{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6HXAE7QULNYFANO2R57ATJQY4R","short_pith_number":"pith:6HXAE7QU","schema_version":"1.0","canonical_sha256":"f1ee027e145b705035da8f7e09a618e4545f9ff8b69abb540ff5fce097dda2d4","source":{"kind":"arxiv","id":"2509.04619","version":1},"attestation_state":"computed","paper":{"title":"$\\mathcal{L}_1$-DRAC: Distributionally Robust Adaptive Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","math.DS"],"primary_cat":"eess.SY","authors_text":"Aditya Gahlawat, Naira Hovakimyan, Sambhu H. Karumanchi","submitted_at":"2025-09-04T19:07:16Z","abstract_excerpt":"Data-driven machine learning methodologies have attracted considerable attention for the control and estimation of dynamical systems. However, such implementations suffer from a lack of predictability and robustness. Thus, adoption of data-driven tools has been minimal for safety-aware applications despite their impressive empirical results. While classical tools like robust adaptive control can ensure predictable performance, their consolidation with data-driven methods remains a challenge and, when attempted, leads to conservative results. The difficulty of consolidation stems from the inher"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2509.04619","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-09-04T19:07:16Z","cross_cats_sorted":["cs.SY","math.DS"],"title_canon_sha256":"6ed5360d7e697cbd50ba975a83ddf35f5b66c471621862a78e9b321a298e3248","abstract_canon_sha256":"c7473f076e0ed312a5794c17806d73c82f81b407d412f6614977813aeaa3542d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:26.416414Z","signature_b64":"r9+lehpDHur0SJ45LEn0Ol1cIamqTH8BPLkQnLweWj1joLtZoOwrlihlKXujx+oOHRt0jV2UaPAVhRB4W/CoAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1ee027e145b705035da8f7e09a618e4545f9ff8b69abb540ff5fce097dda2d4","last_reissued_at":"2026-07-05T12:05:26.415829Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:26.415829Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"$\\mathcal{L}_1$-DRAC: Distributionally Robust Adaptive Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","math.DS"],"primary_cat":"eess.SY","authors_text":"Aditya Gahlawat, Naira Hovakimyan, Sambhu H. Karumanchi","submitted_at":"2025-09-04T19:07:16Z","abstract_excerpt":"Data-driven machine learning methodologies have attracted considerable attention for the control and estimation of dynamical systems. However, such implementations suffer from a lack of predictability and robustness. Thus, adoption of data-driven tools has been minimal for safety-aware applications despite their impressive empirical results. While classical tools like robust adaptive control can ensure predictable performance, their consolidation with data-driven methods remains a challenge and, when attempted, leads to conservative results. The difficulty of consolidation stems from the inher"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.04619","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.04619/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2509.04619","created_at":"2026-07-05T12:05:26.415905+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.04619v1","created_at":"2026-07-05T12:05:26.415905+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.04619","created_at":"2026-07-05T12:05:26.415905+00:00"},{"alias_kind":"pith_short_12","alias_value":"6HXAE7QULNYF","created_at":"2026-07-05T12:05:26.415905+00:00"},{"alias_kind":"pith_short_16","alias_value":"6HXAE7QULNYFANO2","created_at":"2026-07-05T12:05:26.415905+00:00"},{"alias_kind":"pith_short_8","alias_value":"6HXAE7QU","created_at":"2026-07-05T12:05:26.415905+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.28758","citing_title":"Distributionally Robust Planning with $\\mathcal{L}_1$ Adaptive Control","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R","json":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R.json","graph_json":"https://pith.science/api/pith-number/6HXAE7QULNYFANO2R57ATJQY4R/graph.json","events_json":"https://pith.science/api/pith-number/6HXAE7QULNYFANO2R57ATJQY4R/events.json","paper":"https://pith.science/paper/6HXAE7QU"},"agent_actions":{"view_html":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R","download_json":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R.json","view_paper":"https://pith.science/paper/6HXAE7QU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.04619&json=true","fetch_graph":"https://pith.science/api/pith-number/6HXAE7QULNYFANO2R57ATJQY4R/graph.json","fetch_events":"https://pith.science/api/pith-number/6HXAE7QULNYFANO2R57ATJQY4R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R/action/storage_attestation","attest_author":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R/action/author_attestation","sign_citation":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R/action/citation_signature","submit_replication":"https://pith.science/pith/6HXAE7QULNYFANO2R57ATJQY4R/action/replication_record"}},"created_at":"2026-07-05T12:05:26.415905+00:00","updated_at":"2026-07-05T12:05:26.415905+00:00"}