086420a335e6c1741c0df05ff66","observation_id":"8e80284e-45ef-4037-a7a8-c89666075b23","resolution":{"observed_at":"2026-08-07T12:08:01.442545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:08:01.213254Z","title":"Theoretical perspectives on user engagement","venue":null,"work_id":"04070308-cb25-40eb-bfc4-539ba6dd97c8","year":2016},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.260843Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:14c64bbf9b84f499e2919198d3f0a353a5018e0a4a8a7e9f1daab4ca5933b4ea","observation_id":"437362b0-d1b9-49f7-8cbb-7e7e3107d003","resolution":{"observed_at":"2026-08-07T12:08:01.306020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:08:01.030987Z","title":"Problems of monetary management: the UK experience","venue":null,"work_id":"90105787-597a-4817-a627-d179d1d9098f","year":1984},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.371290Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:f184f41a2adbd5f6f16db7cec855ea519ddd682f50aa474c6ae9b65d245c9911","observation_id":"cd80c61f-8ff8-4328-a84e-11de269d6710","resolution":{"observed_at":"2026-08-07T12:08:01.114261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:08:00.852366Z","title":"Preference-based policy learning","venue":null,"work_id":"07c5f53c-a1fa-45e4-9ef1-0b113744de8f","year":2011},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.499628Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:d4d43c742623665b683e7550a6c7b488b8956b479320636a9a8b5a03fa882529","observation_id":"ec69b33a-8464-40bb-abc1-19ce6958dc74","resolution":{"observed_at":"2026-08-07T12:08:00.916435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:08:00.679715Z","title":"Training language models to follow instructions with human feedback","venue":null,"work_id":"f866188a-8e5a-4187-81f6-269f50736cfa","year":2022},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.604349Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:9f4f223b18b8583d77044fd1ceff137b3008dc27a4cc17fa20343d64cbea5c6f","observation_id":"8875f411-a565-4bb6-a814-8ea424681816","resolution":{"observed_at":"2026-08-07T12:08:00.747194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:08:00.517531Z","title":"Pairwise learning to rank by neural networks revisited: Reconstruction, theoretical analysis and practical performance","venue":null,"work_id":"443a592b-ead3-47a0-a291-443dbdb4091f","year":2020},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.687646Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:41988af7ffc1689b7a3c843c1cd460a858e1226420ca6a610aafe5d9adc33639","observation_id":"426d8c63-96ed-4f3c-8ce2-78e8d8170bb7","resolution":{"observed_at":"2026-08-07T12:08:00.596679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:08:00.303246Z","title":"A contextual-bandit approach to personalized news article recommendation","venue":null,"work_id":"30c6528b-fd86-4515-9dc4-3b4d874d65ca","year":2010},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.755445Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:98392e2fe1ea331a3c3feafb4b4a67290d6f1d1656774d0bc5521f84bea0655e","observation_id":"521da1b9-9423-45cc-b054-8fa1e8a50ba0","resolution":{"observed_at":"2026-08-07T12:08:00.418140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:08:00.125854Z","title":"A machine learning approach for online automated optimization of super-resolution optical microscopy","venue":null,"work_id":"b4f26560-2d43-49b2-b383-e77c0e1aaa23","year":2018},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.849361Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:dc208d35ecb9cac0a10954763f4b25ee5d668cb025f2992072381613b867e064","observation_id":"d9137e50-bab1-454b-947c-caf6f8746786","resolution":{"observed_at":"2026-08-07T12:08:00.199984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:59.942458Z","title":"Large scale learning to rank","venue":null,"work_id":"05ca156a-04a3-4a9f-a01c-33b36236629b","year":2009},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:56.964094Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:469bdf92212ad6833a60f3f927f70c9d224ec50f23b963fefdcf3c90dc476b3a","observation_id":"ff7c0eae-f0d3-45ea-84f1-5530bac881f1","resolution":{"observed_at":"2026-08-07T12:08:00.024913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:59.775843Z","title":"Power law distributions in information science: Making the case for logarithmic binning","venue":null,"work_id":"c65295dd-28f1-4d57-94e0-5b27d7be75ba","year":2010},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.051466Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:b21b4ab787bec2062bef2b3304aad9ad11195f9a715b8944232d636a5b09d55c","observation_id":"bc696515-9503-4642-b85f-0ef9e6b9a516","resolution":{"observed_at":"2026-08-07T12:07:59.842200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:59.589698Z","title":"Accroître l'impact journalistique : ordonnancement automatique d'articles de nouvelles sur les médias sociaux","venue":null,"work_id":"c43433b8-3db5-4de0-a96d-1ede519d0397","year":2024},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.182908Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:cf6ae61c2b6f6f6cebbc1e07b75e548f10b0803a9c08222e0506ad5eff23351f","observation_id":"9247b84b-abf4-46d8-929a-927e2760b277","resolution":{"observed_at":"2026-08-07T12:07:59.653611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:59.446443Z","title":"Google translate vs","venue":null,"work_id":"bd2de210-ec95-4d86-b133-ff096941e4b0","year":2021},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.262005Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:356e2283dfd6e083137886c7cc36deb2598b274ee8cc7bb0223b2687088e2a81","observation_id":"6dfe5ff0-2c14-4879-ab8a-ace9ba91f88f","resolution":{"observed_at":"2026-08-07T12:07:59.508400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17428","last_updated":"2025-02-25T00:35:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-27T17:59:45Z","title":"NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17428","snapshot_observed_at":"2026-08-07T12:07:57.327989Z","title":"Nv-embed: Improved techniques for training llms as generalist embedding models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.327989Z"},"links":{"cited_paper":"/paper/2405.17428","citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:9a48a37f4f108d14e67a8310ee46679d404eea989b7931935d10b0fee812fdc9","observation_id":"17bd3cff-cce5-4381-b77e-9ec9d95c6e49","resolution":{"observed_at":"2026-08-07T12:07:57.327989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.15700","last_updated":"2024-09-24T03:30:19Z","snapshot_observed_at":"2026-07-06T19:20:55.427960Z","submitted_at":"2024-09-24T03:30:19Z","title":"Making Text Embedders Few-Shot Learners","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.15700","snapshot_observed_at":"2026-08-07T12:07:57.413913Z","title":"Making text embedders few-shot learners","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.413913Z"},"links":{"cited_paper":"/paper/2409.15700","citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:ae64de505f99f30efe41780da18502bc8a344e729e82ab35af2d410b0dfa5f94","observation_id":"50fed3e8-e022-49de-bea6-3cc9a33da9da","resolution":{"observed_at":"2026-08-07T12:07:57.413913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:59.239904Z","title":"An empirical evaluation of Thompson Sampling","venue":null,"work_id":"3096ef7b-41c3-4cff-8b4d-bbe2db62c6f1","year":2011},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.510944Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:aeae92f5f5a13f8f6d400e61121061d12a383395e3f9de556df8d728179a4c70","observation_id":"daaf29d1-f856-4d4b-8f52-98aeed95897d","resolution":{"observed_at":"2026-08-07T12:07:59.337515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1407.2806","last_updated":"2014-07-10T14:32:37Z","snapshot_observed_at":"2026-07-06T03:48:39.896655Z","submitted_at":"2014-07-10T14:32:37Z","title":"Bandits Warm-up Cold Recommender Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1407.2806","snapshot_observed_at":"2026-08-07T12:07:57.620853Z","title":"Bandits warm-up cold recommender systems","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.620853Z"},"links":{"cited_paper":"/paper/1407.2806","citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:af16b219c7473a433c6d0ddf639fb3e1febc9077ceea539c101f7fdbdbbf683f","observation_id":"99c9b51f-182b-4ed1-b2ea-b60e0731420a","resolution":{"observed_at":"2026-08-07T12:07:57.620853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:59.009686Z","title":"Modeling delayed feedback in display advertising","venue":null,"work_id":"091c6489-276e-4b92-ae4a-cff887a018a5","year":2014},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.739978Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:284cfdcf9dcd4095d9f146fe0cc90b53b9782c703c6f04b9f3ebd79575cb302b","observation_id":"dd6d9cb0-6664-490b-a6bc-3fff300f0584","resolution":{"observed_at":"2026-08-07T12:07:59.125540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:58.766038Z","title":"Neural Thompson Sampling","venue":null,"work_id":"11cb9d15-d79e-497b-ae68-80d2c9b0dd42","year":2021},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.841862Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:3fb3924ad9309d4fb564c1826d65a3086ab0b02de2108008bdcd004a943476be","observation_id":"fb711797-c222-442e-b1df-a95f97483bf9","resolution":{"observed_at":"2026-08-07T12:07:58.854605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:57.984886Z","title":"Optuna: A next-generation hyperparameter optimization framework","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:57.984886Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:755d491df5d82c73618bd8e84944440b82664b2cebbb4d856d06ed8962736255","observation_id":"8a9bc564-6f28-4c9e-b4ea-1bd1e4225fc8","resolution":{"observed_at":"2026-08-07T12:07:57.984886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:58.555630Z","title":"A smoothed analysis of the greedy algorithm for the linear contextual bandit problem","venue":null,"work_id":"481c391b-395a-4bcc-9779-14b904838158","year":2018},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:58.079193Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:7d7b24ebbe3f0ca75ea69e5eb0d56784c22b7255f7a62b2b36def20cdb21e534","observation_id":"d7a6ed71-8bd6-4823-aa7b-193faabb038d","resolution":{"observed_at":"2026-08-07T12:07:58.641644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:07:58.390547Z","title":"Unreasonable effectiveness of greedy algorithms in multi-armed bandit with many arms","venue":null,"work_id":"47585ac1-ed6b-458d-9fea-2f4e3b83b6a4","year":2020},"citing_paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T12:07:58.191210Z"},"links":{"citing_paper":"/paper/2506.06334"},"observation_digest":"sha256:a1d17c90b2b6bc178e13ec0c5d1f912ca97de6b5bb1a7c0f7b3c8c72ba5ea8dc","observation_id":"d97c03a7-3171-4047-956a-2e2e00ccd79b","resolution":{"observed_at":"2026-08-07T12:07:58.466116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.06334","last_updated":"2025-05-31T12:57:56Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-07T12:00:51.075343Z","submitted_at":"2025-05-31T12:57:56Z","title":"Preference-based learning for news headline recommendation"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":21},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2506.06334."}