{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QYZHTCMSZTYIZHFKVE5RK7C3CD","short_pith_number":"pith:QYZHTCMS","schema_version":"1.0","canonical_sha256":"8632798992ccf08c9caaa93b157c5b10f8dd6790accc1480086f4ff25c0fc3a7","source":{"kind":"arxiv","id":"2605.15354","version":1},"attestation_state":"computed","paper":{"title":"Controllable Molecular Generative Foundation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Molecular generation gains reliable control by operating on motifs rather than atoms in a diffusion process.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Meng Jiang, Tengfei Luo, Weijiang Li, Yihan Zhu, Yuhan Liu","submitted_at":"2026-05-14T19:27:01Z","abstract_excerpt":"Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tasks with reliable controllability. While reinforcement learning (RL) offers a natural post-training mechanism for task-specific optimization, applying it to graph generative models is hindered by the vast atom-wise action spaces and chemically invalid intermediate states. We propose \\textbf{Co}ntrollable \\textbf{Mole}cular Generative Foundation Models (CoMole), built with a unified motif-aware graph diffusion pipeline. By learning a motif-aware "},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2605.15354","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T19:27:01Z","cross_cats_sorted":[],"title_canon_sha256":"e80cbc36f7c861e320df05616abae2574915a210c825a0b3e18d5ec20e6327cf","abstract_canon_sha256":"efee410f7132209f6858c3fd0af3641e000c34b6632f4649eda05fa86c07d291"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:54.078477Z","signature_b64":"acSE1h2XDjKnnpPMeCEevdAwwqt2D6fIMtjARIPtuaVRm27lGx9L7aMYjHx0GbqKJozOlo0vRhO58almlZWRDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8632798992ccf08c9caaa93b157c5b10f8dd6790accc1480086f4ff25c0fc3a7","last_reissued_at":"2026-05-20T00:00:54.077691Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:54.077691Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Controllable Molecular Generative Foundation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Molecular generation gains reliable control by operating on motifs rather than atoms in a diffusion process.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Meng Jiang, Tengfei Luo, Weijiang Li, Yihan Zhu, Yuhan Liu","submitted_at":"2026-05-14T19:27:01Z","abstract_excerpt":"Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tasks with reliable controllability. While reinforcement learning (RL) offers a natural post-training mechanism for task-specific optimization, applying it to graph generative models is hindered by the vast atom-wise action spaces and chemically invalid intermediate states. We propose \\textbf{Co}ntrollable \\textbf{Mole}cular Generative Foundation Models (CoMole), built with a unified motif-aware graph diffusion pipeline. By learning a motif-aware "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Across three heterogeneous benchmarks spanning materials and drug discovery, CoMole ranks first in controllability on all nine targets, reduces MAE by up to 48.2% relative to the strongest baselines, and maintains validity above 0.94 without rule-based correction or post-hoc filtering.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The central premise that learning a motif-aware graph space successfully transfers pretrained structural priors into controllable generation and enables RL to optimize conditional reverse policies over chemically meaningful decisions without the bottlenecks of atom-level action spaces.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CoMole uses a motif-aware graph diffusion pipeline with RL to rank first in controllability on nine targets across materials and drug benchmarks while keeping validity above 0.94 without post-processing.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Molecular generation gains reliable control by operating on motifs rather than atoms in a diffusion process.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8cb680b513078cca04fc7cab049b86e8bd2653ad3ed2c642d1fa052be965f5b1"},"source":{"id":"2605.15354","kind":"arxiv","version":1},"verdict":{"id":"2d56abb1-3329-4c64-ae1a-7f676981fcea","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T15:33:22.937303Z","strongest_claim":"Across three heterogeneous benchmarks spanning materials and drug discovery, CoMole ranks first in controllability on all nine targets, reduces MAE by up to 48.2% relative to the strongest baselines, and maintains validity above 0.94 without rule-based correction or post-hoc filtering.","one_line_summary":"CoMole uses a motif-aware graph diffusion pipeline with RL to rank first in controllability on nine targets across materials and drug benchmarks while keeping validity above 0.94 without post-processing.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The central premise that learning a motif-aware graph space successfully transfers pretrained structural priors into controllable generation and enables RL to optimize conditional reverse policies over chemically meaningful decisions without the bottlenecks of atom-level action spaces.","pith_extraction_headline":"Molecular generation gains reliable control by operating on motifs rather than atoms in a diffusion process."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15354/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T16:01:18.091265Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T15:41:03.329573Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.200138Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.747162Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"92c9470d412117c32bd6744bb69f63e09c79755a2a4dc73371cedf4679d7ca9f"},"references":{"count":38,"sample":[{"doi":"","year":2023,"title":"DiGress: Discrete Denoising diffusion for graph generation , author=. 2023 , eprint=","work_id":"e13fa227-cf9a-4b55-9487-57aa23b92c17","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation , author=. 2023 , eprint=","work_id":"41a68084-5d9f-404f-bad1-5ebcee6bfc27","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Graph Diffusion Transformers for Multi-Conditional Molecular Generation , author=. 2024 , eprint=","work_id":"dfaadffa-9654-4e72-989e-e40fd4416029","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Junction Tree Variational Autoencoder for Molecular Graph Generation , author=. 2019 , eprint=","work_id":"6282d275-9c4c-4993-b1a8-58280b469f40","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Advances in Neural Information Processing Systems , volume=","work_id":"133d8876-939a-45a3-b693-8d3a1d71022b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":38,"snapshot_sha256":"7397bf3417dcc746a983dc6c00eb56fc18b9586048fa26f9c2fa0f4036489080","internal_anchors":1},"formal_canon":{"evidence_count":2,"snapshot_sha256":"8c2cb68b3458c821a9eda5f5ed717b5df3443abaab3f1e42789997b89f628310"},"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":"2605.15354","created_at":"2026-05-20T00:00:54.077799+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.15354v1","created_at":"2026-05-20T00:00:54.077799+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15354","created_at":"2026-05-20T00:00:54.077799+00:00"},{"alias_kind":"pith_short_12","alias_value":"QYZHTCMSZTYI","created_at":"2026-05-20T00:00:54.077799+00:00"},{"alias_kind":"pith_short_16","alias_value":"QYZHTCMSZTYIZHFK","created_at":"2026-05-20T00:00:54.077799+00:00"},{"alias_kind":"pith_short_8","alias_value":"QYZHTCMS","created_at":"2026-05-20T00:00:54.077799+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD","json":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD.json","graph_json":"https://pith.science/api/pith-number/QYZHTCMSZTYIZHFKVE5RK7C3CD/graph.json","events_json":"https://pith.science/api/pith-number/QYZHTCMSZTYIZHFKVE5RK7C3CD/events.json","paper":"https://pith.science/paper/QYZHTCMS"},"agent_actions":{"view_html":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD","download_json":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD.json","view_paper":"https://pith.science/paper/QYZHTCMS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.15354&json=true","fetch_graph":"https://pith.science/api/pith-number/QYZHTCMSZTYIZHFKVE5RK7C3CD/graph.json","fetch_events":"https://pith.science/api/pith-number/QYZHTCMSZTYIZHFKVE5RK7C3CD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD/action/storage_attestation","attest_author":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD/action/author_attestation","sign_citation":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD/action/citation_signature","submit_replication":"https://pith.science/pith/QYZHTCMSZTYIZHFKVE5RK7C3CD/action/replication_record"}},"created_at":"2026-05-20T00:00:54.077799+00:00","updated_at":"2026-05-20T00:00:54.077799+00:00"}