{"as_of":"2026-08-10T13:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0c20072add4e16fef1d407c9cb88603ee725b051dc39eb88d88d11188fcc3e31","coverage":[{"denominator":17,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T11:25:58.503982Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T01:02:15.404416Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T15:25:48.476063Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"cited_work":{"arxiv_id":"2509.02815","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.02815","snapshot_observed_at":"2026-07-01T15:25:48.476063Z","title":"Multi-embodiment locomotion at scale with extreme embodiment randomization","venue":null,"work_id":"af893ce3-5b78-4622-aaec-a5952414cd15","year":2025},"citing_paper":{"arxiv_id":"2605.08020","last_updated":"2026-05-08T17:07:58Z","snapshot_observed_at":"2026-07-06T23:20:19.821342Z","submitted_at":"2026-05-08T17:07:58Z","title":"Active Embodiment Identification with Reinforcement Learning for Legged Robots","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-11T02:33:18.736942Z"},"links":{"cited_paper":"/paper/2509.02815","citing_paper":"/paper/2605.08020"},"observation_digest":"sha256:92a165917de655b9753bbcdacdb3d809920f09e214c7f2f0822dd490010cc7b8","observation_id":"2731ab20-5aed-4616-814d-29921ceb32ba","resolution":{"observed_at":"2026-05-11T03:15:56.468859Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"cited_work":{"arxiv_id":"2509.02815","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.02815","snapshot_observed_at":"2026-07-01T15:25:48.476063Z","title":"Multi-embodiment locomotion at scale with extreme embodiment randomization","venue":null,"work_id":"af893ce3-5b78-4622-aaec-a5952414cd15","year":2025},"citing_paper":{"arxiv_id":"2606.00702","last_updated":"2026-05-30T12:21:09Z","snapshot_observed_at":"2026-08-06T23:32:01.777916Z","submitted_at":"2026-05-30T12:21:09Z","title":"Shape Your Body: Value Gradients for Multi-Embodiment Robot Design","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T18:41:46.839905Z"},"links":{"cited_paper":"/paper/2509.02815","citing_paper":"/paper/2606.00702"},"observation_digest":"sha256:d1f4347dc9dba5062fdc0544d521f0f35a62a1e124958470ad71aa7139eed101","observation_id":"bf569ee8-2027-4f98-9b4b-4d93febfb01b","resolution":{"observed_at":"2026-06-28T18:42:28.950196Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"cited_work":{"arxiv_id":"2509.02815","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.02815","snapshot_observed_at":"2026-07-01T15:25:48.476063Z","title":"Multi-embodiment locomotion at scale with extreme embodiment randomization","venue":null,"work_id":"af893ce3-5b78-4622-aaec-a5952414cd15","year":2025},"citing_paper":{"arxiv_id":"2606.28476","last_updated":"2026-06-26T16:05:10Z","snapshot_observed_at":"2026-08-02T18:54:24.801863Z","submitted_at":"2026-06-26T16:05:10Z","title":"FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-06-30T01:28:29.441778Z"},"links":{"cited_paper":"/paper/2509.02815","citing_paper":"/paper/2606.28476"},"observation_digest":"sha256:bce583ce09a6ee48ff20b5fca50a4643dc1cf7f0261e188e970104c6726218fc","observation_id":"94f9e8a4-46cc-4a3d-9162-cdf9b9a2f3fb","resolution":{"observed_at":"2026-07-01T15:25:48.477690Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.02815","snapshot_observed_at":"2026-08-06T00:09:48.228413Z","title":"Multi-Embodiment Loco- motion at Scale with extreme Embodiment Randomization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01506","last_updated":"2026-08-05T23:16:29Z","snapshot_observed_at":"2026-08-10T11:23:41.845745Z","submitted_at":"2026-08-02T21:31:49Z","title":"Rapid Embodiment Adaptation for Quadrupedal Locomotion","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T00:09:48.228413Z"},"links":{"cited_paper":"/paper/2509.02815","citing_paper":"/paper/2608.01506"},"observation_digest":"sha256:bbbeaec8caa3ee1f257e2155e2e4d24700c719d84b5ca931fde2140c81389989","observation_id":"f8918361-c1dd-4800-af1a-29f786c24aa1","resolution":{"observed_at":"2026-08-06T00:09:48.228413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.02815","snapshot_observed_at":"2026-08-07T01:02:15.404416Z","title":"Multi-Embodiment Loco- motion at Scale with extreme Embodiment Randomization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.01506","last_updated":"2026-08-05T23:16:29Z","snapshot_observed_at":"2026-08-10T11:23:41.845745Z","submitted_at":"2026-08-02T21:31:49Z","title":"Rapid Embodiment Adaptation for Quadrupedal Locomotion","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T01:02:15.404416Z"},"links":{"cited_paper":"/paper/2509.02815","citing_paper":"/paper/2608.01506"},"observation_digest":"sha256:1a8dc16502b717ee14eaa650e140ebb9e646658afb9374cb199483800fe9cdee","observation_id":"5fd03ab5-f90f-4828-a612-78f820da217c","resolution":{"observed_at":"2026-08-07T01:02:15.404416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2509.02815/citation-record","integrity":"/paper/2509.02815/integrity","json":"/paper/2509.02815/citation-record.json","paper":"/paper/2509.02815"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:26:01.498049Z","title":"Learning coor- dinated badminton skills for legged manipulators,","venue":null,"work_id":"1a2983e2-5139-49e3-8cef-51b7e0a408b1","year":2025},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.160286Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:1ba50911fc135792f56f08e3f54cda01e54ea2be673ea586a983e3308c525ab8","observation_id":"c35c86d9-bd9f-4932-8ec9-9fde86c359a8","resolution":{"observed_at":"2026-08-05T11:26:01.563208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.21043","last_updated":"2025-09-04T13:32:15Z","snapshot_observed_at":"2026-08-06T05:37:57.983283Z","submitted_at":"2025-08-28T17:49:12Z","title":"HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.21043","snapshot_observed_at":"2026-08-05T11:25:57.229092Z","title":"Hitter: A humanoid table ten- nis robot via hierarchical planning and learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.229092Z"},"links":{"cited_paper":"/paper/2508.21043","citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:05abb243162bef73b6c08671e9855ac15e98d126694005c88e9feeb1428e4b4c","observation_id":"8fe9b27b-56cf-4b30-ad59-59e25f851204","resolution":{"observed_at":"2026-08-05T11:25:57.229092Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:26:01.254897Z","title":"Walk these ways: Tuning robot control for generalization with multiplicity of behavior,","venue":null,"work_id":"5b1afc25-5a2e-438b-b1b3-a32bccadce75","year":2023},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.295119Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:7193766555bb48fad8a1a02367e78c18abf4633f71e24e25fb98deddc6696e61","observation_id":"f359b3aa-1dac-486d-93a3-dbf496b15d3d","resolution":{"observed_at":"2026-08-05T11:26:01.381684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:26:01.085274Z","title":"Extreme parkour with legged robots,","venue":null,"work_id":"f9cf78fa-b79d-4991-a7b4-488d12733da3","year":2023},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.386007Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:65a41bafaad82f0eb3678b82d77f8877bfafcbc2f0741fef28757a56caf031cb","observation_id":"43525bfc-6e70-4244-881c-09f5f4de07a9","resolution":{"observed_at":"2026-08-05T11:26:01.180820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:26:00.874013Z","title":"One policy to run them all: an end-to-end learning approach to multi-embodiment locomotion,","venue":null,"work_id":"0828754a-df4e-4af7-b213-de3868646744","year":2024},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.447424Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:586254393b2df39a8a5fcc51240ee6263d32148d7c06612df42e5a702455bfdf","observation_id":"91268c5d-90b0-4b75-9377-9ad203235a90","resolution":{"observed_at":"2026-08-05T11:26:00.997360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:26:00.650473Z","title":"Bridge the gap: Enhancing quadruped locomotion with vertical ground perturbations,","venue":null,"work_id":"fd5bd598-8917-4479-9d01-25c5a2c24405","year":2025},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.554617Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:bc62bd9f6b5aed4ae8badb2c3dab49f1504202f2a2f6cb01d56812d3be4848cc","observation_id":"8d93428e-c0ae-4759-aca4-241a67b4901c","resolution":{"observed_at":"2026-08-05T11:26:00.755554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:26:00.423395Z","title":"Orbit: A unified simulation framework for interactive robot learning environments,","venue":null,"work_id":"5fc459e0-2ef2-4936-87d4-29fc8f889939","year":2023},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.614061Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:b73154836b50ca337d3f335581539255314950aa5e1c5a344cafcd07810d4d51","observation_id":"193d701c-3680-4d3d-bfca-754029ef7345","resolution":{"observed_at":"2026-08-05T11:26:00.522100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:26:00.200172Z","title":"Mujoco: A physics engine for model-based control,","venue":null,"work_id":"f4c74894-63d4-41d2-88c4-51dd74e53dfb","year":2012},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.697834Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:19de0b52900af81b3c9f1a64be9ba2d7c991bee95c2a13269ca355a057f1a643","observation_id":"a43e485c-0b4c-4d0f-98d5-331388b56e59","resolution":{"observed_at":"2026-08-05T11:26:00.313273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-05T11:25:57.812714Z","title":"Proximal policy optimization algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.812714Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:6e4c40b3d0aa36b13935f832bbb374d43537873aba02be8cacbb2cee049d7bc7","observation_id":"d6db12d1-0243-4a35-8554-546499706bcf","resolution":{"observed_at":"2026-08-05T11:25:57.812714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:25:59.990703Z","title":"Domain randomization for transferring deep neural networks from simulation to the real world,","venue":null,"work_id":"4d9271b6-717d-4ff8-84f9-9b5a982441ae","year":2017},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.894819Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:d561309a6d9f38d28ec9a0f7dcc6a322a4c06c4e2ecdde2ca36cf2622bae52c1","observation_id":"84223205-8d1d-4cc9-a67b-984b393e3904","resolution":{"observed_at":"2026-08-05T11:26:00.085530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:25:59.816626Z","title":"Rapid locomotion via reinforcement learning,","venue":null,"work_id":"9b08e040-40ab-4d27-9753-ea2ce6964cd0","year":2024},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:57.998729Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:0fbc91f18427ce614d14784d6c9d61a88c48487e45ae0a075f42e0b1e8555171","observation_id":"fd8d3cbd-b13b-4d7b-b6a1-58004570d73d","resolution":{"observed_at":"2026-08-05T11:25:59.895746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:25:59.676810Z","title":"Gait in eight: Efficient on-robot learning for omnidirectional quadruped locomotion,","venue":null,"work_id":"2774dc43-83d7-438d-92f8-63a8d7712a5d","year":2025},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:58.080088Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:4906d5a96fa34494841de4a78553e6f4decb898e012b6b1b3cfa00521f380ff0","observation_id":"ad5dbbcd-3226-4bf5-a8d7-34fc6a9a105d","resolution":{"observed_at":"2026-08-05T11:25:59.741423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:25:59.500335Z","title":"Nervenet: Learning structured policy with graph neural networks,","venue":null,"work_id":"1171e894-018a-44c4-a4ce-484d8bb6853c","year":2018},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:58.173656Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:4d9c2b78dc2713140adbbb36ed694983c2ba8db6d6958f254774603546275454","observation_id":"b0c94c42-d62a-407f-b9e5-b29a642f3384","resolution":{"observed_at":"2026-08-05T11:25:59.560505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:25:59.367044Z","title":"Metamorph: learning universal controllers with transformers,","venue":null,"work_id":"c7e9005e-422d-4b70-ba17-5dd7fc9f43e2","year":2022},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:58.231505Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:b2a6987fdd5ee5557d844f22ac956994991c7518c408f26421145911afcc6b13","observation_id":"da5e9dbd-992b-49b0-8d93-29b827afca8c","resolution":{"observed_at":"2026-08-05T11:25:59.425257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:25:59.130921Z","title":"Towards embodiment scaling laws in robot locomotion,","venue":null,"work_id":"c6d559a2-770e-4261-84b8-1ad59f92e421","year":2025},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:58.283525Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:7217496d9b5654fc66938d5b1fe239ea7ed4018d814a0eb6fb290d82e872afbc","observation_id":"638d9961-b4cc-4078-99c0-f6e22d4a5bd6","resolution":{"observed_at":"2026-08-05T11:25:59.271925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:25:58.943649Z","title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks,","venue":null,"work_id":"16e5c0ef-9e73-4738-9189-e13421c62150","year":2016},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:58.378557Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:3b1615d910a045af8542bf488e9659be041c07ad4a1e0a8aae78a2bf0b85c43f","observation_id":"01be2772-af4d-461c-b035-0faaa9e0d800","resolution":{"observed_at":"2026-08-05T11:25:59.028693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:25:58.678192Z","title":"Rl-x: A deep reinforcement learning library (not only) for robocup,","venue":null,"work_id":"81b22fab-b3ee-4d90-970f-a58f9622e962","year":2023},"citing_paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T11:25:58.503982Z"},"links":{"citing_paper":"/paper/2509.02815"},"observation_digest":"sha256:997b99687719e150680aff30972a7ddc6fbdb77156ffc914f80fde4ca0c30b5c","observation_id":"19f25b7c-9244-4581-a12b-51afaccfbc36","resolution":{"observed_at":"2026-08-05T11:25:58.822730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.02815","last_updated":"2025-09-02T20:32:02Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-08T19:19:58.140367Z","submitted_at":"2025-09-02T20:32:02Z","title":"Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomization"},"reference_resolution":{"displayed":17,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":17},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 5 inbound Pith citation observations for arXiv:2509.02815."}