{"as_of":"2026-08-17T03:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:356f0de4d9ad3664afb120cd68f798506244c128ab98ce12928188965087ab47","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T14:45:44.879975Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.11663/citation-record","integrity":"/paper/2412.11663/integrity","json":"/paper/2412.11663/citation-record.json","paper":"/paper/2412.11663"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:45:44.817992Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.817992Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:2129a8cfb2dad2340444eddb09ea915918257ffa0ee7f57e70f85fec0fffdbbe","observation_id":"0ccaf34c-13f4-4856-83cb-9026ace0d370","resolution":{"observed_at":"2026-08-11T14:45:44.817992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.14613","last_updated":"2023-10-16T05:37:06Z","snapshot_observed_at":"2026-08-16T15:45:20.015335Z","submitted_at":"2023-03-26T03:35:46Z","title":"GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.14613","snapshot_observed_at":"2026-08-11T14:45:44.822200Z","title":"Gesturediffuclip: Gesture diffusion model with clip latents","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.822200Z"},"links":{"cited_paper":"/paper/2303.14613","citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:75968b7a5859c19f0be05ffc05680d5c4ee5b53baf7160e3a2d02fc7dd6f0f44","observation_id":"bff2d027-2e6c-4b3b-8206-82be550f3672","resolution":{"observed_at":"2026-08-11T14:45:44.822200Z","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":"2026-08-11T14:45:45.066051Z","title":"Delving into clip latent space for video anomaly recognition","venue":null,"work_id":"5569d8b9-b7f8-46ca-9dcf-703dc7eb1581","year":2024},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.826196Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:db219b7e3c7208b71ae1719e19598364db9dd3095adfb1b0da88918b58199a2f","observation_id":"e3017430-f5b6-449f-9971-2adb852e1ad2","resolution":{"observed_at":"2026-08-11T14:45:45.069767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-14T10:40:26.323157Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-11T14:45:44.829990Z","title":"A survey of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.829990Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:48a998e85ea283701b1bce7b891a0de7db066e32c47b4cb7c9d75cbc62b9d1af","observation_id":"ad9fdcca-b4c0-493d-8815-ad472a137480","resolution":{"observed_at":"2026-08-11T14:45:44.829990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.13549","last_updated":"2024-11-29T15:51:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-23T15:21:52Z","title":"A Survey on Multimodal Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.13549","snapshot_observed_at":"2026-08-11T14:45:44.834033Z","title":"A survey on multimodal large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.834033Z"},"links":{"cited_paper":"/paper/2306.13549","citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:b94058cf09567260a7aa40d38d4eaacf48e7a414a956a0c403da751d9d23ec1d","observation_id":"80e931cc-c0db-493e-9f9d-2e8744e85ee5","resolution":{"observed_at":"2026-08-11T14:45:44.834033Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.10592","last_updated":"2023-10-02T16:38:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-20T18:25:35Z","title":"MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.10592","snapshot_observed_at":"2026-08-11T14:45:44.838160Z","title":"Minigpt-4: Enhancing vision-language understanding with advanced large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.838160Z"},"links":{"cited_paper":"/paper/2304.10592","citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:b35dadd4d9d484358cf237d5f67b4342f4752816aa3a0b2148cc7db1b09615cf","observation_id":"51e91cb0-b2c8-41fb-85bd-152915dd2aba","resolution":{"observed_at":"2026-08-11T14:45:44.838160Z","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":"2026-08-11T14:45:45.055444Z","title":"Graph embedded convolutional neural networks in human crowd detection for drone flight safety","venue":null,"work_id":"2c3d8275-2414-44d3-9fa9-e5b2be908238","year":2019},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.842426Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:19429b65cc03602a3ab99211726ae949505770a5564d6048b36e009196f3184b","observation_id":"7bf1e90a-37f0-4fc5-aaa3-3e90e3a7a762","resolution":{"observed_at":"2026-08-11T14:45:45.059429Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:45:44.845777Z","title":"Learning to prompt for vision-language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.845777Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:704afeeaf9ad1caff0eee04fbd22e944faa2635eb62815aa9006b812d87bc702","observation_id":"c6f337d1-6a24-4637-85cc-1d5291fac1d6","resolution":{"observed_at":"2026-08-11T14:45:44.845777Z","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":"2026-08-11T14:45:45.038280Z","title":"Conditional prompt learning for vision-language models","venue":null,"work_id":"132b06b6-53cc-412e-92cd-22bb7bd634b2","year":2022},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.849154Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:040c4e2d61de000073ca93169cdbfa6f0a705b5756d447a6475e645f09188c00","observation_id":"f02c98b0-69ba-48dc-9003-aed864977b57","resolution":{"observed_at":"2026-08-11T14:45:45.042173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:45:45.026707Z","title":"Language in a bottle: Language model guided concept bottlenecks for interpretable image classification","venue":null,"work_id":"df46f088-6b05-4a09-81e3-e06e26d259eb","year":2023},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.852508Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:f469fec87b9d0c8cd6d284de4da77a4a443c1de4904b11152d91b2a31a1405d6","observation_id":"e8c474e3-522b-422d-87c2-a096e4333eca","resolution":{"observed_at":"2026-08-11T14:45:45.030547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:45:44.855619Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.855619Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:4a0b543ed8b09522b0381b255a19744f2ca42fd1e75bfa74fb4660a4cd7be745","observation_id":"0140eeeb-c6d1-4c3a-a6d3-7ad68c9eaf54","resolution":{"observed_at":"2026-08-11T14:45:44.855619Z","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":"2026-08-11T14:45:45.008778Z","title":"En- hancing clip with gpt-4: Harnessing visual descriptions as prompts","venue":null,"work_id":"6ab8e25b-6a11-4b23-a7ca-915ead2ae123","year":2023},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.859052Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:649f9a40a079ebda8f6c078c835ad30bd41d8395768770c43c5066f2f811a2e1","observation_id":"98ff77d4-3c2f-48f5-a56e-80b242af9b1e","resolution":{"observed_at":"2026-08-11T14:45:45.012438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-16T19:40:28.523700Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-11T14:45:44.862809Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.862809Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:9a5485157d7e1b132f04a87e8a6a684e16dcf6d6b711a4ab16125070391f7517","observation_id":"4f2ecd95-2697-4bb9-9e48-2285ca6f157c","resolution":{"observed_at":"2026-08-11T14:45:44.862809Z","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":"2026-08-11T14:45:44.997739Z","title":"Exploiting lmm-based knowledge for image classification tasks","venue":null,"work_id":"88851ed2-7443-4cea-825d-68c0d998ab79","year":2024},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.866432Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:3065fea3bdd9e66aa43eeefd47d7693930e58ed2bc70200fc985341f2e5d573b","observation_id":"b685c802-8fae-4abc-8f85-0037d437b2a5","resolution":{"observed_at":"2026-08-11T14:45:45.001520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:45:44.987082Z","title":"Disturbing image detection using lmm-elicited emotion embeddings","venue":null,"work_id":"f2285866-e43b-4eef-b648-c947e5fcfceb","year":2024},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.869730Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:571bdb9fb4f038472dbe56be93ba8f4307f926d038ad74bd5d73e1c8e0c67e49","observation_id":"684881ee-5e3d-472e-af5c-cd6cb9315d5e","resolution":{"observed_at":"2026-08-11T14:45:44.990963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1212.0402","last_updated":"2012-12-03T14:45:31Z","snapshot_observed_at":"2026-08-16T21:21:44.768787Z","submitted_at":"2012-12-03T14:45:31Z","title":"UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1212.0402","snapshot_observed_at":"2026-08-11T14:45:44.872958Z","title":"Ucf101: A dataset of 101 human actions classes from videos in the wild","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.872958Z"},"links":{"cited_paper":"/paper/1212.0402","citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:5a85d0fecd7f4726c09463436fc0132297ec23f521fd0a11255b718310e45a4b","observation_id":"07e99017-8748-4185-9ee3-97db8e30c678","resolution":{"observed_at":"2026-08-11T14:45:44.872958Z","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":"2026-08-11T14:45:44.976274Z","title":null,"venue":null,"work_id":"7735a2b2-2d56-4703-84a0-35aeb46f9ebc","year":null},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.876510Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:0cd69df790e25c81b32c3554d44655650cdf85b93276be396efa47b8354845a6","observation_id":"16b33324-d2fa-4497-9fe9-38f7cc0f2802","resolution":{"observed_at":"2026-08-11T14:45:44.980271Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T14:45:44.963190Z","title":"Learning from failure: De-biasing classifier from biased classifier","venue":null,"work_id":"161ae3bd-1cf4-40ce-beb9-eda9077a9ff5","year":2020},"citing_paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T14:45:44.879975Z"},"links":{"citing_paper":"/paper/2412.11663"},"observation_digest":"sha256:9dcc164b3d3b5837528e792e6cd7fd1c74e9d409acf5d200848532a16620dbea","observation_id":"fc70647e-bf9e-4efa-9771-47db5b465892","resolution":{"observed_at":"2026-08-11T14:45:44.968822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.11663","last_updated":"2024-12-16T11:11:23Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T06:46:47.746049Z","submitted_at":"2024-12-16T11:11:23Z","title":"LMM-Regularized CLIP Embeddings for Image Classification"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":18},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2412.11663."}