{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JYA2Q5DQG2BPEABZUSRZR7LSCT","short_pith_number":"pith:JYA2Q5DQ","schema_version":"1.0","canonical_sha256":"4e01a874703682f20039a4a398fd7214de5fd91101e7ce10204e96d5dabd3401","source":{"kind":"arxiv","id":"2606.20761","version":1},"attestation_state":"computed","paper":{"title":"Integrating Large Language Model Agents with Digital Twins for Industrial Autonomous Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.MA","cs.SY","eess.SY"],"primary_cat":"cs.SE","authors_text":"Yuchen Xia","submitted_at":"2026-06-18T09:48:44Z","abstract_excerpt":"Industrial automation is being transformed by digitalization and the increasing use of cyber-physical systems. Modern production environments require greater adaptability, faster reconfiguration, and more intuitive human-machine interaction. However, traditional rule-based systems rely on fixed logic and cannot autonomously adapt to changing conditions. Consequently, current automation systems lack a systematic approach for integrating adaptive and generalizable reasoning capabilities for interpreting, planning, and executing user tasks across dynamic environments and heterogeneous components."},"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":"2606.20761","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SE","submitted_at":"2026-06-18T09:48:44Z","cross_cats_sorted":["cs.AI","cs.ET","cs.MA","cs.SY","eess.SY"],"title_canon_sha256":"3ffdfc6b407146997ddc3b2ff421c326b31e452017e220a93f9c61a13a556ae3","abstract_canon_sha256":"20d818d238423903494e2ff6c14681e8530483d161f1ee3adc9c194989609778"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T00:11:56.040521Z","signature_b64":"Hndg/AAVEB+oixL4LhZuK3UtPca/J5XwEOnngjwrFjRN6EV568yqSHeqh0th09mYGl4f35WTiov1OHrZ4DQPCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e01a874703682f20039a4a398fd7214de5fd91101e7ce10204e96d5dabd3401","last_reissued_at":"2026-06-23T00:11:56.040133Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T00:11:56.040133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Integrating Large Language Model Agents with Digital Twins for Industrial Autonomous Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.MA","cs.SY","eess.SY"],"primary_cat":"cs.SE","authors_text":"Yuchen Xia","submitted_at":"2026-06-18T09:48:44Z","abstract_excerpt":"Industrial automation is being transformed by digitalization and the increasing use of cyber-physical systems. Modern production environments require greater adaptability, faster reconfiguration, and more intuitive human-machine interaction. However, traditional rule-based systems rely on fixed logic and cannot autonomously adapt to changing conditions. Consequently, current automation systems lack a systematic approach for integrating adaptive and generalizable reasoning capabilities for interpreting, planning, and executing user tasks across dynamic environments and heterogeneous components."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.20761","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/2606.20761/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":"2606.20761","created_at":"2026-06-23T00:11:56.040188+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.20761v1","created_at":"2026-06-23T00:11:56.040188+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.20761","created_at":"2026-06-23T00:11:56.040188+00:00"},{"alias_kind":"pith_short_12","alias_value":"JYA2Q5DQG2BP","created_at":"2026-06-23T00:11:56.040188+00:00"},{"alias_kind":"pith_short_16","alias_value":"JYA2Q5DQG2BPEABZ","created_at":"2026-06-23T00:11:56.040188+00:00"},{"alias_kind":"pith_short_8","alias_value":"JYA2Q5DQ","created_at":"2026-06-23T00:11:56.040188+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/JYA2Q5DQG2BPEABZUSRZR7LSCT","json":"https://pith.science/pith/JYA2Q5DQG2BPEABZUSRZR7LSCT.json","graph_json":"https://pith.science/api/pith-number/JYA2Q5DQG2BPEABZUSRZR7LSCT/graph.json","events_json":"https://pith.science/api/pith-number/JYA2Q5DQG2BPEABZUSRZR7LSCT/events.json","paper":"https://pith.science/paper/JYA2Q5DQ"},"agent_actions":{"view_html":"https://pith.science/pith/JYA2Q5DQG2BPEABZUSRZR7LSCT","download_json":"https://pith.science/pith/JYA2Q5DQG2BPEABZUSRZR7LSCT.json","view_paper":"https://pith.science/paper/JYA2Q5DQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.20761&json=true","fetch_graph":"https://pith.science/api/pith-number/JYA2Q5DQG2BPEABZUSRZR7LSCT/graph.json","fetch_events":"https://pith.science/api/pith-number/JYA2Q5DQG2BPEABZUSRZR7LSCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JYA2Q5DQG2BPEABZUSRZR7LSCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JYA2Q5DQG2BPEABZUSRZR7LSCT/action/storage_attestation","attest_author":"https://pith.science/pith/JYA2Q5DQG2BPEABZUSRZR7LSCT/action/author_attestation","sign_citation":"https://pith.science/pith/JYA2Q5DQG2BPEABZUSRZR7LSCT/action/citation_signature","submit_replication":"https://pith.science/pith/JYA2Q5DQG2BPEABZUSRZR7LSCT/action/replication_record"}},"created_at":"2026-06-23T00:11:56.040188+00:00","updated_at":"2026-06-23T00:11:56.040188+00:00"}