{"as_of":"2026-08-17T00:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f0db25657504b5ef25979fa16a6e357313581bcf48506f2810b1262ba42abbc9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":17,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":17,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":17,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T04:17:47.318565Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T20:10:07.737880Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-08-04T11:38:12.683587Z","title":null,"venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2510.03896","last_updated":"2026-06-11T03:17:22Z","snapshot_observed_at":"2026-08-13T14:22:12.349032Z","submitted_at":"2025-10-04T18:33:27Z","title":"GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-04T11:38:12.683587Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2510.03896"},"observation_digest":"sha256:2008c7e65c8a8df394f6deaf9c4d346385ab7552be11f35ae5fc8d9bcce2d45c","observation_id":"0a948c24-6221-4b87-8eb5-5b8394f2e3e1","resolution":{"observed_at":"2026-08-04T11:38:12.683587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2604.05226","last_updated":"2026-04-06T22:42:05Z","snapshot_observed_at":"2026-08-11T06:41:30.928596Z","submitted_at":"2026-04-06T22:42:05Z","title":"RoboPlayground: Democratizing Robotic Evaluation through Structured Physical Domains","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T18:46:08.897540Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2604.05226"},"observation_digest":"sha256:24f3411bd376d1c4e73171df056ac9b630e09799e26aac70732456f2386aad5d","observation_id":"7a9ce7ce-9b1d-4f22-a376-d4d2e01ec282","resolution":{"observed_at":"2026-05-11T00:00:52.026819Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2604.19683","last_updated":"2026-04-22T17:44:56Z","snapshot_observed_at":"2026-08-15T06:57:44.935343Z","submitted_at":"2026-04-21T17:05:37Z","title":"Mask World Model: Predicting What Matters for Robust Robot Policy Learning","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T02:14:17.676675Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2604.19683"},"observation_digest":"sha256:f990096abd66114f76d34f4ff582064ea295acbf6e393fb62bdc15d9fd6f57ce","observation_id":"165dcbe5-d8a8-4e00-9c70-f6fd48c289b5","resolution":{"observed_at":"2026-05-11T13:11:06.106855Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2605.12090","last_updated":"2026-05-12T13:10:52Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-12T13:10:52Z","title":"World Action Models: The Next Frontier in Embodied AI","version":1},"reference_index":222,"source":"pdf_text","source_observed_at":"2026-05-13T05:01:16.802019Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2605.12090"},"observation_digest":"sha256:cc58a6893f13dcd33298aaf8f1336b559995f3871283f272a5413501260e7ede","observation_id":"866505f8-ed92-4d4a-a1c6-1e824245d9fa","resolution":{"observed_at":"2026-05-13T05:07:18.030614Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2606.06491","last_updated":"2026-07-19T18:14:26Z","snapshot_observed_at":"2026-08-02T12:17:07.877886Z","submitted_at":"2026-06-04T17:59:40Z","title":"TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-28T01:14:14.972805Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2606.06491"},"observation_digest":"sha256:c85415958b8fa79c3a044069a4e7c5bd4c11f7456a1e06ffbba095bcd3270d39","observation_id":"307d186e-1329-4890-a476-5545dd1d9a9b","resolution":{"observed_at":"2026-07-02T13:36:58.830410Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-08-02T12:17:09.420240Z","title":"James, Z","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2606.06491","last_updated":"2026-07-19T18:14:26Z","snapshot_observed_at":"2026-08-02T12:17:07.877886Z","submitted_at":"2026-06-04T17:59:40Z","title":"TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T12:17:09.420240Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2606.06491"},"observation_digest":"sha256:dc6573935a47e462514a45d6f5e5c01299f57635396de9289f74cfeb8c14ec5c","observation_id":"df314f4e-5f2c-4ab5-a2c1-3e61f592a571","resolution":{"observed_at":"2026-08-02T12:17:09.420240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2606.17446","last_updated":"2026-06-16T03:00:58Z","snapshot_observed_at":"2026-08-12T12:09:17.391394Z","submitted_at":"2026-06-16T03:00:58Z","title":"AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-06-27T01:15:50.904741Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2606.17446"},"observation_digest":"sha256:4b6bacade4962c08b177142f50eef211c8ac450fca0b4d2e2fdf9df442c7a5c8","observation_id":"9b493b7f-7602-43e8-b9e8-84d9e3f1b7f3","resolution":{"observed_at":"2026-07-03T20:38:55.416307Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2606.17511","last_updated":"2026-06-16T04:42:43Z","snapshot_observed_at":"2026-08-15T04:28:13.640734Z","submitted_at":"2026-06-16T04:42:43Z","title":"MagicSim: A Unified Infrastructure for Executable Embodied Interaction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-27T01:00:07.465292Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2606.17511"},"observation_digest":"sha256:6a612a9ff2d20077c8c6c6a2b526db8feabe14b0020530473642c483c86d82d9","observation_id":"0a92a2f1-7391-4dc8-ad23-6a4c30079db0","resolution":{"observed_at":"2026-07-03T20:58:58.018952Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2606.25939","last_updated":"2026-06-26T02:40:52Z","snapshot_observed_at":"2026-08-11T15:07:35.049158Z","submitted_at":"2026-06-24T15:15:38Z","title":"DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-25T20:38:40.242221Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2606.25939"},"observation_digest":"sha256:6507695424e32c10b54e1233b1f7517d9f499eabe03e3eba39e07a66cfaefa71","observation_id":"6d87baf5-341c-48cc-9610-d65a2fe32eec","resolution":{"observed_at":"2026-07-04T20:10:07.739719Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2606.25939","last_updated":"2026-06-26T02:40:52Z","snapshot_observed_at":"2026-08-11T15:07:35.049158Z","submitted_at":"2026-06-24T15:15:38Z","title":"DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T04:53:12.335945Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2606.25939"},"observation_digest":"sha256:22e55c2c044cb41848c6c2f62a805e250ce429458eb93984d6118e0fc8033b5f","observation_id":"3f2ad7a9-4304-4285-9335-943b472b0cb3","resolution":{"observed_at":"2026-06-29T19:13:53.023113Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2606.26694","last_updated":"2026-06-28T04:16:09Z","snapshot_observed_at":"2026-08-14T14:01:42.165937Z","submitted_at":"2026-06-25T07:27:09Z","title":"PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-06-26T05:46:21.198781Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2606.26694"},"observation_digest":"sha256:276f06079274b61d4bf31dc81cc5939851fda3d4eb3a66d4d7d5373299289f2e","observation_id":"0f98de11-ba13-4468-8cb9-a12ad5c77127","resolution":{"observed_at":"2026-07-04T12:49:53.086502Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":"1909.12271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-04T20:10:07.737880Z","title":"Jia, Y ., Liu, J., Liu, S., Zhou, R., Yu, W., Yan, Y ., Chi, X., Guo, Y ., Shi, B., and Zhang, S","venue":null,"work_id":"a02aec79-052b-435e-8df1-1927cc82fd3d","year":1909},"citing_paper":{"arxiv_id":"2606.26694","last_updated":"2026-06-28T04:16:09Z","snapshot_observed_at":"2026-08-14T14:01:42.165937Z","submitted_at":"2026-06-25T07:27:09Z","title":"PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-06-30T10:19:06.268547Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2606.26694"},"observation_digest":"sha256:c87993f73ed6cbcd8909b744776277545ed213a96a53f569454e9e2f58b41b4c","observation_id":"d02c8927-2f2c-4a2c-9e50-1d9f9357d209","resolution":{"observed_at":"2026-06-30T12:04:39.308594Z","resolver_source":"arxiv_id","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":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-11T19:13:23.494763Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.04434","last_updated":"2026-07-05T17:58:02Z","snapshot_observed_at":"2026-08-13T01:18:42.931884Z","submitted_at":"2026-07-05T17:58:02Z","title":"RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-11T19:13:23.494763Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2607.04434"},"observation_digest":"sha256:90701549155801d0b6ad61b4ce9cb33ac32c46b954dda71acebd67a23f9b8637","observation_id":"82ab1d84-6500-4383-979a-40bc24d5f346","resolution":{"observed_at":"2026-07-11T19:13:23.494763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-07-31T06:18:55.622068Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.24744","last_updated":"2026-08-08T15:15:40Z","snapshot_observed_at":"2026-08-16T11:31:42.282108Z","submitted_at":"2026-07-27T17:59:58Z","title":"Data Pyramid for Embodied Manipulation: A Survey","version":1},"reference_index":168,"source":"pdf_text","source_observed_at":"2026-07-31T06:18:55.622068Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2607.24744"},"observation_digest":"sha256:bf7efd758dbd8639d32a7001c6a6192fb5c57520b65f1e2a77cfcbbfc9ca7e6d","observation_id":"68a3a9bf-82bc-4e18-84d1-ed29a5c5571b","resolution":{"observed_at":"2026-07-31T06:18:55.622068Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-08-04T19:45:34.775883Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.01851","last_updated":"2026-08-03T07:58:35Z","snapshot_observed_at":"2026-08-15T02:22:30.824958Z","submitted_at":"2026-08-03T07:58:35Z","title":"Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills","version":1},"reference_index":105,"source":"pdf_text","source_observed_at":"2026-08-04T19:45:34.775883Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2608.01851"},"observation_digest":"sha256:99c5d3377fe2461fdea41974037751103c49976e77befb723a126e786e264f84","observation_id":"e7a7163b-cab0-4037-a2b5-9ecfdbf7b92b","resolution":{"observed_at":"2026-08-04T19:45:34.775883Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-08-11T04:54:23.820537Z","title":"Rlbench: The robot learning benchmark & learning environment,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.09892","last_updated":"2026-08-11T17:41:52Z","snapshot_observed_at":"2026-08-16T15:34:10.165022Z","submitted_at":"2026-08-10T17:41:04Z","title":"XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T04:54:23.820537Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2608.09892"},"observation_digest":"sha256:7b26b0bc3bb001979395ef207930107311c74934988267b16f4f8ae860d6d060","observation_id":"6da7c31f-7f3c-42ac-b73e-1d35b270a58d","resolution":{"observed_at":"2026-08-11T04:54:23.820537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.12271","snapshot_observed_at":"2026-08-14T04:17:47.318565Z","title":"Rlbench: The robot learning benchmark & learning environment,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.09892","last_updated":"2026-08-11T17:41:52Z","snapshot_observed_at":"2026-08-16T15:34:10.165022Z","submitted_at":"2026-08-10T17:41:04Z","title":"XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T04:17:47.318565Z"},"links":{"cited_paper":"/paper/1909.12271","citing_paper":"/paper/2608.09892"},"observation_digest":"sha256:815627f4a6ebba35992693e09a0d041b78c338266803a30caef85de2fe57f91b","observation_id":"1bbf6d81-4430-4c01-85b1-b978df09bf9d","resolution":{"observed_at":"2026-08-14T04:17:47.318565Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1909.12271/citation-record","integrity":"/paper/1909.12271/integrity","json":"/paper/1909.12271/citation-record.json","paper":"/paper/1909.12271"},"outbound":[],"paper":{"arxiv_id":"1909.12271","last_updated":"2019-09-26T17:26:18Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-16T18:33:23.994288Z","submitted_at":"2019-09-26T17:26:18Z","title":"RLBench: The Robot Learning Benchmark & Learning Environment"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:1909.12271."}