{"as_of":"2026-08-07T23:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b4ac13ed03a606d1e46e40a8f12008a27f4e557c4a6b16ababf2b1ddebeb5a13","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:33:35.505115Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-09T20:05:21.069489Z","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":"2026-05-11T15:26:08.149245Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"cited_work":{"arxiv_id":"2506.21476","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.21476","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2506.21476 , year=","venue":null,"work_id":"c7039388-8d6a-454d-871e-650ccad182ec","year":null},"citing_paper":{"arxiv_id":"2605.00265","last_updated":"2026-05-29T06:53:40Z","snapshot_observed_at":"2026-08-07T17:06:17.077296Z","submitted_at":"2026-04-30T22:02:19Z","title":"Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-09T20:05:21.069489Z"},"links":{"cited_paper":"/paper/2506.21476","citing_paper":"/paper/2605.00265"},"observation_digest":"sha256:191d2899601612fbf42d0a2c0776e9286c06db71dd0fe2d61e1bd28d19828093","observation_id":"95454e06-4331-4bbe-8af5-073e2ee9594b","resolution":{"observed_at":"2026-05-11T15:26:08.152519Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.21476/citation-record","integrity":"/paper/2506.21476/integrity","json":"/paper/2506.21476/citation-record.json","paper":"/paper/2506.21476"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:41.963391Z","title":"Emergent visual- semantic hierarchies in image-text representations","venue":null,"work_id":"ef6ec2a7-35d6-41da-9d80-f285a2055140","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:30.772338Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:da3cf2297671e69d41f857a51ea8b3967a8269fc69135b2de9efb5226677e35b","observation_id":"83d1a7ed-358b-499d-a14e-9d2830a8d932","resolution":{"observed_at":"2026-08-06T22:33:42.074702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:41.757842Z","title":"Multi-relational poincar ´e graph embeddings","venue":null,"work_id":"5bc243fd-8f02-4844-877b-baa7ed3ded35","year":2019},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:30.926779Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:02c8445c878e842d89fe0f8bda1b46fdd314695397d0cb06353b64c467562f3e","observation_id":"8ae2afdb-cce2-43ea-950b-cae969c0e5a2","resolution":{"observed_at":"2026-08-06T22:33:41.853157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:31.056105Z","title":"Recognition in terra incognita","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.056105Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:83c73882e53801e8c6e6325b831a0cdd2b4e0263b1e0e25d364f3871054f9341","observation_id":"ee9f7430-dd7d-480f-861f-47bd17da1e59","resolution":{"observed_at":"2026-08-06T22:33:31.056105Z","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":"2026-08-06T22:33:41.569058Z","title":"Hyperbolic graph convolutional neural networks","venue":null,"work_id":"8148f20d-552d-4c53-9ec7-1b647dacdd58","year":null},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.155068Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:30aca5ed9f30520cf104e65300f29d4345b998b631867c31568bf8cc9f850d78","observation_id":"2665e193-9a78-40a3-9f8f-aad944215b79","resolution":{"observed_at":"2026-08-06T22:33:41.651146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00545","last_updated":"2020-05-01T18:00:02Z","snapshot_observed_at":"2026-08-05T01:03:21.419855Z","submitted_at":"2020-05-01T18:00:02Z","title":"Low-Dimensional Hyperbolic Knowledge Graph Embeddings","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.00545","snapshot_observed_at":"2026-08-06T22:33:31.250766Z","title":"Low-dimensional hy- perbolic knowledge graph embeddings","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.250766Z"},"links":{"cited_paper":"/paper/2005.00545","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:7aba25c742e2c2dbbb550b2eb512cf67ce518444994e5e9857e3473f45f9e691","observation_id":"ac751a6b-0092-4bfd-a377-90e1c4a98dad","resolution":{"observed_at":"2026-08-06T22:33:31.250766Z","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":"2026-08-06T22:33:41.367358Z","title":"Mammalnet: A large-scale video benchmark for mammal recognition and behavior understanding","venue":null,"work_id":"cb11f4dd-d9a9-4273-9a21-f08296fafe3d","year":2023},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.347592Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:176fd5500ebf9bdda3ce08b6bb8128cd193e10f2c8cf350a5afcedd2e4a5e3d3","observation_id":"fabb0f11-7468-4714-9555-2690c1eb246f","resolution":{"observed_at":"2026-08-06T22:33:41.456713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:41.147127Z","title":"Mitree: Multi-input transformer ecoregion encoder for species distribution mod- elling","venue":null,"work_id":"bf4b8128-fe22-4834-a42e-fe3a43497351","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.485847Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:1d82461df853c6a70209f7940fd95ab707fa84d6e4befa6e8a776281a399b90d","observation_id":"0f202ed4-f5c8-4568-b639-3dfe9e5f4f48","resolution":{"observed_at":"2026-08-06T22:33:41.272540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:41.004268Z","title":"Revis- iting multimodal representation in contrastive learning: from patch and token embeddings to finite discrete tokens","venue":null,"work_id":"d7a8a6c4-1ffb-454d-be46-e67ed26223b2","year":2023},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.609621Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:46f0f4d407c8a18c9ea6fab9d1695a0268398bb4a297e1424bbdf2d959029d14","observation_id":"0754a917-d5c5-4484-9e9b-6bc05772ec56","resolution":{"observed_at":"2026-08-06T22:33:41.067117Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:31.770151Z","title":"Probabilistic language-image pre-training","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.770151Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:00d17d4725324e638afe912b40abfe02d83d9e1bfa55420eab3ea74dca458694","observation_id":"69470baf-af21-4f91-aa57-05ef544c51e9","resolution":{"observed_at":"2026-08-06T22:33:31.770151Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.14428","last_updated":"2025-08-08T14:58:18Z","snapshot_observed_at":"2026-07-06T20:09:42.677491Z","submitted_at":"2024-12-19T00:52:25Z","title":"WildSAT: Learning Satellite Image Representations from Wildlife Observations","version":2},"cited_work":{"arxiv_id":"2412.14428","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.14428","snapshot_observed_at":"2026-08-06T22:33:36.146666Z","title":"WildSAT: Learning Satellite Image Representations from Wildlife Observations","venue":"cs.CV","work_id":"ec876f86-7002-43da-afa3-a339c112ff1d","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.869715Z"},"links":{"cited_paper":"/paper/2412.14428","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:566b0f513f99426b1bc932b460a5eb1c557b9aa3695e10cc7d1e2d827a555a7f","observation_id":"b9f2e2c1-4a3b-4234-8764-265678d466c2","resolution":{"observed_at":"2026-08-06T22:33:36.226591Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:40.834061Z","title":"Hyper- bolic image-text representations","venue":null,"work_id":"df3018ad-4bd8-4f1d-8587-58c92a1fefd9","year":2023},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:31.977146Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:3576497ec3c935f4c31d721b9cc63f4fe455ecfdb382d3c32b75c0b3b96092e1","observation_id":"01cc59c7-8dfa-4f5e-abc3-4d4d9baa9e18","resolution":{"observed_at":"2026-08-06T22:33:40.943341Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:40.703183Z","title":"Embedding text in hyperbolic spaces","venue":null,"work_id":"654ab0ea-eb50-4b42-9277-2b4673b928b1","year":2018},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:32.085837Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:44c80d9eb6ff8d9ff1ec23323d8cb1bd6a1cd153e4f29f66b5e25f50b1ed87e7","observation_id":"036ef8f8-5788-4bd1-bced-376e0246b2cf","resolution":{"observed_at":"2026-08-06T22:33:40.756465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:40.552778Z","title":"Logics for approximate and strong en- tailments","venue":null,"work_id":"b14da1e8-710e-4073-bbb4-1ffe18d75951","year":2012},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:32.226360Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:0ec72af62374f506e4be25304a73c7d94a89a9b2273b324fe6f78e0a33f27604","observation_id":"b3183e52-f2d8-4066-a7db-dd54e96658c6","resolution":{"observed_at":"2026-08-06T22:33:40.638011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:40.360989Z","title":"Hyperbolic entailment cones for learning hierarchical em- beddings","venue":null,"work_id":"057ad603-1053-466c-8667-792bce04b87e","year":2018},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:32.355991Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:d4b35d5ab5f3e805ae66ed29e8c096e3569b49ac414f4030a1b02f0243fa1890","observation_id":"0eabdd7e-51fa-4088-b6e8-2e7a06941f9f","resolution":{"observed_at":"2026-08-06T22:33:40.459822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:40.137858Z","title":"Lowe, Graham W","venue":null,"work_id":"03707b67-82ee-4295-baf9-c9a3ea485d55","year":2025},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:32.539325Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:5cbc5512e89d019b2aa6c01d67cef6a825ac5b228408dbbe41b6337e45a7d113","observation_id":"548c7539-436c-4148-a4bf-625c16547190","resolution":{"observed_at":"2026-08-06T22:33:40.252057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:39.999323Z","title":"Pigeon: Predicting image geolocations","venue":null,"work_id":"3d0df813-5d30-47bb-bdc1-7c764420913b","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:32.686532Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:df8bc18983b49e380cb125352df210ccc6269b9e1f2550289ba3e38405a13e3c","observation_id":"17d50def-9bd9-4ca8-ae88-7d1b77cec287","resolution":{"observed_at":"2026-08-06T22:33:40.062364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:39.827000Z","title":"Contrastive ground-level image and remote sensing pre- training improves representation learning for natural world imagery","venue":null,"work_id":"ec954479-8d14-4fa8-a72e-d423d2090993","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:32.816909Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:bb780f44d4635af526cfc7f4a34542ae7abbd8626fa00132e3a86fb6a70ea9a5","observation_id":"0ad4fd8f-94b2-4d54-8b64-64cdd7640f6f","resolution":{"observed_at":"2026-08-06T22:33:39.879393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:39.649308Z","title":"Open- clip, 2021","venue":null,"work_id":"35411fbd-6dcd-4887-b98c-ac2aee2d7bf4","year":2021},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:32.969284Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:193d26c45058675237bd400c854be29c2e6b54c15eee4994ddc633d876861aec","observation_id":"d577da68-242d-4ff7-af68-084fbb0ff9cc","resolution":{"observed_at":"2026-08-06T22:33:39.719041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:33.065260Z","title":"Scaling up visual and vision-language representa- tion learning with noisy text supervision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.065260Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:17ffb1a74f2441932e3b74a2c1ae9e02be848af7d809914a5dbfb780128bf683","observation_id":"ff99ac94-0e0c-46ed-b85b-7c744ed35a9f","resolution":{"observed_at":"2026-08-06T22:33:33.065260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.00913","last_updated":"2019-02-03T16:03:29Z","snapshot_observed_at":"2026-08-05T20:43:02.215613Z","submitted_at":"2019-02-03T16:03:29Z","title":"Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.00913","snapshot_observed_at":"2026-08-06T22:33:33.178388Z","title":"Inferring concept hierarchies from text corpora via hyperbolic embeddings.arXiv preprint arXiv:1902.00913, 2019","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.178388Z"},"links":{"cited_paper":"/paper/1902.00913","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:3cb80de9559018f107a21a3af65415f2fef72879d56526ea94e32f1c831e61ee","observation_id":"01eb54e7-67f3-4298-af7b-b6d533fc3fe8","resolution":{"observed_at":"2026-08-06T22:33:33.178388Z","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":"2026-08-06T22:33:39.523001Z","title":"9 Align before fuse: Vision and language representation learn- ing with momentum distillation","venue":null,"work_id":"a9f00bc9-5be6-4a66-8fd1-1e09ca8dc5ae","year":2021},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.346472Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:e83e33b96918d8724fe882720477de51636c73ae332f05a8f7b3529d2b4fa6f9","observation_id":"846f7b7c-4c4b-4f1f-a647-c8ed869a4e9d","resolution":{"observed_at":"2026-08-06T22:33:39.577439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:39.351984Z","title":"Fine-grained semantically aligned vision-language pre-training","venue":null,"work_id":"40cff8cf-cb96-4458-8288-77f31cfb6993","year":2022},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.479946Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:eb18bdd0d1cfcb9254e368f6109b0aab33df4e60eb66051b5e45ee2df5dab16a","observation_id":"1366ec4b-864e-40e3-aa18-eec60a35be1b","resolution":{"observed_at":"2026-08-06T22:33:39.453591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:39.139283Z","title":"Maruf, Arka Daw, Kazi Sajeed Mehrab, Harish Babu Manogaran, Abhilash Neog, Medha Sawhney, Mridul Khu- rana, James P","venue":null,"work_id":"73dfea8c-909b-4212-a7c7-1b56d6dcc5e5","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.616977Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:8fc68bdc41d705bc37da75f3680ebc8058899e6365f386a4514041a9a7828582","observation_id":"ecd24597-3e4e-4509-aee6-0abbdf9ed4e8","resolution":{"observed_at":"2026-08-06T22:33:39.240300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04263","last_updated":"2025-02-06T17:58:59Z","snapshot_observed_at":"2026-07-06T20:32:18.814472Z","submitted_at":"2025-02-06T17:58:59Z","title":"Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04263","snapshot_observed_at":"2026-08-06T22:33:33.700571Z","title":"Cross the gap: Exposing the intra-modal misalignment in clip via modality inversion","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.700571Z"},"links":{"cited_paper":"/paper/2502.04263","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:ce75dd3d6feef78b44b135c142e2f659d5f22b5625acd4d61242e1e839172526","observation_id":"a3bd064c-3c80-479b-a405-5813f35c1596","resolution":{"observed_at":"2026-08-06T22:33:33.700571Z","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":"2026-08-06T22:33:38.957006Z","title":"Animal kingdom: A large and diverse dataset for animal behavior understanding","venue":null,"work_id":"6e1764a0-a69b-4d43-bb98-8caac4fe2351","year":2022},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.837269Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:5026a2455976511729e08fd708e0a4652ea1eaeaefd25d089acdecd056527eaa","observation_id":"026bbf44-9cb0-4e7a-8d5f-18d924bf1475","resolution":{"observed_at":"2026-08-06T22:33:39.063899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:38.799778Z","title":"Poincar ´e embeddings for learning hierarchical representations","venue":null,"work_id":"48658a52-f42b-43f0-901f-2e49ea87a21d","year":2017},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.906918Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:e171f65467a69a04750f78cfcf0b31ae9b92f07c1e78a1689e1949b2397508ee","observation_id":"c8894a8f-6133-49af-9e66-2d0639ebae1d","resolution":{"observed_at":"2026-08-06T22:33:38.847600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:38.603396Z","title":"Learning continuous hierarchies in the lorentz model of hyperbolic geometry","venue":null,"work_id":"14fd8897-16a7-491a-b49b-6ff316be1e78","year":null},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:33.984142Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:9c4984562242ac93b05bdfcd76c211ad2ef1befdac0de9163fde806f971f8229","observation_id":"3725d289-5144-4fd9-a6e4-4e95404265bc","resolution":{"observed_at":"2026-08-06T22:33:38.703237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:38.429245Z","title":"Compositional entailment learning for hyperbolic vision-language models","venue":null,"work_id":"2d0376ef-b238-4b8d-b5c8-20317b3657c8","year":2025},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.047864Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:c6b4ffc89870e64926a039b43de5e87d2e2b2658c04377b7127957c052dd7f84","observation_id":"e9484950-50ac-4684-9041-5844c40453f5","resolution":{"observed_at":"2026-08-06T22:33:38.486736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:38.253827Z","title":"Har- nessing artificial intelligence to fill global shortfalls in biodi- versity knowledge","venue":null,"work_id":"2579e1dd-2fa2-402d-9a99-74d1115f5091","year":null},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.112050Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:9fe2e4fc545d9f57cec48706235320a9334774932f31b1861d940ed4c8252c64","observation_id":"761e23be-e08d-4434-9bb0-5da276d7bff6","resolution":{"observed_at":"2026-08-06T22:33:38.345424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:38.104829Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"afc9e366-42b8-45d9-89d9-eb3c31c11671","year":2021},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.176797Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:bae5d5b97311e371748bbdfd8e30a20846fa414358f8ab508d02f1f8e6c449e0","observation_id":"37462d67-1a16-4468-8606-c8703c6aa76f","resolution":{"observed_at":"2026-08-06T22:33:38.179346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:37.913259Z","title":"Accept the modality gap: An exploration in the hyperbolic space","venue":null,"work_id":"e41ea7be-2891-4edb-89bd-b70dd3a87c21","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.236292Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:ff71d49c5270bab29e374eb140efa51e86e0bbcf52e6e1cb6e376c8c7aabb043","observation_id":"0b2da370-685e-4faa-be53-2e86d5dcf278","resolution":{"observed_at":"2026-08-06T22:33:38.000097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:37.724304Z","title":"Accelerating ocean species discovery and laying the foundations for the future of marine biodiver- sity research and monitoring","venue":null,"work_id":"50f2396d-a21a-44b6-b12d-6381001bfe18","year":2023},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.317438Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:0f55b49e836b2ca128a64447489863d2066058517cc2f82a609466a2f2c6901d","observation_id":"a6e64047-eade-485a-86ec-0d82706ada24","resolution":{"observed_at":"2026-08-06T22:33:37.794851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:37.541906Z","title":"Birdsat: Cross-view contrastive masked autoencoders for bird species classification and mapping","venue":null,"work_id":"e81084b5-9c82-43b2-b24f-732890b62b29","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.409231Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:569b3b4cc967ca80997261984892f47e53541449b53a97f5cda5ec9c45be46c1","observation_id":"eb488fa6-56a0-4387-b2c4-bd768b999cec","resolution":{"observed_at":"2026-08-06T22:33:37.628811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:37.316253Z","title":"Taxabind: A unified embedding space for ecological applications","venue":null,"work_id":"406d12b1-d6d2-4cf5-b6fb-85e235edcb1a","year":2025},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.488299Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:d0d6cc605e745e846f2c54bb5c806ec29845bde2b914c59d8c13d82dc777ab78","observation_id":"ecf4acab-692e-470b-9945-2367d23b4440","resolution":{"observed_at":"2026-08-06T22:33:37.412050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:37.183644Z","title":"Deepwild: Wildlife identification, localisation and estima- tion on camera trap videos using deep learning","venue":null,"work_id":"25706829-6b00-4851-acc2-3e7866891b38","year":2023},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.576885Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:81a7b2d32c83cb4e4053ef91d3b501aa6c74b071ba09e1133815f45b6d9f39c6","observation_id":"c4b6d7f0-62ad-4f81-a11e-90a3e8c51aab","resolution":{"observed_at":"2026-08-06T22:33:37.212874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:34.646209Z","title":"Bioclip: A vision foundation model for the tree of life","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.646209Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:e4e920d5952ad5fb340bc05204d40abb8b07af3ce826b820ea622b0e9ea47245","observation_id":"b8d4410c-a531-4846-aa8e-6629d50bfc3d","resolution":{"observed_at":"2026-08-06T22:33:34.646209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.11595","last_updated":"2021-11-23T00:50:25Z","snapshot_observed_at":"2026-07-06T12:11:12.158992Z","submitted_at":"2021-11-23T00:50:25Z","title":"Semi-Supervised Learning with Taxonomic Labels","version":1},"cited_work":{"arxiv_id":"2111.11595","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.11595","snapshot_observed_at":"2026-08-06T22:33:35.986208Z","title":"Semi-Supervised Learning with Taxonomic Labels","venue":"cs.CV","work_id":"e8da5341-2042-46a7-99d8-11a90f92a2c5","year":2021},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.727436Z"},"links":{"cited_paper":"/paper/2111.11595","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:9aa7bb2265e57f509b38b03b52f7a1df18989d1b335af4144dcc918f8ede4c43","observation_id":"8424fb22-1491-4915-9ce3-114fed5de528","resolution":{"observed_at":"2026-08-06T22:33:36.045868Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.06546","last_updated":"2018-11-22T15:46:17Z","snapshot_observed_at":"2026-07-06T07:08:12.503941Z","submitted_at":"2018-10-15T17:54:36Z","title":"Poincar\\'e GloVe: Hyperbolic Word Embeddings","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.06546","snapshot_observed_at":"2026-08-06T22:33:34.795508Z","title":"Poincar \\’e glove: Hyperbolic word embeddings","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.795508Z"},"links":{"cited_paper":"/paper/1810.06546","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:45d609056edf09556437ba18f5bf1b3cd932fcf2f289887dd54dbf44792ebc76","observation_id":"8339fde5-67b6-453d-977e-a1b978561d1f","resolution":{"observed_at":"2026-08-06T22:33:34.795508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:34.869447Z","title":"The inaturalist species classification and de- tection dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.869447Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:84683e39fd7cb1ecdeedd995a79f1f9694e1b80e08f24d6b4e2076da67078bb0","observation_id":"dc296444-4dff-44a3-870f-94255ee149d3","resolution":{"observed_at":"2026-08-06T22:33:34.869447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06361","last_updated":"2016-03-01T08:23:50Z","snapshot_observed_at":"2026-08-07T18:55:40.634051Z","submitted_at":"2015-11-19T20:56:14Z","title":"Order-Embeddings of Images and Language","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06361","snapshot_observed_at":"2026-08-06T22:33:34.946732Z","title":"Order-embeddings of images and language","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:34.946732Z"},"links":{"cited_paper":"/paper/1511.06361","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:aa3b6e9dfe3c1da58171cce8f78d439eaa566196b75da984d4e401f7dfbaeb3f","observation_id":"7064894b-fd49-4068-9b58-702c8c023b2d","resolution":{"observed_at":"2026-08-06T22:33:34.946732Z","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":"2026-08-06T22:33:37.031878Z","title":"Jones, Oisin Mac Aodha, Sara Beery, and Grant Van Horn","venue":null,"work_id":"cf6ca867-e635-4b2b-aa9d-2be8c7fd3785","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:35.004621Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:54302d67160a28a4ed17f4e2bd6ebc8f9d254ab449378e35e2b23768ebc4a702","observation_id":"6c80a563-8b84-4fbc-8882-849237a0047b","resolution":{"observed_at":"2026-08-06T22:33:37.095687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:36.889407Z","title":"Inquire: A natural world text-to-image retrieval benchmark","venue":null,"work_id":"304089c9-595a-403e-bdf2-9f03dbd77b9c","year":2025},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:35.071759Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:ea895c146f4d9702b3aa0132986b6990e4f536340b59c44a189358631edc1407","observation_id":"257ccfc2-22ac-4b8b-a7ac-a11a87924b4e","resolution":{"observed_at":"2026-08-06T22:33:36.957598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:36.738320Z","title":"Geoclip: Clip-inspired alignment be- tween locations and images for effective worldwide geo- localization","venue":null,"work_id":"962750ec-0f34-4b6b-8295-4a73c0bd140b","year":2023},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:35.159596Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:35ca731c9d606470e918df7be8dd107caec4dd241511817fa12eeca8aad63bfc","observation_id":"bcdaa094-6d18-485f-a166-d31964673ebb","resolution":{"observed_at":"2026-08-06T22:33:36.801590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2411.17490","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:35.857304Z","title":"10 Learning visual hierarchies with hyperbolic embeddings","venue":null,"work_id":"1e444545-8251-4791-83fc-eefd2222a32c","year":2024},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:35.246729Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:77bc9393fe42b1a234767fefec29ed0af5d1ef85f01821a4b3c49ec0ef558534","observation_id":"a1829513-dd65-4fe3-9f4c-c670d6c30f1e","resolution":{"observed_at":"2026-08-06T22:33:35.898214Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:36.599762Z","title":"Biotrove: A large curated image dataset enabling ai for bio- diversity","venue":null,"work_id":"068775ef-4116-4d0d-9123-40aa249e642b","year":2025},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:35.320606Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:7e16ba0aac06f5e79617865dc5dfaf21563fe8afd0561f37dcbeae8b6da15f7a","observation_id":"d717c493-1392-401a-8b25-5196cfbfdaeb","resolution":{"observed_at":"2026-08-06T22:33:36.663179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07783","last_updated":"2021-11-09T17:15:38Z","snapshot_observed_at":"2026-07-06T12:08:35.761426Z","submitted_at":"2021-11-09T17:15:38Z","title":"FILIP: Fine-grained Interactive Language-Image Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.07783","snapshot_observed_at":"2026-08-06T22:33:35.382549Z","title":"Filip: Fine-grained interactive language-image pre-training","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:35.382549Z"},"links":{"cited_paper":"/paper/2111.07783","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:9d2648b0e041b776dcdd29ce39656034d5ada4b641c0ae8d4b36fae8b53d1f21","observation_id":"aeff4d42-cf50-4119-b915-40f1fc14ae37","resolution":{"observed_at":"2026-08-06T22:33:35.382549Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15215","last_updated":"2024-04-08T22:03:54Z","snapshot_observed_at":"2026-07-06T15:32:35.999476Z","submitted_at":"2023-05-24T14:52:56Z","title":"Shadow Cones: A Generalized Framework for Partial Order Embeddings","version":3},"cited_work":{"arxiv_id":"2305.15215","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.15215","snapshot_observed_at":"2026-08-06T22:33:35.599172Z","title":"Shadow Cones: A Generalized Framework for Partial Order Embeddings","venue":"cs.LG","work_id":"839db2b8-7b4e-404e-8f19-a40278739a9b","year":2023},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:35.450182Z"},"links":{"cited_paper":"/paper/2305.15215","citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:e461cc4d572812867322c33015404a25cab490b916a5e029bcec0dadcf38280a","observation_id":"5d73b2f0-8ef4-4411-a7c4-7967d85d2adb","resolution":{"observed_at":"2026-08-06T22:33:35.615951Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:33:36.436582Z","title":null,"venue":null,"work_id":"d747e846-24b4-4674-9c1c-5fc532f3b25b","year":2021},"citing_paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T22:33:35.505115Z"},"links":{"citing_paper":"/paper/2506.21476"},"observation_digest":"sha256:9aa1279d221a2d00fae52bed5461a778f5a21cc8fdff22578e9038384a026b4c","observation_id":"313efdb7-6b36-4625-b55a-b6b3255b457b","resolution":{"observed_at":"2026-08-06T22:33:36.482489Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.21476","last_updated":"2025-06-26T17:05:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T22:22:07.574344Z","submitted_at":"2025-06-26T17:05:06Z","title":"Global and Local Entailment Learning for Natural World Imagery"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":4,"verified_fuzzy":32},"total_outbound_references":48},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 1 inbound Pith citation observation for arXiv:2506.21476."}