{"as_of":"2026-08-05T12:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:53444a63b4ce743b2d88f7805adb8663e167b692098a1fe1b6ac2bbf4cee9fe9","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T11:17:54.667906Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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/2509.02969/citation-record","integrity":"/paper/2509.02969/integrity","json":"/paper/2509.02969/citation-record.json","paper":"/paper/2509.02969"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:17:55.601622Z","title":null,"venue":null,"work_id":"32f764bb-1033-4c42-ade1-1c10f458fc88","year":2015},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.421309Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:4d7b661cb45e167ab99f91102594f3e23d96a473cfe8cc757e081ad66e570000","observation_id":"0f749ec7-d044-4c58-a035-d158780c3cf4","resolution":{"observed_at":"2026-08-05T11:17:55.606657Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-08-05T11:17:54.425971Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.425971Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:81516c564de80a274bec0ddff873e5405a52fddc34f906285d1b6d9e55d30835","observation_id":"510dbecb-c5d4-47e4-9e19-6821c4659e5b","resolution":{"observed_at":"2026-08-05T11:17:54.425971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.02273","last_updated":"2020-11-02T14:20:19Z","snapshot_observed_at":"2026-07-31T03:53:16.829342Z","submitted_at":"2020-11-02T14:20:19Z","title":"VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement","version":1},"cited_work":{"arxiv_id":"2011.02273","doi":null,"metadata_source":"pith","pith_arxiv_id":"2011.02273","snapshot_observed_at":"2026-08-05T11:17:54.908104Z","title":"VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement","venue":"cs.CY","work_id":"cf844cd3-20cd-4b81-af6b-ecf26b8c6763","year":2020},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.430610Z"},"links":{"cited_paper":"/paper/2011.02273","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:6ac35408cfa957078dd1e8a3b01e9008c27df0d1804e426d26235e8019fa1e6b","observation_id":"577c29c6-eb4d-469c-a903-7110b55f6ea7","resolution":{"observed_at":"2026-08-05T11:17:54.913075Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.585884Z","title":"Learning generalized spatial-temporal deep feature representation for no-reference video quality as- sessment","venue":null,"work_id":"f519dfdb-3fb0-4110-a625-4691f150b55e","year":1903},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.435334Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:faddf8f3e2fdc3d5f1ee68a67b6945b52c289326d66dbdb3772e68abeacead8a","observation_id":"ddfccd28-5cef-4880-903e-4c7c5c51d47f","resolution":{"observed_at":"2026-08-05T11:17:55.590753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.571705Z","title":"Rirnet: Recurrent-in-recurrent network for video quality assessment","venue":null,"work_id":"72b255c1-68f2-411a-a17e-83f5d75db0d2","year":2020},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.439490Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:173e0e10a4e64ffcf63b4ac2ffe5f2bd053353b70e7d89e119378a59d7c59b09","observation_id":"b8d5c0b3-1ffb-435e-a9c8-9b6bfb6cad1b","resolution":{"observed_at":"2026-08-05T11:17:55.576232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09058","last_updated":"2022-12-18T10:41:55Z","snapshot_observed_at":"2026-08-02T19:02:22.939940Z","submitted_at":"2022-12-18T10:41:55Z","title":"BEATs: Audio Pre-Training with Acoustic Tokenizers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.09058","snapshot_observed_at":"2026-08-05T11:17:54.443943Z","title":"Beats: Au- dio pre-training with acoustic tokenizers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.443943Z"},"links":{"cited_paper":"/paper/2212.09058","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:aae2f9c3c76fc82391905f5116318b5382617a81bd6f543fb1aa8a5ce25094db","observation_id":"88a4edce-cff7-4396-89c3-402c0ff4e7df","resolution":{"observed_at":"2026-08-05T11:17:54.443943Z","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":"2026-08-05T11:17:55.557035Z","title":"Xgboost: A scalable tree boosting system","venue":null,"work_id":"6f32c042-c880-4b71-ade5-26ab1c20a0b9","year":2016},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.448995Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:c0da9f7d68f12d832ee6e26ff0844c015b12780353875999c7523de1629868f9","observation_id":"9a5aabcb-428e-4ae7-9c56-7efaa12304e3","resolution":{"observed_at":"2026-08-05T11:17:55.561383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.03261","last_updated":"2025-05-06T07:42:24Z","snapshot_observed_at":"2026-07-06T21:19:29.591475Z","submitted_at":"2025-05-06T07:42:24Z","title":"DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor","version":1},"cited_work":{"arxiv_id":"2505.03261","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.03261","snapshot_observed_at":"2026-08-05T11:17:54.868705Z","title":"DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor","venue":"cs.CV","work_id":"f3922ed0-0f5b-4ff9-bbbb-677469d3b818","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.452860Z"},"links":{"cited_paper":"/paper/2505.03261","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:ba5e2bf6e284e98c69b2a1b7a3737a905c3914bd4490af2ca58f60869e692bc2","observation_id":"ee3f365f-b592-4237-a8e0-5a7805617b04","resolution":{"observed_at":"2026-08-05T11:17:54.873690Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.542358Z","title":"Vquala 2025 challenge on genai-bench aigc video quality assessment: Methods and re- sults","venue":null,"work_id":"31a86efc-0a79-445d-b630-b19e1cd4923e","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.457276Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:1a543086fdc85dfc2abee01337db92101e886557cf495b2a84c35316001d5972","observation_id":"a496611d-6979-4fb0-beea-ebdc4ce09887","resolution":{"observed_at":"2026-08-05T11:17:55.547247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07476","last_updated":"2024-10-30T06:49:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-11T17:22:23Z","title":"VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07476","snapshot_observed_at":"2026-08-05T11:17:54.465720Z","title":"Videollama 2: Advancing spatial- temporal modeling and audio understanding in video-llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.465720Z"},"links":{"cited_paper":"/paper/2406.07476","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:ef4cf087f897e6f1d302e5fb1c3cd9adecb0e4729d3d899e1c54854b058899f4","observation_id":"72475147-1c3a-4f9a-bf0a-f2ccda91caba","resolution":{"observed_at":"2026-08-05T11:17:54.465720Z","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":"2026-08-05T11:17:55.526721Z","title":"Imagenet: A large-scale hierarchical im- age database","venue":null,"work_id":"1a070123-07fb-4601-ac90-988b3f8ffde1","year":2009},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.470115Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:cd2729de9280ccf627494ff743d20963d3bec5ecbdb56150a193cc9d97f48133","observation_id":"206d8df4-0dd2-4a70-8897-1135883594b7","resolution":{"observed_at":"2026-08-05T11:17:55.531517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.511908Z","title":"Bert: Pre-training of deep bidirectional trans- formers for language understanding","venue":null,"work_id":"d144559c-7731-4138-b22e-744eeec05e24","year":2019},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.474214Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:891451f15cd8351c8fbd5a409bbfc1b0d6eeb15636bf8f64908ce9c45939eb78","observation_id":"857520c7-8e84-473c-8e13-31d2296ad42a","resolution":{"observed_at":"2026-08-05T11:17:55.516515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T11:17:54.478131Z","title":"An image is worth 16x16 words: Trans- formers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.478131Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:738535af32f0c305a72ae81d0564a587c5d348af66584496def839932e698854","observation_id":"ece5e4be-59fa-43da-ace9-bbec8fe4bfb5","resolution":{"observed_at":"2026-08-05T11:17:54.478131Z","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":"2026-08-05T11:17:55.497818Z","title":"Konvid-150k: A dataset for no-reference video qual- ity assessment of videos in-the-wild","venue":null,"work_id":"3b3781fb-ff80-42cb-affc-f831dd74fab0","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.482377Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:ca9fbbf39788a585a9b436818e27c824dde5e1553e7eea19789322c04e39f8d1","observation_id":"ec8c7906-a654-437f-a3f1-2b0ead3fe535","resolution":{"observed_at":"2026-08-05T11:17:55.502475Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.19026","last_updated":"2025-02-26T10:34:14Z","snapshot_observed_at":"2026-07-06T20:43:00.304358Z","submitted_at":"2025-02-26T10:34:14Z","title":"InternVQA: Advancing Compressed Video Quality Assessment with Distilling Large Foundation Model","version":1},"cited_work":{"arxiv_id":"2502.19026","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.19026","snapshot_observed_at":"2026-08-05T11:17:54.814206Z","title":"InternVQA: Advancing Compressed Video Quality Assessment with Distilling Large Foundation Model","venue":"eess.IV","work_id":"2a4c0240-1e91-4585-9bc2-2b5eb642b48b","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.486342Z"},"links":{"cited_paper":"/paper/2502.19026","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:50f103854ee69ea7f3ae9d289e892b4b091d498207e4a64b935f5b04a8d3f646","observation_id":"0afa97aa-f29f-4689-a674-2ba202e809ea","resolution":{"observed_at":"2026-08-05T11:17:54.819315Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.482002Z","title":null,"venue":null,"work_id":"c632c991-451f-45c3-8dfb-06c25621ccac","year":2018},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.490423Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:2dcc20c223a37b9cc3f2ed0013452eed7e63efc3ac074a94762cd23baccfbee4","observation_id":"5ee5cc87-446e-4418-b6de-7410f32e26f2","resolution":{"observed_at":"2026-08-05T11:17:55.487180Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.466141Z","title":"Deep residual learning for image recognition","venue":null,"work_id":"bf28c864-6659-4727-8f7b-1fe53e15c6d0","year":2016},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.494521Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:47bf6ad8ebbc69f566f4ee1bca07f2cefe0cc9132b36e5d1cb62f89ccae26676","observation_id":"6e16190a-56d0-4709-bc93-bd98477dce1b","resolution":{"observed_at":"2026-08-05T11:17:55.470785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.451234Z","title":"The konstanz natural video database (konvid-1k)","venue":null,"work_id":"24f5534e-cdb1-489a-a077-b7a49fb60aa6","year":2017},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.498560Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:aef8e1255760c012f28dd490dabbdb4e5269917cfcf56c9822224cd345aed65d","observation_id":"5d359bcb-70d4-4a38-a5ae-523df4aed969","resolution":{"observed_at":"2026-08-05T11:17:55.456235Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.435481Z","title":"Vquala 2025 doc- ument image quality assessment challenge","venue":null,"work_id":"b5d38f74-4551-4fa9-b00f-a1ff3bd4ff3b","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.502571Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:8a98735cb0b444dd2441edcab3e94bdf5b7803f51937e79670ab2195f3427e6a","observation_id":"ea0f3c11-e4bb-496b-8610-6ce3214c7e26","resolution":{"observed_at":"2026-08-05T11:17:55.440116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.06950","last_updated":"2017-05-19T12:07:01Z","snapshot_observed_at":"2026-07-06T05:43:22.028213Z","submitted_at":"2017-05-19T12:07:01Z","title":"The Kinetics Human Action Video Dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.06950","snapshot_observed_at":"2026-08-05T11:17:54.506452Z","title":"The kinetics human action video dataset","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.506452Z"},"links":{"cited_paper":"/paper/1705.06950","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:d0b3f33cecd371c5523ceb49f78babd60ad4902cd2570628733b38c1358e9f9b","observation_id":"14fd20bb-f2a3-46af-8a5e-6220a96917cc","resolution":{"observed_at":"2026-08-05T11:17:54.506452Z","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":"2026-08-05T11:17:55.420503Z","title":"Guo, Daniel T","venue":null,"work_id":"5304221e-9dac-411d-8097-8279487579f0","year":2014},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.510629Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:86aedb60bf153fca41b1fe033f5e8ba56cb69c58580e9bec998540fc586b6a29","observation_id":"35207e93-d117-4a36-9320-be75a0237429","resolution":{"observed_at":"2026-08-05T11:17:55.425056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.406148Z","title":"Melu: Meta-learned user preference esti- mator for cold-start recommendation","venue":null,"work_id":"97544992-5c2b-4633-bfc3-5abbf3425f39","year":2019},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.514788Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:d0ca69e7ba3f60890d64223b76f877f95f13e3a9f6862238c2a2cd012ca96375","observation_id":"0855f57f-4115-4b6e-aa8f-a3cf07157ee4","resolution":{"observed_at":"2026-08-05T11:17:55.410725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.389229Z","title":"Quality assessment of in-the-wild videos","venue":null,"work_id":"55ae3ec5-de0b-4b62-ba35-177c90b90a51","year":2019},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.518675Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:941170c53a1d93e2125dd631bc78819a7247a6f2bbb8844ec0477943afbf9af0","observation_id":"36356bb7-895b-442e-a3db-79ee158cd3bd","resolution":{"observed_at":"2026-08-05T11:17:55.394692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.373169Z","title":"Learning degradation rep- resentations for image deblurring","venue":null,"work_id":"818cc61c-547b-4469-a339-4277a7253efd","year":2022},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.522575Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:201d66e2edf237e07572c0d5613607968983e6b5ec2583c0f529fc1243f9a8ef","observation_id":"31fb43e3-d028-4206-9f65-5007a5ed8e6e","resolution":{"observed_at":"2026-08-05T11:17:55.377931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.358478Z","title":"Efficient burst raw denois- ing with variance stabilization and multi-frequency denois- ing network, 2022","venue":null,"work_id":"a2c73271-e8c5-4ea2-9991-a1b7d174312e","year":2022},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.526389Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:b43cf6866ea888a1e87520846b02cf4fb7e4d1f9cf86027dde7ff21866be8f8a","observation_id":"6fb2873b-145d-4c75-bdf4-057022098263","resolution":{"observed_at":"2026-08-05T11:17:55.362967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.345221Z","title":"A simple baseline for video restoration with grouped spatial- temporal shift","venue":null,"work_id":"8725328e-7d2d-41e9-8857-5394cf042f59","year":2023},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.530629Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:cc6fdd83d9cf8ec6a8a8a98c7ba2cfdecbd8d2dc70cc012d05528864e70dee37","observation_id":"3b978900-1f6d-47e0-9559-8c4f1791e838","resolution":{"observed_at":"2026-08-05T11:17:55.349253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.330933Z","title":"Delving deep into engagement prediction of short videos","venue":null,"work_id":"fce66b5e-0190-4221-a81b-fdb907e3a17d","year":2024},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.535249Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:af335a714c98fcea850c3b44f582ee18cbd2566560b220016bad6951447ca889","observation_id":"6570ccc8-0fe6-43cc-b3dd-6830307a3219","resolution":{"observed_at":"2026-08-05T11:17:55.335464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.317407Z","title":"Vquala 2025 challenge on image super-resolution generated content qual- ity assessment: Methods and results","venue":null,"work_id":"5bcaea56-ee8e-413d-8b16-c2bd7686f351","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.539341Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:e6dc7ac7ade94572b4d0a98abdc04a0431fdc49a6d857afe8494d68f74112029","observation_id":"cd65929c-8db5-4a9d-be1d-1d8575e13b14","resolution":{"observed_at":"2026-08-05T11:17:55.321581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.303582Z","title":"Efficient video quality assessment with deeper spatiotemporal feature extraction and integra- tion","venue":null,"work_id":"11b8a883-07bf-4e15-8998-0634de266c89","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.543051Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:f828846d45fe40bfb385513889fabebcbdbdcbb34d196b6f6af363f1c1b85642","observation_id":"bd2011aa-6145-4e04-b283-dffc5ab5805d","resolution":{"observed_at":"2026-08-05T11:17:55.308006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.01655","last_updated":"2025-04-02T12:02:57Z","snapshot_observed_at":"2026-07-06T21:03:02.194337Z","submitted_at":"2025-04-02T12:02:57Z","title":"Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.01655","snapshot_observed_at":"2026-08-05T11:17:54.546855Z","title":"Q-adapt: Adapting lmm for visual quality as- sessment with progressive instruction tuning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.546855Z"},"links":{"cited_paper":"/paper/2504.01655","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:aef73374384030d841801fbe6bfa4b41691f6e7d7a4ad0e9e642ce0e8f37ef5b","observation_id":"284aecf8-b25e-4250-9122-593649119f73","resolution":{"observed_at":"2026-08-05T11:17:54.546855Z","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":"2026-08-05T11:17:55.288468Z","title":"Reduced-reference video quality assessment of compressed video sequences","venue":null,"work_id":"f9e74ca6-2497-4a97-88ab-f4654bfd9955","year":2012},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.551075Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:6b4c4fe6e05eaeafc9c097135a2eb66560604a3f844f2f49f31dea95994072a3","observation_id":"ad82aadb-acbd-4514-b114-cd8987f4cfc6","resolution":{"observed_at":"2026-08-05T11:17:55.293010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.273548Z","title":"Vquala 2025 challenge on face image quality assessment: Methods and results","venue":null,"work_id":"ed7b4f71-3852-4eb1-bfaf-84d74c229002","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.554894Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:9b31889dffef24e6167bc5befe2c8c20dc7b3e527bb44f661fedebf836cd4287","observation_id":"736591ef-54de-47b4-afcc-1c040193f45b","resolution":{"observed_at":"2026-08-05T11:17:55.278750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.259130Z","title":"Warm up cold-start advertisements: Improving ctr predictions via learning to learn id embeddings","venue":null,"work_id":"d382d64d-00d2-4895-9f5f-65459a28ebd6","year":2019},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.558885Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:a53d23595c64a61e51268fff8c89cc31ed4ad6ac53f4bdfa0a56b5ff96b1bcdc","observation_id":"41a15aa9-0a68-495c-8e96-c3ba628bafe3","resolution":{"observed_at":"2026-08-05T11:17:55.263545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.244999Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"38e09971-7541-41a7-930d-ee61da45cbbe","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.562707Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:be13a7f3247f3b2a6e9bbbb855b1f572a620450955b0e6ac36a1bd7f9f5c3216","observation_id":"cf5533a2-542a-4227-81f6-3264c606c35c","resolution":{"observed_at":"2026-08-05T11:17:55.249753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10084","last_updated":"2019-08-27T08:50:17Z","snapshot_observed_at":"2026-07-06T08:17:05.681370Z","submitted_at":"2019-08-27T08:50:17Z","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10084","snapshot_observed_at":"2026-08-05T11:17:54.566786Z","title":"Sentence-bert: Sentence embeddings using siamese bert-networks","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.566786Z"},"links":{"cited_paper":"/paper/1908.10084","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:2aadf69c0adf18f34f0452234abd240af0dc5dfe28f04d54a88e1156cd6abed1","observation_id":"9c8d6710-63c7-40c4-a557-1827e4e50a0b","resolution":{"observed_at":"2026-08-05T11:17:54.566786Z","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":"2026-08-05T11:17:55.232412Z","title":"Large-scale study of perceptual video quality","venue":null,"work_id":"0bbf65e7-ba5a-461a-b5cf-7404de7fd73a","year":2019},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.571264Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:c659a1b927d16cd1904e154373f18b971304db8767d710eebfbb10f66aa8e3f3","observation_id":"9e9270f4-9f4e-41e3-9a8b-76927a8c7d9b","resolution":{"observed_at":"2026-08-05T11:17:55.236351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.219379Z","title":"Video quality as- sessment by reduced reference spatio-temporal entropic dif- ferencing","venue":null,"work_id":"c782267c-fbfb-48d4-8e29-40f200196d2b","year":2012},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.575904Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:e64c1bc49df0374e3596b1642631c33beab6021173e76e7e9a1947e503ed07b9","observation_id":"116a71f5-91b1-403c-aed5-3a0f07759d60","resolution":{"observed_at":"2026-08-05T11:17:55.223330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.205963Z","title":"Deep learning based full-reference and no-reference quality assessment models for compressed ugc videos","venue":null,"work_id":"34259656-fad6-44a4-8c36-943260cc54ad","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.579755Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:9289d742958ed2bf89589d2a3b2c7d798f9599243668f0d5a8e3626b02301064","observation_id":"97817fb5-1a5a-44c8-bf1a-a9986abff643","resolution":{"observed_at":"2026-08-05T11:17:55.210063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.192379Z","title":"A deep learning based no-reference quality assessment model for ugc videos","venue":null,"work_id":"812afd1a-9730-4776-b7f8-df39497eb788","year":2022},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.583712Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:ee225b57d17b4f8647f37fa3dc4c1a3a4bdbabf13a9dbbb2c91c8b26ee12c455","observation_id":"b8a47dfa-aa15-4143-9de7-9c1199cfe24d","resolution":{"observed_at":"2026-08-05T11:17:55.196703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.177532Z","title":"Engagement prediction of short videos with large multimodal models","venue":null,"work_id":"45ac7ef8-567c-42af-8524-b9d3f8abb909","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.587844Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:53c8e62b7338469ba412b16959a52383bad11224276108bb4b2587a411b166d5","observation_id":"1cf596a9-0769-4313-b09b-09a1567eac71","resolution":{"observed_at":"2026-08-05T11:17:55.181973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.161936Z","title":null,"venue":null,"work_id":"1d37a4d7-12c1-418b-8df6-ab58916ce55b","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.591881Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:1d374af878f19976b78a9a644bfe4fe5b7fc93a66ae776dc0124eec2aa6a2393","observation_id":"2c17e6e6-39fc-4dc0-867c-fd9ea7c8e2df","resolution":{"observed_at":"2026-08-05T11:17:55.166728Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.145354Z","title":"Ugc-vqa: Benchmarking blind video quality assessment for user generated content","venue":null,"work_id":"4cf25172-39ab-4618-acf6-e7e474c71a3d","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.595874Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:66f139778eab3ab08fbef54d0fc8d3bf2bb34bdd392cc85cfc136ef5c8ce6df0","observation_id":"ff5730e4-229c-45a4-b06c-a7f2c67f8db5","resolution":{"observed_at":"2026-08-05T11:17:55.149755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.130324Z","title":"Dropoutnet: Addressing cold start in recommender systems","venue":null,"work_id":"12199cd7-273f-4583-a871-0243f7ae3456","year":2017},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.600165Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:b0f02ab16d4a6305c17d12d4fe102a513c0b462c63d07d085f76b338e96698ad","observation_id":"99c30a70-a0de-44bb-a5e5-8c6c65371acd","resolution":{"observed_at":"2026-08-05T11:17:55.135416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.115183Z","title":"Skywork-vl re- ward: An effective reward model for multimodal understand- ing and reasoning, 2025","venue":null,"work_id":"c594e843-8bd4-4309-971b-6f8cbe7a47bd","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.604196Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:f9554af04c4904f999403854e24d033f1e6a7940c69cf035bfe6960553d6f241","observation_id":"a9569612-f94a-4f03-aadd-570ed68cd874","resolution":{"observed_at":"2026-08-05T11:17:55.119473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.100955Z","title":"Rich features for perceptual quality assessment of ugc videos","venue":null,"work_id":"42cdc598-4271-4fbc-a6dc-293b96af0337","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.608325Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:2dc4280e7fa8b3ca566c45ff98dd51c734c4e820b9ed57d740ab03f12b5433c8","observation_id":"b88e2455-89da-49ca-9ecf-c2e48fac3e18","resolution":{"observed_at":"2026-08-05T11:17:55.105891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.085550Z","title":"Exploring video quality assessment on user gener- ated contents from aesthetic and technical perspectives","venue":null,"work_id":"763ae9a1-7381-451b-b5a2-6c3b6ed37c41","year":2023},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.612235Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:2d6839e0db804f4afdddcf97065fb8b474fccf8053f3ae0665bbb9780282b5bf","observation_id":"4e6be097-a0e6-4e03-9045-8b7a727912c6","resolution":{"observed_at":"2026-08-05T11:17:55.090256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.17090","last_updated":"2023-12-28T16:10:25Z","snapshot_observed_at":"2026-08-02T07:14:02.308302Z","submitted_at":"2023-12-28T16:10:25Z","title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.17090","snapshot_observed_at":"2026-08-05T11:17:54.616319Z","title":"Q-align: Teaching lmms for visual scoring via discrete text-defined levels","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.616319Z"},"links":{"cited_paper":"/paper/2312.17090","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:d6fc12afb2dde7c64401f57ff162beeb0c66d23074f8d080880a816ca0ed473c","observation_id":"c729cbd6-b18c-446f-ace6-74bab7f56458","resolution":{"observed_at":"2026-08-05T11:17:54.616319Z","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":"2026-08-05T11:17:55.070345Z","title":"Q-instruct: Improving low-level visual abilities for multi-modality foundation models","venue":null,"work_id":"2fd2e5d1-4fb9-47b5-bed0-62b29b8f3b67","year":2024},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.620687Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:9794161ce59426cbaf4242a9808b74f078476ae4abbcc585b8e725dacb743240","observation_id":"95b64b22-d3f3-430e-949d-ca942339f12a","resolution":{"observed_at":"2026-08-05T11:17:55.074644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.056027Z","title":"Be- yond views: Measuring and predicting engagement in online videos","venue":null,"work_id":"24974aeb-6825-4f9e-b6f4-6b2b34874478","year":2018},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.625107Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:2aeac78e7fa4be01b4cf0fe0659398cf6728db27e76d144cf45004ba6f981f41","observation_id":"48327759-bc64-4632-bd39-9830205a499f","resolution":{"observed_at":"2026-08-05T11:17:55.060552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.00402","last_updated":"2023-02-01T12:40:03Z","snapshot_observed_at":"2026-07-06T14:47:00.945435Z","submitted_at":"2023-02-01T12:40:03Z","title":"mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.00402","snapshot_observed_at":"2026-08-05T11:17:54.629910Z","title":"mplug-2: A modularized multi-modal foundation model across text, image and video","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.629910Z"},"links":{"cited_paper":"/paper/2302.00402","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:7cb0065a66aafdf165b139953020d1b936bb2480ee4cb072b38957553668ba9a","observation_id":"ac6ea12a-aa4c-494e-8766-a622a54fa52e","resolution":{"observed_at":"2026-08-05T11:17:54.629910Z","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":"2026-08-05T11:17:55.040669Z","title":"Subjective quality assessment for youtube ugc dataset","venue":null,"work_id":"8529382c-6baa-4b77-b0da-661c22a534ea","year":2020},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.635168Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:d0f6736be3f11768139c7471605b568fe97d3225b68cb6d871f9973cf7774cdd","observation_id":"c161e794-2642-4e15-ae62-3eab0b7a9c93","resolution":{"observed_at":"2026-08-05T11:17:55.045389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.024945Z","title":"Patch-vq: ’patching up’ the video quality problem","venue":null,"work_id":"571f77cb-08fa-48fd-b168-1218307d545f","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.639428Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:526e15762af440a99a3d1796c86e7d3a97e39f00a2989c4c91e661a3ef8c4197","observation_id":"381f9c29-a4d0-47f2-af56-54e0ff5f9323","resolution":{"observed_at":"2026-08-05T11:17:55.030477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08522","last_updated":"2024-01-16T17:33:54Z","snapshot_observed_at":"2026-07-06T17:16:17.240820Z","submitted_at":"2024-01-16T17:33:54Z","title":"Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy","version":1},"cited_work":{"arxiv_id":"2401.08522","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.08522","snapshot_observed_at":"2026-08-05T11:17:54.706329Z","title":"Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy","venue":"cs.CV","work_id":"08e8a147-a985-478d-be70-274c96b16bf3","year":2024},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.643218Z"},"links":{"cited_paper":"/paper/2401.08522","citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:cd17f4f24fffae8f93c1373db9cd260aaa327931d4f777a113534ea718ec7f50","observation_id":"50f36328-4ba6-4819-9981-0261e526940f","resolution":{"observed_at":"2026-08-05T11:17:54.713006Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:55.009409Z","title":"Deconfounding duration bias in watch-time pre- diction for video recommendation","venue":null,"work_id":"37dc50f3-22d2-4e7c-80d8-9c33d0ca2a53","year":null},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.647276Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:20ce4d93f923c8be77330e7a96dcac54cafdb47ee5a1d5e1170b48049db4ebff","observation_id":"938171fa-6178-4e11-8baf-823b3e1e2f1b","resolution":{"observed_at":"2026-08-05T11:17:55.014197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:54.984344Z","title":"The unreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":"b049fa2d-4826-493d-8b90-98c3f3c5fa34","year":2018},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.655330Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:ca95e299a2ea7eca517cf7c9b082958329723dfd2edba66e0ea82283c38c2078","observation_id":"b0cab571-9fdf-4977-bbba-a1a86c013f66","resolution":{"observed_at":"2026-08-05T11:17:54.988766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:54.969044Z","title":"Md-vqa: Multi-dimensional quality assessment for ugc live videos","venue":null,"work_id":"3a10e088-109d-4a87-a277-427e7a9c2771","year":null},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.659325Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:985f7803952ed41787aaea57ba37cd555fafb75b8bcbccbe7a7fe745c80f17e7","observation_id":"61d78413-d667-4411-9b61-4fc475e05de7","resolution":{"observed_at":"2026-08-05T11:17:54.973180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:54.954676Z","title":"Vquala 2025 challenge on visual quality comparison for large multimodal models: Methods and results","venue":null,"work_id":"d655c116-4bd7-4b95-b02c-c53814bb8e40","year":2025},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.663490Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:1c620e42fab618c3e81fffd8115cfec0feff01d66544766d102ad1853d59bb07","observation_id":"78b01f39-c5fb-45c5-a7e3-87221d869151","resolution":{"observed_at":"2026-08-05T11:17:54.959169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:54.940297Z","title":"Learning to warm up cold item embeddings for cold-start recommenda- tion with meta scaling and shifting networks","venue":null,"work_id":"a91668a1-b442-40b7-be27-5cf3317dbdb7","year":2021},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.667906Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:1e1ff09b816cc67873958a34f362ae352cca753a1d215dccb97848806c979c21","observation_id":"9717284a-ceca-4903-83a2-5b804e74e3eb","resolution":{"observed_at":"2026-08-05T11:17:54.944855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-08-05T11:17:54.651318Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-05T11:17:54.651318Z"},"links":{"citing_paper":"/paper/2509.02969"},"observation_digest":"sha256:944e252309e2f5aa2cc31198639455cb53b5ea33cb15e1b2c0bcf453b7858658","observation_id":"b055a25f-2e30-4168-ae47-41b488254e16","resolution":{"observed_at":"2026-08-05T11:17:54.651318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.02969","last_updated":"2025-09-03T03:14:23Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-05T11:17:53.652592Z","submitted_at":"2025-09-03T03:14:23Z","title":"VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":4,"verified_fuzzy":42},"total_outbound_references":59},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2509.02969."}