{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WSF6OKOWUDD2PBJGMHESJNNIXT","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":"5ac448227a916fcf20de77e79a94052f93d46e38b4ed3ab089fa6ab7b7b63278","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-13T01:40:21Z","title_canon_sha256":"8c17a4b6360e064a5fe78c8994759c8a6cf3cb29d7b29fb075c1bd37d5ef4a01"},"schema_version":"1.0","source":{"id":"2607.22550","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.22550","created_at":"2026-07-28T00:21:41Z"},{"alias_kind":"arxiv_version","alias_value":"2607.22550v1","created_at":"2026-07-28T00:21:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22550","created_at":"2026-07-28T00:21:41Z"},{"alias_kind":"pith_short_12","alias_value":"WSF6OKOWUDD2","created_at":"2026-07-28T00:21:41Z"},{"alias_kind":"pith_short_16","alias_value":"WSF6OKOWUDD2PBJG","created_at":"2026-07-28T00:21:41Z"},{"alias_kind":"pith_short_8","alias_value":"WSF6OKOW","created_at":"2026-07-28T00:21:41Z"}],"graph_snapshots":[{"event_id":"sha256:608030ebab04eb2419d224872373440a9dbe8679ef4b2abeaf77ae83bc2c6c12","target":"graph","created_at":"2026-07-28T00:21:41Z","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/2607.22550/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a learning-augmented Benders decomposition framework to solve large-scale two-stage stochastic mixed-integer programs. We focus on the two-stage stochastic capacitated lot-sizing problem (TSSCLSP) under demand uncertainty. Our method accelerates the convergence of the decomposition by using a pre-trained TransfORmer model to rapidly generate high-quality approximate solutions for the scenario subproblems. This hybrid strategy uses the TransfORmer predictions to generate strong optimality and feasibility cuts, effectively guiding the Benders master problem. Our framework includes a n","authors_text":"Esra Buyuktahtakin Toy, Josh Cooper, Kimiya Jozani, Seung Jin Choi","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-13T01:40:21Z","title":"Learning to Optimize at Scale: A Benders Decomposition-TransfORmers Framework for Stochastic Combinatorial Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22550","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:ea5befd0a72514dcd30f56d0c06372c65ca69f97f71b0af1ba2a6f9a2a66565c","target":"record","created_at":"2026-07-28T00:21:41Z","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":"5ac448227a916fcf20de77e79a94052f93d46e38b4ed3ab089fa6ab7b7b63278","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-05-13T01:40:21Z","title_canon_sha256":"8c17a4b6360e064a5fe78c8994759c8a6cf3cb29d7b29fb075c1bd37d5ef4a01"},"schema_version":"1.0","source":{"id":"2607.22550","kind":"arxiv","version":1}},"canonical_sha256":"b48be729d6a0c7a7852661c924b5a8bced291aa56e4707cad5b0bfdd0b6fe7b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b48be729d6a0c7a7852661c924b5a8bced291aa56e4707cad5b0bfdd0b6fe7b5","first_computed_at":"2026-07-28T00:21:41.734864Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T00:21:41.734864Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v1kK2M6JUXkA3Jb7uUZsnlYG3mJZmJtVOaeQftN6ZDbT7l4M0LYUZSVoae5HL9e4XNl6/Wal3pZeUUEp5Dj1BA==","signature_status":"signed_v1","signed_at":"2026-07-28T00:21:41.735748Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.22550","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea5befd0a72514dcd30f56d0c06372c65ca69f97f71b0af1ba2a6f9a2a66565c","sha256:608030ebab04eb2419d224872373440a9dbe8679ef4b2abeaf77ae83bc2c6c12"],"state_sha256":"ebb0cd34ef73603536cba10203454bd0d383644885f7703966dcd37e70d3b12f"}