{"as_of":"2026-08-16T15:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f6f121e9a56fa0f62c57bddd9c9ddc8813d2b77086b86bc0aa0ec7a0230886f5","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:56:17.903022Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:46:40.861952Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-16T04:46:42.063716Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"cited_work":{"arxiv_id":"2411.10203","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.10203","snapshot_observed_at":"2026-08-16T04:46:42.063716Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","venue":"cs.CV","work_id":"db54aa98-29ae-4a7f-aab7-cf2525954fca","year":2024},"citing_paper":{"arxiv_id":"2505.01458","last_updated":"2026-06-09T09:31:25Z","snapshot_observed_at":"2026-08-16T06:07:00.394267Z","submitted_at":"2025-05-01T09:22:23Z","title":"A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI","version":2},"reference_index":139,"source":"pdf_text","source_observed_at":"2026-08-16T04:46:40.861952Z"},"links":{"cited_paper":"/paper/2411.10203","citing_paper":"/paper/2505.01458"},"observation_digest":"sha256:e59a74897516b30d71e535a9401a0f1696b8dc5a1ac945e9fdc9ca9d96ad2f75","observation_id":"c593154d-d587-4848-b2b7-6d7ac65ca473","resolution":{"observed_at":"2026-08-16T04:46:42.070761Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.10203/citation-record","integrity":"/paper/2411.10203/integrity","json":"/paper/2411.10203/citation-record.json","paper":"/paper/2411.10203"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:56:17.766752Z","title":"Catgrasp: Learning category-level task-relevant grasping in clutter from simulation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.766752Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:28b3174bfc6e186bebc7b3e86e25e18c11156c303c178d61159f05d4e444c15f","observation_id":"abe03853-51f0-4a8a-9aef-0118578691f2","resolution":{"observed_at":"2026-08-12T19:56:17.766752Z","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":"2026-08-12T19:56:18.274938Z","title":"You only demonstrate once: Category-level manipulation from single visual demonstration,","venue":null,"work_id":"d761c262-e2e3-4000-b67f-e75c78c4f520","year":2022},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.770754Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:050cf1ae48d254e7f144b98ed611da35059a5911fd3db58ce23538decdc69e2f","observation_id":"412a5734-9143-4e1a-8131-4be7893b7979","resolution":{"observed_at":"2026-08-12T19:56:18.279040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:18.263660Z","title":"Autonomous manipulation learning for similar deformable objects via only one demonstration,","venue":null,"work_id":"8cc79b5c-4da5-4f44-b448-b8d7b48c8943","year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.774445Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:0a0a5936a990ebf566f837a13c899fd2a66aceaf4aaab5bab4b600fb72935c25","observation_id":"7e1c54d2-fca3-4b9a-b71f-60a31acac6bc","resolution":{"observed_at":"2026-08-12T19:56:18.267430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:18.253341Z","title":"Instructing robots by sketching: Learning from demonstration via probabilistic diagram- matic teaching,","venue":null,"work_id":"d33c399e-8ce8-4e64-9365-eef78856748d","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.778071Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:72a32eeb6aac1c1a062576ebb16cb5c0afc93b8ced1723658655ee99d7c77e40","observation_id":"24ec20c1-e762-4ec2-b567-953beb61a4a4","resolution":{"observed_at":"2026-08-12T19:56:18.257232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.14070","last_updated":"2021-06-26T17:54:16Z","snapshot_observed_at":"2026-08-15T09:20:36.992882Z","submitted_at":"2021-06-26T17:54:16Z","title":"Vision-driven Compliant Manipulation for Reliable, High-Precision Assembly Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.14070","snapshot_observed_at":"2026-08-12T19:56:17.781786Z","title":"Vision-driven compliant manipulation for reliable, high- precision assembly tasks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.781786Z"},"links":{"cited_paper":"/paper/2106.14070","citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:e6ea3ccddbd27d7bbb4ca5817264fe35642b43a7516d42bbceb8e0530db1f34d","observation_id":"eb72a421-20bd-4b77-9a24-cc86e1e7c41e","resolution":{"observed_at":"2026-08-12T19:56:17.781786Z","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":"2026-08-12T19:56:18.242353Z","title":"Multi-level reasoning for robotic assembly: From sequence inference to contact selection,","venue":null,"work_id":"f3439444-a06c-4b89-bf7f-0e9d7d231f2e","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.785818Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:08c93551a43926ca5d3a51faafb699b2431736024f3f186b63eb97937947e96a","observation_id":"b422c772-4926-4268-b425-da8cd20187ea","resolution":{"observed_at":"2026-08-12T19:56:18.246154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12716","last_updated":"2022-05-06T14:46:46Z","snapshot_observed_at":"2026-08-13T16:50:28.300300Z","submitted_at":"2022-01-30T03:59:14Z","title":"You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12716","snapshot_observed_at":"2026-08-12T19:56:17.789489Z","title":"You only demonstrate once: Category-level manipulation from single visual demonstration,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.789489Z"},"links":{"cited_paper":"/paper/2201.12716","citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:4ccb0a350c7f1f5dd8e41919856ba0102e63e4a7cbc7885ae7145e903b32a882","observation_id":"5c01087d-c641-4805-bc37-e5766dd28ac6","resolution":{"observed_at":"2026-08-12T19:56:17.789489Z","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":"2026-08-12T19:56:18.231130Z","title":"Miles: Making imitation learning easy with self-supervision,","venue":null,"work_id":"5bfc5e4e-c2d9-40c2-887e-4c98616bd2f3","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.793296Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:b06336c2e80a64c644dd14bdca4e467ea783977aac700398d6f8ab2b25e26f88","observation_id":"efdf94dc-e0ab-4463-b6be-e89ff3dd163a","resolution":{"observed_at":"2026-08-12T19:56:18.235810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:18.220883Z","title":"Mail: Improving im- itation learning with selective state space models,","venue":null,"work_id":"7b82bc9a-dd86-4893-bfcf-3ef3ad2e67b4","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.796546Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:467b94242d5bfed53db99fe31181123a88496df144cb3da27a787e0c8848e84d","observation_id":"021d148d-caa3-4f22-9f8a-2bad2300384b","resolution":{"observed_at":"2026-08-12T19:56:18.224527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.799831Z","title":"Diffusion policy: Visuomotor policy learning via action diffusion,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.799831Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:0c52a01871761e6d337f5b329e3d665f0d2f37ae19025ac3cf2f67d1e7902f77","observation_id":"8911676b-bfb9-4f8f-bd1d-476485cb6e30","resolution":{"observed_at":"2026-08-12T19:56:17.799831Z","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":"2026-08-12T19:56:17.802978Z","title":"3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.802978Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:1cb7e9e36a2ac50412ea08cb7749f6eafffeaf3baef837f21a56eccc724ad36b","observation_id":"49f0d00b-d58f-40a8-9ac1-5da5fea31d4d","resolution":{"observed_at":"2026-08-12T19:56:17.802978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01586","last_updated":"2025-09-06T02:40:42Z","snapshot_observed_at":"2026-08-16T13:46:36.463979Z","submitted_at":"2024-06-03T17:59:23Z","title":"ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01586","snapshot_observed_at":"2026-08-12T19:56:17.806468Z","title":"Manicm: Real-time 3d diffusion policy via consistency model for robotic manipulation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.806468Z"},"links":{"cited_paper":"/paper/2406.01586","citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:9a1fbccc3e11bbe1efdb19ecfae95b01e06c1f5227c0d196ba3420533e2c6530","observation_id":"cf6fad84-855d-4879-855f-904fb82457d9","resolution":{"observed_at":"2026-08-12T19:56:17.806468Z","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":"2026-08-12T19:56:18.198659Z","title":"Commonsense spatial knowledge-aware 3-d human motion and object interaction prediction,","venue":null,"work_id":"f9ee20ae-5361-4987-b8fe-bea9cc0b6ddc","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.810078Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:aeb11711bf5ecc4223d28e1080eb5f1f0289f0d332da8ae8d7f58240105b0b2b","observation_id":"57a0c473-2770-4c2f-a62e-bf1c47846f79","resolution":{"observed_at":"2026-08-12T19:56:18.202170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:18.187137Z","title":"Grasp manipulation relationship detection based on graph sample and aggregation,","venue":null,"work_id":"20448f97-62d3-4a5e-846a-3764a4f70597","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.813382Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:e41dfb897e24c77df13a63595a82364143b07afc05263cc06abef9f6cf520de9","observation_id":"4e7c83e0-94bc-4f33-994a-7f21ec86bf28","resolution":{"observed_at":"2026-08-12T19:56:18.191656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01029","last_updated":"2024-10-17T19:05:50Z","snapshot_observed_at":"2026-08-16T13:46:52.868115Z","submitted_at":"2024-06-03T06:24:55Z","title":"CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos","version":4},"cited_work":{"arxiv_id":"2406.01029","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.01029","snapshot_observed_at":"2026-08-12T19:56:17.950415Z","title":"CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos","venue":"cs.CV","work_id":"65a8e535-4830-4f53-931b-a60a6ff5c28a","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.816739Z"},"links":{"cited_paper":"/paper/2406.01029","citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:4ef9e059c982a13dca4ee39fde69655b354883fda7d2e9af5c65eb3e3bc84f39","observation_id":"ce0d29cc-02a4-4b8e-a1b3-aedc66b08a53","resolution":{"observed_at":"2026-08-12T19:56:17.956521Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.820625Z","title":"Cliport: What and where pathways for robotic manipulation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.820625Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:3d5d968c1c3e2d12de8c45bfa32ac692916f59ef5ecf2bab5fd5c717f5fd72f9","observation_id":"ef94e301-ab44-4d48-ad5a-68ef39d64336","resolution":{"observed_at":"2026-08-12T19:56:17.820625Z","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":"2026-08-12T19:56:18.169025Z","title":"Mechanical intelligence for prehensile in-hand manipulation of spatial trajectories,","venue":null,"work_id":"a3eef1fc-0ce1-4048-bbc8-dd400d8e7ce4","year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.823758Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:d2a07e28a493e49f7203893938528e877f586c904f29505eb19cc1c7648088a4","observation_id":"d057dc7b-52eb-4b0c-9a63-b70e3995a195","resolution":{"observed_at":"2026-08-12T19:56:18.174142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.827190Z","title":"Dall-e-bot: Introducing web- scale diffusion models to robotics,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.827190Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:4d8b1a02f0e8169239abc14385f4583d860191c13be77c3eab1229d1b8c8e407","observation_id":"9714c296-96bf-4073-a17c-a56e291232b2","resolution":{"observed_at":"2026-08-12T19:56:17.827190Z","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":"2026-08-12T19:56:18.152372Z","title":"Playfusion: Skill acquisition via diffusion from language-annotated play,","venue":null,"work_id":"88ed495d-66af-4137-adae-c42d6151123e","year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.830421Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:6e73005dd83df96c0431e173713eb4649d90fb51e0f659fb0770f308d5fec126","observation_id":"5af0ef49-bf6a-4c7b-95e1-8e2b973e2d1a","resolution":{"observed_at":"2026-08-12T19:56:18.156253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.834068Z","title":"Generative skill chaining: Long-horizon skill planning with diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.834068Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:b84e947be942f020aabd5bdf39b96ac1a44e7a182acbb98f1a1963f15a46c660","observation_id":"6c17b298-99b8-4ba2-8c47-22a791e36f99","resolution":{"observed_at":"2026-08-12T19:56:17.834068Z","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":"2026-08-12T19:56:18.135516Z","title":"Movement primitive diffusion: Learning gentle robotic manipulation of deformable objects,","venue":null,"work_id":"6568e253-77fa-474c-a05c-3dbf3dcf92d0","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.837278Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:30afa2bee2488e7084bf5ec921430c1083204292360fc3aa1f3932bd5085a5f7","observation_id":"edb610bc-b7a4-4c09-8a24-f4b12d6cbe85","resolution":{"observed_at":"2026-08-12T19:56:18.139520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:18.124685Z","title":"Crossway diffusion: Im- proving diffusion-based visuomotor policy via self-supervised learn- ing,","venue":null,"work_id":"dcdf4af6-ebf9-4e3c-9d1b-f6b417de5923","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.840554Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:5a720c97a2b7df60fcb9b995a5e3cf2ecab05f4dac94d6280ed2512c706d8887","observation_id":"fea9054c-9ac1-4bd3-afcf-7aeef34aca7d","resolution":{"observed_at":"2026-08-12T19:56:18.128817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.843882Z","title":"Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.843882Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:fb2975302668a06b5e123c4a046cf9542b8a998b865ce4f9f64473e57c391220","observation_id":"dc9bfebb-49b0-4387-9cbd-56ecef81da3c","resolution":{"observed_at":"2026-08-12T19:56:17.843882Z","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":"2026-08-12T19:56:17.847121Z","title":"Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.847121Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:9f7f135c8bdd9cf6e4150d183befb9d66f8c784cf75be18a3107c54c82fc161e","observation_id":"f30bdb64-3e8b-4d9c-9a68-994a01c48627","resolution":{"observed_at":"2026-08-12T19:56:17.847121Z","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":"2026-08-12T19:56:17.850305Z","title":"Scaling up and distilling down: Language-guided robot skill acquisition,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.850305Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:37d3150b9945f8a8145da1e62c58e642d9c6cf488d2fa498ec58a804195f11bc","observation_id":"c9df1b1d-d738-47f8-a187-3dc18b707bb9","resolution":{"observed_at":"2026-08-12T19:56:17.850305Z","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":"2026-08-12T19:56:18.096027Z","title":"Xskill: Cross em- bodiment skill discovery,","venue":null,"work_id":"b017837a-e84e-4aa9-a245-39c68bf600b6","year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.853587Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:ef9c93e8ffd27a2bb3017d6b40235afcb7f82603200649127899dd1cb2600dd4","observation_id":"25c5f377-a510-4888-baee-b813ac45e806","resolution":{"observed_at":"2026-08-12T19:56:18.099649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:18.085226Z","title":"Chaineddiffuser: Unifying trajectory diffusion and keypose predic- tion for robotic manipulation,","venue":null,"work_id":"f7f26239-c095-47b1-a477-4a46f7b2c47e","year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.856845Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:b56d42380f17a7d1acfef1fc9704f43f119e548339798838c297fb027c2dfe94","observation_id":"25e374a2-e510-4d58-9065-a5597cb0bfe8","resolution":{"observed_at":"2026-08-12T19:56:18.089144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.860400Z","title":"Act3d: 3d feature field transformers for multi-task robotic manipulation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.860400Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:2c0f46d2d2438479904921abf86bb6962d387f0f2d7ea2aa417d7024df637ce3","observation_id":"7bbef4cd-527a-4ab0-8ff3-deb778505f3b","resolution":{"observed_at":"2026-08-12T19:56:17.860400Z","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":"2026-08-12T19:56:18.068288Z","title":"Learning generalizable manipulation policies with object-centric 3d representations,","venue":null,"work_id":"cb2bf04d-8427-4efb-bd81-4dee9b28cceb","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.863573Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:2a4be250866c3fb9b80778723f3409fc09993b0914fe8736bb1e8756ec30ca89","observation_id":"31f3936a-0ea2-4df5-992e-5a4d2f61c6b1","resolution":{"observed_at":"2026-08-12T19:56:18.072446Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.866842Z","title":"Hierarchical diffu- sion policy for kinematics-aware multi-task robotic manipulation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.866842Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:cd79856926bcc1001514775836bd04a783e8a23ea4ef2047a60a146b26b60bfd","observation_id":"bacae316-f0f1-4b9c-8efa-a9218e1c63a3","resolution":{"observed_at":"2026-08-12T19:56:17.866842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20321","last_updated":"2025-09-04T08:23:37Z","snapshot_observed_at":"2026-08-16T13:47:38.229864Z","submitted_at":"2024-05-30T17:56:54Z","title":"Vision-based Manipulation from Single Human Video with Open-World Object Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20321","snapshot_observed_at":"2026-08-12T19:56:17.870023Z","title":"Vision-based manipulation from single human video with open-world object graphs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.870023Z"},"links":{"cited_paper":"/paper/2405.20321","citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:af52659b933b477b641ab5bf7da860aaec1f60985cb754a81b9be4dd09c8c44b","observation_id":"fc440a6d-67d4-47b7-8246-6548d8bfcc92","resolution":{"observed_at":"2026-08-12T19:56:17.870023Z","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":"2026-08-12T19:56:17.873529Z","title":"Adding conditional control to text-to-image diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.873529Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:a714b8be73ef6dd13d2482ba37ac927e20712ed68606c3c961648c0effc974de","observation_id":"a0c1f71f-7137-457f-9220-81884f31d041","resolution":{"observed_at":"2026-08-12T19:56:17.873529Z","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":"2026-08-12T19:56:18.046117Z","title":"Emergent correspondence from image diffusion,","venue":null,"work_id":"751cee2f-7b49-48a7-80bd-16e4b24c295a","year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.876740Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:0d6cb1a0135972441187801e7141c040458df5f9747e2a21c6ae8cbc31564bcb","observation_id":"c8112bb5-c7e3-46df-a8a6-e169cbceb4bb","resolution":{"observed_at":"2026-08-12T19:56:18.049762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:18.035749Z","title":"Plug-and-play diffusion features for text-driven image-to-image translation,","venue":null,"work_id":"1ef1ef39-668a-4bf5-b27b-ddbcc897a3ce","year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.880192Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:8165285f1a81578ae088801abd1a99079b589f4f1cd9641e28d4c6d6e1a7c019","observation_id":"6bffcec2-c115-4987-9cc8-aa64e04ce5d5","resolution":{"observed_at":"2026-08-12T19:56:18.039365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.883430Z","title":"Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.883430Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:e55d7ee47c915402d95015e930a62f3a46c5f62634f28511fecc9e787941b7f0","observation_id":"adc23130-5b2c-4113-8681-ba43e61531e4","resolution":{"observed_at":"2026-08-12T19:56:17.883430Z","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":"2026-08-12T19:56:17.886796Z","title":"Segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.886796Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:bb67bbe1da7edbbe054acc5f1c6b2bedec650b11b40f161a6de1392508699325","observation_id":"dc304cef-c189-4cf2-85bf-e54f3d41be39","resolution":{"observed_at":"2026-08-12T19:56:17.886796Z","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":"2026-08-12T19:56:18.012937Z","title":"Putting the object back into video object segmentation,","venue":null,"work_id":"291261bb-4c1d-482a-ab98-dc16e815a106","year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.889909Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:1a5e7fe5368478f53fb7579c914a177316176bb3fd108dec604f31e7b8637291","observation_id":"297e4a84-b177-4914-929e-f2280633dd41","resolution":{"observed_at":"2026-08-12T19:56:18.016902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.893049Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.893049Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:2380522046d606b02baac232abe4be32fe019e5ab0b562d063f4561ced288f79","observation_id":"9d3a21c8-b76b-4fe8-baf3-b9b0ecda64d3","resolution":{"observed_at":"2026-08-12T19:56:17.893049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-12T19:56:17.896325Z","title":"Sam 2: Segment anything in images and videos,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.896325Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:8e0a95e30893b0e78cd21e3eb413125358908b88a685b1b814b00f39d76cb3fe","observation_id":"eb7ce4ac-1549-429d-bf6d-62a12b8f4125","resolution":{"observed_at":"2026-08-12T19:56:17.896325Z","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":"2026-08-12T19:56:17.996829Z","title":"Mask dino: Towards a unified transformer-based framework for object detection and segmentation,","venue":null,"work_id":"e22e06dc-d75a-4155-ad5c-127b7656a003","year":2023},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.899953Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:580ea466df0376c95f18f0dd02458ce21dabf97db5cd9a1eaad5c5bbdfbb201d","observation_id":"516a63f7-1dfb-4851-9192-6ea5ee5a9528","resolution":{"observed_at":"2026-08-12T19:56:18.000368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-12T19:56:17.903022Z","title":"Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T19:56:17.903022Z"},"links":{"citing_paper":"/paper/2411.10203"},"observation_digest":"sha256:7ec044e0975ddd37b4280753b6ac127e3399a77354ec7624c2731e4fac556d8b","observation_id":"00debf6e-5a00-4cbe-9281-85bef26fc4e9","resolution":{"observed_at":"2026-08-12T19:56:17.903022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.10203","last_updated":"2024-11-15T14:01:02Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T13:58:07.662650Z","submitted_at":"2024-11-15T14:01:02Z","title":"Learning Generalizable 3D Manipulation With 10 Demonstrations"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":1,"verified_fuzzy":19},"total_outbound_references":41},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2411.10203."}