{"as_of":"2026-08-08T05:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b3bc49d03dea6e622391fdc857f07c4d9a7fb44126651eafa9c0d953282b2b8a","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:48:02.078916Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.07062/citation-record","integrity":"/paper/2506.07062/integrity","json":"/paper/2506.07062/citation-record.json","paper":"/paper/2506.07062"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:48:02.327812Z","title":"(2022) Do as i can, not as i say: grounding language in robotic affordances","venue":null,"work_id":"c8f16713-9ac6-4c0d-844d-28655487f7bf","year":2022},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.003992Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:2bc78581550a2d752e90028c273b1d8e00f635c9927967c83400f495fa155fec","observation_id":"a035ee80-fecc-4c68-a461-fc85a927300a","resolution":{"observed_at":"2026-08-07T05:48:02.332077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:48:02.304195Z","title":"(2021) Rhh-lgp: Receding Horizon and Heuristics-Based Logic-Geometric Programming for Task and Motion Planning","venue":null,"work_id":"5f7c9816-fc58-4c38-a982-e67f5ed6817e","year":2021},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.012613Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:4709ba813264856e49b23c5c7528560ba0eb4071d6b1dedeae22121a99bdcaee","observation_id":"7e722757-fdd7-4964-bb79-cd0372df55ff","resolution":{"observed_at":"2026-08-07T05:48:02.308981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:48:02.280739Z","title":"(2011) Continuous upper con fidence trees","venue":null,"work_id":"703300b9-6c78-407e-96b0-379a8f6d4d52","year":2011},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.022772Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:28b0c4a6acb7be5f1e34a42d3137172b820046f5dbf6d6e4953bb73efa084aa6","observation_id":"1d8315ec-4cff-41d3-a93a-ac9acf6355f1","resolution":{"observed_at":"2026-08-07T05:48:02.284697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:48:02.292342Z","title":"Therefore, if the grill is placed first, it prevents the robot from placing another object (red can in the hand) in the region","venue":null,"work_id":"d247288f-055e-48f7-8147-59bbdd904103","year":2016},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.017338Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:83b33db484f425a228c642098e027a3ebdfbf38fafd363de8431811ebb0f9197","observation_id":"453ff003-2cdd-4404-80e6-c13794c7c915","resolution":{"observed_at":"2026-08-07T05:48:02.296283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:48:02.254493Z","title":"Washington, DC: The Association for the Advancement of Arti ficial Intelligence","venue":null,"work_id":"e73bc7d6-bb2f-4ddd-89c1-3b759c0ab609","year":2020},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.036826Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:4f8ee32771fbccd45d706ac1a70c451ab7dbff237627a3b0c31e4c59dfd4ae0c","observation_id":"ec03fac1-3693-4ae3-95cb-09c2bd58f373","resolution":{"observed_at":"2026-08-07T05:48:02.259174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:48:02.241305Z","title":"Washington, DC: The Association for the Advancement of Arti ficial Intelligence","venue":null,"work_id":"b3a839fc-62da-4535-bb5f-ce1156bc4da6","year":2018},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.040458Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:bec9c4933503307dae796717041f48aacc14b0e770ba24f400efe9e12ef8722c","observation_id":"ed877441-c8e5-436a-9408-4770b1eaa7d4","resolution":{"observed_at":"2026-08-07T05:48:02.245761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:48:02.228114Z","title":"(2023) Learning efficient abstract planning models that choose what to predict","venue":null,"work_id":"db984b71-ed6c-49b1-ab96-f12a3cdaf731","year":2023},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.044141Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:b470138e03330769cf0227fcad153964d1f6a8f073f944fb94cb03cdd677fef3","observation_id":"b6113536-2ea6-4a4a-9812-9e2d0b5f246f","resolution":{"observed_at":"2026-08-07T05:48:02.232349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:48:02.214983Z","title":"In: The Conference on Lifelong Learning Agents, Pisa, Italy, 29-1 August","venue":null,"work_id":"b08e7bce-22c8-4686-9dcc-9ec584c9345d","year":2023},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.048604Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:a2704583b433391ab2cd3ed8afa5b8afcb1c2868e52df58c3031450e2d7b84ef","observation_id":"e1de5f07-141a-45e8-9adc-340d6e3952c9","resolution":{"observed_at":"2026-08-07T05:48:02.219283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11477","last_updated":"2023-09-27T07:29:44Z","snapshot_observed_at":"2026-08-01T15:58:01.346044Z","submitted_at":"2023-04-22T20:34:03Z","title":"LLM+P: Empowering Large Language Models with Optimal Planning Proficiency","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11477","snapshot_observed_at":"2026-08-07T05:48:02.053445Z","title":"(2023) Text2motion: from natural language instructions to feasible plans","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.053445Z"},"links":{"cited_paper":"/paper/2304.11477","citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:9abfa03ea74760d687c20850d82885adfa6373814de2f20a63c915feefba5ce4","observation_id":"afe3aae5-1dd9-4a89-92b8-934aae73e42d","resolution":{"observed_at":"2026-08-07T05:48:02.053445Z","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":"2026-08-07T05:48:02.201288Z","title":"(2022) Con flict- directed diverse planning for logic-geometric program- ming","venue":null,"work_id":"46bd3b98-5300-4ade-aa14-ce740f493696","year":2022},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.058568Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:5b17442450b130e77f83cd41cf9d2c88493a2a944341d49ae32de8d759eabb9b","observation_id":"0f08e57d-b66d-4a04-97ba-6ba0ddac983b","resolution":{"observed_at":"2026-08-07T05:48:02.206340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.05456","last_updated":"2021-10-15T14:31:17Z","snapshot_observed_at":"2026-08-07T15:42:34.350794Z","submitted_at":"2021-03-09T14:44:08Z","title":"Extended Tree Search for Robot Task and Motion Planning","version":3},"cited_work":{"arxiv_id":"2103.05456","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.05456","snapshot_observed_at":"2026-08-07T05:48:02.147373Z","title":"Extended Tree Search for Robot Task and Motion Planning","venue":"cs.RO","work_id":"2fef7685-ffc3-4ce8-b67d-c6e88dc37add","year":2021},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.062924Z"},"links":{"cited_paper":"/paper/2103.05456","citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:79e2f2e16e4f4614985e62107a851172bd6e1ce89404b432fc5ce26a1d90ee5b","observation_id":"b7210834-596d-4b5b-aa37-bf8a02d7d41b","resolution":{"observed_at":"2026-08-07T05:48:02.153874Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.14100","last_updated":"2023-03-24T16:06:11Z","snapshot_observed_at":"2026-07-06T15:07:41.634914Z","submitted_at":"2023-03-24T16:06:11Z","title":"Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.14100","snapshot_observed_at":"2026-08-07T05:48:02.066703Z","title":"(2023) Inventing Relational State and Action Abstractions for Effective and Ef ficient Bilevel Planning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.066703Z"},"links":{"cited_paper":"/paper/2303.14100","citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:777e074180261f7f79c31e9ee18a223425af8f4c85b0e14fc73f28ca67e03f09","observation_id":"06d36984-0f03-4332-8054-a929c4556733","resolution":{"observed_at":"2026-08-07T05:48:02.066703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.05128","last_updated":"2023-02-10T09:17:52Z","snapshot_observed_at":"2026-08-07T05:05:17.591349Z","submitted_at":"2023-02-10T09:17:52Z","title":"Translating Natural Language to Planning Goals with Large-Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.05128","snapshot_observed_at":"2026-08-07T05:48:02.070869Z","title":"Piscataway: 2017 IEEE International Conference on Robotics and Automation (ICRA)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.070869Z"},"links":{"cited_paper":"/paper/2302.05128","citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:7c74803ccc328ea492d9ff5c244ff25a2ef6ae2a4e7768fddb39388f5005aa93","observation_id":"ff373c79-1789-4d9b-a49d-ba28ee60cae4","resolution":{"observed_at":"2026-08-07T05:48:02.070869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.01219","last_updated":"2025-09-14T09:34:46Z","snapshot_observed_at":"2026-07-06T16:13:46.112815Z","submitted_at":"2023-09-03T16:56:48Z","title":"Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.01219","snapshot_observed_at":"2026-08-07T05:48:02.074876Z","title":"(2023) Siren’s song in the ai ocean: a survey on hallucination in large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.074876Z"},"links":{"cited_paper":"/paper/2309.01219","citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:f62be2eda9dfa9f71a1bd126a8b27e800f1d1318d45311936b974d80ede3c88b","observation_id":"d9e2fb9d-ece4-4bb2-bb51-37ab89fd8766","resolution":{"observed_at":"2026-08-07T05:48:02.074876Z","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":"2026-08-07T05:48:02.188490Z","title":"For WarmStartUCT of STaLM, We use UCT exploration constant c = 50, PW constants ( kα, cα) = (1.5, 0.15)","venue":null,"work_id":"cf3bf8f3-5325-492c-996e-43f26b7efda9","year":2024},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.078916Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:b90749491577e8983b561e84a0ac77984ce71b571fae010f64ba6bc728848c23","observation_id":"6d66885d-4439-4e50-9efd-8ae60774f281","resolution":{"observed_at":"2026-08-07T05:48:02.192917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T05:48:02.267103Z","title":"(2020b) Deep Visual Heuristics: Learning Feasibility of Mixed-Integer Programs for Ma- nipulation Planning","venue":null,"work_id":"aee445e1-0a65-4e4a-bf19-000ddf866e02","year":2020},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.027172Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:af20e8975171efbc63301c135ae490abbed758e61b980dba82fe086eaa4b338c","observation_id":"8bfe94c9-e568-4852-9468-9106710de4aa","resolution":{"observed_at":"2026-08-07T05:48:02.272467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17842","last_updated":"2023-12-24T03:48:40Z","snapshot_observed_at":"2026-08-07T02:37:30.672574Z","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":"2026-08-07T05:48:02.032044Z","title":"(2021) Integrated task and motion planning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.032044Z"},"links":{"cited_paper":"/paper/2311.17842","citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:96fa770629b5273e58348e6015638d0353bc1adec7516873b0a3ef9d0d4aede6","observation_id":"ba8792ae-56ba-418b-951d-0ee3d2da5641","resolution":{"observed_at":"2026-08-07T05:48:02.032044Z","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":"2026-08-07T05:48:02.316506Z","title":"(2023) Preference learning for guiding the tree search in continuous pomdps","venue":null,"work_id":"9a65ff94-32f2-4c19-a7e5-72842bd3ac12","year":2023},"citing_paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T05:48:02.008738Z"},"links":{"citing_paper":"/paper/2506.07062"},"observation_digest":"sha256:615b357ef9c74f2b431783a98ce0ccdc2235902d1c8982db8f4ca678f87d5f91","observation_id":"9d2a723a-7a45-46a5-bcd4-079108159cbb","resolution":{"observed_at":"2026-08-07T05:48:02.320564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.07062","last_updated":"2025-06-08T09:47:54Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-07T05:40:35.337244Z","submitted_at":"2025-06-08T09:47:54Z","title":"Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree search"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":18},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2506.07062."}