{"as_of":"2026-08-14T14:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:978b10d92a1c724ca0d8d7370b8eefc1fb326a9564503b1438130858dcee8ba0","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":"2026-08-06T19:21:39.501045Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2507.05880/citation-record","integrity":"/paper/2507.05880/integrity","json":"/paper/2507.05880/citation-record.json","paper":"/paper/2507.05880"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:21:36.862166Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:36.862166Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:9194b5a7dee25fb8eeee45bc3cf88972dec9c7fe84d6298c597d632f922fc8ce","observation_id":"bd610c98-ffb9-46f8-ae3a-e9f6db71707e","resolution":{"observed_at":"2026-08-06T19:21:36.862166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.08084","last_updated":"2022-05-19T06:50:31Z","snapshot_observed_at":"2026-08-13T15:42:57.474747Z","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":"2026-08-06T19:21:37.015142Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:37.015142Z"},"links":{"cited_paper":"/paper/2205.08084","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:13c94fd2cd7587882dcfa33c13891aa90156e8ebed8d1572bad8edaac000765d","observation_id":"d324a02b-e29d-40d6-beb3-b34f835f0b5a","resolution":{"observed_at":"2026-08-06T19:21:37.015142Z","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":"2026-08-06T19:21:40.476992Z","title":null,"venue":null,"work_id":"4f87de68-0977-47b5-94c8-be85dd75a308","year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:37.148705Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:4bec50dea1b3e71b160f0245e2125d1c84f77add705850fd94041c8353bbfd26","observation_id":"3ac89b80-4a54-4b73-89cc-9f576c4d1772","resolution":{"observed_at":"2026-08-06T19:21:40.482547Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-06T19:21:37.326237Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:37.326237Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:cb71a59cddc89675e76f029a4a036076a0ed98643cd9aef3f303f59b4a58ef7b","observation_id":"381dd1ef-47ee-4ae9-9a43-b055a242fe71","resolution":{"observed_at":"2026-08-06T19:21:37.326237Z","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":"2026-08-06T19:21:37.461572Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:37.461572Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:ee4faf2ee33ccde3dc0abe7a558dcad2cfca78c9a1a114649cc2e70a2100b896","observation_id":"561d5c75-430f-42e3-a955-a83412b0b356","resolution":{"observed_at":"2026-08-06T19:21:37.461572Z","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":"2026-08-06T19:21:40.433174Z","title":null,"venue":null,"work_id":"156fe82f-ecc7-4840-a6ef-0d6a3718025d","year":1979},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:37.725615Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:c5b02a115d2a6e2da729bb98f5cbb565a186e2bae12ab3125f81be12578be286","observation_id":"5affe31e-27d1-4ddd-bdbc-a2e0db729d71","resolution":{"observed_at":"2026-08-06T19:21:40.439467Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.415286Z","title":null,"venue":null,"work_id":"8d444e9b-35e6-4b11-a465-3f0be8103c19","year":2020},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:37.832647Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:9b2571f00ead9ac46878b4d5b8536922908611906e0dda02fbd99cd44787f4f9","observation_id":"6e8fa4cc-66b4-425c-96e9-7bc7e1bb1dff","resolution":{"observed_at":"2026-08-06T19:21:40.420698Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.396259Z","title":null,"venue":null,"work_id":"1de69677-b83b-4e28-8110-19b4073dc230","year":2022},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.045253Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:c1f2d1b78b52fdb05bb16d8b2397c895656a0e6743eae59d6442147654be67cc","observation_id":"e96a84bb-10a3-4ab2-865b-de85e537aea3","resolution":{"observed_at":"2026-08-06T19:21:40.403347Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.380098Z","title":null,"venue":null,"work_id":"fc97a24e-0150-4124-9317-8f1baceccbb7","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.211961Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:081a39fb7b6b380900a933968551c5a5fe91cc5f7ec3d1da5fcc6c23353b2d23","observation_id":"07a3dd2a-87a0-4f32-9a0d-ca019f9ca052","resolution":{"observed_at":"2026-08-06T19:21:40.385349Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.362739Z","title":null,"venue":null,"work_id":"bd92d638-d446-43de-90d7-2d0d827c84b4","year":2009},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.327144Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:d72e9ed8f95b02fc344d6858b3ac0c0631f8f0bb51004ac50c8b180d16eb6540","observation_id":"bdcac04c-1215-4ec4-a7f1-117aaa492bf1","resolution":{"observed_at":"2026-08-06T19:21:40.368210Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.346517Z","title":null,"venue":null,"work_id":"673ed65f-5979-4bde-9154-266f880a9878","year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.396611Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:250ee79efcc93c8aeada8ac00526b16ebdce530d7f1a3234934f8af94537ea5f","observation_id":"4261cd7a-ca49-4d97-bed6-384c2b2e77d1","resolution":{"observed_at":"2026-08-06T19:21:40.351207Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:38.562140Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.562140Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:a47e0b53efe67f0c8e120cbe67d8f99361e2b0228ee7ff74399bed66383e43dc","observation_id":"841e0202-e54d-4352-bcd5-dec25e4f2fd1","resolution":{"observed_at":"2026-08-06T19:21:38.562140Z","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":"2026-08-06T19:21:40.320172Z","title":null,"venue":null,"work_id":"b68afe67-c115-4ba1-9edc-2a2733f998dd","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.636044Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:233b46e12bfd95faba030d962ec841d0fc78c7ebc0f8876e57623c5ee3c0d5fb","observation_id":"95d20a15-9ad7-4fce-b632-96a74e7810ae","resolution":{"observed_at":"2026-08-06T19:21:40.324869Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.305011Z","title":null,"venue":null,"work_id":"b6160766-5afd-4ae6-ad0c-6a34a60f37cb","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.646350Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:8135efa3b0ce892a39425f8ede9ce07f5f12e36134fcd8b8e5effb8f1f582817","observation_id":"adc7a035-9793-4cb0-9920-af9759b8a929","resolution":{"observed_at":"2026-08-06T19:21:40.309651Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:38.586663Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.586663Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:28b8a50735529aa21da2663427e4f0380b606aaac76f5294a21602b8993babda","observation_id":"ed420a04-e290-4124-8720-36550a10a2ac","resolution":{"observed_at":"2026-08-06T19:21:38.586663Z","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":"2026-08-06T19:21:40.275135Z","title":null,"venue":null,"work_id":"701c3131-b72d-4abf-837c-a9a32af087d9","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.671215Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:eebdabfc948b70c1825d1e4c51d51e6eafc2504d8c8072eb2ff091f94ebb394a","observation_id":"918fd2c9-ef92-40aa-b6c6-895aaa973390","resolution":{"observed_at":"2026-08-06T19:21:40.279981Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11114","last_updated":"2023-06-19T18:27:54Z","snapshot_observed_at":"2026-08-13T11:13:55.031036Z","submitted_at":"2023-06-19T18:27:54Z","title":"Generative Sequential Recommendation with GPTRec","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11114","snapshot_observed_at":"2026-08-06T19:21:38.752265Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.752265Z"},"links":{"cited_paper":"/paper/2306.11114","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:2a5c3b8313d85ae8816e41188b8190f3e00cb3c6ff51c0825e41fa6777423fa8","observation_id":"dd5ab97f-4f78-4853-8fe4-28c5c511f06c","resolution":{"observed_at":"2026-08-06T19:21:38.752265Z","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":"2026-08-06T19:21:40.290136Z","title":null,"venue":null,"work_id":"3674c0ab-78c3-4d48-8b63-a842302e658b","year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.654296Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:689d45325b17218f506df3bb8b31281d641fa09dbcfab719712aee0f5bb0f66f","observation_id":"59240dfe-3158-4f02-a16f-d75b52cef672","resolution":{"observed_at":"2026-08-06T19:21:40.294773Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00573","last_updated":"2021-05-24T03:17:05Z","snapshot_observed_at":"2026-08-13T19:17:56.394753Z","submitted_at":"2021-05-24T03:17:05Z","title":"One4all User Representation for Recommender Systems in E-commerce","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.00573","snapshot_observed_at":"2026-08-06T19:21:38.954295Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.954295Z"},"links":{"cited_paper":"/paper/2106.00573","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:175d8ec87cde656ad22f7db4c223f7d4686263d4111942b84d881c4fb7451014","observation_id":"c35456c8-c91d-45bb-b710-d3bb6f14348f","resolution":{"observed_at":"2026-08-06T19:21:38.954295Z","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":"2026-08-06T19:21:40.245343Z","title":null,"venue":null,"work_id":"f5ee368c-643b-403f-85e5-99d4d5389c2f","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.046943Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:0cae7435b6f771a35243bbc7dc6fd419de369553f8d9b4a717d7c2c5f1809c92","observation_id":"8a8582eb-7874-4957-be18-968de8a6a588","resolution":{"observed_at":"2026-08-06T19:21:40.250186Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.260664Z","title":null,"venue":null,"work_id":"1fcf94fc-7bfd-4201-ac75-c0d1513f6747","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:38.867727Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:16d63714b9f83d930b23a89c7ba9aa459f0e304696844d535d4c0985d943d207","observation_id":"edfc7ec4-6b31-43fb-8c38-03f24d1df7c5","resolution":{"observed_at":"2026-08-06T19:21:40.265008Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.03153","last_updated":"2023-04-06T15:35:11Z","snapshot_observed_at":"2026-08-13T12:07:57.899463Z","submitted_at":"2023-04-06T15:35:11Z","title":"Zero-Shot Next-Item Recommendation using Large Pretrained Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.03153","snapshot_observed_at":"2026-08-06T19:21:39.187521Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.187521Z"},"links":{"cited_paper":"/paper/2304.03153","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:9cb1c1b5ac40e5397724a56f2c148330a7ccc3af535fd713e56e77015465d19c","observation_id":"dd09a3fb-8d30-458a-9bb0-931eeca85353","resolution":{"observed_at":"2026-08-06T19:21:39.187521Z","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":"2026-08-06T19:21:40.227401Z","title":null,"venue":null,"work_id":"d3a8586a-cecc-440b-abed-77e2b98e7783","year":2015},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.218311Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:ec8ff54125b5ac4e5375a987152822860dfc5ad0022fc216ea25529b3a800fb8","observation_id":"a4ebc8ff-cb4c-4e3b-a5f3-638e7cd613e1","resolution":{"observed_at":"2026-08-06T19:21:40.234111Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-06T19:21:39.125430Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.125430Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:c8185caf6fd85a5e49b7b6ee28d8b144dfd940652ef257ccab8a15d802a96144","observation_id":"4864addd-067f-4b65-b559-14695f5d5443","resolution":{"observed_at":"2026-08-06T19:21:39.125430Z","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":"2026-08-06T19:21:40.193963Z","title":null,"venue":null,"work_id":"265254bb-7c99-47dc-a8ff-5e3d3da5d9a6","year":2019},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.286254Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:f6b53556761c3ed951c4403cae8d115509a629de54d886f15193be698bfb890d","observation_id":"93fe4d4c-74bd-4c49-9faa-06aff1afc555","resolution":{"observed_at":"2026-08-06T19:21:40.199021Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.176634Z","title":null,"venue":null,"work_id":"151a996b-1bee-4462-a363-79a68565457a","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.298489Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:fb07928521724e586ab2635bd68bcc44459b0479a97d7d5d2511bcfa1a74f7b5","observation_id":"57a1e21a-5153-43c4-a981-1418cc0c83ac","resolution":{"observed_at":"2026-08-06T19:21:40.182514Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.210271Z","title":null,"venue":null,"work_id":"e339a187-ccde-4b08-8189-6f28f08976ef","year":2019},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.236291Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:2e3da02a81926fab1f137fc7dba79cf81f8fd667055f0f1fcf2a6dbcda078d97","observation_id":"660bfd9b-1c58-4b1e-91da-34be3c8923a0","resolution":{"observed_at":"2026-08-06T19:21:40.215756Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.143489Z","title":null,"venue":null,"work_id":"071bb582-d7f3-406b-acc7-e08bfa7e6ed0","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.332936Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:3b67ab8fa469495eba37fd03f7b08536bb047819caa0157423e0a31998abcddd","observation_id":"b3fcb749-237e-4e14-8e59-fa04e66652ac","resolution":{"observed_at":"2026-08-06T19:21:40.149374Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10420","last_updated":"2023-12-23T12:53:02Z","snapshot_observed_at":"2026-08-13T18:45:11.939291Z","submitted_at":"2023-03-18T14:02:04Z","title":"A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10420","snapshot_observed_at":"2026-08-06T19:21:39.342420Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.342420Z"},"links":{"cited_paper":"/paper/2303.10420","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:1b503b61eb37b5bae5ec4c52be8bfcae0a0d2335a820fa916d1751ba781ff0e9","observation_id":"14e0efcb-2704-461d-a2d8-d4908910908a","resolution":{"observed_at":"2026-08-06T19:21:39.342420Z","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":"2026-08-06T19:21:40.159543Z","title":null,"venue":null,"work_id":"6cd1e9f1-5673-45e9-965c-9ad6642eef14","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.321964Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:44bd2708714229b3b6352f0ecfea7d63ff4b5b0975c7c43c9bfbe59491830599","observation_id":"43c73ab5-4a28-4c0b-b261-167e4dd486a2","resolution":{"observed_at":"2026-08-06T19:21:40.165000Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:39.360728Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.360728Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:d0e9630cf317362a5a375c99330244284cd13b79f2361ed2cd5aa991c72cbf05","observation_id":"8655711c-9297-4100-8874-cf6e100c8267","resolution":{"observed_at":"2026-08-06T19:21:39.360728Z","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":"2026-08-06T19:21:40.107075Z","title":null,"venue":null,"work_id":"7fe9f448-80b3-4d1d-9d50-49c2b2af3ac6","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.376631Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:2a64c72f8588b616be94add1063875def663aa3d5465e4880fe96575582d85fa","observation_id":"770d716c-a413-4214-a8a5-ccd26cf6535c","resolution":{"observed_at":"2026-08-06T19:21:40.112011Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.20343","last_updated":"2023-11-03T09:51:38Z","snapshot_observed_at":"2026-08-13T05:36:45.858441Z","submitted_at":"2023-10-31T10:33:23Z","title":"Large Multi-modal Encoders for Recommendation","version":2},"cited_work":{"arxiv_id":"2310.20343","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.20343","snapshot_observed_at":"2026-08-06T19:21:39.578878Z","title":"Large Multi-modal Encoders for Recommendation","venue":"cs.IR","work_id":"68d2297f-db78-4824-bd7a-1480739ad4cc","year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.352654Z"},"links":{"cited_paper":"/paper/2310.20343","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:4cf24462a926304a5dca50aa936243bfa0d4ee5e674fa9c2ad2c471cbd8c1bee","observation_id":"b4729f21-3267-4dcf-9d34-bbb96da2fe08","resolution":{"observed_at":"2026-08-06T19:21:39.596867Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.073171Z","title":null,"venue":null,"work_id":"87ec5d13-6ee3-4242-8152-d0e9edb3dc83","year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.395049Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:1f617107088a20d6cdcc0344e1e3c655642fefeeac324b9c26c35985dd45080d","observation_id":"65132258-cb62-40d9-acab-59c867c2415e","resolution":{"observed_at":"2026-08-06T19:21:40.078882Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.057634Z","title":null,"venue":null,"work_id":"083bcd2a-0a7e-4a93-88cb-772ea077927b","year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.402725Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:d02319ae68ac28da3b98b01edc4a0656c68fb458c04cc2d1d291ee6aeae88255","observation_id":"c8bd4348-6420-405b-87f6-831ee11ace4e","resolution":{"observed_at":"2026-08-06T19:21:40.062276Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.03326","last_updated":"2024-04-04T09:52:45Z","snapshot_observed_at":"2026-08-13T00:37:51.430027Z","submitted_at":"2024-04-04T09:52:45Z","title":"A Directional Diffusion Graph Transformer for Recommendation","version":1},"cited_work":{"arxiv_id":"2404.03326","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.03326","snapshot_observed_at":"2026-08-06T19:21:39.542603Z","title":"A Directional Diffusion Graph Transformer for Recommendation","venue":"cs.IR","work_id":"150e3ca0-549c-4424-8723-a10a0ea8ce22","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.411034Z"},"links":{"cited_paper":"/paper/2404.03326","citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:b1d24ebd3ce8637ff9c9ae9cbc4ed8207d7b22c9aca754740da69661fe52da61","observation_id":"f8fb6ad0-ae6d-4b9f-bdf9-d067c0a90a75","resolution":{"observed_at":"2026-08-06T19:21:39.556400Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.090065Z","title":null,"venue":null,"work_id":"26a19b67-c08d-4b3e-aefd-990bdff40ee8","year":2025},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.384067Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:8aebab52f8a673ab8e13c43b6f3d977a9e7d2b93a6138596293db2ced57a8e62","observation_id":"db34307a-6163-4cbd-acd4-88ec680c10e5","resolution":{"observed_at":"2026-08-06T19:21:40.095503Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.030587Z","title":null,"venue":null,"work_id":"b81fd96f-26ba-48ad-ab5e-b1dcf90ee7df","year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.427908Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:827c3b9a6db6efa5dafea67e08f4771305f484be02b90ce03719ccffde4c50a6","observation_id":"ff86130f-d2e3-489e-b014-4adf7c1602fa","resolution":{"observed_at":"2026-08-06T19:21:40.035441Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:39.436168Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.436168Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:d2018e26d5437040c9161ea076131e926e18b5dd080860db36a558ded5698356","observation_id":"6ff159fc-b235-4fac-8121-4f0c389b31d7","resolution":{"observed_at":"2026-08-06T19:21:39.436168Z","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":"2026-08-06T19:21:39.451622Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.451622Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:de768de66b01e79c80ef21cfa2ffc116b4852055a9c35b37cec28ff6535b2616","observation_id":"608f1058-e563-484c-8345-301964a62d98","resolution":{"observed_at":"2026-08-06T19:21:39.451622Z","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":"2026-08-06T19:21:39.419037Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.419037Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:5a21fef620a1e77d88893e56e748059e8da8531ace222219004822fe01199c89","observation_id":"1d850902-61b4-4dd7-8517-e6bd2cb6b97b","resolution":{"observed_at":"2026-08-06T19:21:39.419037Z","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":"2026-08-06T19:21:39.940444Z","title":null,"venue":null,"work_id":"0fe8b260-8adb-49e2-8368-47991bb946e0","year":2021},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.475448Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:e3a1c1cbabeef1be04ea2efc401ef08690aaec3f0e6aa9a3b72c56612205cded","observation_id":"c62c43cf-fd65-4dc1-a05b-f603152b17e0","resolution":{"observed_at":"2026-08-06T19:21:39.945566Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:39.923517Z","title":null,"venue":null,"work_id":"ab816931-9df4-404b-87b0-8b1ce7c6a828","year":2018},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.483429Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:016a9d9c7311971a083619edced502ac6d420b089c4c02a10f541695b4fd9ec4","observation_id":"5ce57f87-ee52-4259-bb5f-3499d628d8c5","resolution":{"observed_at":"2026-08-06T19:21:39.928991Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.002910Z","title":null,"venue":null,"work_id":"81e77a5a-22ec-484d-a5d6-d0d95251f0a4","year":2023},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.445216Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:b6c41181d17c0e6e679999490406c28250d0d3ac592e244e8cc692644063dbae","observation_id":"c1f6b162-b180-48c4-ab26-96c5950c6fc0","resolution":{"observed_at":"2026-08-06T19:21:40.008906Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:39.889082Z","title":null,"venue":null,"work_id":"02957484-fc3d-4067-bb99-8a2b30715eac","year":2005},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.501045Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:9c1db0efa4270a0537f79d522375ff057feb2940ce64190333d1e46b74210f09","observation_id":"445120d9-b86a-4559-9adb-e91318b523b3","resolution":{"observed_at":"2026-08-06T19:21:39.895310Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:39.956430Z","title":null,"venue":null,"work_id":"f01f7611-257d-45ff-91aa-8d8c85f00000","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.467279Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:2e6f1c4ec7718b7074a64a40321dc1b2b8a2b33f6727602923bafb5e0ff3cfd3","observation_id":"ede432a1-6a7d-4d50-bfed-6c4ea2283e4d","resolution":{"observed_at":"2026-08-06T19:21:39.962163Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:39.907456Z","title":null,"venue":null,"work_id":"9ccf1d69-1baf-45d2-9d86-8a26685f955a","year":2024},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.491122Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:cb389356baee8bb8750a5e37c2d0f4588c87da065520dd90a63d3661e6e998bc","observation_id":"0c86b384-28f4-4ce2-b356-f451998df991","resolution":{"observed_at":"2026-08-06T19:21:39.912523Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.450997Z","title":null,"venue":null,"work_id":"3e586a4b-aaad-46c2-bf15-ff078537b1e0","year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:37.594777Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:4bb6a462a89a79ef1c07b3d12d4cad71073748cdf5a4f8559a203e6fe760931b","observation_id":"00f275c1-339e-4d7c-b595-573731e16a69","resolution":{"observed_at":"2026-08-06T19:21:40.455734Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:40.494649Z","title":null,"venue":null,"work_id":"d50f6ce8-b160-4f56-bd27-c9fca2b3c426","year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:36.931421Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:44d72b53b1979e904fb43734a4cf1ed8ba8f136721fc94a49cbd883330800c98","observation_id":"b29b65b9-a028-46f7-aadc-d1203cc15350","resolution":{"observed_at":"2026-08-06T19:21:40.500883Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:39.974857Z","title":null,"venue":null,"work_id":"4380c512-7ae5-45a6-886b-3267343608e3","year":null},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.460105Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:dd35045379320472d60e0ebfcc87d7c3884424537f53b2a47a94a6211dfe7744","observation_id":"696b1284-5433-43c6-8d2b-669af67216a6","resolution":{"observed_at":"2026-08-06T19:21:39.980060Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T19:21:39.367861Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T19:21:39.367861Z"},"links":{"citing_paper":"/paper/2507.05880"},"observation_digest":"sha256:198976d7ab85e71bd7488a3a6fd6ae4c551c79b810becd8a92a9e3a2cbb339b1","observation_id":"4849084e-1587-4513-8c39-1ebb3815bfb3","resolution":{"observed_at":"2026-08-06T19:21:39.367861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.05880","last_updated":"2025-07-08T11:04:17Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-09T20:47:37.898750Z","submitted_at":"2025-07-08T11:04:17Z","title":"RecRankerEval: A Flexible and Extensible Framework for Top-k LLM-based Recommendation"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2507.05880."}