{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OOZ62HB3SORD6DJJDULXY2OUXJ","short_pith_number":"pith:OOZ62HB3","schema_version":"1.0","canonical_sha256":"73b3ed1c3b93a23f0d291d177c69d4ba67f68aa7c321a25ee7a621f9460bd8a6","source":{"kind":"arxiv","id":"2602.04883","version":2},"attestation_state":"computed","paper":{"title":"Protein Autoregressive Modeling via Multiscale Structure Generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","q-bio.BM","q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Cheng-Yen Hsieh, Ge Liu, Quanquan Gu, Yanru Qu, Zaixiang Zheng","submitted_at":"2026-02-04T18:59:49Z","abstract_excerpt":"We present protein autoregressive modeling (PAR), the first multi-scale autoregressive framework for protein backbone generation via coarse-to-fine next-scale prediction. Using the hierarchical nature of proteins, PAR generates structures that mimic sculpting a statue, forming a coarse topology and refining structural details over scales. To achieve this, PAR consists of three key components: (i) multi-scale downsampling operations that represent protein structures across multiple scales during training; (ii) an autoregressive transformer that encodes multi-scale information and produces condi"},"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":"2602.04883","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-04T18:59:49Z","cross_cats_sorted":["cs.AI","q-bio.BM","q-bio.QM"],"title_canon_sha256":"2f9b5512ed429e853853fc079a8ec654d45b7c90313342e687d4abc7dd8604eb","abstract_canon_sha256":"c894097f5f62ec5481d67f217fb7ea5f43a4a3b2ef300da15cc1ecc8daa485de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:04:30.420922Z","signature_b64":"JOdcpzKFtYrK++j8KdJOe9pHJWWGD+w4y+K8rUPJRS6rg4bsbuocuja1/ZFjMhTjC8AX2n64Nc7hHvr5NYEyCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73b3ed1c3b93a23f0d291d177c69d4ba67f68aa7c321a25ee7a621f9460bd8a6","last_reissued_at":"2026-05-20T01:04:30.420182Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:04:30.420182Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Protein Autoregressive Modeling via Multiscale Structure Generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","q-bio.BM","q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Cheng-Yen Hsieh, Ge Liu, Quanquan Gu, Yanru Qu, Zaixiang Zheng","submitted_at":"2026-02-04T18:59:49Z","abstract_excerpt":"We present protein autoregressive modeling (PAR), the first multi-scale autoregressive framework for protein backbone generation via coarse-to-fine next-scale prediction. Using the hierarchical nature of proteins, PAR generates structures that mimic sculpting a statue, forming a coarse topology and refining structural details over scales. To achieve this, PAR consists of three key components: (i) multi-scale downsampling operations that represent protein structures across multiple scales during training; (ii) an autoregressive transformer that encodes multi-scale information and produces condi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.04883","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/2602.04883/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":"2602.04883","created_at":"2026-05-20T01:04:30.420298+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.04883v2","created_at":"2026-05-20T01:04:30.420298+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.04883","created_at":"2026-05-20T01:04:30.420298+00:00"},{"alias_kind":"pith_short_12","alias_value":"OOZ62HB3SORD","created_at":"2026-05-20T01:04:30.420298+00:00"},{"alias_kind":"pith_short_16","alias_value":"OOZ62HB3SORD6DJJ","created_at":"2026-05-20T01:04:30.420298+00:00"},{"alias_kind":"pith_short_8","alias_value":"OOZ62HB3","created_at":"2026-05-20T01:04:30.420298+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/OOZ62HB3SORD6DJJDULXY2OUXJ","json":"https://pith.science/pith/OOZ62HB3SORD6DJJDULXY2OUXJ.json","graph_json":"https://pith.science/api/pith-number/OOZ62HB3SORD6DJJDULXY2OUXJ/graph.json","events_json":"https://pith.science/api/pith-number/OOZ62HB3SORD6DJJDULXY2OUXJ/events.json","paper":"https://pith.science/paper/OOZ62HB3"},"agent_actions":{"view_html":"https://pith.science/pith/OOZ62HB3SORD6DJJDULXY2OUXJ","download_json":"https://pith.science/pith/OOZ62HB3SORD6DJJDULXY2OUXJ.json","view_paper":"https://pith.science/paper/OOZ62HB3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.04883&json=true","fetch_graph":"https://pith.science/api/pith-number/OOZ62HB3SORD6DJJDULXY2OUXJ/graph.json","fetch_events":"https://pith.science/api/pith-number/OOZ62HB3SORD6DJJDULXY2OUXJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OOZ62HB3SORD6DJJDULXY2OUXJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OOZ62HB3SORD6DJJDULXY2OUXJ/action/storage_attestation","attest_author":"https://pith.science/pith/OOZ62HB3SORD6DJJDULXY2OUXJ/action/author_attestation","sign_citation":"https://pith.science/pith/OOZ62HB3SORD6DJJDULXY2OUXJ/action/citation_signature","submit_replication":"https://pith.science/pith/OOZ62HB3SORD6DJJDULXY2OUXJ/action/replication_record"}},"created_at":"2026-05-20T01:04:30.420298+00:00","updated_at":"2026-05-20T01:04:30.420298+00:00"}