{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JQNETVQ65N5BLBH223OUH5BRNA","short_pith_number":"pith:JQNETVQ6","schema_version":"1.0","canonical_sha256":"4c1a49d61eeb7a1584fad6dd43f431681c9253dc88c2f9a2b5098f2d90382154","source":{"kind":"arxiv","id":"2312.01090","version":2},"attestation_state":"computed","paper":{"title":"Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"C.Yu, J.Zhao, W.Wang, X.Zhou, Y.Sun","submitted_at":"2023-12-02T09:45:45Z","abstract_excerpt":"The large language models represented by ChatGPT have a disruptive impact on the field of artificial intelligence. But it mainly focuses on natural language processing, speech recognition, machine learning and natural language understanding. This paper innovatively applies the large language model to the field of intelligent decision-making, places the large language model in the decision-making center, and constructs an agent architecture with the large language model as the core. Based on this, it further proposes a two-layer agent task planning, issues and executes decision commands through"},"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":"2312.01090","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-12-02T09:45:45Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"d2566f4e7c842fe3e031892751142897453a3f12ad01ffe37e514481c5155d17","abstract_canon_sha256":"8590fa72b4666e775ccf6f03846dbc79afdc2df15d411c5bee52526adbd8bdd7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:57.212145Z","signature_b64":"ZaQATHIRjqLc84UNo36mFL/C4VGTW2/BCgPLCdLYSjskhYx6etCQd2NxsxbwXuIA3MGzyu/qiV2lkILKh65ZAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c1a49d61eeb7a1584fad6dd43f431681c9253dc88c2f9a2b5098f2d90382154","last_reissued_at":"2026-07-05T07:24:57.211733Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:57.211733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"C.Yu, J.Zhao, W.Wang, X.Zhou, Y.Sun","submitted_at":"2023-12-02T09:45:45Z","abstract_excerpt":"The large language models represented by ChatGPT have a disruptive impact on the field of artificial intelligence. But it mainly focuses on natural language processing, speech recognition, machine learning and natural language understanding. This paper innovatively applies the large language model to the field of intelligent decision-making, places the large language model in the decision-making center, and constructs an agent architecture with the large language model as the core. Based on this, it further proposes a two-layer agent task planning, issues and executes decision commands through"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01090","kind":"arxiv","version":2},"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/2312.01090/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":"2312.01090","created_at":"2026-07-05T07:24:57.211790+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.01090v2","created_at":"2026-07-05T07:24:57.211790+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01090","created_at":"2026-07-05T07:24:57.211790+00:00"},{"alias_kind":"pith_short_12","alias_value":"JQNETVQ65N5B","created_at":"2026-07-05T07:24:57.211790+00:00"},{"alias_kind":"pith_short_16","alias_value":"JQNETVQ65N5BLBH2","created_at":"2026-07-05T07:24:57.211790+00:00"},{"alias_kind":"pith_short_8","alias_value":"JQNETVQ6","created_at":"2026-07-05T07:24:57.211790+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/JQNETVQ65N5BLBH223OUH5BRNA","json":"https://pith.science/pith/JQNETVQ65N5BLBH223OUH5BRNA.json","graph_json":"https://pith.science/api/pith-number/JQNETVQ65N5BLBH223OUH5BRNA/graph.json","events_json":"https://pith.science/api/pith-number/JQNETVQ65N5BLBH223OUH5BRNA/events.json","paper":"https://pith.science/paper/JQNETVQ6"},"agent_actions":{"view_html":"https://pith.science/pith/JQNETVQ65N5BLBH223OUH5BRNA","download_json":"https://pith.science/pith/JQNETVQ65N5BLBH223OUH5BRNA.json","view_paper":"https://pith.science/paper/JQNETVQ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.01090&json=true","fetch_graph":"https://pith.science/api/pith-number/JQNETVQ65N5BLBH223OUH5BRNA/graph.json","fetch_events":"https://pith.science/api/pith-number/JQNETVQ65N5BLBH223OUH5BRNA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JQNETVQ65N5BLBH223OUH5BRNA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JQNETVQ65N5BLBH223OUH5BRNA/action/storage_attestation","attest_author":"https://pith.science/pith/JQNETVQ65N5BLBH223OUH5BRNA/action/author_attestation","sign_citation":"https://pith.science/pith/JQNETVQ65N5BLBH223OUH5BRNA/action/citation_signature","submit_replication":"https://pith.science/pith/JQNETVQ65N5BLBH223OUH5BRNA/action/replication_record"}},"created_at":"2026-07-05T07:24:57.211790+00:00","updated_at":"2026-07-05T07:24:57.211790+00:00"}