{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:UO7E3TLNEUAS7V56Q2CTHITSYZ","short_pith_number":"pith:UO7E3TLN","canonical_record":{"source":{"id":"2206.06965","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-14T16:35:58Z","cross_cats_sorted":["cs.AI","cs.RO","math.OC"],"title_canon_sha256":"918b9a32d0bfd6f57b73ca638d931a2465bf3eb00e71b7795a8f4bfe18a36dc0","abstract_canon_sha256":"97e667377ced3e85972145aa40e7a97797059c5d7ccd53ca81a8a8ca9a9b81c0"},"schema_version":"1.0"},"canonical_sha256":"a3be4dcd6d25012fd7be868533a272c67b2953d2f79a0f9ca87d54e71253f9ad","source":{"kind":"arxiv","id":"2206.06965","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.06965","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"arxiv_version","alias_value":"2206.06965v1","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.06965","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"pith_short_12","alias_value":"UO7E3TLNEUAS","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"pith_short_16","alias_value":"UO7E3TLNEUAS7V56","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"pith_short_8","alias_value":"UO7E3TLN","created_at":"2026-07-05T04:31:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:UO7E3TLNEUAS7V56Q2CTHITSYZ","target":"record","payload":{"canonical_record":{"source":{"id":"2206.06965","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-14T16:35:58Z","cross_cats_sorted":["cs.AI","cs.RO","math.OC"],"title_canon_sha256":"918b9a32d0bfd6f57b73ca638d931a2465bf3eb00e71b7795a8f4bfe18a36dc0","abstract_canon_sha256":"97e667377ced3e85972145aa40e7a97797059c5d7ccd53ca81a8a8ca9a9b81c0"},"schema_version":"1.0"},"canonical_sha256":"a3be4dcd6d25012fd7be868533a272c67b2953d2f79a0f9ca87d54e71253f9ad","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:31:47.749971Z","signature_b64":"MqfHi8tbjFr3VQ0tkLBC2ZE+O1R0y5qSfJuFnusZOzFE5Coy0yYfJOJeXYKCpEykDg1R2CEXNIDRKJJc1f+wDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3be4dcd6d25012fd7be868533a272c67b2953d2f79a0f9ca87d54e71253f9ad","last_reissued_at":"2026-07-05T04:31:47.749528Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:31:47.749528Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.06965","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":"2026-07-05T04:31:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PeuzuixALan9tRVx88NIO/XQt093lLtbD+nlCqUNfdXDJ0PLu9eG199+TwgEyEpaWQYEOqvpTddpw41TqT1PAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T10:40:10.268968Z"},"content_sha256":"f16315a50d0587918f0040dd1b07e2080c8a78898eb6b43697d79d5607e94860","schema_version":"1.0","event_id":"sha256:f16315a50d0587918f0040dd1b07e2080c8a78898eb6b43697d79d5607e94860"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:UO7E3TLNEUAS7V56Q2CTHITSYZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO","math.OC"],"primary_cat":"cs.LG","authors_text":"Amin Banitalebi-Dehkordi, Tianyu Zhang, Yong Zhang","submitted_at":"2022-06-14T16:35:58Z","abstract_excerpt":"Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use P"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.06965","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/2206.06965/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":"2026-07-05T04:31:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gwk/cTz+mUEtqROhMQfLVevvH7iSr0TDe5Ycon6xrBZnTTtjT/YV58H+kWYdjhYeunu5gcLRjr21V6jpIx0yAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T10:40:10.269353Z"},"content_sha256":"eb602459428a8f179cec2ba6e828a7e78134da28ad5fc568af912fcd7e5db15d","schema_version":"1.0","event_id":"sha256:eb602459428a8f179cec2ba6e828a7e78134da28ad5fc568af912fcd7e5db15d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UO7E3TLNEUAS7V56Q2CTHITSYZ/bundle.json","state_url":"https://pith.science/pith/UO7E3TLNEUAS7V56Q2CTHITSYZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UO7E3TLNEUAS7V56Q2CTHITSYZ/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":"2026-08-13T10:40:10Z","links":{"resolver":"https://pith.science/pith/UO7E3TLNEUAS7V56Q2CTHITSYZ","bundle":"https://pith.science/pith/UO7E3TLNEUAS7V56Q2CTHITSYZ/bundle.json","state":"https://pith.science/pith/UO7E3TLNEUAS7V56Q2CTHITSYZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UO7E3TLNEUAS7V56Q2CTHITSYZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:UO7E3TLNEUAS7V56Q2CTHITSYZ","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":"97e667377ced3e85972145aa40e7a97797059c5d7ccd53ca81a8a8ca9a9b81c0","cross_cats_sorted":["cs.AI","cs.RO","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-14T16:35:58Z","title_canon_sha256":"918b9a32d0bfd6f57b73ca638d931a2465bf3eb00e71b7795a8f4bfe18a36dc0"},"schema_version":"1.0","source":{"id":"2206.06965","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.06965","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"arxiv_version","alias_value":"2206.06965v1","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.06965","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"pith_short_12","alias_value":"UO7E3TLNEUAS","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"pith_short_16","alias_value":"UO7E3TLNEUAS7V56","created_at":"2026-07-05T04:31:47Z"},{"alias_kind":"pith_short_8","alias_value":"UO7E3TLN","created_at":"2026-07-05T04:31:47Z"}],"graph_snapshots":[{"event_id":"sha256:eb602459428a8f179cec2ba6e828a7e78134da28ad5fc568af912fcd7e5db15d","target":"graph","created_at":"2026-07-05T04:31:47Z","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/2206.06965/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-the-art handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use P","authors_text":"Amin Banitalebi-Dehkordi, Tianyu Zhang, Yong Zhang","cross_cats":["cs.AI","cs.RO","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-14T16:35:58Z","title":"Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.06965","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:f16315a50d0587918f0040dd1b07e2080c8a78898eb6b43697d79d5607e94860","target":"record","created_at":"2026-07-05T04:31:47Z","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":"97e667377ced3e85972145aa40e7a97797059c5d7ccd53ca81a8a8ca9a9b81c0","cross_cats_sorted":["cs.AI","cs.RO","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-14T16:35:58Z","title_canon_sha256":"918b9a32d0bfd6f57b73ca638d931a2465bf3eb00e71b7795a8f4bfe18a36dc0"},"schema_version":"1.0","source":{"id":"2206.06965","kind":"arxiv","version":1}},"canonical_sha256":"a3be4dcd6d25012fd7be868533a272c67b2953d2f79a0f9ca87d54e71253f9ad","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a3be4dcd6d25012fd7be868533a272c67b2953d2f79a0f9ca87d54e71253f9ad","first_computed_at":"2026-07-05T04:31:47.749528Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:31:47.749528Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MqfHi8tbjFr3VQ0tkLBC2ZE+O1R0y5qSfJuFnusZOzFE5Coy0yYfJOJeXYKCpEykDg1R2CEXNIDRKJJc1f+wDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:31:47.749971Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.06965","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f16315a50d0587918f0040dd1b07e2080c8a78898eb6b43697d79d5607e94860","sha256:eb602459428a8f179cec2ba6e828a7e78134da28ad5fc568af912fcd7e5db15d"],"state_sha256":"dfc7267af164e223318bab5bca0c00b2ca7d05779d887e75ea546940add12da8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AC1nechGtmJzY7DeragTT1pRaqm/IZMni4wWXG+k4MnbROMBqmWo/Fe7U94X04fd6oTW0x61IZmxQK47i9YoAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T10:40:10.297363Z","bundle_sha256":"a7ac2643325acf57a69a69f8d9d1a0be3462a7db4bd744087c6c485886fb2da6"}}