{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PDGXW56F2WXIEVBKBZVWXDAR6Q","short_pith_number":"pith:PDGXW56F","schema_version":"1.0","canonical_sha256":"78cd7b77c5d5ae82542a0e6b6b8c11f405a5d9807258a4671e6313616cfe9988","source":{"kind":"arxiv","id":"2508.09005","version":1},"attestation_state":"computed","paper":{"title":"MechaFormer: Sequence Learning for Kinematic Mechanism Design Automation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Diana Bolanos, Mohammadmehdi Ataei, Pradeep Kumar Jayaraman","submitted_at":"2025-08-12T15:17:30Z","abstract_excerpt":"Designing mechanical mechanisms to trace specific paths is a classic yet notoriously difficult engineering problem, characterized by a vast and complex search space of discrete topologies and continuous parameters. We introduce MechaFormer, a Transformer-based model that tackles this challenge by treating mechanism design as a conditional sequence generation task. Our model learns to translate a target curve into a domain-specific language (DSL) string, simultaneously determining the mechanism's topology and geometric parameters in a single, unified process. MechaFormer significantly outperfor"},"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":"2508.09005","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-12T15:17:30Z","cross_cats_sorted":[],"title_canon_sha256":"d89390f662f8adb15b67a9de5b7341d327742f98726c04b6b7c4c6c07f2eda79","abstract_canon_sha256":"1102458064a705b24a179ca975ce332513337c0713efc28286efe32ddb1e1b2d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:38.227513Z","signature_b64":"tNtmJm4zsGVE97CcYwL9P8CaUQR9RXslMn5/MDCU5Dgl6Hc1SvPD7cGDKubu/NuAvJsBfeOt7xO+ToiFBxhJCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78cd7b77c5d5ae82542a0e6b6b8c11f405a5d9807258a4671e6313616cfe9988","last_reissued_at":"2026-07-05T11:52:38.227024Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:38.227024Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MechaFormer: Sequence Learning for Kinematic Mechanism Design Automation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Diana Bolanos, Mohammadmehdi Ataei, Pradeep Kumar Jayaraman","submitted_at":"2025-08-12T15:17:30Z","abstract_excerpt":"Designing mechanical mechanisms to trace specific paths is a classic yet notoriously difficult engineering problem, characterized by a vast and complex search space of discrete topologies and continuous parameters. We introduce MechaFormer, a Transformer-based model that tackles this challenge by treating mechanism design as a conditional sequence generation task. Our model learns to translate a target curve into a domain-specific language (DSL) string, simultaneously determining the mechanism's topology and geometric parameters in a single, unified process. MechaFormer significantly outperfor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.09005","kind":"arxiv","version":1},"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/2508.09005/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":"2508.09005","created_at":"2026-07-05T11:52:38.227081+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.09005v1","created_at":"2026-07-05T11:52:38.227081+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.09005","created_at":"2026-07-05T11:52:38.227081+00:00"},{"alias_kind":"pith_short_12","alias_value":"PDGXW56F2WXI","created_at":"2026-07-05T11:52:38.227081+00:00"},{"alias_kind":"pith_short_16","alias_value":"PDGXW56F2WXIEVBK","created_at":"2026-07-05T11:52:38.227081+00:00"},{"alias_kind":"pith_short_8","alias_value":"PDGXW56F","created_at":"2026-07-05T11:52:38.227081+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17409","citing_title":"Discrete Autoregressive Transformer for Generative Mechanism Synthesis","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q","json":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q.json","graph_json":"https://pith.science/api/pith-number/PDGXW56F2WXIEVBKBZVWXDAR6Q/graph.json","events_json":"https://pith.science/api/pith-number/PDGXW56F2WXIEVBKBZVWXDAR6Q/events.json","paper":"https://pith.science/paper/PDGXW56F"},"agent_actions":{"view_html":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q","download_json":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q.json","view_paper":"https://pith.science/paper/PDGXW56F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.09005&json=true","fetch_graph":"https://pith.science/api/pith-number/PDGXW56F2WXIEVBKBZVWXDAR6Q/graph.json","fetch_events":"https://pith.science/api/pith-number/PDGXW56F2WXIEVBKBZVWXDAR6Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q/action/storage_attestation","attest_author":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q/action/author_attestation","sign_citation":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q/action/citation_signature","submit_replication":"https://pith.science/pith/PDGXW56F2WXIEVBKBZVWXDAR6Q/action/replication_record"}},"created_at":"2026-07-05T11:52:38.227081+00:00","updated_at":"2026-07-05T11:52:38.227081+00:00"}