{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YZJDQHZLEMFRMNNRPI357Y5PTY","short_pith_number":"pith:YZJDQHZL","schema_version":"1.0","canonical_sha256":"c652381f2b230b1635b17a37dfe3af9e1973ea27d4eb7f37528d422d6434e7a0","source":{"kind":"arxiv","id":"2509.07381","version":1},"attestation_state":"computed","paper":{"title":"TransMPC: Transformer-based Explicit MPC with Variable Prediction Horizon","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fawang Zhang, Fei Ma, Guangyuan Yu, Jiang Wu, Jingliang Duan, Junjie Zhao, Sichao Wu, Xingyu Cao, Yue Qu","submitted_at":"2025-09-09T04:14:42Z","abstract_excerpt":"Traditional online Model Predictive Control (MPC) methods often suffer from excessive computational complexity, limiting their practical deployment. Explicit MPC mitigates online computational load by pre-computing control policies offline; however, existing explicit MPC methods typically rely on simplified system dynamics and cost functions, restricting their accuracy for complex systems. This paper proposes TransMPC, a novel Transformer-based explicit MPC algorithm capable of generating highly accurate control sequences in real-time for complex dynamic systems. Specifically, we formulate the"},"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":"2509.07381","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-09-09T04:14:42Z","cross_cats_sorted":[],"title_canon_sha256":"f97c1b87e99a7194d1b74979e54a7891881c242f8a06796d763a67002927c199","abstract_canon_sha256":"08d4fb9f6baeeb44643c123d35f65b9dfdabcd439317a9d1ead25c92d7dfd055"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:07:20.563987Z","signature_b64":"T29B3H1F6ltpmtVZ6D0zG6vrUQUiP3reSFBubNaQFL4z9kmJZSk7Vq4aZNTPqjGXFv6RcId3/mYKeezS5H/zAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c652381f2b230b1635b17a37dfe3af9e1973ea27d4eb7f37528d422d6434e7a0","last_reissued_at":"2026-07-05T12:07:20.563495Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:07:20.563495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TransMPC: Transformer-based Explicit MPC with Variable Prediction Horizon","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fawang Zhang, Fei Ma, Guangyuan Yu, Jiang Wu, Jingliang Duan, Junjie Zhao, Sichao Wu, Xingyu Cao, Yue Qu","submitted_at":"2025-09-09T04:14:42Z","abstract_excerpt":"Traditional online Model Predictive Control (MPC) methods often suffer from excessive computational complexity, limiting their practical deployment. Explicit MPC mitigates online computational load by pre-computing control policies offline; however, existing explicit MPC methods typically rely on simplified system dynamics and cost functions, restricting their accuracy for complex systems. This paper proposes TransMPC, a novel Transformer-based explicit MPC algorithm capable of generating highly accurate control sequences in real-time for complex dynamic systems. Specifically, we formulate the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.07381","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/2509.07381/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":"2509.07381","created_at":"2026-07-05T12:07:20.563553+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.07381v1","created_at":"2026-07-05T12:07:20.563553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.07381","created_at":"2026-07-05T12:07:20.563553+00:00"},{"alias_kind":"pith_short_12","alias_value":"YZJDQHZLEMFR","created_at":"2026-07-05T12:07:20.563553+00:00"},{"alias_kind":"pith_short_16","alias_value":"YZJDQHZLEMFRMNNR","created_at":"2026-07-05T12:07:20.563553+00:00"},{"alias_kind":"pith_short_8","alias_value":"YZJDQHZL","created_at":"2026-07-05T12:07:20.563553+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/YZJDQHZLEMFRMNNRPI357Y5PTY","json":"https://pith.science/pith/YZJDQHZLEMFRMNNRPI357Y5PTY.json","graph_json":"https://pith.science/api/pith-number/YZJDQHZLEMFRMNNRPI357Y5PTY/graph.json","events_json":"https://pith.science/api/pith-number/YZJDQHZLEMFRMNNRPI357Y5PTY/events.json","paper":"https://pith.science/paper/YZJDQHZL"},"agent_actions":{"view_html":"https://pith.science/pith/YZJDQHZLEMFRMNNRPI357Y5PTY","download_json":"https://pith.science/pith/YZJDQHZLEMFRMNNRPI357Y5PTY.json","view_paper":"https://pith.science/paper/YZJDQHZL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.07381&json=true","fetch_graph":"https://pith.science/api/pith-number/YZJDQHZLEMFRMNNRPI357Y5PTY/graph.json","fetch_events":"https://pith.science/api/pith-number/YZJDQHZLEMFRMNNRPI357Y5PTY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YZJDQHZLEMFRMNNRPI357Y5PTY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YZJDQHZLEMFRMNNRPI357Y5PTY/action/storage_attestation","attest_author":"https://pith.science/pith/YZJDQHZLEMFRMNNRPI357Y5PTY/action/author_attestation","sign_citation":"https://pith.science/pith/YZJDQHZLEMFRMNNRPI357Y5PTY/action/citation_signature","submit_replication":"https://pith.science/pith/YZJDQHZLEMFRMNNRPI357Y5PTY/action/replication_record"}},"created_at":"2026-07-05T12:07:20.563553+00:00","updated_at":"2026-07-05T12:07:20.563553+00:00"}