{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:3COD6MQZ4TTTN67VSAM2BSDJYW","short_pith_number":"pith:3COD6MQZ","canonical_record":{"source":{"id":"2505.11636","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T19:00:02Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"4ffd8e92aa7def8806af2e76333797772cf32c988d821eb03f112d9f3b4b6381","abstract_canon_sha256":"dc79ca00aae9954becf0aedd3f0e44fbe7650bde2ca1a0fca8125f4e26d9b8ff"},"schema_version":"1.0"},"canonical_sha256":"d89c3f3219e4e736fbf59019a0c869c5b49335d5e95dea277222edf7c828a70c","source":{"kind":"arxiv","id":"2505.11636","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.11636","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"arxiv_version","alias_value":"2505.11636v1","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11636","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"pith_short_12","alias_value":"3COD6MQZ4TTT","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"pith_short_16","alias_value":"3COD6MQZ4TTTN67V","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"pith_short_8","alias_value":"3COD6MQZ","created_at":"2026-07-05T11:04:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:3COD6MQZ4TTTN67VSAM2BSDJYW","target":"record","payload":{"canonical_record":{"source":{"id":"2505.11636","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T19:00:02Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"4ffd8e92aa7def8806af2e76333797772cf32c988d821eb03f112d9f3b4b6381","abstract_canon_sha256":"dc79ca00aae9954becf0aedd3f0e44fbe7650bde2ca1a0fca8125f4e26d9b8ff"},"schema_version":"1.0"},"canonical_sha256":"d89c3f3219e4e736fbf59019a0c869c5b49335d5e95dea277222edf7c828a70c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:04:24.648903Z","signature_b64":"VNSv14jyi9Vos62L3HRR9zdJkA9YN437npkMfpmZPuIjp4ycv68vQcMtYWScYcc6W1G/Ufj6Uyifzt4dXQt/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d89c3f3219e4e736fbf59019a0c869c5b49335d5e95dea277222edf7c828a70c","last_reissued_at":"2026-07-05T11:04:24.648276Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:04:24.648276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.11636","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":"2026-07-05T11:04:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+WZVfVRIQsPKiEf74Ai+6131APTH+C8VFmHMsYC/AyWIvL8mtHBWhjLCL1Q/y8GTeYLib2yxg+/1w0Qnz+yQBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T14:40:54.300438Z"},"content_sha256":"ea74088ec4ce2e57620e0af1e4a537fd57fd1ad4360ff620d0f414177fff35b8","schema_version":"1.0","event_id":"sha256:ea74088ec4ce2e57620e0af1e4a537fd57fd1ad4360ff620d0f414177fff35b8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:3COD6MQZ4TTTN67VSAM2BSDJYW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalization Guarantees for Learning Branch-and-Cut Policies in Integer Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Amitabh Basu, Hongyu Cheng","submitted_at":"2025-05-16T19:00:02Z","abstract_excerpt":"Mixed-integer programming (MIP) provides a powerful framework for optimization problems, with Branch-and-Cut (B&C) being the predominant algorithm in state-of-the-art solvers. The efficiency of B&C critically depends on heuristic policies for making sequential decisions, including node selection, cut selection, and branching variable selection. While traditional solvers often employ heuristics with manually tuned parameters, recent approaches increasingly leverage machine learning, especially neural networks, to learn these policies directly from data. A key challenge is to understand the theo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11636","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/2505.11636/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:04:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0JtqYtXFsNEMR9vjRql7jf9gLDD/m+nkFXdjFHxfqgtkSJph4moQGeeZ+1dXi8nGp6bIjZRXXQxfir7lM9poBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T14:40:54.300928Z"},"content_sha256":"a6c4ff68487f66c758a179a362aa847f5d53183b7c5e39994c5d8ef2105b2d49","schema_version":"1.0","event_id":"sha256:a6c4ff68487f66c758a179a362aa847f5d53183b7c5e39994c5d8ef2105b2d49"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3COD6MQZ4TTTN67VSAM2BSDJYW/bundle.json","state_url":"https://pith.science/pith/3COD6MQZ4TTTN67VSAM2BSDJYW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3COD6MQZ4TTTN67VSAM2BSDJYW/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":"2026-07-31T14:40:54Z","links":{"resolver":"https://pith.science/pith/3COD6MQZ4TTTN67VSAM2BSDJYW","bundle":"https://pith.science/pith/3COD6MQZ4TTTN67VSAM2BSDJYW/bundle.json","state":"https://pith.science/pith/3COD6MQZ4TTTN67VSAM2BSDJYW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3COD6MQZ4TTTN67VSAM2BSDJYW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:3COD6MQZ4TTTN67VSAM2BSDJYW","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":"dc79ca00aae9954becf0aedd3f0e44fbe7650bde2ca1a0fca8125f4e26d9b8ff","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T19:00:02Z","title_canon_sha256":"4ffd8e92aa7def8806af2e76333797772cf32c988d821eb03f112d9f3b4b6381"},"schema_version":"1.0","source":{"id":"2505.11636","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.11636","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"arxiv_version","alias_value":"2505.11636v1","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.11636","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"pith_short_12","alias_value":"3COD6MQZ4TTT","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"pith_short_16","alias_value":"3COD6MQZ4TTTN67V","created_at":"2026-07-05T11:04:24Z"},{"alias_kind":"pith_short_8","alias_value":"3COD6MQZ","created_at":"2026-07-05T11:04:24Z"}],"graph_snapshots":[{"event_id":"sha256:a6c4ff68487f66c758a179a362aa847f5d53183b7c5e39994c5d8ef2105b2d49","target":"graph","created_at":"2026-07-05T11:04:24Z","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/2505.11636/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Mixed-integer programming (MIP) provides a powerful framework for optimization problems, with Branch-and-Cut (B&C) being the predominant algorithm in state-of-the-art solvers. The efficiency of B&C critically depends on heuristic policies for making sequential decisions, including node selection, cut selection, and branching variable selection. While traditional solvers often employ heuristics with manually tuned parameters, recent approaches increasingly leverage machine learning, especially neural networks, to learn these policies directly from data. A key challenge is to understand the theo","authors_text":"Amitabh Basu, Hongyu Cheng","cross_cats":["math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T19:00:02Z","title":"Generalization Guarantees for Learning Branch-and-Cut Policies in Integer Programming"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.11636","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:ea74088ec4ce2e57620e0af1e4a537fd57fd1ad4360ff620d0f414177fff35b8","target":"record","created_at":"2026-07-05T11:04:24Z","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":"dc79ca00aae9954becf0aedd3f0e44fbe7650bde2ca1a0fca8125f4e26d9b8ff","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-16T19:00:02Z","title_canon_sha256":"4ffd8e92aa7def8806af2e76333797772cf32c988d821eb03f112d9f3b4b6381"},"schema_version":"1.0","source":{"id":"2505.11636","kind":"arxiv","version":1}},"canonical_sha256":"d89c3f3219e4e736fbf59019a0c869c5b49335d5e95dea277222edf7c828a70c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d89c3f3219e4e736fbf59019a0c869c5b49335d5e95dea277222edf7c828a70c","first_computed_at":"2026-07-05T11:04:24.648276Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:04:24.648276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VNSv14jyi9Vos62L3HRR9zdJkA9YN437npkMfpmZPuIjp4ycv68vQcMtYWScYcc6W1G/Ufj6Uyifzt4dXQt/Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:04:24.648903Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.11636","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea74088ec4ce2e57620e0af1e4a537fd57fd1ad4360ff620d0f414177fff35b8","sha256:a6c4ff68487f66c758a179a362aa847f5d53183b7c5e39994c5d8ef2105b2d49"],"state_sha256":"c6a87fe1110de4330f26c8d7ce097897482bcbaf16d6f0bdd7fc8c18226bfad5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VaJbqCNTEPwQvQg6bYBxkGGt8CpcSd82o50Cp2CxTum+nAHl53y7gSvyQf507tQjAI5KppuFk5QS++0lsB3WDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T14:40:54.305100Z","bundle_sha256":"ba392bece78ca767b919833afd65596de9dc9ff11d07a416229072ab03929851"}}