{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JHPDVV6CIC2PKSWKOM2CTHP6MP","short_pith_number":"pith:JHPDVV6C","schema_version":"1.0","canonical_sha256":"49de3ad7c240b4f54aca7334299dfe63fea8dee32483ca8c31b0dbc2756015ee","source":{"kind":"arxiv","id":"2607.26560","version":1},"attestation_state":"computed","paper":{"title":"Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Baichuan Liu, Chenxi Zhang, Feiyu Cai, Jing Qiu, Junhua Zhao, Xinlei Wang, Yi Yang","submitted_at":"2026-07-29T07:33:29Z","abstract_excerpt":"As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal carbon intensity (NCI), a critical factor in carbon-oriented demand response, has traditionally been determined through ex-post calculations. However, this ex-post approach introduces latency in low-carbon dispatch. To address this, this paper presents a proactive ex-ante spatial-temporal carbon response framework. At its core, we develop a novel deep learning-based hierarchical design, enhanced by a dual-stage attention mechanism and a large language model (LLM)-"},"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":"2607.26560","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-07-29T07:33:29Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"be5af8bbc9ba4b627d34e4a4b0622aba468952a31dd44ced0610f004597523b5","abstract_canon_sha256":"c3ca060744f6c801ebe7b523411319b5dc4ad885f5ea8ed6e8fd309c362fafd9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49de3ad7c240b4f54aca7334299dfe63fea8dee32483ca8c31b0dbc2756015ee","last_reissued_at":"2026-07-30T01:20:53.257011Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:20:53.257011Z"},"graph_snapshot":{"paper":{"title":"Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Baichuan Liu, Chenxi Zhang, Feiyu Cai, Jing Qiu, Junhua Zhao, Xinlei Wang, Yi Yang","submitted_at":"2026-07-29T07:33:29Z","abstract_excerpt":"As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal carbon intensity (NCI), a critical factor in carbon-oriented demand response, has traditionally been determined through ex-post calculations. However, this ex-post approach introduces latency in low-carbon dispatch. To address this, this paper presents a proactive ex-ante spatial-temporal carbon response framework. At its core, we develop a novel deep learning-based hierarchical design, enhanced by a dual-stage attention mechanism and a large language model (LLM)-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26560","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/2607.26560/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":"2607.26560","created_at":"2026-07-30T01:20:53.262122+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.26560v1","created_at":"2026-07-30T01:20:53.262122+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26560","created_at":"2026-07-30T01:20:53.262122+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHPDVV6CIC2P","created_at":"2026-07-30T01:20:53.262122+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHPDVV6CIC2PKSWK","created_at":"2026-07-30T01:20:53.262122+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHPDVV6C","created_at":"2026-07-30T01:20:53.262122+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/JHPDVV6CIC2PKSWKOM2CTHP6MP","json":"https://pith.science/pith/JHPDVV6CIC2PKSWKOM2CTHP6MP.json","graph_json":"https://pith.science/api/pith-number/JHPDVV6CIC2PKSWKOM2CTHP6MP/graph.json","events_json":"https://pith.science/api/pith-number/JHPDVV6CIC2PKSWKOM2CTHP6MP/events.json","paper":"https://pith.science/paper/JHPDVV6C"},"agent_actions":{"view_html":"https://pith.science/pith/JHPDVV6CIC2PKSWKOM2CTHP6MP","download_json":"https://pith.science/pith/JHPDVV6CIC2PKSWKOM2CTHP6MP.json","view_paper":"https://pith.science/paper/JHPDVV6C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.26560&json=true","fetch_graph":"https://pith.science/api/pith-number/JHPDVV6CIC2PKSWKOM2CTHP6MP/graph.json","fetch_events":"https://pith.science/api/pith-number/JHPDVV6CIC2PKSWKOM2CTHP6MP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHPDVV6CIC2PKSWKOM2CTHP6MP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHPDVV6CIC2PKSWKOM2CTHP6MP/action/storage_attestation","attest_author":"https://pith.science/pith/JHPDVV6CIC2PKSWKOM2CTHP6MP/action/author_attestation","sign_citation":"https://pith.science/pith/JHPDVV6CIC2PKSWKOM2CTHP6MP/action/citation_signature","submit_replication":"https://pith.science/pith/JHPDVV6CIC2PKSWKOM2CTHP6MP/action/replication_record"}},"created_at":"2026-07-30T01:20:53.262122+00:00","updated_at":"2026-07-30T01:20:53.262122+00:00"}