{"as_of":"2026-08-10T18:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8f9a996ed310893c844ac9ce03a11ef0525e5b57a738c35baf32701d600da3ba","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":"2026-08-03T16:07:32.180007Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T22:25:49.423121Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-28T22:32:44.691808Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"cited_work":{"arxiv_id":"2512.14926","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2512.14926","snapshot_observed_at":"2026-07-07T03:18:11.090558Z","title":"vorbe s, ti românes, te?","venue":null,"work_id":"ee1b3b3e-2a31-48e7-9691-62531a87043c","year":2026},"citing_paper":{"arxiv_id":"2605.31401","last_updated":"2026-06-01T04:58:07Z","snapshot_observed_at":"2026-08-10T01:01:09.204968Z","submitted_at":"2026-05-29T15:04:20Z","title":"\"\\^{I}n\\c{t}elegi Rom\\^ane\\c{s}te?'' A Recipe for Romanian Vision-Language Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T22:25:49.423121Z"},"links":{"cited_paper":"/paper/2512.14926","citing_paper":"/paper/2605.31401"},"observation_digest":"sha256:26b62ede4a7d141536ec32b0c0b72671002a719a6a65d5417221c7371760a200","observation_id":"9da6a4a3-173d-4b47-afb4-514a6b02791e","resolution":{"observed_at":"2026-07-07T03:18:11.090558Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2512.14926/citation-record","integrity":"/paper/2512.14926/integrity","json":"/paper/2512.14926/citation-record.json","paper":"/paper/2512.14926"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.07922","last_updated":"2024-07-16T06:37:43Z","snapshot_observed_at":"2026-08-10T14:32:40.134467Z","submitted_at":"2024-04-11T17:09:28Z","title":"LaVy: Vietnamese Multimodal Large Language Model","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07922","snapshot_observed_at":"2026-08-03T16:07:31.610201Z","title":"Lavy: Vietnamese multimodal large language model.arXiv preprint arXiv:2404.07922,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.610201Z"},"links":{"cited_paper":"/paper/2404.07922","citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:8683a43f604f814d83d1934a2e475d1a3cd2c88accd87001b17eaa9567e7f711","observation_id":"5f7508b3-83d3-44a1-a955-72120d106b22","resolution":{"observed_at":"2026-08-03T16:07:31.610201Z","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":"10.18653/v1/2024.findings-naacl.158","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"X-LLaV A: Optimizing bilingual large vision-language alignment","venue":null,"work_id":"1c1118ac-e4a6-4611-bda7-1e76a6b273f3","year":2024},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.646452Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:f85c9b2398e36334b5bc6a479ca2e292e697bde6ebf9b2590c85f350f5f8a9f4","observation_id":"5d3d4449-0b89-4edb-80bc-0b05036a750c","resolution":{"observed_at":"2026-08-03T16:08:19.731753Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-03T16:07:31.731286Z","title":"Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.731286Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:e62fa07ae619218d60da2ff0fdf98f8d5d9702242b3fae368c67cd99a36c93e7","observation_id":"c533da01-5c8f-46df-95a3-803620420f26","resolution":{"observed_at":"2026-08-03T16:07:31.731286Z","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":"2026-08-03T16:07:31.769584Z","title":"Kangyu Zhu, Ziyuan Qin, Huahui Yi, Zekun Jiang, Qicheng Lao, Shaoting Zhang, and Kang Li","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.769584Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:fbfe9bb1337f8e3972e8995b09cd9a8757c97cbe4ff0de6b0a713db8be0910a0","observation_id":"16f70462-e368-479b-8ee2-4b583a58b48c","resolution":{"observed_at":"2026-08-03T16:07:31.769584Z","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":"2026-08-03T16:07:31.835260Z","title":"Lu, Bowen Chen, Andrew Zhang, Richard J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.835260Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:be5e33bee7f7dcb3913e52677072e5d67766e2c0f1753ed8d09f3817d64220fd","observation_id":"24fcf355-63d9-4138-aa92-1b55db5d2d37","resolution":{"observed_at":"2026-08-03T16:07:31.835260Z","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":"2026-08-03T16:07:31.877007Z","title":"URL https://doi.org/10.1038/ s41591-024-02857-3","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.877007Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:b206e57537205bedc394f30d81a2188e7e865cbeae264d78964e9f1dc4cb626c","observation_id":"9cbb5e7f-30bf-440e-ba2a-855e17bb2e5d","resolution":{"observed_at":"2026-08-03T16:07:31.877007Z","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":"2026-08-03T16:07:31.924073Z","title":"doi: 10.18653/v1/2024.findings-emnlp.268","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.924073Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:63aac92077e39ead9ac4f133e0095e908f7240e43307e7a62a620f08e18bf9ab","observation_id":"5836a262-0c89-49df-b6ed-36cb5692bd22","resolution":{"observed_at":"2026-08-03T16:07:31.924073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.11399","last_updated":"2024-04-01T06:57:20Z","snapshot_observed_at":"2026-08-06T13:31:45.691922Z","submitted_at":"2024-03-18T01:14:47Z","title":"X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.11399","snapshot_observed_at":"2026-08-03T16:07:31.968873Z","title":"Dongjae Shin, HyeonSeok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, and Kyungtae Lim","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.968873Z"},"links":{"cited_paper":"/paper/2403.11399","citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:2cd5b8244aa22c4a6dc19156c083012ab2649e6b1c03bd08b328bf3ac1e00f8f","observation_id":"19cd0887-5ae5-4e9e-a56a-e8cbeb8e2110","resolution":{"observed_at":"2026-08-03T16:07:31.968873Z","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":"2026-08-03T16:07:32.013279Z","title":"Anwer, Tim Baldwin, Michael Felsberg, and Fahad S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:32.013279Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:81df3e09f89a77cad592dc07b72502befa9fddc26ad7dfe9af3683e4a34e19bd","observation_id":"9cebc39b-e7f3-44d7-931b-035284aa7207","resolution":{"observed_at":"2026-08-03T16:07:32.013279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.07112","last_updated":"2024-12-10T01:57:17Z","snapshot_observed_at":"2026-08-10T00:59:30.096910Z","submitted_at":"2024-12-10T01:57:17Z","title":"Maya: An Instruction Finetuned Multilingual Multimodal Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.07112","snapshot_observed_at":"2026-08-03T16:07:32.033663Z","title":"Maya: An instruction finetuned multilingual multimodal model.arXiv preprint arXiv:2412.07112,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:32.033663Z"},"links":{"cited_paper":"/paper/2412.07112","citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:5ee433b5fed46b4dc507b2192c625aa15615e58d7043471310fec906ba889560","observation_id":"17417c65-eed7-4054-8678-b36a85a47f69","resolution":{"observed_at":"2026-08-03T16:07:32.033663Z","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":"2026-08-03T16:07:32.067790Z","title":"Chitrarth: Bridging vision and language for a billion people","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:32.067790Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:de70139ea3abf727d9acd5b1e01a7b10b068e4f50f4f5e5c8aa4cc228d99f976","observation_id":"50d2c4cc-33b9-43c8-9ee9-dd2b1e5a1aad","resolution":{"observed_at":"2026-08-03T16:07:32.067790Z","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":"2026-08-03T16:07:32.180007Z","title":"The question generation prompt is used to produce four candidate questions for each image during the dataset augmentation phase","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:32.180007Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:c0e4e25ff2f17742c91dbc74e33d92d71edb41cb403e5fa64da4bda92d229d23","observation_id":"7d472efe-7ba9-45bf-8d68-4448c88b715b","resolution":{"observed_at":"2026-08-03T16:07:32.180007Z","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":"2026-08-03T16:07:32.088934Z","title":"Rouge: A package for automatic evaluation of summaries","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":2002,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:32.088934Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:b4172b10816da66487b0bcc0da5f6098bbc208d0077a2a9298a710566fc1eb82","observation_id":"c3cfd856-15c2-484a-9111-350b4b13766b","resolution":{"observed_at":"2026-08-03T16:07:32.088934Z","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":"2026-08-03T16:07:32.145621Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:32.145621Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:f3310f6b5c61430d47ca803d305f540df4a861e84064c064a2f3f21de8120b20","observation_id":"dfafbf25-8ee1-482d-b7b7-9e9204b343a5","resolution":{"observed_at":"2026-08-03T16:07:32.145621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00020","last_updated":"2021-02-26T19:04:58Z","snapshot_observed_at":"2026-07-06T10:45:03.059688Z","submitted_at":"2021-02-26T19:04:58Z","title":"Learning Transferable Visual Models From Natural Language Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00020","snapshot_observed_at":"2026-08-03T16:07:31.687214Z","title":"Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.687214Z"},"links":{"cited_paper":"/paper/2103.00020","citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:c1191670a082cd9c8dfe319341bc4c615c8140a6ba6110af3e3a99f86340f6f9","observation_id":"56e94657-71ad-4ca5-b182-ef61ddfe9696","resolution":{"observed_at":"2026-08-03T16:07:31.687214Z","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":"2026-08-03T16:07:31.721667Z","title":"Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.721667Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:48348941c9f30d525ee5be09250be28d441cd5025bd330f82ed4d197b90505b4","observation_id":"9d22b67e-869b-41a7-9e3a-8c0ae15fec3b","resolution":{"observed_at":"2026-08-03T16:07:31.721667Z","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":"2026-08-03T16:07:31.549446Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.549446Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:2da7936c03b5afc2448891c1ae9923c1ffec1460d41573d069d0a25d9eaa883c","observation_id":"938a7b14-f115-42cf-a7c5-8022e86ce656","resolution":{"observed_at":"2026-08-03T16:07:31.549446Z","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":"2026-08-03T16:07:31.789054Z","title":"ISBN 979-8-89176-189-6","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T16:07:31.789054Z"},"links":{"citing_paper":"/paper/2512.14926"},"observation_digest":"sha256:b9babca21411b5842513cfe8ff6704c0437be8973c4e6e12902665268e1a673f","observation_id":"b49eb0df-c817-423c-b839-e3948df40c37","resolution":{"observed_at":"2026-08-03T16:07:31.789054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.14926","last_updated":"2026-07-05T06:57:07Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T01:00:45.957761Z","submitted_at":"2025-12-16T21:36:28Z","title":"Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":1,"verified_fuzzy":0},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2512.14926."}