{"as_of":"2026-08-10T02:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:330c4a0ca7099376cd7e9d02f61cf92939b10dcf14f23520db969b8cc1138d4d","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T18:11:25.983511Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2604.09114/citation-record","integrity":"/paper/2604.09114/integrity","json":"/paper/2604.09114/citation-record.json","paper":"/paper/2604.09114"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sentence-level prompts benefit composed im- age retrieval","venue":null,"work_id":"614e9ca0-20ef-4817-80a0-30aecb38b715","year":2024},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:a98cea13f2924126b53cd3e9cdb7e6fe3a452063234ca68860331671b6202189","observation_id":"7c206aed-5a03-4b13-81d4-0b40e3c5938b","resolution":{"observed_at":"2026-05-17T06:41:36.858747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Conditioned and composed image retrieval combining and partially fine-tuning clip-based features","venue":null,"work_id":"338eedd0-5494-455c-9122-22ff409477f1","year":2022},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:f8cf96d54dc1ca625207a8c3d61299ea7b49fa5658a543de00243ad11ad9afdb","observation_id":"1783536b-5f41-4d0c-b84d-e2efecf2441f","resolution":{"observed_at":"2026-05-17T06:41:36.853177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Vqa4cir: Boosting composed image retrieval with visual question answering","venue":null,"work_id":"df8b5042-4f81-4442-881c-601ad6958abd","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:543b0004d5a3ea827ed6b05118c754dc0ec2700f39e44294b329cad3bbc68bb2","observation_id":"d7faa2df-e4cd-47f6-a421-ae0e59e91bb6","resolution":{"observed_at":"2026-05-17T06:41:36.855867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Improving composed image retrieval via contrastive learning with scal- ing positives and negatives","venue":null,"work_id":"f323d6f2-03f2-403f-a26a-28269d4de9ea","year":null},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:2c5d845392a72d5f3ec4b223ad20c60845ebcfe99f81aa7a034d4fbb630cb0dd","observation_id":"e1bb602b-970b-4009-b3ef-28a0b0a932e7","resolution":{"observed_at":"2026-05-17T06:41:36.862421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.07135","last_updated":"2025-07-08T23:02:10Z","snapshot_observed_at":"2026-08-10T01:34:44.777886Z","submitted_at":"2025-07-08T23:02:10Z","title":"FACap: A Large-scale Fashion Dataset for Fine-grained Composed Image Retrieval","version":1},"cited_work":{"arxiv_id":"2507.07135","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.07135","snapshot_observed_at":"2026-07-03T14:08:21.995393Z","title":"arXiv preprint arXiv:2507.07135 (2025)","venue":null,"work_id":"6f1c5227-017d-424b-a379-6a285372d870","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"cited_paper":"/paper/2507.07135","citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:d76f07d5ea263b267a18bb517d170dcdce343c1659a18a91fc22aca1c903ae69","observation_id":"0b3ddad0-3331-4822-ac58-d62e5dc25091","resolution":{"observed_at":"2026-05-11T05:21:00.362109Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Fashionvil: Fashion-focused vision-and- language representation learning","venue":null,"work_id":"db85699b-c5bc-493b-a9ec-f660b7e87b15","year":2022},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:1c5db46f2ff85282e24ed26d0dc5675fb7abf7c2dcfd2f6a2266c49a8f489648","observation_id":"82edfb84-b9f5-4207-9a60-a2c0d6994df7","resolution":{"observed_at":"2026-05-17T06:41:36.880484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Fame-vil: Multi-tasking vision-language model for heterogeneous fashion tasks","venue":null,"work_id":"090ce6cc-3349-447d-ab1e-e94534bbfefa","year":2023},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:5f845d301296642bacce4e9b3d070e256445c527ea0305b8258de836a183caab","observation_id":"98abe24b-abd5-4a2f-b5cc-ebee98b6768e","resolution":{"observed_at":"2026-05-17T06:41:36.883060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Lora: Low-rank adaptation of large language models.ICLR, 1(2):3","venue":null,"work_id":"1883f840-418a-4cb7-a57d-bd815cefde7d","year":2022},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:b861f7e70239cf975aef0dd3502679882af37fae3066cfbdc4c30346d5151674","observation_id":"10eda2cf-92a7-4208-8456-edb27e81062e","resolution":{"observed_at":"2026-05-17T06:41:36.872841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Collm: A large language model for composed image retrieval","venue":null,"work_id":"0b5749ab-3e6e-4f68-8398-0cf63b3db170","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:e1caf0e264db9d3c9d004b245996e67d8df81ecb0fd6b67deb2ccf2f04cbe9b6","observation_id":"09869870-cd90-4eb1-a5ec-acbee4c272bb","resolution":{"observed_at":"2026-05-17T06:41:36.908906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation","venue":null,"work_id":"0b70927a-4a83-4c25-9721-7b781e57bddd","year":2022},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:df9dde5a35288a62f86bf2016aaa337f54bfe399c7c27291b91fa96b2371bc9d","observation_id":"9166ee85-c0c5-407a-8dba-79af5b4bd987","resolution":{"observed_at":"2026-05-17T06:41:36.902979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models","venue":null,"work_id":"72395310-223d-406f-bbfe-10d733b519ee","year":2023},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:1d33ce9a43d16585c682cfaa39b4fe1d4b0c5acf34be7dd5ab8189c7d2ad8ea0","observation_id":"a036bd47-35bb-4928-a676-3bd2641de1ed","resolution":{"observed_at":"2026-05-17T06:41:36.924888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"MM-EMBED: UNIVERSAL MULTIMODAL RETRIEV AL WITH MUL- TIMODAL LLMS","venue":null,"work_id":"56949de9-748c-4f77-aa5e-2e9ad0b57a9a","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:76be496ff5ea316bca2aa5350e1e8e9def4be633d2db3c4f4b6fc723ceb75a86","observation_id":"88e70180-7876-4f80-b7cb-429fe93f290a","resolution":{"observed_at":"2026-05-17T06:41:36.928851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Improved baselines with visual instruction tuning","venue":null,"work_id":"a238b757-a0b1-4563-9f25-1f2f42deb7ae","year":2024},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:22f353be081d3ad77df1b6254144c4e882a24f8392c885823dd1adab9cd77ac8","observation_id":"8d4fef7a-4a0e-4c20-9252-883782982856","resolution":{"observed_at":"2026-05-17T06:41:36.867956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Lamra: Large multimodal model as your advanced retrieval assistant","venue":null,"work_id":"b4f4feaa-de69-404c-bdc3-f7a63fc7b7cd","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:a346a7b2e3ba0bbc7c7c92db540f1ebad2635712bfa72cb26b0cd3210c82cafe","observation_id":"762e0216-316e-4377-8294-6e77067e56e2","resolution":{"observed_at":"2026-05-17T06:41:36.870435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Bi-directional training for composed im- age retrieval via text prompt learning","venue":null,"work_id":"6b910b87-c4c8-4981-919f-afb8bb156505","year":2024},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:20348ac6413d9d8912ef05b45345dcf30b74f70256763c0130d9349519ede614","observation_id":"833d1286-9db0-4f52-88d6-f83c62283997","resolution":{"observed_at":"2026-05-17T06:41:36.877957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Candidate set re-ranking for composed image re- trieval with dual multi-modal encoder.Transactions on Ma- chine Learning Research","venue":null,"work_id":"f3f56fec-42b0-4596-907c-2404425c8095","year":2024},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:e65cb431dbc1de4e46c58815140c7ca1d3620e4cf432ab5c37c15a01a76bf341","observation_id":"84f8934b-033e-4851-aa26-c9f4413ea795","resolution":{"observed_at":"2026-05-17T06:41:36.897126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Imagescope: Unifying language-guided im- age retrieval via large multimodal model collective reason- ing","venue":null,"work_id":"2472d186-8a71-450b-a57b-37d4646d26e8","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:d1d1dddbb033c499362b2da385b2b8d42ed02efd6d2f8fa8adb6154cc7d36065","observation_id":"6ddca16d-6478-4e16-beda-e15cb52e77c3","resolution":{"observed_at":"2026-05-17T06:41:36.888984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Thinking fast and slow: Effi- cient text-to-visual retrieval with transformers","venue":null,"work_id":"dc5c5cd3-18b3-4b4f-8ebd-c6a1ffea4775","year":2021},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:c9645c50e11fe9a65a54404821b9e6956ca4932cc9270b22e2dda4440275236a","observation_id":"362eb7bd-5307-406d-bdb5-9e0d813d05a8","resolution":{"observed_at":"2026-05-17T06:41:36.899815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"3966a6e0-67df-4bba-92c3-28045a79a14f","year":2021},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:0891b2dc24364c29e94c223f270d8c655f8a7aea6d40bdae3292f8258421855c","observation_id":"00e7ddcc-5d0f-4d3f-a8a7-1a8d5c213e0b","resolution":{"observed_at":"2026-05-17T06:41:36.936375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Training- free zero-shot composed image retrieval with local concept reranking","venue":null,"work_id":"110f35f1-455d-468b-ab77-8043af77fa0b","year":2024},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:81b969a819b24500a22181f5fdf812a89799942f84b006f1de6f6c2b54d59451","observation_id":"5f89a6fe-230c-445f-94ce-cb4f59349554","resolution":{"observed_at":"2026-05-17T06:41:36.912949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Fashion- vqa: A domain-specific visual question answering system","venue":null,"work_id":"37e1995e-3d21-401e-8174-6a7f4d59c62d","year":2023},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:37d0820c6cef9d38a9a7b23fb2344bf782e6ee0d530c29f0d4349b7c5ba4aeb2","observation_id":"4d32e2b9-b6e1-4bd3-92a4-fe52a55fd99f","resolution":{"observed_at":"2026-05-17T06:41:36.916665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Target-guided composed image retrieval","venue":null,"work_id":"b652f5ec-8b65-4a98-8b3c-56a1af126bb7","year":2023},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:673c0903481d7c3cfbeb8a90cc8ee4c00435dab06d3f96f8cc251357fd3c2742","observation_id":"952d8b43-6a77-4e8d-bd53-5e53691cb9ad","resolution":{"observed_at":"2026-05-17T06:41:36.932674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Fashion iq: A new dataset towards retrieving images by natural language feedback","venue":null,"work_id":"23de6a5b-50b1-4c98-a70c-8898131b4c1d","year":2021},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:34ecd6a908abfba288db57f5f902ba81a6ce226f1bd6efed6857a0594f4a0b46","observation_id":"b2aa4bc0-9b05-4dc1-973a-8d8d0a1d11b0","resolution":{"observed_at":"2026-05-17T06:41:36.920698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Square: Se- mantic query-augmented fusion and efficient batch reranking for training-free zero-shot composed image retrieval","venue":null,"work_id":"15e909d4-0ddb-4cfc-8157-fa58238e9f3c","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:98d00a1ecc127f66713b373343cb8d8c8d676ebce929ccefc802b50f678e594e","observation_id":"c4eef3e8-5c07-450a-a2eb-284194f841b2","resolution":{"observed_at":"2026-05-17T06:41:36.891909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Setr: A two-stage semantic- enhanced framework for zero-shot composed image re- trieval","venue":null,"work_id":"734a6bec-2597-4af9-a403-fed405bbf390","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:4dfed6824b9378a98c124804639019a7c523c6bb39e6eb3b6b8ef255208b70fc","observation_id":"f94ec52a-5665-45fe-91ff-93ecc6bb61b4","resolution":{"observed_at":"2026-05-17T06:41:36.875325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.17796","last_updated":"2025-05-23T12:15:23Z","snapshot_observed_at":"2026-08-10T01:34:45.337180Z","submitted_at":"2025-05-23T12:15:23Z","title":"DetailFusion: A Dual-branch Framework with Detail Enhancement for Composed Image Retrieval","version":1},"cited_work":{"arxiv_id":"2505.17796","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.17796","snapshot_observed_at":"2026-07-02T15:17:07.293578Z","title":"Detailfusion: A dual-branch framework with detail enhancement for composed image retrieval","venue":null,"work_id":"d415ef21-b8d2-49a1-b913-b18b9b32c0f6","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"cited_paper":"/paper/2505.17796","citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:3889708eff9944ac669b9cb51ab17ec16869cc7e8ae17fde11c8c85c11baf984","observation_id":"bd154788-16db-4ba3-92d1-516fb324fc4e","resolution":{"observed_at":"2026-05-11T05:21:00.335945Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"UniFashion: A unified vision-language model for multimodal fashion retrieval and generation","venue":null,"work_id":"165f7a46-2e97-4a85-939d-75a4ea04c34c","year":2024},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:1e7b2772cc75c1e102f3a29f2c252f82eccf1c2a74c3c6f416ccdfd1a54bf862","observation_id":"de74d39f-b6be-4a15-b7a5-8bf158acc594","resolution":{"observed_at":"2026-05-17T06:41:36.865106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Progressive learning for image retrieval with hybrid-modality queries","venue":null,"work_id":"f01b04f8-e3b2-4f60-8e33-85f91dbcce35","year":2022},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:cb081eb029febc814bfc9f530241a108f6b3844ce66a1a51187576187dc30d28","observation_id":"7ff9e571-e811-4b12-ad10-5f560de6e9fb","resolution":{"observed_at":"2026-05-17T06:41:36.894401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models","venue":null,"work_id":"f8ea7cb3-eb1b-4290-adcd-ca2da467355c","year":2025},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:d47bf1b408dcd5e235615c1f8379e1e182a215650f771d151d0641ac7ea81818","observation_id":"318149ca-8bba-44c8-af97-4f70f0cd24c5","resolution":{"observed_at":"2026-05-17T06:41:36.886052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-05T21:23:00.469572Z","title":"dress” subset for Figure 6 and Figure 7, and “shirt","venue":null,"work_id":"8fc47568-d8b2-4089-ac76-5a994d8dc913","year":2021},"citing_paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T18:11:25.983511Z"},"links":{"citing_paper":"/paper/2604.09114"},"observation_digest":"sha256:be533bd40e8cc03dabc5a86b89f51cd83174e50485dad6329095822f0c08d77c","observation_id":"0d43ea17-cfd4-4d8b-b497-2d911875f141","resolution":{"observed_at":"2026-05-17T06:41:36.905773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.09114","last_updated":"2026-04-10T08:50:47Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T22:58:04.106299Z","submitted_at":"2026-04-10T08:50:47Z","title":"FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":2,"verified_fuzzy":28},"total_outbound_references":30},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2604.09114."}