{"as_of":"2026-08-07T18:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5d30424dd9fbf48d9851b7ce9d63406d7bce6a3c44364f1c952b5f6e3f4b3fd7","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:36:15.528322Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-01T00:16:37.248943Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.13861","last_updated":"2024-10-21T15:42:46Z","snapshot_observed_at":"2026-07-06T19:35:30.732080Z","submitted_at":"2024-10-17T17:59:57Z","title":"PUMA: Empowering Unified MLLM with Multi-granular Visual Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13861","snapshot_observed_at":"2026-08-07T05:36:15.528322Z","title":"Puma: Empowering unified mllm with multi- granular visual generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07575","last_updated":"2025-06-09T09:20:20Z","snapshot_observed_at":"2026-08-07T05:28:03.889420Z","submitted_at":"2025-06-09T09:20:20Z","title":"Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:36:15.528322Z"},"links":{"cited_paper":"/paper/2410.13861","citing_paper":"/paper/2506.07575"},"observation_digest":"sha256:c7f347972676ffbcb2c0d4acd898fdb41c1827a986e835621ae9b5f0097efecd","observation_id":"e38279b1-d0d4-49ef-bde6-99226a0032bb","resolution":{"observed_at":"2026-08-07T05:36:15.528322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13861","last_updated":"2024-10-21T15:42:46Z","snapshot_observed_at":"2026-07-06T19:35:30.732080Z","submitted_at":"2024-10-17T17:59:57Z","title":"PUMA: Empowering Unified MLLM with Multi-granular Visual Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13861","snapshot_observed_at":"2026-08-04T18:48:03.640677Z","title":"Puma: Empowering unified mllm with multi-granular visual generation.arXiv preprint arXiv:2410.13861, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.09680","last_updated":"2025-09-11T17:59:59Z","snapshot_observed_at":"2026-08-04T18:48:02.197558Z","submitted_at":"2025-09-11T17:59:59Z","title":"FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning Dataset and Comprehensive Benchmark","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T18:48:03.640677Z"},"links":{"cited_paper":"/paper/2410.13861","citing_paper":"/paper/2509.09680"},"observation_digest":"sha256:7a166594be538ef5c2768581e915d41e76fd6f14e94aad70fb4770862951b0a1","observation_id":"c3c5cd98-926e-4ae3-afbd-c637bb033a3c","resolution":{"observed_at":"2026-08-04T18:48:03.640677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13861","last_updated":"2024-10-21T15:42:46Z","snapshot_observed_at":"2026-07-06T19:35:30.732080Z","submitted_at":"2024-10-17T17:59:57Z","title":"PUMA: Empowering Unified MLLM with Multi-granular Visual Generation","version":2},"cited_work":{"arxiv_id":"2410.13861","doi":"10.48550/arxiv.2410.13861","metadata_source":"pith","pith_arxiv_id":"2410.13861","snapshot_observed_at":"2026-08-01T00:16:37.248943Z","title":"PUMA: Empowering Unified MLLM with Multi-granular Visual Generation","venue":"cs.CV","work_id":"19ecfd39-6222-42fb-adb4-068a072a986f","year":2024},"citing_paper":{"arxiv_id":"2607.27902","last_updated":"2026-08-03T05:25:10Z","snapshot_observed_at":"2026-08-06T23:32:48.083624Z","submitted_at":"2026-07-30T09:17:48Z","title":"One Patch Is Enough: Reinforcement-Optimized Visual Token Grounding for MLLM-Based Scene Text Spotting","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-31T23:25:16.202874Z"},"links":{"cited_paper":"/paper/2410.13861","citing_paper":"/paper/2607.27902"},"observation_digest":"sha256:5c8e21f463ac3ae5dbdf148f44f750fd1b60c4b138ec1bc703bdb0649713bc82","observation_id":"8ee746e9-4511-49a6-a392-8991fc6675e0","resolution":{"observed_at":"2026-07-31T23:26:27.967706Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13861","last_updated":"2024-10-21T15:42:46Z","snapshot_observed_at":"2026-07-06T19:35:30.732080Z","submitted_at":"2024-10-17T17:59:57Z","title":"PUMA: Empowering Unified MLLM with Multi-granular Visual Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13861","snapshot_observed_at":"2026-08-04T03:22:09.894169Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.27902","last_updated":"2026-08-03T05:25:10Z","snapshot_observed_at":"2026-08-06T23:32:48.083624Z","submitted_at":"2026-07-30T09:17:48Z","title":"One Patch Is Enough: Reinforcement-Optimized Visual Token Grounding for MLLM-Based Scene Text Spotting","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T03:22:09.894169Z"},"links":{"cited_paper":"/paper/2410.13861","citing_paper":"/paper/2607.27902"},"observation_digest":"sha256:d4413d5525d956d3b19b7140cf1b5dba831ccafda5b0c8fe85d1d43f90d8b3e6","observation_id":"747e83ae-07e7-4aac-89f3-25c83b3a7ebd","resolution":{"observed_at":"2026-08-04T03:22:09.894169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2410.13861/citation-record","integrity":"/paper/2410.13861/integrity","json":"/paper/2410.13861/citation-record.json","paper":"/paper/2410.13861"},"outbound":[],"paper":{"arxiv_id":"2410.13861","last_updated":"2024-10-21T15:42:46Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T19:35:30.732080Z","submitted_at":"2024-10-17T17:59:57Z","title":"PUMA: Empowering Unified MLLM with Multi-granular Visual Generation"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.13861."}