{"as_of":"2026-08-10T09:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b53dee15002376cf22f905d7dac62fda6176938a02c4078d4af906a3c65ba3b","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T21:17:45.272123Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2508.14910/citation-record","integrity":"/paper/2508.14910/integrity","json":"/paper/2508.14910/citation-record.json","paper":"/paper/2508.14910"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:46.016815Z","title":"Sinkhorn distances: Lightspeed computation of optimal transport","venue":null,"work_id":"c9ff00d0-eae8-4215-b18a-8580d3ae0894","year":2013},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:41.646010Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:725344c3fb9ab15ced27fd66f99963738e3bdf3bfd1114b82dedef8b6cc8aaee","observation_id":"e7d8b4db-fe70-4e95-879f-54dc37fcc367","resolution":{"observed_at":"2026-08-05T21:17:46.021202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:46.002632Z","title":"Moshi: a speech-text foundation model for real-time dialogue","venue":null,"work_id":"9cc57cb4-a33e-4e93-907c-f4a7c31307c2","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:41.721681Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:3161d0ed2b76115feef31f115a3dc82ef72ac0cec5ba6a6b6ff3b5768e04245d","observation_id":"1e377051-2f19-4f6b-817d-79f2c2ae80f7","resolution":{"observed_at":"2026-08-05T21:17:46.007252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.988608Z","title":"A review of modern recommender systems using generative models (gen-recsys)","venue":null,"work_id":"cc1d8b6b-b284-4c2c-9256-55b221523e5b","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:41.827554Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:d80b7e7d78ea753179d0f61acc8715f330da8d756675f3fb5f9662269bcd1363","observation_id":"c376b4a7-6f70-475b-ba70-48e8048583cd","resolution":{"observed_at":"2026-08-05T21:17:45.992984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.974495Z","title":"Recommender forest for efficient retrieval","venue":null,"work_id":"0da0dc82-19d6-4afb-8725-1c27647de417","year":2022},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:41.928262Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:68731d7a71e3a3e111ea2f2d58240627f474e1503fcb4ab4e4fcbd0d97ef806b","observation_id":"6c754ed7-dc5e-4562-a2c0-bc816857a127","resolution":{"observed_at":"2026-08-05T21:17:45.978810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.959819Z","title":"Recommendation as language processing (RLP): A unified pretrain, personalized prompt & predict paradigm (P5)","venue":null,"work_id":"0b569a87-c085-4ba0-9ca8-bff78df13f9a","year":2022},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.003917Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:2a95eb9e8c5fb458a84af458486df86ab0bfba605d069b74df5a1e204bb9ed87","observation_id":"0d3606cc-97f8-47cd-ac87-744ee1d171ec","resolution":{"observed_at":"2026-08-05T21:17:45.964971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.944996Z","title":"InProceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016)","venue":null,"work_id":"e0b623c3-78e0-4c7a-8c3b-8561979cf8ab","year":2016},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.090105Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:18734ee80dbfb1cbf2a2730201ea294ffe6358432cb1acd54d8d64d22e75240b","observation_id":"22c3c3e2-8bcc-41f0-9756-5771c318f421","resolution":{"observed_at":"2026-08-05T21:17:45.949604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.930838Z","title":"A survey on user behavior modeling in recommender systems","venue":null,"work_id":"36b93915-64c1-4bbb-a996-dd388f65e741","year":2023},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.141018Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:b1bc3b4008572224a7552619c4f347784c7aee6473d5e397864d4e15f636435b","observation_id":"c00d7cee-740d-4855-b482-3658d724391b","resolution":{"observed_at":"2026-08-05T21:17:45.935144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"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":"2026-08-05T21:17:42.237786Z","title":"Session-based rec- ommendations with recurrent neural networks","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.237786Z"},"links":{"cited_paper":"/paper/1511.06939","citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:bdfc117475fbe42d37202b017214b8cae3f67361dba5709d6151d25433835064","observation_id":"376b56a3-2e5a-462b-8163-df27a2deb5bb","resolution":{"observed_at":"2026-08-05T21:17:42.237786Z","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":"2026-08-05T21:17:45.915989Z","title":null,"venue":null,"work_id":"a737b574-5dcb-46ae-a40b-50ab9c5bf9df","year":2023},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.340213Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:b81e4b24d49cc659ff90510ea3bb50d0ec059abe6192127798cabce54d4b3a21","observation_id":"35ae4ee0-99e6-44e9-8a68-e96cbdb9b2e4","resolution":{"observed_at":"2026-08-05T21:17:45.920152Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03952","last_updated":"2026-04-20T06:05:54Z","snapshot_observed_at":"2026-08-03T00:56:47.399756Z","submitted_at":"2024-03-06T18:56:36Z","title":"Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03952","snapshot_observed_at":"2026-08-05T21:17:42.419363Z","title":"Bridging language and items for retrieval and recommendation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.419363Z"},"links":{"cited_paper":"/paper/2403.03952","citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:fd9fdf00b8bd528858c5da213bd90ef6d477ca7323d98e33e1b74fa7f93228fe","observation_id":"d6787225-175e-4daf-9396-ffa4afa68f67","resolution":{"observed_at":"2026-08-05T21:17:42.419363Z","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":"2026-08-05T21:17:45.900717Z","title":"In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (2023)","venue":null,"work_id":"5ad0ffe8-8890-4db4-8f92-eab98d329824","year":2023},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.477814Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:b45f7e03ce94a588770800ae66696bb4ad70f746faee5e94a61d7f2f9e3d6026","observation_id":"40d4fdcd-0ec7-4fc4-90a0-0c4bcd0b8e6b","resolution":{"observed_at":"2026-08-05T21:17:45.905849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.886006Z","title":"Billion-scale similarity search with GPUs","venue":null,"work_id":"f89ab322-ca0f-4096-9000-620428367863","year":2019},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.573356Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:86b770c87c3244c8acf361f1f119c796bd946b36bdb6a6d2f812494a76a1214a","observation_id":"229eb17c-a055-4966-b82d-c164453ec9d0","resolution":{"observed_at":"2026-08-05T21:17:45.890465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.871693Z","title":"Self-attentive sequential recommendation","venue":null,"work_id":"42b0ddd7-1f04-4a53-81c7-899e9811fac2","year":2018},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.645835Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:0caef613ad4445a4b3ac06b6cb599cec72b655c95c31d184a8caea4ab4eb5a4a","observation_id":"dff4fcb2-91e6-447b-beba-44bed946c99e","resolution":{"observed_at":"2026-08-05T21:17:45.876031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-05T21:17:42.743690Z","title":"B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.743690Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:a0d7d35fae91efe19244aaab141465ec93996a49f9e16cbdf98f893d56390318","observation_id":"4db8d6a6-d2d7-44ab-b76d-1be8e1050635","resolution":{"observed_at":"2026-08-05T21:17:42.743690Z","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":"2026-08-05T21:17:45.857023Z","title":"Turning dross into gold loss: is bert4rec really better than sasrec? In Proceedings of the 17th ACM Conference on Recommender Systems (2023)","venue":null,"work_id":"97301a48-72a7-4933-a4d7-c45cfe0547a8","year":2023},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.836613Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:0f6a20fbf928db8c01733836768f5579a1b44a1abe45109f1ee1f1c914f0e256","observation_id":"a6d637e5-4811-4e90-acd8-c8d5afe9fc5b","resolution":{"observed_at":"2026-08-05T21:17:45.861440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.843007Z","title":"Autoregressive image gener- ation using residual quantization","venue":null,"work_id":"1d3661b3-4df8-48b6-8096-787146b1a3a1","year":2022},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.906572Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:ca1e33af0f5dde2b0ec7c3d666ff60713eaa6a02bcee529195b82a3eb5b1e83c","observation_id":"a66cb15c-1c69-4343-b7de-f31b5ceefbaf","resolution":{"observed_at":"2026-08-05T21:17:45.847374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.828366Z","title":"Text is all you need: Learning language representations for sequential recommendation","venue":null,"work_id":"271bf051-1217-43df-9853-f910b6372791","year":2023},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:42.986993Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:7ce11ffcd26c462ae34a8171603b083802f896b3dce7251505ba97efe72f135d","observation_id":"55f2590d-e20d-4d61-b92d-83f5467b1361","resolution":{"observed_at":"2026-08-05T21:17:45.833184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.814445Z","title":"Embedding optimization for training large-scale deep learning recommendation systems with embark","venue":null,"work_id":"67e8dbf0-f5a2-48b9-a3ee-939b86bd4018","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.068732Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:578817cf1cbafff44953ec9067aa259c97659d076e0bf85b02d6b8fced3c468c","observation_id":"bf26e42c-e21e-4878-bbb8-6fa1a1b6e684","resolution":{"observed_at":"2026-08-05T21:17:45.818547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.800290Z","title":"Hierarchical gating networks for sequential recom- mendation","venue":null,"work_id":"27c91aee-417b-44b7-9fa8-2cf77a389ea9","year":2019},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.170998Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:43ea490e1509773478e60e68f323a07cf87906793ab10a5f627ef6fb57406c12","observation_id":"1fb407f8-3296-47cc-9129-31b49b46c421","resolution":{"observed_at":"2026-08-05T21:17:45.805141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.785373Z","title":"Image-based recom- mendations on styles and substitutes","venue":null,"work_id":"98403d81-4685-48c1-895f-41215c52a3eb","year":2015},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.241313Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:ae760e0e664f1b4e8eda5b1dec454f8210e215664f9edd7524d7cc05290bab06","observation_id":"2ad93c47-a2bf-40a1-8694-737fa66355e0","resolution":{"observed_at":"2026-08-05T21:17:45.789727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.771172Z","title":"In Findings of the Association for Computational Linguistics (2022)","venue":null,"work_id":"2bea2095-e931-49e5-8674-0af3b31a7afc","year":2022},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.293427Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:b752ddac6ea9b66725db89c57f6439132bb96973310e673a9388b6000deebafe","observation_id":"b1705045-30d7-41a3-89df-98dbdf18c8e7","resolution":{"observed_at":"2026-08-05T21:17:45.775406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.03748","last_updated":"2019-01-22T18:47:12Z","snapshot_observed_at":"2026-07-06T06:49:24.960992Z","submitted_at":"2018-07-10T16:52:11Z","title":"Representation Learning with Contrastive Predictive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.03748","snapshot_observed_at":"2026-08-05T21:17:43.349530Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.349530Z"},"links":{"cited_paper":"/paper/1807.03748","citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:c54a850d536e20714ba2cad50a2774952822c2dcca944527edb475e12dfb0859","observation_id":"d1b58326-ad2a-42ff-86ef-862caf84e3bd","resolution":{"observed_at":"2026-08-05T21:17:43.349530Z","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":"2026-08-05T21:17:45.757212Z","title":"TIGER reviewing process","venue":null,"work_id":"a7b3f633-09ab-4c75-9534-d654c926763c","year":null},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.411596Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:c55e78ab7bd4b37ae40ef913faf403ec1433e9110c76ed3ca32b264010b04151","observation_id":"0605460c-d202-4fe2-b67c-a544ba85e0b9","resolution":{"observed_at":"2026-08-05T21:17:45.761636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.743522Z","title":"H., Vu, T., Heldt, L., Hong, L., Tay, Y., Tran, V","venue":null,"work_id":"681f02e1-6538-45bb-a4b1-7b8d9d70333b","year":2023},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.491336Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:865479b0448df5fd3a1b736445196845d2fd4aff48bffd7e7134f5dbd58c49be","observation_id":"ccb9d1ea-8001-486a-8bdf-8dd7a2c38d38","resolution":{"observed_at":"2026-08-05T21:17:45.747865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00430","last_updated":"2025-02-19T09:23:03Z","snapshot_observed_at":"2026-07-06T19:59:24.405557Z","submitted_at":"2024-11-30T10:56:30Z","title":"Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.00430","snapshot_observed_at":"2026-08-05T21:17:43.595591Z","title":"Y., Guo, W., Liu, Y., Guo, H., Lian, D., Tang, R., and Chen, E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.595591Z"},"links":{"cited_paper":"/paper/2412.00430","citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:ee2611d6cfb0f728052ef5bcc607a52d5fd5575704d8b38fdcf93869e6f494bd","observation_id":"120cf197-ba00-4293-a067-7d23b3f8a7a9","resolution":{"observed_at":"2026-08-05T21:17:43.595591Z","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":"2026-08-05T21:17:45.730839Z","title":"Better generalization with semantic ids: A case study in ranking for recommendations","venue":null,"work_id":"90550a1b-b416-49dc-830a-415b50030763","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.692305Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:18613badb14347ecba1c1bac92d57e890f8daa3221eff8e12bd24f765440f882","observation_id":"97fe4f7a-bbeb-426c-952e-afdcdd1a2a14","resolution":{"observed_at":"2026-08-05T21:17:45.734803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.716205Z","title":"Improved Deep Metric Learning with Multi-class N-pair Loss Objective","venue":null,"work_id":"203b07b9-9d37-420a-a29b-1424a0d52aca","year":2016},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.752074Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:72187b94f14204f6e04ad50f1e1c3ebcdcb3f0b6abeb349b0fea60d4ce8bd110","observation_id":"5a4206bb-9987-4c79-b224-da7b92039858","resolution":{"observed_at":"2026-08-05T21:17:45.721495Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.702141Z","title":"In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019)","venue":null,"work_id":"bf64cb59-e2ac-4f1d-a033-45714b027c65","year":2019},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.852765Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:de8700d2ee4aa9518e4ea1268d8b82b6574203314e59703e720bf703d02d654c","observation_id":"0c7bd96c-2073-4fbc-ba8a-0451bb5d7c79","resolution":{"observed_at":"2026-08-05T21:17:45.706599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.688016Z","title":"In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (2018)","venue":null,"work_id":"918113b3-4c04-4f63-ba8d-228e6be5163d","year":2018},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.912784Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:b71b88017001a89f35362460adea16f19931ee5b71ecf9359497082879d497ae","observation_id":"cae3c609-7311-4d5b-b24a-b0fb369d9d41","resolution":{"observed_at":"2026-08-05T21:17:45.692428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.674256Z","title":"Q., Dehghani, M., Ni, J., Bahri, D., Mehta, H., Qin, Z., Hui, K., Zhao, Z., Gupta, J., Schuster, T., Cohen, W","venue":null,"work_id":"09d279e5-5271-4a35-8c70-5e71ebaad97d","year":2022},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.988525Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:8265810cb6dd5d88e390b644c941bfcc2917fb142d9bd3277f61f98aecbcd932","observation_id":"628f9dab-0945-4562-9d9c-25c397e1f3d6","resolution":{"observed_at":"2026-08-05T21:17:45.678496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.659281Z","title":"Neural discrete representation learning","venue":null,"work_id":"496b87f4-3c76-4d20-bb86-493caf23d66e","year":2017},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:43.997136Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:0005b0eefdfd86c89591d2a0928f6aacba757c421e875a3d330a6484683d3a35","observation_id":"f94b0004-dae1-4e26-8092-faa257ac6b65","resolution":{"observed_at":"2026-08-05T21:17:45.665115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.645168Z","title":"N., Kaiser, Ł., and Polosukhin, I","venue":null,"work_id":"51348c5c-bdb8-4866-9a39-873bd8768802","year":2017},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.114540Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:69da72045d376c9cb9d75c8305f414cd21a54f95ab1b668858acf9ccef687311","observation_id":"661de1b7-0a55-4e59-96b1-b0dde5873a5e","resolution":{"observed_at":"2026-08-05T21:17:45.649744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.630172Z","title":"Learnable item tokenization for generative recommendation","venue":null,"work_id":"59a3a7b4-9be9-45c5-a27b-fc6fa6bb6dc4","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.192379Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:e14118c0c27125953f254f5ae27032991a131fc35028661329a3a68deaa4f74d","observation_id":"b6816819-76c3-4d47-b3c7-827e6b8fda53","resolution":{"observed_at":"2026-08-05T21:17:45.634412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.616647Z","title":"Eager: Two-stream generative recommender with behavior- semantic collaboration","venue":null,"work_id":"98bc28a9-20c7-44f7-b9e8-61f74f442982","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.321138Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:50edc7460d481e1b7296dbf53f78041eac0c18dff942196f1f49f94e431cc722","observation_id":"3ce93be6-fd11-4dc4-9cb3-cdb5d0f98ad1","resolution":{"observed_at":"2026-08-05T21:17:45.620989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.602532Z","title":"A survey on large language models for recommendation","venue":null,"work_id":"734628bd-d03a-48a1-aa19-f9584ae1b711","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.431911Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:f426bc42f31898d0fe424b7f6a8708c9a2375d5873dd0ec0f5421391c383ca6b","observation_id":"373576df-7150-42fd-b066-871401ccc777","resolution":{"observed_at":"2026-08-05T21:17:45.607208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.588403Z","title":"Session-based recom- mendation with graph neural networks","venue":null,"work_id":"a3aaeb4e-f633-408d-a801-76dc8dced558","year":2019},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.547727Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:3768f02a779196cf1f219e94498c8a81d227e70c66c4ca245f4e34ca2d76a9b0","observation_id":"bdcbd739-5364-4ab1-be22-01ed40e67a4d","resolution":{"observed_at":"2026-08-05T21:17:45.592973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.573775Z","title":"Uniaudio: An audio foundation model toward universal audio generation","venue":null,"work_id":"e81ee902-c197-4b20-83e4-bfdb926877d9","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.628329Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:d3e1979d2fca8ea7e4c25dc7bdc8aaa7f2f06924af22c64e75e7509a630a0d6d","observation_id":"60dede86-7919-48c9-8394-b0afd67365fc","resolution":{"observed_at":"2026-08-05T21:17:45.578146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:44.717353Z","title":"Soundstream: An end-to-end neural audio codec, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.717353Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:6b1d8f608814ff485dd5a5b1e35ab539b35778720fe03830b5e23e3fbe62af67","observation_id":"224391cd-9722-4454-9a81-abe2c02fcc9f","resolution":{"observed_at":"2026-08-05T21:17:44.717353Z","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":"2026-08-05T21:17:45.549547Z","title":"In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023)","venue":null,"work_id":"0f54901f-f392-491e-91cf-e4a1463609f5","year":2023},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.826315Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:4a98d31d257735662894834c98f5057a100e997d25bdbaa406beaf4c0e1a6e7c","observation_id":"3a06829c-c89f-405e-bf32-6721cc249615","resolution":{"observed_at":"2026-08-05T21:17:45.553816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.535651Z","title":"Actions speak louder than words: trillion-parameter sequential transducers for generative recommendations","venue":null,"work_id":"d666d30b-2b54-40c2-8d5c-e94ded0fa88c","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:44.962255Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:95e2e84d00f7094b3463a2906f4be29ba5469fea2df4dc79c3eb52542d6501c2","observation_id":"8d6523d7-5d0d-4d75-97f5-e34b3ccc00b8","resolution":{"observed_at":"2026-08-05T21:17:45.540178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.521391Z","title":"Multi- modal quantitative language for generative recommendation","venue":null,"work_id":"f3bfca2a-2b07-40c5-a089-d9efafe9f1b9","year":2025},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.033812Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:74f8c442541942aaf54ac6bd34fa3d517d45b7151c995f0934ecfb5a6a13550d","observation_id":"d8b22f9a-4979-4041-9f51-67bec672b848","resolution":{"observed_at":"2026-08-05T21:17:45.525654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.506377Z","title":"Sigmoid loss for language image pre-training","venue":null,"work_id":"c2acbadb-c17b-4bac-a891-e9c86f70a872","year":2023},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.113403Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:ef52f9405c9aeb18c4ef3ef71be1148f4890a6112b687020f89cc3f20001f358","observation_id":"a51beb2f-faf4-45b8-a93f-03e19f4da116","resolution":{"observed_at":"2026-08-05T21:17:45.511475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.491728Z","title":"X., and Wen, J.-R","venue":null,"work_id":"68ce8b27-60a3-4375-8953-778aee10daf3","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.117774Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:7cd4ce8733768a8f201eb11d3e3d2b4894ee19e7ba504f199e3cf3637aca5de7","observation_id":"a19237cf-cf40-4588-b9fb-654df27cba4d","resolution":{"observed_at":"2026-08-05T21:17:45.496302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.477015Z","title":"S., Xu, J., W ang, D., Liu, G., and Zhou, X","venue":null,"work_id":"79ac9c09-598a-4a67-b69f-410dd6b03509","year":2019},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.122605Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:7b2303c23595387c78199bf3f19cb0b379f2233e6df963638f1bdba984ca6406","observation_id":"b3da906e-7c6c-48f3-80ce-756e9c65104f","resolution":{"observed_at":"2026-08-05T21:17:45.481428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.460230Z","title":"Recommender systems in the era of large language models (llms)","venue":null,"work_id":"02f7045f-7266-494b-803a-ab7e112b928a","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.127067Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:e8589a15686d599106be8821762d7097f1aeed46c8a8538529600fe73732f99a","observation_id":"6b916f19-dc11-41fa-9afd-d4744cffd63f","resolution":{"observed_at":"2026-08-05T21:17:45.465438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.444274Z","title":"X., Chen, M., and Wen, J.-R","venue":null,"work_id":"71103dcb-bf80-446d-a4fa-690185d7a162","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.133423Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:58b071441cd304c8ec8b87fd1cf1fea95fcd60dc27fa1c30390642ca13a19774","observation_id":"0ed76635-e5fe-48cf-aec9-cbe6a9edd761","resolution":{"observed_at":"2026-08-05T21:17:45.448699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.429927Z","title":"X., Zhu, Y., W ang, S., Zhang, F., W ang, Z., and Wen, J","venue":null,"work_id":"38e06444-3b9e-49d8-bcd7-82cb8fa10ef2","year":2020},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.171691Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:1f86a7be54960364a6d768b05a23e3e76a5e37efd6de245169873f1b65941299","observation_id":"933b73c5-03d1-4d53-aef3-02922956fe0a","resolution":{"observed_at":"2026-08-05T21:17:45.434304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.415560Z","title":"Learning tree-based deep model for recommender systems","venue":null,"work_id":"5370fe3c-7df8-4faa-b5c7-32bb3b8c5bc2","year":2018},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.221981Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:d6e48bd328892ab1467bf2744c16b203261fef0268e6f0874273832ad852059c","observation_id":"c94423bc-a59e-4abe-af2f-252e63c60bb0","resolution":{"observed_at":"2026-08-05T21:17:45.420214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.399357Z","title":"CoST: Contrastive quantiza- tion based semantic tokenization for generative recommendation","venue":null,"work_id":"4444eef8-ac21-46be-97ef-5d1efb5173ee","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.268061Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:26e3c11e048a47714759e3b8f3213fba3c3773d0026e8ddde316918c19c64ebd","observation_id":"593661d6-9df6-4b58-8cd6-9d6c497d85c9","resolution":{"observed_at":"2026-08-05T21:17:45.404305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T21:17:45.382232Z","title":"Generative pre-trained speech language model with efficient hierarchical transformer","venue":null,"work_id":"9c0551a1-5ab2-45c2-89e1-200f66213121","year":2024},"citing_paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T21:17:45.272123Z"},"links":{"citing_paper":"/paper/2508.14910"},"observation_digest":"sha256:464b174f171010fc7e6d1b7d038dc748c5ec6518d4416d6d79b182a7f4994f08","observation_id":"a466bb6e-2975-40c6-b528-4fc94e302246","resolution":{"observed_at":"2026-08-05T21:17:45.388668Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.14910","last_updated":"2025-08-12T17:06:55Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-09T15:38:05.945716Z","submitted_at":"2025-08-12T17:06:55Z","title":"Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":42},"total_outbound_references":50},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2508.14910."}