{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AYTV65BWGRFM345HN4HZZEVZN7","short_pith_number":"pith:AYTV65BW","schema_version":"1.0","canonical_sha256":"06275f7436344acdf3a76f0f9c92b96fca173a2db7e938a9293861d88f8099ed","source":{"kind":"arxiv","id":"2306.02689","version":3},"attestation_state":"computed","paper":{"title":"Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hyeonah Kim, Jinkyoo Park, Jiwoo Son, Minsu Kim, Sanghyeok Choi","submitted_at":"2023-06-05T08:29:55Z","abstract_excerpt":"Min-max routing problems aim to minimize the maximum tour length among multiple agents by having agents conduct tasks in a cooperative manner. These problems include impactful real-world applications but are known as NP-hard. Existing methods are facing challenges, particularly in large-scale problems that require the coordination of numerous agents to cover thousands of cities. This paper proposes Equity-Transformer to solve large-scale min-max routing problems. First, we employ sequential planning approach to address min-max routing problems, allowing us to harness the powerful sequence gene"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2306.02689","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-05T08:29:55Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"02f6ae7995de2c17313427a3271a5837e196ae1de0ab12db21ece73f041c8413","abstract_canon_sha256":"687c94cfe2ad1ac1add792c28f06b7c4ba08f2da375a17f73c7ba3a3c33573cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:40:54.024367Z","signature_b64":"WbaO5vlnL7T5FBIVv/y41x03TxwZJZj9Uw5KovowWVOzwHL4HOCCI/kmqhDSYCwNv3cBmO4Hl8P005g8JK5mAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06275f7436344acdf3a76f0f9c92b96fca173a2db7e938a9293861d88f8099ed","last_reissued_at":"2026-07-05T07:40:54.023931Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:40:54.023931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hyeonah Kim, Jinkyoo Park, Jiwoo Son, Minsu Kim, Sanghyeok Choi","submitted_at":"2023-06-05T08:29:55Z","abstract_excerpt":"Min-max routing problems aim to minimize the maximum tour length among multiple agents by having agents conduct tasks in a cooperative manner. These problems include impactful real-world applications but are known as NP-hard. Existing methods are facing challenges, particularly in large-scale problems that require the coordination of numerous agents to cover thousands of cities. This paper proposes Equity-Transformer to solve large-scale min-max routing problems. First, we employ sequential planning approach to address min-max routing problems, allowing us to harness the powerful sequence gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.02689","kind":"arxiv","version":3},"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/2306.02689/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.02689","created_at":"2026-07-05T07:40:54.023992+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.02689v3","created_at":"2026-07-05T07:40:54.023992+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.02689","created_at":"2026-07-05T07:40:54.023992+00:00"},{"alias_kind":"pith_short_12","alias_value":"AYTV65BWGRFM","created_at":"2026-07-05T07:40:54.023992+00:00"},{"alias_kind":"pith_short_16","alias_value":"AYTV65BWGRFM345H","created_at":"2026-07-05T07:40:54.023992+00:00"},{"alias_kind":"pith_short_8","alias_value":"AYTV65BW","created_at":"2026-07-05T07:40:54.023992+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.17252","citing_title":"A Coalition Game for On-demand Multi-modal 3D Automated Delivery System","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7","json":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7.json","graph_json":"https://pith.science/api/pith-number/AYTV65BWGRFM345HN4HZZEVZN7/graph.json","events_json":"https://pith.science/api/pith-number/AYTV65BWGRFM345HN4HZZEVZN7/events.json","paper":"https://pith.science/paper/AYTV65BW"},"agent_actions":{"view_html":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7","download_json":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7.json","view_paper":"https://pith.science/paper/AYTV65BW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.02689&json=true","fetch_graph":"https://pith.science/api/pith-number/AYTV65BWGRFM345HN4HZZEVZN7/graph.json","fetch_events":"https://pith.science/api/pith-number/AYTV65BWGRFM345HN4HZZEVZN7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7/action/storage_attestation","attest_author":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7/action/author_attestation","sign_citation":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7/action/citation_signature","submit_replication":"https://pith.science/pith/AYTV65BWGRFM345HN4HZZEVZN7/action/replication_record"}},"created_at":"2026-07-05T07:40:54.023992+00:00","updated_at":"2026-07-05T07:40:54.023992+00:00"}