{"as_of":"2026-08-09T14:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a68bbfad0ca244cf5482836dcf82b5f34a3335561c63bb63f2417a7170e7a21c","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T00:22:37.562028Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2511.01531/citation-record","integrity":"/paper/2511.01531/integrity","json":"/paper/2511.01531/citation-record.json","paper":"/paper/2511.01531"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T00:22:31.107074Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.107074Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:a04b252956cc57f811456f97ab1f15eef6aab347c5d60fb6467d7909682db726","observation_id":"f7e9296d-f742-4f05-b873-d130a1b53311","resolution":{"observed_at":"2026-08-04T00:22:31.107074Z","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":"2026-08-04T00:22:31.183447Z","title":"Gyenis, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.183447Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:a8f73df22c4f65bb3a384bc53856259d776111f29dc458443d64ced0b97bbcdd","observation_id":"9ef134b7-9578-4f43-a4d1-3f4e5ac1adb3","resolution":{"observed_at":"2026-08-04T00:22:31.183447Z","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":"2026-08-04T00:22:31.351847Z","title":"Danon, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.351847Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:74b700915b639a7c755ca314bdeb30930aad496daae57dd65d2eb9ec70efcf36","observation_id":"2db60dce-2fa2-4a40-91c1-2f58ec0db992","resolution":{"observed_at":"2026-08-04T00:22:31.351847Z","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":"2026-08-04T00:22:31.446023Z","title":null,"venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.446023Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:967c1bd7324eaf1699493fc2e24050d621b03ffb72d25ff9b50d295db49666b4","observation_id":"64b75c73-2f57-4677-8c64-e2e47f9129dd","resolution":{"observed_at":"2026-08-04T00:22:31.446023Z","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":"2026-08-04T00:22:31.508575Z","title":"Ithier, E","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.508575Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:960ed19273e3433e4add2d9b90ad1d864ec9955b24e69e32b41ccbb3020c061e","observation_id":"5ea3af06-72d1-4478-8c2e-40b1022a3744","resolution":{"observed_at":"2026-08-04T00:22:31.508575Z","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":"2026-08-04T00:22:31.567348Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.567348Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:e991599dbd4624ca9b2bcddeeb19a692a628aaea680b3f7165320c3351b15d6d","observation_id":"ea57a00b-db2f-4fb7-85b5-e77aa1079d3f","resolution":{"observed_at":"2026-08-04T00:22:31.567348Z","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":"2026-08-04T00:22:31.629317Z","title":"Martins, F","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.629317Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:d0057ad070bc83798f6336a7de1510fa515c3ff68588950332b92be102b933b1","observation_id":"b6d1b192-0955-4d95-9934-36754f4b55dc","resolution":{"observed_at":"2026-08-04T00:22:31.629317Z","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":"2026-08-04T00:22:31.728855Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.728855Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:19901bd10afb2fe7b7757545704ed77af69ed814086fd1a5d4a4639f6acb26e0","observation_id":"ef24d425-e696-4b4f-841c-fe74408c1a8a","resolution":{"observed_at":"2026-08-04T00:22:31.728855Z","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":"2026-08-04T00:22:31.775255Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.775255Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:1b762d5c8a0cab7a445ebc31a0c82b55a85795da82ba88f4523e4af7561ed5d6","observation_id":"144373bb-d02a-4d0a-8278-e091973a28b3","resolution":{"observed_at":"2026-08-04T00:22:31.775255Z","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":"2026-08-04T00:22:31.852459Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.852459Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:f930bf46a5215c1a931a49d91348afdc234845ba3140b743a4283923c1d2916e","observation_id":"b3a42bb1-6297-4176-a1e9-335e77056cc0","resolution":{"observed_at":"2026-08-04T00:22:31.852459Z","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":"2026-08-04T00:22:31.935017Z","title":"Didier, E","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:31.935017Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:b8886fa198a25c503402dbe9e83d3fe5877b7fd082ff9a3c6c12fbfd078fb7c4","observation_id":"db054c22-130e-478c-9bc6-80df6234bd11","resolution":{"observed_at":"2026-08-04T00:22:31.935017Z","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":"2026-08-04T00:22:32.013574Z","title":"Frees, S","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.013574Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:b0f10df7e4bc8c3a821f5c973f0b93e971b2ebee9515c6924f09a60a6d202397","observation_id":"46a6f389-4a3e-4fc8-818f-8125e3567c8d","resolution":{"observed_at":"2026-08-04T00:22:32.013574Z","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":"2026-08-04T00:22:32.064969Z","title":"Huang, P","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.064969Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:7977627bdc211ce2b3b1150d9dc1d89df22b90f4b9d47c2959f2b4de12c3352c","observation_id":"7748b28c-b820-4f16-aff9-c43b34a29c6b","resolution":{"observed_at":"2026-08-04T00:22:32.064969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.09416","last_updated":"2019-12-19T17:35:07Z","snapshot_observed_at":"2026-07-06T08:45:47.486421Z","submitted_at":"2019-12-19T17:35:07Z","title":"Flux control of superconducting qubits at dynamical sweet spots","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.09416","snapshot_observed_at":"2026-08-04T00:22:32.121396Z","title":"Didier, Flux control of superconducting qubits at dy- namical sweet spots, arXiv:1912.09416 (2019)","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.121396Z"},"links":{"cited_paper":"/paper/1912.09416","citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:e377028303b682838a685d8794aecc3d6d1c11b2bba744d4b61db8e6983521a1","observation_id":"bccd6cad-11fd-44d3-99b2-5a9fa1015ef8","resolution":{"observed_at":"2026-08-04T00:22:32.121396Z","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":"2026-08-04T00:22:32.129645Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.129645Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:94d0d5a69f9dddcd5887e6ca042f8c7d24f40296e67af4c7843adca914949a6c","observation_id":"0dd9e331-0dbb-49a8-b2cc-fb299a6a1340","resolution":{"observed_at":"2026-08-04T00:22:32.129645Z","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":"2026-08-04T00:22:32.246088Z","title":"Nayak, S","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.246088Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:1bd108c54ff18b3721ce692b9d64c10c1029e62c185bb3137b302a7848c33a88","observation_id":"88b37e7a-f092-402d-a3e3-b15f61be2a4f","resolution":{"observed_at":"2026-08-04T00:22:32.246088Z","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":"2026-08-04T00:22:32.339362Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.339362Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:33ab8daf7390ddf595963b9b8b3fb6884f5b7c660cd2d87de8ad0bef68652a3d","observation_id":"51e80154-5fbb-4c47-991f-768c3f6e12c7","resolution":{"observed_at":"2026-08-04T00:22:32.339362Z","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":"2026-08-04T00:22:32.435053Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.435053Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:60b0de37abdf16031a8f9d2a1c2f7be9ddcbb49a6f759df97fb6722ae295da92","observation_id":"3f7ea107-90cf-429c-9283-41d1bf67312c","resolution":{"observed_at":"2026-08-04T00:22:32.435053Z","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":"2026-08-04T00:22:32.560876Z","title":"Leijnse and K","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.560876Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:c477936b8580c5b85d46d22b0a223cb4e59eac4ac9a6e4c171f3e402c3220111","observation_id":"dc502175-14e8-4339-b549-8a2abc1a33e8","resolution":{"observed_at":"2026-08-04T00:22:32.560876Z","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":"2026-08-04T00:22:32.663371Z","title":"Alicea, New directions in the pursuit of Majorana fermions in solid state systems, Reports on progress in physics75, 076501 (2012)","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.663371Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:e776ea3e04752b783d5855b35e2989fbc838ae7451e7e47347a0a8a684be278f","observation_id":"2b1fd2db-b17a-4947-aa6c-ebd821315a65","resolution":{"observed_at":"2026-08-04T00:22:32.663371Z","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":"2026-08-04T00:22:32.742792Z","title":"Das Sarma, M","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.742792Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:14471442237f9bc4474f248f9ab336bb26a90213baf6a17901ab07dd2703c11b","observation_id":"1a8cd262-596d-41ad-9ad0-f1cebcf8648c","resolution":{"observed_at":"2026-08-04T00:22:32.742792Z","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":"2026-08-04T00:22:32.854579Z","title":"Aguado, Majorana quasiparticles in condensed mat- ter, La Rivista del Nuovo Cimento40, 523 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.854579Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:fa26eb66d4c1ad468d4faf53e6a546bae9d3ac94d8139af1d69af845129f111d","observation_id":"22a52ea8-c17a-4e92-887a-0a476bee260c","resolution":{"observed_at":"2026-08-04T00:22:32.854579Z","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":"2026-08-04T00:22:32.983978Z","title":"Flensberg, F","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:32.983978Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:2a8821bc9de8ddcb28cc6a54298a92f1773d07659593abc717c778869d887c05","observation_id":"40241d4c-07ec-49cc-b05f-67b5a7924127","resolution":{"observed_at":"2026-08-04T00:22:32.983978Z","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":"2026-08-04T00:22:33.120667Z","title":"Prada, P","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:33.120667Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:fbffd2d66476f2bbad1f0885c451e8d9e983d908d476dd404ab3c9fa8dc65737","observation_id":"62d63154-1f3b-478b-ac52-8308f02aa870","resolution":{"observed_at":"2026-08-04T00:22:33.120667Z","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":"2026-08-04T00:22:33.177340Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:33.177340Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:0bffe287c1bae7a458f583655ae26733a909baffd550957ac4897b0a854bfa5f","observation_id":"f0b8440d-5e55-414d-a21f-9e1c31ce7154","resolution":{"observed_at":"2026-08-04T00:22:33.177340Z","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":"2026-08-04T00:22:33.274560Z","title":"Das Sarma, In search of Majorana, Nature Physics19, 165 (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:33.274560Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:4e38ba061e47dc3d142f3d84c1b07eab8f8b8e46e4cc45bc1d5f36f17e9677e3","observation_id":"88ac540c-2e08-4646-8da8-bdfc8a9d0248","resolution":{"observed_at":"2026-08-04T00:22:33.274560Z","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":"2026-08-04T00:22:33.366944Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:33.366944Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:7662d199273847040a8b25abc3569247d4b565f7f0075b7b6aef0cc4d1a63cce","observation_id":"00e823ce-0dff-46d8-b9f0-09c42f23feea","resolution":{"observed_at":"2026-08-04T00:22:33.366944Z","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":"2026-08-04T00:22:33.533243Z","title":"Seoane Souto and R","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:33.533243Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:86689469ee75aea226685219ccb93536a188fd102f7c70b8ee7b382e8b5c861b","observation_id":"b554cd62-8567-451b-93fd-9b0173d0c491","resolution":{"observed_at":"2026-08-04T00:22:33.533243Z","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":"2026-08-04T00:22:33.644647Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:33.644647Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:cf027d93e62b6ee0642e0006fbbcbdc3b0d470f641f870e837058214c7aff3bd","observation_id":"d759f689-fd35-4802-bb5a-3d844b61ba3e","resolution":{"observed_at":"2026-08-04T00:22:33.644647Z","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":"2026-08-04T00:22:33.761200Z","title":"Leijnse and K","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:33.761200Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:4d9b7b6ef7623ac1277a398030d0fc767677871c4d330997b047b354cc6b5923","observation_id":"91234f02-41c1-45c8-8fbb-33952121d888","resolution":{"observed_at":"2026-08-04T00:22:33.761200Z","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":"2026-08-04T00:22:33.869662Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:33.869662Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:f61bcea51ee0c3b5eb25b5ea270d5870693b522fbb04013ac51cbaad8e7ec57d","observation_id":"200886ff-7008-4c7e-bafc-958390627f12","resolution":{"observed_at":"2026-08-04T00:22:33.869662Z","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":"2026-08-04T00:22:34.018071Z","title":"Aghaee, A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.018071Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:6833ab9778951e95349f4ceb77ac2049089e1d97a21204900401da195c4f112c","observation_id":"3d788500-523f-4bff-8b49-8c8d9f15c3f6","resolution":{"observed_at":"2026-08-04T00:22:34.018071Z","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":"2026-08-04T00:22:34.136572Z","title":"Thamm and B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.136572Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:3b04c63e133b1e9effe62ae1baaca2f90f8a3be2d342dd6bd80c64e83b4459c5","observation_id":"4a63c107-e4ec-4a9f-8435-212a3baa8acb","resolution":{"observed_at":"2026-08-04T00:22:34.136572Z","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":"2026-08-04T00:22:34.236698Z","title":"Thamm and B","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.236698Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:ae7282a7b8a84d02733b359cc267a9c8cdf6bf99387ba20823ac6cdf66032201","observation_id":"176df9bb-4a5d-4329-805f-44ce4d30a55b","resolution":{"observed_at":"2026-08-04T00:22:34.236698Z","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":"2026-08-04T00:22:34.393041Z","title":"Benestad, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.393041Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:fb03fe8a026789670d9edca5761d5f9b9b60700faea114756ac47ab902ac539e","observation_id":"812ce703-d7e3-4931-a4c1-e72df488541c","resolution":{"observed_at":"2026-08-04T00:22:34.393041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04596","last_updated":"2026-06-24T12:29:34Z","snapshot_observed_at":"2026-07-06T18:11:13.757248Z","submitted_at":"2024-05-07T18:17:50Z","title":"Cross-Platform Autonomous Control of Minimal Kitaev Chains","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04596","snapshot_observed_at":"2026-08-04T00:22:34.566854Z","title":"van Driel, R","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.566854Z"},"links":{"cited_paper":"/paper/2405.04596","citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:702c5f89779a19175b22dd4e743d949a8e677b2eacdadce0f318f96e57fa0a0f","observation_id":"51120400-0db5-4b4d-a824-a0ea7baea851","resolution":{"observed_at":"2026-08-04T00:22:34.566854Z","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":"2026-08-04T00:22:34.651500Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.651500Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:126463d51a9eab6d12632753dd918971e03e5e30640df5197170bb60d2e422ae","observation_id":"b8ad5de4-e4b4-4cd4-a214-af001fa951bc","resolution":{"observed_at":"2026-08-04T00:22:34.651500Z","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":"2026-08-04T00:22:34.693144Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.693144Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:1dd04bc38a7f5781f2aa284279e6cb614216f5565483094be3d5e299035d9888","observation_id":"9edc460d-5552-4565-842a-8950d6138fb6","resolution":{"observed_at":"2026-08-04T00:22:34.693144Z","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":"2026-08-04T00:22:34.788303Z","title":"Tsintzis, R","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.788303Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:6ab59f0ee194de74ac649fc92e5dbb10f62b57005bb1cfb054dadd4644892e4e","observation_id":"654e13a2-1597-4e30-8de4-44b099ec0b7f","resolution":{"observed_at":"2026-08-04T00:22:34.788303Z","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":"2026-08-04T00:22:34.855484Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.855484Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:c408f6a2e5493d4258b204759555e6946b90107edb59f4c9a623e123feeccf0d","observation_id":"09d477d3-8160-4499-90e8-41891ee9c916","resolution":{"observed_at":"2026-08-04T00:22:34.855484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.20696","last_updated":"2025-07-29T12:05:47Z","snapshot_observed_at":"2026-08-06T13:30:57.805163Z","submitted_at":"2025-07-28T10:27:42Z","title":"Measuring coherence factors of states in superconductors through local current","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.20696","snapshot_observed_at":"2026-08-04T00:22:34.895643Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.895643Z"},"links":{"cited_paper":"/paper/2507.20696","citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:4f3aa9b1101098984062d09b3e5963f6cc95cf80631f05f08d84660f6219e0bd","observation_id":"426538cc-dc34-4ccf-af54-e48d6bb04eb0","resolution":{"observed_at":"2026-08-04T00:22:34.895643Z","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":"2026-08-04T00:22:34.961536Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:34.961536Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:6ecbff84d0a8af6794eb79b862f81a4dfd5f27b6581e80e444facc697065b867","observation_id":"79e6f9c8-4ca4-40a6-9e2b-812ca426aab9","resolution":{"observed_at":"2026-08-04T00:22:34.961536Z","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":"2026-08-04T00:22:35.063035Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:35.063035Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:31602cf880c7c2c255ed334167ea086075f3c21e25fd02b8892186af00b276aa","observation_id":"2d668d49-8ba5-4f91-a1b2-2fed5b3bd18e","resolution":{"observed_at":"2026-08-04T00:22:35.063035Z","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":"2026-08-04T00:22:35.161644Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:35.161644Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:df009256d77780c0f858519ea9aaafbe56ef614783e7c83a545cf2c2fe9c818c","observation_id":"dcfc0edb-c87d-4e49-a7e3-9f4f449e5f74","resolution":{"observed_at":"2026-08-04T00:22:35.161644Z","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":"2026-08-04T00:22:35.259049Z","title":"Bordin, C.-X","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:35.259049Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:6a377159e687f436fdfb5751b90bb016881c9e7c708977bf9a4f1eb6c046be65","observation_id":"a66a063f-fb61-48bd-8f38-6c2e9e5c33bb","resolution":{"observed_at":"2026-08-04T00:22:35.259049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13702","last_updated":"2025-04-18T14:03:10Z","snapshot_observed_at":"2026-08-07T16:01:07.132701Z","submitted_at":"2025-04-18T14:03:10Z","title":"Probing Majorana localization of a phase-controlled three-site Kitaev chain with an additional quantum dot","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13702","snapshot_observed_at":"2026-08-04T00:22:35.550295Z","title":"Bordin, F","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:35.550295Z"},"links":{"cited_paper":"/paper/2504.13702","citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:82170b363c340dc1b62a8c0a1f7b48ee6a6872edaab0b7d50875c42090311243","observation_id":"7276374d-dff8-4503-90ea-585b07ef0c46","resolution":{"observed_at":"2026-08-04T00:22:35.550295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.01606","last_updated":"2025-07-02T11:14:54Z","snapshot_observed_at":"2026-08-06T20:44:57.158875Z","submitted_at":"2025-07-02T11:14:54Z","title":"Single-shot parity readout of a minimal Kitaev chain","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.01606","snapshot_observed_at":"2026-08-04T00:22:35.701888Z","title":"van Loo, F","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:35.701888Z"},"links":{"cited_paper":"/paper/2507.01606","citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:023793035b799a616ac625fbec58831773f6b14449cd4c92054370dac6adce54","observation_id":"3a29da9d-e93a-4bfa-9855-65e136e936eb","resolution":{"observed_at":"2026-08-04T00:22:35.701888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.06403","last_updated":"2025-08-08T15:37:55Z","snapshot_observed_at":"2026-08-05T22:46:16.565428Z","submitted_at":"2025-08-08T15:37:55Z","title":"Gate reflectometry in a minimal Kitaev chain device","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.06403","snapshot_observed_at":"2026-08-04T00:22:35.812996Z","title":"Zhang, I","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:35.812996Z"},"links":{"cited_paper":"/paper/2508.06403","citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:535b5637314c468e6cc2b09f9dc11fa07b20ba15c6cbc2436bc8d277257603b3","observation_id":"30021e71-4c58-4db0-ba04-a4f8ca9814a7","resolution":{"observed_at":"2026-08-04T00:22:35.812996Z","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":"2026-08-04T00:22:35.959592Z","title":"Luethi, H","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:35.959592Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:9d9aa324bae9da817dc2971cb3e27ec16c51d0e238a254619f2afd7a2eee0920","observation_id":"02fd2ce7-f154-4b52-b788-30d462ed2c4b","resolution":{"observed_at":"2026-08-04T00:22:35.959592Z","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":"2026-08-04T00:22:36.119922Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:36.119922Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:fe82fe374f0e4eb7af5974ea3b7f47637257c61dcebc55b3a63505c548f8e16c","observation_id":"32851f70-da07-4cc5-ace7-c859be638ff9","resolution":{"observed_at":"2026-08-04T00:22:36.119922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1604.00772","last_updated":"2023-03-10T09:45:23Z","snapshot_observed_at":"2026-07-06T04:51:38.670553Z","submitted_at":"2016-04-04T08:16:12Z","title":"The CMA Evolution Strategy: A Tutorial","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.00772","snapshot_observed_at":"2026-08-04T00:22:36.263496Z","title":"Hansen, The cma evolution strategy: A tutorial (2023), arXiv:1604.00772 [cs.LG]","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:36.263496Z"},"links":{"cited_paper":"/paper/1604.00772","citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:7cfd28a3be21e5dd93971725edea0cf5f5c9711fef6643fb06828ceb8fa6e4e7","observation_id":"cc7b89d1-bcc4-4160-9742-3e41ded208f7","resolution":{"observed_at":"2026-08-04T00:22:36.263496Z","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":"2026-08-04T00:22:36.463826Z","title":"Sticlet, C","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:36.463826Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:62c61e6ebcebf3758a896cc2fad2b75b644884f1a8a8487444cfb092641cebc7","observation_id":"4983c3fa-7e35-4bd8-a4a9-be89a59b3f0b","resolution":{"observed_at":"2026-08-04T00:22:36.463826Z","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":"2026-08-04T00:22:36.623587Z","title":"Sedlmayr and C","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:36.623587Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:7f9fad22dee9c2c078e0441b292e9997ed3c43d4348a558cd878ffde86b591ec","observation_id":"e624962f-4307-47be-a502-8f3c02a09c29","resolution":{"observed_at":"2026-08-04T00:22:36.623587Z","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":"2026-08-04T00:22:36.771551Z","title":"Sedlmayr, J","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:36.771551Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:a9bef2f365fd3f42aeb6857a682a35094fcd48b58dfe5f3ac92a6fd4bc7bb580","observation_id":"842d780c-d7b3-4740-b322-0f3097d0d81b","resolution":{"observed_at":"2026-08-04T00:22:36.771551Z","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":"2026-08-04T00:22:36.894856Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:36.894856Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:9ea364eeb8c3f082e2f352a87df273c92cf3066f95e4702977fd94842829d85b","observation_id":"f16f0de0-6b3b-4da6-9ef6-94aa33cb5a92","resolution":{"observed_at":"2026-08-04T00:22:36.894856Z","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":"2026-08-04T00:22:37.052886Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:37.052886Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:0dc9210064443189b05eb615dce08e6709f683c341afbd8dd5a900c27c063f1c","observation_id":"e9d01760-2452-4c92-82b4-44d6513d9bb6","resolution":{"observed_at":"2026-08-04T00:22:37.052886Z","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":"2026-08-04T00:22:37.250915Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:37.250915Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:a6cd65fde134660af7d1b30d93b98f316873c3de2e17dedfec292092d8044251","observation_id":"e72288ba-5c89-4bd3-b7f7-ceaae607e25c","resolution":{"observed_at":"2026-08-04T00:22:37.250915Z","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":"2026-08-04T00:22:37.400084Z","title":"Tsintzis, R","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:37.400084Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:a43a5059b7f5f33783db33697d25ae0152e5c3f00473d548eedd595e32a7c67a","observation_id":"a153fd8d-3ce1-4e9b-9105-c77925199aa8","resolution":{"observed_at":"2026-08-04T00:22:37.400084Z","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":"2026-08-04T00:22:37.562028Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-04T00:22:37.562028Z"},"links":{"citing_paper":"/paper/2511.01531"},"observation_digest":"sha256:c3d8bf9fb07cc95d0998787c0f3efad73bfbe48034ff1dfaa6f939fa43662786","observation_id":"3a9f192d-88a9-40f4-b942-8ea1ae1dad35","resolution":{"observed_at":"2026-08-04T00:22:37.562028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2511.01531","last_updated":"2025-11-03T12:51:10Z","latest_version":1,"primary_category":"cond-mat.mes-hall","snapshot_observed_at":"2026-08-04T00:22:30.699857Z","submitted_at":"2025-11-03T12:51:10Z","title":"Machine-learned tuning to protected states by probing noise resilience"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":59,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":59},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2511.01531."}