{"as_of":"2026-08-08T13:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3ab649f2a7f0e3b1f571eb2f9c9bb7c9d8870f5580aec94e880088c01a84511f","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T01:28:02.329050Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.09650/citation-record","integrity":"/paper/2607.09650/integrity","json":"/paper/2607.09650/citation-record.json","paper":"/paper/2607.09650"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.04005","last_updated":"2024-11-06T15:44:10Z","snapshot_observed_at":"2026-08-06T18:58:29.423189Z","submitted_at":"2024-11-06T15:44:10Z","title":"Object-Centric Dexterous Manipulation from Human Motion Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.04005","snapshot_observed_at":"2026-07-13T01:28:02.329050Z","title":"Object-centric dexterous manipulation from human motion data.arXiv preprint arXiv:2411.04005,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"cited_paper":"/paper/2411.04005","citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:8608b9f4aeb33b2c38b3194adf88e43b98d4c31097fe0823f5b80e3f569aabf5","observation_id":"da5c8c85-423e-4e3c-b749-a9f3a373b12c","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","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":"2026-07-13T01:28:02.329050Z","title":"Opensim: open-source software to create and analyze dynamic simulations of movement.IEEE transactions on biomedical engineering, 54(11):1940–1950,","venue":null,"work_id":null,"year":1940},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:577bf41926aef7462d366555cedb8ce936d976fd3c302f9543b6eb74a8964455","observation_id":"d62fa609-9e11-47df-8436-9bcd022c5230","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.11735","last_updated":"2024-06-19T10:17:54Z","snapshot_observed_at":"2026-07-06T18:01:53.720064Z","submitted_at":"2024-04-17T20:37:29Z","title":"Learning with 3D rotations, a hitchhiker's guide to SO(3)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.11735","snapshot_observed_at":"2026-07-13T01:28:02.329050Z","title":"Learning with 3d rotations, a hitchhiker’s guide to so (3).arXiv preprint arXiv:2404.11735,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"cited_paper":"/paper/2404.11735","citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:1697f5fb590d04987499b6bf42389785a6dd3dbc16d6193b12dba3bbd930581a","observation_id":"eff737de-46d9-4b42-a6a4-f5c9a7d0a6a8","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00462","last_updated":"2023-12-01T09:56:29Z","snapshot_observed_at":"2026-08-04T13:37:59.203836Z","submitted_at":"2023-12-01T09:56:29Z","title":"Learning Unorthogonalized Matrices for Rotation Estimation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00462","snapshot_observed_at":"2026-07-13T01:28:02.329050Z","title":"Learning unorthogonalized matrices for rotation estimation.arXiv preprint arXiv:2312.00462,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"cited_paper":"/paper/2312.00462","citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:0c0fc8da514e0b9e1e203ee8b357cca85163670ba12ac3b95f58fd14f134dda2","observation_id":"e328b24c-3569-46a3-864d-ad3ac632f0ef","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19756","last_updated":"2025-02-09T21:09:09Z","snapshot_observed_at":"2026-07-06T18:07:47.744531Z","submitted_at":"2024-04-30T17:58:29Z","title":"KAN: Kolmogorov-Arnold Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19756","snapshot_observed_at":"2026-07-13T01:28:02.329050Z","title":"Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y Hou, and Max Tegmark","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"cited_paper":"/paper/2404.19756","citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:5d1f469b67b2c57e654d95fbfd6761dcacea51cb7c26357466fdd3b465130bc3","observation_id":"523f9307-4a3c-4bb3-88d6-4b125b7a6df7","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","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":"2026-07-13T01:28:02.329050Z","title":"From kepler to newton: Inductive biases guide learned world models in transformers.arXiv preprint arXiv:2602.06923,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:9de81b2d31039ecf989ad51fdf26d08d9429a9e118d70c316dab70ad3057dac1","observation_id":"b8098c00-aeb5-4c66-98e5-7613c804357f","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.13913","last_updated":"2026-06-30T04:13:32Z","snapshot_observed_at":"2026-08-05T16:23:42.137544Z","submitted_at":"2026-01-20T12:41:08Z","title":"On the Role of Rotation Equivariance in Monocular 2D-to-3D Human Pose Lifting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.13913","snapshot_observed_at":"2026-07-13T01:28:02.329050Z","title":"On the role of rotation equivariance in monocular 3d human pose estimation.arXiv preprint arXiv:2601.13913,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"cited_paper":"/paper/2601.13913","citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:6ae3792d9d69d65d843513e6a763214cbcdc5f1aa6579a0093bbb564425ced81","observation_id":"574a8a27-5b54-404d-9703-902e2ae986ec","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.01031","last_updated":"2021-01-17T19:47:56Z","snapshot_observed_at":"2026-07-06T09:25:03.937338Z","submitted_at":"2020-06-01T15:57:45Z","title":"A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.01031","snapshot_observed_at":"2026-07-13T01:28:02.329050Z","title":"A smooth representation of belief over so (3) for deep rotation learning with uncertainty.arXiv preprint arXiv:2006.01031,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"cited_paper":"/paper/2006.01031","citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:ca2db12fe2cb32c94450214343618b46d2f112441e7d805a4f4d7db02aa33784","observation_id":"fe98eba2-ab1a-44eb-9538-3285c066ae92","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.11103","last_updated":"2026-06-26T09:13:33Z","snapshot_observed_at":"2026-08-05T04:39:42.101648Z","submitted_at":"2025-10-13T07:49:21Z","title":"A Primer on SO(3) Action Representations in Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.11103","snapshot_observed_at":"2026-07-13T01:28:02.329050Z","title":"A primer on so (3) action representations in deep reinforcement learning.arXiv preprint arXiv:2510.11103,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"cited_paper":"/paper/2510.11103","citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:e7cbdcf88670398b7cb37bddf3cca55ad54a816c6b68cb723981c1cd01196276","observation_id":"04c8e684-ad91-4371-b019-98abaae2804d","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","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":"2026-07-13T01:28:02.329050Z","title":"Are euler angles a useful rotation parameterisation for pose estimation with normalizing flows?arXiv preprint arXiv:2511.02277,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:5102a5892b8fad6fba4bc599420e8112beb179572ad44e2c650f6be0f0f5e2bf","observation_id":"3115a1f7-8866-4aa4-983f-9efb196a8082","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14817","last_updated":"2024-04-04T16:27:06Z","snapshot_observed_at":"2026-08-05T21:43:14.979886Z","submitted_at":"2024-02-22T18:59:56Z","title":"Cameras as Rays: Pose Estimation via Ray Diffusion","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14817","snapshot_observed_at":"2026-07-13T01:28:02.329050Z","title":"Cameras as rays: Pose estimation via ray diffusion.arXiv preprint arXiv:2402.14817, 2024a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"cited_paper":"/paper/2402.14817","citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:38d8389af1aa8a6476bbc72deba4165159d09c28cf778432f42f3a6fe453786b","observation_id":"1e00f435-d9aa-4a39-9e31-ebe6f07e592d","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","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":"2026-07-13T01:28:02.329050Z","title":"•Appendix B: Theoretical Analysis of Euler-Angle Regression with KAN","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:9f4006f6f7e17c0d719ca87bc287beb5648c1c0d1473149c346998659ca214bc","observation_id":"2b8b3f0f-4114-4900-b4ee-e68750e58cd1","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","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":"2026-07-13T01:28:02.329050Z","title":"Rep.” refers to the employed rotation repre- sentation, and “Params","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:83d9499b2d1b7216f394b4a35f292cf328333ec01e56d5c0e5b0f5dce07a0c74","observation_id":"54d9e741-7641-498c-afa8-2036b7795a16","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","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":"2026-07-13T01:28:02.329050Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:e7502a2f5c018b0a9bcb05a16b8c5cf612735535710bbc59e01e016943644acc","observation_id":"67d48f1f-8b4d-4f7e-8f7d-f6162a438d5e","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","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":"2026-07-13T01:28:02.329050Z","title":"The output is the vector of active joint angles, and all models are trained with MSE loss in joint-angle space","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-13T01:28:02.329050Z"},"links":{"citing_paper":"/paper/2607.09650"},"observation_digest":"sha256:3cab49e2eac43398a269f6516b207b9248b0d9cdfd5f4e5c5d9dfdb57bc23b13","observation_id":"d7171dc3-9167-4305-8918-ac514dc17d7f","resolution":{"observed_at":"2026-07-13T01:28:02.329050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.09650","last_updated":"2026-07-10T17:48:06Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T04:40:51.133128Z","submitted_at":"2026-07-10T17:48:06Z","title":"Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":15},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.09650."}