{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:JTEWYTDSMRWIX6ZJXSE3A3472X","short_pith_number":"pith:JTEWYTDS","canonical_record":{"source":{"id":"2012.10658","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-19T11:06:30Z","cross_cats_sorted":[],"title_canon_sha256":"3e2632320c038057bbff68ff191191846d756b5ba5fce4c40ff3aaab581fef98","abstract_canon_sha256":"2c31a762f1ca69b9dca68896100a4599e783f5de7408225c87040223fcfcd239"},"schema_version":"1.0"},"canonical_sha256":"4cc96c4c72646c8bfb29bc89b06f9fd5d3f0baf0da59b8a5883d504867fa0b4c","source":{"kind":"arxiv","id":"2012.10658","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.10658","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"arxiv_version","alias_value":"2012.10658v2","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.10658","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_12","alias_value":"JTEWYTDSMRWI","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_16","alias_value":"JTEWYTDSMRWIX6ZJ","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_8","alias_value":"JTEWYTDS","created_at":"2026-07-05T02:18:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:JTEWYTDSMRWIX6ZJXSE3A3472X","target":"record","payload":{"canonical_record":{"source":{"id":"2012.10658","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-19T11:06:30Z","cross_cats_sorted":[],"title_canon_sha256":"3e2632320c038057bbff68ff191191846d756b5ba5fce4c40ff3aaab581fef98","abstract_canon_sha256":"2c31a762f1ca69b9dca68896100a4599e783f5de7408225c87040223fcfcd239"},"schema_version":"1.0"},"canonical_sha256":"4cc96c4c72646c8bfb29bc89b06f9fd5d3f0baf0da59b8a5883d504867fa0b4c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:18:06.939711Z","signature_b64":"g2Hso5KxzEPTXyQsW2uHktNegC/0Z1l3DM5BNSUAIJzwwlsHd+BTN5sHXNOnuakI/K7WMORaKZUgc+aUuMoJBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4cc96c4c72646c8bfb29bc89b06f9fd5d3f0baf0da59b8a5883d504867fa0b4c","last_reissued_at":"2026-07-05T02:18:06.939249Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:18:06.939249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2012.10658","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":"2026-07-05T02:18:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zJiQMTAERyW6Q10q/gWaAqURp3I+kUKFIod4W5MLRmjGJ1qfIJYeVXkx7ZzcWmxf4PpUFczVCo43JeOXq6bACw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T05:03:43.713309Z"},"content_sha256":"fc6e6afeb61e1e183983fc070452444527631d1a103b2491c72d3f5aabc1ccd9","schema_version":"1.0","event_id":"sha256:fc6e6afeb61e1e183983fc070452444527631d1a103b2491c72d3f5aabc1ccd9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:JTEWYTDSMRWIX6ZJXSE3A3472X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Hongyuan Zha, Kai-Bin Qiu, Zhang-Hua Fu","submitted_at":"2020-12-19T11:06:30Z","abstract_excerpt":"For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of arbitrarily large size, based on a series of techniques such as graph sampling, graph converting and heat maps merging. Furthermore, the heat maps are fed into a reinforcement learning approach (Monte Carlo tree search), to guide the search of high-quality solutions. Experimental"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.10658","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/2012.10658/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":"2026-07-05T02:18:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+/VeTeKt1v4+y/nNSkbGd0Nd5zsyVwaqDZCdvQtiaeoP1/wF6aOX8TzJAS4BZnBR8sekgfBkERgtIMsGalUFBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T05:03:43.713845Z"},"content_sha256":"1762365c5468349fc66ef29d584f15b5bae517bfc7dd0f2cc6b4478834bce6a9","schema_version":"1.0","event_id":"sha256:1762365c5468349fc66ef29d584f15b5bae517bfc7dd0f2cc6b4478834bce6a9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JTEWYTDSMRWIX6ZJXSE3A3472X/bundle.json","state_url":"https://pith.science/pith/JTEWYTDSMRWIX6ZJXSE3A3472X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JTEWYTDSMRWIX6ZJXSE3A3472X/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":"2026-08-05T05:03:43Z","links":{"resolver":"https://pith.science/pith/JTEWYTDSMRWIX6ZJXSE3A3472X","bundle":"https://pith.science/pith/JTEWYTDSMRWIX6ZJXSE3A3472X/bundle.json","state":"https://pith.science/pith/JTEWYTDSMRWIX6ZJXSE3A3472X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JTEWYTDSMRWIX6ZJXSE3A3472X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:JTEWYTDSMRWIX6ZJXSE3A3472X","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":"2c31a762f1ca69b9dca68896100a4599e783f5de7408225c87040223fcfcd239","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-19T11:06:30Z","title_canon_sha256":"3e2632320c038057bbff68ff191191846d756b5ba5fce4c40ff3aaab581fef98"},"schema_version":"1.0","source":{"id":"2012.10658","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.10658","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"arxiv_version","alias_value":"2012.10658v2","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.10658","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_12","alias_value":"JTEWYTDSMRWI","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_16","alias_value":"JTEWYTDSMRWIX6ZJ","created_at":"2026-07-05T02:18:06Z"},{"alias_kind":"pith_short_8","alias_value":"JTEWYTDS","created_at":"2026-07-05T02:18:06Z"}],"graph_snapshots":[{"event_id":"sha256:1762365c5468349fc66ef29d584f15b5bae517bfc7dd0f2cc6b4478834bce6a9","target":"graph","created_at":"2026-07-05T02:18:06Z","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/2012.10658/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised manner) a small-scale model, which could be repetitively used to build heat maps for TSP instances of arbitrarily large size, based on a series of techniques such as graph sampling, graph converting and heat maps merging. Furthermore, the heat maps are fed into a reinforcement learning approach (Monte Carlo tree search), to guide the search of high-quality solutions. Experimental","authors_text":"Hongyuan Zha, Kai-Bin Qiu, Zhang-Hua Fu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-19T11:06:30Z","title":"Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.10658","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:fc6e6afeb61e1e183983fc070452444527631d1a103b2491c72d3f5aabc1ccd9","target":"record","created_at":"2026-07-05T02:18:06Z","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":"2c31a762f1ca69b9dca68896100a4599e783f5de7408225c87040223fcfcd239","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-12-19T11:06:30Z","title_canon_sha256":"3e2632320c038057bbff68ff191191846d756b5ba5fce4c40ff3aaab581fef98"},"schema_version":"1.0","source":{"id":"2012.10658","kind":"arxiv","version":2}},"canonical_sha256":"4cc96c4c72646c8bfb29bc89b06f9fd5d3f0baf0da59b8a5883d504867fa0b4c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4cc96c4c72646c8bfb29bc89b06f9fd5d3f0baf0da59b8a5883d504867fa0b4c","first_computed_at":"2026-07-05T02:18:06.939249Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:18:06.939249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g2Hso5KxzEPTXyQsW2uHktNegC/0Z1l3DM5BNSUAIJzwwlsHd+BTN5sHXNOnuakI/K7WMORaKZUgc+aUuMoJBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:18:06.939711Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.10658","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc6e6afeb61e1e183983fc070452444527631d1a103b2491c72d3f5aabc1ccd9","sha256:1762365c5468349fc66ef29d584f15b5bae517bfc7dd0f2cc6b4478834bce6a9"],"state_sha256":"11d39f20227eb639215cfd605ee194f179e35fbfac0e5d59d325755a9a5bb8a0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jRBi3827xkVBU8tKRHgUN/nBQYIve+ufU7/B2B733/10+e/GzHTiV02t9tqx0bRr4Niry5YalolFt3KNJw+FDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T05:03:43.716928Z","bundle_sha256":"06401779c1405d0e22c58ad1a723d696cd24cefe4fc8125985307f641c39b6e9"}}