{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:LW3WXH67HKLLIRB25IHFZFQE5R","short_pith_number":"pith:LW3WXH67","schema_version":"1.0","canonical_sha256":"5db76b9fdf3a96b4443aea0e5c9604ec7414f495801e35e91626493af097358e","source":{"kind":"arxiv","id":"1611.03398","version":5},"attestation_state":"computed","paper":{"title":"XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"C\\'edric Piette, Christophe Lecoutre, Frederic Boussemart, Gilles Audemard","submitted_at":"2016-11-10T17:00:56Z","abstract_excerpt":"We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated representations of combinatorial constrained problems. This new format is able to deal with mono/multi optimization, many types of variables, cost functions, reification, views, annotations, variable quantification, distributed, probabilistic and qualitative reasoning. The new format is made compact, highly readable, and rather easy to parse. Interestingly, it captures the structure of the problem models, through the possibilities of declaring arrays of variables, and identifying syntactic and semantic groups"},"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":"1611.03398","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2016-11-10T17:00:56Z","cross_cats_sorted":[],"title_canon_sha256":"dfc7c5a14824ddeb994f4fd35d5383703e9929998c0399e13cd36e2eb08beb15","abstract_canon_sha256":"6fbc804b13e8f49ca60a6c3f6c22c33a5a66db4f306dad54252e6aaf28e8422c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:33.582213Z","signature_b64":"IA4dxUWH6c7J2kKEgk8/qspDH3E0A3cgpM8picACQk1dkPyULO8Au5ptjH4HU0DkSj9uCkTSCetkxpAdnaW/DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5db76b9fdf3a96b4443aea0e5c9604ec7414f495801e35e91626493af097358e","last_reissued_at":"2026-07-05T09:00:33.581775Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:33.581775Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"C\\'edric Piette, Christophe Lecoutre, Frederic Boussemart, Gilles Audemard","submitted_at":"2016-11-10T17:00:56Z","abstract_excerpt":"We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated representations of combinatorial constrained problems. This new format is able to deal with mono/multi optimization, many types of variables, cost functions, reification, views, annotations, variable quantification, distributed, probabilistic and qualitative reasoning. The new format is made compact, highly readable, and rather easy to parse. Interestingly, it captures the structure of the problem models, through the possibilities of declaring arrays of variables, and identifying syntactic and semantic groups"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.03398","kind":"arxiv","version":5},"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/1611.03398/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":"1611.03398","created_at":"2026-07-05T09:00:33.581834+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.03398v5","created_at":"2026-07-05T09:00:33.581834+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.03398","created_at":"2026-07-05T09:00:33.581834+00:00"},{"alias_kind":"pith_short_12","alias_value":"LW3WXH67HKLL","created_at":"2026-07-05T09:00:33.581834+00:00"},{"alias_kind":"pith_short_16","alias_value":"LW3WXH67HKLLIRB2","created_at":"2026-07-05T09:00:33.581834+00:00"},{"alias_kind":"pith_short_8","alias_value":"LW3WXH67","created_at":"2026-07-05T09:00:33.581834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.02188","citing_title":"Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14559","citing_title":"PyCSP3-Scheduling: A Scheduling Extension for PyCSP3","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R","json":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R.json","graph_json":"https://pith.science/api/pith-number/LW3WXH67HKLLIRB25IHFZFQE5R/graph.json","events_json":"https://pith.science/api/pith-number/LW3WXH67HKLLIRB25IHFZFQE5R/events.json","paper":"https://pith.science/paper/LW3WXH67"},"agent_actions":{"view_html":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R","download_json":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R.json","view_paper":"https://pith.science/paper/LW3WXH67","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.03398&json=true","fetch_graph":"https://pith.science/api/pith-number/LW3WXH67HKLLIRB25IHFZFQE5R/graph.json","fetch_events":"https://pith.science/api/pith-number/LW3WXH67HKLLIRB25IHFZFQE5R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R/action/storage_attestation","attest_author":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R/action/author_attestation","sign_citation":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R/action/citation_signature","submit_replication":"https://pith.science/pith/LW3WXH67HKLLIRB25IHFZFQE5R/action/replication_record"}},"created_at":"2026-07-05T09:00:33.581834+00:00","updated_at":"2026-07-05T09:00:33.581834+00:00"}