{"as_of":"2026-08-21T01:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d1ea9993609b4314d3517efae1a96e2e636002239731db544cff7f0af555ee10","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:06:22.975019Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2507.16718/citation-record","integrity":"/paper/2507.16718/integrity","json":"/paper/2507.16718/citation-record.json","paper":"/paper/2507.16718"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-08-14T04:17:22.593941Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-08-06T15:06:22.938905Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.938905Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:57007cdf672f0fcb85f9b5a1566f497b88b78118016d319a8ebb30530b472dcb","observation_id":"38c28270-dfbb-43b8-9abc-c2780e9f88a4","resolution":{"observed_at":"2026-08-06T15:06:22.938905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14576","last_updated":"2024-06-17T12:47:04Z","snapshot_observed_at":"2026-08-16T14:42:15.800927Z","submitted_at":"2024-06-17T12:47:04Z","title":"Towards Intelligent Speech Assistants in Operating Rooms: A Multimodal Model for Surgical Workflow Analysis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14576","snapshot_observed_at":"2026-08-06T15:06:22.942371Z","title":"Towards intelligent speech assistants in operating rooms: A multimodal model for surgical workflow analysis.arXiv preprint arXiv:2406.14576, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.942371Z"},"links":{"cited_paper":"/paper/2406.14576","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:a5d40755051d9417571a9e541dba6a20d2384fbc8860edced7479f244725428a","observation_id":"3f2176c7-d82d-4ff1-a70c-a5d5436599ad","resolution":{"observed_at":"2026-08-06T15:06:22.942371Z","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":"2026-08-06T15:06:23.116133Z","title":"A spatio- temporal network for video semantic segmentation in surgical videos.International Journal of Computer Assisted Radiology and Surgery , 19(2):375–382, 2024","venue":null,"work_id":"4abfb6e0-4ae8-4401-9960-c4779ad4c95b","year":2024},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.945334Z"},"links":{"citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:5590f04328f4c2af940e9ae58ee348e874d4a33b23a4f9cf5304354d9c430ddd","observation_id":"51299db0-0732-41a4-8e82-c53c0aa15b54","resolution":{"observed_at":"2026-08-06T15:06:23.119196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T15:06:23.106897Z","title":"Temporal memory relation network for workflow recognition from surgical video","venue":null,"work_id":"0c2db40c-6a92-4825-a0a1-644c8875663b","year":1911},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.948068Z"},"links":{"citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:154bc461cdc43fee55cd81d8087464b47eaf7fd8065ab7fd4818f089e95028cc","observation_id":"f854057d-338e-4e6c-9d27-a28e435522a2","resolution":{"observed_at":"2026-08-06T15:06:23.109869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T15:06:23.097621Z","title":"Lisa: Reasoning segmentation via large language model","venue":null,"work_id":"6954e367-23a6-4674-9611-d112d8f04f18","year":2024},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.950833Z"},"links":{"citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:3efef712c279d237985dbf919cd5eb4f5d15e3ffd0c70963af679506237c34a8","observation_id":"b557b853-3366-48ef-884e-1855b990b60c","resolution":{"observed_at":"2026-08-06T15:06:23.100640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T15:06:22.953421Z","title":"Improved baselines with visual instruction tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.953421Z"},"links":{"citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:ec3797464321e38f40f983235217e2c26f216a2e4b3db59891ac26840446b6db","observation_id":"bcda117c-9cef-428e-9288-5287183f452f","resolution":{"observed_at":"2026-08-06T15:06:22.953421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-06T15:06:22.956091Z","title":"Sam 2: Segment anything in images and videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.956091Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:512c05f5e0b23e355a1a4d6493c73923ec05aabb2340acbe579e36b00940a567","observation_id":"e9ee0c61-ecca-434e-b589-1fe233297714","resolution":{"observed_at":"2026-08-06T15:06:22.956091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.03798","last_updated":"2025-05-01T22:17:41Z","snapshot_observed_at":"2026-08-17T18:26:23.400123Z","submitted_at":"2025-05-01T22:17:41Z","title":"Position: Foundation Models Need Digital Twin Representations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.03798","snapshot_observed_at":"2026-08-06T15:06:22.959059Z","title":"Position: Foundation models need digital twin representations","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.959059Z"},"links":{"cited_paper":"/paper/2505.03798","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:07521296e09f84b8a061c86ddaffa87340daf8f583ca2b2fda1bedd1b3a74b51","observation_id":"0e0199d5-8493-4970-832a-e4eeb65e3214","resolution":{"observed_at":"2026-08-06T15:06:22.959059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11838","last_updated":"2025-05-17T04:58:09Z","snapshot_observed_at":"2026-08-17T18:27:28.415155Z","submitted_at":"2025-05-17T04:58:09Z","title":"RVTBench: A Benchmark for Visual Reasoning Tasks","version":1},"cited_work":{"arxiv_id":"2505.11838","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.11838","snapshot_observed_at":"2026-08-06T15:06:23.031057Z","title":"RVTBench: A Benchmark for Visual Reasoning Tasks","venue":"cs.CV","work_id":"b44d5f46-531b-4934-b79f-308f7f23969a","year":2025},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.961694Z"},"links":{"cited_paper":"/paper/2505.11838","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:b4af57421c6ec71090e117110c3e5da42a0e110d943a49f58bc997d43b5a0b11","observation_id":"9c38a73e-c019-4a1e-a51a-9686a1255dcc","resolution":{"observed_at":"2026-08-06T15:06:23.035823Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21054","last_updated":"2025-03-26T23:59:32Z","snapshot_observed_at":"2026-08-19T18:23:39.052467Z","submitted_at":"2025-03-26T23:59:32Z","title":"Operating Room Workflow Analysis via Reasoning Segmentation over Digital Twins","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.21054","snapshot_observed_at":"2026-08-06T15:06:22.964559Z","title":"Operating room workflow analysis via reasoning segmentation over digital twins","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.964559Z"},"links":{"cited_paper":"/paper/2503.21054","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:fc230e3636db9684c5de98b1ec3d812d54fa65f1d5de144f18a53fa05c436137","observation_id":"caf26f12-4e97-48c3-b8e7-52893dc495fb","resolution":{"observed_at":"2026-08-06T15:06:22.964559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.18816","last_updated":"2025-05-24T18:23:14Z","snapshot_observed_at":"2026-08-15T05:56:46.861476Z","submitted_at":"2025-05-24T18:23:14Z","title":"Reasoning Segmentation for Images and Videos: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.18816","snapshot_observed_at":"2026-08-06T15:06:22.967237Z","title":"Reasoning segmentation for images and videos: A survey","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.967237Z"},"links":{"cited_paper":"/paper/2505.18816","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:5985fe4993d27f673428005b34e1134bb6187a62dd75468564e2009dceb5edf3","observation_id":"6507a37d-5882-44da-844d-3acb78c39a8e","resolution":{"observed_at":"2026-08-06T15:06:22.967237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1808.08180","last_updated":"2021-08-20T10:39:50Z","snapshot_observed_at":"2026-08-14T18:37:27.977005Z","submitted_at":"2018-08-24T15:47:48Z","title":"MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.08180","snapshot_observed_at":"2026-08-06T15:06:22.969839Z","title":"Mvor: A multi-view rgb-d op- erating room dataset for 2d and 3d human pose estimation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.969839Z"},"links":{"cited_paper":"/paper/1808.08180","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:b6cbccd0f93a2d7bd373869674d5a19da83291c1c326f3c561ba975a17fac06f","observation_id":"86b35c74-1a23-4b64-8af0-19d51503112d","resolution":{"observed_at":"2026-08-06T15:06:22.969839Z","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":"2026-08-06T15:06:23.081594Z","title":null,"venue":null,"work_id":"81495d7d-e0ed-485b-bf06-b5bf6f158e0b","year":2024},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.972763Z"},"links":{"citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:20082135d6cf4b6f1d7f92a362581ee0908a2d01fc43b9fde3bcfffe87e4fcf6","observation_id":"bda4ed03-9f55-4b56-a48d-4da90aa1cd5f","resolution":{"observed_at":"2026-08-06T15:06:23.084249Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09414","last_updated":"2024-10-20T11:24:09Z","snapshot_observed_at":"2026-07-06T18:30:32.982860Z","submitted_at":"2024-06-13T17:59:56Z","title":"Depth Anything V2","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.09414","snapshot_observed_at":"2026-08-06T15:06:22.975019Z","title":"Depth anything v2.arXiv preprint arXiv:2406.09414, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:06:22.975019Z"},"links":{"cited_paper":"/paper/2406.09414","citing_paper":"/paper/2507.16718"},"observation_digest":"sha256:f8fe701f399b011273502ddea0fdfc582573a5f7a6e21203e289988aa1e4fe69","observation_id":"e6792027-f9b5-4af3-855a-b28b710000d3","resolution":{"observed_at":"2026-08-06T15:06:22.975019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.16718","last_updated":"2025-07-22T15:59:21Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T16:52:14.598315Z","submitted_at":"2025-07-22T15:59:21Z","title":"Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":1,"verified_fuzzy":3},"total_outbound_references":14},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2507.16718."}