{"as_of":"2026-08-18T02:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:87fad78d472e37524a8c1c3463d3e37516525704037db383f1acc2feec7da612","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:42:41.687023Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.09139/citation-record","integrity":"/paper/2505.09139/integrity","json":"/paper/2505.09139/citation-record.json","paper":"/paper/2505.09139"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:42:41.572893Z","title":"Learning to prompt for vision- language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.572893Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:fdf3c4c34b20c66b09536ffdbd2ae5816b9cb8b8163fc55489a2f83f65bdcfaf","observation_id":"7603201b-6dcb-498c-a005-1043a692404a","resolution":{"observed_at":"2026-08-15T21:42:41.572893Z","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":"2026-08-15T21:42:42.037492Z","title":"Sugarcrepe++ dataset: Vision-language model sensitivity to semantic and lexical alterations,","venue":null,"work_id":"9eb40e56-6dce-49eb-abad-c989321fe61a","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.576906Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:2c77dfc75290d354ab1a3abe9e3c5c41ebd5bc8e2ea2d4e0bdb17681ab4e84b7","observation_id":"75ec0ca4-74e9-448a-a90e-f5c49e317127","resolution":{"observed_at":"2026-08-15T21:42:42.041256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:42.026851Z","title":"Language- driven active learning for diverse open-set 3d object detection,","venue":null,"work_id":"039812bd-77e5-4f20-8619-3f42d86dc088","year":2025},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.580479Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:25c95fecd903bb71f31b52ebe9eed21ec72e37261f3e8f2397a38e79f18f687a","observation_id":"d256b88a-8032-4862-853d-846f44c8634b","resolution":{"observed_at":"2026-08-15T21:42:42.030510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:42.014812Z","title":"Evaluating multimodal vision- language model prompting strategies for visual question answering in road scene understanding,","venue":null,"work_id":"879c8117-a576-4932-bf31-aea4faae7996","year":2025},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.583857Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:975561e7f935c82df0b999abfd7fa45f0668b7db4f88dc3c86a8fb586f187710","observation_id":"26550ea8-a1ad-445f-96ce-d728351dfbf3","resolution":{"observed_at":"2026-08-15T21:42:42.018973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:42.003200Z","title":"Ipo: Interpretable prompt optimization for vision-language models,","venue":null,"work_id":"536f2c38-afee-4015-a13d-adb40273212c","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.587726Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:33ee0c266f730a07ad1ca2b69efbcbe57372f44e7df1e69198c53eb0f6b9e392","observation_id":"f5a869cc-dda4-466b-b7aa-c4c867924982","resolution":{"observed_at":"2026-08-15T21:42:42.007172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14496","last_updated":"2024-06-20T16:59:39Z","snapshot_observed_at":"2026-08-16T13:41:01.777489Z","submitted_at":"2024-06-20T16:59:39Z","title":"African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14496","snapshot_observed_at":"2026-08-15T21:42:41.591013Z","title":"African or european swallow? benchmarking large vision-language models for fine-grained object classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.591013Z"},"links":{"cited_paper":"/paper/2406.14496","citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:3ff8201bc778577bfaad58c393114844f76dc058f8f550f0b1b43efab28bb7ab","observation_id":"3ba5883b-648a-4ed1-829b-2acbf046928d","resolution":{"observed_at":"2026-08-15T21:42:41.591013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16315","last_updated":"2025-01-07T16:05:16Z","snapshot_observed_at":"2026-08-16T14:15:26.838078Z","submitted_at":"2024-02-26T05:43:51Z","title":"Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16315","snapshot_observed_at":"2026-08-15T21:42:41.594933Z","title":"Finer: Investigating and enhancing fine-grained visual concept recognition in large vision language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.594933Z"},"links":{"cited_paper":"/paper/2402.16315","citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:8e2cae82d35f48a26696b1da1314fd9c0afea95cb391c6513ffc5fe8cfd7839d","observation_id":"9ec0c7fa-59b7-4954-ad58-62752c0cfd0f","resolution":{"observed_at":"2026-08-15T21:42:41.594933Z","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":"2026-08-15T21:42:41.991413Z","title":"Litai: Enhanc- ing multimodal literature understanding and mining with generative ai,","venue":null,"work_id":"323ca57a-1aea-415f-ac23-0ff7a11ffd18","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.598481Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:dd76b0ce732cdf6178ae66f9bca3810c630b87fe2e0c4748a25498fb050dda01","observation_id":"8f3f4180-d557-4cc0-9e10-f145d97e4f83","resolution":{"observed_at":"2026-08-15T21:42:41.995160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.980115Z","title":"Gaugetracker: Ai- powered cost-effective analog gauge monitoring system,","venue":null,"work_id":"301e1a42-2bd8-453e-9c3d-2383594df724","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.601946Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:85ffe999ad5720ec0f7f12c1615c02d16a11376172748c0067c8dcea60d5447c","observation_id":"c85506d2-1af8-4bef-bcc2-9fc3ee759f99","resolution":{"observed_at":"2026-08-15T21:42:41.984307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.968621Z","title":"Reframing: Detector-specific prompt tuning for enhancing open-vocabulary object detection,","venue":null,"work_id":"e80e496b-4423-4dcd-af58-00f22c396cee","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.605371Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:bf42e79bfe650324766b8e2b3608c8c82756ad83e6a9f965d51a50228c2cb8f1","observation_id":"b36ad1f3-5eea-4d71-b974-add9164df451","resolution":{"observed_at":"2026-08-15T21:42:41.971796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.957803Z","title":"Learning to prompt for open-vocabulary object detection with vision-language model,","venue":null,"work_id":"210576de-7cec-419b-8ad8-16f637024317","year":2022},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.609192Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:78b62ead037721540ea86c8db76adf49568153144e064b3d3645eef90412a8cc","observation_id":"1cc6e409-038b-475c-8652-9992f3eea356","resolution":{"observed_at":"2026-08-15T21:42:41.961622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.946046Z","title":"T-rex2: Towards generic object detection via text-visual prompt synergy,","venue":null,"work_id":"196d8f8a-3078-46c5-8168-ebe0c913184e","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.612650Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:a1d39b8fc5f957140fd478134761e791f0b1346df2b955ab9082b87bfcf3a3ee","observation_id":"f7203793-ab5e-4c3e-a475-021eb53b7c98","resolution":{"observed_at":"2026-08-15T21:42:41.950009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.934756Z","title":"Fine-grained visual prompting,","venue":null,"work_id":"83827fef-8ee6-4867-aac8-9d8658ff4059","year":2023},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.615652Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:ca044c2e037e92152fe4a2c251893961d1484aa51ec33af72792d55747f56911","observation_id":"fbd40de9-9d96-482f-8195-296874da1597","resolution":{"observed_at":"2026-08-15T21:42:41.938726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.923780Z","title":"Prompt distribution learning,","venue":null,"work_id":"fba4bf2b-12af-4dc8-8d65-8d4d17a04a38","year":2022},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.618570Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:cde14b99ec48fb55e75c46af8217cf8b6c30555466498cf8eedc8b45e34e936a","observation_id":"2b90c45a-8fbe-4687-90fc-46188ddab070","resolution":{"observed_at":"2026-08-15T21:42:41.927311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.00375","last_updated":"2026-06-06T06:52:21Z","snapshot_observed_at":"2026-08-16T12:45:06.556551Z","submitted_at":"2025-04-01T02:45:17Z","title":"CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detection","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.00375","snapshot_observed_at":"2026-08-15T21:42:41.621140Z","title":"Camosam2: Motion-appearance induced auto-refining prompts for video camouflaged object detection,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.621140Z"},"links":{"cited_paper":"/paper/2504.00375","citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:74658222105cc161ae150323fc8b64d8fa48bc314c0e3089ad412c1ef1177f9f","observation_id":"7a43598e-c383-484d-8744-279cd31a5bd1","resolution":{"observed_at":"2026-08-15T21:42:41.621140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:42:41.624757Z","title":"Segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.624757Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:1f8c16a23780f1b11f8ebe8eb7b39a32e4bc63aa04a24253f42eef92fef1c76b","observation_id":"6b2d8bd4-5cdd-48b6-b769-a5aa720bd6ef","resolution":{"observed_at":"2026-08-15T21:42:41.624757Z","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":"2026-08-15T21:42:41.905833Z","title":"Zero-shot nuclei detection via visual-language pre-trained models,","venue":null,"work_id":"439428e9-f3ee-4713-adcc-3aa27de84008","year":2023},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.628232Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:c48dbbb07ed4c13fd10a5caf605d2670382f6c9a8bd4369fb4ceebf70770c827","observation_id":"beeb63d5-7da6-47d7-a429-cf8ad1fb9caa","resolution":{"observed_at":"2026-08-15T21:42:41.909683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.893546Z","title":"Attriprompter: Auto-prompting with attribute semantics for zero-shot nuclei detection via visual-language pre-trained models.,","venue":null,"work_id":"b285de4f-9fac-48e3-b1f8-8a517b5c2333","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.631462Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:11425fbcb1b6de6618adfe20dddfce6fc5cac305f7ee22d21f1ea5858cf688b9","observation_id":"ee667a81-6997-4b54-9b86-cf36c04a0c9d","resolution":{"observed_at":"2026-08-15T21:42:41.898033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.884078Z","title":"Self-driving cars dataset","venue":null,"work_id":"fef4a47a-9685-4ee1-b77a-116aa7a2d029","year":2023},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.635379Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:df7ae5d0090ab03fb5405383d3ff7fefbb70bb9ee092152f571bf53a7bd0caa7","observation_id":"a1f85efc-6c0c-40f7-932b-4b3eed88544a","resolution":{"observed_at":"2026-08-15T21:42:41.887232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-08-15T14:02:47.366139Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-15T21:42:41.639292Z","title":"Gpt-4o system card,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.639292Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:81c87c25dc35ff5119698a9d83c3bf7523fa26fac2d3a0168f3136605e88b97c","observation_id":"2a49d303-1671-44e0-903d-91029f66a08b","resolution":{"observed_at":"2026-08-15T21:42:41.639292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:42:41.644751Z","title":"Minilm: Deep self-attention distillation for task-agnostic compression of pre- trained transformers,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.644751Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:bc6b4f76a2f72d3661e540b935e6cfa00d538e6249d469052f87efaec4cb6354","observation_id":"2312ff62-e95c-409a-8b8c-f3dd0afbd53c","resolution":{"observed_at":"2026-08-15T21:42:41.644751Z","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":"2026-08-15T21:42:41.866863Z","title":"Scaling open-vocabulary object detection,","venue":null,"work_id":"e0b64411-2ec3-481d-ae68-a7863a7bfe09","year":2023},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.648426Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:2c01ffc82148f907590aacbb14039c78991ca89235941c655c0ca84c7c96ee2b","observation_id":"c0694e4e-bc3e-4a2c-b8c6-cbf330af1f56","resolution":{"observed_at":"2026-08-15T21:42:41.870537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.856018Z","title":"Crowdtruth measures for language ambiguity,","venue":null,"work_id":"f2fb2c16-c22c-4b5f-b76e-33316de31fdc","year":2015},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.652150Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:9b8335c34e2bd35fd57457648d6e640c0dc68e20a66e1b497ef56d9831e118cc","observation_id":"e41b3136-eddb-46a5-9a3f-c2bcded86580","resolution":{"observed_at":"2026-08-15T21:42:41.859813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.844989Z","title":"Requirements for tools for ambiguity identification and measurement in natural lan- guage requirements specifications,","venue":null,"work_id":"2ecdee43-e2e0-4b61-99ee-9945c2840489","year":2008},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.656065Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:92539c12afab5765c4622ca11d09e860905940ab88496b44db61a05a4750f520","observation_id":"8cd4e493-8926-4a22-94da-75d1cf9b541c","resolution":{"observed_at":"2026-08-15T21:42:41.848694Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.833920Z","title":"Ambiguity identification and measurement in natural language texts,","venue":null,"work_id":"dbdae08a-e1bb-47b0-8a94-424a186edb0f","year":2004},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.659683Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:990db07ed9e4bfa405c26bed6c6b1e0a666390dde46d5d6ade474c45ad52729a","observation_id":"6461ef41-3c37-4a3f-ad35-3902f5c59b33","resolution":{"observed_at":"2026-08-15T21:42:41.837427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.663523Z","title":"Generation and comprehension of unambiguous object descriptions,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.663523Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:b54ff330b4e1e31be70ae2abeff3a7deb507503befc0a62bc8a50b3abfa1a7ae","observation_id":"bea0b1a8-3902-47f7-ae95-a52248a7f035","resolution":{"observed_at":"2026-08-15T21:42:41.663523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07320","last_updated":"2024-02-11T22:53:21Z","snapshot_observed_at":"2026-08-16T14:19:30.552217Z","submitted_at":"2024-02-11T22:53:21Z","title":"Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07320","snapshot_observed_at":"2026-08-15T21:42:41.667620Z","title":"Towards explainable, safe autonomous driving with language embeddings for novelty identification and active learning: Framework and experimental analysis with real-world data sets,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.667620Z"},"links":{"cited_paper":"/paper/2402.07320","citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:174c98fbe2e550e0e80daedaabc1610290308a548b283760f09faaa49e9ce6c8","observation_id":"aef0477e-610f-4326-929c-da3b15c8869e","resolution":{"observed_at":"2026-08-15T21:42:41.667620Z","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":"2026-08-15T21:42:41.814494Z","title":null,"venue":null,"work_id":"a4ae43ee-bb77-4623-965c-7b002e9a5d2d","year":2025},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.671849Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:bb8e59f1ddf92819b45de6dd2b4991f5136aedb5a26f2539e1a800502a039159","observation_id":"0ee895a9-a78b-4357-9175-a5057f7e8411","resolution":{"observed_at":"2026-08-15T21:42:41.820024Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12225","last_updated":"2024-10-16T04:42:10Z","snapshot_observed_at":"2026-08-16T19:16:08.855221Z","submitted_at":"2024-10-16T04:42:10Z","title":"Evaluating Cascaded Methods of Vision-Language Models for Zero-Shot Detection and Association of Hardhats for Increased Construction Safety","version":1},"cited_work":{"arxiv_id":"2410.12225","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.12225","snapshot_observed_at":"2026-08-15T21:42:41.728602Z","title":"Evaluating Cascaded Methods of Vision-Language Models for Zero-Shot Detection and Association of Hardhats for Increased Construction Safety","venue":"cs.CV","work_id":"db396a85-933c-46c1-b45d-fe5f69525830","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.675797Z"},"links":{"cited_paper":"/paper/2410.12225","citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:134700c24684db7d2b7aa5fb8afa910359333f6aa4ac1abba05e1019edccb649","observation_id":"1c0b5a4d-c29a-412c-87a6-dc6994cf51c0","resolution":{"observed_at":"2026-08-15T21:42:41.734538Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.802876Z","title":"Evaluating vision-language models for zero- shot detection, classification, and association of motorcycles, passengers, and helmets,","venue":null,"work_id":"56adbabe-5c9f-4fb8-90b6-5e86644e150a","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.679824Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:a9b304ea07699e8de4fb0672058091dc37273f0b4dae6930f4716fe3ab50f9c5","observation_id":"c6070081-f4a4-4a2b-9927-5c10c50846c7","resolution":{"observed_at":"2026-08-15T21:42:41.806986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:42:41.791732Z","title":"Driver activity classification using generalizable representations from vision- language models,","venue":null,"work_id":"c7d8dc27-940a-4b46-a901-3804b355c0f0","year":2024},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.683485Z"},"links":{"citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:739a5451ad032e9577dffe522b62ac83f1bd3943e6dbc38414d421efe145d8ea","observation_id":"a94be189-c20e-4da3-b11d-7f05f0df61f4","resolution":{"observed_at":"2026-08-15T21:42:41.795635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13399","last_updated":"2025-04-18T01:25:02Z","snapshot_observed_at":"2026-08-17T21:28:19.169684Z","submitted_at":"2025-04-18T01:25:02Z","title":"Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13399","snapshot_observed_at":"2026-08-15T21:42:41.687023Z","title":"Towards a multi-agent vision-language system for zero-shot novel hazardous object detection for autonomous driving safety,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T21:42:41.687023Z"},"links":{"cited_paper":"/paper/2504.13399","citing_paper":"/paper/2505.09139"},"observation_digest":"sha256:1ad22761629d940f7f2e675811de0a31293cc73fcc1466ba542b184c9a0b67ca","observation_id":"55e5b0ed-2023-4475-8c7c-239c54ad6b01","resolution":{"observed_at":"2026-08-15T21:42:41.687023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.09139","last_updated":"2025-05-14T04:43:36Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T21:08:58.972133Z","submitted_at":"2025-05-14T04:43:36Z","title":"Beyond General Prompts: Automated Prompt Refinement using Contrastive Class Alignment Scores for Disambiguating Objects in Vision-Language Models"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":20},"total_outbound_references":32},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2505.09139."}