{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WULVMVBDEZYZSHK5OQEFA6N2TT","short_pith_number":"pith:WULVMVBD","schema_version":"1.0","canonical_sha256":"b5175654232671991d5d74085079ba9cc234899993abcce97fee42c3e6b0888d","source":{"kind":"arxiv","id":"2601.11650","version":2},"attestation_state":"computed","paper":{"title":"Large Language Model Agent for User-friendly Chemical Process Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.chem-ph","authors_text":"G\\\"urkan Sin, Jingkang Liang, Niklas Groll","submitted_at":"2026-01-15T12:18:45Z","abstract_excerpt":"Modern process simulators enable detailed process design, simulation, and optimization; however, constructing and interpreting simulations is time-consuming and requires expert knowledge. This limits early exploration by inexperienced users. To address this, a large language model (LLM) agent is integrated with AVEVA Process Simulation (APS) via Model Context Protocol (MCP), allowing natural language interaction with rigorous process simulations. An MCP server toolset enables the LLM to communicate programmatically with APS using Python, allowing it to execute complex simulation tasks from pla"},"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":"2601.11650","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2026-01-15T12:18:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e7786ea52883bd026ab978b6c12b4cda392439cf73547c888355b355e2ddc1de","abstract_canon_sha256":"54833268337e3e6e3e7ac1da860a660fd00e079967beeeb0614ffa219af214d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:03:17.026443Z","signature_b64":"ilGo4CF3T/ZIgFZErlfH7BofimEdX+3ifX42Li9bBvS6/RwlZJFUVIDn6Iv0zN/fEyRurhVGDObH3HvCxTbsCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b5175654232671991d5d74085079ba9cc234899993abcce97fee42c3e6b0888d","last_reissued_at":"2026-05-22T01:03:17.025572Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:03:17.025572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Model Agent for User-friendly Chemical Process Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.chem-ph","authors_text":"G\\\"urkan Sin, Jingkang Liang, Niklas Groll","submitted_at":"2026-01-15T12:18:45Z","abstract_excerpt":"Modern process simulators enable detailed process design, simulation, and optimization; however, constructing and interpreting simulations is time-consuming and requires expert knowledge. This limits early exploration by inexperienced users. To address this, a large language model (LLM) agent is integrated with AVEVA Process Simulation (APS) via Model Context Protocol (MCP), allowing natural language interaction with rigorous process simulations. An MCP server toolset enables the LLM to communicate programmatically with APS using Python, allowing it to execute complex simulation tasks from pla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.11650","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/2601.11650/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":"2601.11650","created_at":"2026-05-22T01:03:17.025674+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.11650v2","created_at":"2026-05-22T01:03:17.025674+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.11650","created_at":"2026-05-22T01:03:17.025674+00:00"},{"alias_kind":"pith_short_12","alias_value":"WULVMVBDEZYZ","created_at":"2026-05-22T01:03:17.025674+00:00"},{"alias_kind":"pith_short_16","alias_value":"WULVMVBDEZYZSHK5","created_at":"2026-05-22T01:03:17.025674+00:00"},{"alias_kind":"pith_short_8","alias_value":"WULVMVBD","created_at":"2026-05-22T01:03:17.025674+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/WULVMVBDEZYZSHK5OQEFA6N2TT","json":"https://pith.science/pith/WULVMVBDEZYZSHK5OQEFA6N2TT.json","graph_json":"https://pith.science/api/pith-number/WULVMVBDEZYZSHK5OQEFA6N2TT/graph.json","events_json":"https://pith.science/api/pith-number/WULVMVBDEZYZSHK5OQEFA6N2TT/events.json","paper":"https://pith.science/paper/WULVMVBD"},"agent_actions":{"view_html":"https://pith.science/pith/WULVMVBDEZYZSHK5OQEFA6N2TT","download_json":"https://pith.science/pith/WULVMVBDEZYZSHK5OQEFA6N2TT.json","view_paper":"https://pith.science/paper/WULVMVBD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.11650&json=true","fetch_graph":"https://pith.science/api/pith-number/WULVMVBDEZYZSHK5OQEFA6N2TT/graph.json","fetch_events":"https://pith.science/api/pith-number/WULVMVBDEZYZSHK5OQEFA6N2TT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WULVMVBDEZYZSHK5OQEFA6N2TT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WULVMVBDEZYZSHK5OQEFA6N2TT/action/storage_attestation","attest_author":"https://pith.science/pith/WULVMVBDEZYZSHK5OQEFA6N2TT/action/author_attestation","sign_citation":"https://pith.science/pith/WULVMVBDEZYZSHK5OQEFA6N2TT/action/citation_signature","submit_replication":"https://pith.science/pith/WULVMVBDEZYZSHK5OQEFA6N2TT/action/replication_record"}},"created_at":"2026-05-22T01:03:17.025674+00:00","updated_at":"2026-05-22T01:03:17.025674+00:00"}