{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:3T4K4FK6MPAK2QEPGZCR4DVW6S","short_pith_number":"pith:3T4K4FK6","canonical_record":{"source":{"id":"2207.01443","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T14:31:47Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"0b3dbc4c46de0a65706c1c535465da41d2be7ae825ed5cf16270d67f6517a0d6","abstract_canon_sha256":"687bf265ed3e51cf0ae76a7dff7c29a02d37fe548012c1c7ef9030822ebe3090"},"schema_version":"1.0"},"canonical_sha256":"dcf8ae155e63c0ad408f36451e0eb6f4a5cf2de9cf91c6703b41f73827865540","source":{"kind":"arxiv","id":"2207.01443","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.01443","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"arxiv_version","alias_value":"2207.01443v2","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.01443","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"pith_short_12","alias_value":"3T4K4FK6MPAK","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"pith_short_16","alias_value":"3T4K4FK6MPAK2QEP","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"pith_short_8","alias_value":"3T4K4FK6","created_at":"2026-07-05T04:58:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:3T4K4FK6MPAK2QEPGZCR4DVW6S","target":"record","payload":{"canonical_record":{"source":{"id":"2207.01443","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T14:31:47Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"0b3dbc4c46de0a65706c1c535465da41d2be7ae825ed5cf16270d67f6517a0d6","abstract_canon_sha256":"687bf265ed3e51cf0ae76a7dff7c29a02d37fe548012c1c7ef9030822ebe3090"},"schema_version":"1.0"},"canonical_sha256":"dcf8ae155e63c0ad408f36451e0eb6f4a5cf2de9cf91c6703b41f73827865540","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:58:37.759020Z","signature_b64":"80+XChkZFzl5XCsCUWv4uun/vev/WRp4o8ZLYfPwoRwNb596He5ilIh4jQGAUcknx35961PAMH35ddUWKZfrCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcf8ae155e63c0ad408f36451e0eb6f4a5cf2de9cf91c6703b41f73827865540","last_reissued_at":"2026-07-05T04:58:37.758358Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:58:37.758358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2207.01443","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:58:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9LvnUZJXtiHUThlkizHjTsgIWNzHC7oIIS1LR2xisBv0CsYXwSv0AsVO3JA7t4glshvSGYzh0dxFeWi5iM5qDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T13:51:35.486452Z"},"content_sha256":"87717541a7411bb319756396275707b729375f7065649834302bd40529ce9ce5","schema_version":"1.0","event_id":"sha256:87717541a7411bb319756396275707b729375f7065649834302bd40529ce9ce5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:3T4K4FK6MPAK2QEPGZCR4DVW6S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.LG","authors_text":"Alexander Gembus, Christian L\\\"owens, Genesis Cuizon, Inaam Ashraf, Jonas K. Falkner, Lars Schmidt-Thieme","submitted_at":"2022-07-04T14:31:47Z","abstract_excerpt":"This work presents solutions to the Traveling Salesperson Problem with precedence constraints (TSPPC) using Deep Reinforcement Learning (DRL) by adapting recent approaches that work well for regular TSPs. Common to these approaches is the use of graph models based on multi-head attention (MHA) layers. One idea for solving the pickup and delivery problem (PDP) is using heterogeneous attentions to embed the different possible roles each node can take. In this work, we generalize this concept of heterogeneous attentions to the TSPPC. Furthermore, we adapt recent ideas to sparsify attentions for b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.01443","kind":"arxiv","version":2},"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/2207.01443/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":"2026-07-05T04:58:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7sl6e0gRRPRrf3GlTsBmyzHq9izqzl6jIeIboaAPNf5p0kenvCWFEbW6VflS9Iapc3KXj3+/ymsGmQGfMvWxAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T13:51:35.486961Z"},"content_sha256":"5bf163d79c755ce14d23deebcf77325db025d1d3a7709181a51aae1f64a2863d","schema_version":"1.0","event_id":"sha256:5bf163d79c755ce14d23deebcf77325db025d1d3a7709181a51aae1f64a2863d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3T4K4FK6MPAK2QEPGZCR4DVW6S/bundle.json","state_url":"https://pith.science/pith/3T4K4FK6MPAK2QEPGZCR4DVW6S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3T4K4FK6MPAK2QEPGZCR4DVW6S/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":"2026-08-23T13:51:35Z","links":{"resolver":"https://pith.science/pith/3T4K4FK6MPAK2QEPGZCR4DVW6S","bundle":"https://pith.science/pith/3T4K4FK6MPAK2QEPGZCR4DVW6S/bundle.json","state":"https://pith.science/pith/3T4K4FK6MPAK2QEPGZCR4DVW6S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3T4K4FK6MPAK2QEPGZCR4DVW6S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:3T4K4FK6MPAK2QEPGZCR4DVW6S","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":"687bf265ed3e51cf0ae76a7dff7c29a02d37fe548012c1c7ef9030822ebe3090","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T14:31:47Z","title_canon_sha256":"0b3dbc4c46de0a65706c1c535465da41d2be7ae825ed5cf16270d67f6517a0d6"},"schema_version":"1.0","source":{"id":"2207.01443","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.01443","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"arxiv_version","alias_value":"2207.01443v2","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.01443","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"pith_short_12","alias_value":"3T4K4FK6MPAK","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"pith_short_16","alias_value":"3T4K4FK6MPAK2QEP","created_at":"2026-07-05T04:58:37Z"},{"alias_kind":"pith_short_8","alias_value":"3T4K4FK6","created_at":"2026-07-05T04:58:37Z"}],"graph_snapshots":[{"event_id":"sha256:5bf163d79c755ce14d23deebcf77325db025d1d3a7709181a51aae1f64a2863d","target":"graph","created_at":"2026-07-05T04:58:37Z","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/2207.01443/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work presents solutions to the Traveling Salesperson Problem with precedence constraints (TSPPC) using Deep Reinforcement Learning (DRL) by adapting recent approaches that work well for regular TSPs. Common to these approaches is the use of graph models based on multi-head attention (MHA) layers. One idea for solving the pickup and delivery problem (PDP) is using heterogeneous attentions to embed the different possible roles each node can take. In this work, we generalize this concept of heterogeneous attentions to the TSPPC. Furthermore, we adapt recent ideas to sparsify attentions for b","authors_text":"Alexander Gembus, Christian L\\\"owens, Genesis Cuizon, Inaam Ashraf, Jonas K. Falkner, Lars Schmidt-Thieme","cross_cats":["math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T14:31:47Z","title":"Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.01443","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":"sha256:87717541a7411bb319756396275707b729375f7065649834302bd40529ce9ce5","target":"record","created_at":"2026-07-05T04:58:37Z","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":"687bf265ed3e51cf0ae76a7dff7c29a02d37fe548012c1c7ef9030822ebe3090","cross_cats_sorted":["math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-07-04T14:31:47Z","title_canon_sha256":"0b3dbc4c46de0a65706c1c535465da41d2be7ae825ed5cf16270d67f6517a0d6"},"schema_version":"1.0","source":{"id":"2207.01443","kind":"arxiv","version":2}},"canonical_sha256":"dcf8ae155e63c0ad408f36451e0eb6f4a5cf2de9cf91c6703b41f73827865540","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dcf8ae155e63c0ad408f36451e0eb6f4a5cf2de9cf91c6703b41f73827865540","first_computed_at":"2026-07-05T04:58:37.758358Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:58:37.758358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"80+XChkZFzl5XCsCUWv4uun/vev/WRp4o8ZLYfPwoRwNb596He5ilIh4jQGAUcknx35961PAMH35ddUWKZfrCA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:58:37.759020Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.01443","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:87717541a7411bb319756396275707b729375f7065649834302bd40529ce9ce5","sha256:5bf163d79c755ce14d23deebcf77325db025d1d3a7709181a51aae1f64a2863d"],"state_sha256":"1adae6e97813474425498aa3b96d6092dcd37fc2eb33e58a502a5e8526d173c2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DqqxxSlpEGBhPDTJUyAHW5ELgntgbNzToNQNSlnL5fA0L8YUlxsflj8iMcCAK+F9ImJuRLnhiWI62xnmciwhAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T13:51:35.491784Z","bundle_sha256":"00ca4752988f3dd6473d0f99508ab7a5e7d041731c007226f27baa82b979a8d3"}}