{"as_of":"2026-08-16T14:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6afd221addf6842dacfbe67a8d68676cf75bf270ff7e91ccb34aadfed628747","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:19:47.047794Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-27T05:30:36.170712Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.19829","last_updated":"2024-10-25T07:30:45Z","snapshot_observed_at":"2026-08-16T13:30:18.524234Z","submitted_at":"2024-07-29T09:31:19Z","title":"Generative Retrieval with Preference Optimization for E-commerce Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.19829","snapshot_observed_at":"2026-08-15T16:19:47.047794Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.06887","last_updated":"2025-09-10T17:17:28Z","snapshot_observed_at":"2026-08-15T16:11:58.368798Z","submitted_at":"2025-09-08T17:08:26Z","title":"UniSearch: Rethinking Search System with a Unified Generative Architecture","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T16:19:47.047794Z"},"links":{"cited_paper":"/paper/2407.19829","citing_paper":"/paper/2509.06887"},"observation_digest":"sha256:a730378c4ae2908f6a1a6939c6782645c5e35194f6956b102f7ced0612274d4e","observation_id":"0dfd45ce-3b04-44ae-84b1-2d5400f94dcb","resolution":{"observed_at":"2026-08-15T16:19:47.047794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.19829","last_updated":"2024-10-25T07:30:45Z","snapshot_observed_at":"2026-08-16T13:30:18.524234Z","submitted_at":"2024-07-29T09:31:19Z","title":"Generative Retrieval with Preference Optimization for E-commerce Search","version":2},"cited_work":{"arxiv_id":"2407.19829","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.19829","snapshot_observed_at":"2026-06-27T05:30:36.170712Z","title":null,"venue":null,"work_id":"3c786655-5b64-4141-ab80-d0bab0c4e231","year":2024},"citing_paper":{"arxiv_id":"2602.23620","last_updated":"2026-04-27T03:42:57Z","snapshot_observed_at":"2026-08-11T05:12:13.902859Z","submitted_at":"2026-02-27T02:53:17Z","title":"Synthetic Data Powers Product Retrieval for Long-tail Knowledge-Intensive Queries in E-commerce Search","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-15T19:27:17.802597Z"},"links":{"cited_paper":"/paper/2407.19829","citing_paper":"/paper/2602.23620"},"observation_digest":"sha256:183ae7bf97972bb35d5a295ea6ebfe17dc2009370f61e40d16d8a407cc61720b","observation_id":"3457f512-1dd3-4b36-a6c5-4a3aa91de7c9","resolution":{"observed_at":"2026-05-15T19:30:16.795453Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.19829","last_updated":"2024-10-25T07:30:45Z","snapshot_observed_at":"2026-08-16T13:30:18.524234Z","submitted_at":"2024-07-29T09:31:19Z","title":"Generative Retrieval with Preference Optimization for E-commerce Search","version":2},"cited_work":{"arxiv_id":"2407.19829","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.19829","snapshot_observed_at":"2026-06-27T05:30:36.170712Z","title":null,"venue":null,"work_id":"3c786655-5b64-4141-ab80-d0bab0c4e231","year":2024},"citing_paper":{"arxiv_id":"2602.23964","last_updated":"2026-04-28T09:24:17Z","snapshot_observed_at":"2026-08-16T05:00:16.829040Z","submitted_at":"2026-02-27T12:17:06Z","title":"RAD-DPO: Robust Adaptive Denoising Direct Preference Optimization for Generative Retrieval in E-commerce","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-15T19:05:00.826786Z"},"links":{"cited_paper":"/paper/2407.19829","citing_paper":"/paper/2602.23964"},"observation_digest":"sha256:813501ac8ae7f7d573725e4fbe3709e7c39e147945599f09379688cf5d2490e5","observation_id":"367969ef-af93-4eb4-a5e7-b423c1656026","resolution":{"observed_at":"2026-05-15T19:06:30.674909Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.19829","last_updated":"2024-10-25T07:30:45Z","snapshot_observed_at":"2026-08-16T13:30:18.524234Z","submitted_at":"2024-07-29T09:31:19Z","title":"Generative Retrieval with Preference Optimization for E-commerce Search","version":2},"cited_work":{"arxiv_id":"2407.19829","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.19829","snapshot_observed_at":"2026-06-27T05:30:36.170712Z","title":null,"venue":null,"work_id":"3c786655-5b64-4141-ab80-d0bab0c4e231","year":2024},"citing_paper":{"arxiv_id":"2602.23978","last_updated":"2026-04-28T11:08:55Z","snapshot_observed_at":"2026-08-15T13:44:01.985016Z","submitted_at":"2026-02-27T12:39:38Z","title":"Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-15T18:58:29.985859Z"},"links":{"cited_paper":"/paper/2407.19829","citing_paper":"/paper/2602.23978"},"observation_digest":"sha256:015d94ded0cc7eb7689a8c9c81cd1d6ef78219b08cbce510db0f77b9f4efa494","observation_id":"cda1973d-4e2b-46b1-96b1-8272cc379088","resolution":{"observed_at":"2026-05-15T19:00:15.542062Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.19829","last_updated":"2024-10-25T07:30:45Z","snapshot_observed_at":"2026-08-16T13:30:18.524234Z","submitted_at":"2024-07-29T09:31:19Z","title":"Generative Retrieval with Preference Optimization for E-commerce Search","version":2},"cited_work":{"arxiv_id":"2407.19829","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.19829","snapshot_observed_at":"2026-06-27T05:30:36.170712Z","title":null,"venue":null,"work_id":"3c786655-5b64-4141-ab80-d0bab0c4e231","year":2024},"citing_paper":{"arxiv_id":"2606.13533","last_updated":"2026-06-22T06:53:02Z","snapshot_observed_at":"2026-08-15T11:45:10.239946Z","submitted_at":"2026-06-11T16:21:13Z","title":"OneRetrieval: Unifying Multi-Branch E-commerce Retrieval with an Editable Generative Model","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T05:21:23.186557Z"},"links":{"cited_paper":"/paper/2407.19829","citing_paper":"/paper/2606.13533"},"observation_digest":"sha256:4150adb67ad588d26c762cdddb57b68de80741487423e7716d49545fe444bae6","observation_id":"c07a1e78-1748-4ee7-a38c-d400438cdd0c","resolution":{"observed_at":"2026-06-27T05:30:36.172105Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.19829","last_updated":"2024-10-25T07:30:45Z","snapshot_observed_at":"2026-08-16T13:30:18.524234Z","submitted_at":"2024-07-29T09:31:19Z","title":"Generative Retrieval with Preference Optimization for E-commerce Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.19829","snapshot_observed_at":"2026-08-06T00:42:54.087119Z","title":"InProceedings of the 2020 Conference on Empirical Methods in Natural Lan- guage Processing, 6769–6781","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.03150","last_updated":"2026-08-04T05:29:02Z","snapshot_observed_at":"2026-08-14T21:58:19.085237Z","submitted_at":"2026-08-04T05:29:02Z","title":"UniGD: A Unified Generative-Discriminative Framework for Industrial Retrieval","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T00:42:54.087119Z"},"links":{"cited_paper":"/paper/2407.19829","citing_paper":"/paper/2608.03150"},"observation_digest":"sha256:c59b8ecc3805e45072656889ad3a6463b79e62c4e8a408a7ae04a9aa6dc1d290","observation_id":"61dc917f-d514-40e0-9314-f7673a22258c","resolution":{"observed_at":"2026-08-06T00:42:54.087119Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2407.19829/citation-record","integrity":"/paper/2407.19829/integrity","json":"/paper/2407.19829/citation-record.json","paper":"/paper/2407.19829"},"outbound":[],"paper":{"arxiv_id":"2407.19829","last_updated":"2024-10-25T07:30:45Z","latest_version":2,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-16T13:30:18.524234Z","submitted_at":"2024-07-29T09:31:19Z","title":"Generative Retrieval with Preference Optimization for E-commerce Search"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.19829."}