{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VMYIVWSTSC7PFOXGM65G5VU5YI","short_pith_number":"pith:VMYIVWST","schema_version":"1.0","canonical_sha256":"ab308ada5390bef2bae667ba6ed69dc21c12500a5e32e10fef3b7e2b1592fac8","source":{"kind":"arxiv","id":"2405.20042","version":4},"attestation_state":"computed","paper":{"title":"CycleFormer : TSP Solver Based on Language Modeling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Byung-Ro Moon, Han Joon Byun, Jieun Yook, Joon Huh, Junpyo Seo","submitted_at":"2024-05-30T13:23:02Z","abstract_excerpt":"We propose a new transformer model for the Traveling Salesman Problem (TSP) called CycleFormer. We identified distinctive characteristics that need to be considered when applying a conventional transformer model to TSP and aimed to fully incorporate these elements into the TSP-specific transformer. Unlike the token sets in typical language models, which are limited and static, the token (node) set in TSP is unlimited and dynamic. To exploit this fact to the fullest, we equated the encoder output with the decoder linear layer and directly connected the context vector of the encoder to the decod"},"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":"2405.20042","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-30T13:23:02Z","cross_cats_sorted":[],"title_canon_sha256":"4a110ae77af2853dd85db18356460234688568c4fbda689d11ce29a59e8e4ef8","abstract_canon_sha256":"3d2aaa47df1d73b963234168a4eb30f9c435ce8e82436d60aadfa10551214d40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:16:15.640504Z","signature_b64":"ZzDjmZemQZO/ex0PakvDY87G/ACvZ5jKdc53BKvptwNg9JSSdynkb3TItw5hGgVAODjl7g+LRU03a3KCGU7tDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab308ada5390bef2bae667ba6ed69dc21c12500a5e32e10fef3b7e2b1592fac8","last_reissued_at":"2026-07-05T09:16:15.640042Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:16:15.640042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CycleFormer : TSP Solver Based on Language Modeling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Byung-Ro Moon, Han Joon Byun, Jieun Yook, Joon Huh, Junpyo Seo","submitted_at":"2024-05-30T13:23:02Z","abstract_excerpt":"We propose a new transformer model for the Traveling Salesman Problem (TSP) called CycleFormer. We identified distinctive characteristics that need to be considered when applying a conventional transformer model to TSP and aimed to fully incorporate these elements into the TSP-specific transformer. Unlike the token sets in typical language models, which are limited and static, the token (node) set in TSP is unlimited and dynamic. To exploit this fact to the fullest, we equated the encoder output with the decoder linear layer and directly connected the context vector of the encoder to the decod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20042","kind":"arxiv","version":4},"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/2405.20042/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":"2405.20042","created_at":"2026-07-05T09:16:15.640102+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.20042v4","created_at":"2026-07-05T09:16:15.640102+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20042","created_at":"2026-07-05T09:16:15.640102+00:00"},{"alias_kind":"pith_short_12","alias_value":"VMYIVWSTSC7P","created_at":"2026-07-05T09:16:15.640102+00:00"},{"alias_kind":"pith_short_16","alias_value":"VMYIVWSTSC7PFOXG","created_at":"2026-07-05T09:16:15.640102+00:00"},{"alias_kind":"pith_short_8","alias_value":"VMYIVWST","created_at":"2026-07-05T09:16:15.640102+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.00767","citing_title":"Learning-Based TSP-Solvers Tend to Be Overly Greedy","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI","json":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI.json","graph_json":"https://pith.science/api/pith-number/VMYIVWSTSC7PFOXGM65G5VU5YI/graph.json","events_json":"https://pith.science/api/pith-number/VMYIVWSTSC7PFOXGM65G5VU5YI/events.json","paper":"https://pith.science/paper/VMYIVWST"},"agent_actions":{"view_html":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI","download_json":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI.json","view_paper":"https://pith.science/paper/VMYIVWST","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.20042&json=true","fetch_graph":"https://pith.science/api/pith-number/VMYIVWSTSC7PFOXGM65G5VU5YI/graph.json","fetch_events":"https://pith.science/api/pith-number/VMYIVWSTSC7PFOXGM65G5VU5YI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI/action/storage_attestation","attest_author":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI/action/author_attestation","sign_citation":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI/action/citation_signature","submit_replication":"https://pith.science/pith/VMYIVWSTSC7PFOXGM65G5VU5YI/action/replication_record"}},"created_at":"2026-07-05T09:16:15.640102+00:00","updated_at":"2026-07-05T09:16:15.640102+00:00"}