{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ELUVWENEOPLCY6OYNG7XFNILTM","short_pith_number":"pith:ELUVWENE","schema_version":"1.0","canonical_sha256":"22e95b11a473d62c79d869bf72b50b9b3e86a4007a5aa777f065876c7f7f806a","source":{"kind":"arxiv","id":"2504.05946","version":3},"attestation_state":"computed","paper":{"title":"InstructMPC: A Human-LLM-in-the-Loop Framework for Context-Aware Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Jiahao Ai, Ruixiang Wu, Tongxin Li","submitted_at":"2025-04-08T11:59:00Z","abstract_excerpt":"Model Predictive Control (MPC) is a powerful control strategy widely utilized in domains like energy management, building control, and autonomous systems. However, its effectiveness in real-world settings is challenged by the need to incorporate context-specific predictions and expert instructions, which traditional MPC often neglects. We propose InstructMPC, a novel framework that addresses this gap by integrating real-time human instructions through a Large Language Model (LLM) to produce context-aware predictions for MPC. Our method employs a Language-to-Distribution (L2D) module to transla"},"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":"2504.05946","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-04-08T11:59:00Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"c17743e15548cb0ba942c63a5fc0bbc269a94c6f609a896dda55fe51fbd605d1","abstract_canon_sha256":"a9fcff624586bee7ddb5086fd869c490269d5737a22062decde59ed92081889e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:05:15.721014Z","signature_b64":"ScNXs/kwrsBGFvIJNQrxwngto8vQd4WLfBVxtOfpgz5mxD6al88GEP81gJ0imSrNmmBHasuCQrAgsRBuQ+MqCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22e95b11a473d62c79d869bf72b50b9b3e86a4007a5aa777f065876c7f7f806a","last_reissued_at":"2026-07-05T12:05:15.720443Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:05:15.720443Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"InstructMPC: A Human-LLM-in-the-Loop Framework for Context-Aware Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Jiahao Ai, Ruixiang Wu, Tongxin Li","submitted_at":"2025-04-08T11:59:00Z","abstract_excerpt":"Model Predictive Control (MPC) is a powerful control strategy widely utilized in domains like energy management, building control, and autonomous systems. However, its effectiveness in real-world settings is challenged by the need to incorporate context-specific predictions and expert instructions, which traditional MPC often neglects. We propose InstructMPC, a novel framework that addresses this gap by integrating real-time human instructions through a Large Language Model (LLM) to produce context-aware predictions for MPC. Our method employs a Language-to-Distribution (L2D) module to transla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05946","kind":"arxiv","version":3},"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/2504.05946/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":"2504.05946","created_at":"2026-07-05T12:05:15.720506+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.05946v3","created_at":"2026-07-05T12:05:15.720506+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05946","created_at":"2026-07-05T12:05:15.720506+00:00"},{"alias_kind":"pith_short_12","alias_value":"ELUVWENEOPLC","created_at":"2026-07-05T12:05:15.720506+00:00"},{"alias_kind":"pith_short_16","alias_value":"ELUVWENEOPLCY6OY","created_at":"2026-07-05T12:05:15.720506+00:00"},{"alias_kind":"pith_short_8","alias_value":"ELUVWENE","created_at":"2026-07-05T12:05:15.720506+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12774","citing_title":"Agentic MPC for Semantic Control System Resynthesis","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM","json":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM.json","graph_json":"https://pith.science/api/pith-number/ELUVWENEOPLCY6OYNG7XFNILTM/graph.json","events_json":"https://pith.science/api/pith-number/ELUVWENEOPLCY6OYNG7XFNILTM/events.json","paper":"https://pith.science/paper/ELUVWENE"},"agent_actions":{"view_html":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM","download_json":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM.json","view_paper":"https://pith.science/paper/ELUVWENE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.05946&json=true","fetch_graph":"https://pith.science/api/pith-number/ELUVWENEOPLCY6OYNG7XFNILTM/graph.json","fetch_events":"https://pith.science/api/pith-number/ELUVWENEOPLCY6OYNG7XFNILTM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM/action/storage_attestation","attest_author":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM/action/author_attestation","sign_citation":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM/action/citation_signature","submit_replication":"https://pith.science/pith/ELUVWENEOPLCY6OYNG7XFNILTM/action/replication_record"}},"created_at":"2026-07-05T12:05:15.720506+00:00","updated_at":"2026-07-05T12:05:15.720506+00:00"}