{"as_of":"2026-08-09T23:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7f0480cc7e0d08c091a3472ed1fcd3e51862cb79410acd69c5a1bdf57224ef1b","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T05:06:30.214646Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.04378/citation-record","integrity":"/paper/2502.04378/integrity","json":"/paper/2502.04378/citation-record.json","paper":"/paper/2502.04378"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.041130Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.041130Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:ff846058f497a518590369310c1c1e2482efca421ff202f7757e2179b71f516b","observation_id":"38547892-6bc1-494c-8b44-d3825a5f6e52","resolution":{"observed_at":"2026-08-09T05:06:30.041130Z","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":"2026-08-09T05:06:30.829721Z","title":"Machine learning test- ing: Survey, landscapes and horizons,","venue":null,"work_id":"75db0112-ed8c-41ee-947c-04cabb756106","year":2022},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.050810Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:ef478b2eebdfac987364117c787385c08d454b9fff348ecacef3b55e014c2e7e","observation_id":"84e5b482-ddac-4bd1-a1fa-6a40584079e4","resolution":{"observed_at":"2026-08-09T05:06:30.834955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.814670Z","title":"Deepbillboard: systematic physical-world testing of autonomous driving systems,","venue":null,"work_id":"0db190c5-cc12-4e43-9a1b-a44f83582093","year":2020},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.074759Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:1afdb0f4dabe9379f0cc2b7a0c00b6936c577e3f4c84290262b8fdc04a35896e","observation_id":"849b3800-b8f7-43be-9b65-e29929383dc8","resolution":{"observed_at":"2026-08-09T05:06:30.819630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.793665Z","title":"A miss is as good as A mile: Metamorphic testing for deep learning operators,","venue":null,"work_id":"7133ebee-4a60-4bbd-8e58-7f941ea91890","year":2024},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.092957Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:9a3c28adc58c6aa63c3993ca4829b192f49e4f0ef5e7beae9b09232efad5194f","observation_id":"6e1deaba-a54c-4eb1-9be5-fd34a5b10ef2","resolution":{"observed_at":"2026-08-09T05:06:30.803608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.747708Z","title":"Validating a deep learning framework by metamorphic testing,","venue":null,"work_id":"7bea114c-0e3c-4b02-b526-2730d6ac67c1","year":2017},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.105319Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:b1ebe451ed53a5dcd7b139b21a5495b5b8d8f45b96035730b25a98beb9f71d8d","observation_id":"31dd2035-d67f-4cc4-8372-4a68c27d8c61","resolution":{"observed_at":"2026-08-09T05:06:30.769576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.702581Z","title":"Deeptest: automated testing of deep-neural-network-driven autonomous cars,","venue":null,"work_id":"0660c4ad-acf8-4e7a-a78b-4cbdd867555e","year":2018},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.116300Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:a5ba0e7a4edcc287ca0d55744cc66fd7eaab461a1ddc27e80da39c3e04bb3b81","observation_id":"dec69247-f5d3-48c3-89a8-88c052370fe9","resolution":{"observed_at":"2026-08-09T05:06:30.722299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.625619Z","title":"Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems,","venue":null,"work_id":"31ae8511-1164-4f52-a163-b64ed1235628","year":2018},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.122250Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:dc071d4d835d6e686e5097f3ff8bd2db165d6d7383bbe39c2bc24b2c02897048","observation_id":"9145ce15-8102-49a6-87ea-b3f6331aa99d","resolution":{"observed_at":"2026-08-09T05:06:30.659794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.555719Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":"135c89ff-616b-4088-91f8-49648b08f23a","year":2009},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.126982Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:afb8d87517e9fada701ae488ecaa2db4f4a09186f8424eaf535114054a66518b","observation_id":"5214dc1e-4324-4131-9b83-0c46510acb85","resolution":{"observed_at":"2026-08-09T05:06:30.577967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.538804Z","title":"SHIFT: A synthetic driving dataset for continuous multi- task domain adaptation,","venue":null,"work_id":"e64848b3-c8e0-4d00-9bf0-a891a5f84109","year":2022},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.131823Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:823b706be184608576c367def6af72a8c0e4db2596239b866b5ccb8efb073a46","observation_id":"f388bf4e-d350-4d28-b981-8a8758f64016","resolution":{"observed_at":"2026-08-09T05:06:30.545098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.521942Z","title":"High- resolution image synthesis with latent diffusion models,","venue":null,"work_id":"9cee756d-ec6b-43f2-bdee-9dccee2f165f","year":2022},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.136670Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:ae910f25ccde8546191e59ef2fb19e5f9b8f6d5e9d56aad6afefb90d2e5156b6","observation_id":"0c243ac3-e801-4fd3-9033-d05a8e781a4e","resolution":{"observed_at":"2026-08-09T05:06:30.526848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.506949Z","title":"Adding conditional control to text-to-image diffusion models,","venue":null,"work_id":"582126fb-374f-45cb-8a32-2c732def3903","year":2023},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.141222Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:1be606cdeca8359a65b83aaece24befbfe457f0c22be7e09c1c1fa0c704bb9fa","observation_id":"3d909125-b371-4fce-bfdc-14c29c812386","resolution":{"observed_at":"2026-08-09T05:06:30.512048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.491833Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"708c7f69-3404-4124-ad18-0420b0218630","year":2016},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.146068Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:54f65c319dac238086366ffb25d9fa9f34387e44e39b760f2cc4bc22616461b1","observation_id":"ace1db1a-596d-4150-bc59-afe58b2caf64","resolution":{"observed_at":"2026-08-09T05:06:30.496823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.477431Z","title":"Encoder- decoder with atrous separable convolution for semantic image segmen- tation,","venue":null,"work_id":"1fc5776c-b308-4252-8066-67e7b487aa3c","year":2018},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.150777Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:55b74a3b12f1aabecc2a51f88473d164333af4e797b4f7b051ee197e5202c6fe","observation_id":"26dc80aa-3d31-4b24-a001-feea646c3268","resolution":{"observed_at":"2026-08-09T05:06:30.481887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.461212Z","title":"BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models,","venue":null,"work_id":"42c58438-018e-4a15-a484-c9e279762161","year":2023},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.155189Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:9c8665be07dd1671a8f50f423c60745b62877e040ff970706bb340f456e1aa3d","observation_id":"918a658a-76d9-4d72-890d-aecda933e9d9","resolution":{"observed_at":"2026-08-09T05:06:30.466923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-09T05:06:30.159709Z","title":"Llama 2: Open foundation and fine-tuned chat models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.159709Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:2e7b9cc76e65704b729b39528588cd2361dbab046eb05c190bbed70eccb24656","observation_id":"10743386-c7b8-44d2-b1ef-7cea27ffd579","resolution":{"observed_at":"2026-08-09T05:06:30.159709Z","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":"2026-08-09T05:06:30.164879Z","title":"Chain-of-thought prompting elicits reasoning in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.164879Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:3010c5a6f496a06d83239e4fd6563c5666e79050efb1584e24423c4ac81e8773","observation_id":"c8f713f5-be8c-468c-aca9-d5309b1f8303","resolution":{"observed_at":"2026-08-09T05:06:30.164879Z","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":"2026-08-09T05:06:30.431161Z","title":"Deepxplore: automated whitebox testing of deep learning systems,","venue":null,"work_id":"530e2d5b-b7e6-4993-a0f9-aebd33ccaa55","year":2019},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.169419Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:b143322eabe754b9ff3423c9f3f58ef5c5ab3e6c3778e2b53f9ee6b7147abdc1","observation_id":"d25f079a-27db-44cc-b002-db722de82b8f","resolution":{"observed_at":"2026-08-09T05:06:30.439277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04103","last_updated":"2025-01-10T15:37:26Z","snapshot_observed_at":"2026-07-06T18:41:40.732196Z","submitted_at":"2024-07-04T18:06:48Z","title":"Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04103","snapshot_observed_at":"2026-08-09T05:06:30.174003Z","title":"Advances in diffusion models for image data augmentation: A review of methods, models, evaluation metrics and future research directions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.174003Z"},"links":{"cited_paper":"/paper/2407.04103","citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:1d72b4fb62cfffce11b513a0f309231ab46dbec580cc8a19fd4dc9e2fb38cb00","observation_id":"70486406-33fb-4fcc-9ba4-ac5ecd4db055","resolution":{"observed_at":"2026-08-09T05:06:30.174003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13661","last_updated":"2025-02-17T15:48:30Z","snapshot_observed_at":"2026-08-04T01:15:05.784984Z","submitted_at":"2024-09-20T17:09:45Z","title":"Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models","version":3},"cited_work":{"arxiv_id":"2409.13661","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.13661","snapshot_observed_at":"2026-08-09T05:06:30.343863Z","title":"Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models","venue":"cs.SE","work_id":"449762c8-b9b8-493d-8bdc-4df919e67599","year":2024},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.178772Z"},"links":{"cited_paper":"/paper/2409.13661","citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:72500d3b4dec434fc98ffc5a04d7dcdd1e633ab8ed0981f7161a0b21d06efce4","observation_id":"508035ed-2a92-4663-afa9-2fb06692b812","resolution":{"observed_at":"2026-08-09T05:06:30.349556Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.412211Z","title":"Assessing quality metrics for neural reality gap input mitigation in autonomous driving testing,","venue":null,"work_id":"89231b1c-0a7d-4140-912f-fab6ddba0e69","year":2024},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.183783Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:dda31795dd69352eee10c8f4efd6c61bb691f87d4bb6c1ef706c8eac567cba5a","observation_id":"e3b1f8e9-30b4-46c9-b47b-9094e6671a80","resolution":{"observed_at":"2026-08-09T05:06:30.417696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07865","last_updated":"2023-06-19T15:41:07Z","snapshot_observed_at":"2026-07-06T14:52:15.762461Z","submitted_at":"2023-02-15T18:56:26Z","title":"Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation","version":2},"cited_work":{"arxiv_id":"2302.07865","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.07865","snapshot_observed_at":"2026-08-09T05:06:30.285957Z","title":"Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation","venue":"cs.LG","work_id":"667f1e56-2fbb-4551-a151-5800ac5d0e9c","year":2023},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.189360Z"},"links":{"cited_paper":"/paper/2302.07865","citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:c1d1dd688dace39beda70c4161df6f64151a44892e4283336d433e0ec5d1dd96","observation_id":"9128f74f-f5d1-47ef-8195-18c9223ad5d8","resolution":{"observed_at":"2026-08-09T05:06:30.316430Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T05:06:30.394796Z","title":"Diversify your vision datasets with automatic diffusion-based augmen- tation,","venue":null,"work_id":"e636f62b-b970-42dd-a011-90e25eeccf2e","year":2023},"citing_paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T05:06:30.214646Z"},"links":{"citing_paper":"/paper/2502.04378"},"observation_digest":"sha256:879c230ee4281baead83ee56f387af2390e02cd4445da2cdaa8d2b1665712041","observation_id":"6880bb20-937a-4661-9ef1-94825de45b1e","resolution":{"observed_at":"2026-08-09T05:06:30.400084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.04378","last_updated":"2025-02-05T16:35:42Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T05:00:59.739039Z","submitted_at":"2025-02-05T16:35:42Z","title":"DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":16},"total_outbound_references":22},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2502.04378."}