{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:RUMTGENQMR4O2F43P2VF5XJZQT","short_pith_number":"pith:RUMTGENQ","canonical_record":{"source":{"id":"1807.05517","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-07-15T09:12:08Z","cross_cats_sorted":[],"title_canon_sha256":"788d1c25e154cfe572dbf0c2cd0cc58ea95d89a6ae7f4eba912ce12541577f72","abstract_canon_sha256":"37e8d4e245760fad1636cbd6d5a251b528b824448d457b733a0733a70aacf3d5"},"schema_version":"1.0"},"canonical_sha256":"8d193311b06478ed179b7eaa5edd3984ed3fb4f8230e06f21e1b01f20e5cb396","source":{"kind":"arxiv","id":"1807.05517","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.05517","created_at":"2026-05-18T00:10:43Z"},{"alias_kind":"arxiv_version","alias_value":"1807.05517v1","created_at":"2026-05-18T00:10:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05517","created_at":"2026-05-18T00:10:43Z"},{"alias_kind":"pith_short_12","alias_value":"RUMTGENQMR4O","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RUMTGENQMR4O2F43","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RUMTGENQ","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:RUMTGENQMR4O2F43P2VF5XJZQT","target":"record","payload":{"canonical_record":{"source":{"id":"1807.05517","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-07-15T09:12:08Z","cross_cats_sorted":[],"title_canon_sha256":"788d1c25e154cfe572dbf0c2cd0cc58ea95d89a6ae7f4eba912ce12541577f72","abstract_canon_sha256":"37e8d4e245760fad1636cbd6d5a251b528b824448d457b733a0733a70aacf3d5"},"schema_version":"1.0"},"canonical_sha256":"8d193311b06478ed179b7eaa5edd3984ed3fb4f8230e06f21e1b01f20e5cb396","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:43.352249Z","signature_b64":"PiQkKohciPy7wsRHT+CJehJ7iO5R6jTmaNCryInO1GJt7l+iDZ+ZJgMtbtXWGM7zGQMLQKz5g3SvLLu2nGNHBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d193311b06478ed179b7eaa5edd3984ed3fb4f8230e06f21e1b01f20e5cb396","last_reissued_at":"2026-05-18T00:10:43.351630Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:43.351630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.05517","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L+snr5Eu1uiKEH11UDs0m5VUUYFH19qZb+ckrtHCgBN+EiluxMlWtxEosy1KPQwNDOENkH0aQBgZ/fD14fzXAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T08:31:50.745769Z"},"content_sha256":"d1a2b208ccbf545e04105846281bf58f0666e276f559199c6098bdc8e20c65b4","schema_version":"1.0","event_id":"sha256:d1a2b208ccbf545e04105846281bf58f0666e276f559199c6098bdc8e20c65b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:RUMTGENQMR4O2F43P2VF5XJZQT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Boosting Combinatorial Problem Modeling with Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Michela Milano, Michele Lombardi","submitted_at":"2018-07-15T09:12:08Z","abstract_excerpt":"In the past few years, the area of Machine Learning (ML) has witnessed tremendous advancements, becoming a pervasive technology in a wide range of applications. One area that can significantly benefit from the use of ML is Combinatorial Optimization. The three pillars of constraint satisfaction and optimization problem solving, i.e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness. In this survey we focus on the modeling component, whose effectiveness is crucial for solving the problem. The modeling activity has been tradition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05517","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RNMtfSZKk0zDI8AxdhsORYhoYrVqwbzAO4Slpro2ocKKzacZouXaWOk5fge749ejO7gvvBYpWaINXSgQer7eDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T08:31:50.746112Z"},"content_sha256":"8e8a5893ff5c19ffed5696fd41d83b70407e229f2a225effb3225b2925c4a66c","schema_version":"1.0","event_id":"sha256:8e8a5893ff5c19ffed5696fd41d83b70407e229f2a225effb3225b2925c4a66c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RUMTGENQMR4O2F43P2VF5XJZQT/bundle.json","state_url":"https://pith.science/pith/RUMTGENQMR4O2F43P2VF5XJZQT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RUMTGENQMR4O2F43P2VF5XJZQT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-25T08:31:50Z","links":{"resolver":"https://pith.science/pith/RUMTGENQMR4O2F43P2VF5XJZQT","bundle":"https://pith.science/pith/RUMTGENQMR4O2F43P2VF5XJZQT/bundle.json","state":"https://pith.science/pith/RUMTGENQMR4O2F43P2VF5XJZQT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RUMTGENQMR4O2F43P2VF5XJZQT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:RUMTGENQMR4O2F43P2VF5XJZQT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"37e8d4e245760fad1636cbd6d5a251b528b824448d457b733a0733a70aacf3d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-07-15T09:12:08Z","title_canon_sha256":"788d1c25e154cfe572dbf0c2cd0cc58ea95d89a6ae7f4eba912ce12541577f72"},"schema_version":"1.0","source":{"id":"1807.05517","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.05517","created_at":"2026-05-18T00:10:43Z"},{"alias_kind":"arxiv_version","alias_value":"1807.05517v1","created_at":"2026-05-18T00:10:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.05517","created_at":"2026-05-18T00:10:43Z"},{"alias_kind":"pith_short_12","alias_value":"RUMTGENQMR4O","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RUMTGENQMR4O2F43","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RUMTGENQ","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:8e8a5893ff5c19ffed5696fd41d83b70407e229f2a225effb3225b2925c4a66c","target":"graph","created_at":"2026-05-18T00:10:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In the past few years, the area of Machine Learning (ML) has witnessed tremendous advancements, becoming a pervasive technology in a wide range of applications. One area that can significantly benefit from the use of ML is Combinatorial Optimization. The three pillars of constraint satisfaction and optimization problem solving, i.e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness. In this survey we focus on the modeling component, whose effectiveness is crucial for solving the problem. The modeling activity has been tradition","authors_text":"Michela Milano, Michele Lombardi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-07-15T09:12:08Z","title":"Boosting Combinatorial Problem Modeling with Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.05517","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d1a2b208ccbf545e04105846281bf58f0666e276f559199c6098bdc8e20c65b4","target":"record","created_at":"2026-05-18T00:10:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"37e8d4e245760fad1636cbd6d5a251b528b824448d457b733a0733a70aacf3d5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-07-15T09:12:08Z","title_canon_sha256":"788d1c25e154cfe572dbf0c2cd0cc58ea95d89a6ae7f4eba912ce12541577f72"},"schema_version":"1.0","source":{"id":"1807.05517","kind":"arxiv","version":1}},"canonical_sha256":"8d193311b06478ed179b7eaa5edd3984ed3fb4f8230e06f21e1b01f20e5cb396","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d193311b06478ed179b7eaa5edd3984ed3fb4f8230e06f21e1b01f20e5cb396","first_computed_at":"2026-05-18T00:10:43.351630Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:43.351630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PiQkKohciPy7wsRHT+CJehJ7iO5R6jTmaNCryInO1GJt7l+iDZ+ZJgMtbtXWGM7zGQMLQKz5g3SvLLu2nGNHBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:43.352249Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.05517","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1a2b208ccbf545e04105846281bf58f0666e276f559199c6098bdc8e20c65b4","sha256:8e8a5893ff5c19ffed5696fd41d83b70407e229f2a225effb3225b2925c4a66c"],"state_sha256":"6ce18101bcf5dd2b652da98e0088b565e2ab5c402d730a8f10fa09c22d1886a1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OXajQx3Zce5psvrLKFh2Qgv3VFuS9vQC7JiQ9A0Uh7fWF7BxqLY1AUWxdP8mFn2P6cSVseYXri3Pdy/5zjo6Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T08:31:50.748051Z","bundle_sha256":"dba338bbde361d84d64b01654c756c506c00230cff7598af1abaf8dd868e04fb"}}