{"as_of":"2026-08-19T20:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b0ee4d2af22b63022a182a887a2679d78beeb740e006ffdd1441296b8580073a","coverage":[{"denominator":72,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":72,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:53:00.728545Z","state":"measured"},{"denominator":74,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":74,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:24:28.502441Z","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":"2026-05-20T21:49:05.265568Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.11576","snapshot_observed_at":"2026-08-15T23:24:28.502441Z","title":"Aligning visual and seman- tic interpretability through visually grounded concept bottle- neck models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.04835","last_updated":"2025-05-07T22:19:55Z","snapshot_observed_at":"2026-08-18T15:21:31.690901Z","submitted_at":"2025-05-07T22:19:55Z","title":"Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions?","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T23:24:28.502441Z"},"links":{"cited_paper":"/paper/2412.11576","citing_paper":"/paper/2505.04835"},"observation_digest":"sha256:68b9c3e00cb8800f80f69c1c8f28715f7f621144fa9c8388f17dc9279ac13be1","observation_id":"ecebc595-3407-47e6-94a7-901d44cf22db","resolution":{"observed_at":"2026-08-15T23:24:28.502441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"cited_work":{"arxiv_id":"2412.11576","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.11576","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dcbm: Data-efficient visual concept bottleneck models.arXiv:2412.11576","venue":null,"work_id":"ab41c928-0c86-421c-b299-414a2a29c77f","year":null},"citing_paper":{"arxiv_id":"2605.16405","last_updated":"2026-05-13T10:07:11Z","snapshot_observed_at":"2026-08-16T20:21:12.239108Z","submitted_at":"2026-05-13T10:07:11Z","title":"Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-20T21:47:42.958875Z"},"links":{"cited_paper":"/paper/2412.11576","citing_paper":"/paper/2605.16405"},"observation_digest":"sha256:87e7ba007f9015cbba85fa3c963aa28476ba7caaef7c5113784772aa79b8e6b3","observation_id":"51e0d120-470f-4406-866c-40a8c3d81d7d","resolution":{"observed_at":"2026-05-20T21:49:05.267070Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.11576/citation-record","integrity":"/paper/2412.11576/integrity","json":"/paper/2412.11576/citation-record.json","paper":"/paper/2412.11576"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:53:00.559054Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.559054Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:168dfedf78b45078928737ec9dd147ebe0abd04c9b243f7f9b70227f3361806e","observation_id":"2107fa27-7d10-432d-a643-4c26a79044b3","resolution":{"observed_at":"2026-08-11T14:53:00.559054Z","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":"2026-08-11T14:53:01.229440Z","title":"and Jaakkola, T","venue":null,"work_id":"d1c35c9f-01a9-4bce-8148-b9133827219d","year":2018},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.563293Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:4c6e2168bb92e80e8fad1b4e5b5022c50ea8f45856f0363ce3ed0cad25bfdc6c","observation_id":"8eb21bae-811d-4c9a-86fa-2dff79cf4626","resolution":{"observed_at":"2026-08-11T14:53:01.232034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.223176Z","title":"Convolutional dynamic alignment networks for interpretable classifications","venue":null,"work_id":"ab8f4b80-55ef-469b-babc-d535403fa475","year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.566248Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:01175fc54a36388571ae4d78c3498031036c10b771722ab8d159068c681f70c9","observation_id":"df686b54-1426-4549-b751-e3f5f8a02f1f","resolution":{"observed_at":"2026-08-11T14:53:01.225485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.216973Z","title":"B-cos networks: Alignment is all we need for interpretability","venue":null,"work_id":"b4be0a5d-c898-4cdf-acd4-edd07e46f891","year":2022},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.569180Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:fa0bc75c212d4ca324e7cad7b54bd2b005562975dd97fff64477f30b7e6ea814","observation_id":"30b93a0b-9e4e-4319-8171-0b21998546ff","resolution":{"observed_at":"2026-08-11T14:53:01.219213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.210427Z","title":"End-to-end object detection with transformers","venue":null,"work_id":"0c30ffe0-7b2e-4db7-8a05-e9f0798a656a","year":2020},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.571786Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:58d94c03cbc6f38d8c815d04f834a7e8b0286a7fa3581acda70084f12d1f7299","observation_id":"be53f92f-cfba-441d-9cf2-d9bd51535d01","resolution":{"observed_at":"2026-08-11T14:53:01.212804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.203841Z","title":"Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers","venue":null,"work_id":"9c56c0ff-710a-4523-8528-4a0e2b92c1a9","year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.574502Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:d76bb3528492eff5fa7ff92a897aabd5a97dc5807bde025451f12175ab0e0a7a","observation_id":"5a493d22-a836-46b9-af1a-2be264cfd87c","resolution":{"observed_at":"2026-08-11T14:53:01.206335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.196709Z","title":null,"venue":null,"work_id":"c5282595-74b1-4e38-80f8-0dccfc8e397f","year":2019},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.576997Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:89a2cf959f8732aa3e8666cd16e1e8bae226b30dbb9436d69835e2035c72ab94","observation_id":"598a014b-7478-4a36-9dc3-d666b40c12d7","resolution":{"observed_at":"2026-08-11T14:53:01.199369Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.189109Z","title":"Detect what you can: Detecting and representing objects using holistic models and body parts","venue":null,"work_id":"cdf67c16-38ad-4a79-a0fb-9a58a050fdd1","year":2014},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.579755Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:e66f61220024c462b2dc3a730097fe09c47d9582551a78b24292f03ca568f836","observation_id":"d550c88c-100a-4a47-9a0e-18922af19d59","resolution":{"observed_at":"2026-08-11T14:53:01.191905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.181414Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"a2a225e5-b081-494d-a9d2-eae8a7f1cf3c","year":2009},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.582173Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:9f66ebf290cb9041245c6bc6284125996f1898dadec9a4ce46f82e5594421647","observation_id":"df511e0b-2d02-4c86-82fe-8277cf6c4ab4","resolution":{"observed_at":"2026-08-11T14:53:01.184295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.173889Z","title":"J., and Chen, C","venue":null,"work_id":"0d076d03-cf9f-4426-aca8-60574547c8f7","year":2022},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.584883Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:52105de72aff9395e9e942aeb8e7cb70c8289de3f6a057d0a1796478c67c47d0","observation_id":"d8c74f1c-db7b-435a-8cb5-87f3df0d653a","resolution":{"observed_at":"2026-08-11T14:53:01.176622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.166344Z","title":"Craft: Concept recursive activation factorization for explainability","venue":null,"work_id":"58cc4ee4-7fde-44fe-b48c-aa06e06af80b","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.587229Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:2498f7650518d5d8d4bc24ec5f175dc21ee125d351b80b220b6f578ac2ccd7cd","observation_id":"d1f247a5-225a-4cd6-9489-ce02f322f21e","resolution":{"observed_at":"2026-08-11T14:53:01.169206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.158881Z","title":"Y., and Kim, B","venue":null,"work_id":"327a958e-069a-46a0-82fd-e8a014a8d984","year":2019},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.589841Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:b6bbbe9df7232ac976ea70d98698cca229e3a54b1afb8838a3bf4db14eaa9fe7","observation_id":"366e94cb-3a2d-4b95-a94a-f96becdd887e","resolution":{"observed_at":"2026-08-11T14:53:01.161654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.592315Z","title":"and contributors","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.592315Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:907d0e2ba3a654e65de490efce0e17f2737a96f8d148cfb88efa424c195aa5ac","observation_id":"59c53d51-5d3a-4c98-948f-761412c64c4e","resolution":{"observed_at":"2026-08-11T14:53:00.592315Z","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":"2026-08-11T14:53:01.146484Z","title":"Partimagenet: A large, high-quality dataset of parts","venue":null,"work_id":"c1444e51-cf15-472a-86e5-7b780010520c","year":2022},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.595028Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:9c94dff1ed176fe97609baa13c7654b838d788657b6c1baa05fba9b8092ac60c","observation_id":"1814c018-6f37-46d5-a0fe-314f3f4bcaf2","resolution":{"observed_at":"2026-08-11T14:53:01.149811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.138426Z","title":"Mask r-cnn","venue":null,"work_id":"1e7709f5-9976-4a73-b049-eeb3bc0da5f2","year":2017},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.597480Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:4bee08d1f280433a0101ed21e010d4b7148b3d6b06a1ef5df53ac716b0e2b75a","observation_id":"05df55ac-9ea6-4afa-8741-cf2ffa1d22bc","resolution":{"observed_at":"2026-08-11T14:53:01.141278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.131346Z","title":"Concept correlation and its effects on concept-based models","venue":null,"work_id":"e6cafc8d-d0c4-4ea2-b736-18e4334699a5","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.599911Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:daa59d21422f4664cef54cbef8c236229223ecaef4f241b93ff0dc4cd732566b","observation_id":"e76caa68-2bac-43f8-91dd-1559121610ee","resolution":{"observed_at":"2026-08-11T14:53:01.133723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.124759Z","title":"The many faces of robustness: A critical analysis of out-of-distribution generalization","venue":null,"work_id":"d3caaf45-2b5e-48b2-a5ce-6a056a30f033","year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.602452Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:8837045d5ec63470ff1221bbc9b86a602b99cf7a4c0d450ec46dc512a6781e13","observation_id":"5e129b7b-6ed9-4aa6-924e-6ce6831b446b","resolution":{"observed_at":"2026-08-11T14:53:01.127152Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.117775Z","title":"Imagenette: A smaller subset of 10 easily classified classes from imagenet","venue":null,"work_id":"0e5d96a7-202b-44ad-9a12-e96a9060c1b6","year":2019},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.604971Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:62dc6a2213762abf390afe1e96c98c89507cd90ea7ee2f9a31b3bff75031fbb9","observation_id":"debb66d1-9096-45d4-8eda-059249a34e72","resolution":{"observed_at":"2026-08-11T14:53:01.120198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.110975Z","title":"Imagewoof: a subset of 10 classes from imagenet that aren't so easy to classify","venue":null,"work_id":"4e42716c-4006-439a-a033-689e9d2ac21a","year":2019},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.607391Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:d53c84320cf78e217ffcab2448dae53613eb81aa24c8c63e451810692790291e","observation_id":"77036f6b-8a08-467b-b44c-ae8ebf223dea","resolution":{"observed_at":"2026-08-11T14:53:01.113400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.609796Z","title":"R., Ewart, A., and Sharkey, L","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.609796Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:44831ecc5d780b47d33ec7a3f77cb41279f38a37222d05b79fb4b37c59dbd84d","observation_id":"20d705c9-01cf-4da2-a0bf-33bf26ff8417","resolution":{"observed_at":"2026-08-11T14:53:00.609796Z","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":"2026-08-11T14:53:01.099415Z","title":"J., and Adelson, E","venue":null,"work_id":"5186040e-9112-4e70-be31-f329f01ae517","year":2015},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.611831Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:66e40c302d609c27f2d0b567e61f6dc8730f1686e70147308d0fcd0e587a2a31","observation_id":"03b63f84-bed0-4514-b4ca-053c47afa065","resolution":{"observed_at":"2026-08-11T14:53:01.102051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.092188Z","title":"google-10000-english, 2012","venue":null,"work_id":"30cbb761-3b54-44f8-affd-08f6932484d5","year":2012},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.614122Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:1bd5f345851142c0bdfb9f44fee95937fdc2e1f4da8286488fe5b126ba19ad70","observation_id":"ab791523-2e37-4e1e-95a6-45edc3c1b0de","resolution":{"observed_at":"2026-08-11T14:53:01.094942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.084721Z","title":"C., Lo, W.-Y., et al","venue":null,"work_id":"e0912872-fbae-4ef2-abf7-d08f4e9f8f14","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.616472Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:2bd82063e0c9a3dd1ef2719697e8dc586ae01d974f38a54539603ec8201efbec","observation_id":"e10c694a-5e13-46ff-a2dd-e1a805f2e48b","resolution":{"observed_at":"2026-08-11T14:53:01.087674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.076953Z","title":"Beyond pixels: Enhancing LIME with hierarchical features and segmentation foundation models","venue":null,"work_id":"c9eae449-17e3-4ed0-9e97-6784be3cc3be","year":2025},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.618718Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:a1b252fe9b30e10d811c2490fc0deff4eec8555b93fc487d1cf8571598ec1902","observation_id":"8e22409c-2754-4db5-aa0e-394d0fe11f97","resolution":{"observed_at":"2026-08-11T14:53:01.079884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.069270Z","title":"W., Nguyen, T., Tang, Y","venue":null,"work_id":"d311de99-4175-41fb-b91f-f7c52d12bcb5","year":2020},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.620809Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:f6ac8616148d4cc5f434618187a877faf2c1a4a202724f0e19429a0c399d0c77","observation_id":"16384866-a20b-48b8-90de-82504a20e2e4","resolution":{"observed_at":"2026-08-11T14:53:01.072108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.061292Z","title":"P., and Derpanis, K","venue":null,"work_id":"0c90e657-4656-4d06-b805-9a5e444ddb51","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.623069Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:29e6e1cfd94d128b6739d603ed080d65f57782c331eb1039d9ba8438e670eeae","observation_id":"a5d94900-a6f2-44ef-b3a2-47418baa0c35","resolution":{"observed_at":"2026-08-11T14:53:01.064114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.625045Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.625045Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:aa24f1223aae115152f198e3a56337600a59fea646ffeb5f9e6af0b0d7cbc90e","observation_id":"e2ceb3cf-a80b-4f39-b37b-4fa3d2ec64c9","resolution":{"observed_at":"2026-08-11T14:53:00.625045Z","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":"2026-08-11T14:53:01.049761Z","title":"H., Nickisch, H., and Harmeling, S","venue":null,"work_id":"e6e215d5-294c-4703-926b-95a591168d76","year":2009},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.627731Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:0f1d3477491ffad65420cc9788e972f50d2862602e3b8f441295451cc25481ad","observation_id":"5b6c8ba4-2097-4ea0-83e3-47017c46a739","resolution":{"observed_at":"2026-08-11T14:53:01.052567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.629745Z","title":"Segment and recognize anything at any granularity","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.629745Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:5b3d1e26f68c6d6ef3edee93aac82126e368e7d9df710a502eaefe8d224553f1","observation_id":"d627aedf-8e09-4d15-a353-72ce559848ee","resolution":{"observed_at":"2026-08-11T14:53:00.629745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.07895","last_updated":"2024-07-28T19:58:08Z","snapshot_observed_at":"2026-08-13T00:09:23.835117Z","submitted_at":"2024-07-10T17:59:43Z","title":"LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.07895","snapshot_observed_at":"2026-08-11T14:53:00.631912Z","title":"Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.631912Z"},"links":{"cited_paper":"/paper/2407.07895","citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:1fd2eb2abd299a12979513589d58178883b766ab48271fe37305db3b90f869f4","observation_id":"cd0e0359-7387-4146-9c45-6c0cbc4f64d0","resolution":{"observed_at":"2026-08-11T14:53:00.631912Z","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":"2026-08-11T14:53:01.041504Z","title":"Deal: Disentangle and localize concept-level explanations for vlms","venue":null,"work_id":"037aaf14-d831-42dc-8ef4-711367bb0390","year":2025},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.634232Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:4bd0b733ccd8ce045fc2d7a8c0f4dd21489ab80dc02f0c89b47ade7f03960338","observation_id":"6fc2a142-b5e6-4f26-990e-8e7ee6750f13","resolution":{"observed_at":"2026-08-11T14:53:01.044517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.033948Z","title":"W., Zhang, Y., Kwon, Y., Yeung, S., and Zou, J","venue":null,"work_id":"6b5e4cf4-3f2a-4157-baad-da55baa67b49","year":2022},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.636186Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:1422e540ddfe026f35e164b9d5771098ac603afefa64ace76b7738d88c67df5f","observation_id":"5a74725d-80c6-46cf-aab0-8570b3855ed9","resolution":{"observed_at":"2026-08-11T14:53:01.036837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.027272Z","title":"Grounding dino: Marrying dino with grounded pre-training for open-set object detection","venue":null,"work_id":"35beb469-946f-4477-9820-01351555cca4","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.638146Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:01554ee987105d361cda232fa7c825fc45227b4bf43ce0ae4eed935f35f765b8","observation_id":"bde2e64a-591b-47d6-9c92-796caf2fd687","resolution":{"observed_at":"2026-08-11T14:53:01.029799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.640171Z","title":"Deep learning face attributes in the wild","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.640171Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:76c46a6fbe7fd03e34fa6b341bdf8200462ab396e0efec8e220db841a9b46f60","observation_id":"554d23c1-2c04-49d4-a9d9-de1f0b8299ee","resolution":{"observed_at":"2026-08-11T14:53:00.640171Z","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":"2026-08-11T14:53:01.017231Z","title":"Explainable artificial intelligence (xai) to enhance trust management in intrusion detection systems using decision tree model","venue":null,"work_id":"bb309e94-700e-4e5e-9640-a672e0a7c396","year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.642146Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:f8a21b6145da0bb38e7a9bf07c401aaf3387b207e353d9d9f28411a8cbd8a8b7","observation_id":"83179115-89be-44c0-a990-7913c6770fd8","resolution":{"observed_at":"2026-08-11T14:53:01.019488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.010918Z","title":"and Vondrick, C","venue":null,"work_id":"126ef4d3-6ee2-4d93-aba6-33c73a625960","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.644172Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:e38802ad34ed905c6ebe087620059e03801f979831a67ae4b7f7c501070d6d29","observation_id":"682b9b8a-fc0c-45a7-bba7-7bfd9eaa796e","resolution":{"observed_at":"2026-08-11T14:53:01.013102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:01.004572Z","title":"Neural prototype trees for interpretable fine-grained image recognition","venue":null,"work_id":"e22762d9-e6ed-4868-a6ab-cd48259f6750","year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.646070Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:449d329681936600db643b2b215ef61a9b9766d0f5b6b449005d89772f7f9000","observation_id":"9ce51621-d548-472a-8e34-7e3f439a59ef","resolution":{"observed_at":"2026-08-11T14:53:01.006889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.997694Z","title":"Label-free concept bottleneck models","venue":null,"work_id":"4e2076e8-f681-416a-b734-8129e31dfb49","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.648487Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:5f62cbb9359889508082d31370b333137233a3c224f0146fc0d2b35213cd4ac9","observation_id":"596bc005-0800-41c5-97e2-dfe9e3742750","resolution":{"observed_at":"2026-08-11T14:53:01.000259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.990641Z","title":"P., Ienco, D., and Marcos, D","venue":null,"work_id":"c9cd9830-6ef5-4906-b12e-93a21768a27d","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.651064Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:b3f9d40aeba7d1bcb44880a1aadd0d3241d952a229e42c78d3dfd68971106503","observation_id":"15be4223-5da3-4a03-986c-19b27373e75e","resolution":{"observed_at":"2026-08-11T14:53:00.993183Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.983198Z","title":"P., Ienco, D., and Marcos, D","venue":null,"work_id":"f693dedf-02e2-43c0-bdb3-cf295172bcce","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.653482Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:49143e2c6a3ec2ace6b218d28cff918480e9df198ae614b04aecbfdc608539c8","observation_id":"0dc7d9ef-9599-44ce-b879-b1c0302a036e","resolution":{"observed_at":"2026-08-11T14:53:00.985830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.975364Z","title":"The sun attribute database: Beyond categories for deeper scene understanding","venue":null,"work_id":"bc801f42-e07d-4356-94f7-baf00a9e8e87","year":2014},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.656097Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:76e5e0c249f2b5ec3ac9145e217cea413edd68f146f5ea5f928f9343ed746b58","observation_id":"b946c1ba-e2e0-4e13-a9a0-d95804cbcec2","resolution":{"observed_at":"2026-08-11T14:53:00.978451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.18653/v1/2024.findings-naacl.131","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:53:00.757840Z","title":"PEEB : Part-based image classifiers with an explainable and editable language bottleneck","venue":null,"work_id":"e210f916-d64f-4fac-9bba-9980790c2d5a","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.658627Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:66dbd08c7ed49b5ffade4961fd935921ad425cff5ee762c2d4eb89c7739afb37","observation_id":"e2683903-7903-4b80-ab57-04b54dd02cb6","resolution":{"observed_at":"2026-08-11T14:53:00.761161Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.967964Z","title":"B., Walter, S., and Keuper, M","venue":null,"work_id":"6b9a0214-ba13-4751-bc41-6e764caed32f","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.661222Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:2336b3d46e0b9ef1068fc76ef6f75ab54baaabc97c1eb5bbaa11eb53aba47d2b","observation_id":"24f517d0-501f-4f12-9db9-0bd3f68c5a14","resolution":{"observed_at":"2026-08-11T14:53:00.970663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.960378Z","title":"Task-driven modular networks for zero-shot compositional learning","venue":null,"work_id":"13a0147d-d4fb-4254-acc0-3edcbd2992c7","year":2019},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.663848Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:acbc65eedcb63ae14f7a0fd36f737729fceace505cc7d19043ac44be9b6eafcd","observation_id":"64a67ba0-c709-48d8-857b-9a5955bccabf","resolution":{"observed_at":"2026-08-11T14:53:00.963167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.666317Z","title":"W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.666317Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:0a909eee11010ae44de319ad4e79f2977b4660c0bcf81348b4fd9d6257966e01","observation_id":"0d2b9388-e9ce-4a10-b1f5-2d0deb330bdc","resolution":{"observed_at":"2026-08-11T14:53:00.666317Z","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":"2026-08-11T14:53:00.949419Z","title":"E., Heo, J., and Jamnik, M","venue":null,"work_id":"b2817555-adfa-4a0f-af5b-67281f1713d3","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.668779Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:f868ae9d152fb3cb92a353bf94d56cb1ee536e0fc065db5bbdb7cc8c9e5ad325","observation_id":"e8f2aada-d2e2-427b-84e9-84b20c620f67","resolution":{"observed_at":"2026-08-11T14:53:00.952066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.942707Z","title":"Discover-then-name: Task-agnostic concept bottlenecks via automated concept discovery","venue":null,"work_id":"1d63052f-a770-49b4-ae35-45d58e92bbe1","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.671123Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:b6fccd596029f22c018c9058360fcdbfd8dc63177b899c76278f2c623a9c6ddc","observation_id":"783ad936-2f23-4d12-9bcd-7f9609b86e1e","resolution":{"observed_at":"2026-08-11T14:53:00.945222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.936449Z","title":"V., Carion, N., Wu, C.-Y., Girshick, R., Dollar, P., and Feichtenhofer, C","venue":null,"work_id":"df72f567-278d-4936-af23-dcf82dc4a185","year":2025},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.673480Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:127d7d33a51e36b9a8c28f78c9a6e69aebccf649b3688cc8bca7190e9f2bd599","observation_id":"2ea9f4e6-4db3-483f-a210-7e68c9a2ee7f","resolution":{"observed_at":"2026-08-11T14:53:00.938751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.930578Z","title":"why should i trust you?","venue":null,"work_id":"22772d28-7ed0-4a71-8f39-c2170aa492a1","year":2016},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.676512Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:f629e37c3f4ed00af24bb7bf2e72236b1884bda7a0832ecba185fb244b393bd4","observation_id":"f2b2294a-ede4-4fc2-be88-edf2cb17c70a","resolution":{"observed_at":"2026-08-11T14:53:00.932657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.924250Z","title":"M., Koepke, A., Vinyals, O., Schmid, C., and Akata, Z","venue":null,"work_id":"0cc57dba-9269-4ca6-a845-e8fa4cb92a3a","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.678983Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:2641c10ff4e571b929b82d30c89b6c8f47397feedd98189e8eaa2c8cc993132e","observation_id":"b525c181-2ed8-4e9e-8b89-0d4836cdfa3e","resolution":{"observed_at":"2026-08-11T14:53:00.926654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.917897Z","title":"Concept bottleneck models without predefined concepts","venue":null,"work_id":"f5e7ffaa-5250-435f-a5f6-baaa35bcf21a","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.681330Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:ea9c0f8fc6c81caccd4001ba379d00317c9c3132a9f645eaccda21c3f5a708d3","observation_id":"8cac0839-3b49-4753-b06e-f6fd4b2338ea","resolution":{"observed_at":"2026-08-11T14:53:00.920222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.911343Z","title":"R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D","venue":null,"work_id":"af871ae7-aacf-4737-902e-416635a556f8","year":2019},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.683752Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:3f1b8a132914385c3ae5069248204c97dc22dd602b61130b1b59424af188ccf4","observation_id":"10dff4c6-716b-49fe-b393-d9bfde57736c","resolution":{"observed_at":"2026-08-11T14:53:00.913747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.903147Z","title":"Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning","venue":null,"work_id":"5cb80797-3759-4ad9-b631-9fd0e87f2434","year":2018},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.686225Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:8e8b7710b2d0936c47d73f018b5fc3f5011dda4a35733b25cfbd1d3746c1d157","observation_id":"895ccf77-52a4-42fd-b159-0ff54973f8ef","resolution":{"observed_at":"2026-08-11T14:53:00.906362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.895577Z","title":"Explain any concept: Segment anything meets concept-based explanation","venue":null,"work_id":"646e7de1-8399-43fd-be3d-28d9ad74ab6d","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.688671Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:cb6b33ce6d1ace78c772b1dcd5faa5cf705e75d8e352f1e5f52cb58422d9b923","observation_id":"985e5366-3e04-46f6-afc3-4b1a6df5e508","resolution":{"observed_at":"2026-08-11T14:53:00.898432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.888537Z","title":"Caltech-ucsd birds 200","venue":null,"work_id":"37835313-6e8e-4775-a04b-466082b47f98","year":2011},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.691124Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:c38f3df5b9b94223a1bdd822ea2f1c3097bd8d89d71b9d21fcb10b2d91620c54","observation_id":"a77fd739-58b9-4080-8bb3-37329e477511","resolution":{"observed_at":"2026-08-11T14:53:00.890919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.882077Z","title":"Learning bottleneck concepts in image classification","venue":null,"work_id":"6626990c-3414-4023-af95-dee10328c49e","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.693590Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:6f71d7b9c952a080d08773a4a3891233ae229fec3d1e5f68e221a5bbb17815ec","observation_id":"21f1a646-0b59-4dec-9c33-87857017fe32","resolution":{"observed_at":"2026-08-11T14:53:00.884356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.874263Z","title":"Interpretable image recognition by constructing transparent embedding space","venue":null,"work_id":"f9159f8b-f961-4890-a1c0-eb2b701c2100","year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.696510Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:e373cfc7b92f2004b699a693cab2920d75460f91d436a0b48b0f827ad7ae6cae","observation_id":"8f6c7990-9c36-4701-93ea-7d0be391b67b","resolution":{"observed_at":"2026-08-11T14:53:00.877339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.698919Z","title":"H., Schiele, B., and Akata, Z","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.698919Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:3de317addfda8c207c3d84adfceac7fd87149ae2b681c13c0c7801243f89d15f","observation_id":"8f345b3e-bb92-4008-95ff-0daefea58a11","resolution":{"observed_at":"2026-08-11T14:53:00.698919Z","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":"2026-08-11T14:53:00.862919Z","title":"Energy-based concept bottleneck models: Unifying prediction, concept intervention, and probabilistic interpretations","venue":null,"work_id":"71577181-099a-424d-81ef-373397e4a901","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.701282Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:8dcd710609cb5f1e29c5ce9af26a38075e0c627c8649c5654f0c7b117dffdd57","observation_id":"de8e3ef8-d3f9-4a43-933d-fb8e39c99bae","resolution":{"observed_at":"2026-08-11T14:53:00.865794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.24963/ijcai.2024/168","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:53:00.747202Z","title":"Protopformer: Concentrating on prototypical parts in vision transformers for interpretable image recognition","venue":null,"work_id":"1601c811-985f-453e-96ad-7b9800259d2c","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.703507Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:74c53aeb86b853e1fef1f418ee2aa6643e02674b89b3892ec9759eef2dc201cb","observation_id":"c5b42950-3451-4713-acac-bd87149751be","resolution":{"observed_at":"2026-08-11T14:53:00.752134Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.855502Z","title":"Language in a bottle: Language model guided concept bottlenecks for interpretable image classification","venue":null,"work_id":"f65ea5f8-9fc4-4c97-97c4-fa59a430f394","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.705704Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:fde11a3760f495601b4488d41e523f07d94534bc731986907288722f12b9f54d","observation_id":"e6f2dbfa-2ef3-469f-9c78-a4ea530066b2","resolution":{"observed_at":"2026-08-11T14:53:00.858261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.848593Z","title":"Post-hoc concept bottleneck models","venue":null,"work_id":"9ca42445-efcd-42c8-8220-5e32501b6012","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.707612Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:a756ff7a4f3c257c92c2574675fe233782ddda855e379d95708078e2eb5de47d","observation_id":"5007e378-45e9-48c3-8f06-e1ec8bca7b4e","resolution":{"observed_at":"2026-08-11T14:53:00.850952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.841205Z","title":"Do vision-language pretrained models learn composable primitive concepts? Transactions on Machine Learning Research, 2023","venue":null,"work_id":"6a03e7a5-e387-4fb4-86a8-5605c5be6f05","year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.709609Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:7e71d33cbdf8eaeb6fed3b244e333e49437cacc8c7e56df593be43e659bd95a4","observation_id":"941ed0a2-038d-4ef4-98d2-e6c43685b1a2","resolution":{"observed_at":"2026-08-11T14:53:00.844171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00192","last_updated":"2023-11-30T21:07:26Z","snapshot_observed_at":"2026-08-17T15:38:57.862321Z","submitted_at":"2023-11-30T21:07:26Z","title":"Benchmarking and Enhancing Disentanglement in Concept-Residual Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00192","snapshot_observed_at":"2026-08-11T14:53:00.711556Z","title":"Benchmarking and enhancing disentanglement in concept-residual models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.711556Z"},"links":{"cited_paper":"/paper/2312.00192","citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:fcf59ba188cfb31522863bee827440bdcc27f9ee23c2c36b07dd7a3c51fc2643","observation_id":"e622c34d-119f-404f-8371-25ac6762ee5b","resolution":{"observed_at":"2026-08-11T14:53:00.711556Z","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":"2026-08-11T14:53:00.833752Z","title":"A Playground for CLIP-like Models , 7 2021","venue":null,"work_id":"1d543235-e84d-46b7-b1a6-011e18e2b585","year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.713963Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:c34dd15aeac3569efcb53b29437f7467f763ece344c6717bfc6fb152b80ce7d5","observation_id":"d32b9187-6aa0-4577-9220-59e60d2f3d8d","resolution":{"observed_at":"2026-08-11T14:53:00.836510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.825070Z","title":"A., Lin, Z., Brandt, J., Shen, X., and Sclaroff, S","venue":null,"work_id":"fda0752e-9eda-46c1-8792-4826ee3570b2","year":2018},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.716102Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:e40c8f9a8ad714f385a31538dbdda57935b5975215d497e35fdd235f0b727e21","observation_id":"d50963ab-5249-49be-82fa-553abf5e91c4","resolution":{"observed_at":"2026-08-11T14:53:00.827875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.818393Z","title":"A., and Rubinstein, B","venue":null,"work_id":"d3549f9a-5dde-45bc-9d66-aba61b4b8900","year":2021},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.718145Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:b76397684a6af7bf949d8a5faa75acef5b79fc27d8d1078d6fa9cd7dcda3935b","observation_id":"ca60148a-3d0a-43e8-89b0-3ea321b04b1d","resolution":{"observed_at":"2026-08-11T14:53:00.820685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.811084Z","title":"The decoupling concept bottleneck model","venue":null,"work_id":"e269aa4e-4ced-4d69-853d-3fb42dffabb2","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.720298Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:6721ca485ae0be488feea63adf40ae88163aaa8efb2d82b6b97a9324224b64b7","observation_id":"84814ccc-e3af-4a4a-bec6-c5b95e2aa61b","resolution":{"observed_at":"2026-08-11T14:53:00.813670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.803189Z","title":"Recognize anything: A strong image tagging model","venue":null,"work_id":"edacf6a3-8568-4b0a-b573-14a610278bf3","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.722291Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:871ee46a3c2aecbacda21a71cd1c2a3cbf50140e570f8f4656e8bf2daf67e98f","observation_id":"4076c86d-cc38-4336-a071-8283381ef0be","resolution":{"observed_at":"2026-08-11T14:53:00.806248Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.795163Z","title":"Places: A 10 million image database for scene recognition","venue":null,"work_id":"75c0fb5a-47dd-4814-9f60-6168ba0f4595","year":2017},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.724472Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:f35c6c2f07fad1ccbd2c2a706f3cf84b423ef9663e3d80259bb9aa5d7c9d3bc8","observation_id":"cc229bc1-8268-438c-b908-e26296e1edc0","resolution":{"observed_at":"2026-08-11T14:53:00.798116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.787195Z","title":"Where you see is what you know: A visual-semantic conceptual explainer","venue":null,"work_id":"be3fa70a-a289-4231-89b7-b2108801bd82","year":2024},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.726626Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:56bfbb1b61bdcccb716373b7d4ebec64f136861b8dc00951c65aca62af3499ff","observation_id":"f907ea23-8061-4c4c-a186-be8ae2198f2b","resolution":{"observed_at":"2026-08-11T14:53:00.789669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T14:53:00.728545Z","title":"J., Wang, Z., Mallen, A., Basart, S., Koyejo, S., Song, D., Fredrikson, M., Kolter, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models","version":3},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-11T14:53:00.728545Z"},"links":{"citing_paper":"/paper/2412.11576"},"observation_digest":"sha256:d027d411072cb6b9753c3f1c6209795d8f149849625573f302c588d2340bca6a","observation_id":"947f6403-ebfd-4938-8ec1-6d8f2fc454af","resolution":{"observed_at":"2026-08-11T14:53:00.728545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.11576","last_updated":"2025-07-02T13:25:58Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T20:48:45.319888Z","submitted_at":"2024-12-16T09:04:58Z","title":"DCBM: Data-Efficient Visual Concept Bottleneck Models"},"reference_resolution":{"displayed":72,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":2,"verified_fuzzy":58},"total_outbound_references":72},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 72 of 72 outbound references and 2 inbound Pith citation observations for arXiv:2412.11576."}