{"as_of":"2026-08-17T22:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:38539f2f6a0ba4f2630994caf2c5cf408747fe07f0410e63ce86568f49943c2c","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T11:50:17.038882Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2607.24554/citation-record","integrity":"/paper/2607.24554/integrity","json":"/paper/2607.24554/citation-record.json","paper":"/paper/2607.24554"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-07-31T11:50:16.795608Z","title":"Llama 2: Open foundation and fine-tuned chat models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.795608Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:fa4543a7ee5ed70f4ed68d573ec5fde844b25691230bdc2c9ada23cb5e9fa823","observation_id":"41829841-1f6c-4b14-a1a1-d2c998ea02cc","resolution":{"observed_at":"2026-07-31T11:50:16.795608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-07-31T11:50:16.801291Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.801291Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:aa8692f79a9df1023167b88b1bd317455d2c5a30833ececbbd435e71ec895b1d","observation_id":"3ba4573b-47b4-4b64-aea9-5276a83c8aed","resolution":{"observed_at":"2026-07-31T11:50:16.801291Z","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":"2026-07-31T11:50:16.807373Z","title":"Retrieval-augmented generation for knowledge- intensive nlp tasks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.807373Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:e831b73cf9fc50d09f10df081568bbe6e964dbc6236810c5e2241d1f0ee7b9f4","observation_id":"59715cea-d6ec-4368-9e6c-93948870bf95","resolution":{"observed_at":"2026-07-31T11:50:16.807373Z","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":"2026-07-31T11:50:16.812261Z","title":"REALM: Retrieval-augmented language model pre-training,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.812261Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:0e512ae4a3c88421c263c416362fe90e31ae552a1c49d1e0cfd87c375413ba68","observation_id":"92687639-6069-42f9-bcac-6985d6d4149a","resolution":{"observed_at":"2026-07-31T11:50:16.812261Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-07-31T11:50:16.816904Z","title":"Retrieval-augmented generation for large language models: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.816904Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:4fa2463f787d816900d115a691555face09d2a0c066a856987bd6006a69a63a3","observation_id":"5e244521-82db-4030-a6f9-6d450cb971a9","resolution":{"observed_at":"2026-07-31T11:50:16.816904Z","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":"2026-07-31T11:50:16.821943Z","title":"Dense passage retrieval for open-domain question answering,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.821943Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:f205d8e9a0967dd938993f668e5e43ba1c2636c20d09ab8f12ad0d16a0e3b678","observation_id":"db3aa38a-246b-4b70-bc5f-b7de7189ef98","resolution":{"observed_at":"2026-07-31T11:50:16.821943Z","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":"2026-07-31T11:50:16.827420Z","title":"Leveraging passage retrieval with gener- ative models for open domain question answering,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.827420Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:cd36c1136f48f0bbaf0e15e1d535c8888c5a31d86f198239d26e88e43a8b042a","observation_id":"65338d73-ecc2-4d7e-9b0d-fa3b2d56ed34","resolution":{"observed_at":"2026-07-31T11:50:16.827420Z","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":"2026-07-31T11:50:16.832747Z","title":"Rag-anything: All-in-one rag framework,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.832747Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:4ecbe61df10c23533087dc63fabd46c376be187f92283bcee05c8d516daf493a","observation_id":"8377accc-974b-4f82-85eb-42894bf01566","resolution":{"observed_at":"2026-07-31T11:50:16.832747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16130","last_updated":"2025-02-19T10:49:41Z","snapshot_observed_at":"2026-08-16T12:38:40.131901Z","submitted_at":"2024-04-24T18:38:11Z","title":"From Local to Global: A Graph RAG Approach to Query-Focused Summarization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16130","snapshot_observed_at":"2026-07-31T11:50:16.837528Z","title":"From local to global: A Graph RAG approach to query-focused summarization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.837528Z"},"links":{"cited_paper":"/paper/2404.16130","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:1ec4d2417caedfa8027010bb8874cf0dede2f56ca5bf67fe473bb24beeda1f8f","observation_id":"5c09b5d7-963c-416f-99c1-c874d3b0dc4c","resolution":{"observed_at":"2026-07-31T11:50:16.837528Z","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":"2026-07-31T11:50:16.844740Z","title":"G-Retriever: Retrieval-augmented generation for textual graph understanding and question answering,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.844740Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:db078e2a6d0f44c28546b31bc2d590de065fe67fe29740e000f1d4231ea873fe","observation_id":"929ad0b4-5c31-4378-8de2-afcd54952882","resolution":{"observed_at":"2026-07-31T11:50:16.844740Z","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":"2026-07-31T11:50:16.849336Z","title":"LayoutLMv3: Pre- training for document ai with unified text and image masking,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.849336Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:1e7bd95dee4a85393131e29d7918d5664dd51536467ed0ae0c0e4a1d77139f94","observation_id":"6040179f-ffba-4a53-b643-2f1dbdadce3b","resolution":{"observed_at":"2026-07-31T11:50:16.849336Z","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":"2026-07-31T11:50:16.853632Z","title":"Ocr-free document understanding transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.853632Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:987c6c29f25729bb0a71f8efeb2d887c8dbaad6001b4995ff6b1b55518b22dd1","observation_id":"016f70e2-b95a-43d6-95a1-6c4d95a3705e","resolution":{"observed_at":"2026-07-31T11:50:16.853632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13418","last_updated":"2023-08-25T15:03:36Z","snapshot_observed_at":"2026-08-12T03:48:04.422679Z","submitted_at":"2023-08-25T15:03:36Z","title":"Nougat: Neural Optical Understanding for Academic Documents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13418","snapshot_observed_at":"2026-07-31T11:50:16.858739Z","title":"Nougat: Neural optical understanding for academic documents,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.858739Z"},"links":{"cited_paper":"/paper/2308.13418","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:bbcb718dcab936f569cc82ebba1d923a81389c4d0d7819570fbf4a48d153b990","observation_id":"ab5fa990-c5ab-4651-8637-775fabdc49ef","resolution":{"observed_at":"2026-07-31T11:50:16.858739Z","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":"2026-07-31T11:50:16.863633Z","title":"Docvqa: A dataset for vqa on document images,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.863633Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:2ace9886d9d01432aff7d90941c1c69b6e854859f2dee750e9831e2589564808","observation_id":"37cd1b6a-1025-4541-b38f-62df47cab9db","resolution":{"observed_at":"2026-07-31T11:50:16.863633Z","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":"2026-07-31T11:50:16.868784Z","title":"Towards vqa models that can read,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.868784Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:02e89fcfa70acebe192818ddb95cc3359e915691f9c7f65258ba78c43660a4ea","observation_id":"bb62cb33-c922-4c1a-9a69-5dbe25e44e5b","resolution":{"observed_at":"2026-07-31T11:50:16.868784Z","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":"2026-07-31T11:50:16.873298Z","title":"Visual instruction tuning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.873298Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:2c0b72bf6c63501cbd2be84098991fa15cfce7ef7df5f7d9d7013b3d176afbbd","observation_id":"a71fd1b7-d9cc-444f-beba-2aa666c20168","resolution":{"observed_at":"2026-07-31T11:50:16.873298Z","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":"2026-07-31T11:50:16.877480Z","title":"Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.877480Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:0ef5a84f2f84a98f926f68b87d3b20157b6ab418ebc343b133897b6e7591409a","observation_id":"34d858f3-609b-47a7-92fc-5c55517ae89a","resolution":{"observed_at":"2026-07-31T11:50:16.877480Z","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":"2026-07-31T11:50:16.881979Z","title":"Flamingo: a visual language model for few-shot learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.881979Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:450a02733468f50822db76df07310faf8d415c5bbdc7528ef573804c7f6bb983","observation_id":"8499f592-9750-43be-867c-ab0b790d6756","resolution":{"observed_at":"2026-07-31T11:50:16.881979Z","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":"2026-07-31T11:50:16.886600Z","title":"Instructblip: Towards general-purpose vision-language models with instruc- tion tuning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.886600Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:1a65224dee23443258d32fbfb32f7b6c30b2764cbb33e4903d50dc62912cf7d7","observation_id":"1000e979-cf55-475b-9a02-8e9d787dcd6a","resolution":{"observed_at":"2026-07-31T11:50:16.886600Z","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":"2026-07-31T11:50:16.891078Z","title":"Llms for knowledge graph construction and reasoning: Recent capabilities and future opportunities,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.891078Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:26348c50ce1fe795783932e65ae5eaa5e36c6d6635a2678805e81dffa0a6b965","observation_id":"d74ca9f0-2d69-435f-8060-248fec30caf7","resolution":{"observed_at":"2026-07-31T11:50:16.891078Z","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":"2026-07-31T11:50:16.896317Z","title":"Mitigating object hallucinations in large vision-language mod- els through visual contrastive decoding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.896317Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:dbb549e7865aee07fea02b28e87277e572d97163736f418f997c025b31b1ce13","observation_id":"9c96694d-d056-4bea-9113-5e7633cf9f7a","resolution":{"observed_at":"2026-07-31T11:50:16.896317Z","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":"2026-07-31T11:50:16.901016Z","title":"Woodpecker: Hallucination correction for multimodal large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.901016Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:f2e20fd7718829d8296c866af8b0410cae16bb1e13e7ecd76d124355ad884b49","observation_id":"9a1e40bb-c73b-41f5-a1cf-6e23e423581f","resolution":{"observed_at":"2026-07-31T11:50:16.901016Z","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":"2026-07-31T11:50:16.906124Z","title":"Hallusionbench: An advanced diagnostic suite for entangled language halluci- nation and visual illusion in large vision-language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.906124Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:0fe3b6ade6db7cfd5546c56d627be1d404ba68b78e0bb6b29753bcaa96a0849b","observation_id":"5002b4f4-1704-438a-b5cd-b637d5b38468","resolution":{"observed_at":"2026-07-31T11:50:16.906124Z","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":"2026-07-31T11:50:16.911208Z","title":"Evaluating object hallucination in large vision-language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.911208Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:7a8a7c9675596e66e9af36d30892f0083345b6fba3126f561fc98b3b88552294","observation_id":"0e831a6f-5d79-4c84-8152-ffde74c09792","resolution":{"observed_at":"2026-07-31T11:50:16.911208Z","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":"2026-07-31T11:50:16.915847Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.915847Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:ed365d2bd24c4c80734633b2d2038f1a0b2a84a18ad466e8064cb2185d026d07","observation_id":"b708b922-a340-4b59-91b1-5e9242f1636c","resolution":{"observed_at":"2026-07-31T11:50:16.915847Z","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":"2026-07-31T11:50:16.919876Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.919876Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:5e1d8229326325290fad2e8404fa1296915d2911723b45f4175538e9010d3827","observation_id":"8210469d-d5a2-4bec-86ed-8c6713159bb6","resolution":{"observed_at":"2026-07-31T11:50:16.919876Z","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":"2026-07-31T11:50:16.924569Z","title":"Flashattention: Fast and memory-efficient exact attention with io-awareness,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.924569Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:e639a600d0a9f4531b4696112f4dd49b3f747f661d3fb8960b2debf7d7365a88","observation_id":"86920734-58a7-444a-981d-c75646e06780","resolution":{"observed_at":"2026-07-31T11:50:16.924569Z","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":"2026-07-31T11:50:16.928529Z","title":"Vision trans- formers need registers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.928529Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:72a311f0a82ded3f2e1b34b8049fc9bca3b762d6ae1423a023682c1251c46731","observation_id":"130e3855-dc80-4f7f-a9d7-242826f6d2bf","resolution":{"observed_at":"2026-07-31T11:50:16.928529Z","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":"2026-07-31T11:50:16.932442Z","title":"Efficient streaming language models with attention sinks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.932442Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:d2f05bd4073308f9eceda734ddb2c4e881ee03d98c28adf2cb42f07bc62c3701","observation_id":"219bf8f2-f9c6-4904-9f19-09e36d4dcd62","resolution":{"observed_at":"2026-07-31T11:50:16.932442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.16137","last_updated":"2024-06-24T21:22:00Z","snapshot_observed_at":"2026-08-16T15:04:33.687975Z","submitted_at":"2023-08-30T16:47:51Z","title":"LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.16137","snapshot_observed_at":"2026-07-31T11:50:16.936655Z","title":"Lm-infinite: Zero-shot extreme length generalization for large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.936655Z"},"links":{"cited_paper":"/paper/2308.16137","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:91e70921c425b84a2b83dee74b2e3040d3335e9f7b63bbc4bfb13d9442bef3b4","observation_id":"14e89e45-5f30-4b1a-b4a0-638c9cb50e0b","resolution":{"observed_at":"2026-07-31T11:50:16.936655Z","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":"2026-07-31T11:50:16.940891Z","title":"H 2O: Heavy- hitter oracle for efficient generative inference of large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.940891Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:d5d7756f0474de0bcacb386843e4c489808d69b2777bd9d0786906db4184e083","observation_id":"da4ab357-7e51-49fb-8683-076a8cb7ed62","resolution":{"observed_at":"2026-07-31T11:50:16.940891Z","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":"2026-07-31T11:50:16.945194Z","title":"Colpali: Efficient document retrieval with vision language models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.945194Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:21f46b66bcc96ef2afcdc801092aeb3802c1479a824660242234270df21ef123","observation_id":"43b563f1-2cf4-4cc3-932d-2c5f07c518be","resolution":{"observed_at":"2026-07-31T11:50:16.945194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.20804","last_updated":"2026-07-18T05:33:43Z","snapshot_observed_at":"2026-08-17T05:14:56.012925Z","submitted_at":"2025-07-28T13:16:23Z","title":"MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.20804","snapshot_observed_at":"2026-07-31T11:50:16.949425Z","title":"Mmgraphrag: Bridging vision and language with interpretable multimodal knowledge graphs,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.949425Z"},"links":{"cited_paper":"/paper/2507.20804","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:18581841c127a46f7e20d76e014f9ae18bc758e38d81d061da3171262e6e54a1","observation_id":"5550d6fc-fb7c-40e6-80a9-6cfac09e2742","resolution":{"observed_at":"2026-07-31T11:50:16.949425Z","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":"2026-07-31T11:50:16.953929Z","title":"VisRAG: Vision-based retrieval-augmented generation on multi-modality documents,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.953929Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:8bd522a64a3ab5fdc1808a2971dc2e3adf21e25764b22dab1259faed6d2603a1","observation_id":"a516b7d0-04b3-4d08-b298-b5ff3c44d164","resolution":{"observed_at":"2026-07-31T11:50:16.953929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.20626","last_updated":"2026-04-20T06:49:02Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-11-26T05:00:03Z","title":"MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.20626","snapshot_observed_at":"2026-07-31T11:50:16.958407Z","title":"Megarag: Multimodal knowledge graph-based retrieval aug- mented generation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.958407Z"},"links":{"cited_paper":"/paper/2512.20626","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:cedfee0df3fc352f61e635c2ffa367e4acf1c6f3de0390ac6e1b59af20c1c9bf","observation_id":"34bb4fae-e9b9-4051-b9bd-b1dec10dd87c","resolution":{"observed_at":"2026-07-31T11:50:16.958407Z","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":"2026-07-31T11:50:16.963066Z","title":"Image cropping with spatial-aware feature and rank consistency,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.963066Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:3227ee6cd30b75d61e0d8de30a8ff51d1e69cdd27ec567a0510d547fc3d37cdb","observation_id":"c45b05a9-1a2a-48f9-a2d2-f1467c838980","resolution":{"observed_at":"2026-07-31T11:50:16.963066Z","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":"2026-07-31T11:50:16.967383Z","title":"Reliable and efficient image cropping: A grid anchor based approach,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.967383Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:a0614a441e6b781ff98d1a43a479c726a6f671539cd6c54541158400846ca44f","observation_id":"232506d8-055a-4bd7-9857-27d92765e705","resolution":{"observed_at":"2026-07-31T11:50:16.967383Z","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":"2026-07-31T11:50:16.971375Z","title":"Cropper: Vision-language model for image cropping through in-context learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.971375Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:70560e03a8e4dd9577edc94a880fe01f3d465754e3c224f5bcb569e3f897ff7a","observation_id":"867d78a0-9d82-40bd-8d18-1e2c2e415ea4","resolution":{"observed_at":"2026-07-31T11:50:16.971375Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.02499","last_updated":"2023-07-04T11:28:07Z","snapshot_observed_at":"2026-08-16T15:18:52.045487Z","submitted_at":"2023-07-04T11:28:07Z","title":"mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.02499","snapshot_observed_at":"2026-07-31T11:50:16.975786Z","title":"mPLUG-DocOwl: Modularized multimodal large language model for document understanding,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.975786Z"},"links":{"cited_paper":"/paper/2307.02499","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:3de7394755a03195f7493831b1abd3cc820b690dc1171cc68c6033b8c4f1a496","observation_id":"dd04d064-6e10-4f74-8517-0dc5168f0875","resolution":{"observed_at":"2026-07-31T11:50:16.975786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12966","last_updated":"2023-10-13T02:41:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-08-24T17:59:17Z","title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12966","snapshot_observed_at":"2026-07-31T11:50:16.980324Z","title":"Qwen-vl: A versatile vision-language model for un- derstanding, localization, text reading, and beyond,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.980324Z"},"links":{"cited_paper":"/paper/2308.12966","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:8575bf4a3ffdb6eaa589a6d3d45a1c7f05691d3f82441e137a57e30a2b935f84","observation_id":"7471fcc5-67e8-4014-8bb6-3a55551cfc4a","resolution":{"observed_at":"2026-07-31T11:50:16.980324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23628","last_updated":"2025-08-01T06:39:45Z","snapshot_observed_at":"2026-08-14T18:56:29.304736Z","submitted_at":"2025-05-29T16:34:58Z","title":"AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23628","snapshot_observed_at":"2026-07-31T11:50:16.984679Z","title":"Au- toschemakg: Autonomous knowledge graph construction through dynamic schema induction from web-scale corpora,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.984679Z"},"links":{"cited_paper":"/paper/2505.23628","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:0de0d5e4c01192eeb0466213da99683332eb6f27495f306fa99d769d15b1a666","observation_id":"fd152c35-110d-4982-b441-20419457569e","resolution":{"observed_at":"2026-07-31T11:50:16.984679Z","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":"2026-07-31T11:50:16.989303Z","title":"Spiqa: A dataset for multimodal question answering on scientific papers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.989303Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:eadf8a839c4b503e3a0835f8669c0796046e88cf11c7b3a3290cdfa78ea528e4","observation_id":"1ab9c76e-b4ce-42b2-9040-fba8b3d44a12","resolution":{"observed_at":"2026-07-31T11:50:16.989303Z","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":"2026-07-31T11:50:16.994579Z","title":"Deformable detr: Deformable transformers for end-to-end object detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.994579Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:e6fd7872b38790ed2f6cb6868f852faa40db29d0ac7fde900709431bdae17394","observation_id":"ead5d471-58ad-4e3c-9aa5-d5156bedca8f","resolution":{"observed_at":"2026-07-31T11:50:16.994579Z","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":"2026-07-31T11:50:16.998840Z","title":"Chartqa: A benchmark for question answering about charts with visual and logical reasoning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:16.998840Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:6b61bd0873fa7845c6fb2768c726009faebe5dc43a76b8bc9c28f03d925e17ee","observation_id":"262f7719-6c94-42fd-8518-58518700aaf1","resolution":{"observed_at":"2026-07-31T11:50:16.998840Z","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":"2026-07-31T11:50:17.003727Z","title":"Deplot: One-shot visual language reasoning by plot-to-table translation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:17.003727Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:111023144f6c37e16f2f0859942aae07641d8a1bea7096dd89df730d3a1d7b4f","observation_id":"ad5d0b5a-1a43-4196-84e0-ac37f230d527","resolution":{"observed_at":"2026-07-31T11:50:17.003727Z","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":"2026-07-31T11:50:17.008938Z","title":"Plotqa: Reasoning over scientific plots,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:17.008938Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:6be09f1a67a529e82868b6c690e63f39467631f659c795bf3b9b9e093f951d0b","observation_id":"2bc540e9-230a-4de2-b5a0-ef3973cbac23","resolution":{"observed_at":"2026-07-31T11:50:17.008938Z","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":"2026-07-31T11:50:17.013678Z","title":"Slidevqa: A dataset for document visual question an- swering on multiple images,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:17.013678Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:5f19e76679ee600faec661516719583022e242309aef9f281a51ffa802d02eac","observation_id":"92360df7-3e07-4e1d-8c0f-c82c25fe49cf","resolution":{"observed_at":"2026-07-31T11:50:17.013678Z","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":"2026-07-31T11:50:17.018502Z","title":"UDA: A benchmark suite for retrieval augmented generation in real-world document analysis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:17.018502Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:670b05df0b0702c2ee06ea9a7bfe50e69e50838bfdaa1b79a835bb974eb5dc7c","observation_id":"4003b05b-452d-451e-8e25-d8e26bb03c97","resolution":{"observed_at":"2026-07-31T11:50:17.018502Z","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":"2026-07-31T11:50:17.022999Z","title":"TabFact: A large-scale dataset for table-based fact verification,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:17.022999Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:98bc7d2326cc196653f7ea49694036a958f4828360e698e8b1969ccbf4221684","observation_id":"7fd32828-45bb-4cb7-bd97-fb7755a5d14e","resolution":{"observed_at":"2026-07-31T11:50:17.022999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12191","last_updated":"2024-10-03T15:54:49Z","snapshot_observed_at":"2026-08-06T05:35:29.109022Z","submitted_at":"2024-09-18T17:59:32Z","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12191","snapshot_observed_at":"2026-07-31T11:50:17.027871Z","title":"Qwen2-vl: Enhancing vision- language model’s perception of the world at any resolution,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:17.027871Z"},"links":{"cited_paper":"/paper/2409.12191","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:a55362b619f584366d8837c8d001c5f829da34fab79209f88d9785a47372128f","observation_id":"6fb2e673-797b-42a2-82cc-288015b77ae7","resolution":{"observed_at":"2026-07-31T11:50:17.027871Z","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":"2026-07-31T11:50:17.033929Z","title":"Judging LLM-as-a-Judge with MT-Bench and Chat- bot Arena,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:17.033929Z"},"links":{"citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:60b8a4ceb5a38b7880e8e856290c2dc75f13c08833f76fc484422910e2cadca0","observation_id":"2ea06288-f13a-47b9-88be-c7b61a182201","resolution":{"observed_at":"2026-07-31T11:50:17.033929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.16634","last_updated":"2023-05-23T22:12:16Z","snapshot_observed_at":"2026-08-02T04:02:36.848064Z","submitted_at":"2023-03-29T12:46:54Z","title":"G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.16634","snapshot_observed_at":"2026-07-31T11:50:17.038882Z","title":"G-eval: Nlg evaluation using gpt-4 with better human alignment,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-31T11:50:17.038882Z"},"links":{"cited_paper":"/paper/2303.16634","citing_paper":"/paper/2607.24554"},"observation_digest":"sha256:a45ad3ef6704ca0a0f8279bcf06f9e805e875f63875edc95073e902edb6b3764","observation_id":"9f7fdeec-b2ad-4ee7-9e22-d8be414ddd71","resolution":{"observed_at":"2026-07-31T11:50:17.038882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.24554","last_updated":"2026-07-27T15:28:02Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-07T04:39:15.958635Z","submitted_at":"2026-07-27T15:28:02Z","title":"DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":52,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":52},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2607.24554."}