{"id":"285686de70e67824756717704d8e9f5aadda110d9933a8043dc1346e6916b3c6","title":"cuArms coordinates multiple robot arms with GPU-accelerated planning","argument":"The authors of “cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation” present a planner for coordinating multiple robot arms working in the same workcell. They argue that existing approaches can be slow, depend on domain-specific assumptions, or scale poorly as robot numbers and task horizons grow.\n\nTheir approach combines GPU parallelism with reusable multi-modal roadmaps for individual robots, then uses a lazy search to resolve conflicts and deadlocks. A high-level scheduler proposes candidate task skeletons for cuArms to turn into coordinated manipulation plans. The authors report real-time initial solutions and later improvements in motion quality and scheduling, and say the approach does not require the days of domain-specific training they associate with learning approaches.\n\nEscape Meridian interpretation: If these results transfer beyond the reported experiments, faster coordination could help industrial tasks that benefit from several arms working together. The abstract also says videos cover simulated and real-world industrial tasks, but does not provide benchmark details, numerical comparisons, or evidence about performance in sustained deployment. How does cuArms perform against the compared approaches as the number of arms and task horizon increase?","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The abstract does not provide benchmark details or numerical comparisons, so the reported performance cannot be assessed from this evidence alone. The abstract does not establish performance in sustained industrial deployment.","speaker":{"name":"Huang, Philip, Ramos, Fabio, Li, Jiaoyang, Garrett, Caelan, Johannsmeier, Lars","roleAtSource":"Article author"},"source":{"title":"cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.36250","publishedAt":"2026-10-26","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors present cuArms as a conflict-based planner co-designed with GPU parallelism for multi-arm manipulation.","excerpt":"we introduce cuArms, a conflict-based planner co-designed with GPU parallelism","url":"https://arxiv.org/abs/2610.36250","relationship":"Source-stated"},{"text":"The planner uses reusable multi-modal roadmaps for individual robots and lazy search to resolve conflicts and deadlocks.","excerpt":"using reusable multi-modal roadmaps for individual robots and resolves conflicts and deadlocks through a lazy search.","url":"https://arxiv.org/abs/2610.36250","relationship":"Source-stated"},{"text":"The authors report real-time initial solutions and anytime improvements in motion quality and scheduling, without days of domain-specific training required by learning approaches.","excerpt":"with real-time initial solutions and anytime improvements in both motion quality and scheduling, without days of domain-specific training required by learning approaches.","url":"https://arxiv.org/abs/2610.36250","relationship":"Source-stated"},{"text":"If the reported results transfer beyond the experiments, faster coordination could help industrial tasks that benefit from multiple arms working together.","excerpt":"Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks.","url":"https://arxiv.org/abs/2610.36250","relationship":"Escape Meridian interpretation"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.36250","checkedAt":"2026-11-04T17:08:09.297Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How does cuArms perform against the compared approaches as the number of arms and task horizon increase?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"cuArms coordinates multiple robot arms with GPU-accelerated planning","summary":"The authors of “cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation” present a planner for coordinating multiple robot arms working in the same workcell. They argue that existing approaches can be slow, depend on domain-specific assumptions, or scale poorly as robot numbers and task horizons grow.\n\nTheir approach combines GPU parallelism with reusable multi-modal roadmaps for individual robots, then uses a lazy search to resolve conflicts and deadlocks. A high-level scheduler proposes candidate task skeletons for cuArms to turn into coordinated manipulation plans. The authors report real-time initial solutions and later improvements in motion quality and scheduling, and say the approach does not require the days of domain-specific training they associate with learning approaches.\n\nEscape Meridian interpretation: If these results transfer beyond the reported experiments, faster coordination could help industrial tasks that benefit from several arms working together. The abstract also says videos cover simulated and real-world industrial tasks, but does not provide benchmark details, numerical comparisons, or evidence about performance in sustained deployment. How does cuArms perform against the compared approaches as the number of arms and task horizon increase?","question":"How does cuArms perform against the compared approaches as the number of arms and task horizon increase?","claims":[{"text":"The authors present cuArms as a conflict-based planner co-designed with GPU parallelism for multi-arm manipulation.","excerpt":"we introduce cuArms, a conflict-based planner co-designed with GPU parallelism","support":"source-stated"},{"text":"The planner uses reusable multi-modal roadmaps for individual robots and lazy search to resolve conflicts and deadlocks.","excerpt":"using reusable multi-modal roadmaps for individual robots and resolves conflicts and deadlocks through a lazy search.","support":"source-stated"},{"text":"The authors report real-time initial solutions and anytime improvements in motion quality and scheduling, without days of domain-specific training required by learning approaches.","excerpt":"with real-time initial solutions and anytime improvements in both motion quality and scheduling, without days of domain-specific training required by learning approaches.","support":"source-stated"},{"text":"If the reported results transfer beyond the experiments, faster coordination could help industrial tasks that benefit from multiple arms working together.","excerpt":"Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks.","support":"interpretation"}],"limitations":["The abstract does not provide benchmark details or numerical comparisons, so the reported performance cannot be assessed from this evidence alone.","The abstract does not establish performance in sustained industrial deployment."],"mediaId":"6475d2107c09030a35f0fc5571cad7e86c13dacab68fb81ea67d6d299688af51","correctionReason":null,"id":"285686de70e67824756717704d8e9f5aadda110d9933a8043dc1346e6916b3c6","publicationId":"83f60daa-c5de-45c5-84e1-3ce0304d53a5","version":1,"sourceUrl":"https://arxiv.org/abs/2610.36250","sourceTitle":"cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-26","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-28T05:02:33.401Z","createdAt":"2026-11-04T15:01:54.388Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"6475d2107c09030a35f0fc5571cad7e86c13dacab68fb81ea67d6d299688af51","kind":"article","url":"https://arxiv.org/abs/2610.36250","sourceUrl":"https://arxiv.org/abs/2610.36250","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-29T21:31:03.235Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.36250","checkedAt":"2026-11-04T17:08:09.297Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"6475d2107c09030a35f0fc5571cad7e86c13dacab68fb81ea67d6d299688af51","confidence":0.76,"provider":"typesafe","model":"jev-1.13.0","inputHash":"9af68638d4c169b52c64f1e2f520a6e7d8094fd5eae145dc68252d08e41486f1","decidedAt":"2026-10-29T21:31:04.000Z","redecided":{"from":"stream-admission-v5","under":"stream-admission-v5+confidence-0.5"}},"provenance":{"textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observationId":"5d67f0f6119f596d89dbb42c173d38d95399081c93bad95dd5f1cb7dc79ab1ea","retrievedAt":"2026-10-29T21:31:03.110Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"acdb916d-cef9-4321-8c20-44c78cb12009"},"reviewer":{"model":"gpt-6-luna","runId":"5920803c-4cfd-4d56-884c-a40b8dd6c8de"}},"audit":{"status":"passed","reviewedAt":"2026-11-04T15:01:54.388Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Huang, Philip","url":null,"kind":"unknown"},{"name":"Ramos, Fabio","url":null,"kind":"unknown"},{"name":"Li, Jiaoyang","url":null,"kind":"unknown"},{"name":"Garrett, Caelan","url":null,"kind":"unknown"},{"name":"Johannsmeier, Lars","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Huang, Philip","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Huang, Philip","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}},{"actor":{"name":"Ramos, Fabio","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Ramos, Fabio","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}},{"actor":{"name":"Li, Jiaoyang","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Li, Jiaoyang","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}},{"actor":{"name":"Garrett, Caelan","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Garrett, Caelan","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}},{"actor":{"name":"Johannsmeier, Lars","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Johannsmeier, Lars","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:4","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}}],"participationCoverage":{"total":5,"returned":5,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.36250","snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z","basis":"publisher-page-metadata","sourceLinks":46,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/285686de70e67824756717704d8e9f5aadda110d9933a8043dc1346e6916b3c6/source"}},"canonicalUrl":"https://escapemeridian.com/readings/cuarms-coordinates-multiple-robot-arms-with-gpu-accelerated-285686de","formats":{"html":"/readings/cuarms-coordinates-multiple-robot-arms-with-gpu-accelerated-285686de","markdown":"/readings/cuarms-coordinates-multiple-robot-arms-with-gpu-accelerated-285686de.md","json":"/readings/cuarms-coordinates-multiple-robot-arms-with-gpu-accelerated-285686de.json"},"attribution":"Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.","maintainedGuideLinks":[],"readerContext":{"version":"reader-context-v1","attribution":"Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.","terms":[{"id":"term-gpu","name":"Graphics processing unit","explanation":"A chip that performs many calculations at once; the main hardware used to train and run AI models.","referenceUrl":"https://en.wikipedia.org/wiki/Graphics_processing_unit"},{"id":"term-multimodal-model","name":"Multimodal model","explanation":"A model that works with more than one kind of input or output, such as text, images and audio.","referenceUrl":"https://developers.google.com/machine-learning/glossary#multimodal-model"},{"id":"term-benchmark","name":"Benchmark","explanation":"A defined set of tests used to compare systems. Results depend on the tasks and measures selected.","referenceUrl":"https://arxiv.org/abs/2211.09110"}],"entities":[{"id":"arxiv","name":"arXiv","kind":"organization","context":"arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.","referenceUrls":["https://arxiv.org/","https://info.arxiv.org/help/api/index.html"],"path":"/entities/arxiv","json":"/entities/arxiv.json","markdown":"/entities/arxiv.md"}]},"researchQuestions":[],"withdrawn":false,"references":{"schemaVersion":1,"target":{"type":"reading","id":"285686de70e67824756717704d8e9f5aadda110d9933a8043dc1346e6916b3c6","revision":"83f60daa-c5de-45c5-84e1-3ce0304d53a5","revisionKind":"publication-id","title":"cuArms coordinates multiple robot arms with GPU-accelerated planning","url":"https://escapemeridian.com/readings/cuarms-coordinates-multiple-robot-arms-with-gpu-accelerated-285686de"},"source":{"type":"source","id":"https://arxiv.org/abs/2610.36250","revision":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","revisionKind":"sha256-source-text","title":"cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation","url":"https://arxiv.org/abs/2610.36250"},"status":"available","items":[],"next":null,"scope":"Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter."},"revisions":[{"id":"285686de70e67824756717704d8e9f5aadda110d9933a8043dc1346e6916b3c6","title":"cuArms coordinates multiple robot arms with GPU-accelerated planning","argument":"The authors of “cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation” present a planner for coordinating multiple robot arms working in the same workcell. They argue that existing approaches can be slow, depend on domain-specific assumptions, or scale poorly as robot numbers and task horizons grow.\n\nTheir approach combines GPU parallelism with reusable multi-modal roadmaps for individual robots, then uses a lazy search to resolve conflicts and deadlocks. A high-level scheduler proposes candidate task skeletons for cuArms to turn into coordinated manipulation plans. The authors report real-time initial solutions and later improvements in motion quality and scheduling, and say the approach does not require the days of domain-specific training they associate with learning approaches.\n\nEscape Meridian interpretation: If these results transfer beyond the reported experiments, faster coordination could help industrial tasks that benefit from several arms working together. The abstract also says videos cover simulated and real-world industrial tasks, but does not provide benchmark details, numerical comparisons, or evidence about performance in sustained deployment. How does cuArms perform against the compared approaches as the number of arms and task horizon increase?","topicIds":["intelligence-and-discovery"],"domain":"science","claimType":"attributed reading","limits":"The abstract does not provide benchmark details or numerical comparisons, so the reported performance cannot be assessed from this evidence alone. The abstract does not establish performance in sustained industrial deployment.","speaker":{"name":"Huang, Philip, Ramos, Fabio, Li, Jiaoyang, Garrett, Caelan, Johannsmeier, Lars","roleAtSource":"Article author"},"source":{"title":"cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation","publisher":"arXiv cs.AI","format":"Article","url":"https://arxiv.org/abs/2610.36250","publishedAt":"2026-10-26","dateBasis":"Date basis not provided."},"evidence":[{"text":"The authors present cuArms as a conflict-based planner co-designed with GPU parallelism for multi-arm manipulation.","excerpt":"we introduce cuArms, a conflict-based planner co-designed with GPU parallelism","url":"https://arxiv.org/abs/2610.36250","relationship":"Source-stated"},{"text":"The planner uses reusable multi-modal roadmaps for individual robots and lazy search to resolve conflicts and deadlocks.","excerpt":"using reusable multi-modal roadmaps for individual robots and resolves conflicts and deadlocks through a lazy search.","url":"https://arxiv.org/abs/2610.36250","relationship":"Source-stated"},{"text":"The authors report real-time initial solutions and anytime improvements in motion quality and scheduling, without days of domain-specific training required by learning approaches.","excerpt":"with real-time initial solutions and anytime improvements in both motion quality and scheduling, without days of domain-specific training required by learning approaches.","url":"https://arxiv.org/abs/2610.36250","relationship":"Source-stated"},{"text":"If the reported results transfer beyond the experiments, faster coordination could help industrial tasks that benefit from multiple arms working together.","excerpt":"Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks.","url":"https://arxiv.org/abs/2610.36250","relationship":"Escape Meridian interpretation"}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.36250","checkedAt":"2026-11-04T17:08:09.297Z","items":[],"note":"No suitable media was found by the supported page extraction."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"How does cuArms perform against the compared approaches as the number of arms and task horizon increase?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"cuArms coordinates multiple robot arms with GPU-accelerated planning","summary":"The authors of “cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation” present a planner for coordinating multiple robot arms working in the same workcell. They argue that existing approaches can be slow, depend on domain-specific assumptions, or scale poorly as robot numbers and task horizons grow.\n\nTheir approach combines GPU parallelism with reusable multi-modal roadmaps for individual robots, then uses a lazy search to resolve conflicts and deadlocks. A high-level scheduler proposes candidate task skeletons for cuArms to turn into coordinated manipulation plans. The authors report real-time initial solutions and later improvements in motion quality and scheduling, and say the approach does not require the days of domain-specific training they associate with learning approaches.\n\nEscape Meridian interpretation: If these results transfer beyond the reported experiments, faster coordination could help industrial tasks that benefit from several arms working together. The abstract also says videos cover simulated and real-world industrial tasks, but does not provide benchmark details, numerical comparisons, or evidence about performance in sustained deployment. How does cuArms perform against the compared approaches as the number of arms and task horizon increase?","question":"How does cuArms perform against the compared approaches as the number of arms and task horizon increase?","claims":[{"text":"The authors present cuArms as a conflict-based planner co-designed with GPU parallelism for multi-arm manipulation.","excerpt":"we introduce cuArms, a conflict-based planner co-designed with GPU parallelism","support":"source-stated"},{"text":"The planner uses reusable multi-modal roadmaps for individual robots and lazy search to resolve conflicts and deadlocks.","excerpt":"using reusable multi-modal roadmaps for individual robots and resolves conflicts and deadlocks through a lazy search.","support":"source-stated"},{"text":"The authors report real-time initial solutions and anytime improvements in motion quality and scheduling, without days of domain-specific training required by learning approaches.","excerpt":"with real-time initial solutions and anytime improvements in both motion quality and scheduling, without days of domain-specific training required by learning approaches.","support":"source-stated"},{"text":"If the reported results transfer beyond the experiments, faster coordination could help industrial tasks that benefit from multiple arms working together.","excerpt":"Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks.","support":"interpretation"}],"limitations":["The abstract does not provide benchmark details or numerical comparisons, so the reported performance cannot be assessed from this evidence alone.","The abstract does not establish performance in sustained industrial deployment."],"mediaId":"6475d2107c09030a35f0fc5571cad7e86c13dacab68fb81ea67d6d299688af51","correctionReason":null,"id":"285686de70e67824756717704d8e9f5aadda110d9933a8043dc1346e6916b3c6","publicationId":"83f60daa-c5de-45c5-84e1-3ce0304d53a5","version":1,"sourceUrl":"https://arxiv.org/abs/2610.36250","sourceTitle":"cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation","publisher":"arXiv cs.AI","publishedAt":null,"originalPublished":{"value":"2026-10-26","precision":"day","basis":"publisher_citation_date"},"firstObservedAt":"2026-10-28T05:02:33.401Z","createdAt":"2026-11-04T15:01:54.388Z","historical":true,"entityIds":["arxiv"],"topicIds":["intelligence-and-discovery"],"media":[{"id":"6475d2107c09030a35f0fc5571cad7e86c13dacab68fb81ea67d6d299688af51","kind":"article","url":"https://arxiv.org/abs/2610.36250","sourceUrl":"https://arxiv.org/abs/2610.36250","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-10-29T21:31:03.235Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."}],"sourceMedia":{"status":"not_found","sourceUrl":"https://arxiv.org/abs/2610.36250","checkedAt":"2026-11-04T17:08:09.297Z","items":[],"note":"No suitable media was found by the supported page extraction."},"editorialDecision":{"outcome":"accept","reasonCodes":["material_development"],"rubricVersion":"stream-admission-v5","mediaId":"6475d2107c09030a35f0fc5571cad7e86c13dacab68fb81ea67d6d299688af51","confidence":0.76,"provider":"typesafe","model":"jev-1.13.0","inputHash":"9af68638d4c169b52c64f1e2f520a6e7d8094fd5eae145dc68252d08e41486f1","decidedAt":"2026-10-29T21:31:04.000Z","redecided":{"from":"stream-admission-v5","under":"stream-admission-v5+confidence-0.5"}},"provenance":{"textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observationId":"5d67f0f6119f596d89dbb42c173d38d95399081c93bad95dd5f1cb7dc79ab1ea","retrievedAt":"2026-10-29T21:31:03.110Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"acdb916d-cef9-4321-8c20-44c78cb12009"},"reviewer":{"model":"gpt-6-luna","runId":"5920803c-4cfd-4d56-884c-a40b8dd6c8de"}},"audit":{"status":"passed","reviewedAt":"2026-11-04T15:01:54.388Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Huang, Philip","url":null,"kind":"unknown"},{"name":"Ramos, Fabio","url":null,"kind":"unknown"},{"name":"Li, Jiaoyang","url":null,"kind":"unknown"},{"name":"Garrett, Caelan","url":null,"kind":"unknown"},{"name":"Johannsmeier, Lars","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Huang, Philip","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Huang, Philip","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}},{"actor":{"name":"Ramos, Fabio","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Ramos, Fabio","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:1","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}},{"actor":{"name":"Li, Jiaoyang","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Li, Jiaoyang","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:2","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}},{"actor":{"name":"Garrett, Caelan","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Garrett, Caelan","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:3","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}},{"actor":{"name":"Johannsmeier, Lars","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Johannsmeier, Lars","status":"stated","provenance":{"basis":"head-meta","sourceUrl":"https://arxiv.org/abs/2610.36250","locator":"head/meta[name=author|citation_author]"},"assertionId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716:participation:4","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-10-29T21:31:03.110Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","sourceUrl":"https://arxiv.org/abs/2610.36250","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z"}}],"participationCoverage":{"total":5,"returned":5,"complete":true,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://arxiv.org/abs/2610.36250","snapshotId":"a3728df4c49893a31d347c216b3f738a236e1ef7920c4ecc7de01bc5aac3e716","contentSha256":"52eea4dc83cbc19047f52b590ad0a6a5b053e3edb959ea15f8de8cc1ebc3fce7","textHash":"80c79118683d64a71f5ab5e1abf9cbd0fe62d50d4c3d88cd235e289a7bd02aff","observedAt":"2026-10-29T21:31:03.110Z","basis":"publisher-page-metadata","sourceLinks":46,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/285686de70e67824756717704d8e9f5aadda110d9933a8043dc1346e6916b3c6/source"}}}],"audits":[{"publicationId":"83f60daa-c5de-45c5-84e1-3ce0304d53a5","verdict":"passed","findings":[{"dimension":"attribution","status":"pass","explanation":"The reading attributes the work and its claims to the authors of the named arXiv paper. Performance claims are framed as the authors' reports, and the interpretive paragraph is clearly labeled as Escape Meridian interpretation.","excerpt":"Authors:Philip Huang, Fabio Ramos, Jiaoyang Li, Caelan Garrett, Lars Johannsmeier"},{"dimension":"summary","status":"pass","explanation":"The summary states the actor, the problem (coordinating multiple arms in a shared workcell), the method and the reported results, and keeps the source's technical terms exactly. Lazy search, multi-modal roadmaps and skeletons would benefit from term-library tooltips, but the gist stays readable. Minor softening: the source says prior approaches are too slow, while the draft says they can be slow. The draft also omits the state-of-the-art claim. Neither changes the meaning. The question follows from the scaling problem the source raises.","excerpt":"So far, these approaches are either too slow, rely on domain-specific assumptions, or scale poorly with the number of robots and task horizon."},{"dimension":"date","status":"pass","explanation":"The originalPublished value of 2026-10-26 matches the v1 submission date on the arXiv page. The later feed date is correctly not used.","excerpt":"[Submitted on 26 Oct 2026]"},{"dimension":"support","status":"pass","explanation":"Each source-stated claim matches the abstract verbatim. The industrial-benefit inference is explicitly conditional and labeled as interpretation. It is anchored to the abstract's own statement about the potential of multi-arm manipulation. The limitations correctly note the absence of numbers and deployment evidence, and no stakeholder costs or benefits are invented.","excerpt":"Experimental results show that our method establishes a new state of the art, with real-time initial solutions and anytime improvements in both motion quality and scheduling"},{"dimension":"media","status":"pass","explanation":"The selected media is the verified direct link to the original arXiv abstract page, which is the most direct access to the source.","excerpt":null}],"createdAt":"2026-11-04T15:01:54.388Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}