{"as_of":"2026-08-23T16:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:59c18e9cce1ca08f7229b52738381367b5aee9f75d8355d3217ceeb161ed5ab2","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T01:10:47.622232Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-03T08:05:16.851267Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-03T08:07:44.881263Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"cited_work":{"arxiv_id":"2605.24924","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.24924","snapshot_observed_at":"2026-07-03T08:07:44.881263Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","venue":"cs.RO","work_id":"490b8906-dfe8-4cc8-9d29-a1016d038038","year":2026},"citing_paper":{"arxiv_id":"2607.01819","last_updated":"2026-07-02T07:31:47Z","snapshot_observed_at":"2026-08-04T21:14:25.257579Z","submitted_at":"2026-07-02T07:31:47Z","title":"Koopman operator theory: fundamentals, control, and applications","version":1},"reference_index":194,"source":"pdf_text","source_observed_at":"2026-07-03T08:05:16.851267Z"},"links":{"cited_paper":"/paper/2605.24924","citing_paper":"/paper/2607.01819"},"observation_digest":"sha256:117266e9e80e548f23e4f3d5602780b9d51d0780c000c0a94d466567397206de","observation_id":"39ce51c8-7140-4104-8f8d-004e8027d613","resolution":{"observed_at":"2026-07-03T08:07:44.883068Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2605.24924/citation-record","integrity":"/paper/2605.24924/integrity","json":"/paper/2605.24924/citation-record.json","paper":"/paper/2605.24924"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T01:10:47.622232Z","title":"A survey on deep generative models for robot learning from multimodal demonstrations,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:67b258055a9603b23e7149951d075e71c25b7c96bc29e6a33ef0de54f8df5c45","observation_id":"c0e14488-7d44-42c3-9a3f-6e81a2308abb","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Diffusion models for intelligent transportation systems: A survey,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:b97851846801ab8ec163fa86898e60703116305f63a54db31dba2b56c51014e6","observation_id":"8df82f49-54a2-45d8-a44a-ad8b9f81278d","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Model-based diffusion for trajectory optimization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:e6cc24f1f1e4e88425e1e724cb237227d88c5436f14cc822640db091c1cece94","observation_id":"b6603f1f-78cc-49f6-a7b4-b24d29493131","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Diffusion policy: Visuomotor policy learning via action diffusion,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:a46692895c7425440c56ef2ccb9f1cc16de67633be0287e58ae67950e216b320","observation_id":"7ccf7ff0-75a5-4a72-b63b-c7a3a27c5571","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2601.15729","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T15:45:48.220653Z","title":"DualShield: Safe model predictive diffusion via reachability analysis for interactive autonomous driving,","venue":null,"work_id":"7da8871f-fa50-4007-a698-0d71b6127133","year":2026},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:cace2f6b3a101105006cfffd1687e902107990b576629a6aa8d65fc940b05a13","observation_id":"8ae3d771-127e-42c4-933f-051cab63d8be","resolution":{"observed_at":"2026-07-01T15:45:48.222428Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T01:10:47.622232Z","title":"SICNav-Diffusion: Safe and interactive crowd navigation with diffusion trajectory predictions,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:66e6b8a5b8422f370e3973cbb84903c319d1756470044db4f41a4259401a8fdf","observation_id":"8f2b99f7-bdfb-4316-a499-c5a9dfceaf58","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"DARE: Diffu- sion policy for autonomous robot exploration,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:a17482313d9b2f56a6688573d2908ca7a684f461fbbf1fb9cc045a524739adc3","observation_id":"6370b3c0-45ed-4998-a80e-4848d3d72a37","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Score-based generative modeling through stochastic differ- ential equations,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:04f2e080df86027696851d08e46651702e8d1fc4a23984ac02139c6a7496cd1a","observation_id":"95a575cc-aa2e-4f67-b37d-b00ebf66a66f","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:bb51c4582cccda1e2cd0ed3ad13168cb93179c48c2fae98d38fb6a016d17eeea","observation_id":"6a8188ba-9f7a-4fa6-ac01-f9d92752d8fb","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Learning agile and dynamic motor skills for legged robots,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:1f42fac2151db7b0d1daf6bcfd4db925664dee2f7c58070d5c304a7f333badf3","observation_id":"0cb50c73-1813-4c5f-aa6b-6f4caf91be7f","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Consistency models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:9deb2a9db1d17c0dd83e0593c5ff61e6c1f9f75d0599d2cdd30ee52c4ab66a5f","observation_id":"abb410eb-78e1-4555-b0b0-fae32898fbdb","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Improved techniques for training consistency models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:977fcd3223996d6db4099f44e9619d5ceeee0d14900ea67016e78ebad62feab1","observation_id":"77d7d4c7-8be1-4c5a-9251-d7ebd94b3b35","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Progressive distillation for fast sampling of diffusion models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:465d014f1637ce40d1c7c98df69ab031ecb40ad78f43261a81822ceababa224a","observation_id":"5b0765ba-69c3-4206-8a9a-e4040f9bdc19","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"One-step diffusion policy: Fast visuomotor policies via diffusion distillation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:ec425e015e16b4bd1d4f2f9e183365dd3fbd1ffac54210bdef90f47de5ca4611","observation_id":"eb1f1c45-b5bb-4072-8eab-2da20e58e497","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Koopman operator dynamical models: Learning, analysis and control,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:ab29a83808d3f59ffb6f131b79daa258912d1d4cee4b0629983c34ee5686018d","observation_id":"7e9bd99b-4be3-41b9-84d6-8ebaa279fc7c","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Koopman operators in robot learning,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:6edce12c67efc7f481419c6ddc1d518e06aeb26b7ff06758fb9f4a143b3091e9","observation_id":"84e0a5c3-42d1-4b47-82b4-aedfd317b7dc","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Imitation learning with limited actions via diffusion planners and deep Koopman controllers,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:82cab9146b2b13b1d6a73087f1d22b28849433d2781e40b81c8710091b33d604","observation_id":"02496d07-00f4-4130-98d8-ee7376305776","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"An overview of Koopman-based control: From error bounds to closed-loop guarantees,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:c93768cb086193e6bba59c2536648745738a7b074cc9a9ad2f2bf84030f1c904","observation_id":"ad769f17-d9b9-4c00-b30d-dbf59bea4e7a","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"One- Step Offline Distillation of Diffusion-based Models via Koopman Mod- eling,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:383d08a7d3b495e05df86342f06ba2f1076279a19ea4993802883e02eabc3968","observation_id":"2f83465f-ad50-411f-924a-9dd6450e3c2a","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Spectral properties of dynamical systems, model reduction and decompositions,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:f59a27550d7e71dfef42f3739d29ac74ee78f56eeebea7d3d8c37009b0303c1a","observation_id":"f499167e-429b-49f2-b292-ba33b4972b99","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"CoPlanner: An interactive motion planner with contingency-aware diffusion for autonomous driving,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:ca5db3e1b35e63b3c199ab72488be6f4ba4e2683b20ed2b6aadd7f7d18812ad4","observation_id":"a4bcd2d7-4f36-454f-8d6c-31016a6a69a9","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Cle- andiffuser: An easy-to-use modularized library for diffusion models in decision making,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:c31935a8117c9028eead5cc78c251e4a8a15d937b8710c5e79ccd61d4902c3f0","observation_id":"1be0a2c2-98a5-4548-99aa-56577c503064","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:ceb4dff0d21401f2aa3385d4edd0049199a2e36a33a2bb0e8364bc90baa353b6","observation_id":"f965e0d5-861d-4640-84db-32f094dbc916","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:97691965ad7c157c8c62063252def84d6ba5befd2a367b401b5861c40775b119","observation_id":"5d158adf-1534-4da9-985c-633dbb86004d","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Elucidating the design space of diffusion-based generative models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:3de9e72a121733f718f29690bdbcdec372131b989b3d809c48a9a1d2a0e895ea","observation_id":"59f1535e-4e83-4470-be3f-03e2322fdd79","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Continual learning and lifting of Koopman dynamics for linear control of legged robots,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:82b315a44d5aed6149104ff1ebcbb8ff0a01ec4b8904f47be44a9a1942d468ce","observation_id":"4fffa9c3-d341-4d86-bcae-1b1d7c1be1ba","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Koopman kernel regression,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:50deff571be5a4d7e0e9c6fd7944c897f75a723b682be1e026a16efa2f8eb561","observation_id":"a8be0349-6508-4f93-9673-4b74718d138e","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Offline reinforcement learning with implicit Q-learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:3224bc72e8388dcec9a6096370679a3564974ce497c5daca1b23af84fba11477","observation_id":"4851bd75-e544-4d26-8ac2-5b4920be9b16","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","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":"2026-06-30T01:10:47.622232Z","title":"Toward near-globally optimal nonlinear model predictive control via diffusion models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:713c9795276f8c2dfe9f53c74789f26287a63f050c21476b938d73b0032443e3","observation_id":"e046e1be-fe21-47c6-9f49-86886faa7400","resolution":{"observed_at":"2026-06-30T01:10:47.622232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.07219","last_updated":"2021-02-06T01:57:28Z","snapshot_observed_at":"2026-08-16T08:32:46.407746Z","submitted_at":"2020-04-15T17:18:19Z","title":"D4RL: Datasets for Deep Data-Driven Reinforcement Learning","version":4},"cited_work":{"arxiv_id":"2004.07219","doi":"10.48550/arxiv.2004.07219","metadata_source":"pith","pith_arxiv_id":"2004.07219","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"D4RL: Datasets for Deep Data-Driven Reinforcement Learning","venue":"cs.LG","work_id":"47082e4e-a4a5-418b-bf4f-4667355065fc","year":2020},"citing_paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-30T01:10:47.622232Z"},"links":{"cited_paper":"/paper/2004.07219","citing_paper":"/paper/2605.24924"},"observation_digest":"sha256:ce94637fd1faaf6172ec98f4fe508e7ac76d1a63208895acf254eb4bb3735d62","observation_id":"620cc88a-f8c3-4a09-b8de-8368248bf548","resolution":{"observed_at":"2026-07-01T15:45:48.219518Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.24924","last_updated":"2026-05-24T08:03:49Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-07-06T23:34:57.148983Z","submitted_at":"2026-05-24T08:03:49Z","title":"Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":28,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":30},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2605.24924."}