{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3GAM3IDW7EJUVFWEDHC5LRXIEU","short_pith_number":"pith:3GAM3IDW","schema_version":"1.0","canonical_sha256":"d980cda076f9134a96c419c5d5c6e8250c66fd08e6bb3a3e8963d5385a783935","source":{"kind":"arxiv","id":"2509.02494","version":1},"attestation_state":"computed","paper":{"title":"GridMind: LLMs-Powered Agents for Power System Analysis and Operations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Hongwei Jin, Jonghwan Kwon, Kibaek Kim","submitted_at":"2025-09-02T16:42:18Z","abstract_excerpt":"The complexity of traditional power system analysis workflows presents significant barriers to efficient decision-making in modern electric grids. This paper presents GridMind, a multi-agent AI system that integrates Large Language Models (LLMs) with deterministic engineering solvers to enable conversational scientific computing for power system analysis. The system employs specialized agents coordinating AC Optimal Power Flow and N-1 contingency analysis through natural language interfaces while maintaining numerical precision via function calls. GridMind addresses workflow integration, knowl"},"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":"2509.02494","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-09-02T16:42:18Z","cross_cats_sorted":[],"title_canon_sha256":"bfbaaa4a07cdb2ef0539cf7b89d5e93b577207991c50255d4d11125b91d346b7","abstract_canon_sha256":"b20cc595e999456eecda1b922fd166a4a4e95babadb81f30c1f30acfcfdea920"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:39.569492Z","signature_b64":"GyeUvy3ncITW4VFZYqHHT6kaS4Q0d0J3DHfckZsayobcXwEUHjbQRL6kuA5x3JbCcopeV9f5jvyIHImlBEzUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d980cda076f9134a96c419c5d5c6e8250c66fd08e6bb3a3e8963d5385a783935","last_reissued_at":"2026-07-05T12:03:39.569021Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:39.569021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GridMind: LLMs-Powered Agents for Power System Analysis and Operations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Hongwei Jin, Jonghwan Kwon, Kibaek Kim","submitted_at":"2025-09-02T16:42:18Z","abstract_excerpt":"The complexity of traditional power system analysis workflows presents significant barriers to efficient decision-making in modern electric grids. This paper presents GridMind, a multi-agent AI system that integrates Large Language Models (LLMs) with deterministic engineering solvers to enable conversational scientific computing for power system analysis. The system employs specialized agents coordinating AC Optimal Power Flow and N-1 contingency analysis through natural language interfaces while maintaining numerical precision via function calls. GridMind addresses workflow integration, knowl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.02494","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/2509.02494/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":"2509.02494","created_at":"2026-07-05T12:03:39.569094+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.02494v1","created_at":"2026-07-05T12:03:39.569094+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.02494","created_at":"2026-07-05T12:03:39.569094+00:00"},{"alias_kind":"pith_short_12","alias_value":"3GAM3IDW7EJU","created_at":"2026-07-05T12:03:39.569094+00:00"},{"alias_kind":"pith_short_16","alias_value":"3GAM3IDW7EJUVFWE","created_at":"2026-07-05T12:03:39.569094+00:00"},{"alias_kind":"pith_short_8","alias_value":"3GAM3IDW","created_at":"2026-07-05T12:03:39.569094+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20950","citing_title":"Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineering","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20950","citing_title":"Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineering","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31478","citing_title":"Knowledge Boundary Probing and Demand-Guided Intervention for LLM-Based Power System Code Generation","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12728","citing_title":"Grid-Orch: An LLM-Powered Orchestrator for Distribution Grid Simulation and Analytics","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU","json":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU.json","graph_json":"https://pith.science/api/pith-number/3GAM3IDW7EJUVFWEDHC5LRXIEU/graph.json","events_json":"https://pith.science/api/pith-number/3GAM3IDW7EJUVFWEDHC5LRXIEU/events.json","paper":"https://pith.science/paper/3GAM3IDW"},"agent_actions":{"view_html":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU","download_json":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU.json","view_paper":"https://pith.science/paper/3GAM3IDW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.02494&json=true","fetch_graph":"https://pith.science/api/pith-number/3GAM3IDW7EJUVFWEDHC5LRXIEU/graph.json","fetch_events":"https://pith.science/api/pith-number/3GAM3IDW7EJUVFWEDHC5LRXIEU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU/action/storage_attestation","attest_author":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU/action/author_attestation","sign_citation":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU/action/citation_signature","submit_replication":"https://pith.science/pith/3GAM3IDW7EJUVFWEDHC5LRXIEU/action/replication_record"}},"created_at":"2026-07-05T12:03:39.569094+00:00","updated_at":"2026-07-05T12:03:39.569094+00:00"}