{"as_of":"2026-08-08T01:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e7dee63f6e1fd5ba0ff3184b804eb94643f59fac64398050a5cd01c08864899","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:44:50.292508Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2507.21520/citation-record","integrity":"/paper/2507.21520/integrity","json":"/paper/2507.21520/citation-record.json","paper":"/paper/2507.21520"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.14762","last_updated":"2024-11-05T16:40:21Z","snapshot_observed_at":"2026-07-06T17:34:07.737296Z","submitted_at":"2024-02-22T18:21:59Z","title":"MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14762","snapshot_observed_at":"2026-08-06T12:44:49.134508Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.134508Z"},"links":{"cited_paper":"/paper/2402.14762","citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:1ba5ef66fbb1f52348958bd1b3d6b2b055eb5215517a7fc49c01744fc52f51e3","observation_id":"6a187fa1-5fae-4d64-be44-61702cb8e1ae","resolution":{"observed_at":"2026-08-06T12:44:49.134508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-08-06T12:44:49.171519Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.171519Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:39cf1f62704cd8bdc58d30eda20de3af811979541139e69d95e14623472c4820","observation_id":"a5e47152-8c06-4b10-a0e1-d53eb613a711","resolution":{"observed_at":"2026-08-06T12:44:49.171519Z","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":"2026-08-06T12:44:49.254306Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.254306Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:3f4db466eeb03940bfefa85affcd371d5f1b321926f8e48306670238d8e34e07","observation_id":"54fd92ba-667c-4307-a156-46641d107b36","resolution":{"observed_at":"2026-08-06T12:44:49.254306Z","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":"2026-08-06T12:44:52.472205Z","title":null,"venue":null,"work_id":"6b9c450c-3d54-4407-a335-4c92c57e3b83","year":2024},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.344545Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:a08fa15e47e8beb77ae1d20ffabb26fbb2b5d33d0ec0a2d861fb4e9f5dfbe38e","observation_id":"16f9d260-3511-4cce-8ce2-3b4624680b34","resolution":{"observed_at":"2026-08-06T12:44:52.572548Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:44:52.265032Z","title":"Gonzalez, Hao Zhang, and Ion Stoica","venue":null,"work_id":"371696d8-58ac-4102-97e1-2cace419e8fe","year":2023},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.404329Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:c0fb31982b795e14d32f8ae9f37bf0cd9b4e92a60c321ecc3dbc4b27d164c75a","observation_id":"8ff5afa0-4e17-4277-97ba-231721bf3777","resolution":{"observed_at":"2026-08-06T12:44:52.386479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:44:52.020159Z","title":"Gonzalez, Ion Stoica, Sohier Dane, Maggie Demkin, and Nate Keating","venue":null,"work_id":"c8ead894-8623-494f-bdbe-dc56f79537b6","year":2024},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.465767Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:c2907cc5a2a1d2982774703db80fa923ca3427264d5bc1344f37695ddb048f2d","observation_id":"9ca4fc3a-3d6a-48f8-b019-9f5e98593d29","resolution":{"observed_at":"2026-08-06T12:44:52.115957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04735","last_updated":"2024-03-07T18:38:17Z","snapshot_observed_at":"2026-07-06T17:41:10.677334Z","submitted_at":"2024-03-07T18:38:17Z","title":"SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04735","snapshot_observed_at":"2026-08-06T12:44:49.581330Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.581330Z"},"links":{"cited_paper":"/paper/2403.04735","citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:47da2030d918b67c043897f6cd2245ec9a0283c6876c15a3fd198073bb973460","observation_id":"c2c88776-3aac-43d6-b35b-ceed2381d9cd","resolution":{"observed_at":"2026-08-06T12:44:49.581330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.00005","last_updated":"2024-09-13T06:10:42Z","snapshot_observed_at":"2026-07-06T19:24:43.745786Z","submitted_at":"2024-09-13T06:10:42Z","title":"Winning Solution For Meta KDD Cup' 24","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.00005","snapshot_observed_at":"2026-08-06T12:44:49.671498Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.671498Z"},"links":{"cited_paper":"/paper/2410.00005","citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:bc3010a3410cbe49c6149a36e485530ac93f6cde0e5f38713450b8d9c841fb19","observation_id":"1ff1a0a2-e10c-4acf-a3eb-4d68fa1de595","resolution":{"observed_at":"2026-08-06T12:44:49.671498Z","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":"2026-08-06T12:44:51.755966Z","title":null,"venue":null,"work_id":"c5932247-a1f2-4e47-9d6b-47fca2ea88f1","year":2024},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.753735Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:5bd5674c2d6ccec5cce77b5a87a7c75c27d37835b3a6691549fbb8f5b25f4f70","observation_id":"6fd5afae-a374-4be7-adcd-a305fe845379","resolution":{"observed_at":"2026-08-06T12:44:51.858589Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.02490","last_updated":"2024-12-01T05:46:03Z","snapshot_observed_at":"2026-08-06T13:03:19.727877Z","submitted_at":"2023-08-04T17:59:47Z","title":"MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.02490","snapshot_observed_at":"2026-08-06T12:44:49.868313Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:49.868313Z"},"links":{"cited_paper":"/paper/2308.02490","citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:ade58b4d5526de56ace2903c1fe64fe8061a4388d25a7139f8d77226cd8bf562","observation_id":"61c513d0-6f99-440a-8e39-287cc735ad13","resolution":{"observed_at":"2026-08-06T12:44:49.868313Z","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":"2026-08-06T12:44:51.413548Z","title":null,"venue":null,"work_id":"cb1d1442-b340-416d-8f2e-dbd75ae7106e","year":null},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:50.043637Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:1f2fcae54403d6b1f320a6b418aeea9ca8d740ce4b0e58157310c2a6f776ee84","observation_id":"7cdff741-97d7-43b7-b210-1044bfd32633","resolution":{"observed_at":"2026-08-06T12:44:51.570923Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:44:51.144025Z","title":"Question: {query} Standard answer: {ans_full} Data Augmentation Prompt for Task1 Figure 5: Data augmentation prompt for Task1","venue":null,"work_id":"223581e8-5db5-46d0-8bb2-da82ab4572ef","year":null},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:50.102640Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:6c7ca526240a5bb2c470492bf491a27e6992e2d9a89af1a33ef09c1cd350e95d","observation_id":"0ffbd882-01a6-4e4b-95b2-2eba69c94e09","resolution":{"observed_at":"2026-08-06T12:44:51.300201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:44:50.891239Z","title":null,"venue":null,"work_id":"41e5f0a7-6557-417c-9234-324be8eac00c","year":null},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:50.176136Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:f63c7886a994b8f7b5fbb8e919f352844fa84fa29941f6ea05bcf30ac54e3808","observation_id":"96b3ed29-78ca-4f8b-ba98-9a97f79f99fd","resolution":{"observed_at":"2026-08-06T12:44:50.996961Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:44:50.767134Z","title":null,"venue":null,"work_id":"8cd621e5-bafa-4797-aef9-b239dce32de5","year":null},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:50.214658Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:ebafbceee656bf0ae50274b487a284840dcfdd26519b2cfa32aa7943e38277c9","observation_id":"162aecdc-59e1-4ad0-bab7-31ccd08a8b2b","resolution":{"observed_at":"2026-08-06T12:44:50.794221Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-06T12:44:50.589292Z","title":"For example, when answer a question about time, it's better to answer with day, month and year","venue":null,"work_id":"819269be-fff2-4260-948b-c560eb3f40da","year":null},"citing_paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:44:50.292508Z"},"links":{"citing_paper":"/paper/2507.21520"},"observation_digest":"sha256:f7678d1be369adb056c1438bd535976b56de945b7ec989c8b9be28b2d7af71a9","observation_id":"cdfeaa14-a5da-48ee-9b11-f350500ba276","resolution":{"observed_at":"2026-08-06T12:44:50.679810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21520","last_updated":"2025-07-29T06:07:59Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-06T12:44:48.421569Z","submitted_at":"2025-07-29T06:07:59Z","title":"Solution for Meta KDD Cup'25: A Comprehensive Three-Step Framework for Vision Question Answering"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":4},"total_outbound_references":15},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2507.21520."}