{"as_of":"2026-08-11T13:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eff15962ace499fa5ca6853f36b73305e41bebc5ba9f5fb0f6bcdd11423a57fc","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:17:27.802900Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding","version":3},"cited_work":{"arxiv_id":"2408.15045","doi":"10.48550/arxiv.2408.15045","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.15045","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Doclayllm: An efficient and effective multi-modal extension of large language models for text-rich document understanding","venue":"arXiv (Cornell University)","work_id":"6ee2679a-f720-4fc3-93c6-11edcfc19b69","year":2024},"citing_paper":{"arxiv_id":"2501.00321","last_updated":"2025-06-05T02:59:05Z","snapshot_observed_at":"2026-08-07T17:13:10.057242Z","submitted_at":"2024-12-31T07:32:35Z","title":"OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-17T20:33:26.613927Z"},"links":{"cited_paper":"/paper/2408.15045","citing_paper":"/paper/2501.00321"},"observation_digest":"sha256:1b3bab7f3f774891a8d74305386f00916576333a5fdb2348d818dd8cb3532c39","observation_id":"840efc04-9dbd-4cac-bce6-e3a3808c3239","resolution":{"observed_at":"2026-05-17T20:33:26.793729Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15045","snapshot_observed_at":"2026-08-10T22:17:27.802900Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.02235","last_updated":"2025-03-10T03:41:41Z","snapshot_observed_at":"2026-08-11T12:44:48.413598Z","submitted_at":"2025-01-04T08:45:24Z","title":"Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-10T22:17:27.802900Z"},"links":{"cited_paper":"/paper/2408.15045","citing_paper":"/paper/2501.02235"},"observation_digest":"sha256:fbb7905485001415cf8f6c25f99ee38b6839dbe2e663c6b7c6521ea982902ec0","observation_id":"195cf54e-b498-4da7-9066-a0ab28b3dd3f","resolution":{"observed_at":"2026-08-10T22:17:27.802900Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15045","snapshot_observed_at":"2026-08-07T20:06:00.818917Z","title":"Doclayllm: An efficient and effective multi-modal extension of large language models for text-rich document understanding, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09927","last_updated":"2025-02-14T05:36:32Z","snapshot_observed_at":"2026-08-10T18:50:32.127116Z","submitted_at":"2025-02-14T05:36:32Z","title":"Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T20:06:00.818917Z"},"links":{"cited_paper":"/paper/2408.15045","citing_paper":"/paper/2502.09927"},"observation_digest":"sha256:394b7776d28ff79b4fcb6ec7d095b5a7c18c3638c0093f2602855d0c62e6315f","observation_id":"55a9d781-b9c5-4bf2-b988-d2adb86f3101","resolution":{"observed_at":"2026-08-07T20:06:00.818917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15045","snapshot_observed_at":"2026-08-06T17:59:04.645195Z","title":"Doclayllm: An efficient and effective multi-modal extension of large language mod- els for text-rich document understanding","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.09531","last_updated":"2025-07-13T08:15:11Z","snapshot_observed_at":"2026-08-09T19:00:02.677398Z","submitted_at":"2025-07-13T08:15:11Z","title":"VDInstruct: Zero-Shot Key Information Extraction via Content-Aware Vision Tokenization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:59:04.645195Z"},"links":{"cited_paper":"/paper/2408.15045","citing_paper":"/paper/2507.09531"},"observation_digest":"sha256:bf776fd626f04f083668f83a0fd4b1c041c0f7d3a780e7a77df882f2d37c84b5","observation_id":"e2683014-4149-4068-9dce-8ebb7e5ef479","resolution":{"observed_at":"2026-08-06T17:59:04.645195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding","version":3},"cited_work":{"arxiv_id":"2408.15045","doi":"10.48550/arxiv.2408.15045","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.15045","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Doclayllm: An efficient and effective multi-modal extension of large language models for text-rich document understanding","venue":"arXiv (Cornell University)","work_id":"6ee2679a-f720-4fc3-93c6-11edcfc19b69","year":2024},"citing_paper":{"arxiv_id":"2507.09861","last_updated":"2026-04-21T13:31:05Z","snapshot_observed_at":"2026-08-11T13:29:02.180869Z","submitted_at":"2025-07-14T02:10:31Z","title":"A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-19T04:38:49.512293Z"},"links":{"cited_paper":"/paper/2408.15045","citing_paper":"/paper/2507.09861"},"observation_digest":"sha256:4305e1287218d011bc522ea2094e833fb148f01f063587054ad35009aa1d15d2","observation_id":"08b6382f-8eb5-4395-bcee-e630f005b43c","resolution":{"observed_at":"2026-05-19T04:42:04.050413Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding","version":3},"cited_work":{"arxiv_id":"2408.15045","doi":"10.48550/arxiv.2408.15045","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.15045","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Doclayllm: An efficient and effective multi-modal extension of large language models for text-rich document understanding","venue":"arXiv (Cornell University)","work_id":"6ee2679a-f720-4fc3-93c6-11edcfc19b69","year":2024},"citing_paper":{"arxiv_id":"2511.22521","last_updated":"2026-04-16T14:40:49Z","snapshot_observed_at":"2026-07-06T22:37:08.086095Z","submitted_at":"2025-11-27T15:00:58Z","title":"DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-17T04:41:55.030673Z"},"links":{"cited_paper":"/paper/2408.15045","citing_paper":"/paper/2511.22521"},"observation_digest":"sha256:f3975b5baa6b6d1e1ad4f2878d12c627c79ab22a2f52ce7ccab2b8457226efbc","observation_id":"34aaa92a-50c5-40f3-8837-6c4db8922b33","resolution":{"observed_at":"2026-05-17T04:44:02.477273Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding","version":3},"cited_work":{"arxiv_id":"2408.15045","doi":"10.48550/arxiv.2408.15045","metadata_source":"arxiv_reference","pith_arxiv_id":"2408.15045","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Doclayllm: An efficient and effective multi-modal extension of large language models for text-rich document understanding","venue":"arXiv (Cornell University)","work_id":"6ee2679a-f720-4fc3-93c6-11edcfc19b69","year":2024},"citing_paper":{"arxiv_id":"2604.00161","last_updated":"2026-04-21T01:45:08Z","snapshot_observed_at":"2026-07-06T22:51:22.254518Z","submitted_at":"2026-03-31T19:09:55Z","title":"Q-Mask: Query-driven Causal Masks for Text Anchoring in OCR-Oriented Vision-Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-13T23:30:53.449935Z"},"links":{"cited_paper":"/paper/2408.15045","citing_paper":"/paper/2604.00161"},"observation_digest":"sha256:8ca151dbce92cdbd05e8254f4bd2e7bc6f237da6a1f0c3c928e0d686fc552ec7","observation_id":"c01bd59f-4028-4882-9156-c3badc769502","resolution":{"observed_at":"2026-05-13T23:33:26.767230Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15045","snapshot_observed_at":"2026-08-02T14:55:39.244177Z","title":"arXiv preprint arXiv:2408.15045 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16203","last_updated":"2026-05-05T10:59:34Z","snapshot_observed_at":"2026-08-10T18:12:24.304206Z","submitted_at":"2026-05-05T10:59:34Z","title":"DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth","version":1},"reference_index":182,"source":"arxiv_source","source_observed_at":"2026-08-02T14:55:39.244177Z"},"links":{"cited_paper":"/paper/2408.15045","citing_paper":"/paper/2607.16203"},"observation_digest":"sha256:626f1f151b1b4de78ed695288f5f82c7adc9a931f69e979ebe9e27b44b359778","observation_id":"d3a90b89-c08f-4769-8f86-39e8221d455b","resolution":{"observed_at":"2026-08-02T14:55:39.244177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.15045/citation-record","integrity":"/paper/2408.15045/integrity","json":"/paper/2408.15045/citation-record.json","paper":"/paper/2408.15045"},"outbound":[],"paper":{"arxiv_id":"2408.15045","last_updated":"2025-03-19T10:05:04Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T18:59:22.667732Z","submitted_at":"2024-08-27T13:13:38Z","title":"DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document Understanding"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2408.15045."}