{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KF2QUQGUJ6V4QVQ4HH6N3DBMNX","short_pith_number":"pith:KF2QUQGU","schema_version":"1.0","canonical_sha256":"51750a40d44fabc8561c39fcdd8c2c6dd55f2e99eb65955445161afc289ea8f0","source":{"kind":"arxiv","id":"2504.20784","version":3},"attestation_state":"computed","paper":{"title":"Approximate Lifted Model Construction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.DS","cs.LG"],"primary_cat":"cs.AI","authors_text":"Jan Speller, Malte Luttermann, Marcel Gehrke, Mattis Hartwig, Ralf M\\\"oller, Tanya Braun","submitted_at":"2025-04-29T14:01:10Z","abstract_excerpt":"Probabilistic relational models such as parametric factor graphs enable efficient (lifted) inference by exploiting the indistinguishability of objects. In lifted inference, a representative of indistinguishable objects is used for computations. To obtain a relational (i.e., lifted) representation, the Advanced Colour Passing (ACP) algorithm is the state of the art. The ACP algorithm, however, requires underlying distributions, encoded as potential-based factorisations, to exactly match to identify and exploit indistinguishabilities. Hence, ACP is unsuitable for practical applications where pot"},"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":"2504.20784","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2025-04-29T14:01:10Z","cross_cats_sorted":["cs.DS","cs.LG"],"title_canon_sha256":"817f14652f93a086f924aad8d4eb89f99c953e73597eb6febd32d6f285bd5b56","abstract_canon_sha256":"ac167b3846b17ea0c462bdcbb297506f4c1e2f359145c5601c8013213ff10544"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:00.472788Z","signature_b64":"XtOWtlii1DhZmEPiNXQgyex8XWAo8AtOz8AgST7GTTnOyZ2P3SYdjLcTu+HKbLutpK3UhgZiOPOn1eFmDKCNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51750a40d44fabc8561c39fcdd8c2c6dd55f2e99eb65955445161afc289ea8f0","last_reissued_at":"2026-07-05T12:00:00.472351Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:00.472351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximate Lifted Model Construction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.DS","cs.LG"],"primary_cat":"cs.AI","authors_text":"Jan Speller, Malte Luttermann, Marcel Gehrke, Mattis Hartwig, Ralf M\\\"oller, Tanya Braun","submitted_at":"2025-04-29T14:01:10Z","abstract_excerpt":"Probabilistic relational models such as parametric factor graphs enable efficient (lifted) inference by exploiting the indistinguishability of objects. In lifted inference, a representative of indistinguishable objects is used for computations. To obtain a relational (i.e., lifted) representation, the Advanced Colour Passing (ACP) algorithm is the state of the art. The ACP algorithm, however, requires underlying distributions, encoded as potential-based factorisations, to exactly match to identify and exploit indistinguishabilities. Hence, ACP is unsuitable for practical applications where pot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.20784","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/2504.20784/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":"2504.20784","created_at":"2026-07-05T12:00:00.472411+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.20784v3","created_at":"2026-07-05T12:00:00.472411+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.20784","created_at":"2026-07-05T12:00:00.472411+00:00"},{"alias_kind":"pith_short_12","alias_value":"KF2QUQGUJ6V4","created_at":"2026-07-05T12:00:00.472411+00:00"},{"alias_kind":"pith_short_16","alias_value":"KF2QUQGUJ6V4QVQ4","created_at":"2026-07-05T12:00:00.472411+00:00"},{"alias_kind":"pith_short_8","alias_value":"KF2QUQGU","created_at":"2026-07-05T12:00:00.472411+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.22288","citing_title":"Compression versus Accuracy: A Hierarchy of Lifted Models","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX","json":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX.json","graph_json":"https://pith.science/api/pith-number/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/graph.json","events_json":"https://pith.science/api/pith-number/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/events.json","paper":"https://pith.science/paper/KF2QUQGU"},"agent_actions":{"view_html":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX","download_json":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX.json","view_paper":"https://pith.science/paper/KF2QUQGU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.20784&json=true","fetch_graph":"https://pith.science/api/pith-number/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/graph.json","fetch_events":"https://pith.science/api/pith-number/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/action/storage_attestation","attest_author":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/action/author_attestation","sign_citation":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/action/citation_signature","submit_replication":"https://pith.science/pith/KF2QUQGUJ6V4QVQ4HH6N3DBMNX/action/replication_record"}},"created_at":"2026-07-05T12:00:00.472411+00:00","updated_at":"2026-07-05T12:00:00.472411+00:00"}