{"as_of":"2026-08-18T15:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:029c4904463d73e6ece9e97cd73df4b6dcb2a87a52562d63cbc092ace8bacde9","coverage":[{"denominator":107,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:17:13.536817Z","state":"measured"},{"denominator":102,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":102,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T06:18:55.493101Z","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":"2026-05-13T05:07:18.165010Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"cited_work":{"arxiv_id":"2506.10966","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.10966","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"GenManip: LLM-driven simulation for generalizable instruction-following manipulation","venue":null,"work_id":"43fe298b-8e93-49ee-8208-e9a4bc059400","year":2025},"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":239,"source":"pdf_text","source_observed_at":"2026-05-13T05:01:16.802019Z"},"links":{"cited_paper":"/paper/2506.10966","citing_paper":"/paper/2605.12090"},"observation_digest":"sha256:55413575f4a550162d697c3ce2b45ca279d3253ec1440d049928459cd7114c74","observation_id":"ae9a638b-a8ec-48e6-af85-d3d2c2808645","resolution":{"observed_at":"2026-05-13T05:07:18.166697Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.10966","snapshot_observed_at":"2026-07-31T06:18:55.493101Z","title":"GENMANIP: LLM-driven simulation for generalizable instruction-following manipulation","venue":null,"work_id":null,"year":2025},"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":117,"source":"pdf_text","source_observed_at":"2026-07-31T06:18:55.493101Z"},"links":{"cited_paper":"/paper/2506.10966","citing_paper":"/paper/2607.24744"},"observation_digest":"sha256:2429f23ed1821d0d72acca16a554520726a87582d7683531e09094c9a3de9e31","observation_id":"cb854878-da0f-4c31-a11e-b13e85eefffa","resolution":{"observed_at":"2026-07-31T06:18:55.493101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.10966/citation-record","integrity":"/paper/2506.10966/integrity","json":"/paper/2506.10966/citation-record.json","paper":"/paper/2506.10966"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:17:07.236363Z","title":"Pddl— the planning domain definition language","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.236363Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:d8a5d8ec025516a1e3906665be490cde61a93d7fad1d2e094cb8ad7146fa171e","observation_id":"83198efd-4f67-47da-9516-c8cd967ef32f","resolution":{"observed_at":"2026-08-07T04:17:07.236363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.06757","last_updated":"2018-07-18T03:28:02Z","snapshot_observed_at":"2026-08-14T11:12:04.949259Z","submitted_at":"2018-07-18T03:28:02Z","title":"On Evaluation of Embodied Navigation Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.06757","snapshot_observed_at":"2026-08-07T04:17:07.337820Z","title":"On evaluation of embodied navigation agents.arXiv preprint arXiv:1807.06757, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.337820Z"},"links":{"cited_paper":"/paper/1807.06757","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:67c9645eae5ee385c5cbc781f1ddc44d7f6d26cf81a8e2df5db124b72c0aba6a","observation_id":"a0c722ae-fbd4-4321-83f9-af4e88afa36f","resolution":{"observed_at":"2026-08-07T04:17:07.337820Z","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":"2026-08-07T04:17:07.421431Z","title":"Claude ai, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.421431Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:b8632902dbe88bca3e65972f2dc3d06f97124095311c022fe0d7423eb15ba4b5","observation_id":"887326da-1fab-4d7e-bf0c-6247328a7101","resolution":{"observed_at":"2026-08-07T04:17:07.421431Z","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":"2026-08-07T04:17:07.496296Z","title":"Track2act: Predicting point tracks from internet videos enables diverse zero-shot robot manip- ulation, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.496296Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:392bdab12464216bcbe59cc98b096962b0f57867701eaa23d204af1dc89f339a","observation_id":"f9234ff2-7d04-4f81-ae9c-a0a4a6a8c999","resolution":{"observed_at":"2026-08-07T04:17:07.496296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06817","last_updated":"2023-08-11T17:45:27Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-12-13T18:55:15Z","title":"RT-1: Robotics Transformer for Real-World Control at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06817","snapshot_observed_at":"2026-08-07T04:17:07.608183Z","title":"Rt-1: Robotics transformer for real-world control at scale","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.608183Z"},"links":{"cited_paper":"/paper/2212.06817","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:e49d2b6188b963d260d2bcb82afb2c9ef768c9f786e34e6fd5bcb52b6c3adec3","observation_id":"87440fc2-79de-4b75-8ff9-eefb01b605d9","resolution":{"observed_at":"2026-08-07T04:17:07.608183Z","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":"2026-08-07T04:17:07.644953Z","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":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.644953Z"},"links":{"cited_paper":"/paper/2307.15818","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:b1470ec5dd700233e2d0007fed2bcb58d4ccc6eebf77410a4e6fde2c3eec1495","observation_id":"50827982-8d42-48a5-875b-9169e3747c9b","resolution":{"observed_at":"2026-08-07T04:17:07.644953Z","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":"2026-08-07T04:17:07.679138Z","title":"Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.679138Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:6e77b0b47c01f40162808d7291d9566200340251e24af1c21af2c72ff139cb22","observation_id":"e76fc7b9-b122-4a4d-9b03-377f18548c91","resolution":{"observed_at":"2026-08-07T04:17:07.679138Z","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":"2026-08-07T04:17:07.742078Z","title":"Procthor: Large-scale embodied ai using procedural generation.Ad- vances in Neural Information Processing Systems, 35:5982– 5994, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.742078Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:ce582a84293d3645d9d1bfa9155d26fe400c78c10dac67fdcefc7477708b7b54","observation_id":"7035777a-4503-4660-834c-6968da6f259f","resolution":{"observed_at":"2026-08-07T04:17:07.742078Z","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":"2026-08-07T04:17:07.805487Z","title":"Objaverse: A universe of annotated 3d objects","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.805487Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:6f9cdf0c263d80ac5c0ad416b7b46fcd92e8e4f29f509b9dd19b779f2d8aedc9","observation_id":"7fd3207c-a4dc-482f-a723-eb3c84233b7b","resolution":{"observed_at":"2026-08-07T04:17:07.805487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.13303","last_updated":"2024-08-13T17:20:34Z","snapshot_observed_at":"2026-08-16T14:33:09.815860Z","submitted_at":"2023-12-19T23:11:06Z","title":"RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios","version":2},"cited_work":{"arxiv_id":"2312.13303","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.13303","snapshot_observed_at":"2026-08-07T04:17:14.073450Z","title":"RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios","venue":"cs.LG","work_id":"9cfbed93-cc0f-4ff8-ae70-10d515b5287d","year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.854351Z"},"links":{"cited_paper":"/paper/2312.13303","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:e423d35a55c01a6867ff0eae2c8826ab9ececc77f892753e53cc3e1ff5fedbd7","observation_id":"1cdb4ea3-49fc-4b21-9ac8-6d3c8f868483","resolution":{"observed_at":"2026-08-07T04:17:14.079113Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:07.913806Z","title":"A survey on safety-critical driving scenario generation—a methodological perspective.IEEE Transactions on Intelligent Transportation Systems, 24(7): 6971–6988, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.913806Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:6aa58f9b8a057c7c4f84631436f40bb3dd784e1cb77764f69d170ffd3a9438ee","observation_id":"0be860bb-0e51-4034-ab53-f960892040f0","resolution":{"observed_at":"2026-08-07T04:17:07.913806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18915","last_updated":"2024-08-29T16:07:30Z","snapshot_observed_at":"2026-08-16T13:39:12.239182Z","submitted_at":"2024-06-27T06:12:01Z","title":"Manipulate-Anything: Automating Real-World Robots using Vision-Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.18915","snapshot_observed_at":"2026-08-07T04:17:07.939920Z","title":"Manipulate- anything: Automating real-world robots using vision- language models.arXiv preprint arXiv:2406.18915, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.939920Z"},"links":{"cited_paper":"/paper/2406.18915","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:c1fbf3c2820eb32f6335344b6e0ae485cf5a465a6c2eeb3bbb75ba973c37a948","observation_id":"cccfe068-1b98-40b2-8c65-3d6f8c0ad2a7","resolution":{"observed_at":"2026-08-07T04:17:07.939920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.04382","last_updated":"2024-05-02T16:17:25Z","snapshot_observed_at":"2026-08-16T17:01:35.444955Z","submitted_at":"2022-05-09T15:35:33Z","title":"FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.04382","snapshot_observed_at":"2026-08-07T04:17:07.993358Z","title":"Flowbot3d: Learning 3d articulation flow to manipulate articulated ob- jects.arXiv preprint arXiv:2205.04382, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:07.993358Z"},"links":{"cited_paper":"/paper/2205.04382","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:5613d26ffa8074e1d461238cd3409e4118b68a2d92727fa03525323b50e1c3d0","observation_id":"24135056-5e69-46ee-88b9-2f6a495e8dba","resolution":{"observed_at":"2026-08-07T04:17:07.993358Z","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":"2026-08-07T04:17:08.038497Z","title":"Anygrasp: Robust and efficient grasp perception in spa- tial and temporal domains.IEEE Transactions on Robotics,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.038497Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:3524fc887944f5535268b240178768cf7ad03564543641be24a775a72b430410","observation_id":"c2d2de9c-fe83-4da3-9b74-731913b6c216","resolution":{"observed_at":"2026-08-07T04:17:08.038497Z","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":"2026-08-07T04:17:08.103878Z","title":"Active task randomization: Learning visuomotor skills for sequential manipulation by proposing feasible and novel tasks.CoRR, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.103878Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:4fc545ac917a53ffc00791c0db11133317f46df1d312282051b4a1aea22cbabf","observation_id":"72e04039-40be-432b-a06c-a442fb39d959","resolution":{"observed_at":"2026-08-07T04:17:08.103878Z","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":"2026-08-07T04:17:08.156813Z","title":"Helix: A vision-language-action model for gen- eralist humanoid control, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.156813Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:b6356ef603755e0e4b359ec41e81a0c041dcb1460296bf5a0b667d6ef26c1f13","observation_id":"5488a891-ce19-4383-8ae0-5e7a7c15bb69","resolution":{"observed_at":"2026-08-07T04:17:08.156813Z","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":"2026-08-07T04:17:08.211902Z","title":"Scenic: a language for scenario specification and scene generation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.211902Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:0d7288c9fc9e3993d4c85d1fea5b77b6770aceb3428819ab5a95dfb6d2823426","observation_id":"6e33a08d-0799-4257-a1ac-d61e4bb972c5","resolution":{"observed_at":"2026-08-07T04:17:08.211902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02117","last_updated":"2024-01-04T07:55:53Z","snapshot_observed_at":"2026-08-15T12:54:53.410743Z","submitted_at":"2024-01-04T07:55:53Z","title":"Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02117","snapshot_observed_at":"2026-08-07T04:17:08.242838Z","title":"Mobile aloha: Learning bimanual mobile manipulation with low-cost whole-body teleoperation.arXiv preprint arXiv:2401.02117,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.242838Z"},"links":{"cited_paper":"/paper/2401.02117","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:063ab659092a9dc64f6a2eaa45a35e9ebb91027cae8e1052aaab938056089003","observation_id":"c932b5f8-00f3-4d24-bb46-3c2d358a1cca","resolution":{"observed_at":"2026-08-07T04:17:08.242838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.01345","last_updated":"2025-03-01T22:11:10Z","snapshot_observed_at":"2026-08-16T13:13:27.446052Z","submitted_at":"2024-10-02T09:02:34Z","title":"Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.01345","snapshot_observed_at":"2026-08-07T04:17:08.311238Z","title":"To- wards generalizable vision-language robotic manipulation: A benchmark and llm-guided 3d policy.arXiv preprint arXiv:2410.01345, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.311238Z"},"links":{"cited_paper":"/paper/2410.01345","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:a7de1fae0ff62e7bfaea10dbe864e2a5aadf3109739aea92d7d09537de58f21a","observation_id":"5e501f3d-1b26-4343-a987-f881375e7591","resolution":{"observed_at":"2026-08-07T04:17:08.311238Z","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":"2026-08-07T04:17:08.373688Z","title":"Skillmimicgen: Automated demonstration generation for efficient skill learning and deployment","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.373688Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:4a876f836870b3d3fb64838b139097f57f457bbafebacd00b18b3d0455be0e8b","observation_id":"96d067c9-e8c0-4273-b492-d399457ccf5c","resolution":{"observed_at":"2026-08-07T04:17:08.373688Z","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":"2026-08-07T04:17:08.397256Z","title":"Gapartnet: Cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.397256Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:aff06d8f314bbdde835a9f43c08f99100fc2f4215a19c6ab87e112ae0d87d809","observation_id":"ef1acecb-2ba5-4162-b6af-facb2e276ed8","resolution":{"observed_at":"2026-08-07T04:17:08.397256Z","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":"2026-08-07T04:17:08.461345Z","title":"Arnold: A benchmark for language-grounded task learning with con- tinuous states in realistic 3d scenes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.461345Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:7dd3b36cdfbbe41f269fae13c62a366913ee897208466103dbd7b522fc7f35db","observation_id":"38d23a0c-2895-41d1-9af0-0b598be93c26","resolution":{"observed_at":"2026-08-07T04:17:08.461345Z","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":"2026-08-07T04:17:08.529755Z","title":"Gemini api, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.529755Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:dcfb2063d6f8582e2aec0044c50a71358d342eee2a793beb91da1b6a15b32ee8","observation_id":"c2fb5500-db8b-4096-979a-2bc8559e0e04","resolution":{"observed_at":"2026-08-07T04:17:08.529755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08545","last_updated":"2024-06-12T18:00:01Z","snapshot_observed_at":"2026-08-16T13:43:37.560927Z","submitted_at":"2024-06-12T18:00:01Z","title":"RVT-2: Learning Precise Manipulation from Few Demonstrations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08545","snapshot_observed_at":"2026-08-07T04:17:08.566688Z","title":"Rvt-2: Learning precise manipulation from few demonstrations.arXiv preprint arXiv:2406.08545, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.566688Z"},"links":{"cited_paper":"/paper/2406.08545","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:d0701dc82048694136267365d86f69af0052d7f83ed7c2970b931a3494b899e5","observation_id":"d899cff8-9b08-4889-892e-3ab1332a6a6e","resolution":{"observed_at":"2026-08-07T04:17:08.566688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01977","last_updated":"2023-11-06T05:53:08Z","snapshot_observed_at":"2026-08-16T14:46:11.532367Z","submitted_at":"2023-11-03T15:31:51Z","title":"RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01977","snapshot_observed_at":"2026-08-07T04:17:08.602998Z","title":"Rt-trajectory: Robotic task generalization via hindsight trajectory sketches","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.602998Z"},"links":{"cited_paper":"/paper/2311.01977","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:c750ff5b03eff682eacf0d95a28fb9fd35121b7f6319cbb38ec1d8ad703f9f5d","observation_id":"734333b9-a33e-44a7-9c24-c695df072297","resolution":{"observed_at":"2026-08-07T04:17:08.602998Z","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":"2026-08-07T04:17:08.674560Z","title":"Maniskill2: A unified benchmark for generalizable manipulation skills","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.674560Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:3cc2c1e79c95d1c015d8c2d429540c1e19a0dbf88197b901f36ecfe491b01400","observation_id":"e84efe59-9cf7-4bca-a54e-9f4be7b19cd9","resolution":{"observed_at":"2026-08-07T04:17:08.674560Z","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":"2026-08-07T04:17:08.717158Z","title":"MPlib: a Lightweight Motion Planning Library,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.717158Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:3dc2d56c7bc4867d7b22fc38bf6067aacbaa98d77829026de9ae623e32a52463","observation_id":"4e52279b-7af8-4914-ba1e-681bb1ec0f3b","resolution":{"observed_at":"2026-08-07T04:17:08.717158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.12821","last_updated":"2023-05-22T08:29:00Z","snapshot_observed_at":"2026-08-16T15:31:11.618471Z","submitted_at":"2023-05-22T08:29:00Z","title":"FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12821","snapshot_observed_at":"2026-08-07T04:17:08.780101Z","title":"Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation.arXiv preprint arXiv:2305.12821, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.780101Z"},"links":{"cited_paper":"/paper/2305.12821","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:1a04d40ab0460e2ed8e060e9e1de772712044fcf813d359afcb231b38a560242","observation_id":"af9d2852-59a8-442a-92d6-75e858a5a0f6","resolution":{"observed_at":"2026-08-07T04:17:08.780101Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17842","last_updated":"2023-12-24T03:48:40Z","snapshot_observed_at":"2026-08-16T14:39:09.587564Z","submitted_at":"2023-11-29T17:46:25Z","title":"Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17842","snapshot_observed_at":"2026-08-07T04:17:08.821795Z","title":"Look before you leap: Unveiling the power of gpt- 4v in robotic vision-language planning.arXiv preprint arXiv:2311.17842, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.821795Z"},"links":{"cited_paper":"/paper/2311.17842","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:c74aca715c02a4f45f033c84a18a7e3e0a90bf04ec60a05eaa7d273a68a1849c","observation_id":"ade70b3d-d5c4-4b51-8e03-abe6fbd8aba5","resolution":{"observed_at":"2026-08-07T04:17:08.821795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.08248","last_updated":"2024-03-13T05:03:58Z","snapshot_observed_at":"2026-08-16T14:10:20.449738Z","submitted_at":"2024-03-13T05:03:58Z","title":"CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08248","snapshot_observed_at":"2026-08-07T04:17:08.863737Z","title":"Copa: General robotic manipulation through spatial constraints of parts with foundation models.arXiv preprint arXiv:2403.08248, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.863737Z"},"links":{"cited_paper":"/paper/2403.08248","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:d0fce8f21db56c9e6b29e83da162448e5a42134e95e16957ac374edd847c4d01","observation_id":"cac4aca0-2baf-4a07-9777-3321c6d5d14d","resolution":{"observed_at":"2026-08-07T04:17:08.863737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12871","last_updated":"2024-05-09T17:35:44Z","snapshot_observed_at":"2026-08-05T10:01:43.401012Z","submitted_at":"2023-11-18T01:21:38Z","title":"An Embodied Generalist Agent in 3D World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12871","snapshot_observed_at":"2026-08-07T04:17:08.926212Z","title":"An embodied generalist agent in 3d world.arXiv preprint arXiv:2311.12871, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:08.926212Z"},"links":{"cited_paper":"/paper/2311.12871","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:9cdda4ec3dc58e84f0af9c374976e267cd481d418a061855431662fa95096324","observation_id":"cf67ed40-80c8-41f3-b643-74c72305a002","resolution":{"observed_at":"2026-08-07T04:17:08.926212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.05973","last_updated":"2023-11-02T06:53:37Z","snapshot_observed_at":"2026-08-05T01:03:23.456778Z","submitted_at":"2023-07-12T07:40:48Z","title":"VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.05973","snapshot_observed_at":"2026-08-07T04:17:09.009338Z","title":"V oxposer: Composable 3d value maps for robotic manipulation with language models.arXiv preprint arXiv:2307.05973, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.009338Z"},"links":{"cited_paper":"/paper/2307.05973","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:429bcd2ec2a8cfad0281ddc77f6c9b6d339726a92eeab16d3858cb75a171bf2a","observation_id":"6dcb539d-fc74-4e67-acff-821cdd2c4b5d","resolution":{"observed_at":"2026-08-07T04:17:09.009338Z","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":"2026-08-07T04:17:09.054421Z","title":"Rlbench: The robot learning benchmark & learning environment.IEEE Robotics and Automation Let- ters, 5(2):3019–3026, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.054421Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:7803f792dd89088e2261b26c8ca01d3e584ee707c379eac4861358bbf5796b5b","observation_id":"838f89b8-a091-4fcb-826c-c708c0522ddf","resolution":{"observed_at":"2026-08-07T04:17:09.054421Z","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":"2026-08-07T04:17:09.108993Z","title":"Sceneverse: Scaling 3d vision-language learning for grounded scene understanding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.108993Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:e5d003c683e50e76412a444f8f06f48e4b7febb1cb26d840e9c54c66e78b0dbb","observation_id":"e90258c3-f4c1-47b7-b191-9def291c62e1","resolution":{"observed_at":"2026-08-07T04:17:09.108993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03094","last_updated":"2023-05-28T07:32:38Z","snapshot_observed_at":"2026-08-16T16:26:20.587132Z","submitted_at":"2022-10-06T17:50:11Z","title":"VIMA: General Robot Manipulation with Multimodal Prompts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03094","snapshot_observed_at":"2026-08-07T04:17:09.193134Z","title":"Vima: General robot manipulation with multimodal prompts.arXiv preprint arXiv:2210.03094, 2(3):6, 2022","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.193134Z"},"links":{"cited_paper":"/paper/2210.03094","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:4aeb462134bae8996291e2ddb0c80be12ef4715a6f118b2d42aa8b02e84ff662","observation_id":"dac035ed-a010-45a7-99d1-4c5ce2b25572","resolution":{"observed_at":"2026-08-07T04:17:09.193134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02643","last_updated":"2023-04-05T17:59:46Z","snapshot_observed_at":"2026-08-08T05:14:59.435033Z","submitted_at":"2023-04-05T17:59:46Z","title":"Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02643","snapshot_observed_at":"2026-08-07T04:17:09.261005Z","title":"Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.261005Z"},"links":{"cited_paper":"/paper/2304.02643","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:ff589faf3fa39b1d74267dd7d60504d4d5a4d3fb214c69a74529e2f813a2d97e","observation_id":"9177a481-ea0a-47c4-bb3c-b158a60b56fe","resolution":{"observed_at":"2026-08-07T04:17:09.261005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05474","last_updated":"2022-08-26T17:12:17Z","snapshot_observed_at":"2026-08-14T05:42:22.751765Z","submitted_at":"2017-12-14T23:17:24Z","title":"AI2-THOR: An Interactive 3D Environment for Visual AI","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05474","snapshot_observed_at":"2026-08-07T04:17:09.324886Z","title":"Ai2-thor: An interactive 3d environment for visual ai.arXiv preprint arXiv:1712.05474,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.324886Z"},"links":{"cited_paper":"/paper/1712.05474","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:7c9039d9321cf40b7059f6507357a683a1a6dde44532c2f96835e2b8408dbf2b","observation_id":"65cf1253-22de-4e62-8a98-264988fc72db","resolution":{"observed_at":"2026-08-07T04:17:09.324886Z","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":"2026-08-07T04:17:09.394021Z","title":"Visual genome: Connecting language and vision using crowdsourced dense image annotations.International journal of computer vision, 123:32–73, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.394021Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:66b02f06183243cbb38f163534fbc84834e7a58672a5d68730df36acef269dd9","observation_id":"68837f12-c908-495f-b6bb-d9170cb052a1","resolution":{"observed_at":"2026-08-07T04:17:09.394021Z","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":"2026-08-07T04:17:09.468992Z","title":"Behavior-1k: A benchmark for embodied ai with 1,000 ev- eryday activities and realistic simulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.468992Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:94738b458487cfe3b60990d6d5812de08bcf22444d556f20424d305cde2f1b18","observation_id":"b3d3f192-855f-4476-9e03-eedaec392207","resolution":{"observed_at":"2026-08-07T04:17:09.468992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01378","last_updated":"2024-02-05T03:46:00Z","snapshot_observed_at":"2026-08-13T00:43:50.403870Z","submitted_at":"2023-11-02T16:34:33Z","title":"Vision-Language Foundation Models as Effective Robot Imitators","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01378","snapshot_observed_at":"2026-08-07T04:17:09.575868Z","title":"Vision-language foun- dation models as effective robot imitators.arXiv preprint arXiv:2311.01378, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.575868Z"},"links":{"cited_paper":"/paper/2311.01378","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:47ead168feb68a0e8674b2baa95adb2ae4c9cfdc6ef6f0bb3a99b7b401a88f13","observation_id":"ebc8f8a7-6d31-4b44-b8b3-bbad4f0e1b1b","resolution":{"observed_at":"2026-08-07T04:17:09.575868Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05941","last_updated":"2024-05-09T17:30:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-09T17:30:16Z","title":"Evaluating Real-World Robot Manipulation Policies in Simulation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05941","snapshot_observed_at":"2026-08-07T04:17:09.609772Z","title":"Evaluating real-world robot manipulation policies in simulation.arXiv preprint arXiv:2405.05941, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.609772Z"},"links":{"cited_paper":"/paper/2405.05941","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:6958717a757b3fdbb2512827f9bb14939d91a415b267c14f7b4a4caf036d8d47","observation_id":"fc24be92-cc92-4975-9336-e082e702db44","resolution":{"observed_at":"2026-08-07T04:17:09.609772Z","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":"2026-08-07T04:17:09.701613Z","title":"Code as policies: Language model programs for embodied control","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.701613Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:a13cc76f241cca2ffff4383148dddbee478c6e879794cc6b3b99b52b87cd07c2","observation_id":"834884a6-b7da-4954-9010-1755546380ad","resolution":{"observed_at":"2026-08-07T04:17:09.701613Z","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":"2026-08-07T04:17:09.706774Z","title":"Libero: Benchmarking knowl- edge transfer for lifelong robot learning.Advances in Neural Information Processing Systems, 36, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.706774Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:e8472baa82686fb95f750a654f7fa1ede67445c0fb17b26c5a5c77492b008aa5","observation_id":"c1d7077c-9c6b-488a-9acf-cd448285f4bf","resolution":{"observed_at":"2026-08-07T04:17:09.706774Z","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":"2026-08-07T04:17:09.751966Z","title":"Moka: Open-vocabulary robotic manipulation through mark-based visual prompting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.751966Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:8848f1188260a62c2d0ad5894f4773e516870c32cdf0c973c53091bc68c60b47","observation_id":"82bfbe20-4ea6-421d-a789-a32169ce83ad","resolution":{"observed_at":"2026-08-07T04:17:09.751966Z","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":"2026-08-07T04:17:09.840480Z","title":"Zero-1-to- 3: Zero-shot one image to 3d object","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.840480Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:6ec44386a4bae33fcfb5b4c53a878bea3c99424276b5d4d089d0cae2c307720e","observation_id":"4d3c53d7-f540-4419-a0fc-bfd8ece47cee","resolution":{"observed_at":"2026-08-07T04:17:09.840480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.10470","last_updated":"2021-08-25T23:42:59Z","snapshot_observed_at":"2026-07-06T11:40:56.544714Z","submitted_at":"2021-08-24T01:38:11Z","title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.10470","snapshot_observed_at":"2026-08-07T04:17:09.941516Z","title":"Isaac gym: High performance gpu-based physics simulation for robot learning.arXiv preprint arXiv:2108.10470, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:09.941516Z"},"links":{"cited_paper":"/paper/2108.10470","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:939f102f2cbbfa559d916ecc64f1956a9c2159442fed751961a7cb5dd92ca6e2","observation_id":"fa66c84f-c25a-41e7-928b-9a61afb10d11","resolution":{"observed_at":"2026-08-07T04:17:09.941516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17596","last_updated":"2023-10-26T17:17:31Z","snapshot_observed_at":"2026-08-13T22:05:40.944747Z","submitted_at":"2023-10-26T17:17:31Z","title":"MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17596","snapshot_observed_at":"2026-08-07T04:17:10.018884Z","title":"Mimicgen: A data generation system for scalable robot learning using human demonstrations.arXiv preprint arXiv:2310.17596, 2023","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.018884Z"},"links":{"cited_paper":"/paper/2310.17596","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:2ce2820c1cca172fa1b60859d221a63971f2ba2ea444047b7fbb7ab65baafc9a","observation_id":"2cf78bc7-600e-4133-9597-eac5e271e9f9","resolution":{"observed_at":"2026-08-07T04:17:10.018884Z","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":"2026-08-07T04:17:10.061068Z","title":"Calvin: A benchmark for language- conditioned policy learning for long-horizon robot manip- ulation tasks.IEEE Robotics and Automation Letters, 7(3): 7327–7334, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.061068Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:6fcd03b4581dac6fa74ce56ed9f8b62910df067250a1ba32debd46cbe20fe051","observation_id":"0256e076-13c5-4901-9752-4f713e0d10eb","resolution":{"observed_at":"2026-08-07T04:17:10.061068Z","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":"2026-08-07T04:17:10.174111Z","title":"Simple open-vocabulary object detection","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.174111Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:5d2fe5e2b84f4fb1b192cdc81ff7298a478f0e7638e19a31e3d327ea0997af17","observation_id":"d94732b8-e63a-4438-ae42-2ddfe86d2beb","resolution":{"observed_at":"2026-08-07T04:17:10.174111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.02523","last_updated":"2024-06-04T17:41:31Z","snapshot_observed_at":"2026-08-16T06:47:25.140028Z","submitted_at":"2024-06-04T17:41:31Z","title":"RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.02523","snapshot_observed_at":"2026-08-07T04:17:10.262973Z","title":"Robocasa: Large-scale simula- tion of everyday tasks for generalist robots.arXiv preprint arXiv:2406.02523, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.262973Z"},"links":{"cited_paper":"/paper/2406.02523","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:2cb11e37636d26809ecd008ef0273fb24c0b86c09edcdadf6f19447b30a7b2d0","observation_id":"9b580a6b-f0ac-402d-bd69-cefbf422110f","resolution":{"observed_at":"2026-08-07T04:17:10.262973Z","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":"2026-08-07T04:17:14.590871Z","title":"Pivot: Itera- tive visual prompting elicits actionable knowledge for vlms","venue":null,"work_id":"e755c256-1621-4c71-97f1-b5f9b43daf58","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.319888Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:851e1d62000edbc9786efcd50ef19fa461939d7bacca49501d8022b9d7cc12f3","observation_id":"e4e0a8ee-085b-42ef-b593-aaae1ee2491b","resolution":{"observed_at":"2026-08-07T04:17:14.594825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.578192Z","title":"Octo: An open-source generalist robot policy","venue":null,"work_id":"d638b83e-3027-446e-8970-87113f82a5bc","year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.368028Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:d346a4fdd2d85c25e5299011687fcd329974a082f43bafc83dffaf0d3a4b7d60","observation_id":"90e515f7-4f09-47dd-9167-52d35cdc4f75","resolution":{"observed_at":"2026-08-07T04:17:14.582211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08864","last_updated":"2025-05-14T15:22:36Z","snapshot_observed_at":"2026-08-13T13:59:48.091257Z","submitted_at":"2023-10-13T05:20:40Z","title":"Open X-Embodiment: Robotic Learning Datasets and RT-X Models","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08864","snapshot_observed_at":"2026-08-07T04:17:10.464849Z","title":"Open x-embodiment: Robotic learning datasets and rt-x models.arXiv preprint arXiv:2310.08864, 2023","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.464849Z"},"links":{"cited_paper":"/paper/2310.08864","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:a9c1aaf26a76e1ac2a09a7efdd123b8e107bdf7cef774324c176a44a8ac85407","observation_id":"d93f4eac-d7d6-4204-a349-be0be4e47ce6","resolution":{"observed_at":"2026-08-07T04:17:10.464849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","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":"2026-08-07T04:17:10.558832Z","title":"Gpt-4 technical report.arXiv:2303.08774, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.558832Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:144b053e3c80d3ea56b94ecd7a454e08b13c416489586111a046e90f3979f7ad","observation_id":"79d99234-ec30-4418-99e3-e04299e1265b","resolution":{"observed_at":"2026-08-07T04:17:10.558832Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.08191","last_updated":"2024-05-28T00:37:02Z","snapshot_observed_at":"2026-08-16T14:19:07.560970Z","submitted_at":"2024-02-13T03:25:33Z","title":"THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.08191","snapshot_observed_at":"2026-08-07T04:17:10.598519Z","title":"The colosseum: A bench- mark for evaluating generalization for robotic manipulation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.598519Z"},"links":{"cited_paper":"/paper/2402.08191","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:00333e0e2445f15296020b0ad6ee65a140db4d3ebe0f5865aa101e9d0ada23ef","observation_id":"bc7e8207-af92-43ce-8de4-2b23cecd6dae","resolution":{"observed_at":"2026-08-07T04:17:10.598519Z","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":"2026-08-07T04:17:10.645112Z","title":"Keto: Learning keypoint representations for tool manipulation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.645112Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:844bb3edebe9e2c42679c610a4d7812571b4022581499d355e91a773d5cd3922","observation_id":"01338251-9136-4df7-923e-42728e40935c","resolution":{"observed_at":"2026-08-07T04:17:10.645112Z","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":"2026-08-07T04:17:10.738596Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.738596Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:5604abe330f5917a762fcaa252d2c1d6ec390719dfa0985afe9de8ce0837ffea","observation_id":"a57a6c25-9b2d-4f17-a29b-d06dd4eabe8c","resolution":{"observed_at":"2026-08-07T04:17:10.738596Z","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":"2026-08-07T04:17:10.858849Z","title":"Sam 2: Segment anything in images and videos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.858849Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:63704c1af5a717119cb340d135170897aaa026dab93521d0025f6109debeb715","observation_id":"b14d933d-5d0f-4122-b732-cfbfb87e1e5f","resolution":{"observed_at":"2026-08-07T04:17:10.858849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.14159","last_updated":"2024-01-25T13:12:09Z","snapshot_observed_at":"2026-07-06T17:20:25.138890Z","submitted_at":"2024-01-25T13:12:09Z","title":"Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.14159","snapshot_observed_at":"2026-08-07T04:17:10.921465Z","title":"Grounded sam: Assembling open-world models for diverse visual tasks.arXiv preprint arXiv:2401.14159,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.921465Z"},"links":{"cited_paper":"/paper/2401.14159","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:6fce2d5c43c8cb89923b7c19ce409b5db0bf2efe42c094c15cdb7ef0b7db6d03","observation_id":"50c1ff37-12cc-4f3d-be7d-0f4630a9e4b8","resolution":{"observed_at":"2026-08-07T04:17:10.921465Z","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":"2026-08-07T04:17:14.548630Z","title":"Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds","venue":null,"work_id":"c0b88745-8981-4089-8a55-ff38a9e5ff0e","year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:10.972716Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:62a096e46c28bd98b6b94b0a70c42c4ca1d3b6b9b7e5072435778c7226fbf713","observation_id":"e40665f0-ad48-41d7-bcd5-c28293869d31","resolution":{"observed_at":"2026-08-07T04:17:14.552482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.535706Z","title":"Alfred: A benchmark for interpreting grounded instructions for everyday tasks","venue":null,"work_id":"470a8d06-c705-48c9-954b-9b1e848593f5","year":2020},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.079153Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:3552b278a95c4bc8b2285a1e81a9de31400092838af2302180ee2a39fdcb9710","observation_id":"c2e88cba-2c0d-4f92-a699-31dea03dde23","resolution":{"observed_at":"2026-08-07T04:17:14.540018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:11.170009Z","title":"Cliport: What and where pathways for robotic manipulation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.170009Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:60a9348d73c345cf3d39b87333d151bb7db98c9a9438f0f7f2350821688f198a","observation_id":"628cd259-d6b2-4520-b18a-6d68abbdfba3","resolution":{"observed_at":"2026-08-07T04:17:11.170009Z","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":"2026-08-07T04:17:14.515552Z","title":"Perceiver- actor: A multi-task transformer for robotic manipulation","venue":null,"work_id":"efff0a34-ff0e-441d-a849-b7c2276bf105","year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.302795Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:d1f7e830fe3311a5ed8daa66113f3551a4d41929449754c87891d572bc9565eb","observation_id":"5277652e-5a0f-4ce4-925c-ac462aca1a86","resolution":{"observed_at":"2026-08-07T04:17:14.519405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.00905","last_updated":"2023-10-25T21:45:24Z","snapshot_observed_at":"2026-08-16T15:51:32.614520Z","submitted_at":"2023-03-02T01:55:10Z","title":"Open-World Object Manipulation using Pre-trained Vision-Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.00905","snapshot_observed_at":"2026-08-07T04:17:11.391124Z","title":"Open-world ob- ject manipulation using pre-trained vision-language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.391124Z"},"links":{"cited_paper":"/paper/2303.00905","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:05240ebfcad98a97eeea6d4ee5348ad64170f4757c39e4f5a0ca00d7dcf05e02","observation_id":"d66f160d-33b8-4cbe-ac89-b41817523580","resolution":{"observed_at":"2026-08-07T04:17:11.391124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15109","last_updated":"2024-12-19T17:52:50Z","snapshot_observed_at":"2026-08-18T07:25:34.849820Z","submitted_at":"2024-12-19T17:52:50Z","title":"Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15109","snapshot_observed_at":"2026-08-07T04:17:11.569241Z","title":"Predictive inverse dynamics models are scalable learners for robotic manipulation.arXiv preprint arXiv:2412.15109, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.569241Z"},"links":{"cited_paper":"/paper/2412.15109","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:1d6f20a76b00ef727aae9e27e58f71a390b7bbd0f734d621c2857fc04618dd11","observation_id":"0bbbaf3a-e6b7-4e39-b31a-4fc0515692b6","resolution":{"observed_at":"2026-08-07T04:17:11.569241Z","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":"2026-08-07T04:17:14.503398Z","title":"Domain randomization for transferring deep neural networks from simulation to the real world","venue":null,"work_id":"80744a52-5935-4075-9852-4944361117fb","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.655044Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:ccab9947d0093bd768404c851e8a6fd2445f63212f8608ec427a7a71d0c2aa1a","observation_id":"a5041262-c20d-441a-aae7-4f8043630952","resolution":{"observed_at":"2026-08-07T04:17:14.507051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:11.729633Z","title":"Robotap: Tracking arbitrary points for few-shot visual imitation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.729633Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:bfad5b298d4df68d4b534025ef857d678171197ec224e22dd2380deccac470e8","observation_id":"be65a9d9-51ed-40fd-b761-28e5e6b9c1d9","resolution":{"observed_at":"2026-08-07T04:17:11.729633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10943","last_updated":"2024-07-15T17:40:46Z","snapshot_observed_at":"2026-08-16T13:34:03.313546Z","submitted_at":"2024-07-15T17:40:46Z","title":"GRUtopia: Dream General Robots in a City at Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10943","snapshot_observed_at":"2026-08-07T04:17:11.814197Z","title":"Grutopia: Dream general robots in a city at scale.arXiv preprint arXiv:2407.10943, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.814197Z"},"links":{"cited_paper":"/paper/2407.10943","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:e58df2dbce3e897b1ce634267bf99fc7d80cfbdb01d6d658aedf876f87459183","observation_id":"7b2fccbe-2af8-4d9e-98d3-14afc41b1582","resolution":{"observed_at":"2026-08-07T04:17:11.814197Z","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":"2026-08-07T04:17:14.481716Z","title":"Goal-auxiliary actor-critic for 6d robotic grasp- ing with point clouds","venue":null,"work_id":"2998e922-112f-4c20-8e62-6af9b58c48aa","year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:11.935509Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:93432e0ca25035f64a3337ba69dcd3d031adc5d8c37c02275f4e65d7aa77e323","observation_id":"0a873c8a-9e78-494d-80e5-0b144011948d","resolution":{"observed_at":"2026-08-07T04:17:14.486221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.01361","last_updated":"2024-01-21T21:01:12Z","snapshot_observed_at":"2026-08-18T08:16:35.911543Z","submitted_at":"2023-10-02T17:23:48Z","title":"GenSim: Generating Robotic Simulation Tasks via Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.01361","snapshot_observed_at":"2026-08-07T04:17:12.054993Z","title":"Gensim: Generating robotic simulation tasks via large language models.arXiv preprint arXiv:2310.01361,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.054993Z"},"links":{"cited_paper":"/paper/2310.01361","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:c650fc84a1839bc221dd2041982db306c1cf3625c437967432622915954aaa09","observation_id":"97428efc-ef24-4e4d-bfcd-311c6dfe0685","resolution":{"observed_at":"2026-08-07T04:17:12.054993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00025","last_updated":"2024-07-12T12:51:00Z","snapshot_observed_at":"2026-07-06T17:09:59.848387Z","submitted_at":"2023-12-28T23:34:43Z","title":"Any-point Trajectory Modeling for Policy Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00025","snapshot_observed_at":"2026-08-07T04:17:12.170933Z","title":"Any-point trajectory modeling for policy learning.arXiv preprint arXiv:2401.00025, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.170933Z"},"links":{"cited_paper":"/paper/2401.00025","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:811a1238cffa8d8ceef4fd19e8dc625b95faf94a55581c6592b272d0014104da","observation_id":"3551655b-03cb-467c-8764-e2651764ea5b","resolution":{"observed_at":"2026-08-07T04:17:12.170933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.13139","last_updated":"2023-12-21T05:34:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-20T16:00:43Z","title":"Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.13139","snapshot_observed_at":"2026-08-07T04:17:12.308337Z","title":"Unleashing large-scale video generative pre- training for visual robot manipulation.arXiv preprint arXiv:2312.13139, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.308337Z"},"links":{"cited_paper":"/paper/2312.13139","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:89fbd33f31c1b18243bf84b37ca2c1c0a6ac88bca5ac9784522e8dad617485ee","observation_id":"84556921-bde2-41ad-b260-c3a40eea619d","resolution":{"observed_at":"2026-08-07T04:17:12.308337Z","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":"2026-08-07T04:17:14.469641Z","title":"Sapien: A simulated part-based interactive environment","venue":null,"work_id":"70e88a41-b1b2-4b53-9497-22995e1f0cdb","year":2020},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.404887Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:eea151641c6628889068d11cd1c5799731b7482a3943644df8836ab997903d5b","observation_id":"c3d7714c-a152-43a2-9e8d-d8d85e2efaf0","resolution":{"observed_at":"2026-08-07T04:17:14.473606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.457765Z","title":"Flow as the cross-domain manipulation interface","venue":null,"work_id":"07949adc-2add-40cf-85ec-5f9931bb6451","year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.466706Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:7aa6b105dc1febac58e4af413429cdba22488b6961b20447aac24dab11160e56","observation_id":"b890839f-8646-4a0a-9d47-10a422ce9f53","resolution":{"observed_at":"2026-08-07T04:17:14.461621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11441","last_updated":"2023-11-06T07:39:49Z","snapshot_observed_at":"2026-08-12T21:25:32.312122Z","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":"2026-08-07T04:17:12.492921Z","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":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.492921Z"},"links":{"cited_paper":"/paper/2310.11441","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:000580d6b62e6930bd1a7c8bcfa3213bfdaa16cedd974924361954905d262f66","observation_id":"cb7bc328-be32-4553-8f89-6df636db2070","resolution":{"observed_at":"2026-08-07T04:17:12.492921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11439","last_updated":"2024-09-23T08:22:04Z","snapshot_observed_at":"2026-08-16T14:25:49.886675Z","submitted_at":"2024-01-21T09:39:11Z","title":"General Flow as Foundation Affordance for Scalable Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.11439","snapshot_observed_at":"2026-08-07T04:17:12.613817Z","title":"General flow as foundation affordance for scalable robot learning.arXiv preprint arXiv:2401.11439, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.613817Z"},"links":{"cited_paper":"/paper/2401.11439","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:94a6f9b7a6e71373a36c1315508fe4fc761c09e55646f7e20d9deeedc9d306b3","observation_id":"e025c7d8-1fd6-4386-bc00-70e040889545","resolution":{"observed_at":"2026-08-07T04:17:12.613817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10721","last_updated":"2024-06-15T19:22:51Z","snapshot_observed_at":"2026-08-16T13:42:40.054113Z","submitted_at":"2024-06-15T19:22:51Z","title":"RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10721","snapshot_observed_at":"2026-08-07T04:17:12.693223Z","title":"Robopoint: A vision-language model for spatial affordance prediction for robotics.arXiv preprint arXiv:2406.10721, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.693223Z"},"links":{"cited_paper":"/paper/2406.10721","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:dc625860d4cb314b6bb1e2a6a03fee61902a205bb660d59d20f57c3e876d19e4","observation_id":"822ca968-e8f6-4d95-9ddf-8cea73989b70","resolution":{"observed_at":"2026-08-07T04:17:12.693223Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03954","last_updated":"2024-09-27T02:43:48Z","snapshot_observed_at":"2026-08-01T16:34:39.855742Z","submitted_at":"2024-03-06T18:58:49Z","title":"3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03954","snapshot_observed_at":"2026-08-07T04:17:12.774293Z","title":"3d diffusion policy.arXiv preprint arXiv:2403.03954, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.774293Z"},"links":{"cited_paper":"/paper/2403.03954","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:9a12bd74076839b996f38a3eaa2b965933b93816dbbcfd62951ecc78fbb86de3","observation_id":"6b826cab-c762-41ce-80bb-86efa507b5b3","resolution":{"observed_at":"2026-08-07T04:17:12.774293Z","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":"2026-08-07T04:17:14.446274Z","title":"Transporter networks: Rearranging the visual world for robotic manipu- lation","venue":null,"work_id":"b86ff9fc-6191-4a4e-ba4f-43fe6a02f085","year":2021},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.891068Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:9b64128a9515541eb03eeb9e0c56deb0029c2355d605f2546b0dabb74c61914a","observation_id":"a62935ad-ef49-4001-bc89-d3c25fce063b","resolution":{"observed_at":"2026-08-07T04:17:14.449921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.13705","last_updated":"2023-04-23T19:10:53Z","snapshot_observed_at":"2026-08-03T01:22:01.078078Z","submitted_at":"2023-04-23T19:10:53Z","title":"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.13705","snapshot_observed_at":"2026-08-07T04:17:12.984162Z","title":"Learning fine-grained bimanual manipulation with low-cost hardware.arXiv preprint arXiv:2304.13705, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:12.984162Z"},"links":{"cited_paper":"/paper/2304.13705","citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:9392943db98b8e6495e894ac1f81a670833e8fcbc283d6e3f7fd1e9f5fd8792c","observation_id":"188daa05-9857-43f1-b471-62cdaa76dc88","resolution":{"observed_at":"2026-08-07T04:17:12.984162Z","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":"2026-08-07T04:17:14.434239Z","title":"Vlmbench: A compositional benchmark for vision-and-language manipulation.Advances in Neural In- formation Processing Systems, 35:665–678, 2022","venue":null,"work_id":"27295b93-95a6-45ca-a706-67a1b8f40ff9","year":2022},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.038976Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:dbc3d2f27673284612b9eae5e85afafd268db5c12a02fed815343f98c5e41c5a","observation_id":"5bd30194-1779-4faf-b82e-df15d29d93b5","resolution":{"observed_at":"2026-08-07T04:17:14.438263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.423096Z","title":null,"venue":null,"work_id":"beeeafe7-9f90-4a05-9ada-ec4f2f95f9ae","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.157043Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:6949d6d515a342e796d81d3b1816cc0c99ae80e8d668115e9324c398f8e1be5a","observation_id":"de1915ac-7503-40cb-a939-a6a32bd93da4","resolution":{"observed_at":"2026-08-07T04:17:14.426619Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.411423Z","title":"Camera setups for modular manipulation systems and learning-based methods in GENMANIP-BENCH","venue":null,"work_id":"43db9dca-d7d8-4ade-9770-c4cf6a9ed8c4","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.178845Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:3d29f8db4ce6785f89e925ca20086a399c77847b6d1accf45587498580309cb5","observation_id":"059f0616-3b25-4ea3-9ebf-c5fcc13e6605","resolution":{"observed_at":"2026-08-07T04:17:14.415390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.400471Z","title":"instruction","venue":null,"work_id":"85635753-416a-48ec-8bde-aeeb784ef09e","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.321276Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:e52728d9c9f90b2897c6c011d70b905ace9c0f142518228d70a21c32a03c7c6f","observation_id":"df54b8f6-50a6-4e94-a9de-c4c17415ac86","resolution":{"observed_at":"2026-08-07T04:17:14.403890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.389085Z","title":"Algorithm 1 Layout construction pipeline","venue":null,"work_id":"f9b488ae-c3e8-46e1-992f-bf77f1c5b733","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.473649Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:cdb97d4cdf0690fabaac8cc96362eea6fc87a8e2089cddaa0fec248e48fc8246","observation_id":"e44a56d3-4eb4-4dbb-a807-ffaadfb7df35","resolution":{"observed_at":"2026-08-07T04:17:14.392787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.377691Z","title":"Human annotators, with privileged access to the scene graph and USD files, use IsaacSim for detailed inspections","venue":null,"work_id":"add1bc01-bd35-4c7c-b68e-b32b39b2c9d5","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.482280Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:b6d9f5df94202bc4b1ba43c79937b26fde03cebd4fe037b8f22c637f6d4db564","observation_id":"753f2459-402d-4519-8968-4025fe7713a0","resolution":{"observed_at":"2026-08-07T04:17:14.381342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.365913Z","title":null,"venue":null,"work_id":"77f652cc-a344-432f-a2b6-f5c9793d6e1c","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.486829Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:cf4e2f2000b9a3caad2980e5447cde6da057aa7e83613d79e2d0317e23ff2a23","observation_id":"518e912b-4d2e-4c95-8360-92caf31d35c2","resolution":{"observed_at":"2026-08-07T04:17:14.369965Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.354047Z","title":"Figure 8.Human-in-the-Loop corrections of benchmark scenarios","venue":null,"work_id":"6c9a7184-2d92-4a6d-a7d1-29b9d8fae6f0","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.491179Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:c5b9b58be9d6cb7b390b3e70bfaf9ab54a574d4f715d0e93ddb43798ce0cf66c","observation_id":"b175ad91-e44c-4d42-bc36-6daa99598d3c","resolution":{"observed_at":"2026-08-07T04:17:14.357790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.342771Z","title":null,"venue":null,"work_id":"9e07deff-c22d-47d2-839e-4f031f2283b9","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.494972Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:ce2beaecbc87740a676309fc26d90672ca08ad182bec7266daa3c78f0b41dd28","observation_id":"4a98163c-f5ef-49da-8023-f3dd6f2013ab","resolution":{"observed_at":"2026-08-07T04:17:14.346468Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.327588Z","title":"Following the approach of Mimicgen [47], we collect primitive skills from human teleoperation trajectories for these articulated objects, as illustrated in Figure A- 11","venue":null,"work_id":"8af4fb28-7418-4441-8f93-80b81dc1fa37","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.498701Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:325a5617a4dc59d78d58f02c3d005885eeb3bb3ab40b6da6a29a59582488e654","observation_id":"70cd6369-73ef-46cd-9784-2f50782f76fc","resolution":{"observed_at":"2026-08-07T04:17:14.331816Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.315651Z","title":"The BC data collection pipeline is shown in Figure A- 12","venue":null,"work_id":"a25a7241-04f5-44cb-881c-f29a2015c27a","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.502933Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:7837d57c25ccf8feb63083b4da9fc0101780e4e66cb6a2653acce54c5ba1799b","observation_id":"058e3cdf-1893-4530-aab6-7694804084f0","resolution":{"observed_at":"2026-08-07T04:17:14.319510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.303798Z","title":"In this section, we detail the method for determining spatial relationships among these point clouds, encompassing horizontal, vertical, and multi-object interactions","venue":null,"work_id":"b3e30306-920c-4f26-866c-3eff54f11ef9","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.507515Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:3321610b88628d2ffbe899868c4ae403f3300a84cb7399a37e1b9d57c6a8a891","observation_id":"11636d71-9baa-4526-a1e2-3070b3aa8a31","resolution":{"observed_at":"2026-08-07T04:17:14.307767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.291983Z","title":null,"venue":null,"work_id":"20a31421-bd07-4f56-b02d-e8543b3363bc","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.511340Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:a182199c33ab411049c84fdd1d67b8a573877d8015677f98e669adc89cc3f4b6","observation_id":"5c6276f3-ad12-4eba-b53e-789c8bc5fa55","resolution":{"observed_at":"2026-08-07T04:17:14.295837Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.280726Z","title":null,"venue":null,"work_id":"cc2a1354-ffed-4672-a28d-21a7efdd5665","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.514976Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:48886807ef9559f949dc99dac8cf39ddde5b78d9d3684dfde6935bacfe1ac451","observation_id":"dd24eb1c-ff23-4de8-ba33-80fb8292ef0c","resolution":{"observed_at":"2026-08-07T04:17:14.284234Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.269055Z","title":"The possible horizontal relationships are: •Left-Right: This relationship is determined when the point clouds overlap along the X-axis but not the Y-axis","venue":null,"work_id":"431afb94-cb1a-4eea-a61e-1df0b725dd41","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.518742Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:4945746703e0b1d5232e9cccfda1a6ca04ac2f3f1a385f88af11c3748199679e","observation_id":"29212170-27de-4579-99c7-fde7ae56be8b","resolution":{"observed_at":"2026-08-07T04:17:14.272801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.257395Z","title":null,"venue":null,"work_id":"33f05a9a-ecc8-4e8f-aa17-7cd05498436d","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.522405Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:343fefb3dee1df4081c18a6edac1f27a9cc7397550bcac0cf5badcc137446096","observation_id":"562a35ad-ee4d-4790-aab2-40c0b13acc1b","resolution":{"observed_at":"2026-08-07T04:17:14.260915Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.245238Z","title":"•Supporting/Supported by: If the objects are in contact or near each other, the function checks if one object supports the other based on the overlap area ratio","venue":null,"work_id":"20760806-7ba4-40dd-a088-6ce2545422d3","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.526065Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:7aebb96e39a8b7e79245695dd2a82ecda92cbe1e968028ae9d31536d203763e9","observation_id":"34994c6e-62ed-4d3d-a7ad-76b21cf3ebe4","resolution":{"observed_at":"2026-08-07T04:17:14.249156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.232886Z","title":null,"venue":null,"work_id":"3c97320c-efdc-47a5-b31d-4ef49f36cfa8","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.529521Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:b1ad6eb3b0c04a8e847e727498cda45749b389d2ca9f553702c6cb1799c4b527","observation_id":"e13d5166-b9b1-4a6d-a037-55c715549818","resolution":{"observed_at":"2026-08-07T04:17:14.236863Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.220568Z","title":null,"venue":null,"work_id":"70bfca8f-b2ff-4efc-9c66-566b6855b111","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.533084Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:28ea790b1e452b51ec1e8f1df88563c2016bd5df0272f2ef179b7becdab84277","observation_id":"84d0fa3b-461b-4866-b067-1b9e83c7d51f","resolution":{"observed_at":"2026-08-07T04:17:14.224157Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-07T04:17:14.207623Z","title":null,"venue":null,"work_id":"c71e2ea8-7b67-49db-aede-4ebf61b15eaf","year":null},"citing_paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-07T04:17:13.536817Z"},"links":{"citing_paper":"/paper/2506.10966"},"observation_digest":"sha256:1a8f4f85840718cf5775233d43613094219b00c27dad9566e1781a21504991b9","observation_id":"2d861826-0561-404e-b7e2-1a06b13d3dbb","resolution":{"observed_at":"2026-08-07T04:17:14.211737Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.10966","last_updated":"2025-06-12T17:59:04Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-15T06:36:53.043556Z","submitted_at":"2025-06-12T17:59:04Z","title":"GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":77,"verified_exact":1,"verified_fuzzy":20},"total_outbound_references":107},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 100 of 107 outbound references and 2 inbound Pith citation observations for arXiv:2506.10966."}