{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6OPCKLGQGBLE26FC7S6ASZPCHH","short_pith_number":"pith:6OPCKLGQ","schema_version":"1.0","canonical_sha256":"f39e252cd030564d78a2fcbc0965e239d5d388a3bb6c319e157540c7179a1476","source":{"kind":"arxiv","id":"2408.04063","version":1},"attestation_state":"computed","paper":{"title":"From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Gregory Kish, Xiaoting Wang, Yunwei Li, Yuzhuo Li","submitted_at":"2024-08-07T20:00:14Z","abstract_excerpt":"This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations. To fully leverage KAN's interpretability, a general framework is proposed considering complex uncertainties. The stochastic optimal power flow problem in hybrid AC/DC systems is chosen as a particularly tough case study for demonstrating the effectiveness of this framework."},"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":"2408.04063","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2024-08-07T20:00:14Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"29749f1e4e8feb7e479daa102236ac0ac0f2f84a9ea0db27c824cfaac56f65c6","abstract_canon_sha256":"b5345e19d2976067345ac5157bad49cca04d878ae38d7679a958e14183ac12c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:53:27.556452Z","signature_b64":"Crs57oflf1oNArZLxBc8GTkmQikvRSPWaUCLC2sBrhUUOUAx3WdxX4tCn7FCpptbEmkbzT+al/CQQh1HNTKHAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f39e252cd030564d78a2fcbc0965e239d5d388a3bb6c319e157540c7179a1476","last_reissued_at":"2026-07-05T08:53:27.556077Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:53:27.556077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Gregory Kish, Xiaoting Wang, Yunwei Li, Yuzhuo Li","submitted_at":"2024-08-07T20:00:14Z","abstract_excerpt":"This work is the first to adopt Kolmogorov-Arnold Networks (KAN), a recent breakthrough in artificial intelligence, for smart grid optimizations. To fully leverage KAN's interpretability, a general framework is proposed considering complex uncertainties. The stochastic optimal power flow problem in hybrid AC/DC systems is chosen as a particularly tough case study for demonstrating the effectiveness of this framework."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.04063","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/2408.04063/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":"2408.04063","created_at":"2026-07-05T08:53:27.556138+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.04063v1","created_at":"2026-07-05T08:53:27.556138+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.04063","created_at":"2026-07-05T08:53:27.556138+00:00"},{"alias_kind":"pith_short_12","alias_value":"6OPCKLGQGBLE","created_at":"2026-07-05T08:53:27.556138+00:00"},{"alias_kind":"pith_short_16","alias_value":"6OPCKLGQGBLE26FC","created_at":"2026-07-05T08:53:27.556138+00:00"},{"alias_kind":"pith_short_8","alias_value":"6OPCKLGQ","created_at":"2026-07-05T08:53:27.556138+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH","json":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH.json","graph_json":"https://pith.science/api/pith-number/6OPCKLGQGBLE26FC7S6ASZPCHH/graph.json","events_json":"https://pith.science/api/pith-number/6OPCKLGQGBLE26FC7S6ASZPCHH/events.json","paper":"https://pith.science/paper/6OPCKLGQ"},"agent_actions":{"view_html":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH","download_json":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH.json","view_paper":"https://pith.science/paper/6OPCKLGQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.04063&json=true","fetch_graph":"https://pith.science/api/pith-number/6OPCKLGQGBLE26FC7S6ASZPCHH/graph.json","fetch_events":"https://pith.science/api/pith-number/6OPCKLGQGBLE26FC7S6ASZPCHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH/action/storage_attestation","attest_author":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH/action/author_attestation","sign_citation":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH/action/citation_signature","submit_replication":"https://pith.science/pith/6OPCKLGQGBLE26FC7S6ASZPCHH/action/replication_record"}},"created_at":"2026-07-05T08:53:27.556138+00:00","updated_at":"2026-07-05T08:53:27.556138+00:00"}