{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:EXNW2WI4UTXEQAHSCMATF4JW3I","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":"a83086b3f192b5ebfbf513f4e76b9b9f9c2ece5e3b14c089a54ba595f8d3eb0c","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-30T02:33:32Z","title_canon_sha256":"44139054a242b6102ab6015646c5b0f2c0df69c0184af135790a5bbe80c11284"},"schema_version":"1.0","source":{"id":"2206.14987","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.14987","created_at":"2026-07-05T05:29:02Z"},{"alias_kind":"arxiv_version","alias_value":"2206.14987v2","created_at":"2026-07-05T05:29:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.14987","created_at":"2026-07-05T05:29:02Z"},{"alias_kind":"pith_short_12","alias_value":"EXNW2WI4UTXE","created_at":"2026-07-05T05:29:02Z"},{"alias_kind":"pith_short_16","alias_value":"EXNW2WI4UTXEQAHS","created_at":"2026-07-05T05:29:02Z"},{"alias_kind":"pith_short_8","alias_value":"EXNW2WI4","created_at":"2026-07-05T05:29:02Z"}],"graph_snapshots":[{"event_id":"sha256:3358dad9865554330ace82f5e1e366ed4c1b6b3e1dca3150cf0169da89b1f033","target":"graph","created_at":"2026-07-05T05:29:02Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2206.14987/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Program (MILP) solvers. Recent works have demonstrated the effectiveness of such GNNs in replacing the branching (variable selection) heuristic in branch-and-bound (B&B) solvers. These GNNs are trained, offline and on a collection of MILPs, to imitate a very good but computationally expensive branching heuristic, strong branching. Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependenci","authors_text":"Andrea Lodi, Didier Chet\\'elat, Elias B. Khalil, Maxime Gasse, M. Pawan Kumar, Prateek Gupta, Yoshua Bengio","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-30T02:33:32Z","title":"Lookback for Learning to Branch"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.14987","kind":"arxiv","version":2},"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:e15c19a54b0f7f9f027b2a05d2804691f7ccdb4f62ef6e1c464a21ac19b7a9cf","target":"record","created_at":"2026-07-05T05:29:02Z","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":"a83086b3f192b5ebfbf513f4e76b9b9f9c2ece5e3b14c089a54ba595f8d3eb0c","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-30T02:33:32Z","title_canon_sha256":"44139054a242b6102ab6015646c5b0f2c0df69c0184af135790a5bbe80c11284"},"schema_version":"1.0","source":{"id":"2206.14987","kind":"arxiv","version":2}},"canonical_sha256":"25db6d591ca4ee4800f2130132f136da3f7cd679d759a69462133b3bacacd101","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"25db6d591ca4ee4800f2130132f136da3f7cd679d759a69462133b3bacacd101","first_computed_at":"2026-07-05T05:29:02.534959Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:29:02.534959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0DB9j13ENiqZak7Fi6vbQBcYs5VLGiSXUBPOWlmgVbuE6n9dJhDiUDMymz5L4+tv1hlXeZDWhGkSHrNJdQD5DA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:29:02.535551Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.14987","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e15c19a54b0f7f9f027b2a05d2804691f7ccdb4f62ef6e1c464a21ac19b7a9cf","sha256:3358dad9865554330ace82f5e1e366ed4c1b6b3e1dca3150cf0169da89b1f033"],"state_sha256":"b80ab417fb3fdf1bda3ce4bc5e57ad936f5ab456b8b84d377c201596e733235f"}