{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KXAIKJCQ7YFO2DFWZJRZUVDVOF","short_pith_number":"pith:KXAIKJCQ","schema_version":"1.0","canonical_sha256":"55c0852450fe0aed0cb6ca639a54757170a81b2239d54376c1fc4af5fba7601d","source":{"kind":"arxiv","id":"2605.29366","version":1},"attestation_state":"computed","paper":{"title":"Solving Integer Linear Programming with Parallel Tempering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jinkyoo Park, Kyuil Sim, Sanghyeok Choi","submitted_at":"2026-05-28T05:09:21Z","abstract_excerpt":"Integer Linear Programming (ILP) serves as a versatile framework for modeling a wide range of combinatorial optimization problems, typically addressed by sophisticated exact solvers or heuristics. While learning-based approaches have recently shown their effectiveness, they suffer from poor generalization to out-of-distribution instances and inherent dependence on external solvers. In this work, we propose a solver-free, sampling-based optimization framework for ILP that directly explores discrete feasible regions without training or external solvers. Exploiting the linear structure of ILP, we"},"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":"2605.29366","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T05:09:21Z","cross_cats_sorted":[],"title_canon_sha256":"9825788311f556c7a2194e24285480a98c2cc77b15dfa29f49ab1e11dda1783e","abstract_canon_sha256":"18f4b912dcfb00a08f78b99d738fbb6c42b13d167b078414f32c1db18111b2cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:35.578266Z","signature_b64":"oDWcwQVlAXEk96BqmNb1+3SWJ73/Fxn09p3tnpr1uU8tKi6ela6dBOtqItJV+dEIlMfUQGpPTnbFYGL7fiKXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55c0852450fe0aed0cb6ca639a54757170a81b2239d54376c1fc4af5fba7601d","last_reissued_at":"2026-05-29T01:05:35.577629Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:35.577629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solving Integer Linear Programming with Parallel Tempering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jinkyoo Park, Kyuil Sim, Sanghyeok Choi","submitted_at":"2026-05-28T05:09:21Z","abstract_excerpt":"Integer Linear Programming (ILP) serves as a versatile framework for modeling a wide range of combinatorial optimization problems, typically addressed by sophisticated exact solvers or heuristics. While learning-based approaches have recently shown their effectiveness, they suffer from poor generalization to out-of-distribution instances and inherent dependence on external solvers. In this work, we propose a solver-free, sampling-based optimization framework for ILP that directly explores discrete feasible regions without training or external solvers. Exploiting the linear structure of ILP, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29366","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/2605.29366/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":"2605.29366","created_at":"2026-05-29T01:05:35.577722+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29366v1","created_at":"2026-05-29T01:05:35.577722+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29366","created_at":"2026-05-29T01:05:35.577722+00:00"},{"alias_kind":"pith_short_12","alias_value":"KXAIKJCQ7YFO","created_at":"2026-05-29T01:05:35.577722+00:00"},{"alias_kind":"pith_short_16","alias_value":"KXAIKJCQ7YFO2DFW","created_at":"2026-05-29T01:05:35.577722+00:00"},{"alias_kind":"pith_short_8","alias_value":"KXAIKJCQ","created_at":"2026-05-29T01:05:35.577722+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF","json":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF.json","graph_json":"https://pith.science/api/pith-number/KXAIKJCQ7YFO2DFWZJRZUVDVOF/graph.json","events_json":"https://pith.science/api/pith-number/KXAIKJCQ7YFO2DFWZJRZUVDVOF/events.json","paper":"https://pith.science/paper/KXAIKJCQ"},"agent_actions":{"view_html":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF","download_json":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF.json","view_paper":"https://pith.science/paper/KXAIKJCQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29366&json=true","fetch_graph":"https://pith.science/api/pith-number/KXAIKJCQ7YFO2DFWZJRZUVDVOF/graph.json","fetch_events":"https://pith.science/api/pith-number/KXAIKJCQ7YFO2DFWZJRZUVDVOF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF/action/storage_attestation","attest_author":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF/action/author_attestation","sign_citation":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF/action/citation_signature","submit_replication":"https://pith.science/pith/KXAIKJCQ7YFO2DFWZJRZUVDVOF/action/replication_record"}},"created_at":"2026-05-29T01:05:35.577722+00:00","updated_at":"2026-05-29T01:05:35.577722+00:00"}