{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GOAKFLBZ5D43ED7WL2THYCS6Y5","short_pith_number":"pith:GOAKFLBZ","schema_version":"1.0","canonical_sha256":"3380a2ac39e8f9b20ff65ea67c0a5ec747aaea94c4f1fc06943b966020c5e02f","source":{"kind":"arxiv","id":"2401.02771","version":5},"attestation_state":"computed","paper":{"title":"Powerformer: A Section-adaptive Transformer for Power Flow Adjustment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Jie Song, Kaixuan Chen, Mingli Song, Quan Zhang, Shunyu Liu, Wei Luo, Yaoquan Wei, Yihe Zhou, Yunpeng Qing","submitted_at":"2024-01-05T12:01:19Z","abstract_excerpt":"In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. Specifically, our proposed approach, named Powerformer, develops a dedicated section-adaptive attention mechanism, separating itself from the self-attention used in conventional transformers. This mechanism effectively integrates power system states with transmission section information, which facilitates the development of robust state representations. Furthermo"},"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":"2401.02771","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-05T12:01:19Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"514a9617e7167f4bd057c8afff54674f6e772e2c30a353b40e92b595750c95af","abstract_canon_sha256":"d2ed2583966db6e1623e7675693ba4b581bff8ff84e8d989bb845fc679178547"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:31.160406Z","signature_b64":"YrBKpdbQsHxhSGnUXv37KGFmZzRx3id4EGa9D4MWBso2Nzbw6FEr0Y3apeU1iJtDQe+YIPkBrqXSx1J8iMocDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3380a2ac39e8f9b20ff65ea67c0a5ec747aaea94c4f1fc06943b966020c5e02f","last_reissued_at":"2026-07-05T09:41:31.159902Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:31.159902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Powerformer: A Section-adaptive Transformer for Power Flow Adjustment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Jie Song, Kaixuan Chen, Mingli Song, Quan Zhang, Shunyu Liu, Wei Luo, Yaoquan Wei, Yihe Zhou, Yunpeng Qing","submitted_at":"2024-01-05T12:01:19Z","abstract_excerpt":"In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. Specifically, our proposed approach, named Powerformer, develops a dedicated section-adaptive attention mechanism, separating itself from the self-attention used in conventional transformers. This mechanism effectively integrates power system states with transmission section information, which facilitates the development of robust state representations. Furthermo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.02771","kind":"arxiv","version":5},"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/2401.02771/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":"2401.02771","created_at":"2026-07-05T09:41:31.159963+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.02771v5","created_at":"2026-07-05T09:41:31.159963+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.02771","created_at":"2026-07-05T09:41:31.159963+00:00"},{"alias_kind":"pith_short_12","alias_value":"GOAKFLBZ5D43","created_at":"2026-07-05T09:41:31.159963+00:00"},{"alias_kind":"pith_short_16","alias_value":"GOAKFLBZ5D43ED7W","created_at":"2026-07-05T09:41:31.159963+00:00"},{"alias_kind":"pith_short_8","alias_value":"GOAKFLBZ","created_at":"2026-07-05T09:41:31.159963+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.05762","citing_title":"BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5","json":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5.json","graph_json":"https://pith.science/api/pith-number/GOAKFLBZ5D43ED7WL2THYCS6Y5/graph.json","events_json":"https://pith.science/api/pith-number/GOAKFLBZ5D43ED7WL2THYCS6Y5/events.json","paper":"https://pith.science/paper/GOAKFLBZ"},"agent_actions":{"view_html":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5","download_json":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5.json","view_paper":"https://pith.science/paper/GOAKFLBZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.02771&json=true","fetch_graph":"https://pith.science/api/pith-number/GOAKFLBZ5D43ED7WL2THYCS6Y5/graph.json","fetch_events":"https://pith.science/api/pith-number/GOAKFLBZ5D43ED7WL2THYCS6Y5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5/action/storage_attestation","attest_author":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5/action/author_attestation","sign_citation":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5/action/citation_signature","submit_replication":"https://pith.science/pith/GOAKFLBZ5D43ED7WL2THYCS6Y5/action/replication_record"}},"created_at":"2026-07-05T09:41:31.159963+00:00","updated_at":"2026-07-05T09:41:31.159963+00:00"}