{"as_of":"2026-08-11T13:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f7de00a466e16d98dc962c253abb048385e5b6a5a8f37515221d4516748d5c64","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T20:29:33.097470Z","state":"measured"},{"denominator":81,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":81,"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":"2026-08-06T15:33:39.445246Z","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":"2026-07-08T06:04:34.676226Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"cited_work":{"arxiv_id":"2507.02747","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.02747","snapshot_observed_at":"2026-07-08T06:04:34.676226Z","title":"Dexvlg: Dexterous vision-language-grasp model at scale","venue":"cs.CV","work_id":"b0b9aece-20ed-4429-b501-e71041ceb063","year":2025},"citing_paper":{"arxiv_id":"2507.04447","last_updated":"2025-08-26T08:23:50Z","snapshot_observed_at":"2026-08-05T16:56:52.400110Z","submitted_at":"2025-07-06T16:14:29Z","title":"DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-16T15:42:41.363422Z"},"links":{"cited_paper":"/paper/2507.02747","citing_paper":"/paper/2507.04447"},"observation_digest":"sha256:43618e219fd8843f36c4f4ae3c529617c9aaa0aee1532b205d92f1f7ad28c226","observation_id":"8a4016a4-f65d-497a-8bd7-098b8b8eb7f8","resolution":{"observed_at":"2026-05-16T15:42:41.415484Z","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":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.02747","snapshot_observed_at":"2026-08-06T15:33:39.445246Z","title":"Dexvlg: Dexterous vision-language-grasp model at scale.arXiv preprint arXiv:2507.02747, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.15597","last_updated":"2025-07-21T13:19:09Z","snapshot_observed_at":"2026-08-06T15:25:42.125132Z","submitted_at":"2025-07-21T13:19:09Z","title":"Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T15:33:39.445246Z"},"links":{"cited_paper":"/paper/2507.02747","citing_paper":"/paper/2507.15597"},"observation_digest":"sha256:6c555dffe73c81d95a2e1a83315e5f3145b36b78e8b0e7b6f3294a6740b5543d","observation_id":"9c96aaab-977f-4a81-9243-af342b15fcf0","resolution":{"observed_at":"2026-08-06T15:33:39.445246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"cited_work":{"arxiv_id":"2507.02747","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.02747","snapshot_observed_at":"2026-07-08T06:04:34.676226Z","title":"Dexvlg: Dexterous vision-language-grasp model at scale","venue":"cs.CV","work_id":"b0b9aece-20ed-4429-b501-e71041ceb063","year":2025},"citing_paper":{"arxiv_id":"2509.18455","last_updated":"2026-04-09T07:24:53Z","snapshot_observed_at":"2026-07-06T22:30:31.933240Z","submitted_at":"2025-09-22T22:25:35Z","title":"Learning Geometry-Aware Nonprehensile Pushing and Pulling with Dexterous Hands","version":4},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-18T13:52:35.939944Z"},"links":{"cited_paper":"/paper/2507.02747","citing_paper":"/paper/2509.18455"},"observation_digest":"sha256:05f6e13b85175e2d3411cb4384c2e518dabf8d3e7465c2f993d78d972c339462","observation_id":"71f0ccd7-34d9-47a4-8cd1-8c130286bba1","resolution":{"observed_at":"2026-05-18T13:52:38.724758Z","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":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"cited_work":{"arxiv_id":"2507.02747","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.02747","snapshot_observed_at":"2026-07-08T06:04:34.676226Z","title":"Dexvlg: Dexterous vision-language-grasp model at scale","venue":"cs.CV","work_id":"b0b9aece-20ed-4429-b501-e71041ceb063","year":2025},"citing_paper":{"arxiv_id":"2602.10698","last_updated":"2026-02-11T09:57:32Z","snapshot_observed_at":"2026-08-11T10:15:07.202307Z","submitted_at":"2026-02-11T09:57:32Z","title":"AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-16T06:01:30.803128Z"},"links":{"cited_paper":"/paper/2507.02747","citing_paper":"/paper/2602.10698"},"observation_digest":"sha256:fc073d13e1625e2bcc16750c70ef561d56c3d50321d95ce1b05372f84ab1685a","observation_id":"efb12478-110b-431a-ab25-204dd742e19b","resolution":{"observed_at":"2026-05-16T06:02:24.955096Z","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":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"cited_work":{"arxiv_id":"2507.02747","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.02747","snapshot_observed_at":"2026-07-08T06:04:34.676226Z","title":"Dexvlg: Dexterous vision-language-grasp model at scale","venue":"cs.CV","work_id":"b0b9aece-20ed-4429-b501-e71041ceb063","year":2025},"citing_paper":{"arxiv_id":"2604.06589","last_updated":"2026-04-08T02:17:11Z","snapshot_observed_at":"2026-07-06T22:55:01.863342Z","submitted_at":"2026-04-08T02:17:11Z","title":"BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T18:49:49.433772Z"},"links":{"cited_paper":"/paper/2507.02747","citing_paper":"/paper/2604.06589"},"observation_digest":"sha256:b73dd864be011526daf189185bc43cdf9275a3c4abc213aad9721f86b30b221a","observation_id":"47baa568-4a1b-4428-bc15-319925288dd0","resolution":{"observed_at":"2026-05-10T23:50:56.957350Z","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":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"cited_work":{"arxiv_id":"2507.02747","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.02747","snapshot_observed_at":"2026-07-08T06:04:34.676226Z","title":"Dexvlg: Dexterous vision-language-grasp model at scale","venue":"cs.CV","work_id":"b0b9aece-20ed-4429-b501-e71041ceb063","year":2025},"citing_paper":{"arxiv_id":"2604.08410","last_updated":"2026-04-14T05:25:29Z","snapshot_observed_at":"2026-07-06T22:57:31.046146Z","submitted_at":"2026-04-09T16:10:20Z","title":"BLaDA: Bridging Language to Functional Dexterous Actions within 3DGS Fields","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T17:25:09.012355Z"},"links":{"cited_paper":"/paper/2507.02747","citing_paper":"/paper/2604.08410"},"observation_digest":"sha256:4691040c079c3c0c02e1d380f73bc4433c41a87244c9d4ad7e446fdd9dac48b1","observation_id":"5f05e673-cf52-480f-bb0b-9bc2bc9af562","resolution":{"observed_at":"2026-05-11T06:51:25.793833Z","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":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"cited_work":{"arxiv_id":"2507.02747","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.02747","snapshot_observed_at":"2026-07-08T06:04:34.676226Z","title":"Dexvlg: Dexterous vision-language-grasp model at scale","venue":"cs.CV","work_id":"b0b9aece-20ed-4429-b501-e71041ceb063","year":2025},"citing_paper":{"arxiv_id":"2607.06438","last_updated":"2026-07-13T05:01:31Z","snapshot_observed_at":"2026-08-01T16:18:21.827851Z","submitted_at":"2026-07-07T16:06:43Z","title":"WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-08T05:55:32.387354Z"},"links":{"cited_paper":"/paper/2507.02747","citing_paper":"/paper/2607.06438"},"observation_digest":"sha256:92b44d13a4cba35a63ed1b6c3ad13d9e7b46e040d48c9f2c7c35925a927892cc","observation_id":"a7a90edd-ed5b-40ad-bdd1-bc8a9ed4a4b1","resolution":{"observed_at":"2026-07-08T06:04:34.677454Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.02747","snapshot_observed_at":"2026-07-14T16:02:59.358284Z","title":"arXiv preprint arXiv:2507.02747 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.06438","last_updated":"2026-07-13T05:01:31Z","snapshot_observed_at":"2026-08-01T16:18:21.827851Z","submitted_at":"2026-07-07T16:06:43Z","title":"WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T16:02:59.358284Z"},"links":{"cited_paper":"/paper/2507.02747","citing_paper":"/paper/2607.06438"},"observation_digest":"sha256:3467db4d07c151d103a2251db4d055997035140954b946c0d0f8c32d43653bad","observation_id":"798058ca-a00a-4edb-b787-4ba0a6250bdb","resolution":{"observed_at":"2026-07-14T16:02:59.358284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.02747/citation-record","integrity":"/paper/2507.02747/integrity","json":"/paper/2507.02747/citation-record.json","paper":"/paper/2507.02747"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","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":"2026-08-06T20:29:23.959143Z","title":"Gpt-4 technical report.arXiv preprint arXiv:2303.08774,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:23.959143Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:da1e45c612d3e336b6950466d666f2ec5855791197c5a784279a4544376b3d6c","observation_id":"10f0d90b-4765-4165-b495-0467919d6626","resolution":{"observed_at":"2026-08-06T20:29:23.959143Z","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":"2026-08-06T20:29:41.844315Z","title":"Dexterous functional grasping","venue":null,"work_id":"20ddb3aa-5d49-4ee1-ad79-c8a348f845e8","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:24.089645Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:0560d0ceb4cbe51056c947f9978b63b872a008f59ad969d2461d9e76044cf9a2","observation_id":"0c4f6461-ba29-48e5-9111-f06bfd3539c9","resolution":{"observed_at":"2026-08-06T20:29:41.919023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2410.24164","last_updated":"2026-01-08T17:01:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-31T17:22:30Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.24164","snapshot_observed_at":"2026-08-06T20:29:24.283858Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:24.283858Z"},"links":{"cited_paper":"/paper/2410.24164","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:9e77abd588b76496ac38c19d3d18dd16ae3ea61c4b1420edb5100bdde4b53092","observation_id":"ede89f73-b332-4bb4-b453-7dec4abab9d5","resolution":{"observed_at":"2026-08-06T20:29:24.283858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15818","last_updated":"2023-07-28T21:18:02Z","snapshot_observed_at":"2026-08-02T16:17:50.621617Z","submitted_at":"2023-07-28T21:18:02Z","title":"RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15818","snapshot_observed_at":"2026-08-06T20:29:24.467366Z","title":"Rt-2: Vision-language-action models transfer web knowledge to robotic control.arXiv preprint arXiv:2307.15818, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:24.467366Z"},"links":{"cited_paper":"/paper/2307.15818","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:89f6da158e658e2e17c86d3da7d8225a9d772eab628467281de74e68c0e782c6","observation_id":"dfcc98e9-132b-4609-a7d5-71429ee5c4e2","resolution":{"observed_at":"2026-08-06T20:29:24.467366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.13586","last_updated":"2024-04-06T06:20:46Z","snapshot_observed_at":"2026-08-09T20:39:58.921647Z","submitted_at":"2023-09-24T09:01:19Z","title":"Task-Oriented Dexterous Hand Pose Synthesis Using Differentiable Grasp Wrench Boundary Estimator","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.13586","snapshot_observed_at":"2026-08-06T20:29:24.623262Z","title":"Task-oriented dexterous grasp synthesis via differ- entiable grasp wrench boundary estimator.arXiv preprint arXiv:2309.13586, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:24.623262Z"},"links":{"cited_paper":"/paper/2309.13586","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:a1d27ac2e1799fb6ba8aac996117c8405820f4f4f8c552f0ff4bc4d15e04628e","observation_id":"a15c7294-a4df-49b9-ac51-e115657935ca","resolution":{"observed_at":"2026-08-06T20:29:24.623262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16490","last_updated":"2025-09-03T03:39:08Z","snapshot_observed_at":"2026-08-11T10:29:32.062329Z","submitted_at":"2024-12-21T05:22:53Z","title":"BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16490","snapshot_observed_at":"2026-08-06T20:29:24.749825Z","title":"Bodex: Scalable and efficient robotic dexterous grasp synthesis using bilevel op- timization.arXiv preprint arXiv:2412.16490, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:24.749825Z"},"links":{"cited_paper":"/paper/2412.16490","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:83e915ee9f9bc47960d39728073ee4ba69ae7d9db8e79c73dab8563170f79034","observation_id":"60e7b811-93d2-43be-ad01-0fa11a225596","resolution":{"observed_at":"2026-08-06T20:29:24.749825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.13532","last_updated":"2024-04-25T05:21:21Z","snapshot_observed_at":"2026-08-01T08:53:52.895953Z","submitted_at":"2024-04-21T05:07:51Z","title":"SpringGrasp: Synthesizing Compliant, Dexterous Grasps under Shape Uncertainty","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.13532","snapshot_observed_at":"2026-08-06T20:29:24.877321Z","title":"Springgrasp: An optimization pipeline for robust and compliant dexterous pre-grasp synthesis.arXiv preprint arXiv:2404.13532, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:24.877321Z"},"links":{"cited_paper":"/paper/2404.13532","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:21777991e0a44d8663d046e7a845368baa4ebc1acdba80e9fbb24c562f2d5b42","observation_id":"83ecaee0-2962-41ff-8a05-b3960414cf34","resolution":{"observed_at":"2026-08-06T20:29:24.877321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13638","last_updated":"2022-10-24T22:40:33Z","snapshot_observed_at":"2026-08-11T12:44:23.398424Z","submitted_at":"2022-10-24T22:40:33Z","title":"Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13638","snapshot_observed_at":"2026-08-06T20:29:25.009478Z","title":"Learn- ing robust real-world dexterous grasping policies via im- plicit shape augmentation.arXiv preprint arXiv:2210.13638,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.009478Z"},"links":{"cited_paper":"/paper/2210.13638","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:f907004966ed764ad38cc34c60e218336c69ffdd6b3495c727981ac0b85b2a56","observation_id":"60e8948a-ac7d-47c4-b74d-5e1f47b1063a","resolution":{"observed_at":"2026-08-06T20:29:25.009478Z","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":"2026-08-06T20:29:41.730815Z","title":"Syn- thesis and optimization of force closure grasps via sequential semidefinite programming.Robotics Research: Volume 1, pages 285–305, 2018","venue":null,"work_id":"f7477828-b94e-4224-a667-20fb1c7d65ac","year":2018},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.178223Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:24a624971c5be1ba55dfcff8b2207dfd2cae98e4c68fd6325e536e6b249fd506","observation_id":"11dfaaf7-284c-492b-9360-c96c594889e4","resolution":{"observed_at":"2026-08-06T20:29:41.786579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:41.611354Z","title":null,"venue":null,"work_id":"ff2e553b-03a1-493a-aae7-8af0b3ef46f9","year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.316048Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:480295d1491b7e28eaca8a384fc4096b58dd9b5ca4f2352f11adcfa3c8b5433c","observation_id":"c8bb98b4-d7fe-44a9-9a6c-e77b02dd11b5","resolution":{"observed_at":"2026-08-06T20:29:41.670671Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:41.376933Z","title":"Objaverse: A universe of annotated 3d objects","venue":null,"work_id":"b48b419e-00c8-4dad-821c-b53758904cc0","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.408782Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:9a3ecad2fed561208e1ead7f7aa076e568951353b07774756582ca9aca3a76ae","observation_id":"be662dab-1b63-43c1-98ea-fff45681b87e","resolution":{"observed_at":"2026-08-06T20:29:41.529274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:41.123575Z","title":"Open6dor: Benchmarking open-instruction 6-dof object rearrangement and a vlm-based approach","venue":null,"work_id":"98924642-b7d9-476d-9198-a0e2b6904529","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.517974Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:3ce11697b5589a9e047fa50cae5a65722a243e4c240f445c65bf4be762094c79","observation_id":"11c6b362-0cd5-40d8-a3af-8de5df796ed0","resolution":{"observed_at":"2026-08-06T20:29:41.246754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:40.944141Z","title":null,"venue":null,"work_id":"a061d36e-b727-47d1-9bc1-4e200bf0a624","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.587196Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:bee5e7dca24fefd293a58ae321e7192f9175660485eb074a4a85808ce97bf781","observation_id":"ee315b7d-9b6b-46ff-a9b3-46f4cd2896b2","resolution":{"observed_at":"2026-08-06T20:29:40.986668Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:40.792602Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":"fa6b2671-2e50-4403-bbb4-3faafd137932","year":2021},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.708066Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:2c81a2a638aba74968c4f73025bda76764502fa2fdada65426a03e26c50f4fb1","observation_id":"5a7b652c-04d2-4b32-abec-e36d42a1700b","resolution":{"observed_at":"2026-08-06T20:29:40.865746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:40.635601Z","title":"Graspnet-1billion: A large-scale benchmark for general ob- ject grasping","venue":null,"work_id":"fcc8038b-a6e1-40e3-9f73-138f4a34dbf4","year":2020},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.863718Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:305bcb434dcfa85f98b5b1746c9dee15cbc40ccee2106d4cbe5ae4ad39ba17a0","observation_id":"9129cffc-20ea-4882-8833-ecb64fac30c9","resolution":{"observed_at":"2026-08-06T20:29:40.710622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:40.473249Z","title":"Planning optimal grasps","venue":null,"work_id":"cae30916-b9aa-4e1f-8909-153ad30e5bfd","year":1992},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:25.961573Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:5c1170a18d7fa988438dcbef009e482340bbf808601da4320b0ff925e4381816","observation_id":"484ba86f-fe38-42fb-90fb-bde2230ef1e6","resolution":{"observed_at":"2026-08-06T20:29:40.556574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:40.286586Z","title":"Measurement of areas on a sphere using fibonacci and latitude–longitude lattices.Mathematical geo- sciences, 42:49–64, 2010","venue":null,"work_id":"aa44f40c-d2e7-478b-a518-caee5cb5d541","year":2010},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:26.119442Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:fccf4c606bb71df71473677054ae1aac2da8138296660375a6f89a952fac96f4","observation_id":"574b1704-c839-4331-b855-a0d85ce8d7be","resolution":{"observed_at":"2026-08-06T20:29:40.368086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:40.137152Z","title":null,"venue":null,"work_id":"27bc31bc-22d6-430e-8db2-73271f5f7bd8","year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:26.238716Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:7fcbe3195e87dd491b237cbb00269dbc640c3317615f162f8485e97fc4ff7704","observation_id":"328d0923-740b-4194-9452-b0f4a504a948","resolution":{"observed_at":"2026-08-06T20:29:40.213467Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:39.946772Z","title":"Dexfuncgrasp: A robotic dexterous functional grasp dataset constructed from a cost-effective real-simulation annotation system","venue":null,"work_id":"ffd53614-4cfe-4577-a91e-04750e0d2621","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:26.334294Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:21db79663a0e29dbcbde7c6d1d02951b27f08cd5d19131de81a0373be128df13","observation_id":"12912eb0-d9b3-4aeb-ba45-eb7d14097568","resolution":{"observed_at":"2026-08-06T20:29:40.060031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2306.05416","last_updated":"2023-06-08T17:58:45Z","snapshot_observed_at":"2026-08-07T05:47:21.310754Z","submitted_at":"2023-06-08T17:58:45Z","title":"Tracking Objects with 3D Representation from Videos","version":1},"cited_work":{"arxiv_id":"2306.05416","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.05416","snapshot_observed_at":"2026-08-06T20:29:34.100608Z","title":"Tracking Objects with 3D Representation from Videos","venue":"cs.CV","work_id":"8a82852b-56f9-405c-89d6-275e32f2ce02","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:26.478690Z"},"links":{"cited_paper":"/paper/2306.05416","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:7b669fe594aefdbc00769e213d4cfa7d3a37f8ed7e1db9f6b57ae4ecf709f3a8","observation_id":"ad6eb191-2cd4-4300-a171-0bd7ed6e3f45","resolution":{"observed_at":"2026-08-06T20:29:34.283869Z","resolver_source":"local_arxiv","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":"2005.11621","last_updated":"2020-05-23T23:27:44Z","snapshot_observed_at":"2026-08-09T18:02:04.344233Z","submitted_at":"2020-05-23T23:27:44Z","title":"ManifoldPlus: A Robust and Scalable Watertight Manifold Surface Generation Method for Triangle Soups","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.11621","snapshot_observed_at":"2026-08-06T20:29:26.596066Z","title":"Man- ifoldplus: A robust and scalable watertight manifold sur- face generation method for triangle soups.arXiv preprint arXiv:2005.11621, 2020","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:26.596066Z"},"links":{"cited_paper":"/paper/2005.11621","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:fbad6532bee55a49f950d044812a65c41bbb2fde1360c64f02252dd3ef185bde","observation_id":"6b743346-c9cd-4e7f-812a-6e861edd5c70","resolution":{"observed_at":"2026-08-06T20:29:26.596066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.16755","last_updated":"2024-11-24T07:30:54Z","snapshot_observed_at":"2026-07-06T19:56:48.086175Z","submitted_at":"2024-11-24T07:30:54Z","title":"FunGrasp: Functional Grasping for Diverse Dexterous Hands","version":1},"cited_work":{"arxiv_id":"2411.16755","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.16755","snapshot_observed_at":"2026-08-06T20:29:33.873619Z","title":"FunGrasp: Functional Grasping for Diverse Dexterous Hands","venue":"cs.RO","work_id":"b92521ad-471c-4996-bfb4-418c7685bbe8","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:26.745663Z"},"links":{"cited_paper":"/paper/2411.16755","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:efc382384c365108dc488bd3a50474e776bd7b35740fa6e689a96f75bcf19cd0","observation_id":"5e2de85e-1357-489e-b81c-0b4be878dc6c","resolution":{"observed_at":"2026-08-06T20:29:33.942387Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:26.898492Z","title":"Omnispatial: Towards comprehensive spatial reasoning benchmark for vi- sion language models.arXiv preprint arXiv:2506.03135,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:26.898492Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:7e6230ff671ae55b73c245c5cf1266019d3180e812dcebc668de7e8a7435a976","observation_id":"92620677-bbac-42f9-97f0-a5ecd2e1e3a1","resolution":{"observed_at":"2026-08-06T20:29:26.898492Z","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":"2026-08-06T20:29:26.959637Z","title":"Hand-object contact consistency reasoning for human grasps generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:26.959637Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:a49e24d3af93870cffcfadc70273994cda7d7ae48f15809ac62f05b5c6a29e20","observation_id":"c6b8a731-ecee-44c8-8c73-2ba20f0f1003","resolution":{"observed_at":"2026-08-06T20:29:26.959637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09246","last_updated":"2024-09-05T19:46:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-13T15:46:55Z","title":"OpenVLA: An Open-Source Vision-Language-Action Model","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.09246","snapshot_observed_at":"2026-08-06T20:29:27.086264Z","title":"Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:27.086264Z"},"links":{"cited_paper":"/paper/2406.09246","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:dc324e22c778c4762ea9379211862e9e69646d5f50e75f25786d43d59306f093","observation_id":"7e4f90b4-1b89-495e-a7cf-cc7f76242fce","resolution":{"observed_at":"2026-08-06T20:29:27.086264Z","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":"2026-08-06T20:29:39.754345Z","title":"Frogger: Fast robust grasp generation via the min-weight metric","venue":null,"work_id":"630cbb3f-29f3-4f40-86f3-9ab010b3dc24","year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:27.226058Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:6e8cfae16a4398728d6c8c1fa130d01c2b798cf2672fc0495fe7a1577df9433a","observation_id":"1a0222a8-6ba0-4f43-b108-38d71125c67f","resolution":{"observed_at":"2026-08-06T20:29:39.821992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2412.08468","last_updated":"2025-06-07T09:35:13Z","snapshot_observed_at":"2026-07-06T20:05:18.939982Z","submitted_at":"2024-12-11T15:33:35Z","title":"Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.08468","snapshot_observed_at":"2026-08-06T20:29:27.315080Z","title":"Multi-graspllm: A multimodal llm for multi-hand semantic guided grasp generation.arXiv preprint arXiv:2412.08468, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:27.315080Z"},"links":{"cited_paper":"/paper/2412.08468","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:8239389e6b2876a563a5761c5d8e65f1f54ccb2131401d6d831b1eec943f072a","observation_id":"31824d11-5009-4f46-a815-58f492c960c5","resolution":{"observed_at":"2026-08-06T20:29:27.315080Z","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":"2026-08-06T20:29:39.515246Z","title":"Semgrasp: Semantic grasp generation via language aligned discretization","venue":null,"work_id":"a1a6721c-8747-4f9c-a076-918d7ef7edb0","year":2025},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:27.440427Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:4ef3691fa841a37c8d435c73d66ac178224c3a42f788a1e1b77cbe8a1c8067f1","observation_id":"a546af55-d5d8-4155-85bf-900ce30e42e3","resolution":{"observed_at":"2026-08-06T20:29:39.621484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:39.350809Z","title":"Incremental potential con- tact: intersection-and inversion-free, large-deformation dy- namics.ACM Trans","venue":null,"work_id":"5a0e94bf-8336-45d3-8d97-2b52522815af","year":2020},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:27.640359Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:0f6250445632348b9763aaa3f6a329ef503ed0618ecfdd275b45b928839573b3","observation_id":"759c2b4a-9570-4751-b51a-f58fee802b27","resolution":{"observed_at":"2026-08-06T20:29:39.456819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:39.169224Z","title":"Moka: Open-vocabulary robotic manipulation through mark-based visual prompting","venue":null,"work_id":"7535e2ff-1d02-46b1-97fe-b6269412e3de","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:27.764261Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:330f71a69e0064e8438a4e82ddb40202641ad820d5ac859cc59436374fe1e731","observation_id":"1a8d3641-35de-45f5-a327-e42838c22b24","resolution":{"observed_at":"2026-08-06T20:29:39.253008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:38.986496Z","title":"Openshape: Scaling up 3d shape representation towards open-world understanding.Advances in neural information processing systems, 36:44860–44879, 2023","venue":null,"work_id":"c40b5cfa-d97f-4ff7-989c-ba03749ce4ef","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:27.845319Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:8693d26cc04d22047107c281327779a1d2df324f98e206769f35ab5e2591a471","observation_id":"0b60d2f1-f597-4d13-ba6a-e553ca50149f","resolution":{"observed_at":"2026-08-06T20:29:39.068759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:38.823237Z","title":"Partslip: Low-shot part segmentation for 3d point clouds via pretrained image- language models","venue":null,"work_id":"a17ffdc3-1414-4d04-a7ab-8646cde3b158","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:27.989710Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:da1693020e2379ceaaa2039574e2f244cf771ef488577a30338573faf29dd1b8","observation_id":"5c049be5-11c7-4ac1-82cc-b9b1553fe7d8","resolution":{"observed_at":"2026-08-06T20:29:38.921222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:38.660890Z","title":null,"venue":null,"work_id":"31fd4018-452b-4da7-90f3-be8c11cf0920","year":2021},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:28.165184Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:5ebc2914bb5cc9b4f505f79a0278272e00b72dce08f4a80b8b34230c28103133","observation_id":"e68a3880-4e47-4baf-a13e-92e40d38dc7a","resolution":{"observed_at":"2026-08-06T20:29:38.727799Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2502.09614","last_updated":"2025-02-13T18:59:13Z","snapshot_observed_at":"2026-08-11T00:17:03.805560Z","submitted_at":"2025-02-13T18:59:13Z","title":"DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.09614","snapshot_observed_at":"2026-08-06T20:29:28.267833Z","title":"Dextrack: Towards generalizable neural tracking control for dexterous manipulation from human references","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:28.267833Z"},"links":{"cited_paper":"/paper/2502.09614","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:7228bd6b44d17abdbefa1ec6008292d27fe1de463d6b367135df4ff3ab97b9a9","observation_id":"fb3b7c14-7e54-4504-8893-65ac02a53e18","resolution":{"observed_at":"2026-08-06T20:29:28.267833Z","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":"2026-08-06T20:29:38.474342Z","title":"Cross-shape atten- tion for part segmentation of 3d point clouds","venue":null,"work_id":"9f4640ea-1682-4c4c-b192-802a02a62fc7","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:28.417849Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:3254c84e405ba74417dae3d635e31ea0326185f59d7570a6b95a8e494ea86644","observation_id":"0d78fade-e492-4176-b8b5-575977bf57e1","resolution":{"observed_at":"2026-08-06T20:29:38.553779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2411.13550","last_updated":"2025-03-28T04:36:55Z","snapshot_observed_at":"2026-07-06T19:53:21.444233Z","submitted_at":"2024-11-20T18:59:01Z","title":"Find Any Part in 3D","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.13550","snapshot_observed_at":"2026-08-06T20:29:28.549836Z","title":"Find any part in 3d.arXiv preprint arXiv:2411.13550, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:28.549836Z"},"links":{"cited_paper":"/paper/2411.13550","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:6e0c3f8729f870b28e9c376ee8831a55e2464096e12dfafc8bf9b5d9f12e6f50","observation_id":"e4ff4f02-5337-40da-96a4-f85c842c4d7d","resolution":{"observed_at":"2026-08-06T20:29:28.549836Z","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":"2026-08-06T20:29:38.359447Z","title":"Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics, 2017","venue":null,"work_id":"b1c463c8-cbc9-40c9-8d0a-d4e32e801584","year":2017},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:28.696256Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:0811cd4bd2c48bf03c385a383ade219f802db8d5b94534e42cecf8ff711193b9","observation_id":"4fdee6b5-7f06-425c-b0a5-57c2ddd80e28","resolution":{"observed_at":"2026-08-06T20:29:38.415263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:38.245040Z","title":"Isaac gym: High performance GPU based physics simulation for robot learning","venue":null,"work_id":"265c76d8-7b96-40fb-bb7d-1844305dc370","year":2021},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:28.841295Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:daf17da3a5dab19ea4c4fe2387dc4f7af3b24b07ba2dc3e27e546841b05f7d25","observation_id":"8ddbfee3-fb0e-4015-a812-3335b0616212","resolution":{"observed_at":"2026-08-06T20:29:38.302653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:38.132310Z","title":"Introducing gpt-4o and more tools to chatgpt free users","venue":null,"work_id":"0f2c792b-329f-40d4-8eac-1482765e9109","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:28.935516Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:053c0cb117555fb326fc8e9bbbcc1ca24ebd35b8aa57b05619b28ed60436a3a7","observation_id":"74c17cf7-ce50-4338-b471-726231909b35","resolution":{"observed_at":"2026-08-06T20:29:38.183802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:37.964691Z","title":"Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining","venue":null,"work_id":"a8510b6f-f9f8-45fa-bffd-6bd51c7ada89","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:29.096366Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:d71047a0a628c693f40942b26947bc16d75dcc368951af2541d1f6a2bfc722c8","observation_id":"3e4b4fdf-03d6-48c6-86d5-e4618c81f0a5","resolution":{"observed_at":"2026-08-06T20:29:38.050894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:37.618147Z","title":"Vpp: Efficient conditional 3d generation via voxel-point pro- gressive representation.Advances in Neural Information Processing Systems, 36:26744–26763, 2023","venue":null,"work_id":"4591fc56-a386-4f6e-bb51-e646c27c483c","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:29.237495Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:5efdd50fdabfd2097221d2fa4951efc72ada737a24e78d5149904b3a9799282b","observation_id":"ec4a4cd4-a158-4452-93e3-611c56013000","resolution":{"observed_at":"2026-08-06T20:29:37.824946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:29.403897Z","title":"Shapellm: Universal 3d object understanding for embodied interaction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:29.403897Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:4b35880f46caed9d56c9f370a0bf5cd0e257f4f467c78f1b74ad8f8774f2b343","observation_id":"f57c9fbf-f3a9-4b58-a9ae-823e631aae83","resolution":{"observed_at":"2026-08-06T20:29:29.403897Z","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":"2026-08-06T20:29:29.552214Z","title":"So- far: Language-grounded orientation bridges spatial reason- ing and object manipulation.CoRR, abs/2502.13143, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:29.552214Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:53e7501e81437e234b6b8a0d6bf462134417b46ae133a66c15fb481f6d6bb5c5","observation_id":"001b863b-55dd-40ab-be74-f7f969b93e55","resolution":{"observed_at":"2026-08-06T20:29:29.552214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.04577","last_updated":"2024-05-16T21:14:44Z","snapshot_observed_at":"2026-08-08T07:20:23.450723Z","submitted_at":"2023-07-10T14:11:07Z","title":"AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.04577","snapshot_observed_at":"2026-08-06T20:29:29.763561Z","title":"Anyteleop: A general vision-based dexterous robot arm-hand teleoperation system.arXiv preprint arXiv:2307.04577, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:29.763561Z"},"links":{"cited_paper":"/paper/2307.04577","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:c091b9b24616144b3ee7df8b7697034938e2dc3b6d8049f2b796f64bf871ed4d","observation_id":"e24b2bae-4e0d-415f-a519-14d01369cc91","resolution":{"observed_at":"2026-08-06T20:29:29.763561Z","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":"2026-08-06T20:29:29.927894Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:29.927894Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:1ce5c3cd4bdd785a9fa073013e0b84da5f0d7c0887eaf59a108f52ce2ab8526b","observation_id":"a39427a0-da84-4830-82de-88a5eaaede3b","resolution":{"observed_at":"2026-08-06T20:29:29.927894Z","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":"2026-08-06T20:29:30.071613Z","title":"Curobo: Parallelized collision-free robot mo- tion generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.071613Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:8ba010d89a566af24ee5d21e0bdf6e3932849a8be3943e9444f0ce325671eaa9","observation_id":"6731e4e9-09df-439f-8968-8adb5322bf4a","resolution":{"observed_at":"2026-08-06T20:29:30.071613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13679","last_updated":"2025-03-09T21:11:26Z","snapshot_observed_at":"2026-08-11T06:23:55.857320Z","submitted_at":"2024-08-24T22:05:04Z","title":"Segment Any Mesh","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.13679","snapshot_observed_at":"2026-08-06T20:29:30.129937Z","title":"Segment any mesh: Zero-shot mesh part segmentation via lifting segment anything 2 to 3d.arXiv preprint arXiv:2408.13679, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.129937Z"},"links":{"cited_paper":"/paper/2408.13679","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:265dbd0acc85912eab3dc408d2c9191890bffb34ed90561afee55f696494140c","observation_id":"6e298da7-121a-49d0-8d11-301f2dd92c2b","resolution":{"observed_at":"2026-08-06T20:29:30.129937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-06T20:29:30.269101Z","title":"Gemini: a family of highly capable multimodal models.arXiv preprint arXiv:2312.11805, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.269101Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:04bfc18a6866032773bfab300610a7ef47abafb6ea990477a3933e3fe7a54446","observation_id":"ae82df7e-2c73-43ce-b21a-f32634946c89","resolution":{"observed_at":"2026-08-06T20:29:30.269101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12213","last_updated":"2024-05-26T19:55:26Z","snapshot_observed_at":"2026-07-06T18:16:51.116432Z","submitted_at":"2024-05-20T17:57:01Z","title":"Octo: An Open-Source Generalist Robot Policy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12213","snapshot_observed_at":"2026-08-06T20:29:30.354019Z","title":"Octo: An open-source generalist robot policy.arXiv preprint arXiv:2405.12213, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.354019Z"},"links":{"cited_paper":"/paper/2405.12213","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:aa62e992fcbb0e0fa87232b7c213fbedb2d78efde58209b5b64fe8ad17bb0fec","observation_id":"5526c4d9-e073-42cb-9436-3053a2ba8d4d","resolution":{"observed_at":"2026-08-06T20:29:30.354019Z","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":"2026-08-06T20:29:37.446400Z","title":"Easy and fast evaluation of grasp stability by using ellipsoidal approx- imation of friction cone","venue":null,"work_id":"9d816395-7c9f-4c30-8a1c-069b9a354534","year":2009},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.428822Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:28daf2ff0fe5e530f91a6a01c6fc466b0249090a5711c8c93499a8138086076f","observation_id":"40a46158-05c8-4132-b329-968a792b2634","resolution":{"observed_at":"2026-08-06T20:29:37.490954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:30.519370Z","title":"Grasp’d: Differentiable contact-rich grasp syn- thesis for multi-fingered hands","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.519370Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:0257e50bd00956350af98dd02bf7ac7a6fe8d57bd4508f318492652241b3f473","observation_id":"ac00543b-ae4b-4794-a402-01a6edead64b","resolution":{"observed_at":"2026-08-06T20:29:30.519370Z","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":"2026-08-06T20:29:37.244531Z","title":"Fast-grasp’d: Dexterous multi- finger grasp generation through differentiable simulation","venue":null,"work_id":"8b85bf31-4d83-44da-b766-a5d99b64ca3b","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.576277Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:a236428269a81d520ab085ba6ef1582b75f05488b612b509d5713cfd0f80fc8b","observation_id":"3ca1a846-5a69-4aaa-9be9-7219bd5dd0cf","resolution":{"observed_at":"2026-08-06T20:29:37.388564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:37.032787Z","title":"Unidexgrasp++: Im- proving dexterous grasping policy learning via geometry- aware curriculum and iterative generalist-specialist learning","venue":null,"work_id":"17109caa-813f-4863-bf29-3c1a0f3395c6","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.628617Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:eb329c9d02c3a305a5e2e01931600a615b6dc4b230c61c78f11bfa03b5cd14dd","observation_id":"d8fbe70f-fd24-4389-b8ad-9e2ef53d9534","resolution":{"observed_at":"2026-08-06T20:29:37.152665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:30.714416Z","title":"Vlm see, robot do: Human demo video to robot action plan via vision language model.arXiv preprint arXiv:2410.08792, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.714416Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:4085bb64549a96c551e1456ca386f83eb3405d8cadacb5b5d5be601cc50e9a2a","observation_id":"554e3b7f-5dee-42a6-af31-bdc81f454ca8","resolution":{"observed_at":"2026-08-06T20:29:30.714416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07788","last_updated":"2024-07-04T04:35:04Z","snapshot_observed_at":"2026-07-06T17:43:22.965126Z","submitted_at":"2024-03-12T16:23:49Z","title":"DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07788","snapshot_observed_at":"2026-08-06T20:29:30.777176Z","title":"Dexcap: Scalable and portable mocap data collection system for dexterous manipulation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.777176Z"},"links":{"cited_paper":"/paper/2403.07788","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:300a43070f78bd518f24265c887b25876d96cd366965f254bf4dd85788fac8ce","observation_id":"4ac49ab8-ca17-416e-926e-5367312bc825","resolution":{"observed_at":"2026-08-06T20:29:30.777176Z","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":"2026-08-06T20:29:36.856404Z","title":"Dexgraspnet: A large-scale robotic dexterous grasp dataset for general ob- jects based on simulation","venue":null,"work_id":"2cfbfad0-714e-4ca0-8f6f-9212f798eb84","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.843470Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:7f8a067945b7bb61b983c85e083514dbf2e90598fd8296e2aee6ca2d79782381","observation_id":"28018a36-fff2-4a7c-bd17-b74eef8cb7b9","resolution":{"observed_at":"2026-08-06T20:29:36.949010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:36.689213Z","title":"Approx- imate convex decomposition for 3d meshes with collision- aware concavity and tree search.ACM Transactions on Graphics (TOG), 41(4):1–18, 2022","venue":null,"work_id":"77248fac-901b-4acd-9f86-c08888fd696e","year":2022},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.888914Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:8e3adf620ce4fa3152bacad94a4cbd5f7db7c049b09f0954320612fa3a96cfce","observation_id":"6c5e48df-43f6-4884-893b-7e377768460b","resolution":{"observed_at":"2026-08-06T20:29:36.757987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2405.19291","last_updated":"2024-10-31T02:43:00Z","snapshot_observed_at":"2026-07-06T18:22:10.094519Z","submitted_at":"2024-05-29T17:19:15Z","title":"Grasp as You Say: Language-guided Dexterous Grasp Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19291","snapshot_observed_at":"2026-08-06T20:29:30.979171Z","title":"Grasp as you say: Language-guided dexterous grasp genera- tion.arXiv preprint arXiv:2405.19291, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:30.979171Z"},"links":{"cited_paper":"/paper/2405.19291","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:3151d867fe3ca00eb2ba65b78957729420bf955e79c050dfda55f0fda24ba5a3","observation_id":"1f2c84d2-8bce-4fd9-9a4f-1b47cdc73c5d","resolution":{"observed_at":"2026-08-06T20:29:30.979171Z","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":"2026-08-06T20:29:36.633340Z","title":"Cross- category functional grasp transfer.IEEE Robotics and Au- tomation Letters, 2024","venue":null,"work_id":"dbc6a1cd-d2f8-4b39-9a79-2ed600a75991","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:31.042231Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:83e72a48fae7394688b50df4b17f134ddcf4eebefe454dfb07a4270a48fcfec0","observation_id":"1b8bc759-778b-4857-974b-eebede907b71","resolution":{"observed_at":"2026-08-06T20:29:36.679576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:36.333807Z","title":"Florence-2: Advancing a unified representation for a variety of vision tasks","venue":null,"work_id":"315ac9ad-d846-409c-b77a-5d6d3b5757d7","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:31.102850Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:accb329ad60b0c96b89886b214ec5c67b9e28e0203653f8c349e82e3645e70f6","observation_id":"9b1cd857-1350-422b-8ae3-e6bb820692c4","resolution":{"observed_at":"2026-08-06T20:29:36.435931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:36.127745Z","title":"Dexterous grasp transformer, 2024","venue":null,"work_id":"54645cfe-20a0-434b-aa41-2ac3338c3714","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:31.225943Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:f0c25718750d0352f21b85864919b66babdee3260f0ea3c75950982b6228fbc5","observation_id":"e8062937-6c91-4d2a-8726-304fa3ac0de8","resolution":{"observed_at":"2026-08-06T20:29:36.236200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:35.919194Z","title":"Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy","venue":null,"work_id":"b3eba0bc-de52-4417-ad06-cb2b589c588a","year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:31.330212Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:a5294bbdfceed176e82a67e0171186c4ca49bb993eff63c742de09e73195f448","observation_id":"625474bd-9997-49bd-8989-64859e589787","resolution":{"observed_at":"2026-08-06T20:29:36.029771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2310.11441","last_updated":"2023-11-06T07:39:49Z","snapshot_observed_at":"2026-08-04T03:29:49.409446Z","submitted_at":"2023-10-17T17:51:31Z","title":"Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.11441","snapshot_observed_at":"2026-08-06T20:29:31.477714Z","title":"Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v.arXiv preprint arXiv:2310.11441, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:31.477714Z"},"links":{"cited_paper":"/paper/2310.11441","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:8a0ac79535c12832b1d72490449468992d97013ad79f292af7b798c95f9ff754","observation_id":"291ccc3b-36d7-4dfb-b3d9-58a21e24e35b","resolution":{"observed_at":"2026-08-06T20:29:31.477714Z","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":"2026-08-06T20:29:35.723726Z","title":"Oakink: A large-scale knowledge repos- itory for understanding hand-object interaction","venue":null,"work_id":"35ad65a1-c218-4b23-bb3b-b1600059fee2","year":2022},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:31.600400Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:071ab57e891f091ebb99ed5562a4a056c3596a17a557f3960a502711fc96acc4","observation_id":"64cbfbd5-03ec-4f53-863f-3a8d923d1356","resolution":{"observed_at":"2026-08-06T20:29:35.833688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2411.07184","last_updated":"2024-11-16T07:04:42Z","snapshot_observed_at":"2026-08-08T06:47:38.579688Z","submitted_at":"2024-11-11T17:59:10Z","title":"SAMPart3D: Segment Any Part in 3D Objects","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.07184","snapshot_observed_at":"2026-08-06T20:29:31.778163Z","title":"Sampart3d: Segment any part in 3d objects.arXiv preprint arXiv:2411.07184, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:31.778163Z"},"links":{"cited_paper":"/paper/2411.07184","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:612ea3bdb121cf72f25f7f90f6c48f2e80484ebdf5f15232cdd6df3addc52b7f","observation_id":"fa30e64b-9fce-4439-a8c9-2e36a3b99977","resolution":{"observed_at":"2026-08-06T20:29:31.778163Z","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":"2026-08-06T20:29:35.519547Z","title":"Graspxl: Generating grasping motions for di- verse objects at scale","venue":null,"work_id":"9692a936-b431-4a06-b46b-9410082b3f00","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:31.977479Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:6c6514be4cee786afdebe09b650e9e806a64f636abbc28e716f8f74820f20a7c","observation_id":"e58fd632-e8c1-4189-bd3e-b23df31690bb","resolution":{"observed_at":"2026-08-06T20:29:35.618900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:35.280604Z","title":"Dexgrasp- net 2.0: Learning generative dexterous grasping in large- scale synthetic cluttered scenes","venue":null,"work_id":"c2ed5f73-7ac1-4eb2-90e9-5644080d32b1","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:32.179501Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:fe6728cf7025232142d6f15df6d7c2529c8c7835ab87339b7b7b983f0304213f","observation_id":"3722ba1b-55d3-4e96-a9a9-5a5a7b32dfdc","resolution":{"observed_at":"2026-08-06T20:29:35.413575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2407.09899","last_updated":"2024-10-23T13:01:38Z","snapshot_observed_at":"2026-07-06T18:45:52.286287Z","submitted_at":"2024-07-13T14:29:12Z","title":"DexGrasp-Diffusion: Diffusion-based Unified Functional Grasp Synthesis Method for Multi-Dexterous Robotic Hands","version":2},"cited_work":{"arxiv_id":"2407.09899","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.09899","snapshot_observed_at":"2026-08-06T20:29:33.241864Z","title":"DexGrasp-Diffusion: Diffusion-based Unified Functional Grasp Synthesis Method for Multi-Dexterous Robotic Hands","venue":"cs.RO","work_id":"7949fa25-720a-4ba5-89fd-7e51ae90e04c","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:32.349340Z"},"links":{"cited_paper":"/paper/2407.09899","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:bfc83b0add93686894eb32f8aacfddad825e9e75a188774ecbcd43835d6b4526","observation_id":"905ff705-9adf-4d0a-a0f1-1fa1ea1380e2","resolution":{"observed_at":"2026-08-06T20:29:33.373364Z","resolver_source":"local_arxiv","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:32.546464Z","title":"Point transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:32.546464Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:c466b290bcf1bb2bb4065ae49c79f2dfcfa26e53e1fc9ad5421cdb60be8ab72b","observation_id":"9bcd0b44-b774-453c-b81f-6bc6f688c1c5","resolution":{"observed_at":"2026-08-06T20:29:32.546464Z","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":"2026-08-06T20:29:35.014886Z","title":"Transfusion: Pre- dict the next token and diffuse images with one multi- modal model","venue":null,"work_id":"a296dfb3-7cb7-463d-a5a6-71f4cfb44286","year":2025},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:32.668921Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:085d651ecdd56d448646ed8d60d1f8b2d9175b0fb021d99544f079271414513d","observation_id":"fb1b47f1-2cd9-4ef9-8f93-15d57347892a","resolution":{"observed_at":"2026-08-06T20:29:35.139936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T20:29:34.702839Z","title":"Uni3d: Exploring uni- fied 3d representation at scale","venue":null,"work_id":"5164251e-183a-4ed4-b745-7c013b1ea603","year":2024},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:32.831965Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:fa7ca6bfb560f11e242c776768d2ecce26b1651ba5a0a262f11b5710d33cb4de","observation_id":"98c75145-325c-4597-b82a-c9d1b8c10234","resolution":{"observed_at":"2026-08-06T20:29:34.832315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2312.03015","last_updated":"2023-12-05T01:33:04Z","snapshot_observed_at":"2026-08-10T21:09:12.813464Z","submitted_at":"2023-12-05T01:33:04Z","title":"PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.03015","snapshot_observed_at":"2026-08-06T20:29:32.980977Z","title":"Partslip++: Enhancing low-shot 3d part segmentation via multi-view instance segmenta- tion and maximum likelihood estimation.arXiv preprint arXiv:2312.03015, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:32.980977Z"},"links":{"cited_paper":"/paper/2312.03015","citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:5abf3743ddc311e3e5ac7e47bea4ca3ebf3ddbfbc4ec6a118f2913359cc076b5","observation_id":"691352d1-c632-4cef-8c1a-a8e3adc332f2","resolution":{"observed_at":"2026-08-06T20:29:32.980977Z","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":"2026-08-06T20:29:34.483579Z","title":"embedded inside","venue":null,"work_id":"aa9cdb7e-c3b9-494c-9adb-08c6949bad01","year":null},"citing_paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T20:29:33.097470Z"},"links":{"citing_paper":"/paper/2507.02747"},"observation_digest":"sha256:f960d94fe1fe40688f0e63f8b9d3f5108fb299a5f1dd99c51e27610ebfac2ee8","observation_id":"02869e3b-8dfb-4bfb-8f96-8e25208f20f0","resolution":{"observed_at":"2026-08-06T20:29:34.596928Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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"}}],"paper":{"arxiv_id":"2507.02747","last_updated":"2025-07-03T16:05:25Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T02:28:10.262757Z","submitted_at":"2025-07-03T16:05:25Z","title":"DexVLG: Dexterous Vision-Language-Grasp Model at Scale"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":3,"verified_fuzzy":35},"total_outbound_references":73},"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 73 of 73 outbound references and 8 inbound Pith citation observations for arXiv:2507.02747."}