{"as_of":"2026-08-04T10:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d1852780e4429551ac4499a81774eb411bce6a84d9f857a7ac90ebf118be6e6b","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T07:33:51.586265Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+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/2607.11027/citation-record","integrity":"/paper/2607.11027/integrity","json":"/paper/2607.11027/citation-record.json","paper":"/paper/2607.11027"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Sail: Faster-than-demonstration execution of imitation learning policies","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:8e87499bce0ea6eb0cecdd12acd8c8cab50db3de0cf3bf7a7a7fa3c6f16b103c","observation_id":"50406232-6732-46c4-9959-5dcb6b0686f8","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"A framework for behavioural cloning","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:029fc40f5e7bf181e6e8fe4bde3ab4d12550e21275b61bb104908d3cca214e8d","observation_id":"2bfde493-df7d-498a-bfb7-6d4644fe3e62","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Hydra: Hybrid robot actions for imitation learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:c17530379088725541e5c57b2eb15555e793ed8198e8e7f50b16ba6607ac9498","observation_id":"4618333e-b4f4-4d2e-afe7-439b15ed0e44","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.24164","last_updated":"2026-01-08T17:01:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-31T17:22:30Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.24164","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2410.24164","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:6296d9edd5d352dc53e2577724c0bf38f0b8b3d4cb83b757f0a1ad29d99d4d07","observation_id":"96dc9017-7c8b-47eb-af87-20166f40f08a","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07339","last_updated":"2025-12-05T07:35:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-09T01:01:59Z","title":"Real-Time Execution of Action Chunking Flow Policies","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07339","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Real-time execution of action chunking flow policies.arXiv preprint arXiv:2506.07339, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2506.07339","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:a65d6ea0df811088b3d612e8ba3a98d09d90dce29e51c92cd4b5e12499a629c6","observation_id":"e94b3785-5a5d-4b14-b5d6-997ce1dc734e","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Polarnet: 3d point clouds for language-guided robotic manipulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:72d2ade88496ded1ee944e535a17287251d3790f81b416788038131fe23b5861","observation_id":"222d647e-540c-446e-84fd-28080380806e","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Bail: Best-action imitation learning for batch deep reinforcement learning.AdvancesinNeuralInformationProcessingSystems, 33:18353–18363, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:7c24f267e89661837250330febdce7ddaa5eff2ed53d0356dc0897e4a931a141","observation_id":"3fea1f84-61b6-4ff1-bbc5-0bef235a7688","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12724","last_updated":"2025-08-13T03:48:11Z","snapshot_observed_at":"2026-07-06T20:38:29.607020Z","submitted_at":"2025-02-18T10:40:39Z","title":"Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12724","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Responsive noise-relaying diffusion policy: Responsive and efficient visuomotor control.arXivpreprintarXiv:2502.12724, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2502.12724","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:aaaaeb85fea64149f628768adc5235d6615c0d886bfd60a21274ca42f2d3ffcf","observation_id":"a2ead7c8-d934-489a-86c5-8d055fcc397b","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Diffusion policy: Visuomotor policy learning via action diffusion.The International Journal of RoboticsResearch, 44(10-11):1684–1704, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:f4f7019e29c4f4acbc812e17937bd1da6e786ac1a069c41f7be7dbf7ecb77b2c","observation_id":"7f5a21e2-125a-462b-8af2-d75b54e5ec5a","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.05396","last_updated":"2025-08-07T13:49:00Z","snapshot_observed_at":"2026-07-06T22:09:23.364568Z","submitted_at":"2025-08-07T13:49:00Z","title":"Real-Time Iteration Scheme for Diffusion Policy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.05396","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Real-time iteration scheme for diffusion policy.arXiv preprint arXiv:2508.05396, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2508.05396","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:37f7be1041359dfaa4512c97050a462cfc52c85438199f249152d60ddedcb27f","observation_id":"9224e558-a25c-434c-b8e9-c13abcbeb47b","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Sam2act: Integrating visual foundation model with a memory architecture for robotic manipulation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:0ff9be6910c740a17c81840daaa63380842e6dfd7cfeeeef9e5a2893f9a830bb","observation_id":"bee77868-e9b5-49c9-9a89-a79e842b1c96","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Act3d: 3d feature field transformers for multi-task robotic manipulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:3fd73b062fd8efa202fd4f2282115e57f8b7e73d19b31d3fce1d92f0ddc162d1","observation_id":"96e08cc2-cccb-48e1-b5af-43821b3a0628","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Rvt: Robotic view transformer for 3d object manipulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:3a921935b72bbc02a43ab39a16780525e0426fc856d4f2d365e5afce13ce4405","observation_id":"2476b0e0-c46c-454c-b9ad-790fc2738036","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Rvt2: Learning precise manipulation from few demonstrations.RSS, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:49bd8e59accf09f47a929da3dd482f670d3307dc8eaa868ddfe811313fc4a4b1","observation_id":"eb3fbeeb-7414-4652-a1b8-b834b3eb1c90","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Denoisingdiffusionprobabilisticmodels","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:81d7277143f4e2b8331002fa473577cc9e06f98bd401dc3bb4e2b3f6325da365","observation_id":"e3ed2255-2f69-4016-ad29-39abeda567e5","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04806","last_updated":"2024-10-11T16:04:49Z","snapshot_observed_at":"2026-08-04T00:22:32.179014Z","submitted_at":"2024-06-07T10:13:44Z","title":"Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04806","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Streaming diffusion policy: Fast policy synthesis with variable noise diffusion models.arXivpreprint arXiv:2406.04806, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2406.04806","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:e15c5a7badfca789533c113fc939d49b7c637723fec0571a2e51b5fc20abc473","observation_id":"7bf45276-580d-4805-9c38-1c5138b93a81","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Video prediction policy: A generalist robot policy with predictive visual representations","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:e8e54f4209aef765fe326cd81abf158e6b41d55641abd22bbf38b7aeb242cdc4","observation_id":"6bc6de18-e56f-4a37-80b9-8bc1eebf6f2b","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Q-attention: Enablingefficientlearningforvision-basedroboticmanipulation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:77fe9be86a70ac34cc660d29596f10b49899843b24287f8a92fba7f2b71dbe2c","observation_id":"2e2686f4-babd-461c-ad35-17abc4a9ada3","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Rlbench: The robot learning benchmark & learning environment.IEEE Roboticsand AutomationLetters, 5(2):3019–3026, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:d52fd049ffbc1bc4d6aca313bd35c0d94fbbbcdfd9cbd477cedc8ffaab9ce4a8","observation_id":"2fbf4ea7-8ab4-4088-b3f7-3ef054de1581","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Coarse-to-fine q-attention: Efficient learning forvisualroboticmanipulationviadiscretisation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:274af163eacd6265f5579663d618909fcba5d0e24a7b742572ccbdca7694935d","observation_id":"f8fa712c-69cf-4ed2-b4ac-8f7b941ae871","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Sampling-based algorithms for optimal motion planning.Theinternational journal ofroboticsresearch, 30(7):846–894, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:d4ac4496f774c2e187deb64af9512340634cb111587f221f91d7bdb7d3d0a212","observation_id":"20a42e61-9b93-4103-8769-2e332567384e","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Anytime motion planning using the rrt","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:e0e22c2d648f488640709284462ebad96b31a67d2370d9964e83e1b5f9ad43d3","observation_id":"f9beb228-4862-4a0d-9e2f-f34eef1cff64","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"3d diffuser actor: Policy diffusion with 3d scene representations","venue":null,"work_id":null,"year":1949},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:413354770dfbb24f1001102cb84ecb65699322e16f26afb7ab07e54c1833afa0","observation_id":"7f413824-37fb-4e69-af21-e640816abfa1","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.19645","last_updated":"2025-04-28T07:49:39Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-27T00:30:29Z","title":"Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.19645","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Fine-tuning vision-language-action models: Optimizing speed and success","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2502.19645","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:30168e91f08a18a91774581fbd570552399f196483b3487a56074b94e19847c0","observation_id":"17b70279-8c04-4e1a-8ab9-d574f89d8572","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Rrt-connect: An efficient approach to single-query path planning","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:022cc6bbe7a05fab0f4477fe48a74954ba1633e9cc65c66ab83686a3bb296413","observation_id":"504328f5-e7e7-4e17-8989-94ba0dbb110d","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Rapidly-exploring random trees: A new tool for path planning.ResearchReport9811, 1998","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:a886b8252d13aece82feaa89eaed9a8c7b4ffe5aa77954d094856c019906da9c","observation_id":"3934d377-72b0-48b3-a61e-5a56cdbb8982","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Curriculum offline imitating learning.AdvancesinNeuralInformationProcessingSystems, 34:6266–6277, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:8a5f0baf158173661537a711bd34dc2cddbcf38262f4ba2590e7112cd41b36a0","observation_id":"b8d87df1-5a66-4f54-9f0c-a952a13388c2","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Rdt-1b: a diffusion foundation model for bimanual manipulation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:fba918441d351054ba778a2caf59028fd37eaaf418167577e5f32b0fde3bf2e8","observation_id":"5262ce23-7a29-4817-8202-5e09baf7e5f6","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01586","last_updated":"2025-09-06T02:40:42Z","snapshot_observed_at":"2026-07-31T19:12:58.292888Z","submitted_at":"2024-06-03T17:59:23Z","title":"ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01586","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Manicm: Real-time 3d diffusion policy via consistency model for robotic manipulation.arXivpreprintarXiv:2406.01586, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2406.01586","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:6b5ed9b4431f6e7e645d773b5961536402b92936223158703364b9336c9b7fe4","observation_id":"18447fba-bbeb-40de-a420-15404caac84b","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"What matters in learning from offline human demonstrations for robot manipulation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:55b26acdb727ef010196a5e999346004484c045555ba5c7934d9e4f9c91c1baf","observation_id":"051f21b3-2dd9-4d94-8290-7993cc4875eb","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Receding horizon control of nonlinear systems","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:74d5799c1f499d167545a4ebf7a6002d097983159151ecd988ae8d7fe017d8f0","observation_id":"10eca440-38b8-4837-b24c-853c7873ec19","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"An algorithmic perspective on imitation learning.Foundationsand Trends®inRobotics, 7(1-2):1–179, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:a975f2760ee8c4cf88679185a7b42f4947fdb155c7977d9f2e9cba8ec851502a","observation_id":"a99e58fc-6a74-43ac-8fef-28586e02c9e5","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07503","last_updated":"2024-06-28T21:56:25Z","snapshot_observed_at":"2026-08-03T03:05:11.541168Z","submitted_at":"2024-05-13T06:53:42Z","title":"Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07503","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Consistencypolicy: Acceleratedvisuomotor policies via consistency distillation.arXivpreprintarXiv:2405.07503, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2405.07503","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:ca55b51844e62056ba6bcdc85e9cae57b6e92a0e8eb4a92d439653a4bd944b43","observation_id":"61138f34-5cea-43e3-ae56-722bc3a62452","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"V-rep: A versatile and scalable robot simulation framework","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:f4e22be1b4e45f72d7a7e6d239971ab3669e254544c6c7345b9b4a2a326e219a","observation_id":"5531090a-0e9e-4ff7-aded-8722cc653325","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Learning from demonstration.Advancesinneuralinformationprocessingsystems, 9, 1996","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:addf2d26c77c620c372e36f3971db8debac60ac4e1ecc2bb5071f60f4af5e9d1","observation_id":"87ecbf33-c3bd-4e80-b813-a1b5b8934f30","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Is imitation learning the route to humanoid robots?Trendsincognitivesciences, 3(6):233–242, 1999","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:185479f663213554b719d75ebeef853e85052bdf3f925129ac4ffe78fd5b45cf","observation_id":"994e5d7a-f916-49cc-8ecd-568c0e43b2c8","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Waypoint-based imitation learning for robotic manipulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:8729d4ae47410ef80b7d422a1b5a94fa7ae37a00dc395c6ede8bceba87668d75","observation_id":"0792c5fd-a6f7-451c-ba03-c7a97466ba63","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Perceiver-actor: A multi-task transformer for robotic manipulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:57a4eef84d6544c23d4de5d4cff98c749e0f284736e27fd0a9e3e58274f9c54a","observation_id":"211fe646-383c-4352-a594-95cc345b48fc","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Generative image as action models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:d1bb032babf4b16eab610930cce8f50097058b2fbbc61229a04835d2aa9f10f0","observation_id":"65285e5b-c0cc-49df-8b03-32a8346d3117","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:2c2f0150c660c265ad59f4e2374f88cf3fe8b3bad201b9d4aefe6699444c5370","observation_id":"81873772-ec96-4b8c-af5d-3955d85081b5","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"What’s the move? hybrid imitation learning via salient points","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:501e8d7f55f57ac1d3e6843ef04c07dd6692666323cbacab83959ba1a217ed19","observation_id":"9f510978-58db-4b4b-916a-8e469d7ca05a","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12213","last_updated":"2024-05-26T19:55:26Z","snapshot_observed_at":"2026-07-06T18:16:51.116432Z","submitted_at":"2024-05-20T17:57:01Z","title":"Octo: An Open-Source Generalist Robot Policy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12213","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Octo: An open-source generalist robot policy.arXiv preprint arXiv:2405.12213, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2405.12213","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:d87075610ab57a60e6329949d97826414709c2e9a1623c0dec86020fb4a0fcb9","observation_id":"9fb89057-e0d7-48fb-ac62-5b5ef38906db","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Hierarchical diffusion policy: manipulation trajectory generation via contact guidance.IEEETransactionson Robotics, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:b6fcf9103b6bd06bb5c5ea942e4f397824c534beb462f504cb247d2a5fdaadf6","observation_id":"e10a38c2-a73f-4208-94fa-968b539e90b3","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Group normalization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:1fb6c8debbc8a1ab2f5962e667f993c0d17c919ef6dee399b0823a9c18018e60","observation_id":"c9e80cd2-f294-486f-b879-94651c90cfab","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Chaineddiffuser: Unifying trajectory diffusion and keypose prediction for robotic manipulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:38c29087506164f4b1587c92b8edf381faf1ac854250c94df15cffb8f154f377","observation_id":"b51b3543-2cad-460c-80bf-6694fdcd6da9","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Human2robot: Learning robot actions from paired human-robot videos","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:28a9efa3af3b3a844f4235a5b3d3515c3bdefcf3b9a2308839c581ef6e6f5b57","observation_id":"60caf69d-3ae5-4523-a4c7-13cadcd9092a","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10093","last_updated":"2024-09-04T08:20:40Z","snapshot_observed_at":"2026-08-04T00:40:29.437176Z","submitted_at":"2024-06-14T14:49:12Z","title":"BiKC: Keypose-Conditioned Consistency Policy for Bimanual Robotic Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10093","snapshot_observed_at":"2026-07-14T07:33:51.586265Z","title":"Bikc: Keypose-conditioned consistency policy for bimanual robotic manipulation.arXivpreprintarXiv:2406.10093, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2406.10093","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:22e62be2a670dd227cce3f19b67197d5001d7d9b1c943b9abfbe23fadcb51dc2","observation_id":"d72cc96b-d0e4-4834-82e5-cb8655486edc","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Asurveyofimitationlearning: Algorithms, recent developments, and challenges.IEEE Transactionson Cybernetics, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:80fd3dc97ce69c7b81358ba10848e7a4ba64da0ae0937f8435346c69db74517d","observation_id":"165ef2d2-b44d-41e0-adce-e60b5c86873b","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Gnfactor: Multi-task real robot learning with generalizable neural feature fields","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:1b8450747c2f6f5e4559a58447a1acb77649c4e5754e8c5208544f9630222ceb","observation_id":"68854900-70c7-45a6-9270-a6a55a96f924","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations.Robotics: Scienceand SystemsXX, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:6c6879595aad2bb7c42ae05f51c51e0a1383d08236808d8055ad62bfafd2b194","observation_id":"10f14510-c3c3-45a3-af9b-701d0f2bb6f0","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Generalizable humanoid manipulation with 3d diffusion policies","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:ae716e2167cc008d65b5f32e6a0b8076923a124c81ffc21fd90c10db268801fe","observation_id":"0680e248-d232-4c48-92b9-471bf55a9849","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Chain-of-action: Trajectory autoregressive modeling for robotic manipulation.arXiv preprint arXiv:2506.09990, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:a37848ac8ee2095cc5ed93b97dd8a753561e1f1b7dcc5c383021e0c045bbc942","observation_id":"a3cecfcf-3146-4283-9549-d3e9f4ecfe36","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","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":"2026-07-14T07:33:51.586265Z","title":"Effective tuning strategies for generalist robot manipulation policies","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:a9d379914625c2f7cb43da50a84505751a161c15fa8c49410391f5cfac859f4a","observation_id":"acaf30f9-76a0-4c6a-9b0e-2970f44cb163","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-07-14T07:33:51.586265Z","title":"same-mode","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-14T07:33:51.586265Z"},"links":{"cited_paper":"/paper/2304.13705","citing_paper":"/paper/2607.11027"},"observation_digest":"sha256:be224eb9ab64f9760ae80548c839943f30080a375380f45d32e37bd5185243f6","observation_id":"1bb3e63d-009d-47d5-8934-f46b9367ec6e","resolution":{"observed_at":"2026-07-14T07:33:51.586265Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.11027","last_updated":"2026-07-13T02:48:03Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-07-16T23:19:09.214077Z","submitted_at":"2026-07-13T02:48:03Z","title":"SegDiff: Segmented Trajectory Diffusion for Consistent and Adaptive Robot Manipulation"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":53,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":54},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2607.11027."}