{"as_of":"2026-08-07T23:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9c39df8f60c31b7bab545ae20fc118b3d8961f6c6ac760f55c83ac5ccca53219","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:14:53.939726Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:29:06.054516Z","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":"2026-08-06T21:29:10.970289Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"cited_work":{"arxiv_id":"2506.09084","doi":null,"metadata_source":"pith","pith_arxiv_id":"2506.09084","snapshot_observed_at":"2026-08-06T21:29:10.970289Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","venue":"cs.LG","work_id":"0a152ada-fd42-4ec5-990a-0eb0c29ff317","year":2025},"citing_paper":{"arxiv_id":"2506.24124","last_updated":"2025-07-01T03:40:22Z","snapshot_observed_at":"2026-08-07T22:30:29.169423Z","submitted_at":"2025-06-30T17:59:14Z","title":"Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives","version":2},"reference_index":107,"source":"pdf_text","source_observed_at":"2026-08-06T21:29:06.054516Z"},"links":{"cited_paper":"/paper/2506.09084","citing_paper":"/paper/2506.24124"},"observation_digest":"sha256:f73d6064812b21f4ee9a0b9d817cd64bd132dc561d23d24e4e99efcaac294404","observation_id":"4d898b2d-8dbf-44e1-b734-2cdfbaa5aece","resolution":{"observed_at":"2026-08-06T21:29:11.064226Z","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/2506.09084/citation-record","integrity":"/paper/2506.09084/integrity","json":"/paper/2506.09084/citation-record.json","paper":"/paper/2506.09084"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:54.646049Z","title":null,"venue":null,"work_id":"e5f9f2b6-facc-4eb9-84ca-aa3403e0a2b0","year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.689412Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:d27d3ad23124e9be3829ca7440c118dd4c0f96630db272258ccd6e954cc4359f","observation_id":"1c620f20-6546-4467-b8d3-7d6e3ad201fb","resolution":{"observed_at":"2026-08-07T05:14:54.650368Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:54.631863Z","title":null,"venue":null,"work_id":"cee2158b-965a-483e-bc16-6b475c296665","year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.694653Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:68b957796b5b8ae071cc468ea3697f40aee133745984288a340d54bfe906e99f","observation_id":"da7a9f02-6810-42ef-92ee-8bcddfb42a04","resolution":{"observed_at":"2026-08-07T05:14:54.636339Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.699486Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.699486Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:c5cd92d007a765f823374aa85ee10f7c72e7b0c16ff123aa57b4e1d0def1ace8","observation_id":"e7084212-6408-4b5b-9c5c-37e6391a829f","resolution":{"observed_at":"2026-08-07T05:14:53.699486Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.705268Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.705268Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:b25192a2c177b454720eb8641e92541fbc1516e6ceb181579d6745c21978001b","observation_id":"451028c1-0eaa-4c87-b4d8-028dc2e06e45","resolution":{"observed_at":"2026-08-07T05:14:53.705268Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.711001Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.711001Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:40276ab4c65ab07e7e557b774f1a43341be1b968895335c98e6247b9a789d6eb","observation_id":"845a7333-e81d-4c85-b3c3-030d04f21db6","resolution":{"observed_at":"2026-08-07T05:14:53.711001Z","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":"2026-08-07T05:14:54.588969Z","title":null,"venue":null,"work_id":"901e5776-ff4a-42f2-a8c7-549c7dbb53e7","year":null},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.715611Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:baafa5aca5657613c41c61e2bd536282cc75ee827d4c5406d0d088de054bc97c","observation_id":"1017f90b-af28-4f0e-9641-31e06c3eab80","resolution":{"observed_at":"2026-08-07T05:14:54.593436Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2205.08084","last_updated":"2022-05-19T06:50:31Z","snapshot_observed_at":"2026-08-07T18:33:30.581004Z","submitted_at":"2022-05-17T04:13:42Z","title":"M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.08084","snapshot_observed_at":"2026-08-07T05:14:53.725271Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.725271Z"},"links":{"cited_paper":"/paper/2205.08084","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:0eb7938ed847cc9dcdb4b8ced98bde1ab8baa72f8b7bc5ee7558b379938d9320","observation_id":"425c7742-3eb8-47e2-b14d-5554ee5ee6ae","resolution":{"observed_at":"2026-08-07T05:14:53.725271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-07T05:14:53.730243Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.730243Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:467bea1db98d414ef7d129d1e3fef22e9a9cf7e0523538cd0fbe64895eb985f3","observation_id":"680abd0a-6883-4bfe-a153-78357a2b025e","resolution":{"observed_at":"2026-08-07T05:14:53.730243Z","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":"2026-08-07T05:14:54.559337Z","title":null,"venue":null,"work_id":"b7770847-ec21-4d57-b48a-f7c19f98ebae","year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.734738Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:ef4d09019e1fcc41e72cd70982292f0c2825c94afcdacddd3074996a6ad37c51","observation_id":"921da315-344c-49b5-9326-b047c5b66b96","resolution":{"observed_at":"2026-08-07T05:14:54.563897Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:54.544352Z","title":null,"venue":null,"work_id":"8e392ff2-3189-47b1-8a65-485861293d8a","year":2019},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.739709Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:85a87f7ef615945ea03d8add86925666485826a7aa1e4d3ae4298ea5fbb93eb8","observation_id":"e28f4d4d-0af6-49f8-a80d-1d991d38c453","resolution":{"observed_at":"2026-08-07T05:14:54.548752Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2305.07961","last_updated":"2023-05-16T21:21:46Z","snapshot_observed_at":"2026-07-06T15:26:50.277211Z","submitted_at":"2023-05-13T16:40:07Z","title":"Leveraging Large Language Models in Conversational Recommender Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.07961","snapshot_observed_at":"2026-08-07T05:14:53.744862Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.744862Z"},"links":{"cited_paper":"/paper/2305.07961","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:7e619828ef2b853c50c641d9c0dcac1f175e180c09ecd389a526d2dfa3bf6014","observation_id":"264598d0-df33-4734-ba81-49371344cd8b","resolution":{"observed_at":"2026-08-07T05:14:53.744862Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.750172Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.750172Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:26734c0404e3d45a093e07efdd3656671e091d8f233ae6d73203721802b7cf37","observation_id":"75b78ff8-f863-41c2-8dba-d5fd19925ab7","resolution":{"observed_at":"2026-08-07T05:14:53.750172Z","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":"2026-08-07T05:14:54.519655Z","title":null,"venue":null,"work_id":"9135a70c-08b0-4cff-ada3-daa22f4f9b18","year":2013},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.754799Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:1c6c9f46feb3df773268049322c92327b2a28fbbbcadb8c27b504440e965ed41","observation_id":"6fdd30d6-ed98-4529-a695-564fbda6d3c8","resolution":{"observed_at":"2026-08-07T05:14:54.524252Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.759895Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.759895Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:c0e5536ebfd502bf0f35d90be1825bbc1ab318236af920c984814a8e62d7e526","observation_id":"f3bda4e8-e3f8-40cb-9147-4f3741fbfd59","resolution":{"observed_at":"2026-08-07T05:14:53.759895Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.768955Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.768955Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:d2444ff708efde77a362ec45e803bdd58efafb5e4312e34e42f752bf169b046d","observation_id":"7f3bfd67-d442-4fcc-afb7-263c483429f3","resolution":{"observed_at":"2026-08-07T05:14:53.768955Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.772970Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.772970Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:da007f59dd2cb4213b5681856642b5ab9c940db14fad3620b868435074c62e54","observation_id":"1f28dc21-6299-44af-ba69-4deb1b855e3b","resolution":{"observed_at":"2026-08-07T05:14:53.772970Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.777799Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.777799Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:56adf5bab70f153355ed41fe8b2cc769860f99674d075a15e0a881bb9145d79f","observation_id":"87e05273-b990-44e9-a334-ea6de3d8d8c3","resolution":{"observed_at":"2026-08-07T05:14:53.777799Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.787319Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.787319Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:ec949a69648444105ffeff74fb1a73a07d25822569dd0ab1fc74134b7516f6ab","observation_id":"d88540e7-424c-47c3-8013-70545b611bbd","resolution":{"observed_at":"2026-08-07T05:14:53.787319Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.791878Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.791878Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:dd51d40df3951e0b68d0ff545bb32b51d8a254aa65130d383bbf4925621c8000","observation_id":"88ccd9a6-38a9-4c5e-9f56-c8b5bb7acdd0","resolution":{"observed_at":"2026-08-07T05:14:53.791878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06474","last_updated":"2023-05-10T21:43:42Z","snapshot_observed_at":"2026-07-06T15:25:47.518595Z","submitted_at":"2023-05-10T21:43:42Z","title":"Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06474","snapshot_observed_at":"2026-08-07T05:14:53.796277Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.796277Z"},"links":{"cited_paper":"/paper/2305.06474","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:9b61b715719df69df359a78b490de37149b522c664e2f7bbcf271cbcd1aff9b5","observation_id":"f92d5c02-792c-4f55-b0a3-1c9ba68fc5e6","resolution":{"observed_at":"2026-08-07T05:14:53.796277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07862","last_updated":"2023-04-16T19:03:01Z","snapshot_observed_at":"2026-08-02T04:08:00.678775Z","submitted_at":"2023-04-16T19:03:01Z","title":"PBNR: Prompt-based News Recommender System","version":1},"cited_work":{"arxiv_id":"2304.07862","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.07862","snapshot_observed_at":"2026-08-07T05:14:54.158501Z","title":"PBNR: Prompt-based News Recommender System","venue":"cs.IR","work_id":"898eb6c9-5a35-4f3b-9bd8-a973fd304946","year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.801312Z"},"links":{"cited_paper":"/paper/2304.07862","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:4eca6ca69c659a200e23e5ce2b92475976d31e381e39fb376e68da841c94fac8","observation_id":"4726e0b6-1503-4c90-81fc-1907d6f0beb1","resolution":{"observed_at":"2026-08-07T05:14:54.163340Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.811925Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.811925Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:7232df23c52c9675ab72d9965af1c137c04b2b10465dcfaa1a093467f48929f3","observation_id":"4a6d14d7-8deb-4f03-8a0c-2580364b4354","resolution":{"observed_at":"2026-08-07T05:14:53.811925Z","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":"2026-08-07T05:14:54.426287Z","title":null,"venue":null,"work_id":"3cb182a8-4336-4cb6-a208-fd02878ae8e2","year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.816473Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:348ab454fc0691da57da2e995868b5c5c56d4cae08d611d163ea4682bce06a51","observation_id":"8c5e5ca8-e5ac-4b9e-9cda-6055b51bb065","resolution":{"observed_at":"2026-08-07T05:14:54.431005Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:54.412078Z","title":null,"venue":null,"work_id":"b68f6b94-9d7c-4ea9-a612-0e80db4e1cc6","year":2016},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.820592Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:fc40b6d414bf3de083c33c6208e209c5df45c8892715967efd935a62937c4d47","observation_id":"5e410608-4ac8-4f91-9354-bd6efa3c0448","resolution":{"observed_at":"2026-08-07T05:14:54.416630Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:54.397094Z","title":null,"venue":null,"work_id":"c7862242-036b-4f62-bee7-8c7ae87610ac","year":2022},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.825162Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:d3629f0e7f0050acfdc26b4d4fe1df9df9921a6b7fee910fd96b2e33816b0a0d","observation_id":"509a68ad-7c94-4f74-8f76-755d41e84b25","resolution":{"observed_at":"2026-08-07T05:14:54.401637Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:54.381395Z","title":null,"venue":null,"work_id":"fbaac7dd-6d48-48d8-8eac-cbaabf9fda57","year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.829628Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:4513f29eac173157b82a94a67e3e7949f51a78b12668582f8ef26237d053684f","observation_id":"dc3de1d4-52fd-4044-ab10-6e9e03dc2d60","resolution":{"observed_at":"2026-08-07T05:14:54.386138Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.833972Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.833972Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:cf04a438d7d3072945dfabb301e646b1ab91c434596fe03cef3799ea4afe324d","observation_id":"4ec8a601-f1be-4d03-83a7-ab8ef212e01a","resolution":{"observed_at":"2026-08-07T05:14:53.833972Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.839609Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.839609Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:215fd349a983bd4614d9ebb1f2ee8087383280683ece134bdd04e0fd94fa015e","observation_id":"f4ee7dc5-c831-4ecf-bf45-904417d7fc0f","resolution":{"observed_at":"2026-08-07T05:14:53.839609Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.849978Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.849978Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:3f60962785ce78104562c4787fe95298d945caddf7d1031785f3702a6fff09a3","observation_id":"50c1e07e-f0a7-47a3-8cfb-ac3fc6413973","resolution":{"observed_at":"2026-08-07T05:14:53.849978Z","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":"2026-08-07T05:14:54.324565Z","title":null,"venue":null,"work_id":"63ac2619-0b67-4410-98f7-a7b30949a3a0","year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.860054Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:e660fdf6f31d3a49f49f7fabd3cbce1f56aea1e660da944dc2765141badb9178","observation_id":"e47ea2d8-edc4-4dcd-a44e-94021d6163e4","resolution":{"observed_at":"2026-08-07T05:14:54.332254Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09617","last_updated":"2025-07-17T07:51:28Z","snapshot_observed_at":"2026-07-06T17:30:21.100876Z","submitted_at":"2024-02-14T23:12:09Z","title":"LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09617","snapshot_observed_at":"2026-08-07T05:14:53.864785Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.864785Z"},"links":{"cited_paper":"/paper/2402.09617","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:02a410652146d220475d273f543bfe4bc7208ce6529c710e46dd926d9aee97e7","observation_id":"d5acc270-963c-4e46-aed9-40d4bf30708f","resolution":{"observed_at":"2026-08-07T05:14:53.864785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.14296","last_updated":"2024-03-20T18:13:10Z","snapshot_observed_at":"2026-07-06T16:11:02.763364Z","submitted_at":"2023-08-28T04:31:04Z","title":"RecMind: Large Language Model Powered Agent For Recommendation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.14296","snapshot_observed_at":"2026-08-07T05:14:53.869440Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.869440Z"},"links":{"cited_paper":"/paper/2308.14296","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:cc7d52b0051f391d1a5d2620c138d3e49d4008781066373acd1e157b78dd1f62","observation_id":"480ff5e9-e259-4bf5-8983-d94b28945888","resolution":{"observed_at":"2026-08-07T05:14:53.869440Z","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":"2026-08-07T05:14:54.309377Z","title":null,"venue":null,"work_id":"70d789ce-d0a4-4efa-9e3e-0b0bdc042d50","year":2016},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.874562Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:f6e5f5230e1bdf0b43a1eb729ef58b2b94c82c593e905515048d28ec2ec691d7","observation_id":"a854ff20-7293-4fbb-bb7d-bb9c5d7cb40f","resolution":{"observed_at":"2026-08-07T05:14:54.314061Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.879530Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.879530Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:e4d37f21552ce70f83790b38cf74f0cb981e1ccadc0f77c21e4ad97183239165","observation_id":"aa57c608-f9bd-4b07-ae7f-b1974bd60cef","resolution":{"observed_at":"2026-08-07T05:14:53.879530Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.02756","last_updated":"2024-06-04T20:21:45Z","snapshot_observed_at":"2026-07-06T18:25:31.706930Z","submitted_at":"2024-06-04T20:21:45Z","title":"Aligning Large Language Models via Fine-grained Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02756","snapshot_observed_at":"2026-08-07T05:14:53.884160Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.884160Z"},"links":{"cited_paper":"/paper/2406.02756","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:856dc9de02bda7bb2347fd8672f5df80153cfa3424dc9636673889866a7fc71b","observation_id":"fc99c1ea-9aee-470b-8d7a-de9b68633f0a","resolution":{"observed_at":"2026-08-07T05:14:53.884160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04997","last_updated":"2025-01-16T09:58:54Z","snapshot_observed_at":"2026-07-06T17:13:38.033348Z","submitted_at":"2024-01-10T08:28:56Z","title":"Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04997","snapshot_observed_at":"2026-08-07T05:14:53.890011Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.890011Z"},"links":{"cited_paper":"/paper/2401.04997","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:7fe61e2f11ed15e7a9a01d10ec59831e9ceeb429ce37b2d9b218f834de519514","observation_id":"0bcb839f-441e-43e9-8410-effe79021641","resolution":{"observed_at":"2026-08-07T05:14:53.890011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.10779","last_updated":"2023-11-16T07:09:38Z","snapshot_observed_at":"2026-07-06T16:49:13.099946Z","submitted_at":"2023-11-16T07:09:38Z","title":"Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations","version":1},"cited_work":{"arxiv_id":"2311.10779","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.10779","snapshot_observed_at":"2026-08-07T05:14:54.060003Z","title":"Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations","venue":"cs.IR","work_id":"2a2363dd-7ccb-4bf1-8c21-c01377016c73","year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.895417Z"},"links":{"cited_paper":"/paper/2311.10779","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:2b50d281bf97f176ea8d65de3befed8c664e64f529252da38e7829dc26a2859f","observation_id":"a7bced7a-0b29-49b7-a975-30ee6a5c31a0","resolution":{"observed_at":"2026-08-07T05:14:54.067221Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:54.285098Z","title":null,"venue":null,"work_id":"2ae6dce2-ec4c-4d2d-a3bf-c172c3d33766","year":2022},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.900802Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:5672ce845a97aea0583464064b4e56258e222a74c2fa11ff12ec1f7899c6cd29","observation_id":"10c749f9-321a-44d5-9331-94b4450fe5a3","resolution":{"observed_at":"2026-08-07T05:14:54.289612Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2311.02089","last_updated":"2023-10-25T06:23:48Z","snapshot_observed_at":"2026-08-01T15:07:27.003536Z","submitted_at":"2023-10-25T06:23:48Z","title":"LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.02089","snapshot_observed_at":"2026-08-07T05:14:53.905674Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.905674Z"},"links":{"cited_paper":"/paper/2311.02089","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:2db752299017881918a34a3bd06f1f6b4f60854780cb20cd9f2805239b5333d5","observation_id":"b5d0c6fc-5c92-442f-9eff-f048b2fb185d","resolution":{"observed_at":"2026-08-07T05:14:53.905674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17152","last_updated":"2024-05-06T02:05:45Z","snapshot_observed_at":"2026-08-06T03:48:40.952592Z","submitted_at":"2024-02-27T02:37:37Z","title":"Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17152","snapshot_observed_at":"2026-08-07T05:14:53.911401Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.911401Z"},"links":{"cited_paper":"/paper/2402.17152","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:c3d9392fe8dfa1a96123471b1dc853614afc3ede09d86dd947b7a90568d6f881","observation_id":"72fc2e69-0f65-48ee-8047-bd62e8c59ec2","resolution":{"observed_at":"2026-08-07T05:14:53.911401Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.07001","last_updated":"2023-05-11T17:39:07Z","snapshot_observed_at":"2026-07-06T15:26:10.831663Z","submitted_at":"2023-05-11T17:39:07Z","title":"Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.07001","snapshot_observed_at":"2026-08-07T05:14:53.916594Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.916594Z"},"links":{"cited_paper":"/paper/2305.07001","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:5be11661e5663e633b9003363f21833d38f0ba13d20ecd839f7e55f71d33c2cf","observation_id":"5b22be86-88ba-40e9-b5ce-694c1de912c6","resolution":{"observed_at":"2026-08-07T05:14:53.916594Z","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":"2026-08-07T05:14:54.270094Z","title":null,"venue":null,"work_id":"a37c0b5f-7c72-4af9-8dad-7c56de5c630b","year":2019},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.921490Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:26f7bb8b5a80651c9fb21e9cee651eaab6ff213771eba5930457c005607b00c1","observation_id":"f1ad0320-2e6c-4dd1-a19d-fbb02341109c","resolution":{"observed_at":"2026-08-07T05:14:54.275018Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2309.01219","last_updated":"2025-09-14T09:34:46Z","snapshot_observed_at":"2026-07-06T16:13:46.112815Z","submitted_at":"2023-09-03T16:56:48Z","title":"Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.01219","snapshot_observed_at":"2026-08-07T05:14:53.925447Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.925447Z"},"links":{"cited_paper":"/paper/2309.01219","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:da3edbd5cc306bd878759d9a377fd375f58c29243e40b0925c064d1c0ddeac16","observation_id":"cf5aeb96-f3f0-4777-b5cc-a763905f49a8","resolution":{"observed_at":"2026-08-07T05:14:53.925447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-06T23:27:24.356320Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-07T05:14:53.930076Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.930076Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:5593c17302b9182f8fc5452cf86cdffcee096c2ee812a60d87e85ee98d06c7e6","observation_id":"66132f65-7f21-4201-91dd-3c047c34ecdf","resolution":{"observed_at":"2026-08-07T05:14:53.930076Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.934987Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.934987Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:9920d49af4b4bd43c1506ea2b47e8ef1ba6cfe52c9fb40ef370dafcc9dcca252","observation_id":"3175445d-906a-40a5-8e0e-0a036bde2627","resolution":{"observed_at":"2026-08-07T05:14:53.934987Z","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":"2026-08-07T05:14:54.245698Z","title":null,"venue":null,"work_id":"d0ab0aa1-ea07-47ea-83c5-627953662799","year":2022},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.939726Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:d0d83f12652acfb2a4aa5671e1dda880542bac045bc351e227d9c9c46cedc9b7","observation_id":"268ec62c-f0e0-4174-be98-63b3699c5c45","resolution":{"observed_at":"2026-08-07T05:14:54.250810Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06939","last_updated":"2016-03-29T14:52:58Z","snapshot_observed_at":"2026-07-31T19:00:00.136727Z","submitted_at":"2015-11-21T23:42:59Z","title":"Session-based Recommendations with Recurrent Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06939","snapshot_observed_at":"2026-08-07T05:14:53.782554Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.782554Z"},"links":{"cited_paper":"/paper/1511.06939","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:3c2e62906fbba9bba1e43a4ee000f6765527047156c9f3a2c950bc922a224b7f","observation_id":"797d0562-979f-479e-8da0-fdc84616b94f","resolution":{"observed_at":"2026-08-07T05:14:53.782554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-07T05:14:53.844915Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.844915Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:a1b94266ca176ada6bb26dd45170e938dbf951b855b1ead183686829bf068af8","observation_id":"2e7fb0d1-01a8-440a-ab39-ffbad17c1733","resolution":{"observed_at":"2026-08-07T05:14:53.844915Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:53.854847Z","title":"InProceedings of the 28th ACM international conference on information and knowledge management","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.854847Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:b92190fffa9c6cb63a80bb92027f8f96086ac2889f13c3786e5b8f38e60751cc","observation_id":"3ceaaa2e-9528-4644-ba7a-fbab7a0c5891","resolution":{"observed_at":"2026-08-07T05:14:53.854847Z","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":"2026-08-07T05:14:54.495164Z","title":null,"venue":null,"work_id":"34624c87-8e9d-4c41-9d16-47c3dc6a1055","year":2023},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.764640Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:8e416b4fa3d7bb2bab4e83c44bf14051817beff1dfeb83d092764f330b6b198d","observation_id":"0cc4a72f-11d0-4f7c-b39c-7a927b48b238","resolution":{"observed_at":"2026-08-07T05:14:54.499858Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:14:54.574149Z","title":null,"venue":null,"work_id":"4afcec55-980a-4391-aab5-f681805ecff2","year":2024},"citing_paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T05:14:53.720229Z"},"links":{"citing_paper":"/paper/2506.09084"},"observation_digest":"sha256:1ab839892945acab4697caa183315bf95dba7ea53a0c9a3f8a04941aa126c810","observation_id":"ef2417d0-c98c-43d4-85ec-b1b2e57150b1","resolution":{"observed_at":"2026-08-07T05:14:54.578786Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}}],"paper":{"arxiv_id":"2506.09084","last_updated":"2026-05-23T00:31:27Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T05:05:25.370254Z","submitted_at":"2025-06-10T08:05:42Z","title":"PageLLM: A Multi-Grained Reward Framework for Whole-Page Optimization with Large Language Models"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":49,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":51},"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 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2506.09084."}