{"as_of":"2026-08-08T15:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9e3f66da96efbe226922f366e2a79767a0b616b550f71535f2f1ee66b0f5d0f2","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:55:57.401337Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2509.03494/citation-record","integrity":"/paper/2509.03494/integrity","json":"/paper/2509.03494/citation-record.json","paper":"/paper/2509.03494"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/6353522","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:56:01.388565Z","title":"Completely Blind","venue":null,"work_id":"1ba34947-7788-48f6-81be-8e50db3bae6a","year":2012},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.018895Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:197abf3b01dbf3e3313a995c1efa931752af6367c6979cd8b985cf9d698a63c5","observation_id":"11adf929-04cd-492f-9edf-39031eb81514","resolution":{"observed_at":"2026-08-05T10:56:01.550135Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.02665","last_updated":"2019-07-05T03:35:35Z","snapshot_observed_at":"2026-07-06T08:05:18.374393Z","submitted_at":"2019-07-05T03:35:35Z","title":"Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network","version":1},"cited_work":{"arxiv_id":"1907.02665","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.02665","snapshot_observed_at":"2026-08-05T10:56:01.116353Z","title":"Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network","venue":"eess.IV","work_id":"c4780a09-b933-4b15-8d60-ede48287a126","year":2019},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.098456Z"},"links":{"cited_paper":"/paper/1907.02665","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:5888132452069d1e959c3d70a5cafe63d609e310c527a5ff05f9bcde5c675a23","observation_id":"bf867e90-610d-4b37-a201-bab05c1e399d","resolution":{"observed_at":"2026-08-05T10:56:01.185604Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.05997","last_updated":"2021-08-12T23:36:22Z","snapshot_observed_at":"2026-07-06T11:37:59.129170Z","submitted_at":"2021-08-12T23:36:22Z","title":"MUSIQ: Multi-scale Image Quality Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.05997","snapshot_observed_at":"2026-08-05T10:55:55.157777Z","title":"MUSIQ: Multi-scale Image Quality Transformer","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.157777Z"},"links":{"cited_paper":"/paper/2108.05997","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:67d990b81b3e0b91fe62b6b507009ab263495ebc18ab40c50b785422518cd4be","observation_id":"2dbaba8b-b8c6-43e5-a8c5-a328bf92cb29","resolution":{"observed_at":"2026-08-05T10:55:55.157777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.06783","last_updated":"2023-11-12T09:10:51Z","snapshot_observed_at":"2026-07-06T16:46:10.142093Z","submitted_at":"2023-11-12T09:10:51Z","title":"Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.06783","snapshot_observed_at":"2026-08-05T10:55:55.252815Z","title":"Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.252815Z"},"links":{"cited_paper":"/paper/2311.06783","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:d969937bee2ce487c3dde10646b56c4bbc02fc839b0234c4fd02e73fd059fce6","observation_id":"cd0eb886-c7c4-4d11-9518-3a0d4a945e7e","resolution":{"observed_at":"2026-08-05T10:55:55.252815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-05T10:55:55.337864Z","title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.337864Z"},"links":{"cited_paper":"/paper/2312.17090","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:f703fd886040bfd8bc1a0c80b911632d0361ae30d7ee3dc8e187a343fc5f5388","observation_id":"25b9ee8b-9a75-4134-a86b-f5f7ead5bb4c","resolution":{"observed_at":"2026-08-05T10:55:55.337864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12396","last_updated":"2022-11-23T13:17:33Z","snapshot_observed_at":"2026-08-06T08:25:44.406346Z","submitted_at":"2022-07-25T17:58:16Z","title":"Exploring CLIP for Assessing the Look and Feel of Images","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12396","snapshot_observed_at":"2026-08-05T10:55:55.383959Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.383959Z"},"links":{"cited_paper":"/paper/2207.12396","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:b49a8852a066ca93f9e968178fa1bdc3fbfe8d8320fb8e130410f03263e11e46","observation_id":"be8da81c-7580-462f-a660-1fbb792414a0","resolution":{"observed_at":"2026-08-05T10:55:55.383959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.14181","last_updated":"2024-01-01T14:48:48Z","snapshot_observed_at":"2026-07-06T16:23:18.453624Z","submitted_at":"2023-09-25T14:43:43Z","title":"Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.14181","snapshot_observed_at":"2026-08-05T10:55:55.439238Z","title":"Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.439238Z"},"links":{"cited_paper":"/paper/2309.14181","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:be4573bc746b3757c4d37a3474dbe1c74eeb4a9f10df1c9156be1235400cc934","observation_id":"fd9350a5-f19b-4416-a29b-f95cef1e66e5","resolution":{"observed_at":"2026-08-05T10:55:55.439238Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.10854","last_updated":"2024-07-11T04:11:53Z","snapshot_observed_at":"2026-08-06T17:20:43.744913Z","submitted_at":"2024-03-16T08:30:45Z","title":"A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment","version":3},"cited_work":{"arxiv_id":"2403.10854","doi":"10.48550/arxiv.2403.10854","metadata_source":"pith","pith_arxiv_id":"2403.10854","snapshot_observed_at":"2026-08-05T12:16:14.913508Z","title":"A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment","venue":"cs.CV","work_id":"dee31f24-6fd3-4e2a-a98f-d6541b9a0fb3","year":2024},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.514059Z"},"links":{"cited_paper":"/paper/2403.10854","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:13216b7ce69c936a252610d9981514675f11e3692bd64bfcb5a99503c90b828f","observation_id":"d5ebee90-74ef-475e-bb10-56018248cbb8","resolution":{"observed_at":"2026-08-05T10:55:58.298715Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.14968","last_updated":"2023-03-27T07:58:09Z","snapshot_observed_at":"2026-08-06T07:05:56.726025Z","submitted_at":"2023-03-27T07:58:09Z","title":"Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective","version":1},"cited_work":{"arxiv_id":"2303.14968","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.14968","snapshot_observed_at":"2026-08-05T10:56:00.726802Z","title":"Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective","venue":"cs.CV","work_id":"39fcc95e-8959-4a3a-a64c-df1e2f6d20a5","year":2023},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.571317Z"},"links":{"cited_paper":"/paper/2303.14968","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:96148bdf2fd0e95c51be6610ca089bf43932c1ad8e3a229d4fc0007ce73eb5de","observation_id":"c9adff78-b987-4e73-b669-cef5a1080ce7","resolution":{"observed_at":"2026-08-05T10:56:00.802510Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.04257","last_updated":"2023-11-09T01:56:51Z","snapshot_observed_at":"2026-08-06T04:08:15.266897Z","submitted_at":"2023-11-07T14:21:29Z","title":"mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.04257","snapshot_observed_at":"2026-08-05T10:55:55.637449Z","title":"mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.637449Z"},"links":{"cited_paper":"/paper/2311.04257","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:133d9e79318e51cfc47cfda384b0aa51bc0a41f610989e84b96b4b3ef71bb805","observation_id":"98d6f1f5-70c9-4e02-80d5-f8a463baf381","resolution":{"observed_at":"2026-08-05T10:55:55.637449Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.17274","last_updated":"2022-06-03T17:52:04Z","snapshot_observed_at":"2026-07-06T12:55:27.843060Z","submitted_at":"2022-03-31T17:59:30Z","title":"Exploring Visual Prompts for Adapting Large-Scale Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.17274","snapshot_observed_at":"2026-08-05T10:55:55.673736Z","title":"Exploring Visual Prompts for Adapting Large-Scale Models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.673736Z"},"links":{"cited_paper":"/paper/2203.17274","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:f6c72254cc10efe6d50daac4b8b6f1037959bc7bd78d05250ab26b1758f9689b","observation_id":"241f9d26-79d5-4ac8-af64-68c4e92693a7","resolution":{"observed_at":"2026-08-05T10:55:55.673736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.06180","last_updated":"2020-05-27T09:40:51Z","snapshot_observed_at":"2026-07-06T08:29:22.074079Z","submitted_at":"2019-10-14T14:38:48Z","title":"KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment","version":2},"cited_work":{"arxiv_id":"1910.06180","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.06180","snapshot_observed_at":"2026-08-05T10:56:00.414552Z","title":"KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment","venue":"cs.CV","work_id":"7a37f4df-f7f6-4704-bf5d-c8880d93e87c","year":2019},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.718193Z"},"links":{"cited_paper":"/paper/1910.06180","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:a64703bcbbc03bfbeb2d4819efd7e5aedcbccc0740e8e4ba938cf6c9790036c1","observation_id":"de9714dc-682c-4c6c-8283-93dcb726333f","resolution":{"observed_at":"2026-08-05T10:56:00.543758Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/8743252","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:56:00.212095Z","title":"KADID-10k: A Large-scale Artifi- cially Distorted IQA Database","venue":null,"work_id":"d8876ca2-543c-46ff-a3ed-748d28fc024e","year":2019},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.766191Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:6568dc262718b9c676c3d3c9c4dc3f167674c22139ed4f38ebb5ec411710ee32","observation_id":"bab68c99-2e4a-4bec-a125-31f116770661","resolution":{"observed_at":"2026-08-05T10:56:00.336610Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04717","last_updated":"2023-06-12T16:42:59Z","snapshot_observed_at":"2026-07-06T15:39:56.892976Z","submitted_at":"2023-06-07T18:28:21Z","title":"AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment","version":2},"cited_work":{"arxiv_id":"2306.04717","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.04717","snapshot_observed_at":"2026-08-05T10:55:59.929186Z","title":"AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment","venue":"cs.CV","work_id":"1945130f-a318-4ec3-b9dc-16db1626c880","year":2023},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.800693Z"},"links":{"cited_paper":"/paper/2306.04717","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:b87c7921a1896db60985120871d60a1bf50c5f464d030f5f882a033d06b18aba","observation_id":"4e449ccf-8cd5-4309-a198-abcfd0285a44","resolution":{"observed_at":"2026-08-05T10:56:00.005751Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.10556","last_updated":"2023-03-29T06:49:51Z","snapshot_observed_at":"2026-07-06T14:33:11.945106Z","submitted_at":"2022-12-20T18:57:06Z","title":"Unleashing the Power of Visual Prompting At the Pixel Level","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.10556","snapshot_observed_at":"2026-08-05T10:55:55.843948Z","title":"Unleashing the Power of Visual Prompting At the Pixel Level","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.843948Z"},"links":{"cited_paper":"/paper/2212.10556","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:699e653c708416ea24719859393c6daf2aa4b5adbba3759ec923206f1621a4be","observation_id":"2caf8e2d-aabd-41db-b704-64635f427224","resolution":{"observed_at":"2026-08-05T10:55:55.843948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08381","last_updated":"2024-03-10T19:00:00Z","snapshot_observed_at":"2026-07-06T16:31:56.083710Z","submitted_at":"2023-10-12T14:55:31Z","title":"AutoVP: An Automated Visual Prompting Framework and Benchmark","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08381","snapshot_observed_at":"2026-08-05T10:55:55.930192Z","title":"AutoVP: An Automated Visual Prompting Framework and Benchmark","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.930192Z"},"links":{"cited_paper":"/paper/2310.08381","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:cb03e6e593c9df5dcec0196d08b68c26e085b50b95954e4ccecf403743ac8150","observation_id":"2a29d498-df21-4100-9590-31c73398beb3","resolution":{"observed_at":"2026-08-05T10:55:55.930192Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.11635","last_updated":"2023-03-24T18:06:06Z","snapshot_observed_at":"2026-08-05T17:22:36.890959Z","submitted_at":"2022-11-21T16:49:47Z","title":"Understanding and Improving Visual Prompting: A Label-Mapping Perspective","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.11635","snapshot_observed_at":"2026-08-05T10:55:55.979446Z","title":"Understanding and Improving Visual Prompting: A Label-Mapping Perspective","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:55.979446Z"},"links":{"cited_paper":"/paper/2211.11635","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:6aaf11523c406c41bcdfbfe81ac013634f5ef95aba86ba5fc4746908290bc459","observation_id":"ae5c3327-dfa0-45bf-9ea6-74fcf3596313","resolution":{"observed_at":"2026-08-05T10:55:55.979446Z","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":"2026-08-05T10:56:02.111488Z","title":"End-to-End Blind Image Quality Assessment Using Deep Neural Networks","venue":null,"work_id":"658408e9-7a5d-4893-af66-8770fbc33ff3","year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.034531Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:b5cca18741f70561d72538896dd10df088489e7d3dbb2d588946080a349cb6ef","observation_id":"338f4cbf-63d3-4d33-9241-23135d09b9ab","resolution":{"observed_at":"2026-08-05T10:56:02.187144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.13983","last_updated":"2021-02-23T09:45:41Z","snapshot_observed_at":"2026-08-05T23:14:30.169690Z","submitted_at":"2020-05-28T13:35:23Z","title":"Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild","version":6},"cited_work":{"arxiv_id":"2005.13983","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.13983","snapshot_observed_at":"2026-08-05T10:55:59.658866Z","title":"Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild","venue":"cs.CV","work_id":"57bf8659-5eed-46aa-8450-266cf63c02b9","year":2020},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.132975Z"},"links":{"cited_paper":"/paper/2005.13983","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:dea354fa56f41bf99d9c9f942c92b69a6e3710d9f2b15e8535731fb18fc5ad31","observation_id":"e24a9897-aa11-416e-9cca-943b7e43c507","resolution":{"observed_at":"2026-08-05T10:55:59.726919Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/9857249","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:55:59.520965Z","title":"MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment","venue":null,"work_id":"6c0798a0-6414-488f-a825-bc517be93812","year":2022},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.198080Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:72af62319f3157b44a3703c6e09fc587635ed327145b60524ae3e980033682d6","observation_id":"3e0333b8-21f6-4741-9366-a8f084cb5d5a","resolution":{"observed_at":"2026-08-05T10:55:59.578485Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:56:01.787133Z","title":"Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli, and Alan C","venue":null,"work_id":"b48a48d4-5845-4728-966f-5f8c8d295358","year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.271805Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:b4ffad0cdb5a093d1d6bf5d524cdfd99b6721b4da685aeac429c0f642f138a78","observation_id":"fd7c852e-c98b-4f4f-9194-c0039e7d95a8","resolution":{"observed_at":"2026-08-05T10:56:01.946984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00020","last_updated":"2021-02-26T19:04:58Z","snapshot_observed_at":"2026-07-06T10:45:03.059688Z","submitted_at":"2021-02-26T19:04:58Z","title":"Learning Transferable Visual Models From Natural Language Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00020","snapshot_observed_at":"2026-08-05T10:55:56.394377Z","title":"Learning Transferable Visual Models From Natural Language Supervision","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.394377Z"},"links":{"cited_paper":"/paper/2103.00020","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:3f1ee681e22805b85da39fc1a09f4b0228667237a5259100a7a0f8091a67914f","observation_id":"2916d72e-2a0a-44e0-962b-a523ed74966c","resolution":{"observed_at":"2026-08-05T10:55:56.394377Z","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":"2026-08-05T10:55:56.480815Z","title":"Learning to Prompt for Vision-Language Models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.480815Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:8829892e1de8903e4c41e1c552d5dfdc19d58e1f30e9913fc46f6169ec246271","observation_id":"48d30153-be2b-4046-abd6-d03ed49b3b94","resolution":{"observed_at":"2026-08-05T10:55:56.480815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11560","last_updated":"2025-02-17T08:48:07Z","snapshot_observed_at":"2026-08-07T18:13:19.273289Z","submitted_at":"2025-02-17T08:48:07Z","title":"A Survey of Automatic Prompt Engineering: An Optimization Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11560","snapshot_observed_at":"2026-08-05T10:55:56.584536Z","title":"A Survey of Automatic Prompt Engineering: An Optimization Perspective","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.584536Z"},"links":{"cited_paper":"/paper/2502.11560","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:9ddf7581d3679d12540004232b35f2f6eb97cf04f1c7f95d8b1ec8f81161909c","observation_id":"03de08e0-830a-468b-96ae-6c177aa7d57e","resolution":{"observed_at":"2026-08-05T10:55:56.584536Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17491","last_updated":"2026-04-03T23:10:26Z","snapshot_observed_at":"2026-08-02T18:34:18.562354Z","submitted_at":"2024-07-04T02:35:00Z","title":"Robust Adaptation of Foundation Models with Black-Box Visual Prompting","version":4},"cited_work":{"arxiv_id":"2407.17491","doi":"10.48550/arxiv.2407.17491","metadata_source":"pith","pith_arxiv_id":"2407.17491","snapshot_observed_at":"2026-08-05T12:16:14.913508Z","title":"Robust Adaptation of Foundation Models with Black-Box Visual Prompting","venue":"cs.CV","work_id":"a2d7397d-1bde-49d3-bec9-172415d17664","year":2024},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.696609Z"},"links":{"cited_paper":"/paper/2407.17491","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:512e78768613e0f1ff8bd89456cba6f894b5af30456a43b4ed35eed05d369c93","observation_id":"9e168cb3-3a98-4597-bcc2-b76e5b07cce0","resolution":{"observed_at":"2026-08-05T10:55:57.898934Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:55:56.774985Z","title":"Multi-Layer Cross-Modal Prompt Fusion for No-Reference Image Quality Assess- ment","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.774985Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:3c6f545204aba542c189ae6ebe148d0c6c1b5370103022807a055df46b30f3a5","observation_id":"fa6af6b4-4379-45cb-ace9-9ea18d67a053","resolution":{"observed_at":"2026-08-05T10:55:56.774985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14949","last_updated":"2024-05-18T13:29:01Z","snapshot_observed_at":"2026-08-06T05:59:33.914595Z","submitted_at":"2024-04-23T11:45:32Z","title":"Multi-Modal Prompt Learning on Blind Image Quality Assessment","version":2},"cited_work":{"arxiv_id":"2404.14949","doi":"10.48550/arxiv.2404.14949","metadata_source":"pith","pith_arxiv_id":"2404.14949","snapshot_observed_at":"2026-08-05T12:16:14.913508Z","title":"Multi-Modal Prompt Learning on Blind Image Quality Assessment","venue":"cs.CV","work_id":"57fb0a51-47d1-4976-b743-15f1025ec0f8","year":2024},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.842294Z"},"links":{"cited_paper":"/paper/2404.14949","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:8d7cca6f2fcd3217bf5fb7fd8d7581a94844d7e683105a54a2048ec14d28dbce","observation_id":"9198f5a8-6e6c-4df9-8007-65fe93d246c5","resolution":{"observed_at":"2026-08-05T10:55:57.739821Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.01655","last_updated":"2025-04-02T12:02:57Z","snapshot_observed_at":"2026-08-07T16:14:49.314565Z","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":"2026-08-05T10:55:56.903384Z","title":"Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.903384Z"},"links":{"cited_paper":"/paper/2504.01655","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:436d3ca624adbd619ee5d73d0739a88d728b74ef4f139286d43d5dd3e4f1bfea","observation_id":"74ced107-eef2-42be-bf99-e39ccb347e32","resolution":{"observed_at":"2026-08-05T10:55:56.903384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.13549","last_updated":"2024-11-29T15:51:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-23T15:21:52Z","title":"A Survey on Multimodal Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.13549","snapshot_observed_at":"2026-08-05T10:55:57.011740Z","title":"A Survey on Multimodal Large Language Models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:57.011740Z"},"links":{"cited_paper":"/paper/2306.13549","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:dec9ab8206fd81f96fa6d9169eab75d8c950979f50b3415813ff81968814527c","observation_id":"b41370ed-0d29-4847-8126-7da1470c97eb","resolution":{"observed_at":"2026-08-05T10:55:57.011740Z","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":"document/9706735","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:55:58.831826Z","title":"Alireza Golestaneh, Saba Dadsetan, and Kris M","venue":null,"work_id":"d639d384-be2b-49b5-99af-7dfe589a3307","year":2022},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:57.142964Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:59a6fa79f19d6316f390eb918c0944659f157a322383688f9297a2edb671e8f0","observation_id":"1d066402-1aab-4c99-9a97-fdb1e726d985","resolution":{"observed_at":"2026-08-05T10:55:58.914862Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:56:01.635135Z","title":"Perceptual Quality As- sessment of Smartphone Photography","venue":null,"work_id":"694926ff-81ff-4c16-b1b2-a57c0af6a5f2","year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:57.202176Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:944816f744d255652140666481dfe7f62ab9007e4c9bbaf42a4289cc72d3ad06","observation_id":"ac956a8a-66d7-41ab-9e59-52ada205a3e8","resolution":{"observed_at":"2026-08-05T10:56:01.687595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.10088","last_updated":"2019-12-20T20:22:55Z","snapshot_observed_at":"2026-07-06T08:46:09.705605Z","submitted_at":"2019-12-20T20:22:55Z","title":"From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.10088","snapshot_observed_at":"2026-08-05T10:55:57.335047Z","title":"From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality, December 2019.doi:10.48550/arXiv.1912.10088","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:57.335047Z"},"links":{"cited_paper":"/paper/1912.10088","citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:9fd008a0e22229a4448a2d5eec189d3e516f787ee54d9b5eb96d3eed68ccdaaa","observation_id":"d1095579-ba2e-4b4d-b63f-a27a2ecc0cdf","resolution":{"observed_at":"2026-08-05T10:55:57.335047Z","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":"document/7327186","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:55:58.592318Z","title":"https://ieeexplore.ieee.org/document/7327186","venue":null,"work_id":"29d5261d-baec-4457-bf11-e91134a7d76f","year":null},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:57.401337Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:17180b585dbf443cf165baede06f9bb4ecc6417e46e63a204ea0b9d3052f498e","observation_id":"3cbbb798-c313-4953-858d-8740bab2dfc8","resolution":{"observed_at":"2026-08-05T10:55:58.649744Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-05T10:55:56.083618Z","title":"2017.2774045","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":1213,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:56.083618Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:fae160a727064bf77c06b5c7cc15de1e04d48d944b5bc4f9a51246a637b1b66c","observation_id":"b93078ea-c99d-47d3-8e3e-8eb406b30a74","resolution":{"observed_at":"2026-08-05T10:55:56.083618Z","resolver_source":null,"status":"malformed_identifier"},"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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:55:59.029130Z","title":null,"venue":null,"work_id":"4f1f83a9-dbd7-40e4-8aad-9f984be9d563","year":2020},"citing_paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-05T10:55:57.266765Z"},"links":{"citing_paper":"/paper/2509.03494"},"observation_digest":"sha256:75bca6828dd0f9039dd4a8c921774f9765e3ced5b092d7d6192c4d8e4459932d","observation_id":"f3a39b0d-4f2c-4663-a7e6-0c42e8636cd5","resolution":{"observed_at":"2026-08-05T10:55:59.101917Z","resolver_source":"arxiv_id_nonexistent","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.03494","last_updated":"2025-09-06T16:31:33Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T18:57:05.874042Z","submitted_at":"2025-09-03T17:23:24Z","title":"Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":14,"verified_fuzzy":3},"total_outbound_references":35},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2509.03494."}