{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:C6THR7ZLECF7SQFPQGDUB3JANB","short_pith_number":"pith:C6THR7ZL","schema_version":"1.0","canonical_sha256":"17a678ff2b208bf940af818740ed206850d0468592a1a720f260f5a796a77d43","source":{"kind":"arxiv","id":"2508.07515","version":3},"attestation_state":"computed","paper":{"title":"Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Neuro-symbolic guidance accelerates MILP motion planning for temporal logic and chance constraints by about 20 percent.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Bistra Dilkina, Brendan Long, Junyang Cai, Jyotirmoy V. Deshmukh, Lars Lindemann, Matthew Cleaveland, Weimin Huang","submitted_at":"2025-08-11T00:13:36Z","abstract_excerpt":"Motion-planning problems with temporal-logic or chance constraints are often encoded as mixed-integer linear programs (MILPs). Although these encodings provide rigorous specifications, their combinatorial structure can make planning prohibitively slow. Machine learning for combinatorial optimization (ML4CO) has accelerated general-purpose MILP solving, but its standard graph representations discard semantic information available in control problems, such as variable roles, time indices, sample identities, and formula structure. We introduce a domain-aware ML4CO framework for MILP-based motion "},"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":"2508.07515","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2025-08-11T00:13:36Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"ac71cdb9066e3991a0ab3a255caf55ff19634c46228e0c53123e91f7b7058add","abstract_canon_sha256":"587ad48b5349983db7ad971d0a24573ff5b01244092b51df1570f9800f730365"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17a678ff2b208bf940af818740ed206850d0468592a1a720f260f5a796a77d43","last_reissued_at":"2026-07-30T01:23:45.844224Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:23:45.844224Z"},"graph_snapshot":{"paper":{"title":"Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Neuro-symbolic guidance accelerates MILP motion planning for temporal logic and chance constraints by about 20 percent.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Bistra Dilkina, Brendan Long, Junyang Cai, Jyotirmoy V. Deshmukh, Lars Lindemann, Matthew Cleaveland, Weimin Huang","submitted_at":"2025-08-11T00:13:36Z","abstract_excerpt":"Motion-planning problems with temporal-logic or chance constraints are often encoded as mixed-integer linear programs (MILPs). Although these encodings provide rigorous specifications, their combinatorial structure can make planning prohibitively slow. Machine learning for combinatorial optimization (ML4CO) has accelerated general-purpose MILP solving, but its standard graph representations discard semantic information available in control problems, such as variable roles, time indices, sample identities, and formula structure. We introduce a domain-aware ML4CO framework for MILP-based motion "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Through extensive experiments, we show that neuro-symbolic search techniques yield scalability gains. Our approach yields substantial improvements across all three classes of planning problems, achieving an average performance gain of about 20% over state-of-the-art solver across key metrics, including runtime and solution quality.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Graph neural network-based learning methods can reliably guide traditional symbolic MILP solvers on branching variable selection and solver parameter configuration for the three classes of planning problems (STL, CPP, CaTL).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Neuro-symbolic GNN guidance accelerates MILP solvers for STL, CPP chance constraints, and CaTL planning problems with average 20% gains in runtime and solution quality over state-of-the-art solvers.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Neuro-symbolic guidance accelerates MILP motion planning for temporal logic and chance constraints by about 20 percent.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"85be8ed3b8abe71571132dd34823a76fb99caa15e27fa0f9996f05cad6dde1c1"},"source":{"id":"2508.07515","kind":"arxiv","version":3},"verdict":{"id":"5c63d08a-7b4b-4350-8c21-dcc1eec18280","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T00:33:39.993692Z","strongest_claim":"Through extensive experiments, we show that neuro-symbolic search techniques yield scalability gains. Our approach yields substantial improvements across all three classes of planning problems, achieving an average performance gain of about 20% over state-of-the-art solver across key metrics, including runtime and solution quality.","one_line_summary":"Neuro-symbolic GNN guidance accelerates MILP solvers for STL, CPP chance constraints, and CaTL planning problems with average 20% gains in runtime and solution quality over state-of-the-art solvers.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Graph neural network-based learning methods can reliably guide traditional symbolic MILP solvers on branching variable selection and solver parameter configuration for the three classes of planning problems (STL, CPP, CaTL).","pith_extraction_headline":"Neuro-symbolic guidance accelerates MILP motion planning for temporal logic and chance constraints by about 20 percent."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.07515/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":"2508.07515","created_at":"2026-07-30T01:23:45.849316+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.07515v3","created_at":"2026-07-30T01:23:45.849316+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.07515","created_at":"2026-07-30T01:23:45.849316+00:00"},{"alias_kind":"pith_short_12","alias_value":"C6THR7ZLECF7","created_at":"2026-07-30T01:23:45.849316+00:00"},{"alias_kind":"pith_short_16","alias_value":"C6THR7ZLECF7SQFP","created_at":"2026-07-30T01:23:45.849316+00:00"},{"alias_kind":"pith_short_8","alias_value":"C6THR7ZL","created_at":"2026-07-30T01:23:45.849316+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2512.10211","citing_title":"ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programs","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB","json":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB.json","graph_json":"https://pith.science/api/pith-number/C6THR7ZLECF7SQFPQGDUB3JANB/graph.json","events_json":"https://pith.science/api/pith-number/C6THR7ZLECF7SQFPQGDUB3JANB/events.json","paper":"https://pith.science/paper/C6THR7ZL"},"agent_actions":{"view_html":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB","download_json":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB.json","view_paper":"https://pith.science/paper/C6THR7ZL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.07515&json=true","fetch_graph":"https://pith.science/api/pith-number/C6THR7ZLECF7SQFPQGDUB3JANB/graph.json","fetch_events":"https://pith.science/api/pith-number/C6THR7ZLECF7SQFPQGDUB3JANB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB/action/storage_attestation","attest_author":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB/action/author_attestation","sign_citation":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB/action/citation_signature","submit_replication":"https://pith.science/pith/C6THR7ZLECF7SQFPQGDUB3JANB/action/replication_record"}},"created_at":"2026-07-30T01:23:45.849316+00:00","updated_at":"2026-07-30T01:23:45.849316+00:00"}