{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MV7R2XHPBRNWD4BYJEF5XHYQHB","short_pith_number":"pith:MV7R2XHP","schema_version":"1.0","canonical_sha256":"657f1d5cef0c5b61f038490bdb9f103862a4d17183979ecb862ab8a693e536e7","source":{"kind":"arxiv","id":"2604.17838","version":2},"attestation_state":"computed","paper":{"title":"Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A landing mechanism enables efficient diffusion models on nonconvex sets by replacing costly projections with a single-step correction.","cross_cats":["stat.CO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kijung Jeon, Michael Muehlebach, Molei Tao","submitted_at":"2026-04-20T05:47:27Z","abstract_excerpt":"Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.g., molecular generation, robotics). We present a unified framework for constrained diffusion models on generic nonconvex feasible sets $\\Sigma$ that simultaneously enforces equality and inequality constraints throughout the diffusion process. Our framework incorporates both overdamped and underdamped dynamics for forward and backward sampling. A key algorithmic innovation is a computationally efficient landing mechanism that replaces cos"},"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":"2604.17838","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-20T05:47:27Z","cross_cats_sorted":["stat.CO","stat.ML"],"title_canon_sha256":"c9854edb46b17fb8d50c20e13a119c17d836e353a7c8a74d3cb81417dc83adf3","abstract_canon_sha256":"86ac32d3d92a6e00e4544bf34164511f0bc49d14393bca667b9d69b63e3fb5da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:04:16.308739Z","signature_b64":"Vgq5M7edq1diwtY3Kx0YRjwOeLG7JtroTzp2BAFwsDMZTaybBLtnO6w3GvVteb2eQCBAQHwqZh5x0GIu7HWeDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"657f1d5cef0c5b61f038490bdb9f103862a4d17183979ecb862ab8a693e536e7","last_reissued_at":"2026-06-02T01:04:16.308244Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:04:16.308244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A landing mechanism enables efficient diffusion models on nonconvex sets by replacing costly projections with a single-step correction.","cross_cats":["stat.CO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kijung Jeon, Michael Muehlebach, Molei Tao","submitted_at":"2026-04-20T05:47:27Z","abstract_excerpt":"Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.g., molecular generation, robotics). We present a unified framework for constrained diffusion models on generic nonconvex feasible sets $\\Sigma$ that simultaneously enforces equality and inequality constraints throughout the diffusion process. Our framework incorporates both overdamped and underdamped dynamics for forward and backward sampling. A key algorithmic innovation is a computationally efficient landing mechanism that replaces cos"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"A key algorithmic innovation is a computationally efficient landing mechanism that replaces costly and often ill-defined projections onto Σ, ensuring feasibility without iterative Newton solves or projection failures.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The landing mechanism works for generic nonconvex feasible sets Σ and preserves the correct diffusion dynamics without introducing bias or instability.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A landing algorithm lets diffusion models sample from nonconvex constrained sets using underdamped dynamics without repeated projections or Newton solves.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A landing mechanism enables efficient diffusion models on nonconvex sets by replacing costly projections with a single-step correction.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"11e80eacb8ebf5f245249de43ef5fe2592d099e51bee4c50a44a48c8510d7a28"},"source":{"id":"2604.17838","kind":"arxiv","version":2},"verdict":{"id":"35494912-8e7a-444f-9a80-3f0efc107625","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T05:51:06.694675Z","strongest_claim":"A key algorithmic innovation is a computationally efficient landing mechanism that replaces costly and often ill-defined projections onto Σ, ensuring feasibility without iterative Newton solves or projection failures.","one_line_summary":"A landing algorithm lets diffusion models sample from nonconvex constrained sets using underdamped dynamics without repeated projections or Newton solves.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The landing mechanism works for generic nonconvex feasible sets Σ and preserves the correct diffusion dynamics without introducing bias or instability.","pith_extraction_headline":"A landing mechanism enables efficient diffusion models on nonconvex sets by replacing costly projections with a single-step correction."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.17838/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-20T04:42:19.839387Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"94825b64b708e2ebcf26014f2cc38859f2107c7b22742f178bb71d2b4a5ded1e"},"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":"2604.17838","created_at":"2026-06-02T01:04:16.308298+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.17838v2","created_at":"2026-06-02T01:04:16.308298+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.17838","created_at":"2026-06-02T01:04:16.308298+00:00"},{"alias_kind":"pith_short_12","alias_value":"MV7R2XHPBRNW","created_at":"2026-06-02T01:04:16.308298+00:00"},{"alias_kind":"pith_short_16","alias_value":"MV7R2XHPBRNWD4BY","created_at":"2026-06-02T01:04:16.308298+00:00"},{"alias_kind":"pith_short_8","alias_value":"MV7R2XHP","created_at":"2026-06-02T01:04:16.308298+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/MV7R2XHPBRNWD4BYJEF5XHYQHB","json":"https://pith.science/pith/MV7R2XHPBRNWD4BYJEF5XHYQHB.json","graph_json":"https://pith.science/api/pith-number/MV7R2XHPBRNWD4BYJEF5XHYQHB/graph.json","events_json":"https://pith.science/api/pith-number/MV7R2XHPBRNWD4BYJEF5XHYQHB/events.json","paper":"https://pith.science/paper/MV7R2XHP"},"agent_actions":{"view_html":"https://pith.science/pith/MV7R2XHPBRNWD4BYJEF5XHYQHB","download_json":"https://pith.science/pith/MV7R2XHPBRNWD4BYJEF5XHYQHB.json","view_paper":"https://pith.science/paper/MV7R2XHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.17838&json=true","fetch_graph":"https://pith.science/api/pith-number/MV7R2XHPBRNWD4BYJEF5XHYQHB/graph.json","fetch_events":"https://pith.science/api/pith-number/MV7R2XHPBRNWD4BYJEF5XHYQHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MV7R2XHPBRNWD4BYJEF5XHYQHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MV7R2XHPBRNWD4BYJEF5XHYQHB/action/storage_attestation","attest_author":"https://pith.science/pith/MV7R2XHPBRNWD4BYJEF5XHYQHB/action/author_attestation","sign_citation":"https://pith.science/pith/MV7R2XHPBRNWD4BYJEF5XHYQHB/action/citation_signature","submit_replication":"https://pith.science/pith/MV7R2XHPBRNWD4BYJEF5XHYQHB/action/replication_record"}},"created_at":"2026-06-02T01:04:16.308298+00:00","updated_at":"2026-06-02T01:04:16.308298+00:00"}